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Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed
Open accessAbstract
Collaborative networks and data sharing initiatives are broadening the opportunities for the advancement of science. These initiatives offer greater transparency in science, with the opportunity for external research groups to reproduce, replicate, and extend research findings. Further, larger datasets offer the opportunity to identify homogeneous patterns within subgroups of individuals, where these patterns may be obscured by the heterogeneity of the neurobiological measure in smaller samples. However, data sharing and data pooling initiatives are not without their challenges, especially with new laws that may at first glance appear quite restrictive for open science initiatives. Interestingly, what is key to some of these new laws (i.e, the European Union's general data protection regulation) is that they provide greater control of data to those who “give” their data for research purposes. Thus, the most important element in data sharing is allowing the participants to make informed decisions about how they want their data to be used, and, within the law of the specific country, to follow the participants' wishes. This framework encompasses obtaining thorough informed consent and allowing the participant to determine the extent that they want their data shared, many of the ethical and legal obstacles are reduced to just monsters under the bed. In this manuscript we discuss the many options and obstacles for data sharing, from fully open, to federated learning, to fully closed. Importantly, we highlight the intersection of data sharing, privacy, and data ownership and highlight specific examples that we believe are informative to the neuroimaging community.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This review argues that neuroimaging data sharing offers major scientific benefits but raises privacy, ownership, and legal challenges, with participant-informed consent as the key to resolving many of them. It maps sharing options from fully open to federated/decentralized to fully closed, concluding that GDPR/HIPAA and related rules are often “monsters under the bed” rather than absolute barriers.
Key findings
- Data sharing and pooling increase sample size, precision, reproducibility, and the ability to parse heterogeneity; examples include ENIGMA, ABIDE, the Human Connectome Project, and the UK Biobank, which aims to release neuroimaging data from 100,000 participants.
- Legal frameworks shape sharing: GDPR went into effect May 25, 2018; HIPAA was implemented in 1996 and its Privacy Rule on April 14, 2003; GDPR gives participants greater control, including the “Right to be Forgotten,” while HIPAA permits PHI sharing under IRB waiver, preparatory research, decedents, or consent. NIH requires a data sharing plan for applications with yearly direct costs greater than $500,000, and GDPR non-compliance can carry a potential 20 million euro fine.
- Sharing lies on a spectrum—from peak coordinates, unthresholded/network maps, meta-analysis, and mega-analysis to preprocessed, NIfTI, DICOM, or fully open data—with trade-offs in information content, compute load, custom subject-level models, and privacy (Table 1). Decentralized approaches such as COINSTAC, DataSHIELD, and ViPAR allow analysis without centralizing raw data.
- The greatest obstacle is researchers and institutions, not participants, funders, or laws; concerns include lack of credit/compensation, scooping risk, cost, re-identification, and unintended use. Proposed solutions include a share-index/data citation, grace periods, funding support, DUAs/DTAs, and decentralized methods.
Methods
- The paper is a narrative review/commentary; no empirical dataset, sample, or formal statistical analysis is reported.
- It synthesizes legal and ethical frameworks, data-sharing initiatives, and sharing approaches, including a table of trade-offs across sharing levels.
- Not stated in the paper for design, data or sample, and main analysis beyond this narrative discussion.
Limitations
- The paper does not present a formal limitations section.
- Evident from the text: it is a narrative/opinion review rather than a systematic review or empirical study, so examples are selective and proposed solutions are not empirically tested.
- The paper acknowledges that data sharing entails risk, that risk/benefit balances differ by institution and country, and that re-identification concerns remain, including that defacing may degrade downstream image analysis.
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Claims the paper makes
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In the paper
“Privacy concerns have only increased since the initial cautionary tales of the Netflix competition in which competitors inadvertently re‐identified individuals from anonymous datasets (Netflix Prize Privacy Concerns—https://en.wikipedia.org/wiki/Netflix_Prize#Privacy_concerns) and the example of genetic reidentification from datasets anonymized per NIH guidelines (Cassa, Wieland, & Mandl, 2008; El”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > PRIVACY
What the sources say
- Not checked yetcites Cassa, Wieland, & Mandl, 2008
- Not checked yetcites El Emam, 2011
- Not checked yetcites Homer et al., 2008
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIn the paper
“Larger sample sizes also offer the ability to parse the considerable heterogeneity of the population into more homogeneous groups.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > INTRODUCTION
What the sources say
- Not checked yetcites Manrai, Patel, & Ioannidis, 2018
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“These data sharing initiatives help advance the process of scientific discovery through increasing sample sizes, which allows for greater precision and the ability to measure smaller effects, although smaller effects can also be associated with either smaller biological effects or potentially confounding factors (Smith & Nichols, 2018).”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > INTRODUCTION
What the sources say
- Not checked yetcites Smith & Nichols, 2018
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, sharing of the individual datasets can be extremely beneficial for numerous reasons, including increasing sample size (Button et al., 2013), better performance (Boedhoe et al., 2019), greater flexibility in controlling for confounders, and the ability to parse heterogeneous groups to better understand the underlying neurobiology.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > A SPECTRUM OF SHARING
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“Whoever owns the data has control over the data, its dissemination, and the timing of dissemination (Fishbein, 1991).”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > DATA OWNERSHIP
What the sources say
- Not checked yetcites Fishbein, 1991
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“Yet one of the strongest messages inherent in the EU's GDPR is that individuals have much more control of their own data.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > DATA OWNERSHIP
What the sources say
- Not checked yetcites GDPR, 2016
Load-bearingStrength:boosted· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Pooling results for meta‐analyses has the major advantage that data is analyzed locally and thus it is not necessary to share individual data.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > A SPECTRUM OF SHARING
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Thus, even removal of facial features from an MRI scan may not completely ensure privacy.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > PRIVACY
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“When an individual participant living within the EU invokes their “Right to be Forgotten,” any personal and de‐identified data are then erased or destroyed.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > DATA OWNERSHIP
What the sources say
- Not checked yetcites EU, 2018
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“In addition, iterative meta‐analyses can be identical to mega‐analysis (Sarwate, Plis, Turner, Arbabshirani, & Calhoun, 2014).”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > A SPECTRUM OF SHARING
What the sources say
- Not checked yetcites Sarwate, Plis, Turner, Arbabshirani, & Calhoun, 2014
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes
- It may state two separate claims as one. 33
- Probably not a substantial claim. 57
In the paper
“Finally, in our longitudinal study of child development (White et al., 2017), we give children several photos of their brains (i.e., sagittal midline slice from the structural MRI) following their session, of which we have learned that some of the children have placed on social media.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > PRIVACY
What the sources say
- Not checked yetcites White et al., 2017
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“In addition, the GDPR's “Privacy by Design” requires that the safeguarding of data should be discussed and implemented during the design phase of the study.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > INTRODUCTION
What the sources say
- Not checked yetcites GDPR, 2016
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The GDPR places greater responsibility on institutions to safeguard the privacy of personal data, such as assuring that there is a data controller to monitor data security.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > INTRODUCTION
What the sources say
