introduction for ethics research paper

• Original article
• Open access
• Published: 13 July 2021

Assisting you to advance with ethics in research: an introduction to ethical governance and application procedures

• Shivadas Sivasubramaniam 1 ,
• Dita Henek Dlabolová 2 ,
• Veronika Kralikova 3 &
• Zeenath Reza Khan 3  

International Journal for Educational Integrity volume  17 , Article number:  14 ( 2021 ) Cite this article

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Ethics and ethical behaviour are the fundamental pillars of a civilised society. The focus on ethical behaviour is indispensable in certain fields such as medicine, finance, or law. In fact, ethics gets precedence with anything that would include, affect, transform, or influence upon individuals, communities or any living creatures. Many institutions within Europe have set up their own committees to focus on or approve activities that have ethical impact. In contrast, lesser-developed countries (worldwide) are trying to set up these committees to govern their academia and research. As the first European consortium established to assist academic integrity, European Network for Academic Integrity (ENAI), we felt the importance of guiding those institutions and communities that are trying to conduct research with ethical principles. We have established an ethical advisory working group within ENAI with the aim to promote ethics within curriculum, research and institutional policies. We are constantly researching available data on this subject and committed to help the academia to convey and conduct ethical behaviour. Upon preliminary review and discussion, the group found a disparity in understanding, practice and teaching approaches to ethical applications of research projects among peers. Therefore, this short paper preliminarily aims to critically review the available information on ethics, the history behind establishing ethical principles and its international guidelines to govern research.

The paper is based on the workshop conducted in the 5th International conference Plagiarism across Europe and Beyond, in Mykolas Romeris University, Lithuania in 2019. During the workshop, we have detailed a) basic needs of an ethical committee within an institution; b) a typical ethical approval process (with examples from three different universities); and c) the ways to obtain informed consent with some examples. These are summarised in this paper with some example comparisons of ethical approval processes from different universities. We believe this paper will provide guidelines on preparing and training both researchers and research students in appropriately upholding ethical practices through ethical approval processes.

Introduction

Ethics and ethical behaviour (often linked to “responsible practice”) are the fundamental pillars of a civilised society. Ethical behaviour with integrity is important to maintain academic and research activities. It affects everything we do, and gets precedence with anything that would include/affect, transform, or impact upon individuals, communities or any living creatures. In other words, ethics would help us improve our living standards (LaFollette, 2007 ). The focus on ethical behaviour is indispensable in certain fields such as medicine, finance, or law, but is also gaining recognition in all disciplines engaged in research. Therefore, institutions are expected to develop ethical guidelines in research to maintain quality, initiate/own integrity and above all be transparent to be successful by limiting any allegation of misconduct (Flite and Harman, 2013 ). This is especially true for higher education organisations that promote research and scholarly activities. Many European institutions have developed their own regulations for ethics by incorporating international codes (Getz, 1990 ). The lesser developed countries are trying to set up these committees to govern their academia and research. World Health Organization has stated that adhering to “ ethical principles … [is central and important]... in order to protect the dignity, rights and welfare of research participants ” (WHO, 2021 ). Ethical guidelines taught to students can help develop ethical researchers and members of society who uphold values of ethical principles in practice.

As the first European-wide consortium established to assist academic integrity (European Network for Academic Integrity – ENAI), we felt the importance of guiding those institutions and communities that are trying to teach, research, and include ethical principles by providing overarching understanding of ethical guidelines that may influence policy. Therefore, we set up an advisory working group within ENAI in 2018 to support matters related to ethics, ethical committees and assisting on ethics related teaching activities.

Upon preliminary review and discussion, the group found a disparity in understanding, practice and teaching approaches to ethical applications among peers. This became the premise for this research paper. We first carried out a literature survey to review and summarise existing ethical governance (with historical perspectives) and procedures that are already in place to guide researchers in different discipline areas. By doing so, we attempted to consolidate, document and provide important steps in a typical ethical application process with example procedures from different universities. Finally, we attempted to provide insights and findings from practical workshops carried out at the 5th International Conference Plagiarism across Europe and Beyond, in Mykolas Romeris University, Lithuania in 2019, focussing on:

• highlighting the basic needs of an ethical committee within an institution,

• discussing and sharing examples of a typical ethical approval process,

• providing guidelines on the ways to teach research ethics with some examples.

We believe this paper provides guidelines on preparing and training both researchers and research students in appropriately upholding ethical practices through ethical approval processes.

Background literature survey

Responsible research practice (RRP) is scrutinised by the aspects of ethical principles and professional standards (WHO’s Code of Conduct for responsible Research, 2017). The Singapore statement on research integrity (The Singapore Statement on Research integrity, 2010) has provided an internationally acceptable guidance for RRP. The statement is based on maintaining honesty, accountability, professional courtesy in all aspects of research and maintaining fairness during collaborations. In other words, it does not simply focus on the procedural part of the research, instead covers wider aspects of “integrity” beyond the operational aspects (Israel and Drenth, 2016 ).

Institutions should focus on providing ethical guidance based on principles and values reflecting upon all aspects/stages of research (from the funding application/project development stage upto or beyond project closing stage). Figure  1 summarizes the different aspects/stages of a typical research and highlights the needs of RRP in compliance with ethical governance at each stage with examples (the figure is based on Resnik, 2020 ; Žukauskas et al., 2018 ; Anderson, 2011 ; Fouka and Mantzorou, 2011 ).

Summary of the enabling ethical governance at different stages of research. Note that it is imperative for researchers to proactively consider the ethical implications before, during and after the actual research process. The summary shows that RRP should be in line with ethical considerations even long before the ethical approval stage

Individual responsibilities to enhance RRP

As explained in Fig.  1 , a successfully governed research should consider ethics at the planning stages prior to research. Many international guidance are compatible in enforcing/recommending 14 different “responsibilities” that were first highlighted in the Singapore Statement (2010) for researchers to follow and achieve competency in RRP. In order to understand the purpose and the expectation of these ethical guidelines, we have carried out an initial literature survey on expected individual responsibilities. These are summarised in Table  1 .

By following these directives, researchers can carry out accountable research by maximising ethical self-governance whilst minimising misconducts. In our own experiences of working with many researchers, their focus usually revolves around ethical “clearance” rather than behaviour. In other words, they perceive this as a paper exercise rather than trying to “own” ethical behaviour in everything they do. Although the ethical principles and responsibilities are explicitly highlighted in the majority of international guidelines [such as UK’s Research Governance Policy (NICE, 2018 ), Australian Government’s National Statement on Ethical Conduct in Human Research (Difn website a - National Statement on Ethical Conduct in Human Research (NSECHR), 2018 ), the Singapore Statement (2010) etc.]; and the importance of holistic approach has been argued in ethical decision making, many researchers and/or institutions only focus on ethics linked to the procedural aspects.

Studies in the past have also highlighted inconsistencies in institutional guidelines pointing to the fact that these inconsistencies may hinder the predicted research progress (Desmond & Dierickx 2021 ; Alba et al., 2020 ; Dellaportas et al., 2014 ; Speight 2016 ). It may also be possible that these were and still are linked to the institutional perceptions/expectations or the pre-empting contextual conditions that are imposed by individual countries. In fact, it is interesting to note many research organisations and HE institutions establish their own policies based on these directives.

Research governance - origins, expectations and practices

Ethical governance in clinical medicine helps us by providing a structure for analysis and decision-making. By providing workable definitions of benefits and risks as well as the guidance for evaluating/balancing benefits over risks, it supports the researchers to protect the participants and the general population.

According to the definition given by National Institute of Clinical care Excellence, UK (NICE 2018 ), “ research governance can be defined as the broad range of regulations, principles and standards of good practice that ensure high quality research ”. As stated above, our literature-based research survey showed that most of the ethical definitions are basically evolved from the medical field and other disciplines have utilised these principles to develop their own ethical guidance. Interestingly, historical data show that the medical research has been “self-governed” or in other words implicated by the moral behaviour of individual researchers (Fox 2017 ; Shaw et al., 2005 ; Getz, 1990 ). For example, early human vaccination trials conducted in 1700s used the immediate family members as test subjects (Fox, 2017 ). Here the moral justification might have been the fact that the subjects who would have been at risk were either the scientists themselves or their immediate families but those who would reap the benefits from the vaccination were the general public/wider communities. However, according to the current ethical principles, this assumption is entirely not acceptable.

Historically, ambiguous decision-making and resultant incidences of research misconduct have led to the need for ethical research governance in as early as the 1940’s. For instance, the importance of an international governance was realised only after the World War II, when people were astonished to note the unethical research practices carried out by Nazi scientists. As a result of this, in 1947 the Nuremberg code was published. The code mainly focussed on the following:

Informed consent and further insisted the research involving humans should be based on prior animal work,

The anticipated benefits should outweigh the risk,

Research should be carried out only by qualified scientists must conduct research,

Avoiding physical and mental suffering and.

Avoiding human research that would result in which death or disability.

(Weindling, 2001 ).

Unfortunately, it was reported that many researchers in the USA and elsewhere considered the Nuremberg code as a document condemning the Nazi atrocities, rather than a code for ethical governance and therefore ignored these directives (Ghooi, 2011 ). It was only in 1964 that the World Medical Association published the Helsinki Declaration, which set the stage for ethical governance and the implementation of the Institutional Review Board (IRB) process (Shamoo and Irving, 1993 ). This declaration was based on Nuremberg code. In addition, the declaration also paved the way for enforcing research being conducted in accordance with these guidelines.

Incidentally, the focus on research/ethical governance gained its momentum in 1974. As a result of this, a report on ethical principles and guidelines for the protection of human subjects of research was published in 1979 (The Belmont Report, 1979 ). This report paved the way to the current forms of ethical governance in biomedical and behavioural research by providing guidance.

Since 1994, the WHO itself has been providing several guidance to health care policy-makers, researchers and other stakeholders detailing the key concepts in medical ethics. These are specific to applying ethical principles in global public health.

Likewise, World Organization for Animal Health (WOAH), and International Convention for the Protection of Animals (ICPA) provide guidance on animal welfare in research. Due to this continuous guidance, together with accepted practices, there are internationally established ethical guidelines to carry out medical research. Our literature survey further identified freely available guidance from independent organisations such as COPE (Committee of Publication Ethics) and ALLEA (All European Academics) which provide support for maintaining research ethics in other fields such as education, sociology, psychology etc. In reality, ethical governance is practiced differently in different countries. In the UK, there is a clinical excellence research governance, which oversees all NHS related medical research (Mulholland and Bell, 2005 ). Although, the governance in other disciplines is not entirely centralised, many research funding councils and organisations [such as UKRI (UK-Research and Innovation; BBSC (Biotechnology and Biological Sciences Research Council; MRC (Medical Research Council); EPSRC (Economic and Social Research Council)] provide ethical governance and expect institutional adherence and monitoring. They expect local institutional (i.e. university/institutional) research governance for day-to-day monitoring of the research conducted within the organisation and report back to these funding bodies, monthly or annually (Department of Health, 2005). Likewise, there are nationally coordinated/regulated ethics governing bodies such as the US Office for Human Research Protections (US-OHRP), National Institute of Health (NIH) and the Canadian Institutes for Health Research (CIHR) in the USA and Canada respectively (Mulholland and Bell, 2005 ). The OHRP in the USA formally reviews all research activities involving human subjects. On the other hand, in Canada, CIHR works with the Natural Sciences and Engineering Research Council (NSERC), and the Social Sciences and Humanities Research Council (SSHRC). They together have produced a Tri-Council Policy Statement (TCPS) (Stephenson et al., 2020 ) as ethical governance. All Canadian institutions are expected to adhere to this policy for conducting research. As for Australia, the research is governed by the Australian code for the responsible conduct of research (2008). It identifies the responsibilities of institutions and researchers in all areas of research. The code has been jointly developed by the National Health and Medical Research Council (NHMRC), the Australian Research Council (ARC) and Universities Australia (UA). This information is summarized in Table  2 .

Basic structure of an institutional ethical advisory committee (EAC)

The WHO published an article defining the basic concepts of an ethical advisory committee in 2009 (WHO, 2009 - see above). According to this, many countries have established research governance and monitor the ethical practice in research via national and/or regional review committees. The main aims of research ethics committees include reviewing the study proposals, trying to understand the justifications for human/animal use, weighing the merits and demerits of the usage (linking to risks vs. potential benefits) and ensuring the local, ethical guidelines are followed Difn website b - Enago academy Importance of Ethics Committees in Scholarly Research, 2020 ; Guide for Research Ethics - Council of Europe, 2014 ). Once the research has started, the committee needs to carry out periodic surveillance to ensure the institutional ethical norms are followed during and beyond the study. They may also be involved in setting up and/or reviewing the institutional policies.

For these aspects, IRB (or institutional ethical advisory committee - IEAC) is essential for local governance to enhance best practices. The advantage of an IRB/EEAC is that they understand the institutional conditions and can closely monitor the ongoing research, including any changes in research directions. On the other hand, the IRB may be overly supportive to accept applications, influenced by the local agenda for achieving research excellence, disregarding ethical issues (Kotecha et al., 2011 ; Kayser-Jones, 2003 ) or, they may be influenced by the financial interests in attracting external funding. In this respect, regional and national ethics committees are advantageous to ensure ethical practice. Due to their impartiality, they would provide greater consistency and legitimacy to the research (WHO, 2009 ). However, the ethical approval process of regional and national ethics committees would be time consuming, as they do not have the local knowledge.

As for membership in the IRBs, most of the guidelines [WHO, NICE, Council of Europe, (2012), European Commission - Facilitating Research Excellence in FP7 ( 2013 ) and OHRP] insist on having a variety of representations including experts in different fields of research, and non-experts with the understanding of local, national/international conflicts of interest. The former would be able to understand/clarify the procedural elements of the research in different fields; whilst the latter would help to make neutral and impartial decisions. These non-experts are usually not affiliated to the institution and consist of individuals representing the broader community (particularly those related to social, legal or cultural considerations). IRBs consisting of these varieties of representation would not only be in a position to understand the study procedures and their potential direct or indirect consequences for participants, but also be able to identify any community, cultural or religious implications of the study.

Understanding the subtle differences between ethics and morals

Interestingly, many ethical guidelines are based on society’s moral “beliefs” in such a way that the words “ethics”‘and “morals” are reciprocally used to define each other. However, there are several subtle differences between them and we have attempted to compare and contrast them herein. In the past, many authors have interchangeably used the words “morals”‘and “ethics”‘(Warwick, 2003 ; Kant, 2018 ; Hazard, GC (Jr)., 1994 , Larry, 1982 ). However, ethics is linked to rules governed by an external source such as codes of conduct in workplaces (Kuyare et al., 2014 ). In contrast, morals refer to an individual’s own principles regarding right and wrong. Quinn ( 2011 ) defines morality as “ rules of conduct describing what people ought and ought not to do in various situations … ” while ethics is “... the philosophical study of morality, a rational examination into people’s moral beliefs and behaviours ”. For instance, in a case of parents demanding that schools overturn a ban on use of corporal punishment of children by schools and teachers (Children’s Rights Alliance for England, 2005 ), the parents believed that teachers should assume the role of parent in schools and use corporal or physical punishment for children who misbehaved. This stemmed from their beliefs and what they felt were motivated by “beliefs of individuals or groups”. For example, recent media highlights about some parents opposing LGBT (Lesbian, Gay, Bisexual, and Transgender) education to their children (BBC News, 2019 ). One parent argued, “Teaching young children about LGBT at a very early stage is ‘morally’ wrong”. She argued “let them learn by themselves as they grow”. This behaviour is linked to and governed by the morals of an ethnic community. Thus, morals are linked to the “beliefs of individuals or group”. However, when it comes to the LGBT rights these are based on ethical principles of that society and governed by law of the land. However, the rights of children to be protected from “inhuman and degrading” treatment is based on the ethical principles of the society and governed by law of the land. Individuals, especially those who are working in medical or judicial professions have to follow an ethical code laid down by their profession, regardless of their own feelings, time or preferences. For instance, a lawyer is expected to follow the professional ethics and represent a defendant, despite the fact that his morals indicate the defendant is guilty.

In fact, we as a group could not find many scholarly articles clearly comparing or contrasting ethics with morals. However, a table presented by Surbhi ( 2015 ) (Difn website c ) tries to differentiate these two terms (see Table  3 ).

Although Table 3 gives some insight on the differences between these two terms, in practice many use these terms as loosely as possible mainly because of their ambiguity. As a group focussed on the application of these principles, we would recommend to use the term “ethics” and avoid “morals” in research and academia.

Based on the literature survey carried out, we were able to identify the following gaps:

there is some disparity in existing literature on the importance of ethical guidelines in research

there is a lack of consensus on what code of conduct should be followed, where it should be derived from and how it should be implemented

The mission of ENAI’s ethical advisory working group

The Ethical Advisory Working Group of ENAI was established in 2018 to promote ethical code of conduct/practice amongst higher educational organisations within Europe and beyond (European Network for Academic Integrity, 2018 ). We aim to provide unbiased advice and consultancy on embedding ethical principles within all types of academic, research and public engagement activities. Our main objective is to promote ethical principles and share good practice in this field. This advisory group aims to standardise ethical norms and to offer strategic support to activities including (but not exclusive to):

● rendering advice and assistance to develop institutional ethical committees and their regulations in member institutions,

● sharing good practice in research and academic ethics,

● acting as a critical guide to institutional review processes, assisting them to maintain/achieve ethical standards,

● collaborating with similar bodies in establishing collegiate partnerships to enhance awareness and practice in this field,

● providing support within and outside ENAI to develop materials to enhance teaching activities in this field,

● organising training for students and early-career researchers about ethical behaviours in form of lectures, seminars, debates and webinars,

● enhancing research and dissemination of the findings in matters and topics related to ethics.

