Why is this important? (Important Because)
From The Embassy of Good Science
A description to provide more focus to the theme/resource (max. 200 words)
- ⧼SA Foundation Data Type⧽: Text
Q
Besides explaining the principles of quality enhancements, this guideline also deals with evaluations and reviews at different levels: institutional, discipline-wide and national. It also details the procedures to deal with complaints, investigations and appeals. +
Currently, citizen science is becoming more and more important in different fields of science. For example, in natural sciences, it enables large-scale data collection by involving a vast number of individuals which would be challenging to achieve for traditional research methods within the same timeframe and resources. This training will guide you through the crucial elements of responsible citizen science, including protection of human research participants, plants, animals and ecosystems;rights of citizen scientists;conflicts of interest;quality of research outputs etc. By the end of this training, you will gain a deeper understanding of responsible open science and acquire the following skills and attitudes necessary for responsible practising of citizen science. +
Integrity in analysis and reporting of results is important to fully understand your data. Misbehaviors related to analysis and reporting include:
#Report on data driven hypotheses without disclosure [‘HARKing’ ‐ Hypothesizing After Results are Known ‐ typically with a view to make results appear more spectacular (‘Chrysalis effect’)]
#Delete data before performing data analysis without disclosure
#Selectively delete data, modify data or add fabricated data after performing initial data‐analyses [in other words: falsification or fabrication of data]
#Perform data‐analyses not stated in the study protocol without disclosure [or in predefined data‐analysis plan – also called ‘Significance chasing’, ‘P-hacking’, ‘data dredging’, ‘fishing expedition’ or explorative subgroup analyses]
#Report an incorrect downwardly rounded p‐value [e.g. by reporting a p value of .054 as being less than .05]
#Not report all study protocol‐stipulated results [in the aggregate of all published reports on the study at issue]
#Not publish a valid ‘negative’ study [in a form that is publicly available or accessible behind a paywall (article, report, website etc.)]
#Report an unexpected finding as having been hypothesized from the start
#Conceal results that contradict your earlier findings or convictions
#Not report clearly relevant details of study methods
#Not report replication problems
#Selectively cite to enhance your own findings or convictions
#Selectively cite to please editors, reviewers or colleagues
#Selectively cite or cite your own work to improve citation metrics [e.g. Impact Factor, H‐index]
#Let your convictions influence the conclusions substantially
#Insufficiently report study flaws and limitations
#Spread study results over more papers than needed [‘salami slicing’]
#Duplicate publication without disclosure
#Re‐use of previously published data without disclosure [which may lead to double counting in meta‐analyses]
#Modify the results or conclusions of a study due to pressure of a sponsor [commercial or not‐for‐profit funder of the study]
#Failure to disclose a sponsor of the study
#Failure to disclose a relevant financial or intellectual conflict of interest [in publications, when reviewing grant proposals, or evaluating persons or institutions]
#Handle existing conflicts of interest inadequately
#Communicate results to the general public before a peer reviewed publication is available
#Deliberately communicate findings inaccurately in the media or during presentations
#Make no clear distinction between personal views and professional comments (List from Bouter et al 2016'"`UNIQ--ref-00000002-QINU`"')
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Good collaboration is not just about building networks and beneficial relationships, it also entails taking responsibility for research conduct, treating colleagues and collaborators with respect, and giving collaborators full credit for their work. Misbehaviors related to collaborations identifiedby research integrity experts include:
# Take no full responsibility for the integrity of the research project and its reports
# Refuse to share data with bona fide colleagues
# Turn a blind eye to putative breaches of research integrity by others
# Refuse to respond to an allegation of a breach of research integrity
# Use unpublished ideas or phrases of others without their permission [e.g. from reviewing manuscripts or grant applications, or from conference presentations ‐ this is one of the forms plagiarism can take]
# Use published ideas or phrases of others without referencing [this is one of the forms plagiarism can take]
# Re‐use parts of your own publications without referencing [‘self‐plagiarism’]
# Unfairly review papers, grant applications or colleagues applying for promotion
# Review your own papers
# Demand, accept or offer substantial gifts for doing a favor [e.g. authorship, promotion, access to data, favorable review or recommendation]
# Insufficiently supervise or mentor junior coworkers
# Be grossly unfair to your collaborators [e.g. in terms of a just balance of benefits and burdens, including giving those who deserve the opportunity to qualify as author]
# Add an author who doesn’t qualify for authorship [‘honorary or gift authorship’]
# Demand or accept an authorship for which you don’t qualify [‘honorary or gift authorship’]
# Omit a contributor who deserves authorship [‘ghost authorship’]
# Not acknowledge contributors who do not qualify for authorship
# Not ask permission from contributors for the wording of the acknowledgement
# Not share reviewers’ comments with all co‐authors
# Submit or resubmit a paper or grant application without consent from all authors
The importance of the data collection phase cannot be overemphasized. For research results to be trustworthy, the underlying data needs to be of a high quality. ‘Misbehaviors’ related to data collection identified by research integrity experts'"`UNIQ--ref-00000000-QINU`"' include:
#Collect more data after noticing that the results are almost statistically significant [unless specified in a predefined adequate plan for interim analysis – also called ‘peeking’]
#Fabricate data*
#Stop data collection earlier than planned because the results are already statistically significant [unless predefined stopping rules are implemented appropriately ‐ also called ‘peeking’]
#Not adhere to pertinent laws and regulations [including the laws and regulations for human and animal studies, safety regulations, good clinical practice, good laboratory practice etc.]