- Not checked yetcites GDPR, 2016
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Larger sample sizes will offer the opportunity to apply more sophisticated statistical models to the data (Bzdok & Yeo, 2017), since most biological processes have non‐linear and stochastic mechanisms (White, 2019).”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > INTRODUCTION
What the sources say
- Not checked yetcites Bzdok & Yeo, 2017
- Not checked yetcites White, 2019
Strength:hedged· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
In the paper
“The GDPR went into effect on May 25, 2018 and involves data privacy laws involving the storage, transfer, and sharing of data, both within and outside the EU and extending to the European Economic Area.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > INTRODUCTION
What the sources say
- Not checked yetcites GDPR, 2016
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Within the GDPR a clear distinction is made between personal data, de‐identified data, and fully anonymized data.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > FULLY ANONYMIZED VERSUS DE‐IDENTIFIED DATA
What the sources say
- Not checked yetcites GDPR, 2016
Load-bearingStrength:boosted· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
In the paper
“In addition, data sharing initiatives open the door for reproducibility, replication, and increased transparency (Milham et al., 2018).”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > INTRODUCTION
What the sources say
- Not checked yetcites Milham et al., 2018
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Moreover, it does not require advanced analysis methods to account for clustering‐effects within cohorts.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > A SPECTRUM OF SHARING
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While this will provide an indication of representativeness between those willing and not willing to share their data, it does not account for representativeness as a result of potential biases during the inclusion phase of the study (i.e., selection bias).”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > MISSED OPPORTUNITIES
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“The combination of neuroimaging and genetics, coined “imaging genetics” has also emerged, with the Enhancing Neuro Imaging Genetics through Meta Analyses (ENIGMA) consortium playing a leading role in this initiative (Thompson et al., 2014).”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > OPPORTUNITIES FOR DATA SHARING
What the sources say
- Not checked yetcites Thompson et al., 2014
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A consent form written that precludes the option for data sharing dramatically limits the ability for sharing to occur, although if it's possible to fully anonymize the data, then sharing is possible in most situations.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > DATA OWNERSHIP
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes? Based: unclearIt may state two separate claims as one.
In the paper
“Under the recent Horizon 2020 call, as far as possible, research data should be made available to “access, mine, exploit, reproduce, and disseminate (free of charge)” research data (European‐Commission, 2017).”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > DATA OWNERSHIP
What the sources say
- Not checked yetcites European‐Commission, 2017
Strength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In addition, these approaches involve retrospective storage of completed studies and do not allow for novel subject‐level models to be run on the time series data.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > A SPECTRUM OF SHARING
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Moreover, performing analyses at a centralized site has the benefit of being able to work within local or regional restrictions, whether defined by law or by the facility.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > OPPORTUNITIES FOR DATA SHARING
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“Examples of re‐identification, such as highly accurate identification via facial reconstruction (Schwarz et al., 2019) and machine learning identification from generative models (Rocher, Hendrickx, & de Montjoye, 2019) challenge the technical and legal adequacy of the de‐identification release‐and‐forget model, spurring calls for additional privacy guidance (Morris, 2019).”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > PRIVACY
What the sources say
- Not checked yetcites Schwarz et al., 2019
- Not checked yetcites Rocher, Hendrickx, & de Montjoye, 2019
- Not checked yetcites Morris, 2019
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Multivariate and other advanced analytic approaches have the ability to extract a remarkable amount of information from these highly distilled features, for example, intrinsic networks can be captured from covariation among individual datasets (Calhoun & Allen, 2013; Smith et al., 2009).”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > A SPECTRUM OF SHARING
What the sources say
- Not checked yetcites Calhoun & Allen, 2013
- Not checked yetcites Smith et al., 2009
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
In the paper
“from the United Kingdom the UK Biobank has the goal to release neuroimaging data from 100,000 participants (Miller et al., 2016)”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > OPPORTUNITIES FOR DATA SHARING
What the sources say
- Not checked yetcites Miller et al., 2016
Strength:plain· not yet calibratedn = 100,000Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While certain analyses cannot be applied to small datasets, pooling these smaller datasets expands the opportunities to address specific questions and to assess the replicability of the findings.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > A SPECTRUM OF SHARING
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“For example, the perspective of the NIH for nearly two decades has been that “all data should be considered for data sharing” (NIH, 2003).”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > DATA OWNERSHIP
What the sources say
- Not checked yetcites NIH, 2003
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While it may be possible for researchers to design their study and inclusion criteria to include only those participants who are willing to share their data, for clinical studies, this may result in a selection bias.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > MISSED OPPORTUNITIES
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“The NIMH currently mandates data sharing (with an institutionally signed DUA (Miller et al., 2017)) for almost all funded studies (there are some notable exceptions to this in the case of extremely sensitive data which might lead to the ability to re‐identify an individual).”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > OPPORTUNITIES FOR DATA SHARING
What the sources say
- Not checked yetcites Miller et al., 2017
Strength:hedged· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
In the paper
“While there is support that individuals have characteristic patterns of functional brain connectivity, known as functional connectome fingerprinting, these have not been used to identify individuals (Finn et al., 2015).”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > PRIVACY
What the sources say
- Not checked yetcites Finn et al., 2015
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
In the paper
“Nevertheless, in spite of the obstacles, many forms of data sharing are taking place and the benefits of these efforts have been seen (Milham et al., 2018).”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > MISSED OPPORTUNITIES
What the sources say
- Not checked yetcites Milham et al., 2018
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“With the proper safeguards and data agreements, cloud‐based computing will be equally as safe as an encrypted hard drive with a strong password that is behind locked doors.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > PRIVACY
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The reason why these initiatives are gaining momentum is because they foster collaboration and can advance the pace of scientific discovery.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > DISCUSSION
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Finally, if participants provide consent for their data to be shared, which is the most important element, then they can enjoy knowing that researchers across the globe are potentially working with their data to better understand the complexities of brain structure and function and to bring about novel discoveries.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > DISCUSSION
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Preparing data for sharing is work above and beyond what is needed for a group to analyze data locally.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > OBSTACLES FOR DATA SHARING
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Researchers are evaluated not on data sharing, but rather based on publications and grants, thus there is less incentive to engage in the considerable effort necessary to make a dataset available for sharing.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > OBSTACLES FOR DATA SHARING
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
- Rests on an assumption: Research careers are primarily evaluated on the number, quality, and impact of papers published and on the acquisition of grant funding, which depends on the number, quality and impact of publications.