The following sections focus on our suggestions based on collective experiences, review of literature provided in earlier sections and workshop feedback collected:

a) basic needs of an ethical committee within an institution;

b) a typical ethical approval process (with examples from three different universities); and

c) the ways to obtain informed consent with some examples. This would give advice on preparing and training both researchers and research students in appropriately upholding ethical practices through ethical approval processes.

Setting up an institutional ethical committee (ECs)

Institutional Ethical Committees (ECs) are essential to govern every aspect of the activities undertaken by that institute. With regards to higher educational organisations, this is vital to establish ethical behaviour for students and staff to impart research, education and scholarly activities (or everything) they do. These committees should be knowledgeable about international laws relating to different fields of studies (such as science, medicine, business, finance, law, and social sciences). The advantages and disadvantages of institutional, subject specific or common (statutory) ECs are summarised in Fig.  2 . Some institutions have developed individual ECs linked to specific fields (or subject areas) whilst others have one institutional committee that overlooks the entire ethical behaviour and approval process. There is no clear preference between the two as both have their own advantages and disadvantages (see Fig. 2 ). Subject specific ECs are attractive to medical, law and business provisions, as it is perceived the members within respective committees would be able to understand the subject and therefore comprehend the need of the proposed research/activity (Kadam, 2012 ; Schnyder et al., 2018 ). However, others argue, due to this “ specificity ”, the committee would fail to forecast the wider implications of that application. On the other hand, university-wide ECs would look into the wider implications. Yet they find it difficult to understand the purpose and the specific applications of that research. Not everyone understands dynamics of all types of research methodologies, data collection, etc., and therefore there might be a chance of a proposal being rejected merely because the EC could not understand the research applications (Getz, 1990 ).

Summary of advantages and disadvantages of three different forms of ethical committees

[N/B for Fig. 2 : Examples of different types of ethical application procedures and forms used were discussed with the workshop attendees to enhance their understanding of the differences. GDPR = General Data Protection Regulation].

Although we recommend a designated EC with relevant professional, academic and ethical expertise to deal with particular types of applications, the membership (of any EC) should include some non-experts who would represent the wider community (see above). Having some non-experts in EC would not only help the researchers to consider explaining their research in layperson’s terms (by thinking outside the box) but also would ensure efficiency without compromising participants/animal safety. They may even help to address the common ethical issues outside research culture. Some UK universities usually offer this membership to a clergy, councillor or a parliamentarian who does not have any links to the institutions. Most importantly, it is vital for any EC members to undertake further training in addition to previous experience in the relevant field of research ethics.

Another issue that raises concerns is multi-centre research, involving several institutions, where institutionalised ethical approvals are needed from each partner. In some cases, such as clinical research within the UK, a common statutory EC called National Health Services (NHS) Research Ethics Committee (NREC) is in place to cover research ethics involving all partner institutions (NHS, 2018 ). The process of obtaining approval from this type of EC takes time, therefore advanced planning is needed.

Ethics approval forms and process

During the workshop, we discussed some anonymised application forms obtained from open-access sources for qualitative and quantitative research as examples. Considering research ethics, for the purpose of understanding, we arbitrarily divided this in two categories; research based on (a) quantitative and (b) qualitative methodologies. As their name suggests their research approach is extremely different from each other. The discussion elicited how ECs devise different types of ethical application form/questions. As for qualitative research, these are often conducted as “face-to-face” interviews, which would have implications on volunteer anonymity.

Furthermore, discussions posited when the interviews are replaced by on-line surveys, they have to be administered through registered university staff to maintain confidentiality. This becomes difficult when the research is a multi-centre study. These types of issues are also common in medical research regarding participants’ anonymity, confidentially, and above all their right to withdraw consent to be involved in research.

Storing and protecting data collected in the process of the study is also a point of consideration when applying for approval.

Finally, the ethical processes of invasive (involving human/animals) and non-invasive research (questionnaire based) may slightly differ from one another. Following research areas are considered as investigations that need ethical approval:

research that involves human participants (see below)

use of the ‘products’ of human participants (see below)

work that potentially impacts on humans (see below)

research that involves animals

In addition, it is important to provide a disclaimer even if an ethical approval is deemed unnecessary. Following word cloud (Fig.  3 ) shows the important variables that need to be considered at the brainstorming stage before an ethical application. It is worth noting the importance of proactive planning predicting the “unexpected” during different phases of a research project (such as planning, execution, publication, and future directions). Some applications (such as working with vulnerable individuals or children) will require safety protection clearance (such as DBS - Disclosure and Barring Service, commonly obtained from the local police). Please see section on Research involving Humans - Informed consents for further discussions.

Examples of important variables that need to be considered for an ethical approval

It is also imperative to report or re-apply for ethical approval for any minor or major post-approval changes to original proposals made. In case of methodological changes, evidence of risk assessments for changes and/or COSHH (Control of Substances Hazardous to Health Regulations) should also be given. Likewise, any new collaborative partners or removal of researchers should also be notified to the IEAC.

Other findings include:

in case of complete changes in the project, the research must be stopped and new approval should be seeked,

in case of noticing any adverse effects to project participants (human or non-human), these should also be notified to the committee for appropriate clearance to continue the work, and

the completion of the project must also be notified with the indication whether the researchers may restart the project at a later stage.

Research involving humans - informed consents

While discussing research involving humans and based on literature review, findings highlight the human subjects/volunteers must willingly participate in research after being adequately informed about the project. Therefore, research involving humans and animals takes precedence in obtaining ethical clearance and its strict adherence, one of which is providing a participant information sheet/leaflet. This sheet should contain a full explanation about the research that is being carried out and be given out in lay-person’s terms in writing (Manti and Licari 2018 ; Hardicre 2014 ). Measures should also be in place to explain and clarify any doubts from the participants. In addition, there should be a clear statement on how the participants’ anonymity is protected. We provide below some example questions below to help the researchers to write this participant information sheet:

What is the purpose of the study?

Why have they been chosen?

What will happen if they take part?

What do they have to do?

What happens when the research stops?

What if something goes wrong?

What will happen to the results of the research study?

Will taking part be kept confidential?

How to handle “vulnerable” participants?

How to mitigate risks to participants?

Many institutional ethics committees expect the researchers to produce a FAQ (frequently asked questions) in addition to the information about research. Most importantly, the researchers also need to provide an informed consent form, which should be signed by each human participant. The five elements identified that are needed to be considered for an informed consent statement are summarized in Fig.  4 below (slightly modified from the Federal Policy for the Protection of Human Subjects ( 2018 ) - Diffn website c ).

Five basic elements to consider for an informed consent [figure adapted from Diffn website c ]

The informed consent form should always contain a clause for the participant to withdraw their consent at any time. Should this happen all the data from that participant should be eliminated from the study without affecting their anonymity.

Typical research ethics approval process

In this section, we provide an example flow chart explaining how researchers may choose the appropriate application and process, as highlighted in Fig.  5 . However, it is imperative to note here that these are examples only and some institutions may have one unified application with separate sections to demarcate qualitative and quantitative research criteria.

Typical ethical approval processes for quantitative and qualitative research. [N/B for Fig. 5 - This simplified flow chart shows that fundamental process for invasive and non-invasive EC application is same, the routes and the requirements for additional information are slightly different]

Once the ethical application is submitted, the EC should ensure a clear approval procedure with distinctly defined timeline. An example flow chart showing the procedure for an ethical approval was obtained from University of Leicester as open-access. This is presented in Fig.  6 . Further examples of the ethical approval process and governance were discussed in the workshop.

An example ethical approval procedures conducted within University of Leicester (Figure obtained from the University of Leicester research pages - Difn website d - open access)

Strategies for ethics educations for students

Student education on the importance of ethics and ethical behaviour in research and scholarly activities is extremely essential. Literature posits in the area of medical research that many universities are incorporating ethics in post-graduate degrees but when it comes to undergraduate degrees, there is less appetite to deliver modules or even lectures focussing on research ethics (Seymour et al., 2004 ; Willison and O’Regan, 2007 ). This may be due to the fact that undergraduate degree structure does not really focus on research (DePasse et al., 2016 ). However, as Orr ( 2018 ) suggested, institutions should focus more on educating all students about ethics/ethical behaviour and their importance in research, than enforcing punitive measures for unethical behaviour. Therefore, as an advisory committee, and based on our preliminary literature survey and workshop results, we strongly recommend incorporating ethical education within undergraduate curriculum. Looking at those institutions which focus on ethical education for both under-and postgraduate courses, their approaches are either (a) a lecture-based delivery, (b) case study based approach or (c) a combined delivery starting with a lecture on basic principles of ethics followed by generating a debate based discussion using interesting case studies. The combined method seems much more effective than the other two as per our findings as explained next.

As many academics who have been involved in teaching ethics and/or research ethics agree, the underlying principles of ethics is often perceived as a boring subject. Therefore, lecture-based delivery may not be suitable. On the other hand, a debate based approach, though attractive and instantly generates student interest, cannot be effective without students understanding the underlying basic principles. In addition, when selecting case studies, it would be advisable to choose cases addressing all different types of ethical dilemmas. As an advisory group within ENAI, we are in the process of collating supporting materials to help to develop institutional policies, creating advisory documents to help in obtaining ethical approvals, and teaching materials to enhance debate-based lesson plans that can be used by the member and other institutions.

Concluding remarks

In summary, our literature survey and workshop findings highlight that researchers should accept that ethics underpins everything we do, especially in research. Although ethical approval is tedious, it is an imperative process in which proactive thinking is essential to identify ethical issues that might affect the project. Our findings further lead us to state that the ethical approval process differs from institution to institution and we strongly recommend the researchers to follow the institutional guidelines and their underlying ethical principles. The ENAI workshop in Vilnius highlighted the importance of ethical governance by establishing ECs, discussed different types of ECs and procedures with some examples and highlighted the importance of student education to impart ethical culture within research communities, an area that needs further study as future scope.

Declarations

The manuscript was entirely written by the corresponding author with contributions from co-authors who have also taken part in the delivery of the workshop. Authors confirm that the data supporting the findings of this study are available within the article. We can also confirm that there are no potential competing interests with other organisations.

Availability of data and materials

Authors confirm that the data supporting the findings of this study are available within the article.

Abbreviations

ALL European academics

Australian research council

Biotechnology and biological sciences research council

Canadian institutes for health research

Committee of publication ethics

Ethical committee

European network of academic integrity

Economic and social research council

International convention for the protection of animals

institutional ethical advisory committee

Institutional review board

Immaculata university of Pennsylvania

Lesbian, gay, bisexual, and transgender

Medical research council)

National health services

National health services nih national institute of health (NIH)

National institute of clinical care excellence

National health and medical research council

Natural sciences and engineering research council

National research ethics committee

National statement on ethical conduct in human research

Responsible research practice

Social sciences and humanities research council

Tri-council policy statement

World Organization for animal health

Universities Australia

UK-research and innovation

US office for human research protections

Alba S, Lenglet A, Verdonck K, Roth J, Patil R, Mendoza W, Juvekar S, Rumisha SF (2020) Bridging research integrity and global health epidemiology (BRIDGE) guidelines: explanation and elaboration. BMJ Glob Health 5(10):e003237. https://doi.org/10.1136/bmjgh-2020-003237

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Sivasubramaniam, S., Dlabolová, D.H., Kralikova, V. et al. Assisting you to advance with ethics in research: an introduction to ethical governance and application procedures. Int J Educ Integr 17 , 14 (2021). https://doi.org/10.1007/s40979-021-00078-6

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Introduction: What is Research Ethics?

Research Ethics is defined here to be the ethics of the planning, conduct, and reporting of research. This introduction covers what research ethics is, its ethical distinctions, approaches to teaching research ethics, and other resources on this topic.

What is Research Ethics

Why Teach Research Ethics

Animal Subjects

Biosecurity

Collaboration

Conflicts of Interest

Data Management

Human Subjects

Peer Review

Publication

Research Misconduct

Social Responsibility

Stem Cell Research

Whistleblowing

Descriptions of educational settings , including in the classroom, and in research contexts.

Case Studies

Other Discussion Tools

Information about the history and authors of the Resources for Research Ethics Collection

What is Research Ethics?

Research Ethics is defined here to be the ethics of the planning, conduct, and reporting of research. It is clear that research ethics should include:

• Protections of human and animal subjects

However, not all researchers use human or animal subjects, nor are the ethical dimensions of research confined solely to protections for research subjects. Other ethical challenges are rooted in many dimensions of research, including the:

• Collection, use, and interpretation of research data
• Methods for reporting and reviewing research plans or findings
• Relationships among researchers with one another
• Relationships between researchers and those that will be affected by their research
• Means for responding to misunderstandings, disputes, or misconduct
• Options for promoting ethical conduct in research

The domain of research ethics is intended to include nothing less than the fostering of research that protects the interests of the public, the subjects of research, and the researchers themselves.

Ethical Distinctions

In discussing or teaching research ethics, it is important to keep some basic distinctions in mind.

• It is important not to confuse moral claims about how people ought to behave with descriptive claims about how they in fact do behave. From the fact that gift authorship or signing off on un-reviewed data may be "common practice" in some contexts, it doesn't follow that they are morally or professionally justified. Nor is morality to be confused with the moral beliefs or ethical codes that a given group or society holds (how some group thinks people should live). A belief in segregation is not morally justified simply because it is widely held by a group of people or given society. Philosophers term this distinction between prescriptive and descriptive claims the 'is-ought distinction.'  
• A second important distinction is that between morality and the law. The law may or may not conform to the demands of ethics (Kagan, 1998). To take a contemporary example: many believe that the law prohibiting federally funded stem cell research is objectionable on moral (as well as scientific) grounds, i.e., that such research can save lives and prevent much human misery. History is full of examples of bad laws, that is laws now regarded as morally unjustifiable, e.g., the laws of apartheid, laws prohibiting women from voting or inter-racial couples from marrying.  
• It is also helpful to distinguish between two different levels of discussion (or two different kinds of ethical questions): first-order or "ground-level" questions and second-order questions.  
• First-order moral questions concern what we should do. Such questions may be very general or quite specific. One might ask whether the tradition of 'senior' authorship should be defended and preserved or, more generally, what are the principles that should go into deciding the issue of 'senior' authorship. Such questions and the substantive proposals regarding how to answer them belong to the domain of what moral philosophers call 'normative ethics.'  
• Second-order moral questions concern the nature and purpose of morality itself. When someone claims that falsifying data is wrong, what exactly is the standing of this claim? What exactly does the word 'wrong' mean in the conduct of scientific research? And what are we doing when we make claims about right and wrong, scientific integrity and research misconduct? These second-order questions are quite different from the ground-level questions about how to conduct one's private or professional life raised above. They concern the nature of morality rather than its content, i.e., what acts are required, permitted or prohibited. This is the domain of what moral philosophers call 'metaethics' (Kagan, 1998).

Ethical Approaches

Each of these approaches provides moral principles and ways of thinking about the responsibilities, duties and obligations of moral life. Individually and jointly, they can provide practical guidance in ethical decision-making.

