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.
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Open science and research ethics have in common their foundation on the universality of human rights. In full alignment with the [https://www.un.org/en/about-us/universal-declaration-of-human-rights Universal Declaration of Human Rights], open science assumes and serves the principle that “All human beings are born free and equal in dignity and rights. They are endowed with reason and conscience and should act towards one another in a spirit of brotherhood.” Additionally, article 27 of the Universal Declaration states that “Everyone has the right freely to participate in the cultural life of the community, to enjoy the arts and to share in scientific advancement and its benefits”, as well as “Everyone has the right to the protection of the moral and material interests resulting from any scientific, literary or artistic production of which he is the author”.
The UNESCO Recommendation on Open Science identifies the core values of open science. These values, applicable to the entire scientific research process, include (1) quality and integrity, (2) collective benefit, (3) equity and fairness, and (4) diversity and inclusiveness (UNESCO, 2021). The UNESCO Recommendation also introduces guiding principles of open science, such as transparency, scrutiny, critique, reproducibility, equality of opportunities, responsibility, respect, accountability, collaboration, participation, inclusion, flexibility, and sustainability (UNESCO, 2021).
Open science practices are in principle reciprocal and symmetrical. Everyone contributes knowledge and data by making them openly accessible, and everyone can then use the knowledge and data for further research. Citizen and participatory science is part of open science as one of the types of engagement of social actors and is defined as "models of scientific research conducted by non-professional scientists, following scientifically valid methodologies and frequently carried out in association with formal, scientific programmes or with professional scientists with web-based platforms and social media, as well as open source hardware and software (especially low-cost sensors and mobile apps) as important agents of interaction." (UNESCO, 2021, p. 18-19)
'''References'''
#UNESCO (2021). Recommendation on Open Science. https://doi.org/10.54677/MNMH8546
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. +
<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.''
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]. +
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:''' doctoral students and early career researches, senior researchers, and RE/RI experts''
The RID-SSISS training for academics aims to develop leadership competencies by combining senior academics’ extant knowledge base with RE/RI and new leadership competencies. In addition to the foundational level, and the advanced level, the RID-SSISS project developed a training for supervisors and leaders ([https://www.researchethicstraining.net/leadershiplevel leadership level]). +
'''''Target audience:''' doctoral students and early career researches, senior researchers, and RE/RI experts''
The RID-SSISS training for academics aims to develop leadership competencies by combining senior academics’ extant knowledge base with RE/RI and new leadership competencies. In addition to the foundational level, and the advanced level, the RID-SSISS project developed a training for supervisors and leaders ([https://www.researchethicstraining.net/leadershiplevel leadership level]). +