- Rests on an assumption: Data collection takes considerable time and effort to assure that the quality of the data is high and the data is properly cleaned and used appropriately.
None of the sources cited for this claim have been checked yet.
In the paper
“Assuring that the shared data has the optimum data quality, or including metrics that allow users to understand the underlying quality of pre‐processed images (Esteban et al., 2017; White et al., 2018) is important to reduce noise‐related variability and to increase power (Zuo, Xu, & Milham, 2019).”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > A SPECTRUM OF SHARING
What the sources say
- Not checked yetcites Esteban et al., 2017
- Not checked yetcites White et al., 2018
- Not checked yetcites Zuo, Xu, & Milham, 2019
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“For those living in the United States, most human subjects research falls under the “Common Rule” (45 C.F.R. § 46 Subpart A), which is based on the 1975 revision of the Declaration of Helsinki.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > THE ETHICS OF DATA SHARING > Data sharing and the HIPAA
What the sources say
- Not checked yetcites 45 C.F.R. § 46 Subpart A
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Data sharing requires that those who use the data have a good understanding of the key variables, including the nuances of the data.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > OBSTACLES FOR DATA SHARING
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In addition, data can be shared with investigators from low‐ and middle‐income countries who may not have the resources to conduct expensive neuroimaging studies, but do have the ability to ask interesting and creative questions of the data.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > DISCUSSION
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Thus, under some regulations and certain countries, the data would need to be fully anonymized prior to being uploaded.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > A SPECTRUM OF SHARING
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
- Rests on an assumption: Sharing data within OpenNEURO has the requirement that, following a 36-month grace period following the first successful analysis of the data, the data will become publicly available under a Creative Commons (CCO) license.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In light of recent legal changes, notably in the EU, ethical aspects related to data have actually become more straightforward.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > THE ETHICS OF DATA SHARING
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, specific state regulations such as the “Federal Policy for the Protection of Human Subjects” or the Common Rule still apply.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > THE ETHICS OF DATA SHARING > Data sharing and the HIPAA
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Providing some time for those who collected the data to write up the results, however, can typically circumvent this issue.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > DISCUSSION > Scientists without borders
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
- Rests on an assumption: The most common issue making researchers less willing to share is that collecting and collating data takes considerable effort and others could publish results sooner than those who collected the data.
In the paper
“HIPAA applies to covered entities only, covered entities are health plans, health care clearinghouses, and health care providers electronically transmitting health information in connection with transactions for which Health and Human Services (HHS) has adopted standards (45 C.F.R. § 160.103).”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > THE ETHICS OF DATA SHARING > Data sharing and the HIPAA
What the sources say
- Not checked yetcites 45 C.F.R. § 160.103
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The most notable difference between the GDPR and HIPAA is to whom the regulations apply.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > THE ETHICS OF DATA SHARING > Data sharing and the HIPAA
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In some cases, for example, rare disease, or an extremely high‐profile scientific focus, the risk level may be too high for sharing.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > OBSTACLES FOR DATA SHARING
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“If certain associations reach sub‐threshold significance at individual sites, they will not be taken into account in the pooled meta‐analysis, whereas in a mega‐analysis, those associations might be discovered, simply because of the increased power.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > A SPECTRUM OF SHARING
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Research involving PHI from non‐covered institutions does not fall under HIPAA, but rather under the Common Rule.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > THE ETHICS OF DATA SHARING > Data sharing and the HIPAA
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Pure reproduction can only be established with detailed information on the coding of variables, the approach to missing data, and how the analyses were performed.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > A SPECTRUM OF SHARING
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While the large number of authors may dilute the impact of those authors contributing the most work, without rewarding via authorship would likely impair initiatives such as the ENIGMA consortium.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > OBSTACLES FOR DATA SHARING
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“Research studies involving neuroimaging require approval from the local medical ethics committee or institutional review board in accordance with the Declaration of Helsinki (WMA, 2000).”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > THE ETHICS OF DATA SHARING
What the sources say
- Not checked yetcites WMA, 2000
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“As certain institutions have both covered and non‐covered functions, there is a possibility to elect for being a hybrid entity, where only the covered functions must comply with the HIPAA requirements under the Privacy Rule.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > THE ETHICS OF DATA SHARING > Data sharing and the HIPAA
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Attorneys whose job is to protect the university may side on the being overly risk aversive, limiting the risk to a university, while at the same time potentially limiting the advancement of scientific discovery.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > OBSTACLES FOR DATA SHARING
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“PHI not held by a covered entity can be used and disclosed without regard to the Privacy Rule.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > THE ETHICS OF DATA SHARING > Data sharing and the HIPAA
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The utilization of standardized approaches can dramatically reduce the time necessary to understand the nature of the data and to reduce the number of errors due to misunderstandings surrounding the data.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > A SPECTRUM OF SHARING
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, research taking place with personal health information from covered institutions (i.e., hospitals, clinics, etc.) falls under the HIPAA.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > THE ETHICS OF DATA SHARING > Data sharing and the HIPAA
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“Data sharing allows for greater transparency in science with the ability to promote reproducibility and replication of study findings (Button et al., 2013; Ioannidis, 2005; Open Science Collaboration, 2015).”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > DISCUSSION
What the sources say
- Not checked yetcites Button et al., 2013
- Not checked yetcites Ioannidis, 2005
- Not checked yetcites Open Science Collaboration, 2015