• One of the most influential and familiar approaches to ethics is deontological ethics, associated with Immanuel Kant (1742-1804). Deontological ethics hold certain acts as right or wrong in themselves, e.g., promise breaking or lying. So, for example, in the context of research, fraud, plagiarism and misrepresentation are regarded as morally wrong in themselves, not simply because they (tend to) have bad consequences. The deontological approach is generally grounded in a single fundamental principle: Act as you would wish others to act towards you OR always treat persons as an end, never as a means to an end.  
• From such central principles are derived rules or guidelines for what is permitted, required and prohibited. Objections to principle-based or deontological ethics include the difficulty of applying highly general principles to specific cases, e.g.: Does treating persons as ends rule out physician-assisted suicide, or require it? Deontological ethics is generally contrasted to consequentialist ethics (Honderich, 1995).  
• According to consequentialist approaches, the rightness or wrongness of an action depends solely on its consequences. One should act in such a way as to bring about the best state of affairs, where the best state of affairs may be understood in various ways, e.g., as the greatest happiness for the greatest number of people, maximizing pleasure and minimizing pain or maximizing the satisfaction of preferences. A theory such as Utilitarianism (with its roots in the work of Jeremy Bentham and John Stuart Mill) is generally taken as the paradigm example of consequentialism. Objections to consequentialist ethics tend to focus on its willingness to regard individual rights and values as "negotiable." So, for example, most people would regard murder as wrong independently of the fact that killing one person might allow several others to be saved (the infamous sacrifice of an ailing patient to provide organs for several other needy patients). Similarly, widespread moral opinion holds certain values important (integrity, justice) not only because they generally lead to good outcomes, but in and of themselves.
• Virtue ethics focuses on moral character rather than action and behavior considered in isolation. Central to this approach is the question what ought we (as individuals, as scientists, as physicians) to be rather than simply what we ought to do. The emphasis here is on inner states, that is, moral dispositions and habits such as courage or a developed sense of personal integrity. Virtue ethics can be a useful approach in the context of RCR and professional ethics, emphasizing the importance of moral virtues such as compassion, honesty, and respect. This approach has also a great deal to offer in discussions of bioethical issues where a traditional emphasis on rights and abstract principles frequently results in polarized, stalled discussions (e.g., abortion debates contrasting the rights of the mother against the rights of the fetus).  
• The term 'an ethics of care' grows out of the work of Carol Gilligan, whose empirical work in moral psychology claimed to discover a "different voice," a mode of moral thinking distinct from principle-based moral thinking (e.g., the theories of Kant and Mill). An ethics of care stresses compassion and empathetic understanding, virtues Gilligan associated with traditional care-giving roles, especially those of women.  
• This approach differs from traditional moral theories in two important ways. First, it assumes that it is the connections between persons, e.g., lab teams, colleagues, parents and children, student and mentor, not merely the rights and obligations of discrete individuals that matter. The moral world, on this view, is best seen not as the interaction of discrete individuals, each with his or her own interests and rights, but as an interrelated web of obligations and commitment. We interact, much of the time, not as private individuals, but as members of families, couples, institutions, research groups, a given profession and so on. Second, these human relationships, including relationships of dependency, play a crucial role on this account in determining what our moral obligations and responsibilities are. So, for example, individuals have special responsibilities to care for their children, students, patients, and research subjects.  
• An ethics of care is thus particularly useful in discussing human and animal subjects research, issues of informed consent, and the treatment of vulnerable populations such as children, the infirm or the ill.  
• The case study approach begins from real or hypothetical cases. Its objective is to identify the intuitively plausible principles that should be taken into account in resolving the issues at hand. The case study approach then proceeds to critically evaluate those principles. In discussing whistle-blowing, for example, a good starting point is with recent cases of research misconduct, seeking to identify and evaluate principles such as a commitment to the integrity of science, protecting privacy, or avoiding false or unsubstantiated charges. In the context of RCR instruction, case studies provide one of the most interesting and effective approaches to developing sensitivity to ethical issues and to honing ethical decision-making skills.  
• Strictly speaking, casuistry is more properly understood as a method for doing ethics rather than as itself an ethical theory. However, casuistry is not wholly unconnected to ethical theory. The need for a basis upon which to evaluate competing principles, e.g., the importance of the well-being of an individual patient vs. a concern for just allocation of scarce medical resources, makes ethical theory relevant even with case study approaches.  
• Applied ethics is a branch of normative ethics. It deals with practical questions particularly in relation to the professions. Perhaps the best known area of applied ethics is bioethics, which deals with ethical questions arising in medicine and the biological sciences, e.g., questions concerning the application of new areas of technology (stem cells, cloning, genetic screening, nanotechnology, etc.), end of life issues, organ transplants, and just distribution of healthcare. Training in responsible conduct of research or "research ethics" is merely one among various forms of professional ethics that have come to prominence since the 1960s. Worth noting, however, is that concern with professional ethics is not new, as ancient codes such as the Hippocratic Oath and guild standards attest (Singer, 1986).
• Adams D, Pimple KD (2005): Research Misconduct and Crime: Lessons from Criminal Science on Preventing Misconduct and Promoting Integrity. Accountability in Research 12(3):225-240.
• Anderson MS, Horn AS, Risbey KR, Ronning EA, De Vries R, Martinson BC (2007): What Do Mentoring and Training in the Responsible Conduct of Research Have To Do with Scientists' Misbehavior? Findings from a National Survey of NIH-Funded Scientists . Academic Medicine 82(9):853-860.
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• Pimple KD (2002): Six Domains of Research Ethics: A Heuristic Framework for the Responsible Conduct of Research. Science and Engineering Ethics 8(2):191-205.
• Steneck NH (2006): Fostering Integrity in Research: Definitions, Current Knowledge, and Future Directions. Science and Engineering Ethics 12:53-74.
• Steneck NH, Bulger RE (2007): The History, Purpose, and Future of Instruction in the Responsible Conduct of Research. Academic Medicine. 82(9):829-834.
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The Resources for Research Ethics Education site was originally developed and maintained by Dr. Michael Kalichman, Director of the Research Ethics Program at the University of California San Diego. The site was transferred to the Online Ethics Center in 2021 with the permission of the author.

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An Introduction to Research Ethics and Scientific Integrity

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This chapter outlines the aims for the handbook. A main aim is to be a first point of contact for contemporary information, issues, and challenges in the fields of research ethics and scientific integrity. It is aimed at researchers, reviewers, and policymakers to help them pursue the best ways forward in seeking ethics and integrity in all research across disciplines, methods, subjects, participants, and contexts. The authors form a global network of scholars, practitioners, and researchers with a range of experience and insights that scope a challenging field but one that is vital to the maintenance of research standards and public confidence in science. Fact-based policymaking remains under threat from political and ideological pressures. Scientists and researchers in all disciplines and professions hold a clear responsibility to protect their subjects, research participants, and society from pressures, interests, and prejudices that risk undermining the value of their work. This overview outlines how the handbook is constructed and how readers might gain from it.

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Iphofen, R. (2019). An Introduction to Research Ethics and Scientific Integrity. In: Iphofen, R. (eds) Handbook of Research Ethics and Scientific Integrity. Springer, Cham. https://doi.org/10.1007/978-3-319-76040-7_62-1

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DOI : https://doi.org/10.1007/978-3-319-76040-7_62-1

Received : 17 March 2019

Accepted : 01 May 2019

Published : 30 May 2019

Publisher Name : Springer, Cham

Print ISBN : 978-3-319-76040-7

Online ISBN : 978-3-319-76040-7

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Module one: introduction to research ethics

Affiliation.

• 1 University of the Witwatersrand Division of Bioethics WITS 2050, South Africa. [email protected].
• PMID: 15748175
• DOI: 10.1111/j.1471-8847.2005.00099.x

This module will introduce you to the ethical concepts underlying applied ethical decision-making in the area of research involving human participants. We will also learn what the issues are that people involved in research on research ethics are concerned with. Ethics without an understanding of historical and legal context makes arguably little sense. It is for this reason that this module will begin with a brief history of research ethics and ends with a brief overview of the relevant national and international guidelines pertaining to ethical issues in research involving human participants.

• Clinical Trials as Topic / ethics
• Ethical Analysis
• Ethical Theory
• Ethics, Research
• Guidelines as Topic
• Helsinki Declaration
• Human Experimentation / ethics*
• Human Experimentation / standards
• Informed Consent
• Internationality
• Research Subjects

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How to Write an Ethics Paper

Last Updated: May 16, 2023 Approved

This article was co-authored by Emily Listmann, MA . Emily Listmann is a private tutor in San Carlos, California. She has worked as a Social Studies Teacher, Curriculum Coordinator, and an SAT Prep Teacher. She received her MA in Education from the Stanford Graduate School of Education in 2014. wikiHow marks an article as reader-approved once it receives enough positive feedback. In this case, 100% of readers who voted found the article helpful, earning it our reader-approved status. This article has been viewed 251,541 times.

Writing an ethics paper can present some unique challenges. For the most part, the paper will be written like any other essay or research paper, but there are some key differences. An ethics paper will generally require you to argue for a specific position rather than simply present an overview of an issue. Arguing this position will also involve presenting counterarguments and then refuting them. Finally, ensuring that your reasoning is valid and sound and citing the appropriate sources will allow you to write an ethics paper that will satisfy any critic.

Getting Started

• What is the main objective of the assignment?
• What specific things do you need to do in order to get a good grade?
• How much time will you need to complete the assignment?

• For example, you might begin with a topic of "ethical problems of euthanasia." This is very broad, and so forms a good starting point.

• Remember, you may refine your topic even further after you have begun writing your paper. This is perfectly acceptable, and is part of the advantage of writing a paper in multiple drafts.

• For example, you might include issues such as: "describing specifically what is meant by 'extreme, constant pain.' "Other issues might include, "the rights and responsibilities of physicians regarding euthanasia," and "voluntary versus involuntary euthanasia."
• After making this list, group or order them in some way. For example, you might imagine yourself taking the position that euthanasia is acceptable in this circumstance, and you could order the issues based on how you would draw supporting evidence and build your claim.

Developing Your Thesis Statement

• In your thesis, you should take a specific stand on the ethical issue. For example, you might write your thesis as follows: "Euthanasia is an immoral option even when patients are in constant, extreme pain."

• For example, this thesis statement is ambiguous: "Patients should not undergo euthanasia even when suffering constant, extreme pain." With how it's worded, it's unclear whether you mean that euthanasia should be outlawed or that it is morally wrong.
• Clarify your position to create a strong thesis: "Euthanasia is an immoral option even when patients are in constant, extreme pain."

• For example, in the thesis, "It is immoral for patients to choose euthanasia even when suffering constant, extreme pain," the moral burden is on the patient's actions. The author of this thesis would need to make sure to focus on the patient in the essay and not to focus on the moral implications of the doctor's actions.
• If the thesis you have written does not reflect what you want to argue in your paper, start over and draft a new thesis statement.

Conducting Research

• Ask a librarian for help finding sources if you are not sure how to access your library’s databases.
• A simple way to strengthen your argument through citations is by incorporating some relevant statistics. Simple statistics can have a major impact if presented after you've made a bold assertion. For instance, you may claim that the patient's family members would be unduly traumatized if the patient chose euthanasia, and then cite a university study that catalogued a majority of families reporting trauma or stress in this situation.
• Another helpful citation is one in which the broad issue itself is discussed. For instance, you might cite a prominent ethicist's position on your issue to strengthen your position.

• The author and his or her credentials. Does the source provide the author’s first and last name and credentials (M.D., Ph.D, etc.)? Steer clear of sources without an author attached to them or that lack credentials when credentials seem crucial, such as in an article about a medical subject. [3] X Research source
• Type of publication. Is the publication a book, journal, magazine, or website? Is the publisher an academic or educational institution? Does the publisher have a motive other than education? Who is the intended audience? Ask yourself these questions to determine if this source is reliable. For example, a university or government website might be reliable, but a site that sells items may be biased toward what they're selling.
• Citations. How well has the author researched his or her topic? Check the author’s bibliography or works cited page. If the author has not provided any sources, then you may want to look for a different source. [4] X Research source
• Bias. Has the author presented an objective, well-reasoned account of the topic? If the sources seems skewed towards one side of the argument, then it may not be a good choice. [5] X Research source
• Publication date. Does this source present the most up to date information on the subject? If the sources is outdated, then try to find something more recent. [6] X Research source

• To check for comprehension after reading a source, try to summarize the source in your own words and generate a response to the author’s main argument. If you cannot do one or both of these things, then you may need to read the source again.
• Creating notecards for your sources may also help you to organize your ideas. Write the citation for the source on the top of the notecard, then write a brief summary and response to the article in the lined area of the notecard. [7] X Research source

• Remember to indicate when you have quoted a source in your notes by putting it into quotation marks and including information about the source such as the author’s name, article or book title, and page number. [8] X Research source

Writing and Revising Your Ethics Paper

• To expand on your outline, write a couple of sentences describing and/or explaining each of the items in your outline. Include a relevant source for each item as well.

• Check your outline to see if you have covered each of these items in this order. If not, you will need to add a section and use your sources to help inform that section.

• In your first draft, focus on the quality of the argument, rather than the quality of the prose. If the argument is structured well and each conclusion is supported by your reasoning and by cited evidence, you will be able to focus on the writing itself on the second draft.
• Unless major revisions are needed to your argument (for example, if you have decided to change your thesis statement), use the second draft to strengthen your writing. Focus on sentence lengths and structures, vocabulary, and other aspects of the prose itself.

• Try to allow yourself a few days or even a week to revise your paper before it is due. If you do not allow yourself enough time to revise, then you will be more prone to making simple mistakes and your grade may suffer as a result. [10] X Research source

• Does my paper fulfill the requirements of the assignment? How might it score according to the rubric provided by my instructor?
• What is your main point? How might you clarify your main point?
• Who is your audience? Have you considered their needs and expectations?
• What is your purpose? Have you accomplished your purpose with this paper?
• How effective is your evidence? How might your strengthen your evidence?
• Does every part of your paper relate back to your thesis? How might you improve these connections?
• Is anything confusing about your language or organization? How might your clarify your language or organization?
• Have you made any errors with grammar, punctuation, or spelling? How can you correct these errors?
• What might someone who disagrees with you say about your paper? How can you address these opposing arguments in your paper? [11] X Research source

• As you read your paper out loud, highlight or circle any errors and revise as necessary before printing your final copy.

Community Q&A

• If at all possible, have someone else read through your paper before submitting it. They can provide valuable feedback on style as well as catching grammatical errors. Thanks Helpful 0 Not Helpful 1

Things You'll Need

• Word-processing software
• Access to your library’s databases
• Pencil and highlighter

You Might Also Like

• ↑ https://owl.english.purdue.edu/owl/resource/688/1/
• ↑ https://owl.english.purdue.edu/owl/resource/553/03/
• ↑ http://guides.jwcc.edu/content.php?pid=65900&sid=538553
• ↑ http://www.writing.utoronto.ca/advice/reading-and-researching/notes-from-research
• ↑ https://owl.english.purdue.edu/owl/resource/658/05/
• ↑ https://owl.english.purdue.edu/owl/resource/561/05/

About This Article

To write an ethics paper, start by researching the issue you want to write about and evaluating your sources for potential bias and trustworthiness. Next, develop a thesis statement that takes a specific stand on the issue and create an outline that includes the key arguments. As you write, avoid using words like “could” or “might,” which will seem ambiguous to the reader. Once you’ve finished your paper, take a break for a few days so your mind is clear, then go back and revise what you wrote, focusing on the quality of your argument. For tips from our Education reviewer on how to annotate source material as you research, read on! Did this summary help you? Yes No

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Ethical considerations in scientific writing

Jane d. carver.

Department of Pediatrics, University of South Florida, and Clinical and Translational Science Institute (USF-CTSI), Tampa, Florida, USA

Patricia J. Emmanuel

Ritu parchure.

1 Prayas, Pune, India

INTRODUCTION

Fostering scientific advancement requires strict adherence to ethical guidelines for research and scientific writing. Several professional organizations have policies to address the ethics associated with scientific writing and publishing, including the Committee on Publication Ethics and the International Council of Medical Journal Editors (ICMJE); the majority of medical journals follow the ICMJE's Uniform Guidelines. We discuss two issues related to ethics in scientific writing: Plagiarism and authorship. Plagiarism, the most common form of scientific misconduct, is defined as the appropriation of another person's ideas, processes, results or words without giving appropriate credit. While plagiarism is often intentional, it may be unintentional due to confusion regarding the definition of plagiarism and how to avoid it. Other forms of plagiarism include self-plagiarism, whereby authors copy large parts of one of their previous manuscripts word-for-word. Duplicate publication is a form of plagiarism that occurs when an author submits a previously-published work as if it were original. An increasing number of manuscripts are retracted each year due to duplicate publication. The incidence of plagiarism is of particular concern among international trainees in the U.S. and in countries where English is not the primary language, and is often due to issues related to language barriers. The major issues related to authorship include determination of author responsibilities and author order. Awarding authorship to people who have not made sufficient contributions conveys benefit to them inappropriately and it reduces benefit to those who actually contributed to the work, while denying authorship to deserving contributors is a widespread violation of scientific integrity.

The benefits of research can only be realized if results of investigations are published in the literature for others to replicate and expand upon. Fostering scientific advancement requires strict adherence to ethical guidelines for research and scientific writing. Here, we discuss two issues related to ethics in scientific writing: plagiarism and authorship. Violations of the ethical principles associated with these issues are considered as scientific misconduct. However, authors and academic institutions often have difficulty in defining and addressing these complex issues. Fortunately, several professional organizations have developed policies to address these and other issues associated with the ethics of scientific writing. These policies can be readily adopted – and adapted – by academic institutions, but the process still requires that the policies be consistently adhered to. The Committee on Publication Ethics (COPE)-[ 1 ] defines best practice in the ethics of scholarly publication. The COPE's Code of Conduct and Best Practice Guidelines for Journal Editors, ascribed to by many major journals, defines ethical violations that involve publication issues, and provides guidelines for editors and publishers in dealing with these violations. The International Council of Medical Journal Editors (ICMJE)[ 2 ] developed the uniform requirements for manuscripts submitted to biomedical journals. The majority of medical journals follow the uniform guidelines, which provide guidance on many issues including plagiarism and authorship standards. The U.S. Office of Research Integrity (ORI)[ 3 ] oversees and directs public health research in the U.S. The ORI develops policies and procedures related to detecting, investigating and preventing research misconduct, and it implements programs to promote research integrity.

While preparing his dissertation, a graduate student used a colleague's previously-submitted paper to compose much of the introduction and background sections. The professor recognized the duplication and questioned the student. The student argued that the methods, results and discussion section are all original, and the background is mostly common knowledge. He admitted to using the colleague's paper but felt that he had changed enough words, and that citation wasn′t necessary because the information was common knowledge.

The U.S. Office of Science and Technology defines plagiarism as “the appropriation of another person's ideas, processes, results or words without giving appropriate credit, including those obtained through confidential review of others’ research proposal and manuscripts.”[ 4 ] Although plagiarism is considered as a form of scientific misconduct, it is often unintentional. Inexperienced writers and trainees may not be aware of the importance of strict adherence to plagiarism guidelines, they may be confused by vague and conflicting definitions of plagiarism, faculty may assume that trainees understand what plagiarism is and how to avoid it, and authors often have difficulty in paraphrasing complex ideas or methods.[ 5 , 6 ] Further complicating the issue is that institutions in some countries may not require strict adherence to plagiarism guidelines.

Plagiarism, the most common form of scientific misconduct, occurs quite often among students and faculty. Studies have documented persistent plagiarism among medical students, and have found that explicit warnings may not be enough to deter students from engaging in plagiarism.[ 7 ] Faculty at research institutions may succumb to plagiarism due to the tremendous pressure to publish their work, which is essential to effectively compete for grant money and to advance their careers.

