Text (Instruction Step Text)
From The Embassy of Good Science
Describe the actions the user should take to experience the material (including preparation and follow up if any). Write in an active way.
- ⧼SA Foundation Data Type⧽: Text
2
In many cases participation in research does not pose '''risks to research participants''', for example, filling in an anonymous questionnaire usually is easy, and no risks are associated with it (nevertheless, sometimes sensitive questions may pose psychological risk). In some other types of research, participation can pose physical or psychological risks. For example, participants of biomedical research who are exposed to experimental treatments might face risks to physical well-being; research in psychology may lead to emotional distress among participants; studies dealing with sensitive information may raise risks for the privacy and confidentiality of participants; some research topics may be socially sensitive and research participants might face social consequences or stigma. In citizen science, sharing data sometimes might pose a privacy risk to the citizen scientists themselves. This might be a case when, for example, management of citizen science programs requires collecting private information about volunteers.
The '''rights and interests of research participants''' are arguably the cornerstone of research ethics and in the traditional research ethics setting there has been developed a certain framework of how these rights are applied in different fields of research. Citizen science however introduces some additional challenges that need to be addressed. Many citizen science projects are conducted outside traditional academic or commercial settings. This raises the issue of ethics oversight of these studies and whether citizen scientists have the necessary research ethics training.
Research involving human research participants is guided by various laws and ethical guidelines. These legal and ethical standards embody important ethical principles and requirements (Emanuel et al 2000; Resnik 2019):
*'''Social value''' means that to justify the participation of human subjects, research should be expected to yield results that can benefit society.
*Evaluation of the '''risk/benefit ratio''' means that risks posed by participation in a research study should be minimized and justified in terms of the potential benefits to the research participants and society.
*'''Informed consent''' means that research participants should receive adequate information about the planned research and their voluntary consent should be sought and appropriately documented.
*'''Confidentiality''' is required to protect personal data and privacy of research participants.
*'''Data safety''' monitoring means that research data should be adequately protected to avoid harming, e.g., stigmatizing research participants.
*'''Fair selection of subjects''' means that the selection of research participants should be based on sound scientific and ethical criteria.
*'''Protection of vulnerable subjects''' requires to ensure additional protections for research participants who may be vulnerable to coercion, exploitation, or harm.
*'''Independent review''' is a requirement applied to some types of research, e.g. biomedical research involving human research participants should be reviewed by an independent research ethics committee according to the national legal framework.
For a research study to be ethical, researchers, including citizen scientists, must comply with all the requirements and principles mentioned above. For example, poorly designed studies will not yield valuable results and therefore, the risks that research participants have been exposed to during the study will be unjustified. One of the suggested ways to avoid these problems is to closely collaborate with professional scientists who are experts in a particular field of research (Resnik 2019).
'''References'''
#Emanuel, E. J., Wendler, D., & Grady, C. (2000). What Makes Clinical Research Ethical? ''JAMA'', 283(20), 2701–2711. https://doi.org/10.1001/jama.283.20.2701
#Resnik, D. B. (2019). Citizen scientists as human subjects: Ethical issues. ''Citizen Science: Theory and Practice'', 4(1). https://doi.org/10.5334/cstp.150
Citizen science offers valuable opportunities for all stakeholders involved; however, it also raises new issues regarding research ethics and integrity. Some authors have expressed concerns regarding the potential '''exploitation and instrumentalization''' of citizen scientists, where their unpaid work is utilized without proper acknowledgment of their contributions (Resnik, 2019). Therefore, '''recognizing the contributions''' of citizen scientists in all phases of research especially in scientific publications is essential to acknowledge their valuable research inputs. In some cases, citizen scientists may qualify for co-authorship if they have made substantial intellectual contributions to the research, including contributions to study design, data analysis, manuscript writing, and agreement to be accountable for all aspects of the research ([https://bit.ly/N7uoq3 <span lang="EN-GB">ICMJE</span>]<span lang="EN-GB">). While traditional academic authorship criteria may not always directly apply to citizen scientists, there are various other ways to appropriately recognize their involvement. Citizen scientists who have contributed to the research but whose contributions do not justify authorship may be acknowledged as contributors, with their roles and specific tasks described in a contributorship statement or acknowledgments. Open and transparent communication with citizen scientists throughout the research process, involving them in discussions about authorship and recognition, is crucial for building trust and ensuring that everyone involved feels appropriately acknowledged for their contributions.</span>
Additionally, issues of '''data quality and ownership''' have been raised in the context of citizen science, as citizen scientists are often not specifically trained in research ethics and methodologies. The quality of data collected by citizen scientists can be ensured through various methods. Researchers can provide appropriate training to citizen scientists on data collection techniques and emphasize the importance of maintaining good research records. It is also crucial to ensure that the technological solutions chosen for citizen science projects are comprehensible and user-friendly, which can help minimize errors or misunderstandings during data collection and improve the overall quality of the collected data. Moreover, facilitating discussions between professional researchers and citizen scientists on questions of data ownership and future data accessibility is important to establish clear agreements on how the data will be used, shared, and accessed.