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“The collaborative informatics and neuroimaging suite toolkit for anonymous computation (COINSTAC [Plis et al., 2016]; https://github.com/trendscenter/coinstac) tool and approach goes a step further in offering fully decentralized (and potentially privatized analysis), allowing the data to remain local at the site of collection, by leveraging local compute resources for each site's data.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > A SPECTRUM OF SHARING
What the sources say
- Not checked yetcites Plis et al., 2016
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“If the data is released relatively quickly after collection, other groups may access the data and publish prior to those who actually have collected the data.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > OBSTACLES FOR DATA SHARING
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“There are a number of valid concerns raised by researchers related to data sharing, however, for every concern there is a feasible solution (Figure 1):”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > OBSTACLES FOR DATA SHARING
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These laws, in essence, give much of the control back to the participants.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > THE ETHICS OF DATA SHARING
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“Specific rules apply for data sharing with countries with similar privacy protection, (i.e., countries that fall under the GDPR adequacy decision [Council of the European Union and European Parliament, 2016]), and thus to share data with other countries, additional safeguards are often necessary.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > THE ETHICS OF DATA SHARING > Data sharing and the GDPR
What the sources say
- Not checked yetcites Council of the European Union and European Parliament, 2016
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While there are obstacles to data sharing, most of these can be overcome.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > OBSTACLES FOR DATA SHARING
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Junior investigators are especially at risk, as they typically need more time to analyze and write the papers.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > OBSTACLES FOR DATA SHARING
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Changing some of the obstacles would likely require changes in policies of funding agencies and journals to provide support and credit for those who make the time and effort to participate in data sharing.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > OBSTACLES FOR DATA SHARING
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“For example, clinical neuroimaging data is held by covered entities and HIPAA applies to this data.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > THE ETHICS OF DATA SHARING > Data sharing and the HIPAA
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“If a different group publishes first, it may make it difficult for the PhD student to publish, and thus could hamper their career.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > OBSTACLES FOR DATA SHARING
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“another approach called ViPAR (virtual pooling and analysis of research data) leverages federated databases to provide temporal pooling of the actual data for analysis (Carter et al., 2016).”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > A SPECTRUM OF SHARING
What the sources say
- Not checked yetcites Carter et al., 2016
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Researchers can provide the opportunity for the participants to decide whether they are willing, or would like that their data be shared.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > CONCLUSIONS
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While we do not intend to minimize the importance of data security, there is a certain fear that has emerged regarding data sharing where it has become greater than life, monsters under the bed.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > CONCLUSIONS
Strength:plain· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“There are both real and imagined obstacles for data sharing which equate with not all researchers being supportive of data sharing initiatives.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > CONCLUSIONS
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This may result in the setting of a very restrictive bar for individual researchers within certain institutions.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > DISCUSSION > Scientists within borders
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
- Rests on an assumption: Attorneys who work for specific universities have the goal to assure that the university is protected from potential legal actions, such as the potential 20 million euro fine imposed by non-compliance with the GDPR.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, one key element that is often not discussed in regards to data sharing is the wishes of the participant in allowing their data to be shared.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > CONCLUSIONS
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Within the EU and likely other countries, the complexities of these laws and the fears of retribution may serve as a rationale for some researchers for not sharing.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > DISCUSSION > Scientists without borders
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
- Rests on an assumption: The goal of the GDPR is to provide greater control and protection to the individual over whose data has been collected.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Thus, there is a balance.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > DISCUSSION > Scientists within borders
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, there should be a balance, as keeping data under lock and key for use by only a handful of researchers may protect privacy, but will limit scientific discovery.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > CONCLUSIONS
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Collaborative networks and data sharing initiatives are broadening the opportunities for the advancement of science and the ability to ask important research questions that could benefit others.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > CONCLUSIONS
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For researchers who are not keen on data sharing, recent and emerging regulations regarding human subjects data can be used as a barrier, or excuse, for not taking part in data sharing initiatives.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > CONCLUSIONS
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Alternatively, sharing everything with everyone does not safeguard individual privacy.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > CONCLUSIONS
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“There is no question that data sharing will entail some level of risk.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > DISCUSSION > Scientists within borders
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A data leak of sensitive information, for example, could result in individual's data being used for unintended and potentially harmful purposes.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > DISCUSSION > Scientists within borders
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, if all researchers kept their data under tight control with no data sharing outside their research group, this will hamper the progress of scientific discovery.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > DISCUSSION > Scientists within borders
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Different countries may differ slightly in the level of restrictions towards data, however, these differences become equalized to some extent if the participants are allowed to decide how they want their data to be used.”
§ Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed > DISCUSSION > Scientists within borders
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper relies on this without support.
Depending on the type and amount of data, machine learning algorithms could, within a specific probability distribution, trace back to a specific individual.