It has been noted that the incidence of plagiarism is higher among international versus domestic trainees in the U.S. This difference is mainly attributed to differing perspectives of international students toward plagiarism, the lack of formal policies on research misconduct at their home institutions, and language barriers causing difficulties in writing English.[ 6 ] Plagiarism in countries where English is not the primary language is also a significant concern.[ 7 ] English is often the preferred language to communicate scientific ideas and results, and there is increasing pressure to publish papers in reputable English-language journals. However, many faculty and trainees are not skilled in expressing complex ideas in English. This language barrier, along with the ease of internet searches and the ability to “cut and paste” verbiage from Web pages, contribute to the increasing incidence of plagiarism. In all academic settings, the increasing pressure to publish as an important step in advancing careers further contributes to the increasing incidence of plagiarism. The Indian government, in particular, has expressed concern about the country's low research output, and its revised rules for academic promotion link the number of published papers to promotions.[ 8 ] If institutions and faculty are to be competitive in the global research arena, better policies to address research misconduct need to be developed, and training in the skill of scientific writing needs to be recognized as a critically important priority.

Several different forms of plagiarism encountered in scientific writing

Intentional plagiarism, in which one knowingly lifts text directly from other authors without giving appropriate credit, is the most common form of plagiarism. Fisher and Zigmond[ 5 ] believe the common factors that underlie intentional plagiarism are an individual's strong desire to succeed, coupled with a lack of time and lack of interest in learning how to write properly. As in the case study above, some authors may view “common knowledge” in their field quite broadly. However, even basic background information needs to be properly cited, both to give credit to the original author(s) and to aid readers in finding the information provided. When compiling background and introduction sections, it can be easy to lift phrases directly from notes taken from primary sources. However, it is important to remember that taking text directly from a source requires proper citation and the use of quotation marks when word-for-word text is cited.

Self-plagiarism

Also known as text recycling, is another common form of plagiarism. In self-plagiarism, the author copies large parts of one of his or her previous papers word-for-word. This form of plagiarism can be difficult to define, since there is no consensus on how many words of copied text constitute self-plagiarism. Although the ethical breach associated with self-plagiarism is generally less severe than with intentional plagiarism, it is still considered as scientific misconduct. Copying sections of previously published text, for example the methods section of a research paper, is occasionally legitimate. However, copying large parts of an original paper is considered as self-plagiarism, and submitting it for publication is considered as duplicate publication, as discussed below.[ 9 ]

Duplicate publication is a form of plagiarism that occurs when an author submits for publication a previously-published work as if it were original. Submitting previously published work is considered as plagiarism and a form of scientific misconduct, unless the author makes a clear statement that the article is being intentionally republished in part or in whole. Duplicate submission of manuscripts wastes the time of the editor and reviewers. Worse yet, duplicate publication of research distorts the scientific record, since it implies that more than one study has independently achieved the reported results. Readers of published manuscripts have a right to expect that what they read is original content, and they should not be misled into believing a report is original when it is a duplication of the author's own work or that of others.[ 10 ] At the time of submission, most journals require that authors make a statement about any previous submissions that were similar or that were based on the reported results. Some forms of duplicate publication are acceptable, such as clinical trial updates and conference proceedings. According to the ICME guidelines,[ 2 ] submitted manuscripts that are duplicates should be promptly rejected. If the editor is not made aware of the violation prior to publication, a notice of duplicate publication may be published with or without the author's explanation or approval.

The number of published manuscripts that are retracted each year is increasing, and plagiarism is making a significant contribution to this increase. Steen[ 11 ] investigated the reasons for retraction of 742 English language research papers from PubMed between 2000 and 2010. Sixteen percent of papers were retracted due to duplicate publication and 14% were retracted due to plagiarism. Errami and Garner[ 10 ] also searched the published biomedical literature and reported tens of thousands of highly similar articles, and that the number is growing. In their commentary in Nature, the authors state that the “three major sins of modern publishing” are duplication, co-submission and plagiarism.

Academic institutions are increasingly using plagiarism detection software to detect plagiarism in documents submitted by students. Likewise, journals use software tools to detect plagiarism and duplicate publications among submitted manuscripts. Plagiarism detection software compares the text of manuscripts with a database of the existing scholarly literature. The Lancet, which recently adopted the use of plagiarism detection software,[ 12 ] screens all submitted papers before sending them for peer review. If there is substantial overlap with previously published material, the editors may ask authors to put text in quotation marks, rewrite passages, or they may reject the manuscript and contact the head of the author's institution.

Table 1 lists the U.S. Department of Health and Human Services Office of Research Integrity's “Guidelines for Avoiding Plagiarism”. A good rule-of-thumb to follow is to always provide a citation if there is any question about the appropriateness of doing so. Our institution provides an on-line tutorial to assist faculty and students in differentiating plagiarism from paraphrasing,[ 13 ] and the student catalog provides specific definitions for plagiarism, along with punishment guidelines.[ 14 ] Most published guidelines for avoiding, detecting and dealing with plagiarism emphasize that a multi-faceted approach should be used to ensure that all persons understand the meaning of and consequences of plagiarism.[ 6 , 7 , 15 ]

The U.S. Department of Health and Human Services Office of Research Integrity<s “Guidelines for Avoiding Plagiarism”.[ 3 ]

A junior investigator prepared a case series and review article based on a group of interesting patients he has cared for. He worked with one student and a colleague to review the cases and prepare the manuscript, and they were both listed as authors on the paper. When the manuscript was close to completion, the investigator asked his senior mentor to review the manuscript. The mentor returned the paper with several edits and comments, and added his name as the senior author on the paper.

Authorship issues are often contentious and can affect personal and professional relationships. The major issues related to authorship include determination of author responsibilities and author order. There is tremendous pressure among academicians to be listed on as many publications as possible, and students in many graduate programs are required to publish one or more first-authored papers. However, awarding authorship to people who have not made sufficient contributions conveys benefit to them inappropriately, and it reduces the benefit to those who actually contributed to the work.[ 16 ]

Several forms of authorship abuse described by Kevin Strange[ 16 ]

• Coercion authorship, where intimidation is used to gain authorship. This type of authorship can occur when a senior person pressures a more junior person or a student to include their name on a paper to which they have not contributed enough to qualify for authorship;
• Honorary, guest or gift authorship that is awarded to acknowledge friendship, to gain favor, and/or to give the paper a greater sense of legitimacy. It is still quite common for authors to add well-known senior investigators as authors to their papers, even though the senior person may not have made significant contributions to the paper;
• Mutual support authorship, whereby two or more investigators place their names on each other's papers to enhance their perceived productivity;
• Ghost authorship, where papers are written by people who are not included as authors or are not acknowledged. Ghost authorship is quite common in the pharmaceutical industry, which often hires professional writers.
• Denial of authorship, where a work is published without providing authorship or acknowledgement to people who made substantial contributions to the work.

In the case described above, the senior mentor may expect to be added to the junior faculty member's paper because he feels that his position of authority qualifies him for authorship, and/or because he feels that he substantially contributed to the content through his edits and comments. However, even if he did make substantive changes and suggestions, the junior faculty member should not be made to feel coerced into adding the senior mentor as an author. The junior investigator should be able to confidently refer to published guidelines of authorship to determine if the senior mentor qualifies for authorship – and he should have the support of his institution in making this determination.

An often overlooked aspect of authorship is that the agreement implies support for the findings of the study, and a willingness to take public responsibility for the paper. Dr. Strange[ 16 ] describes several high-profile cases in which investigators inappropriately accepted authorship on papers. When serious charges of scientific misconduct were filed against the authors, the inappropriate authors tried to distance themselves from the study – after implicitly supporting the findings by accepting authorship. These cases illustrate the importance of not accepting authorship inappropriately, and of accepting the responsibility that accompanies authorship.

As with plagiarism, many institutions and professional organizations have established formal authorship guidelines. The U.S. Department of Health and Human Services Office of Research Integrity recommends that all research institutions, journals and scientific societies establish and make public their authorship policies.[ 3 ] The ICMJE's standards for authorship have been revised several times, have been adopted by hundreds of journals, and are the most widely accepted[ 2 ] [ Table 2 ]. In general, the ICMJE recommends that authorship be reserved for those who made substantive intellectual contributions to a published study.

Abbreviated version of the International Medical Journal Editors’ “Guidelines for Authorship”.[ 2 ]

Another authorship issue that can be problematic is authorship order. Generally speaking, the first and last author positions are considered as the most desirable. The first author, or “primary author”, is the person who conducted most of the work described in the paper, and is usually the person who drafted the manuscript. The “senior author” is usually the last person named, and is generally the person who directed or oversaw the project. Senior authors are often expected to take responsibility for the project as a whole. The names of “contributing authors’” appear between the primary and senior authors, and the order should reflect their relative contribution to the work.[ 16 ] The importance of these designations to medical school promotion committees, and clarification of these designations in published manuscripts have been described.[ 17 ] Increasingly, journals require that the role(s) of each listed author be specified at the time of submission, and many journals publish this information with the article.

CONCLUSIONS

Ethical lapses in writing and publishing are all too common. The cases presented illustrate a very small sample of the complex issues authors may face. We encourage institutions to adopt formal policies related to scientific misconduct – including plagiarism and authorship. Numerous established policies are available that can be adopted – or adapted – to meet the needs of individual institutions. Institutions should make their policies related to plagiarism readily available to both students and faculty, and they should provide clear guidelines to help students and faculty recognize and avoid plagiarism. Defining roles on projects and establishing authorship order on manuscripts before the writing begins – or even before the project begins – can often circumvent misunderstandings related to authorship. Authors should also clarify authorship expectations when they ask colleagues to review a working manuscript, and when they invite a colleague to participate on a project. Team science can help to foster ethical publishing if the team establishes guiding principles of authorship and publishing, and holds each member accountable to these principles.

Source of Support: Nil.

Conflict of Interest: None declared.

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How to Write an Ethics Paper: Guide & Ethical Essay Examples

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An ethics essay is a type of academic writing that explores ethical issues and dilemmas. Students should evaluates them in terms of moral principles and values. The purpose of an ethics essay is to examine the moral implications of a particular issue, and provide a reasoned argument in support of an ethical perspective.

Writing an essay about ethics is a tough task for most students. The process involves creating an outline to guide your arguments about a topic and planning your ideas to convince the reader of your feelings about a difficult issue. If you still need assistance putting together your thoughts in composing a good paper, you have come to the right place. We have provided a series of steps and tips to show how you can achieve success in writing. This guide will tell you how to write an ethics paper using ethical essay examples to understand every step it takes to be proficient. In case you don’t have time for writing, get in touch with our professional essay writers for hire . Our experts work hard to supply students with excellent essays.

What Is an Ethics Essay?

An ethics essay uses moral theories to build arguments on an issue. You describe a controversial problem and examine it to determine how it affects individuals or society. Ethics papers analyze arguments on both sides of a possible dilemma, focusing on right and wrong. The analysis gained can be used to solve real-life cases. Before embarking on writing an ethical essay, keep in mind that most individuals follow moral principles. From a social context perspective, these rules define how a human behaves or acts towards another. Therefore, your theme essay on ethics needs to demonstrate how a person feels about these moral principles. More specifically, your task is to show how significant that issue is and discuss if you value or discredit it.

Purpose of an Essay on Ethics

The primary purpose of an ethics essay is to initiate an argument on a moral issue using reasoning and critical evidence. Instead of providing general information about a problem, you present solid arguments about how you view the moral concern and how it affects you or society. When writing an ethical paper, you demonstrate philosophical competence, using appropriate moral perspectives and principles.

Things to Write an Essay About Ethics On

Before you start to write ethics essays, consider a topic you can easily address. In most cases, an ethical issues essay analyzes right and wrong. This includes discussing ethics and morals and how they contribute to the right behaviors. You can also talk about work ethic, code of conduct, and how employees promote or disregard the need for change. However, you can explore other areas by asking yourself what ethics mean to you. Think about how a recent game you watched with friends started a controversial argument. Or maybe a newspaper that highlighted a story you felt was misunderstood or blown out of proportion. This way, you can come up with an excellent topic that resonates with your personal ethics and beliefs.

Ethics Paper Outline

Sometimes, you will be asked to submit an outline before writing an ethics paper. Creating an outline for an ethics paper is an essential step in creating a good essay. You can use it to arrange your points and supporting evidence before writing. It also helps organize your thoughts, enabling you to fill any gaps in your ideas. The outline for an essay should contain short and numbered sentences to cover the format and outline. Each section is structured to enable you to plan your work and include all sources in writing an ethics paper. An ethics essay outline is as follows:

• Background information
• Thesis statement
• Restate thesis statement
• Summarize key points
• Final thoughts on the topic

Using this outline will improve clarity and focus throughout your writing process.

Ethical Essay Structure

Ethics essays are similar to other essays based on their format, outline, and structure. An ethical essay should have a well-defined introduction, body, and conclusion section as its structure. When planning your ideas, make sure that the introduction and conclusion are around 20 percent of the paper, leaving the rest to the body. We will take a detailed look at what each part entails and give examples that are going to help you understand them better.  Refer to our essay structure examples to find a fitting way of organizing your writing.

Ethics Paper Introduction

An ethics essay introduction gives a synopsis of your main argument. One step on how to write an introduction for an ethics paper is telling about the topic and describing its background information. This paragraph should be brief and straight to the point. It informs readers what your position is on that issue. Start with an essay hook to generate interest from your audience. It can be a question you will address or a misunderstanding that leads up to your main argument. You can also add more perspectives to be discussed; this will inform readers on what to expect in the paper.

Ethics Essay Introduction Example

You can find many ethics essay introduction examples on the internet. In this guide, we have written an excellent extract to demonstrate how it should be structured. As you read, examine how it begins with a hook and then provides background information on an issue. 

Imagine living in a world where people only lie, and honesty is becoming a scarce commodity. Indeed, modern society is facing this reality as truth and deception can no longer be separated. Technology has facilitated a quick transmission of voluminous information, whereas it's hard separating facts from opinions.

In this example, the first sentence of the introduction makes a claim or uses a question to hook the reader.

Ethics Essay Thesis Statement

An ethics paper must contain a thesis statement in the first paragraph. Learning how to write a thesis statement for an ethics paper is necessary as readers often look at it to gauge whether the essay is worth their time.

When you deviate away from the thesis, your whole paper loses meaning. In ethics essays, your thesis statement is a roadmap in writing, stressing your position on the problem and giving reasons for taking that stance. It should focus on a specific element of the issue being discussed. When writing a thesis statement, ensure that you can easily make arguments for or against its stance.

Ethical Paper Thesis Example

Look at this example of an ethics paper thesis statement and examine how well it has been written to state a position and provide reasons for doing so:

The moral implications of dishonesty are far-reaching as they undermine trust, integrity, and other foundations of society, damaging personal and professional relationships. 

The above thesis statement example is clear and concise, indicating that this paper will highlight the effects of dishonesty in society. Moreover, it focuses on aspects of personal and professional relationships.

Ethics Essay Body

The body section is the heart of an ethics paper as it presents the author's main points. In an ethical essay, each body paragraph has several elements that should explain your main idea. These include:

• A topic sentence that is precise and reiterates your stance on the issue.
• Evidence supporting it.
• Examples that illustrate your argument.
• A thorough analysis showing how the evidence and examples relate to that issue.
• A transition sentence that connects one paragraph to another with the help of essay transitions .

When you write an ethics essay, adding relevant examples strengthens your main point and makes it easy for others to understand and comprehend your argument. 

Body Paragraph for Ethics Paper Example

A good body paragraph must have a well-defined topic sentence that makes a claim and includes evidence and examples to support it. Look at part of an example of ethics essay body paragraph below and see how its idea has been developed:

Honesty is an essential component of professional integrity. In many fields, trust and credibility are crucial for professionals to build relationships and success. For example, a doctor who is dishonest about a potential side effect of a medication is not only acting unethically but also putting the health and well-being of their patients at risk. Similarly, a dishonest businessman could achieve short-term benefits but will lose their client’s trust.

Ethics Essay Conclusion

A concluding paragraph shares the summary and overview of the author's main arguments. Many students need clarification on what should be included in the essay conclusion and how best to get a reader's attention. When writing an ethics paper conclusion, consider the following:

• Restate the thesis statement to emphasize your position.
• Summarize its main points and evidence.
• Final thoughts on the issue and any other considerations.

You can also reflect on the topic or acknowledge any possible challenges or questions that have not been answered. A closing statement should present a call to action on the problem based on your position.

Sample Ethics Paper Conclusion

The conclusion paragraph restates the thesis statement and summarizes the arguments presented in that paper. The sample conclusion for an ethical essay example below demonstrates how you should write a concluding statement.  

In conclusion, the implications of dishonesty and the importance of honesty in our lives cannot be overstated. Honesty builds solid relationships, effective communication, and better decision-making. This essay has explored how dishonesty impacts people and that we should value honesty. We hope this essay will help readers assess their behavior and work towards being more honest in their lives.

In the above extract, the writer gives final thoughts on the topic, urging readers to adopt honest behavior.

How to Write an Ethics Paper?

As you learn how to write an ethics essay, it is not advised to immediately choose a topic and begin writing. When you follow this method, you will get stuck or fail to present concrete ideas. A good writer understands the importance of planning. As a fact, you should organize your work and ensure it captures key elements that shed more light on your arguments. Hence, following the essay structure and creating an outline to guide your writing process is the best approach. In the following segment, we have highlighted step-by-step techniques on how to write a good ethics paper.

1. Pick a Topic

Before writing ethical papers, brainstorm to find ideal topics that can be easily debated. For starters, make a list, then select a title that presents a moral issue that may be explained and addressed from opposing sides. Make sure you choose one that interests you. Here are a few ideas to help you search for topics:

• Review current trends affecting people.
• Think about your personal experiences.
• Study different moral theories and principles.
• Examine classical moral dilemmas.