Citizen scientists should also be provided with information regarding research integrity to ensure ethical conduct. This includes informing them about potential financial and non-financial '''conflicts of interest''', such as relationships with organizations sponsoring research or personal interests (Resnik, 2019). Openly discussing the expectations and motivations of citizen scientists within the research team can help foster transparency and compliance with research ethics principles.
To provide a framework for conducting citizen science projects the European Citizen Science Association (ECSA) has developed the 10 principles of citizen science. Before moving to the next step, please, read: [http://doi.org/10.17605/OSF.IO/XPR2N ECSA (European Citizen Science Association). (2015). Ten Principles of Citizen Science]
'''References'''
#ICMJE. [https://bit.ly/N7uoq3 Defining the role of authors and contributors.]
#Resnik, D.B. (2019). Citizen scientists as human subjects: Ethical issues. ''Citizen Science: Theory and Practice'', 4(1). https://doi.org/10.5334/cstp.150
#The Embassy of Good Science: “[https://embassy.science/wiki-wiki/index.php/Theme:Cbe88760-7f0e-4d6d-952b-b724bb0f375e Authorship criteria]”
Citizen science projects collect and share diverse types of data. As pointed out by Balázs et al.: "Some projects are solely quantitative data projects, while others are solely qualitative. Mixed-method citizen science projects also exist which include both quantitative and qualitative data collection, generation, and manipulation." (Balázs et al., 2021) Due to this variety of data and other reasons, data quality in citizen science encounters various challenges that can impact the reliability and usability of the collected information. For example, analysis of the data collected by iNaturalist project revealed that the data suffers from various kinds of biases, for example, towards certain taxa (such as birds, plants, and mammals). Also, there is some evidence of spatial sampling bias. For example, about 58% of all threatened species observations in iNaturalist come from the U.S., Canada, Mexico, Russia and New Zealand (Soroye et al., 2022).
Balázs et al. point out the two main aspects of data quality in citizen science - reliability and validity. Reliability refers to the stability and consistency of data over time. In the context of citizen science, reliable data means that results can be replicated consistently. (Balázs et al., 2021) For example, in a project tracking water quality in a river, if different citizen scientists using the same measurement tools consistently report similar results for the same water samples, the data is deemed reliable. Validity in data refers to the extent to which the data accurately represents what it is supposed to measure or describe. For example, in a citizen science project on weather monitoring, if citizen scientists consistently report all relevant weather parameters (temperature, humidity, precipitation), the data is valid as it provides a comprehensive view of weather conditions.
Data contextualization refers to the practice of providing essential context and information surrounding a dataset, enabling a better understanding of how the data was generated, its purpose, and its quality. It includes metadata, attribution, and curation details to situate the data within its broader context. (Balázs et al., 2021) For example, in a climate monitoring citizen science project, metadata could include details about the creation of data set, contributors, methodology, instruments used, calibration procedures, and the temporal and spatial resolution of data. Metadata enhances the understanding and usability of the data.
