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
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Human digital twins rely on the continuous collection and integration of large volumes of highly sensitive personal data. This creates significant concerns regarding data security, confidentiality, and the potential misuse of health information (1). Unlike traditional medical records, digital twins may contain highly detailed representations of an individual's health status and future disease risks (1,3).
Another challenge concerns informed consent. Participants may consent to the use of their current data, but future applications of digital twin technologies may be difficult to predict. Researchers must therefore consider whether consent remains valid when new analytical methods or purposes emerge (1).
Questions of responsibility and accountability are equally important. If a treatment decision is influenced by a digital twin prediction that later proves incorrect, it may be unclear whether responsibility lies with clinicians, researchers, software developers, or healthcare institutions (1,4).<div></div> +
Scientific research is often done in a team, hence, one must be prepared to collaborate with others. Every researcher must know their role and what is expected of them before they begin their research, in order to avoid conflict. Having a list of principles that every scientist must follow throughout their research ensures that there won’t be any clashes or misunderstandings in the resulting works. All research components should be in sync and make sense when merged together in the final results. +
Cross-boundary collaborations provide opportunities but also difficulties. It is important to be aware of differences in research practice, guidelines and legislation. Collaborators should try to reach consensus and agreement in the design and implementation of research. +
Collaborations between high-income countries (HICs) and low- and middle-income countries (LMICs) can be mutually beneficial endeavors. Researchers from HIC might benefit from local expertise and experience and gain access to unique resources, environments and participants. Researchers from LMICs potentially benefit from access to funding, international networks and opportunities for local capacity building. Collaborations can also, unfortunately, lead to negative experiences, ranging from different standards in data management and ethics applications to a lack of participation in research agenda setting and even coercive recruitment practices or exploitation of people/samples/resources. +
Confidentiality is grounded in the prima facie duty of researchers and health professionals not to reveal information entrusted to them by participants or patients without their permission. It arises from an implicit or explicit agreement to safeguard confidential or secret information and is central to maintaining trust in the researcher–participant and patient–physician relationship. Confidentiality is more specific than privacy: while privacy concerns a person’s general interest in controlling access to themselves and their data, confidentiality refers to the obligation of those who receive information to keep it from unauthorized disclosure. In research, strong promises of confidentiality are often essential to recruit participants, especially when topics are sensitive or potentially stigmatizing. Breaches of confidentiality can cause direct harm to participants, damage trust in researchers and institutions, and undermine public confidence in research. +
Since children are considered vulnerable population, it is important that parental consent is obtained for research. That consent ensures understanding of purpose, procedures and potential risks/benefits of the study, while children's assent respects their autonomy. +
Peer review process is vital to science, as it provides quality assurance before publication of new knowledge. Any situation which can compromise peer review process by influencing decision making should hence be reported, and prevented.'"`UNIQ--ref-00000008-QINU`"'
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COI is a core concept in research integrity. It can even be argued that most research integrity issues are in some way related to underlying COIs, especially if integrity is understood to refer to doing what is right even if confronted by countervailing incentives.<sup>[2]</sup> Authorship conflicts, for example, often occur because researchers have a strong secondary interest to be listed as authors on as many papers as possible to advance their career, even if they have not contributed to a paper (or if their contribution does not constitute authorship). Usually, discussions on COIs in the research integrity literature focus on the narrower aspect of how COIs can bias research results and thus decrease the reliability of research results, however. In line with most of the relevant literature, this theme page adopts a narrow perspective on COIs.