Once you find a suitable topic and are ready, start to write your ethics essay, conduct preliminary research, and ascertain that there are enough sources to support it.

2. Conduct In-Depth Research

Once you choose a topic for your essay, the next step is gathering sufficient information about it. Conducting in-depth research entails looking through scholarly journals to find credible material. Ensure you note down all sources you found helpful to assist you on how to write your ethics paper. Use the following steps to help you conduct your research:

• Clearly state and define a problem you want to discuss.
• This will guide your research process.
• Develop keywords that match the topic.
• Begin searching from a wide perspective. This will allow you to collect more information, then narrow it down by using the identified words above.

3. Develop an Ethics Essay Outline

An outline will ease up your writing process when developing an ethic essay. As you develop a paper on ethics, jot down factual ideas that will build your paragraphs for each section. Include the following steps in your process:

• Review the topic and information gathered to write a thesis statement.
• Identify the main arguments you want to discuss and include their evidence.
• Group them into sections, each presenting a new idea that supports the thesis.
• Write an outline.
• Review and refine it.

Examples can also be included to support your main arguments. The structure should be sequential, coherent, and with a good flow from beginning to end. When you follow all steps, you can create an engaging and organized outline that will help you write a good essay.

4. Write an Ethics Essay

Once you have selected a topic, conducted research, and outlined your main points, you can begin writing an essay . Ensure you adhere to the ethics paper format you have chosen. Start an ethics paper with an overview of your topic to capture the readers' attention. Build upon your paper by avoiding ambiguous arguments and using the outline to help you write your essay on ethics. Finish the introduction paragraph with a thesis statement that explains your main position.  Expand on your thesis statement in all essay paragraphs. Each paragraph should start with a topic sentence and provide evidence plus an example to solidify your argument, strengthen the main point, and let readers see the reasoning behind your stance. Finally, conclude the essay by restating your thesis statement and summarizing all key ideas. Your conclusion should engage the reader, posing questions or urging them to reflect on the issue and how it will impact them.

5. Proofread Your Ethics Essay

Proofreading your essay is the last step as you countercheck any grammatical or structural errors in your essay. When writing your ethic paper, typical mistakes you could encounter include the following:

• Spelling errors: e.g., there, they’re, their.
• Homophone words: such as new vs. knew.
• Inconsistencies: like mixing British and American words, e.g., color vs. color.
• Formatting issues: e.g., double spacing, different font types.

While proofreading your ethical issue essay, read it aloud to detect lexical errors or ambiguous phrases that distort its meaning. Verify your information and ensure it is relevant and up-to-date. You can ask your fellow student to read the essay and give feedback on its structure and quality.

Ethics Essay Examples

Writing an essay is challenging without the right steps. There are so many ethics paper examples on the internet, however, we have provided a list of free ethics essay examples below that are well-structured and have a solid argument to help you write your paper. Click on them and see how each writing step has been integrated. Ethics essay example 1

Ethics essay example 2

Ethics essay example 3

Ethics essay example 4

College ethics essay example 5

Ethics Essay Writing Tips

When writing papers on ethics, here are several tips to help you complete an excellent essay:

• Choose a narrow topic and avoid broad subjects, as it is easy to cover the topic in detail.
• Ensure you have background information. A good understanding of a topic can make it easy to apply all necessary moral theories and principles in writing your paper.
• State your position clearly. It is important to be sure about your stance as it will allow you to draft your arguments accordingly.
• When writing ethics essays, be mindful of your audience. Provide arguments that they can understand.
• Integrate solid examples into your essay. Morality can be hard to understand; therefore, using them will help a reader grasp these concepts.

Bottom Line on Writing an Ethics Paper

Creating this essay is a common exercise in academics that allows students to build critical skills. When you begin writing, state your stance on an issue and provide arguments to support your position. This guide gives information on how to write an ethics essay as well as examples of ethics papers. Remember to follow these points in your writing:

• Create an outline highlighting your main points.
• Write an effective introduction and provide background information on an issue.
• Include a thesis statement.
• Develop concrete arguments and their counterarguments, and use examples.
• Sum up all your key points in your conclusion and restate your thesis statement.

Contact our academic writing platform and have your challenge solved. Here, you can order essays and papers on any topic and enjoy top quality. 

Daniel Howard is an Essay Writing guru. He helps students create essays that will strike a chord with the readers.

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Professional Ethics Research Paper Topics

This page provides a comprehensive list of professional ethics research paper topics , offering students studying ethics a valuable resource for their academic endeavors. Delve into the diverse world of professional ethics, explore its significance, and discover a wide array of research paper topics that can enrich your studies. Whether you seek to understand the ethical dimensions of various professions or wish to analyze ethical challenges in professional settings, this page is your gateway to a wealth of knowledge in the field of professional ethics. Additionally, learn about the writing services offered by iResearchNet, which can help you excel in your research paper assignments. Explore this page and take the first step toward crafting a compelling research paper on professional ethics.

100 Professional Ethics Research Paper Topics

Professional ethics stands as a cornerstone in the realm of ethical studies, guiding individuals and organizations toward responsible and morally sound conduct within their respective fields. It is through the lens of professional ethics that we scrutinize the behavior, values, and dilemmas that arise within various professions. This page provides an invaluable resource for students delving into the intricate world of professional ethics. By exploring the extensive list of research paper topics curated here, you will gain insights into the ethical dimensions of diverse professions and the challenges they face. Through in-depth analysis and critical examination, you can contribute to the ongoing discourse on professional ethics. Dive into this comprehensive collection, and discover the myriad topics awaiting exploration in this vital field.

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Healthcare Professions

• Ethical considerations in organ transplantation.
• The role of confidentiality in healthcare ethics.
• Medical malpractice and professional responsibility.
• Bioethical dilemmas in end-of-life care.
• Ethical implications of pharmaceutical marketing.
• Mental health professionals’ duty to protect confidentiality.
• Patient autonomy and decision-making in medical ethics.
• Ethical challenges in clinical trials and research.
• Ethics of healthcare resource allocation.
• Euthanasia and physician-assisted suicide debates.

Legal and Judicial Professions

• Legal ethics and attorney-client privilege.
• Judicial impartiality and the rule of law.
• Ethical dilemmas in criminal defense.
• Professional ethics in corporate law.
• Judges’ recusal and conflicts of interest.
• Prosecutorial misconduct and accountability.
• The role of ethics in alternative dispute resolution.
• Legal ethics in the age of technology.
• The death penalty and ethical considerations.
• Ethical issues in international human rights law.

Business and Corporate Ethics

• Corporate social responsibility and ethical business practices.
• Workplace ethics and employee rights.
• Ethical dimensions of business advertising.
• The role of whistleblowers in corporate ethics.
• Environmental ethics in business and sustainability.
• Ethics in international business negotiations.
• Leadership ethics and the impact on organizational culture.
• Ethical considerations in outsourcing and globalization.
• Corporate governance and ethical decision-making.
• Business ethics in the digital age.

Journalism and Media Ethics

• Ethical challenges in reporting on sensitive topics.
• Journalistic objectivity and the pursuit of truth.
• The role of ethics in photojournalism and image manipulation.
• Media ownership and its implications for journalistic ethics.
• The impact of social media on ethical journalism.
• Whistleblowing in the media industry.
• Ethical considerations in celebrity reporting.
• Censorship, freedom of the press, and ethical dilemmas.
• Conflict of interest in journalism.
• Privacy issues in the digital media era.

Education and Academic Ethics

• Plagiarism and academic integrity.
• Ethical issues in student-teacher relationships.
• The role of ethics in academic publishing.
• Educational equity and ethical considerations.
• Ethical dilemmas in standardized testing.
• Academic freedom and its ethical boundaries.
• The ethics of diversity and inclusion in education.
• Ethics in research involving human subjects.
• Student rights and educational ethics.
• The ethics of technology in the classroom.

Engineering and Technology Ethics

• Ethical considerations in artificial intelligence development.
• Privacy concerns in the digital age.
• The impact of automation on job ethics.
• Environmental ethics in engineering and technology.
• Ethical dilemmas in genetic engineering.
• Cybersecurity and ethical responsibilities.
• Ethical dimensions of autonomous vehicles.
• Intellectual property and technology ethics.
• Ethical issues in biotechnology research.
• The role of ethics in software development.

Social Work and Counseling Ethics

• Ethical principles in counseling and psychotherapy.
• Confidentiality and informed consent in counseling.
• Ethical challenges in child welfare services.
• The intersection of spirituality and counseling ethics.
• Cultural competence and ethical practice in social work.
• Ethical considerations in addiction counseling.
• Dual relationships in therapeutic settings.
• The ethics of mental health advocacy.
• Social justice and ethical social work practice.
• Ethical issues in gerontology and elder care.

Environmental Ethics

• Conservation ethics and wildlife protection.
• Climate change ethics and global responsibility.
• Environmental justice and ethics.
• Ethical dimensions of sustainable agriculture.
• The ethics of natural resource management.
• Biodiversity preservation and ethical considerations.
• Pollution control and environmental ethics.
• Eco-friendly technology and ethical innovation.
• Corporate responsibility for environmental ethics.
• Ethical considerations in eco-tourism.

Military and Defense Ethics

• The ethics of military intervention and just war theory.
• Soldiers’ moral responsibilities in combat.
• Ethical dilemmas in intelligence operations.
• The use of drones and unmanned warfare ethics.
• Cyber warfare and its ethical implications.
• Military leadership ethics and responsibility.
• Ethical considerations in nuclear deterrence.
• War crimes, accountability, and international law.
• The ethics of humanitarian assistance in conflict zones.
• Military ethics in the digital age.

Public Service and Government Ethics

• Public officials’ duty to uphold the law and ethical conduct.
• Corruption, transparency, and government ethics.
• Whistleblowing and ethics in public service.
• Ethical dimensions of public health policy.
• Ethical challenges in international diplomacy.
• Accountability and ethical governance.
• The role of ethics in disaster management.
• Ethical considerations in intelligence agencies.
• Lobbying, special interests, and government ethics.
• Ethical issues in public-private partnerships.

The compilation above represents merely a fraction of the vast landscape of research paper topics within professional ethics. As you delve into these categories, you’ll find that professional ethics transcends specific vocations, touching on fundamental principles of responsibility, integrity, and accountability. By exploring these topics, students can gain a deeper understanding of the ethical considerations that underpin various professions and industries. From healthcare to journalism, from business to environmental preservation, professional ethics plays a pivotal role in shaping our society.

The Range of Professional Ethics Research Paper Topics

Introduction.

Professional ethics is a cornerstone of ethical studies, offering a compass to navigate the complex terrain of our working lives. As students delve into the world of ethical research, they encounter the multifaceted domain of professional ethics. This page serves as a gateway to exploring the ethical considerations that underpin various vocations and industries, providing a rich tapestry of research paper topics to explore.

Exploring Professional Ethics

Professional ethics, nestled within the larger field of applied ethics, plays a pivotal role in guiding the moral conduct of individuals and organizations in diverse professions. At its core, it seeks to bridge the gap between theoretical ethical principles and the practical realities encountered in professional life.

In various fields, ethical considerations are woven into the fabric of daily practice. For instance, healthcare professionals grapple with dilemmas related to patient autonomy, confidentiality, and end-of-life decisions. Journalists strive to balance the pursuit of truth with concerns regarding privacy and sensitivity. Business leaders navigate the intricate interplay of corporate social responsibility and profit margins.

Research in professional ethics extends beyond academia, impacting real-world practices. By examining the ethical dimensions of specific professions, scholars and practitioners can contribute to the development of ethical guidelines, policies, and practices. Such research aids in addressing ethical challenges, fostering responsible conduct, and safeguarding the integrity of professions.

Range of Research Paper Topics

Professional ethics offers a diverse array of research paper topics that span different vocations and industries, each with its unique ethical considerations. These topics shed light on the moral dimensions of professions and are integral to ethical studies:

Within the expansive realm of professional ethics, students can explore topics ranging from the ethical dilemmas in organ transplantation to legal ethics and the attorney-client privilege. They can delve into corporate social responsibility in business ethics or investigate ethical challenges in reporting sensitive topics in journalism ethics.

Ethical dimensions are not confined to traditional professions. In the ever-evolving world of technology, the ethics of artificial intelligence development and privacy concerns in the digital age are pressing topics. Additionally, questions about conservation ethics and wildlife protection resonate in environmental ethics.

These are but a few examples of the vast landscape of professional ethics research paper topics. Whether you are drawn to the complexities of healthcare, the legal arena, business ethics, or any other profession, the study of professional ethics offers a captivating journey into the moral dimensions of our working lives.

In conclusion, professional ethics is an essential facet of ethical studies, guiding individuals and organizations to uphold principles of responsibility, integrity, and accountability within their respective professions. As students embark on their exploration of professional ethics research paper topics, they enter a realm where ethical principles are put to the test in real-world scenarios.

The breadth of topics within professional ethics mirrors the diversity of professions themselves. Each field presents unique ethical challenges and dilemmas, providing a fertile ground for ethical inquiry. Through rigorous research and thoughtful analysis, students and scholars have the opportunity to contribute to ethical frameworks that shape our society and professions.

We encourage you to delve into the ethical considerations of your chosen profession, for it is within this exploration that you will uncover insights, perspectives, and solutions that can lead to a more ethical and responsible world of work. Embrace the journey, and let your research on professional ethics illuminate the path toward ethical excellence in your chosen field.

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• Open access
• Published: 26 March 2024

Predicting and improving complex beer flavor through machine learning

• Michiel Schreurs   ORCID: orcid.org/0000-0002-9449-5619 1 , 2 , 3   na1 ,
• Supinya Piampongsant 1 , 2 , 3   na1 ,
• Miguel Roncoroni   ORCID: orcid.org/0000-0001-7461-1427 1 , 2 , 3   na1 ,
• Lloyd Cool   ORCID: orcid.org/0000-0001-9936-3124 1 , 2 , 3 , 4 ,
• Beatriz Herrera-Malaver   ORCID: orcid.org/0000-0002-5096-9974 1 , 2 , 3 ,
• Christophe Vanderaa   ORCID: orcid.org/0000-0001-7443-5427 4 ,
• Florian A. Theßeling 1 , 2 , 3 ,
• Łukasz Kreft   ORCID: orcid.org/0000-0001-7620-4657 5 ,
• Alexander Botzki   ORCID: orcid.org/0000-0001-6691-4233 5 ,
• Philippe Malcorps 6 ,
• Luk Daenen 6 ,
• Tom Wenseleers   ORCID: orcid.org/0000-0002-1434-861X 4 &
• Kevin J. Verstrepen   ORCID: orcid.org/0000-0002-3077-6219 1 , 2 , 3  

Nature Communications volume  15 , Article number:  2368 ( 2024 ) Cite this article

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• Chemical engineering
• Gas chromatography
• Machine learning
• Metabolomics
• Taste receptors

The perception and appreciation of food flavor depends on many interacting chemical compounds and external factors, and therefore proves challenging to understand and predict. Here, we combine extensive chemical and sensory analyses of 250 different beers to train machine learning models that allow predicting flavor and consumer appreciation. For each beer, we measure over 200 chemical properties, perform quantitative descriptive sensory analysis with a trained tasting panel and map data from over 180,000 consumer reviews to train 10 different machine learning models. The best-performing algorithm, Gradient Boosting, yields models that significantly outperform predictions based on conventional statistics and accurately predict complex food features and consumer appreciation from chemical profiles. Model dissection allows identifying specific and unexpected compounds as drivers of beer flavor and appreciation. Adding these compounds results in variants of commercial alcoholic and non-alcoholic beers with improved consumer appreciation. Together, our study reveals how big data and machine learning uncover complex links between food chemistry, flavor and consumer perception, and lays the foundation to develop novel, tailored foods with superior flavors.

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Introduction

Predicting and understanding food perception and appreciation is one of the major challenges in food science. Accurate modeling of food flavor and appreciation could yield important opportunities for both producers and consumers, including quality control, product fingerprinting, counterfeit detection, spoilage detection, and the development of new products and product combinations (food pairing) 1 , 2 , 3 , 4 , 5 , 6 . Accurate models for flavor and consumer appreciation would contribute greatly to our scientific understanding of how humans perceive and appreciate flavor. Moreover, accurate predictive models would also facilitate and standardize existing food assessment methods and could supplement or replace assessments by trained and consumer tasting panels, which are variable, expensive and time-consuming 7 , 8 , 9 . Lastly, apart from providing objective, quantitative, accurate and contextual information that can help producers, models can also guide consumers in understanding their personal preferences 10 .

Despite the myriad of applications, predicting food flavor and appreciation from its chemical properties remains a largely elusive goal in sensory science, especially for complex food and beverages 11 , 12 . A key obstacle is the immense number of flavor-active chemicals underlying food flavor. Flavor compounds can vary widely in chemical structure and concentration, making them technically challenging and labor-intensive to quantify, even in the face of innovations in metabolomics, such as non-targeted metabolic fingerprinting 13 , 14 . Moreover, sensory analysis is perhaps even more complicated. Flavor perception is highly complex, resulting from hundreds of different molecules interacting at the physiochemical and sensorial level. Sensory perception is often non-linear, characterized by complex and concentration-dependent synergistic and antagonistic effects 15 , 16 , 17 , 18 , 19 , 20 , 21 that are further convoluted by the genetics, environment, culture and psychology of consumers 22 , 23 , 24 . Perceived flavor is therefore difficult to measure, with problems of sensitivity, accuracy, and reproducibility that can only be resolved by gathering sufficiently large datasets 25 . Trained tasting panels are considered the prime source of quality sensory data, but require meticulous training, are low throughput and high cost. Public databases containing consumer reviews of food products could provide a valuable alternative, especially for studying appreciation scores, which do not require formal training 25 . Public databases offer the advantage of amassing large amounts of data, increasing the statistical power to identify potential drivers of appreciation. However, public datasets suffer from biases, including a bias in the volunteers that contribute to the database, as well as confounding factors such as price, cult status and psychological conformity towards previous ratings of the product.