''Four aspects of data accuracy in citizen science''. Balázs B. et al. https://doi.org/10.1007/978-3-030-58278-4_8, [https://creativecommons.org/licenses/by/4.0/ CC BY 4.0]
'''References'''
#Balázs, B., Mooney, P., Nováková, E., Bastin, L., Jokar Arsanjani, J. (2021). Data Quality in Citizen Science. In: ''The Science of Citizen Science''. Springer https://doi.org/10.1007/978-3-030-58278-4_8
#Soroye, P. et al. (2022). The risks and rewards of community science for threatened species monitoring. ''Conservation Science and Practice'', 4(9), e12788. https://doi.org/10.1111/csp2.12788
The term “conflict of interest” refers to situations where a person or an organisation has more than one interest (personal, professional, financial, etc.) and pursuing one of them could potentially involve conflict with others. There are two main types of conflicts of interest – financial and non-financial. An example of a '''financial conflict of interest''' is a physician who works for a pharmaceutical company that produces medicine for the same group of patients that she treats. In this case physician’s interest in earning more money conflicts with her role as a physician whose main duty is to find and prescribe the best available treatment to each patient. An example of a '''non-financial conflict of interest''' is a scientist whose personal beliefs or affiliations may impact the interpretation of his research findings. The same applies to a scientist who makes a biased hypothesis that tends to support her preferred theory.
It is important to note that conflict of interests also includes the potential for conflict, and these should always be declared. Whether financial or non-financial conflicts of interests threaten the core virtue of scientific enterprise as it interferes with the role of the scientist as a seeker of truth. Besides that, it also might undermine the public’s trust in science.
Investigators in citizen science projects might not have their pet theory that they might want to see proven true. However, laypeople who are involved in collaboration with scientists might have some political or personal interests that motivate them to participate in the research in the first place. For example, a person might have some strong beliefs about an environmental issue, and she might see involvement in the research as a way of solving the problem. There is some evidence that one of the key reasons why some citizen scientists engage in helping researchers to collect data is to advance their political aims (Riesch & Potter, 2014). These non-financial conflicts of interest might be more common in citizen science than financial conflicts of interest. An example of the latter would be a citizen scientist who receives funding from an environmental group or serves on its board of directors.
A common strategy for dealing with conflicts of interest is to declare them. Although by itself it will not solve all the problems, timely disclosure of a potential conflict of interest avoids situations where the conflict is discovered after the fact. Thus, one might avoid suspicions and loss of trust (Resnik, 2015). The importance of a potential conflict of interests may vary, some might be negligible, and some, on the other hand, very severe. Whatever the case, it is always better to inform about it upfront. One unique problem with this strategy in the context of citizen science is that lead investigators of a study might have to deal with a large number of such disclosures as many citizen scientists might be involved in the study and sharing all this information might be impractical. One strategy to solve the problem could be to disclose the conflict of interest in aggregate (Resnik, 2015). Another strategy, how one can deal with damage, that might be caused by a conflict of interests is to make all the data publicly available. This enables everybody to analyse the data and assess the results independently (Resnik, 2015).
'''References'''
#Riesch, H., & Potter, C. (2014). Citizen science as seen by scientists: Methodological, epistemological and ethical dimensions. Public Understanding of Science, 23(1), 107–120. https://doi.org/10.1177/0963662513497324
#Resnik, D. B., Elliott, K. C., & Miller, A. K. (2015). A framework for addressing ethical issues in citizen science. Environmental Science & Policy, 54, 475–481. https://doi.org/10.1016/j.envsci.2015.05.008
The data collected by citizen scientists are increasingly used in different fields of scientific research. One of the most prominent examples is animal and plant population monitoring programs. This development brings many '''benefits'''. It is a cost-effective way to gather substantial amounts of data for research purposes that otherwise would be impossible or too expensive to collect. The involvement of citizen scientists in monitoring animal and plant populations could also help improve public understanding of science and promote public engagement in conservation. Additionally, these citizen science projects can inform policies.