“However, depending on the type and amount of data, machine learning algorithms could, within a specific probability distribution, trace back to a specific individual.”
The paper relies on this without support.
Within neuroimaging there are considerable missed opportunities for data sharing: thousands of studies with data collected from valuable populations did not include data sharing language within their consent forms and some IRB and medical ethics committees are refusing to allow these data to be shared.
“Within neuroimaging there are considerable missed opportunities for data sharing: thousands of studies with data collected from valuable populations did not include data sharing language within their consent forms and some IRB and medical ethics committees are refusing to allow these data to be shared.”
The paper relies on this without support.
In general, and across most countries, consent from the participants is necessary prior to the sharing of de-identified data.
“In general, and across most countries, consent from the participants is necessary prior to the sharing of de‐identified data.”
The paper relies on this without support.
The GDPR 'Right to be Forgotten' does not apply to any fully anonymized data that has been released.
“However, the GDPR “Right to be Forgotten” does not apply to any fully anonymized data that has been released.”
The paper relies on this without support.
In most cases, de-identified human subject's data, including neuroimaging and its associated meta-data, cannot be shared without adequate consent that specifically states that the data can be shared.
“In most cases, de‐identified human subject's data, including neuroimaging and its associated meta‐data, cannot be shared without adequate consent that specifically states that the data can be shared.”
The paper relies on this without support.
It is the researchers who write the consent forms and thus can ultimately control, to some extent, the opportunities for data sharing.
“However, it is the researchers who write the consent forms and thus can ultimately control, to some extent, the opportunities for data sharing.”
The paper relies on this without support.
The fields that have been at the forefront of data sharing or data pooling initiatives are those fields in which (a) data can be easily harmonized; and (b) large sample sizes are necessary and potentially available to address specific questions.
“The fields that have been at the forefront of data sharing or data pooling initiatives are those fields in which (a) data can be easily harmonized; and (b) large sample sizes are necessary and potentially available to address specific questions.”
The paper relies on this without support.
Within medicine, it is the field of human genetics that has been at the forefront of these initiatives, followed closely by the field of neuroimaging.
“Thus, it is not surprising that within medicine, it is the field of human genetics that has been at the forefront of these initiatives, followed closely by the field of neuroimaging.”
The paper relies on this without support.
Such meta-analytic approaches are limited to low dimensional analyses and do not yet enable the use of voxelwise, surface-based, or iterative machine learning analyses.
“However, such meta‐analytic approaches are limited to low dimensional analyses (such as volumetric analyses) and do not yet enable the use of voxelwise, surface‐based, or iterative machine learning analyses (i.e., those that perform iterative analyses using the entire dataset).”
The paper relies on this without support.
Entering a DUA can help mitigate these issues, but setting up a DUA is often a cumbersome process, requiring multiple agreements, discouraging potential users and still providing no more than a trust-based protection of the data.
“Entering a data use agreement (DUA) can help mitigate these issues, but setting up a DUA is often a cumbersome process, requiring multiple agreements (one per site and sometime even one per researcher, including institutional sign‐off), discouraging potential users and still providing no more than a trust‐based protection of the data.”
The paper relies on this without support.
Differences in data-acquisition protocols and MR platforms introduce noise in the data.
“Differences in data‐acquisition protocols and MR platforms introduce noise in the data.”
The paper relies on this without support.
Sharing data within OpenNEURO has the requirement that, following a 36-month grace period following the first successful analysis of the data, the data will become publicly available under a Creative Commons (CCO) license.
“However, sharing data within OpenNEURO has the requirement that, following a 36‐month grace period following the first successful analysis of the data, the data will be become publicly available under a Creative Commons (CCO) license.”
The paper relies on this without support.
Research careers are primarily evaluated on the number, quality, and impact of papers published and on the acquisition of grant funding, which depends on the number, quality and impact of publications.
“Research careers are primarily evaluated on the number, quality, and impact of papers published; and the acquisition of grant funding, where the acquisition of grant funding being dependent on the number, quality and impact of the publications.”
The paper relies on this without support.
Data collection takes considerable time and effort to assure that the quality of the data is high and the data is properly cleaned and used appropriately.
“Data collection takes considerable time and effort to assure that the quality of the data is high and the data is properly cleaned and used appropriately.”
The paper relies on this without support.
GDPR was implemented on the 25th of May 2018 to provide data protection regulations for the inhabitants living or traveling within the EU.
“The GDPR was implemented on the 25th of May 2018 to provide data protection regulations for the inhabitants living or traveling within the EU.”
The paper relies on this without support.
For data sharing of human subjects data both within and outside of the EU, the GDPR requires that specific information be provided on both participant information forms and consent forms.
“For data sharing of human subjects data both within and outside of the EU, the GDPR requires that specific information be provided on both participant information forms and consent forms.”
The paper relies on this without support.
Funding agencies are nearly unanimous in their support of data sharing.
“Funding agencies are nearly unanimous in their support of data sharing.”
The paper relies on this without support.
With appropriate consent and, if necessary, assent, participants can determine whether they wish to have their data shared or not.
“With appropriate consent and, if necessary, assent, participants can determine whether they wish to have their data shared or not.”
The paper relies on this without support.
Scientists are sometimes not overly keen about sharing their data with others.
“Scientists are sometimes not overly keen about sharing their data with others.”
The paper relies on this without support.
There are very real issues related to data sharing that make researchers less willing to share.
“There are very real issues related to data sharing that make researchers less willing to share.”
The paper relies on this without support.
The most common issue making researchers less willing to share is that collecting and collating data takes considerable effort and others could publish results sooner than those who collected the data.
“The most common is that it takes considerable effort to collect and collate the data and others could then publish results sooner than those who actually collected the data.”
The paper relies on this without support.
Some studies are complex and those using the data may not fully understand the subtle complexities of the data.
“Further, some studies may be quite complex and those using the data may not fully understand the sometimes‐subtle complexities of the data.”