#Inadequately handle or store data or (bio)materials [including archiving for an appropriate period]
#Keep inadequate notes of the research process [with (digital) lab journals or its equivalent in other types of research]
#Ignore basic principles of quality assurance (From Bouter et al 2016'"`UNIQ--ref-00000001-QINU`"').
'"`UNIQ--nowiki-00000002-QINU`"'Included in a separate misconduct section in The Embassy categorisation.'"`UNIQ--references-00000003-QINU`"' +
An appropriate, transparent, and meticulous study design is the foundation on which to build trustworthy, high quality research. Questionable practices related to study design include:
1. Propose study questions which are clearly irrelevant [including questions that have already been or could be answered adequately by a systematic review of the literature]
2. Choose a clearly inadequate research design or using evidently unsuitable measurement instrument [which will not lead to a valid, reproducible and efficient answer to the main study question, taking into account the state‐of‐the‐art in the field at issue]
3. Present grossly misleading information in a grant application
4. Write no or a clearly inadequate research protocol [in which essential details are lacking]
5. Ignore substantial safety risks of the study to participants, workers or environment
6. Ignore substantial risks of the expected findings for society or environment
7. Importantly change the research design during the study without disclosure [or – if applicable‐ without permission of sponsor, Institutional Review Board or Institutional Animal Care and Use Committee]
8. Give insufficient attention to the equipment, skills or expertise which are essential to perform the study (From Bouter et al 2016'"`UNIQ--ref-00000002-QINU`"').
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It can be difficult for researchers to know what to do if they suspect misconduct but do not have concrete evidence. The extreme hierarchies present in scientific departments and labs can exacerbate the problem for junior researchers. +
R
This page is important because it showcases the gold standard of RDA’s contributions outputs that have been vetted, endorsed, and deemed ready for broader adoption. Such endorsement provides legitimacy and confidence that the tools and recommendations are robust, community-supported, and sustainable. For researchers, institutions, and infrastructure providers, these outputs serve as trustworthy blueprints for implementing FAIR data practices. They help reduce duplication, promote interoperability, and accelerate uptake across domains and geographies. In effect, the endorsed set helps translate collective expertise into reliable infrastructure for data-intensive research. +
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These guidelines are important as open science is now considered as standard in the practice of science in the research and innovation programs of the European Commission. To quote the EC, " Open science is a policy priority for the European Commission and the standard method of working under its research and innovation funding programmes as it improves the quality, efficiency and responsiveness of research."