However, some '''risks''' have to be addressed as well. Publishing information about the location of threatened animal and plant species might inadvertently enable poaching. '''Poaching''' refers to illegal hunting, capturing, or harvesting of wildlife, typically for commercial purposes or personal gain. For example, Soroye et al. point out that "human disturbance or poaching and harvesting are listed as major threats for 57.9% of threatened species reported in iNaturalist" compared with 38% of all Red List threatened species are at risk of these threats. (Soroye et al., 2022) This suggests “that the threatened species reported to iNaturalist disproportionately tend to be threatened by disturbance and harvesting.” (Soroye et al., 2022) Moreover, incentivising non-professional monitoring creates a potential for harm even to the species that are not threatened by poaching as some species can be negatively affected just by disturbance (Quinn, 2021).
Citizen scientists can greatly contribute to monitoring threatened species by complementing traditional methods and addressing monitoring gaps. '''To avoid or mitigate''' the above-mentioned risks citizen scientists should be provided with information or trained on species identification and monitoring, citizen science projects should ensure a robust data vetting process and involve threatened species experts, as well as developing plans for data use and security. Some citizen science projects are even directly aimed at fighting against poaching (See this [https://news.mongabay.com/2018/01/crowdsourcing-the-fight-against-poaching-with-the-help-of-remote-cameras/ project in South Africa].)
'''References'''
#Quinn, A. (2021). Transparency and secrecy in citizen science: Lessons from herping. ''Studies in History and Philosophy of Science Part A'', 85, 208–217. https://doi.org/10.1016/j.shpsa.2020.10.010
#Soroye, P. et al. (2022). The risks and rewards of community science for threatened species monitoring. ''Conservation Science and Practice'', 4(9), e12788. https://doi.org/10.1111/csp2.12788
''Target Audience: undergraduate and graduate students.''
The training games developed by the [https://www.academicintegrity.eu/wp/bridge/ BRIDGE project] use the “gamification” approach to raise awareness and provide very basic knowledge on research integrity for university students. Online, board, role-play and scenario games can be found [https://www.academicintegrity.eu/wp/bridge-games/ here]. +
Six different modules on responsible open science can be found [[Guide:E525ee0d-0d7e-4ba5-b19b-89e4a5029b2f|here]], on the Embassy of Good Science and [https://classroom.eneri.eu/node/82 ENERI] platforms. [https://zenodo.org/records/11671024 Open Science Learning Gate] developed by [https://community.embassy.science/c/nerq/105 NERQ] offers the possibility to align trainings with the principles of the research community. To enhance the quality of open science practices, the ‘Open Science Learning Gate’ seeks to unite the research community while aligning and standardizing both a) customized and b) high-quality OS training programs. +
Most of the resources available are made by recommendations and guidelines on the use of responsible AI. Published by the European Commission, the [https://research-and-innovation.ec.europa.eu/document/download/2b6cf7e5-36ac-41cb-aab5-0d32050143dc_en?filename=ec_rtd_ai-guidelines.pdf Living Guidelines on the Responsible Use of Generative AI in Research] provides recommendations for researchers, research institutions and funding organizations. [https://www.academicintegrity.eu/ ENAI] developed [https://www.academicintegrity.eu/wp/wp-content/uploads/2024/06/Using-GenAI-Tools-Practical-Guide-for-Students-Ver1-MAR2024.pdf a practical guide for students] (see also [https://www.academicintegrity.eu/wp/wp-content/uploads/2024/06/USING-GENAI-TOOLS_guide_for_students_ver2.pdf here]) on the use of generative AI. [https://ukrio.org/ UKRIO] provides a full list of resources on [https://ukrio.org/ukrio-resources/ai-in-research/#list the use of AI in research]. +
'''''Target audience:''' Master and bachelor students, doctoral students and early career researchers''
The developed by the [https://cordis.europa.eu/project/id/787580 VIRT2UE] project presents a modified version of the developed by Erasmus University Rotterdam. This exercise supports participants in identifying research integrity principles, virtues and questionable research practices in a hypothetical case. It provides a framework to consider, choose and defend alternative courses of action regarding realistic research integrity dilemmas. +
'''''TARGET audience:''' bachelor and master students, doctoral students and early career researchers''