Classical multivariate statistics and machine learning methods have been used to predict flavor of specific compounds by, for example, linking structural properties of a compound to its potential biological activities or linking concentrations of specific compounds to sensory profiles 1 , 26 . Importantly, most previous studies focused on predicting organoleptic properties of single compounds (often based on their chemical structure) 27 , 28 , 29 , 30 , 31 , 32 , 33 , thus ignoring the fact that these compounds are present in a complex matrix in food or beverages and excluding complex interactions between compounds. Moreover, the classical statistics commonly used in sensory science 34 , 35 , 36 , 37 , 38 , 39 require a large sample size and sufficient variance amongst predictors to create accurate models. They are not fit for studying an extensive set of hundreds of interacting flavor compounds, since they are sensitive to outliers, have a high tendency to overfit and are less suited for non-linear and discontinuous relationships 40 .

In this study, we combine extensive chemical analyses and sensory data of a set of different commercial beers with machine learning approaches to develop models that predict taste, smell, mouthfeel and appreciation from compound concentrations. Beer is particularly suited to model the relationship between chemistry, flavor and appreciation. First, beer is a complex product, consisting of thousands of flavor compounds that partake in complex sensory interactions 41 , 42 , 43 . This chemical diversity arises from the raw materials (malt, yeast, hops, water and spices) and biochemical conversions during the brewing process (kilning, mashing, boiling, fermentation, maturation and aging) 44 , 45 . Second, the advent of the internet saw beer consumers embrace online review platforms, such as RateBeer (ZX Ventures, Anheuser-Busch InBev SA/NV) and BeerAdvocate (Next Glass, inc.). In this way, the beer community provides massive data sets of beer flavor and appreciation scores, creating extraordinarily large sensory databases to complement the analyses of our professional sensory panel. Specifically, we characterize over 200 chemical properties of 250 commercial beers, spread across 22 beer styles, and link these to the descriptive sensory profiling data of a 16-person in-house trained tasting panel and data acquired from over 180,000 public consumer reviews. These unique and extensive datasets enable us to train a suite of machine learning models to predict flavor and appreciation from a beer’s chemical profile. Dissection of the best-performing models allows us to pinpoint specific compounds as potential drivers of beer flavor and appreciation. Follow-up experiments confirm the importance of these compounds and ultimately allow us to significantly improve the flavor and appreciation of selected commercial beers. Together, our study represents a significant step towards understanding complex flavors and reinforces the value of machine learning to develop and refine complex foods. In this way, it represents a stepping stone for further computer-aided food engineering applications 46 .

To generate a comprehensive dataset on beer flavor, we selected 250 commercial Belgian beers across 22 different beer styles (Supplementary Fig.  S1 ). Beers with ≤ 4.2% alcohol by volume (ABV) were classified as non-alcoholic and low-alcoholic. Blonds and Tripels constitute a significant portion of the dataset (12.4% and 11.2%, respectively) reflecting their presence on the Belgian beer market and the heterogeneity of beers within these styles. By contrast, lager beers are less diverse and dominated by a handful of brands. Rare styles such as Brut or Faro make up only a small fraction of the dataset (2% and 1%, respectively) because fewer of these beers are produced and because they are dominated by distinct characteristics in terms of flavor and chemical composition.

Extensive analysis identifies relationships between chemical compounds in beer

For each beer, we measured 226 different chemical properties, including common brewing parameters such as alcohol content, iso-alpha acids, pH, sugar concentration 47 , and over 200 flavor compounds (Methods, Supplementary Table  S1 ). A large portion (37.2%) are terpenoids arising from hopping, responsible for herbal and fruity flavors 16 , 48 . A second major category are yeast metabolites, such as esters and alcohols, that result in fruity and solvent notes 48 , 49 , 50 . Other measured compounds are primarily derived from malt, or other microbes such as non- Saccharomyces yeasts and bacteria (‘wild flora’). Compounds that arise from spices or staling are labeled under ‘Others’. Five attributes (caloric value, total acids and total ester, hop aroma and sulfur compounds) are calculated from multiple individually measured compounds.

As a first step in identifying relationships between chemical properties, we determined correlations between the concentrations of the compounds (Fig.  1 , upper panel, Supplementary Data  1 and 2 , and Supplementary Fig.  S2 . For the sake of clarity, only a subset of the measured compounds is shown in Fig.  1 ). Compounds of the same origin typically show a positive correlation, while absence of correlation hints at parameters varying independently. For example, the hop aroma compounds citronellol, and alpha-terpineol show moderate correlations with each other (Spearman’s rho=0.39 and 0.57), but not with the bittering hop component iso-alpha acids (Spearman’s rho=0.16 and −0.07). This illustrates how brewers can independently modify hop aroma and bitterness by selecting hop varieties and dosage time. If hops are added early in the boiling phase, chemical conversions increase bitterness while aromas evaporate, conversely, late addition of hops preserves aroma but limits bitterness 51 . Similarly, hop-derived iso-alpha acids show a strong anti-correlation with lactic acid and acetic acid, likely reflecting growth inhibition of lactic acid and acetic acid bacteria, or the consequent use of fewer hops in sour beer styles, such as West Flanders ales and Fruit beers, that rely on these bacteria for their distinct flavors 52 . Finally, yeast-derived esters (ethyl acetate, ethyl decanoate, ethyl hexanoate, ethyl octanoate) and alcohols (ethanol, isoamyl alcohol, isobutanol, and glycerol), correlate with Spearman coefficients above 0.5, suggesting that these secondary metabolites are correlated with the yeast genetic background and/or fermentation parameters and may be difficult to influence individually, although the choice of yeast strain may offer some control 53 .

Spearman rank correlations are shown. Descriptors are grouped according to their origin (malt (blue), hops (green), yeast (red), wild flora (yellow), Others (black)), and sensory aspect (aroma, taste, palate, and overall appreciation). Please note that for the chemical compounds, for the sake of clarity, only a subset of the total number of measured compounds is shown, with an emphasis on the key compounds for each source. For more details, see the main text and Methods section. Chemical data can be found in Supplementary Data  1 , correlations between all chemical compounds are depicted in Supplementary Fig.  S2 and correlation values can be found in Supplementary Data  2 . See Supplementary Data  4 for sensory panel assessments and Supplementary Data  5 for correlation values between all sensory descriptors.

Interestingly, different beer styles show distinct patterns for some flavor compounds (Supplementary Fig.  S3 ). These observations agree with expectations for key beer styles, and serve as a control for our measurements. For instance, Stouts generally show high values for color (darker), while hoppy beers contain elevated levels of iso-alpha acids, compounds associated with bitter hop taste. Acetic and lactic acid are not prevalent in most beers, with notable exceptions such as Kriek, Lambic, Faro, West Flanders ales and Flanders Old Brown, which use acid-producing bacteria ( Lactobacillus and Pediococcus ) or unconventional yeast ( Brettanomyces ) 54 , 55 . Glycerol, ethanol and esters show similar distributions across all beer styles, reflecting their common origin as products of yeast metabolism during fermentation 45 , 53 . Finally, low/no-alcohol beers contain low concentrations of glycerol and esters. This is in line with the production process for most of the low/no-alcohol beers in our dataset, which are produced through limiting fermentation or by stripping away alcohol via evaporation or dialysis, with both methods having the unintended side-effect of reducing the amount of flavor compounds in the final beer 56 , 57 .

Besides expected associations, our data also reveals less trivial associations between beer styles and specific parameters. For example, geraniol and citronellol, two monoterpenoids responsible for citrus, floral and rose flavors and characteristic of Citra hops, are found in relatively high amounts in Christmas, Saison, and Brett/co-fermented beers, where they may originate from terpenoid-rich spices such as coriander seeds instead of hops 58 .

Tasting panel assessments reveal sensorial relationships in beer

To assess the sensory profile of each beer, a trained tasting panel evaluated each of the 250 beers for 50 sensory attributes, including different hop, malt and yeast flavors, off-flavors and spices. Panelists used a tasting sheet (Supplementary Data  3 ) to score the different attributes. Panel consistency was evaluated by repeating 12 samples across different sessions and performing ANOVA. In 95% of cases no significant difference was found across sessions ( p  > 0.05), indicating good panel consistency (Supplementary Table  S2 ).

Aroma and taste perception reported by the trained panel are often linked (Fig.  1 , bottom left panel and Supplementary Data  4 and 5 ), with high correlations between hops aroma and taste (Spearman’s rho=0.83). Bitter taste was found to correlate with hop aroma and taste in general (Spearman’s rho=0.80 and 0.69), and particularly with “grassy” noble hops (Spearman’s rho=0.75). Barnyard flavor, most often associated with sour beers, is identified together with stale hops (Spearman’s rho=0.97) that are used in these beers. Lactic and acetic acid, which often co-occur, are correlated (Spearman’s rho=0.66). Interestingly, sweetness and bitterness are anti-correlated (Spearman’s rho = −0.48), confirming the hypothesis that they mask each other 59 , 60 . Beer body is highly correlated with alcohol (Spearman’s rho = 0.79), and overall appreciation is found to correlate with multiple aspects that describe beer mouthfeel (alcohol, carbonation; Spearman’s rho= 0.32, 0.39), as well as with hop and ester aroma intensity (Spearman’s rho=0.39 and 0.35).

Similar to the chemical analyses, sensorial analyses confirmed typical features of specific beer styles (Supplementary Fig.  S4 ). For example, sour beers (Faro, Flanders Old Brown, Fruit beer, Kriek, Lambic, West Flanders ale) were rated acidic, with flavors of both acetic and lactic acid. Hoppy beers were found to be bitter and showed hop-associated aromas like citrus and tropical fruit. Malt taste is most detected among scotch, stout/porters, and strong ales, while low/no-alcohol beers, which often have a reputation for being ‘worty’ (reminiscent of unfermented, sweet malt extract) appear in the middle. Unsurprisingly, hop aromas are most strongly detected among hoppy beers. Like its chemical counterpart (Supplementary Fig.  S3 ), acidity shows a right-skewed distribution, with the most acidic beers being Krieks, Lambics, and West Flanders ales.

Tasting panel assessments of specific flavors correlate with chemical composition

We find that the concentrations of several chemical compounds strongly correlate with specific aroma or taste, as evaluated by the tasting panel (Fig.  2 , Supplementary Fig.  S5 , Supplementary Data  6 ). In some cases, these correlations confirm expectations and serve as a useful control for data quality. For example, iso-alpha acids, the bittering compounds in hops, strongly correlate with bitterness (Spearman’s rho=0.68), while ethanol and glycerol correlate with tasters’ perceptions of alcohol and body, the mouthfeel sensation of fullness (Spearman’s rho=0.82/0.62 and 0.72/0.57 respectively) and darker color from roasted malts is a good indication of malt perception (Spearman’s rho=0.54).

Heatmap colors indicate Spearman’s Rho. Axes are organized according to sensory categories (aroma, taste, mouthfeel, overall), chemical categories and chemical sources in beer (malt (blue), hops (green), yeast (red), wild flora (yellow), Others (black)). See Supplementary Data  6 for all correlation values.

Interestingly, for some relationships between chemical compounds and perceived flavor, correlations are weaker than expected. For example, the rose-smelling phenethyl acetate only weakly correlates with floral aroma. This hints at more complex relationships and interactions between compounds and suggests a need for a more complex model than simple correlations. Lastly, we uncovered unexpected correlations. For instance, the esters ethyl decanoate and ethyl octanoate appear to correlate slightly with hop perception and bitterness, possibly due to their fruity flavor. Iron is anti-correlated with hop aromas and bitterness, most likely because it is also anti-correlated with iso-alpha acids. This could be a sign of metal chelation of hop acids 61 , given that our analyses measure unbound hop acids and total iron content, or could result from the higher iron content in dark and Fruit beers, which typically have less hoppy and bitter flavors 62 .

Public consumer reviews complement expert panel data

To complement and expand the sensory data of our trained tasting panel, we collected 180,000 reviews of our 250 beers from the online consumer review platform RateBeer. This provided numerical scores for beer appearance, aroma, taste, palate, overall quality as well as the average overall score.

Public datasets are known to suffer from biases, such as price, cult status and psychological conformity towards previous ratings of a product. For example, prices correlate with appreciation scores for these online consumer reviews (rho=0.49, Supplementary Fig.  S6 ), but not for our trained tasting panel (rho=0.19). This suggests that prices affect consumer appreciation, which has been reported in wine 63 , while blind tastings are unaffected. Moreover, we observe that some beer styles, like lagers and non-alcoholic beers, generally receive lower scores, reflecting that online reviewers are mostly beer aficionados with a preference for specialty beers over lager beers. In general, we find a modest correlation between our trained panel’s overall appreciation score and the online consumer appreciation scores (Fig.  3 , rho=0.29). Apart from the aforementioned biases in the online datasets, serving temperature, sample freshness and surroundings, which are all tightly controlled during the tasting panel sessions, can vary tremendously across online consumers and can further contribute to (among others, appreciation) differences between the two categories of tasters. Importantly, in contrast to the overall appreciation scores, for many sensory aspects the results from the professional panel correlated well with results obtained from RateBeer reviews. Correlations were highest for features that are relatively easy to recognize even for untrained tasters, like bitterness, sweetness, alcohol and malt aroma (Fig.  3 and below).

RateBeer text mining results can be found in Supplementary Data  7 . Rho values shown are Spearman correlation values, with asterisks indicating significant correlations ( p  < 0.05, two-sided). All p values were smaller than 0.001, except for Esters aroma (0.0553), Esters taste (0.3275), Esters aroma—banana (0.0019), Coriander (0.0508) and Diacetyl (0.0134).

Besides collecting consumer appreciation from these online reviews, we developed automated text analysis tools to gather additional data from review texts (Supplementary Data  7 ). Processing review texts on the RateBeer database yielded comparable results to the scores given by the trained panel for many common sensory aspects, including acidity, bitterness, sweetness, alcohol, malt, and hop tastes (Fig.  3 ). This is in line with what would be expected, since these attributes require less training for accurate assessment and are less influenced by environmental factors such as temperature, serving glass and odors in the environment. Consumer reviews also correlate well with our trained panel for 4-vinyl guaiacol, a compound associated with a very characteristic aroma. By contrast, correlations for more specific aromas like ester, coriander or diacetyl are underrepresented in the online reviews, underscoring the importance of using a trained tasting panel and standardized tasting sheets with explicit factors to be scored for evaluating specific aspects of a beer. Taken together, our results suggest that public reviews are trustworthy for some, but not all, flavor features and can complement or substitute taste panel data for these sensory aspects.

Models can predict beer sensory profiles from chemical data

The rich datasets of chemical analyses, tasting panel assessments and public reviews gathered in the first part of this study provided us with a unique opportunity to develop predictive models that link chemical data to sensorial features. Given the complexity of beer flavor, basic statistical tools such as correlations or linear regression may not always be the most suitable for making accurate predictions. Instead, we applied different machine learning models that can model both simple linear and complex interactive relationships. Specifically, we constructed a set of regression models to predict (a) trained panel scores for beer flavor and quality and (b) public reviews’ appreciation scores from beer chemical profiles. We trained and tested 10 different models (Methods), 3 linear regression-based models (simple linear regression with first-order interactions (LR), lasso regression with first-order interactions (Lasso), partial least squares regressor (PLSR)), 5 decision tree models (AdaBoost regressor (ABR), extra trees (ET), gradient boosting regressor (GBR), random forest (RF) and XGBoost regressor (XGBR)), 1 support vector regression (SVR), and 1 artificial neural network (ANN) model.

To compare the performance of our machine learning models, the dataset was randomly split into a training and test set, stratified by beer style. After a model was trained on data in the training set, its performance was evaluated on its ability to predict the test dataset obtained from multi-output models (based on the coefficient of determination, see Methods). Additionally, individual-attribute models were ranked per descriptor and the average rank was calculated, as proposed by Korneva et al. 64 . Importantly, both ways of evaluating the models’ performance agreed in general. Performance of the different models varied (Table  1 ). It should be noted that all models perform better at predicting RateBeer results than results from our trained tasting panel. One reason could be that sensory data is inherently variable, and this variability is averaged out with the large number of public reviews from RateBeer. Additionally, all tree-based models perform better at predicting taste than aroma. Linear models (LR) performed particularly poorly, with negative R 2 values, due to severe overfitting (training set R 2  = 1). Overfitting is a common issue in linear models with many parameters and limited samples, especially with interaction terms further amplifying the number of parameters. L1 regularization (Lasso) successfully overcomes this overfitting, out-competing multiple tree-based models on the RateBeer dataset. Similarly, the dimensionality reduction of PLSR avoids overfitting and improves performance, to some extent. Still, tree-based models (ABR, ET, GBR, RF and XGBR) show the best performance, out-competing the linear models (LR, Lasso, PLSR) commonly used in sensory science 65 .

GBR models showed the best overall performance in predicting sensory responses from chemical information, with R 2 values up to 0.75 depending on the predicted sensory feature (Supplementary Table  S4 ). The GBR models predict consumer appreciation (RateBeer) better than our trained panel’s appreciation (R 2 value of 0.67 compared to R 2 value of 0.09) (Supplementary Table  S3 and Supplementary Table  S4 ). ANN models showed intermediate performance, likely because neural networks typically perform best with larger datasets 66 . The SVR shows intermediate performance, mostly due to the weak predictions of specific attributes that lower the overall performance (Supplementary Table  S4 ).