In addition to their potential effects on research integrity, COIs have an important research ethics dimension as well, especially in biomedical research.<sup>[3]</sup> An example is the specific role of medical doctors in clinical research: According to the International Code of Medical Ethics, they are obliged to “be dedicated to providing competent medical service in full professional and moral independence, with compassion and respect for human dignity”.<sup>[4]</sup> However, if they act as researchers in clinical research, they are confronted with two potentially conflicting interests: a duty to care (primary duty) and the responsibility to generate new knowledge (which in this case is a secondary interest that can under certain circumstances conflict with the duty to care).<sup>[5]</sup>
Therefore, it is crucial to understand what COIs are, how they affect research integrity and research ethics, and what the research community as well as individual researchers can do to minimize their potential detrimental effects. +
Editors play a special role in science, as they ultimately determine what gets published in their journals. We often say that editors are gate-keepers of science, because of their control over journal policies.'"`UNIQ--ref-00000007-QINU`"' Publishing new discoveries is important in science, and following established methods of control such as the peer-review process is necessary. Sometimes, having an undisclosed interest could influence editorial decisions or hinder the proper review of manuscripts. This could lead to a loss of confidence in science, which can be particularly harmful when science is publicly funded.'"`UNIQ--ref-00000008-QINU`"'
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There is concern that intellectual conflicts of interest may cause bias in research. The idea is that if researchers prefer one intervention over another, they will intentionally or unintentionally introduce bias into the design of the study, data analysis or data interpretation so that the results are in favor of the preferred intervention. However, there is not enough evidence on this. In psychotherapy research, it has been shown that researcher allegiance (i.e. intellectual conflicts of interest) is associated with study results: therapies with higher allegiance are shown to be more effective in randomized clinical trials.'"`UNIQ--ref-00000013-QINU`"''"`UNIQ--ref-00000014-QINU`"''"`UNIQ--ref-00000015-QINU`"' It is not clear, though, whether this correlation is causal. It could be that researchers have allegiance towards more effective interventions because they are effective, rather than that the interventions appear to be more effective because the researchers have an allegiance to them.'"`UNIQ--ref-00000016-QINU`"' Although some argue that the association between researcher allegiance and study results might be causal since the association exists even when there is evidence that two interventions are equally effective, the evidence remains inconclusive on how researcher allegiance/ intellectual conflicts of interest might affect study results.'"`UNIQ--ref-00000017-QINU`"'
At the same time, some argue that intellectual conflicts of interest are unavoidable.'"`UNIQ--ref-00000018-QINU`"' Researchers will have a certain educational background, prior research experience, and personal and professional affiliations, which will naturally make them more likely to favor one research outcome over another.'"`UNIQ--ref-00000019-QINU`"' Not only is this unavoidable, but it is what drives science forward: researchers would not spend years researching a topic that they did not feel passionate about. It is precisely the passion and intellectual interest that researchers have that inspires them to delve into research.
'''What conclusions can we make?'''
If intellectual conflicts of interest lead researchers to introduce systematic biases into their research (e.g. selection bias, reporting bias, etc.), then they can be said to be problematic.'"`UNIQ--ref-0000001A-QINU`"' More evidence is needed to establish whether this is the case. However, if intellectual conflicts of interest do not lead to systematic bias, it is hard to argue that they are problematic even if they do affect study results.'"`UNIQ--ref-0000001B-QINU`"' For example, if researchers that prefer intervention A are more likely to have results that favor intervention A even in the absence of systematic bias, it could be that:
1) they are better trained in intervention A than other researchers, or
2) they know more about intervention A than other researchers , or
3) they carry out intervention A more diligently than other researchers.
It would be difficult to argue that possibilities 1, 2 or 3 are problematic. Innovation in science is driven by passionate researchers who develop and propose new hypotheses, which they often strongly believe in. It makes sense that if these passionate researchers who know more about the hypotheses than others are not able to show their truthfulness, then no one else will be able to, indicating that the hypotheses are not correct. Yet, if the innovative researchers are able to provide evidence on the truthfulness of the hypotheses, the hypotheses remain open to a closer scrutiny by the rest of the scientific community.'"`UNIQ--ref-0000001C-QINU`"' Since science is a self-correcting process, it may not matter if the researcher with the intellectual conflict of interest is more likely to obtain study results in line with their allegiances. Other researchers with different allegiances will be able to scrutinize and test the results, thereby correcting for the intellectual conflict of interest.
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Delayed drug availability may affect research results. +
Correct attribution of research outputs is a central aspect of research integrity. However, identifying individual contributions can be difficult due to common names, name changes, inconsistent authorship practices, and long lists of collaborators. These issues can lead to misattribution, reduced visibility, or unfair recognition of work.
ORCID addresses these challenges by assigning a unique identifier to each researcher, ensuring that their work is accurately connected to them regardless of institutional affiliation or name variations. This improves transparency and accountability in research, while also supporting fair evaluation of academic performance.