The e-learning modules produced by the [[Guide:Bbe860a3-56a9-45f7-b787-031689729e52|VIRT2UE]] project represent an introduction to relevant topics in research integrity, These modules, which foster self-learning introduce [[Instruction:6ceba4e4-fb32-4953-9138-5436807fcde6|research integrity]], [[Instruction:86f47366-a189-4395-9301-36ddb6d1fc68|virtue ethics relevant for RI]] and virtue ethics under current research conditions. Moreover, the training has developed a series of [[Instruction:17705907-d9b2-4f33-bc4f-088d84b4d971|videos]] and [[Instruction:7ce7ad50-499a-4cca-b09d-b2c1573d94f3|reading material]] introducing specific concepts and themes relevant to the field and practice of research integrity, such as ethical decision making in research and moral disengagement in research. +
The BEYOND approach - ‘it’s not the apple, but the orchard’ - reflects the idea that integrity is upheld as a collaborative effort. This is why it is important that training also models the collaborative way. Cases have the capacity to open up discussion space for the complexities of integrity and ethics in research, again, guiding learners to think of the full complexity, not just individuals, but also other systemic levels, including meso and macro levels, that is organisation, research community, and national, international and global context. Scaffolding provides a technique acknowledging where the individual or even a team or research community is at and designing the next steps to facilitate learning and development eventually leading to better alignment with the highest ethical and integrity standards. The point of departure is that there is always room for improvement, even in the strongest of research communities and the work starts with acknowledging status quo and identifying the next goals, which are within reach, irrespective of whether we envision the learning of individuals or communities. With these approaches; case-based and collaborative learning and scaffolding we believe training is well geared towards nurturing the orchard.
The BEYOND Trainer Guide goes beyond simply listing training materials; it adds value by explaining various pedagogical approaches that can be applied to enhance the use of different materials. It shows how learning taxonomies can be applied to create learning-focused training (as opposed to mere information transmission) irrespective of which materials produced in EU-funded projects that are implemented. We have structured the material according to target group, so that trainers can easily identify materials that are suitable for the target group they are training.
Additionally, the content is also structured according to the type of learning activities to support those trainers who wish to work using specific activities but may hesitate whether they are suitable for a particular target group, or simply would like to know more about the activity itself.
To summarise, the BEYOND approach is manifested in the Trainer Guide as:
- A proposal for a research-based approach to an ‘orchard pedagogy’
- Suggestions for measuring training effect to gain an indication of the preparedness of the research community to develop a culture of integrity
Facilitation for using existing RE/RI training resources by providing two alternative structures for trainers, including one, which addresses various actors in ‘the orchard’ through a career-level approach. We wish trainers and other readers, as well as learners taking part in trainings and learning activities utilising the resources referred to in the BEYOND Trainer Guide, a joyful journey through the orchard!
<div>
During the session, participants analyse a case using role play. In subgroups (up to 6 participants in each group) they each impersonate a member of an expert group who has been formed by the executive board of a prestigious institution to examine a difficult case and provide advice. <div>
Every participant plays one of following roles: Healthcare professional (physician); R<span lang="EN-US">epresentative of “HealthAI”;</span> <span lang="EN-US">Patient rights advocacy</span>; <span lang="EN-US">Medical ethicist;</span> <span lang="EN-US">Representative of human resources of the hospital</span><span lang="EN-US">;</span> <span lang="EN-US">Representative of a health insurance company</span>.
</div><div>
The experts are invited to have a dialogue and to learn more from each other’s perspectives. The aim is to formulate an advice for the executive board.
</div></div><div>
Before starting the exercise, it can be useful to emphasize that the groups are invited to engage in [[Theme:6217d06b-c907-4b09-af4e-b4c8a17b9847|dialogue]] rather than debate.
</div><div>
To encourage the dialogue a list of questions has been prepared (see step 5).