The paper relies on this without support.
Misuse or investigators serving as a help desk can result from data users not understanding complex data.
“This can result in either misuse or the investigators who collected the data serving as a “help desk” for those using the data.”
The paper relies on this without support.
Some studies have a business plan in which data sharing is tied to monetary reimbursement to support further work or data collection.
“Further, some studies have a different business plan in which data sharing is tied to monetary reimbursement to help support further work or data collection by the researchers.”
The paper relies on this without support.
The goal of the GDPR is to provide greater control and protection to the individual over whose data has been collected.
“The goal of the GDPR is to provide greater control and protection to the individual over whose data has been collected.”
The paper relies on this without support.
Researchers who utilize shared de-identified human research data often need to sign DUAs that adhere to the laws specific to the country of the participants.
“Further, it is often the case that researchers who utilize shared de‐identified human research data will need to sign DUAs that adhere to the laws specific to the country of the participants (i.e., GDPR for data shared from the EU, or the DUA to use ABCD Study data from NIDA).”
The paper relies on this without support.
Attorneys who work for specific universities have the goal to assure that the university is protected from potential legal actions, such as the potential 20 million euro fine imposed by non-compliance with the GDPR.
“Attorneys who work for specific universities have the goal to assure that the university is protected from potential legal actions, such as the potential 20 million euro fine imposed by non‐compliance with the GDPR.”
Claims other papers make about this one
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In the citing paper
“The conversion of DICOM to NIfTI files has the benefit of eliminating all patient-identifiable metadata from the header portion of the DICOM format80,81.”
From Federated learning enables big data for rare cancer boundary detection (2022) · cites this paper as [81]
In the citing paper
“However, centralized data-sharing systems have limitations, such as data privacy and security concerns [5]interoperability issues [6], and single points of failure [7].”
From Federated learning meets blockchain in decentralized data-sharing: Healthcare use case (2024) · cites this paper as [5]
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Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Connectome fingerprinting identification claim conflicts with cited reference
MinorThe text says functional connectome fingerprinting has not been used to identify individuals, while the title of the cited study explicitly describes identifying individuals with it.
“While there is support that individuals have characteristic patterns of functional brain connectivity, known as functional connectome fingerprinting, these have not been used to identify individuals (Finn et al., 2015).”
“Functional connectome fingerprinting: Identifying individuals using patterns of brain connectivity.”
Conflicting claims about who owns U.S. government-funded data
MinorThe paper first says U.S. funding agencies consider institutions to own the data, but later describes government sponsors such as the NIH as the owners.
“Funding agencies in the United States consider institutions to be the owners of the data.”
“In many cases of government sponsored projects, that is, the U.S. National Institutes of Health, are considered the owners of the data.”
Citations
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“Data sharing allows for greater transparency in science with the ability to promote reproducibility and replication of study findings (Button et al., 2013; Ioannidis, 2005; Open Science Collaboration, 2015).”
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“For additional references and resources related to data sharing, we point the reader to the following articles: Poline et al., 2012; Keator et al., 2013; Poldrack et al., 2014; Gibaud, 2011; Temal, Dojat, Kassel, and Gibaud, 2008; Jack et al., 2008; Zou et al., 2005; and Van Essen et al., 2012.”
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“Examples include the Autism Brain Imaging Data Exchange (ABIDE-I and II)(Di Martino et al., 2014, 2017), 1,000 Functional Connectomes (Biswal et al., 2010), Consortium on Reliability and Reproducibility (Zuo et al., 2014), the REST-meta-MDD consortium (Yan et al., 2019) and the Healthy Brains Consortium (O'Connor et a…”
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Sorted by citations
Federated learning enables big data for rare cancer boundary detection
Pati, S., Baid, U., Edwards, B., Sheller, M., Wang, S.-H., Reina, G. A., Foley, P., Gruzdev, A., Karkada, D., Davatzikos, C., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-33407-5 · Cited by 394
Federated learning meets blockchain in decentralized data-sharing: Healthcare use case
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“However, centralized data-sharing systems have limitations, such as data privacy and security issues [5], interoperability issues [6], and single points of failure [7].”
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“For example, models trained primarily on data from Western populations may struggle to generalize to patients from other regions or cultural backgrounds [263].”
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International data governance for neuroscience
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“35,36 It is imperative that patients are fully aware when AI is processing their information, and that they fully consent to the use of their information for ML. Slowly, as AI improves information processing in healthcare, there could potentially be fewer mistakes than with actual humans.”
BackgroundA hitchhiker’s guide to working with large, open-source neuroimaging datasets
Horien, C. L., Noble, S., Greene, A. S., Lee, K., Barron, D. S., Gao, S., O’Connor, D. A., Salehi, M., Dadashkarimi, J., Shen, X., et al. (2020) · Nature Human Behaviour · doi:10.1038/s41562-020-01005-4 · Cited by 70
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Explainable Artificial Intelligence in Neuroimaging of Alzheimer’s Disease
Taiyeb Khosroshahi, M., Morsali, S., Gharakhanlou, S., Motamedi, A., Hassanbaghlou, S., Vahedi, H., Pedrammehr, S., Kabir, H. M. D., & Jafarizadeh, A. (2025) · Diagnostics · doi:10.3390/diagnostics15050612 · Cited by 68
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“…of COINSTAC also provides heightened privacy protection using advanced statistical algorithms, such as differential privacy, and this model may open up a way to gain access to data that researchers are unable to share due to local regulatory restrictions (Ming et al., 2017; White et al., 2020).”
BackgroundBIDScoin: A User-Friendly Application to Convert Source Data to Brain Imaging Data Structure
Zwiers, M. P., Moia, S., & Oostenveld, R. (2022) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2021.770608 · Cited by 56
“The initial lack of data-structure standardization, data-sharing tools, and data-sharing mindset (Poline et al., 2012; Nichols et al., 2017; White et al., 2020) have led to the use of a large variety of file formats and data management methods, and to the lack of metadata descriptions, leaving…”
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“Initiatives such as Open Brain Consent (Bannier et al. 2020) are also critical in making neuroimaging data more readily shared (also see Brakewood and Poldrack 2013; Shenkin et al. 2017; White et al. 2020).”