Model dissection identifies specific, unexpected compounds as drivers of consumer appreciation

Next, we leveraged our models to infer important contributors to sensory perception and consumer appreciation. Consumer preference is a crucial sensory aspects, because a product that shows low consumer appreciation scores often does not succeed commercially 25 . Additionally, the requirement for a large number of representative evaluators makes consumer trials one of the more costly and time-consuming aspects of product development. Hence, a model for predicting chemical drivers of overall appreciation would be a welcome addition to the available toolbox for food development and optimization.

Since GBR models on our RateBeer dataset showed the best overall performance, we focused on these models. Specifically, we used two approaches to identify important contributors. First, rankings of the most important predictors for each sensorial trait in the GBR models were obtained based on impurity-based feature importance (mean decrease in impurity). High-ranked parameters were hypothesized to be either the true causal chemical properties underlying the trait, to correlate with the actual causal properties, or to take part in sensory interactions affecting the trait 67 (Fig.  4A ). In a second approach, we used SHAP 68 to determine which parameters contributed most to the model for making predictions of consumer appreciation (Fig.  4B ). SHAP calculates parameter contributions to model predictions on a per-sample basis, which can be aggregated into an importance score.

A The impurity-based feature importance (mean deviance in impurity, MDI) calculated from the Gradient Boosting Regression (GBR) model predicting RateBeer appreciation scores. The top 15 highest ranked chemical properties are shown. B SHAP summary plot for the top 15 parameters contributing to our GBR model. Each point on the graph represents a sample from our dataset. The color represents the concentration of that parameter, with bluer colors representing low values and redder colors representing higher values. Greater absolute values on the horizontal axis indicate a higher impact of the parameter on the prediction of the model. C Spearman correlations between the 15 most important chemical properties and consumer overall appreciation. Numbers indicate the Spearman Rho correlation coefficient, and the rank of this correlation compared to all other correlations. The top 15 important compounds were determined using SHAP (panel B).

Both approaches identified ethyl acetate as the most predictive parameter for beer appreciation (Fig.  4 ). Ethyl acetate is the most abundant ester in beer with a typical ‘fruity’, ‘solvent’ and ‘alcoholic’ flavor, but is often considered less important than other esters like isoamyl acetate. The second most important parameter identified by SHAP is ethanol, the most abundant beer compound after water. Apart from directly contributing to beer flavor and mouthfeel, ethanol drastically influences the physical properties of beer, dictating how easily volatile compounds escape the beer matrix to contribute to beer aroma 69 . Importantly, it should also be noted that the importance of ethanol for appreciation is likely inflated by the very low appreciation scores of non-alcoholic beers (Supplementary Fig.  S4 ). Despite not often being considered a driver of beer appreciation, protein level also ranks highly in both approaches, possibly due to its effect on mouthfeel and body 70 . Lactic acid, which contributes to the tart taste of sour beers, is the fourth most important parameter identified by SHAP, possibly due to the generally high appreciation of sour beers in our dataset.

Interestingly, some of the most important predictive parameters for our model are not well-established as beer flavors or are even commonly regarded as being negative for beer quality. For example, our models identify methanethiol and ethyl phenyl acetate, an ester commonly linked to beer staling 71 , as a key factor contributing to beer appreciation. Although there is no doubt that high concentrations of these compounds are considered unpleasant, the positive effects of modest concentrations are not yet known 72 , 73 .

To compare our approach to conventional statistics, we evaluated how well the 15 most important SHAP-derived parameters correlate with consumer appreciation (Fig.  4C ). Interestingly, only 6 of the properties derived by SHAP rank amongst the top 15 most correlated parameters. For some chemical compounds, the correlations are so low that they would have likely been considered unimportant. For example, lactic acid, the fourth most important parameter, shows a bimodal distribution for appreciation, with sour beers forming a separate cluster, that is missed entirely by the Spearman correlation. Additionally, the correlation plots reveal outliers, emphasizing the need for robust analysis tools. Together, this highlights the need for alternative models, like the Gradient Boosting model, that better grasp the complexity of (beer) flavor.

Finally, to observe the relationships between these chemical properties and their predicted targets, partial dependence plots were constructed for the six most important predictors of consumer appreciation 74 , 75 , 76 (Supplementary Fig.  S7 ). One-way partial dependence plots show how a change in concentration affects the predicted appreciation. These plots reveal an important limitation of our models: appreciation predictions remain constant at ever-increasing concentrations. This implies that once a threshold concentration is reached, further increasing the concentration does not affect appreciation. This is false, as it is well-documented that certain compounds become unpleasant at high concentrations, including ethyl acetate (‘nail polish’) 77 and methanethiol (‘sulfury’ and ‘rotten cabbage’) 78 . The inability of our models to grasp that flavor compounds have optimal levels, above which they become negative, is a consequence of working with commercial beer brands where (off-)flavors are rarely too high to negatively impact the product. The two-way partial dependence plots show how changing the concentration of two compounds influences predicted appreciation, visualizing their interactions (Supplementary Fig.  S7 ). In our case, the top 5 parameters are dominated by additive or synergistic interactions, with high concentrations for both compounds resulting in the highest predicted appreciation.

To assess the robustness of our best-performing models and model predictions, we performed 100 iterations of the GBR, RF and ET models. In general, all iterations of the models yielded similar performance (Supplementary Fig.  S8 ). Moreover, the main predictors (including the top predictors ethanol and ethyl acetate) remained virtually the same, especially for GBR and RF. For the iterations of the ET model, we did observe more variation in the top predictors, which is likely a consequence of the model’s inherent random architecture in combination with co-correlations between certain predictors. However, even in this case, several of the top predictors (ethanol and ethyl acetate) remain unchanged, although their rank in importance changes (Supplementary Fig.  S8 ).

Next, we investigated if a combination of RateBeer and trained panel data into one consolidated dataset would lead to stronger models, under the hypothesis that such a model would suffer less from bias in the datasets. A GBR model was trained to predict appreciation on the combined dataset. This model underperformed compared to the RateBeer model, both in the native case and when including a dataset identifier (R 2  = 0.67, 0.26 and 0.42 respectively). For the latter, the dataset identifier is the most important feature (Supplementary Fig.  S9 ), while most of the feature importance remains unchanged, with ethyl acetate and ethanol ranking highest, like in the original model trained only on RateBeer data. It seems that the large variation in the panel dataset introduces noise, weakening the models’ performances and reliability. In addition, it seems reasonable to assume that both datasets are fundamentally different, with the panel dataset obtained by blind tastings by a trained professional panel.

Lastly, we evaluated whether beer style identifiers would further enhance the model’s performance. A GBR model was trained with parameters that explicitly encoded the styles of the samples. This did not improve model performance (R2 = 0.66 with style information vs R2 = 0.67). The most important chemical features are consistent with the model trained without style information (eg. ethanol and ethyl acetate), and with the exception of the most preferred (strong ale) and least preferred (low/no-alcohol) styles, none of the styles were among the most important features (Supplementary Fig.  S9 , Supplementary Table  S5 and S6 ). This is likely due to a combination of style-specific chemical signatures, such as iso-alpha acids and lactic acid, that implicitly convey style information to the original models, as well as the low number of samples belonging to some styles, making it difficult for the model to learn style-specific patterns. Moreover, beer styles are not rigorously defined, with some styles overlapping in features and some beers being misattributed to a specific style, all of which leads to more noise in models that use style parameters.

Model validation

To test if our predictive models give insight into beer appreciation, we set up experiments aimed at improving existing commercial beers. We specifically selected overall appreciation as the trait to be examined because of its complexity and commercial relevance. Beer flavor comprises a complex bouquet rather than single aromas and tastes 53 . Hence, adding a single compound to the extent that a difference is noticeable may lead to an unbalanced, artificial flavor. Therefore, we evaluated the effect of combinations of compounds. Because Blond beers represent the most extensive style in our dataset, we selected a beer from this style as the starting material for these experiments (Beer 64 in Supplementary Data  1 ).

In the first set of experiments, we adjusted the concentrations of compounds that made up the most important predictors of overall appreciation (ethyl acetate, ethanol, lactic acid, ethyl phenyl acetate) together with correlated compounds (ethyl hexanoate, isoamyl acetate, glycerol), bringing them up to 95 th percentile ethanol-normalized concentrations (Methods) within the Blond group (‘Spiked’ concentration in Fig.  5A ). Compared to controls, the spiked beers were found to have significantly improved overall appreciation among trained panelists, with panelist noting increased intensity of ester flavors, sweetness, alcohol, and body fullness (Fig.  5B ). To disentangle the contribution of ethanol to these results, a second experiment was performed without the addition of ethanol. This resulted in a similar outcome, including increased perception of alcohol and overall appreciation.

Adding the top chemical compounds, identified as best predictors of appreciation by our model, into poorly appreciated beers results in increased appreciation from our trained panel. Results of sensory tests between base beers and those spiked with compounds identified as the best predictors by the model. A Blond and Non/Low-alcohol (0.0% ABV) base beers were brought up to 95th-percentile ethanol-normalized concentrations within each style. B For each sensory attribute, tasters indicated the more intense sample and selected the sample they preferred. The numbers above the bars correspond to the p values that indicate significant changes in perceived flavor (two-sided binomial test: alpha 0.05, n  = 20 or 13).

In a last experiment, we tested whether using the model’s predictions can boost the appreciation of a non-alcoholic beer (beer 223 in Supplementary Data  1 ). Again, the addition of a mixture of predicted compounds (omitting ethanol, in this case) resulted in a significant increase in appreciation, body, ester flavor and sweetness.

Predicting flavor and consumer appreciation from chemical composition is one of the ultimate goals of sensory science. A reliable, systematic and unbiased way to link chemical profiles to flavor and food appreciation would be a significant asset to the food and beverage industry. Such tools would substantially aid in quality control and recipe development, offer an efficient and cost-effective alternative to pilot studies and consumer trials and would ultimately allow food manufacturers to produce superior, tailor-made products that better meet the demands of specific consumer groups more efficiently.

A limited set of studies have previously tried, to varying degrees of success, to predict beer flavor and beer popularity based on (a limited set of) chemical compounds and flavors 79 , 80 . Current sensitive, high-throughput technologies allow measuring an unprecedented number of chemical compounds and properties in a large set of samples, yielding a dataset that can train models that help close the gaps between chemistry and flavor, even for a complex natural product like beer. To our knowledge, no previous research gathered data at this scale (250 samples, 226 chemical parameters, 50 sensory attributes and 5 consumer scores) to disentangle and validate the chemical aspects driving beer preference using various machine-learning techniques. We find that modern machine learning models outperform conventional statistical tools, such as correlations and linear models, and can successfully predict flavor appreciation from chemical composition. This could be attributed to the natural incorporation of interactions and non-linear or discontinuous effects in machine learning models, which are not easily grasped by the linear model architecture. While linear models and partial least squares regression represent the most widespread statistical approaches in sensory science, in part because they allow interpretation 65 , 81 , 82 , modern machine learning methods allow for building better predictive models while preserving the possibility to dissect and exploit the underlying patterns. Of the 10 different models we trained, tree-based models, such as our best performing GBR, showed the best overall performance in predicting sensory responses from chemical information, outcompeting artificial neural networks. This agrees with previous reports for models trained on tabular data 83 . Our results are in line with the findings of Colantonio et al. who also identified the gradient boosting architecture as performing best at predicting appreciation and flavor (of tomatoes and blueberries, in their specific study) 26 . Importantly, besides our larger experimental scale, we were able to directly confirm our models’ predictions in vivo.

Our study confirms that flavor compound concentration does not always correlate with perception, suggesting complex interactions that are often missed by more conventional statistics and simple models. Specifically, we find that tree-based algorithms may perform best in developing models that link complex food chemistry with aroma. Furthermore, we show that massive datasets of untrained consumer reviews provide a valuable source of data, that can complement or even replace trained tasting panels, especially for appreciation and basic flavors, such as sweetness and bitterness. This holds despite biases that are known to occur in such datasets, such as price or conformity bias. Moreover, GBR models predict taste better than aroma. This is likely because taste (e.g. bitterness) often directly relates to the corresponding chemical measurements (e.g., iso-alpha acids), whereas such a link is less clear for aromas, which often result from the interplay between multiple volatile compounds. We also find that our models are best at predicting acidity and alcohol, likely because there is a direct relation between the measured chemical compounds (acids and ethanol) and the corresponding perceived sensorial attribute (acidity and alcohol), and because even untrained consumers are generally able to recognize these flavors and aromas.

The predictions of our final models, trained on review data, hold even for blind tastings with small groups of trained tasters, as demonstrated by our ability to validate specific compounds as drivers of beer flavor and appreciation. Since adding a single compound to the extent of a noticeable difference may result in an unbalanced flavor profile, we specifically tested our identified key drivers as a combination of compounds. While this approach does not allow us to validate if a particular single compound would affect flavor and/or appreciation, our experiments do show that this combination of compounds increases consumer appreciation.

It is important to stress that, while it represents an important step forward, our approach still has several major limitations. A key weakness of the GBR model architecture is that amongst co-correlating variables, the largest main effect is consistently preferred for model building. As a result, co-correlating variables often have artificially low importance scores, both for impurity and SHAP-based methods, like we observed in the comparison to the more randomized Extra Trees models. This implies that chemicals identified as key drivers of a specific sensory feature by GBR might not be the true causative compounds, but rather co-correlate with the actual causative chemical. For example, the high importance of ethyl acetate could be (partially) attributed to the total ester content, ethanol or ethyl hexanoate (rho=0.77, rho=0.72 and rho=0.68), while ethyl phenylacetate could hide the importance of prenyl isobutyrate and ethyl benzoate (rho=0.77 and rho=0.76). Expanding our GBR model to include beer style as a parameter did not yield additional power or insight. This is likely due to style-specific chemical signatures, such as iso-alpha acids and lactic acid, that implicitly convey style information to the original model, as well as the smaller sample size per style, limiting the power to uncover style-specific patterns. This can be partly attributed to the curse of dimensionality, where the high number of parameters results in the models mainly incorporating single parameter effects, rather than complex interactions such as style-dependent effects 67 . A larger number of samples may overcome some of these limitations and offer more insight into style-specific effects. On the other hand, beer style is not a rigid scientific classification, and beers within one style often differ a lot, which further complicates the analysis of style as a model factor.

Our study is limited to beers from Belgian breweries. Although these beers cover a large portion of the beer styles available globally, some beer styles and consumer patterns may be missing, while other features might be overrepresented. For example, many Belgian ales exhibit yeast-driven flavor profiles, which is reflected in the chemical drivers of appreciation discovered by this study. In future work, expanding the scope to include diverse markets and beer styles could lead to the identification of even more drivers of appreciation and better models for special niche products that were not present in our beer set.

In addition to inherent limitations of GBR models, there are also some limitations associated with studying food aroma. Even if our chemical analyses measured most of the known aroma compounds, the total number of flavor compounds in complex foods like beer is still larger than the subset we were able to measure in this study. For example, hop-derived thiols, that influence flavor at very low concentrations, are notoriously difficult to measure in a high-throughput experiment. Moreover, consumer perception remains subjective and prone to biases that are difficult to avoid. It is also important to stress that the models are still immature and that more extensive datasets will be crucial for developing more complete models in the future. Besides more samples and parameters, our dataset does not include any demographic information about the tasters. Including such data could lead to better models that grasp external factors like age and culture. Another limitation is that our set of beers consists of high-quality end-products and lacks beers that are unfit for sale, which limits the current model in accurately predicting products that are appreciated very badly. Finally, while models could be readily applied in quality control, their use in sensory science and product development is restrained by their inability to discern causal relationships. Given that the models cannot distinguish compounds that genuinely drive consumer perception from those that merely correlate, validation experiments are essential to identify true causative compounds.

Despite the inherent limitations, dissection of our models enabled us to pinpoint specific molecules as potential drivers of beer aroma and consumer appreciation, including compounds that were unexpected and would not have been identified using standard approaches. Important drivers of beer appreciation uncovered by our models include protein levels, ethyl acetate, ethyl phenyl acetate and lactic acid. Currently, many brewers already use lactic acid to acidify their brewing water and ensure optimal pH for enzymatic activity during the mashing process. Our results suggest that adding lactic acid can also improve beer appreciation, although its individual effect remains to be tested. Interestingly, ethanol appears to be unnecessary to improve beer appreciation, both for blond beer and alcohol-free beer. Given the growing consumer interest in alcohol-free beer, with a predicted annual market growth of >7% 84 , it is relevant for brewers to know what compounds can further increase consumer appreciation of these beers. Hence, our model may readily provide avenues to further improve the flavor and consumer appreciation of both alcoholic and non-alcoholic beers, which is generally considered one of the key challenges for future beer production.

Whereas we see a direct implementation of our results for the development of superior alcohol-free beverages and other food products, our study can also serve as a stepping stone for the development of novel alcohol-containing beverages. We want to echo the growing body of scientific evidence for the negative effects of alcohol consumption, both on the individual level by the mutagenic, teratogenic and carcinogenic effects of ethanol 85 , 86 , as well as the burden on society caused by alcohol abuse and addiction. We encourage the use of our results for the production of healthier, tastier products, including novel and improved beverages with lower alcohol contents. Furthermore, we strongly discourage the use of these technologies to improve the appreciation or addictive properties of harmful substances.

The present work demonstrates that despite some important remaining hurdles, combining the latest developments in chemical analyses, sensory analysis and modern machine learning methods offers exciting avenues for food chemistry and engineering. Soon, these tools may provide solutions in quality control and recipe development, as well as new approaches to sensory science and flavor research.