In addition, ORCID simplifies administrative processes such as manuscript submission and grant applications, as researchers can reuse their verified profiles instead of repeatedly entering the same information. As its adoption grows, many publishers and funding bodies increasingly require ORCID identifiers, making it an essential tool in modern research practice. +
Children are a vulnerable population in research, as they cannot make participation decisions on their own. For this reason, informed consent from parents or legal caregivers is typically documented through a written, signed, and dated form. In pediatric research, informed consent ensures that parents or caregivers receive all relevant information and can voluntarily decide whether their child will participate. Failure to follow ethical standards can have legal, professional, and reputational consequences, particularly when data are not securely protected or informed consent procedures are ignored. Following proper ethical procedures, including informed consent, helps protect children and promotes responsible research practices. In all research involving children, maintaining the highest ethical standards is essential, regardless of the research context. +
Data protection is defined as a fundamental human right and using personal data raises significant ethics issues '"`UNIQ--ref-00000003-QINU`"'. Pseudonymisation and anonymisation are methods used to protect one's privacy and minimize the risk in the event of unauthorized access. Pseudonymised data are considered personal data and therefore are in the scope of GDPR, unlike irreversibly anonymised data which are no longer defined as personal data and are outside of the scope of GDPR '"`UNIQ--ref-00000004-QINU`"'
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Since research funders, organisations, reviewers, and individual researchers all have different needs from data management , DPMs are becoming integral parts of grant applications and research organisations.'"`UNIQ--ref-00000016-QINU`"' They are usually short, and state which data will be created and how. DMPs also define the plans for data sharing and presentation,'"`UNIQ--ref-00000017-QINU`"' prescribe how the data will be stored, who will have access to it, what documentation and metadata will be created with it, and how it will be preserved.'"`UNIQ--ref-00000018-QINU`"' Creating DMPs provides several benefits for researchers, such as reducing the loss of data, monitoring the research progress, and preparing data for future use. Apart from that, they are also time saving.'"`UNIQ--ref-00000019-QINU`"'
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While conducting our research we (as researchers) produce different kind of data: we write research designs and workflows, we collect raw data, we analyse them, we write about them. However, most of the time the only part of our research which is publicly shared is represented by the articles which we produce as a result of the entire research cycle. In fact, all phases of research could be of potential interest for other researchers, who could reuse the data we produce in a different way.
By maximizing access to and re-use of research data we can optimize the impact of the data that we produce. In line with the principles of Open Science, good data management becomes a fundamental instrument to promote and facilitate the reusability, accessibility and exploitation of research data, thereby allowing for the generation of new knowledge.'"`UNIQ--ref-00000002-QINU`"'
In 2014, a workshop named [https://www.lorentzcenter.nl/lc/web/2014/602/info.php3?wsid=602 ‘Jointly Designing a data FAIRPORT’] was organized in Leiden by a group of academic and private sector stakeholders. The aim of the workshop was to improve the infrastructure supporting humans and machines in the discovery and analysis of scientific data and their associated algorithms and workflows. A set of guidelines was developed by the participants to support data producers, scientists and data publishers to take full advantage of the generation of data.'"`UNIQ--ref-00000003-QINU`"' Four foundational principles were agreed by the community to allow stakeholders to discover, integrate, re-use and adequately cite the massive quantities of information being generated by contemporary data intensive science.
These foundational principles were subsequently improved, detailed and elaborated on by the [https://www.force11.org/group/fairgroup/fairprinciples FORCE 11] working group, which is still engaged in fostering the implementation and continued update of the FAIR principles. These foundational principles are known as FAIR principles, see below for specification of the principles. '"`UNIQ--ref-00000004-QINU`"'
==='''FAIR principles'''===
'''Findable:'''
#(Meta)data are assigned a globally unique and eternally persistent identifier.
#Data are described with rich metadata.
#(meta)data are registered or indexed in a searchable resource.
#Metadata specify the data identifier.
'''Accessible'''
#(meta)data are retrievable by their identifier using a standardized communications protocol.
#the protocol is open, free, and universally implementable.
#the protocol allows for an authentication and authorization procedure, where necessary.