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Revolutionizing Neurology: The Role of Artificial Intelligence in Advancing Diagnosis and Treatment
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“These studies assert that the uniqueness of the brain anatomy is determined by the combination of genetic factors, individual life experiences, and stochastic processes (White et al., 2020).”
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“Healthcare facilities own imaging data in the US, although patients have HIPAA privacy protections [126].”
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“Data availability is impacted by limitations related to data sharing, data ownership and patient privacy [221].”
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“Thus, pri-vacy-protecting laws dominate the way medical imaging data can be shared [31].”
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“(GDPR) [19], and performance or scalability [20], [21].”
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“However, there are several drawbacks to traditional centralized data-sharing systems, such as their restricted accessibility, privacy and data security concerns, inter-operability problems, and reliance on single points of failure [12], [13], [14], [15], [16].”
BackgroundBuimer, E. E. L., Schnack, H. G., Caspi, Y., van Haren, N. E. M., Milchenko, M. V., Pas, P., Initiative, A. D. N., Pol, H. E. H., & Brouwer, R. M. (2021) · Human Brain Mapping · doi:10.1002/hbm.25459 · Cited by 24
“In light of the increasing number of world-wide (public) neuroimaging collaborations (Poline et al., 2012) and technical improvements, the question is whether sharing raw anatomical MRI images is still in line with privacy regulations (White, Blok, & Calhoun, 2020).”
BackgroundLu, B., Chen, X., Castellanos, F. X., Thompson, P. M., Zuo, X., Zang, Y., & Yan, C. (2024) · 中国科学通报：英文版 · doi:10.1016/j.scib.2024.03.006 · Cited by 27
A marathon, not a sprint – neuroimaging, Open Science and ethics
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An evolutionary game model for indirect data sharing in manufacturing big data consortium
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The role of artificial intelligence in paediatric neuroradiology
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Data management strategy for a collaborative research center
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Alsaigh, R., Mehmood, R., Katib, I. A., Liang, X., Alshanqiti, A. M., Corchado, J., & See, S. (2024) · Frontiers Neuroinformatics · doi:10.3389/fninf.2024.1472653 · Cited by 19
Caeyenberghs, K., Imms, P. E., Andrei Irimia, A., Monti, M. M., Esopenko, C., de Souza, N. L., D, J. F. D., Newsome, M. R., Dobryakova, E., Cwiek, A. P., et al. (2024) · NeuroImage Clinical · doi:10.1016/j.nicl.2024.103585 · Cited by 19
“Researchers need to employ techniques like anonymization, removing personally identifiable information, and implementing strict access controls (see critical reviews, Poline et al., 2012, White et al., 2022).”
A Chinese multi-modal neuroimaging data release for increasing diversity of human brain mapping
Gao, P., Dong, H.-M., Liu, S.-M., Fan, X.-R., Jiang, C., Wang, Y., Margulies, D. S., Li, H., & Zuo, X. (2022) · Scientific Data · doi:10.1038/s41597-022-01413-3 · Cited by 24
“Therefore, data protection should be considered and implemented during the design phase of scientific studies 24,26 .”
BackgroundArtificial intelligence in neuroradiology: a scoping review of some ethical challenges
Khosravi, P., & Schweitzer, M. (2023) · Frontiers in Radiology · doi:10.3389/fradi.2023.1149461 · Cited by 18
“There has been a paradigm shift over the last decade with respect to the ethos of data ownership (18).”
BackgroundInfluentialBioChainReward: A Secure and Incentivised Blockchain Framework for Biomedical Data Sharing
Elkhodr, M., Gide, E., Darwish, O., & Al-Eidi, S. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20196825 · Cited by 19
“Medical imaging data, being high-dimensional and uniquely identifying, can be exploited for malicious intent if not handled securely, leading to privacy infringements [1].”
BackgroundReer, A., Wiebe, A., Wang, X., & Rieger, J. W. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2023.1086802 · Cited by 18
“For that reason, data from these devices are less likely to be re-identi fi able (Jwa and Poldrack, 2022; White et al., 2022).”
BackgroundFully synthetic neuroimaging data for replication and exploration
Vaden, K. I., Gebregziabher, M., & Eckert, M. A. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117284 · Cited by 14
“Sharing neuroimaging data is relatively easy when informed consent to share data is obtained ( White et al., 2020 ), and software can be used to reduce privacy risks by de-identifying image datasets ( Song et al., 2015 ).”
MethodVinding, M. C., & Oostenveld, R. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119165 · Cited by 15
Gazula, H., Rootes-Murdy, K., Holla, B., Basodi, S., Zhang, Z., Verner, E., Kelly, R., Murthy, P., Chakrabarti, A., Basu, D., et al. (2022) · Neuroinformatics · doi:10.1007/s12021-022-09604-4 · Cited by 13
“However, open data sharing can be limited by policy or proprietary restrictions or data re-identification concerns (Shringarpure & Bus-tamante, 2015; Sweeney, 2002; White et al., 2020).”
BackgroundSelf-citationSharing brain imaging data in the Open Science era: how and why?
Giehl, K., Mutsaerts, H. J. M. M., Aarts, K., Barkhof, F., Caspers, S., Chételat, G., Colin, M.-E., Düzel, E., Frisoni, G. B., Ikram, M. A., et al. (2024) · The Lancet Digital Health · doi:10.1016/s2589-7500(24)00069-4 · Cited by 19
Blockchain Technology in the Healthcare Sector: Overview and Security Analysis
Madhoun, N. E., & Hammi, B. (2024) · IEEE Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc60891.2024.10427731 · Cited by 17
“Finally, breaching the confidentiality of patients taking part in clinical trials threatens the confidence in medical research [18].”