</div> +
<span lang="EN-US">Trainers should develop specific learning goals for their session.</span> The learning objectives for these sessions should align with those of the e-learning modules. General expected outcomes at the end of these sessions include the following:
*Participants should be knowledgeable on relevant literature, developments and regulations with regards to the topic addressed
*They should be able to indicate what ethical issues are pressing regarding research concerning specific technologies
*They should be able to apply relevant ethical concerns on realistic cases
*<span lang="EN-US">They should be aware how learning materials are relevant for their professional/academic context</span> +
The [https://www.path2integrity.eu/ Path2Integrity] project introduces educators to innovative teaching methods that cover topics in research integrity and ethics. The project provides introductory videos and information on the teaching methodology used, discussing research integrity and its significance. +
The [https://www.path2integrity.eu/ Path2Integrity] project introduces educators to innovative teaching methods that cover topics in research integrity and ethics. The project provides introductory videos and information on the teaching methodology used, discussing research integrity and its significance. +
'''''TARGET audience:''' bachelor and master students, doctoral students and early career researchers''
The e-learning modules produced by the [[Guide:Bbe860a3-56a9-45f7-b787-031689729e52|VIRT2UE]] project represent an introduction to relevant topics in research integrity, These modules, which foster self-learning introduce [[Instruction:6ceba4e4-fb32-4953-9138-5436807fcde6|research integrity]], [[Instruction:86f47366-a189-4395-9301-36ddb6d1fc68|virtue ethics relevant for RI]] and virtue ethics under current research conditions. Moreover, the training has developed a series of [[Instruction:17705907-d9b2-4f33-bc4f-088d84b4d971|videos]] and [[Instruction:7ce7ad50-499a-4cca-b09d-b2c1573d94f3|reading material]] introducing specific concepts and themes relevant to the field and practice of research integrity, such as ethical decision making in research and moral disengagement in research. +
'''''Target audience''': Bachelor and master students, doctoral students and early-career researchers.''
The RID-SSISS training aims to help beginner and more experienced researchers develop their research ethics competencies in HE institutions. A CSCL (Computer-Supported Collaborative Learning) ethics resource was designed that utilised cases, collaboration, and structural scaffolding. This resource provides learners with opportunities to gradually develop research ethics competencies, guiding them through three levels. The [https://en.researchethicscompass.net/ Foundation level] focuses on developing (but also helping learners to recall) central concepts of RE/RI, primarily suitable for bachelor’s, master’s, and doctoral students but also usable with academic staff and researchers. During the Foundation level training, participants learn to guide their own REI practices and behaviour. The RID-SSISS training also provides resources for ECRs and junior academics. This material aims to develop RE/RI competencies by supporting ethical analysis competencies as a step towards increased agency in research ethics and integrity. Ethical analysis involves the following steps: identify ethical issues by determining which ethical principle might be at stake; and utilise the ethical analysis steps to provide solutions to ethical dilemmas. In addition to the foundational level, the project developed training materials for ECRs and junior academics ([https://www.researchethicstraining.net/ advanced level]). +
'''''Target audience''': Bachelor and master students, doctoral students and early career researchers.''
Besides the introductory module, the PRINTEGER Upright training provides [https://printeger.eu/upright/toc/ modules] focusing on specific RE and/or RI issues. These modules address topics in relation the research misconduct, questionable research practices and more research ethics-related topics. Depending on the complexity of the topic, these modules can be used for students and academics with different levels of RE/RI-related competencies. +
'''''Target Audience:''' undergraduate and graduate students.''
The training games developed by the [https://www.academicintegrity.eu/wp/bridge/ BRIDGE project] use the “gamification” approach to raise awareness and provide very basic knowledge on research integrity for university students. Online, board, role-play and scenario games can be found [https://www.academicintegrity.eu/wp/bridge-games/ here]. +
'''''Target Audience:''' undergraduate and graduate students.''
The training games developed by the [https://www.academicintegrity.eu/wp/bridge/ BRIDGE project] use the “gamification” approach to raise awareness and provide very basic knowledge on research integrity for university students. Online, board, role-play and scenario games can be found [https://www.academicintegrity.eu/wp/bridge-games/ here]. +