#metadata are accessible, even when the data are no longer available.
'''Interoperable'''
#(meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
#(meta)data use vocabularies that follow FAIR principles.
#(meta)data include qualified references to other (meta)data.
'''Re-usable'''
#meta(data) have a plurality of accurate and relevant attributes.
#(meta)data are released with a clear and accessible data usage license.
#(meta)data are associated with their provenance.
#(meta)data meet domain-relevant community standards.
All researchers at all levels but also society as a whole can benefit from the implementation of FAIR data management. Granting access to data produced throughout the entire research cycle is also important on a global scale since it allows researchers who operate in countries with less developed research infrastructures to benefit from others scientists’ work.'"`UNIQ--ref-00000005-QINU`"'
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The first sequencing of the whole human genome in 2003 cost roughly $2.7 billion. Advances in whole genome scanning technologies have enabled commercial companies to make genomic information available to the end consumer at competitive prices. Consumers can now have access to commercial genotyping of their genome from direct-to-consumer testing companies. According to NHGRI-funded genome-sequencing groups, the cost to generate a high-quality 'draft' whole human genome sequence in mid-2015 was just above $4,000;by late in 2015, that figure had fallen below $1,500.
This changing of landscape and technologies in which GWAS takes place is likely to affect the problem of reporting of findings. Is it possible that the participants in GWAS will expect a feedback similar to the information provided by consumer genetics companies? +
After deciding what research data to keep for long-term preservation, researchers should select the right repository. There is a variety of repositories – those that are focused on a specific research area and those for general purpose.'"`UNIQ--ref-00000009-QINU`"' Funders, journals, and universities also have their own repositories.'"`UNIQ--ref-0000000A-QINU`"''"`UNIQ--ref-0000000B-QINU`"' Since there are more than 2,000 data repositories,'"`UNIQ--ref-0000000C-QINU`"' researchers should use certain principles and standards in their selection process. They should:
-check whether there are any funder requirements that may mandate which repository to use
-check with the journal they are submitting their paper to, because some journals keep a list of approved repositories they will accept or have specific policies on data archiving
-check for discipline specific repositories because they could be more suitable to their datasets and other researchers expect to find them there
-or deposit their data into a University repository.'"`UNIQ--ref-0000000D-QINU`"'
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Missing data are unavoidable in clinical trials. Frequently, complete cases analysis is used only including individuals with no missing data '"`UNIQ--ref-00000009-QINU`"'. However, that can generate bias and can lead to exclude several individuals, causing loss of precision and power '"`UNIQ--ref-0000000A-QINU`"'. The risk of bias from missing data depends on the cause '"`UNIQ--ref-0000000B-QINU`"':
Missing completely at random: There are no systematic differences between the missing values and the observed values.
Missing at random: Any systematic difference between the missing values and the observed values can be explained by differences in observed data.
Missing not at random: Systematic differences remain between the missing values and the observed values.
The determination of the type of missing values is difficult due to the nature of missing values '"`UNIQ--ref-0000000C-QINU`"'. Therefore, practical guidelines are needed to deal with missing data. +
Data access is extremely important for transparent modern science. The rising number of research studies impedes the filtering of research findings, aggravates peer-review process and increases the possibility of false study reports. Having in mind the direct implications of scientific findings on everyday practice, data availability is further prioritized. The ''open data movement'' follows the principles of transparency, participation, and collaboration (1). Open data policy is important because it nurtures the virtues of transparency and honesty, which allows each respondent to check the authenticity of the published results at any time. Data sharing represents a significant part of research ethics and nowadays, many journals require researchers to publish resources to make them available to other investigators (2,3). However, deposited published data may be incomplete, in some cases intentionally because authors could feel like losing priority in future publishing, which may complicate new analyses on previously published data (2).
In an effort to enhance data-sharing practices, some journals have mandatory data availability statement (DAS). However, according to a recent study on data availability statements, 93% of authors of manuscripts with DASs that stated authors are eager to share their data either didn't respond or refused to share their data. In conclusion, the level of compliance is disappointing even when the authors state in their article that they will share data upon request, indicating that the DAS may not be enough to guarantee data sharing (4). +