Beer selection

250 commercial Belgian beers were selected to cover the broad diversity of beer styles and corresponding diversity in chemical composition and aroma. See Supplementary Fig.  S1 .

Chemical dataset

Sample preparation.

Beers within their expiration date were purchased from commercial retailers. Samples were prepared in biological duplicates at room temperature, unless explicitly stated otherwise. Bottle pressure was measured with a manual pressure device (Steinfurth Mess-Systeme GmbH) and used to calculate CO 2 concentration. The beer was poured through two filter papers (Macherey-Nagel, 500713032 MN 713 ¼) to remove carbon dioxide and prevent spontaneous foaming. Samples were then prepared for measurements by targeted Headspace-Gas Chromatography-Flame Ionization Detector/Flame Photometric Detector (HS-GC-FID/FPD), Headspace-Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry (HS-SPME-GC-MS), colorimetric analysis, enzymatic analysis, Near-Infrared (NIR) analysis, as described in the sections below. The mean values of biological duplicates are reported for each compound.

HS-GC-FID/FPD

HS-GC-FID/FPD (Shimadzu GC 2010 Plus) was used to measure higher alcohols, acetaldehyde, esters, 4-vinyl guaicol, and sulfur compounds. Each measurement comprised 5 ml of sample pipetted into a 20 ml glass vial containing 1.75 g NaCl (VWR, 27810.295). 100 µl of 2-heptanol (Sigma-Aldrich, H3003) (internal standard) solution in ethanol (Fisher Chemical, E/0650DF/C17) was added for a final concentration of 2.44 mg/L. Samples were flushed with nitrogen for 10 s, sealed with a silicone septum, stored at −80 °C and analyzed in batches of 20.

The GC was equipped with a DB-WAXetr column (length, 30 m; internal diameter, 0.32 mm; layer thickness, 0.50 µm; Agilent Technologies, Santa Clara, CA, USA) to the FID and an HP-5 column (length, 30 m; internal diameter, 0.25 mm; layer thickness, 0.25 µm; Agilent Technologies, Santa Clara, CA, USA) to the FPD. N 2 was used as the carrier gas. Samples were incubated for 20 min at 70 °C in the headspace autosampler (Flow rate, 35 cm/s; Injection volume, 1000 µL; Injection mode, split; Combi PAL autosampler, CTC analytics, Switzerland). The injector, FID and FPD temperatures were kept at 250 °C. The GC oven temperature was first held at 50 °C for 5 min and then allowed to rise to 80 °C at a rate of 5 °C/min, followed by a second ramp of 4 °C/min until 200 °C kept for 3 min and a final ramp of (4 °C/min) until 230 °C for 1 min. Results were analyzed with the GCSolution software version 2.4 (Shimadzu, Kyoto, Japan). The GC was calibrated with a 5% EtOH solution (VWR International) containing the volatiles under study (Supplementary Table  S7 ).

HS-SPME-GC-MS

HS-SPME-GC-MS (Shimadzu GCMS-QP-2010 Ultra) was used to measure additional volatile compounds, mainly comprising terpenoids and esters. Samples were analyzed by HS-SPME using a triphase DVB/Carboxen/PDMS 50/30 μm SPME fiber (Supelco Co., Bellefonte, PA, USA) followed by gas chromatography (Thermo Fisher Scientific Trace 1300 series, USA) coupled to a mass spectrometer (Thermo Fisher Scientific ISQ series MS) equipped with a TriPlus RSH autosampler. 5 ml of degassed beer sample was placed in 20 ml vials containing 1.75 g NaCl (VWR, 27810.295). 5 µl internal standard mix was added, containing 2-heptanol (1 g/L) (Sigma-Aldrich, H3003), 4-fluorobenzaldehyde (1 g/L) (Sigma-Aldrich, 128376), 2,3-hexanedione (1 g/L) (Sigma-Aldrich, 144169) and guaiacol (1 g/L) (Sigma-Aldrich, W253200) in ethanol (Fisher Chemical, E/0650DF/C17). Each sample was incubated at 60 °C in the autosampler oven with constant agitation. After 5 min equilibration, the SPME fiber was exposed to the sample headspace for 30 min. The compounds trapped on the fiber were thermally desorbed in the injection port of the chromatograph by heating the fiber for 15 min at 270 °C.

The GC-MS was equipped with a low polarity RXi-5Sil MS column (length, 20 m; internal diameter, 0.18 mm; layer thickness, 0.18 µm; Restek, Bellefonte, PA, USA). Injection was performed in splitless mode at 320 °C, a split flow of 9 ml/min, a purge flow of 5 ml/min and an open valve time of 3 min. To obtain a pulsed injection, a programmed gas flow was used whereby the helium gas flow was set at 2.7 mL/min for 0.1 min, followed by a decrease in flow of 20 ml/min to the normal 0.9 mL/min. The temperature was first held at 30 °C for 3 min and then allowed to rise to 80 °C at a rate of 7 °C/min, followed by a second ramp of 2 °C/min till 125 °C and a final ramp of 8 °C/min with a final temperature of 270 °C.

Mass acquisition range was 33 to 550 amu at a scan rate of 5 scans/s. Electron impact ionization energy was 70 eV. The interface and ion source were kept at 275 °C and 250 °C, respectively. A mix of linear n-alkanes (from C7 to C40, Supelco Co.) was injected into the GC-MS under identical conditions to serve as external retention index markers. Identification and quantification of the compounds were performed using an in-house developed R script as described in Goelen et al. and Reher et al. 87 , 88 (for package information, see Supplementary Table  S8 ). Briefly, chromatograms were analyzed using AMDIS (v2.71) 89 to separate overlapping peaks and obtain pure compound spectra. The NIST MS Search software (v2.0 g) in combination with the NIST2017, FFNSC3 and Adams4 libraries were used to manually identify the empirical spectra, taking into account the expected retention time. After background subtraction and correcting for retention time shifts between samples run on different days based on alkane ladders, compound elution profiles were extracted and integrated using a file with 284 target compounds of interest, which were either recovered in our identified AMDIS list of spectra or were known to occur in beer. Compound elution profiles were estimated for every peak in every chromatogram over a time-restricted window using weighted non-negative least square analysis after which peak areas were integrated 87 , 88 . Batch effect correction was performed by normalizing against the most stable internal standard compound, 4-fluorobenzaldehyde. Out of all 284 target compounds that were analyzed, 167 were visually judged to have reliable elution profiles and were used for final analysis.

Discrete photometric and enzymatic analysis

Discrete photometric and enzymatic analysis (Thermo Scientific TM Gallery TM Plus Beermaster Discrete Analyzer) was used to measure acetic acid, ammonia, beta-glucan, iso-alpha acids, color, sugars, glycerol, iron, pH, protein, and sulfite. 2 ml of sample volume was used for the analyses. Information regarding the reagents and standard solutions used for analyses and calibrations is included in Supplementary Table  S7 and Supplementary Table  S9 .

NIR analyses

NIR analysis (Anton Paar Alcolyzer Beer ME System) was used to measure ethanol. Measurements comprised 50 ml of sample, and a 10% EtOH solution was used for calibration.

Correlation calculations

Pairwise Spearman Rank correlations were calculated between all chemical properties.

Sensory dataset

Trained panel.

Our trained tasting panel consisted of volunteers who gave prior verbal informed consent. All compounds used for the validation experiment were of food-grade quality. The tasting sessions were approved by the Social and Societal Ethics Committee of the KU Leuven (G-2022-5677-R2(MAR)). All online reviewers agreed to the Terms and Conditions of the RateBeer website.

Sensory analysis was performed according to the American Society of Brewing Chemists (ASBC) Sensory Analysis Methods 90 . 30 volunteers were screened through a series of triangle tests. The sixteen most sensitive and consistent tasters were retained as taste panel members. The resulting panel was diverse in age [22–42, mean: 29], sex [56% male] and nationality [7 different countries]. The panel developed a consensus vocabulary to describe beer aroma, taste and mouthfeel. Panelists were trained to identify and score 50 different attributes, using a 7-point scale to rate attributes’ intensity. The scoring sheet is included as Supplementary Data  3 . Sensory assessments took place between 10–12 a.m. The beers were served in black-colored glasses. Per session, between 5 and 12 beers of the same style were tasted at 12 °C to 16 °C. Two reference beers were added to each set and indicated as ‘Reference 1 & 2’, allowing panel members to calibrate their ratings. Not all panelists were present at every tasting. Scores were scaled by standard deviation and mean-centered per taster. Values are represented as z-scores and clustered by Euclidean distance. Pairwise Spearman correlations were calculated between taste and aroma sensory attributes. Panel consistency was evaluated by repeating samples on different sessions and performing ANOVA to identify differences, using the ‘stats’ package (v4.2.2) in R (for package information, see Supplementary Table  S8 ).

Online reviews from a public database

The ‘scrapy’ package in Python (v3.6) (for package information, see Supplementary Table  S8 ). was used to collect 232,288 online reviews (mean=922, min=6, max=5343) from RateBeer, an online beer review database. Each review entry comprised 5 numerical scores (appearance, aroma, taste, palate and overall quality) and an optional review text. The total number of reviews per reviewer was collected separately. Numerical scores were scaled and centered per rater, and mean scores were calculated per beer.

For the review texts, the language was estimated using the packages ‘langdetect’ and ‘langid’ in Python. Reviews that were classified as English by both packages were kept. Reviewers with fewer than 100 entries overall were discarded. 181,025 reviews from >6000 reviewers from >40 countries remained. Text processing was done using the ‘nltk’ package in Python. Texts were corrected for slang and misspellings; proper nouns and rare words that are relevant to the beer context were specified and kept as-is (‘Chimay’,’Lambic’, etc.). A dictionary of semantically similar sensorial terms, for example ‘floral’ and ‘flower’, was created and collapsed together into one term. Words were stemmed and lemmatized to avoid identifying words such as ‘acid’ and ‘acidity’ as separate terms. Numbers and punctuation were removed.

Sentences from up to 50 randomly chosen reviews per beer were manually categorized according to the aspect of beer they describe (appearance, aroma, taste, palate, overall quality—not to be confused with the 5 numerical scores described above) or flagged as irrelevant if they contained no useful information. If a beer contained fewer than 50 reviews, all reviews were manually classified. This labeled data set was used to train a model that classified the rest of the sentences for all beers 91 . Sentences describing taste and aroma were extracted, and term frequency–inverse document frequency (TFIDF) was implemented to calculate enrichment scores for sensorial words per beer.

The sex of the tasting subject was not considered when building our sensory database. Instead, results from different panelists were averaged, both for our trained panel (56% male, 44% female) and the RateBeer reviews (70% male, 30% female for RateBeer as a whole).

Beer price collection and processing

Beer prices were collected from the following stores: Colruyt, Delhaize, Total Wine, BeerHawk, The Belgian Beer Shop, The Belgian Shop, and Beer of Belgium. Where applicable, prices were converted to Euros and normalized per liter. Spearman correlations were calculated between these prices and mean overall appreciation scores from RateBeer and the taste panel, respectively.

Pairwise Spearman Rank correlations were calculated between all sensory properties.

Machine learning models

Predictive modeling of sensory profiles from chemical data.

Regression models were constructed to predict (a) trained panel scores for beer flavors and quality from beer chemical profiles and (b) public reviews’ appreciation scores from beer chemical profiles. Z-scores were used to represent sensory attributes in both data sets. Chemical properties with log-normal distributions (Shapiro-Wilk test, p  <  0.05 ) were log-transformed. Missing chemical measurements (0.1% of all data) were replaced with mean values per attribute. Observations from 250 beers were randomly separated into a training set (70%, 175 beers) and a test set (30%, 75 beers), stratified per beer style. Chemical measurements (p = 231) were normalized based on the training set average and standard deviation. In total, three linear regression-based models: linear regression with first-order interaction terms (LR), lasso regression with first-order interaction terms (Lasso) and partial least squares regression (PLSR); five decision tree models, Adaboost regressor (ABR), Extra Trees (ET), Gradient Boosting regressor (GBR), Random Forest (RF) and XGBoost regressor (XGBR); one support vector machine model (SVR) and one artificial neural network model (ANN) were trained. The models were implemented using the ‘scikit-learn’ package (v1.2.2) and ‘xgboost’ package (v1.7.3) in Python (v3.9.16). Models were trained, and hyperparameters optimized, using five-fold cross-validated grid search with the coefficient of determination (R 2 ) as the evaluation metric. The ANN (scikit-learn’s MLPRegressor) was optimized using Bayesian Tree-Structured Parzen Estimator optimization with the ‘Optuna’ Python package (v3.2.0). Individual models were trained per attribute, and a multi-output model was trained on all attributes simultaneously.

Model dissection

GBR was found to outperform other methods, resulting in models with the highest average R 2 values in both trained panel and public review data sets. Impurity-based rankings of the most important predictors for each predicted sensorial trait were obtained using the ‘scikit-learn’ package. To observe the relationships between these chemical properties and their predicted targets, partial dependence plots (PDP) were constructed for the six most important predictors of consumer appreciation 74 , 75 .

The ‘SHAP’ package in Python (v0.41.0) was implemented to provide an alternative ranking of predictor importance and to visualize the predictors’ effects as a function of their concentration 68 .

Validation of causal chemical properties

To validate the effects of the most important model features on predicted sensory attributes, beers were spiked with the chemical compounds identified by the models and descriptive sensory analyses were carried out according to the American Society of Brewing Chemists (ASBC) protocol 90 .

Compound spiking was done 30 min before tasting. Compounds were spiked into fresh beer bottles, that were immediately resealed and inverted three times. Fresh bottles of beer were opened for the same duration, resealed, and inverted thrice, to serve as controls. Pairs of spiked samples and controls were served simultaneously, chilled and in dark glasses as outlined in the Trained panel section above. Tasters were instructed to select the glass with the higher flavor intensity for each attribute (directional difference test 92 ) and to select the glass they prefer.

The final concentration after spiking was equal to the within-style average, after normalizing by ethanol concentration. This was done to ensure balanced flavor profiles in the final spiked beer. The same methods were applied to improve a non-alcoholic beer. Compounds were the following: ethyl acetate (Merck KGaA, W241415), ethyl hexanoate (Merck KGaA, W243906), isoamyl acetate (Merck KGaA, W205508), phenethyl acetate (Merck KGaA, W285706), ethanol (96%, Colruyt), glycerol (Merck KGaA, W252506), lactic acid (Merck KGaA, 261106).

Significant differences in preference or perceived intensity were determined by performing the two-sided binomial test on each attribute.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

The data that support the findings of this work are available in the Supplementary Data files and have been deposited to Zenodo under accession code 10653704 93 . The RateBeer scores data are under restricted access, they are not publicly available as they are property of RateBeer (ZX Ventures, USA). Access can be obtained from the authors upon reasonable request and with permission of RateBeer (ZX Ventures, USA).  Source data are provided with this paper.

Code availability

The code for training the machine learning models, analyzing the models, and generating the figures has been deposited to Zenodo under accession code 10653704 93 .

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Acknowledgements

We thank all lab members for their discussions and thank all tasting panel members for their contributions. Special thanks go out to Dr. Karin Voordeckers for her tremendous help in proofreading and improving the manuscript. M.S. was supported by a Baillet-Latour fellowship, L.C. acknowledges financial support from KU Leuven (C16/17/006), F.A.T. was supported by a PhD fellowship from FWO (1S08821N). Research in the lab of K.J.V. is supported by KU Leuven, FWO, VIB, VLAIO and the Brewing Science Serves Health Fund. Research in the lab of T.W. is supported by FWO (G.0A51.15) and KU Leuven (C16/17/006).

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These authors contributed equally: Michiel Schreurs, Supinya Piampongsant, Miguel Roncoroni.

Authors and Affiliations

VIB—KU Leuven Center for Microbiology, Gaston Geenslaan 1, B-3001, Leuven, Belgium

Michiel Schreurs, Supinya Piampongsant, Miguel Roncoroni, Lloyd Cool, Beatriz Herrera-Malaver, Florian A. Theßeling & Kevin J. Verstrepen

CMPG Laboratory of Genetics and Genomics, KU Leuven, Gaston Geenslaan 1, B-3001, Leuven, Belgium

Leuven Institute for Beer Research (LIBR), Gaston Geenslaan 1, B-3001, Leuven, Belgium

Laboratory of Socioecology and Social Evolution, KU Leuven, Naamsestraat 59, B-3000, Leuven, Belgium

Lloyd Cool, Christophe Vanderaa & Tom Wenseleers

VIB Bioinformatics Core, VIB, Rijvisschestraat 120, B-9052, Ghent, Belgium

Łukasz Kreft & Alexander Botzki

AB InBev SA/NV, Brouwerijplein 1, B-3000, Leuven, Belgium

Philippe Malcorps & Luk Daenen

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Contributions

S.P., M.S. and K.J.V. conceived the experiments. S.P., M.S. and K.J.V. designed the experiments. S.P., M.S., M.R., B.H. and F.A.T. performed the experiments. S.P., M.S., L.C., C.V., L.K., A.B., P.M., L.D., T.W. and K.J.V. contributed analysis ideas. S.P., M.S., L.C., C.V., T.W. and K.J.V. analyzed the data. All authors contributed to writing the manuscript.

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Correspondence to Kevin J. Verstrepen .

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K.J.V. is affiliated with bar.on. The other authors declare no competing interests.

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Schreurs, M., Piampongsant, S., Roncoroni, M. et al. Predicting and improving complex beer flavor through machine learning. Nat Commun 15 , 2368 (2024). https://doi.org/10.1038/s41467-024-46346-0

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Received : 30 October 2023

Accepted : 21 February 2024

Published : 26 March 2024

DOI : https://doi.org/10.1038/s41467-024-46346-0

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