'''Reference:'''
https://research-and-innovation.ec.europa.eu/strategy/strategy-2020-2024/our-digital-future/open-science_en</div><div></div> +
It shows the adverse effects of strict mentoring. +
The present case shows that plagiarism is not only committed by researchers, but also by publishers. Such plagiarism may not only harm the original authors of the articles by not attributing them for their work, but also the original publishers. It is important to recognize journals that do steal the work of others as fast as possible, so they cannot make many victims. This case may help to recognize such journals. +
Within SINAPSE, you will be able to indicate that you are a trained VIRT2UE trainer, enabling other members of the community to identify you as a potential trainer for research integrity courses. The European Commission also uses SINAPSE to identify experts for policy and research purposes. +
This recommendation is important for researchers and authors, who are responsible for declaring truthful and ethically justified institutional affiliations in scientific publications. It is also crucial for universities and research institutions, as affiliations directly affect institutional reputation, visibility, rankings, and accountability for research outputs. Research Integrity Officer rely on such guidance to investigate concerns, enforce good scientific practice, and develop clear internal policies. Journal editors and academic publishers benefit by using the recommendation to assess, verify, and, when necessary, correct misleading affiliation claims. Finally, funding agencies and research evaluators depend on accurate affiliation information to ensure fair evaluation, responsible research assessment, and appropriate allocation of research funding. +
This recommendation is particularly important for researchers, who must ensure their affiliations are accurate, truthful, and ethically justified in publications. It is also highly relevant for universities, research institutes, and funding organizations, as affiliations affect institutional visibility, reputation, performance metrics, and access to funding. Editors and publishers benefit from the guidance when assessing author information and preventing misleading attribution. In addition, research integrity offices and policy makers can use the recommendation to develop or refine institutional rules and codes of conduct. Finally, it matters to the broader research evaluation community, as it supports responsible assessment practices and helps prevent distortions in rankings and bibliometric indicators caused by inappropriate or strategic affiliation claims. +
Recommendation on Science and Scientific Researchers distils international expectations for research integrity in International and clarifies what researchers and institutions in nan need to do to comply. It reduces ambiguity, aligns local practice with international norms, and offers actionable steps that improve transparency, reproducibility, and equitable access. For policy leads, it is a benchmark;for authors and administrators, it is a practical checklist. Published by nan in 2018, it is a credible reference to cite in institutional policies, training, and grant documentation. +
Mainstreaming citizen science is crucial because it enables more inclusive, real-time, and locally grounded data for better policy decisions. Traditional research alone cannot capture all environmental and societal challenges, especially those requiring continuous monitoring such as air quality, biodiversity loss, and climate impacts. By involving the public, governments gain access to larger datasets, improved societal trust, and greater policy legitimacy. It also empowers communities, increases scientific literacy, and supports behavioral change. Recognizing citizen science in policy unlocks funding, long-term support, and institutional acceptance, shifting it from small-scale initiatives to meaningful governance tools. This contributes directly to EU priorities on sustainability, digital transformation, and democratic engagement. +
This handbook is important because investigating suspected misconduct is a sensitive, intricate process that impacts individual reputations, institutional legitimacy, and public trust in science. Without clear, ethically grounded and robust procedures, investigations risk being unfair, opaque, biased, or inconsistent. The handbook helps fill that gap by offering guidance to make investigations more credible, fair, and transparent. It assists institutions, national bodies, and research communities in handling allegations responsibly protecting both complainants and respondents, promoting consistency across cases, and enabling learning from systemic issues. In cross-institution or cross-country cases, shared reference to this handbook supports smoother cooperation and comparability. +
The Handbook might be of help answering to questions related to how to deal with misconduct cases.
How should research misconduct be defined and how will it differ from unacceptable research practices? How should allegations be handled? What if an allegation involves several institutions and/or researchers in different countries? Why is it important to have a formal research integrity system in each European country to deal with research misconduct and is there any best model? Where should countries with no RI structures start? Should responsibility be local or national? What about openness and transparency versus confidentiality when dealing with possible misconduct cases?
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This handbook is important for research integrity offices and institutional officials responsible for investigating alleged misconduct, as it provides structured, practical guidance. It helps universities and research institutions establish or improve their research integrity systems and formal procedures. Investigation committee members and administrators benefit by understanding best practices for fair, transparent, and unbiased processes. Researchers and whistleblowers gain clarity on how misconduct allegations should be handled and what protections should be in place. Finally, national or regional research policymakers and funders can use it to support harmonised, credible, and trustworthy approaches to upholding research integrity across institutions and borders. +
Replication is of great importance to science, because science aims to discover laws of nature. Since such laws are permanent, experiments on which they are based should be infinitely replicable'"`UNIQ--ref-00000013-QINU`"'. This concept is highly important to medicine. Being able to replicate, for example, an epidemiologic study to determine health effects of certain risk factors could build up existing scientific evidence and impact decision making that might affect the public health'"`UNIQ--ref-00000014-QINU`"'. Replicability also represents a direct public interest since science is significantly funded by public resources. If a study cannot be replicated, the money invested in it is wasted. It is estimated that annual costs of non-replicable preclinical research are approximately US$28 billion'"`UNIQ--ref-00000015-QINU`"'.
Replication can be divided into direct and conceptual'"`UNIQ--ref-00000016-QINU`"'. Direct replication is an exact replication of an experiment and it ensures that the phenomenon is reproducible;however, it does not guarantee that the theory behind the phenomenon is true. Therefore, confirming the same results with a different methodology or a different experimental system adds more credibility to the proposed theory or model'"`UNIQ--ref-00000017-QINU`"'. Nevertheless, we cannot expect that every experiment can be replicated down to the last detail, especially in psychology and medicine. We can always expect to see random deviation in the results and conclusions when conducting an independent experiment'"`UNIQ--ref-00000018-QINU`"'.
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