BackgroundDzianok, P., Antonova, I., Wojciechowski, J., Dreszer, J., & Kublik, E. (2022) · GigaScience · doi:10.1093/gigascience/giac015 · Cited by 14
“Open access to data may also increase the speed of research and reduce the need to collect multiple redundant datasets, such as in computational neuroscience [1–4].”
Rahimzadeh, V. N., Jones, K. M., Majumder, M. A., Kahana, M. J., Rutishauser, U., Williams, Z. M., Cash, S. S., Paulk, A. C., Zheng, J., Beauchamp, M. S., et al. (2023) · Neuron · doi:10.1016/j.neuron.2023.09.029 · Cited by 13
Clunie, D., Flanders, A. E., Taylor, A. J., Erickson, B., Bialecki, B., Brundage, D., Gutman, D. A., Prior, F., Seibert, J. A., Perry, J. H., et al. (2023) · PubMed · doi:10.48550/arxiv.2303.10473 · Cited by 27
Palacios‐García, I., Mhuireach, G. A., Grasso‐Cladera, A., Cryan, J. F., & Parada, F. J. (2022) · BioEssays · doi:10.1002/bies.202100249 · Cited by 14
Shafiei, G., Esper, N. B., Hoffmann, M. S., Ai, L., Chen, A. A., Cluce, J., Covitz, S. C., Giavasis, S., Lane, C., Mehta, K. P., et al. (2025) · Neuron · doi:10.1016/j.neuron.2025.08.026 · Cited by 32
Brain-imaging techniques in educational technologies: A systematic literature review
Tenório, K., Pereira, E., Remigio, S., Costa, D., Oliveira, W., Dermeval, D., da Silva, A. P., Bittencourt, I. I., & Marques, L. B. (2021) · Education and Information Technologies · doi:10.1007/s10639-021-10608-x · Cited by 14
“However, it is possible to overcome this problem with some paths, such as using informed consent forms and providing fully anonymized or de-identified data (Shen et al., 2020) (White et al., 2020).”
BackgroundPersonalized Federated Lithium-Ion Battery Capacity Prediction via Cluster and Fusion Modules
Xiao, F., & Wu, L. (2023) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2023.3334826 · Cited by 13
“However, an inherent challenge in FL is data heterogeneity [11], [12], [13], [14].”
BackgroundMarceau, E. M., Ruocco, A. C., & Grenyer, B. F. S. (2022) · Current Opinion in Psychiatry · doi:10.1097/yco.0000000000000820 · Cited by 12
“More broadly in the field, data sharing and pooling of neuroimaging [56] and/ or geneticdata inBPD, likeother large-scale consortia have achieved via global working groups [57], will maximize resources and collaboration such as through mega-analytic studies [58], helping to overcome many methodological challenges to a…”
BackgroundWang, X., Lin, Y., Yang, Y., Xu, H., & Luo, Z. (2023) · Computer Communications · doi:10.1016/j.comcom.2023.06.019 · Cited by 12
Bridging the Brain and Data Sciences
Horn, J. D. V. (2020) · Big Data · doi:10.1089/big.2020.0065 · Cited by 11
“113 Data management practices vary widely across laboratories— from ad hoc file structures to sophisticated databasing architectures—which has contributed to the challenges of data sharing accompanying journal publication.”
Ehlers, M. R., & Lonsdorf, T. B. (2022) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2022.104958 · Cited by 14
Yelezhanova, S., Seitenov, A., Kenzhegarina, A., Kenzhetayev, A., Kemel, A., Ualiyev, N., Myrzakerimova, A., Mursakimova, G., Orynbek, A., & Сахипов, А. (2025) · International Journal of E-Health and Medical Communications · doi:10.4018/ijehmc.372423 · Cited by 13
“In addition, the violation of the confidentiality of patients participating in clinical trials represents a significant threat to the credibility and trustworthiness of medical research (White et al., 2022).”
FPGA-based Implementation of a Resource-Efficient UNET Model for Brain Tumour Segmentation
Neiso, M. K., Muchuka, N. M., & Mambo, S. M. (2024) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2024.0150161 · Cited by 10
“However with the rise in the use of technology in the medical field, there is a need for the data to be made public and anonymous of patient‟s identity [10].”
BackgroundMachine Learning Applications and Advancements in Alcohol Use Disorder: A Systematic ReviewPreprint
Hurtado, M., Siefkas, A. C., Attwood, M. M., Iqbal, Z., & Hoffman, J. L. (2022) · medRxiv · doi:10.1101/2022.06.06.22276057 · Cited by 8
“(94) As genetic and neuroimaging techniques develop and data become more widely available,(95) the accuracy of predicting AUD with increasingly innovative ML approaches will likely continue to improve over time.”
BackgroundData sharing in neuroimaging: experiences from the BIDS project
Ganz, M., & Poldrack, R. A. (2023) · Nature reviews. Neuroscience · doi:10.1038/s41583-023-00762-1 · Cited by 8
Decentralized Brain Age Estimation Using MRI Data
Basodi, S., Raja, R., Ray, B., Gazula, H., Sarwate, A. D., Plis, S., Liu, J., Verner, E., & Calhoun, V. D. (2022) · Neuroinformatics · doi:10.1007/s12021-022-09570-x · Cited by 10
“COINSTAC (White et al., 2020; Plis et al., 2016; COIN-STAC, 2020) is a platform that enables decentralized analysis of (neuroimaging) data without the need to pool the data at one location.”
BackgroundInfluentialSelf-citationPotential of quantum computing to effectively comprehend the complexity of brain
Sihare, S. R. (2023) · Applied Intelligence · doi:10.1007/s10489-023-04857-1 · Cited by 8
U.S. public perceptions of the sensitivity of brain data
Huang, S., Paul, U. S., Gupta, S., et al. (2024) · Journal of Law and the Biosciences · doi:10.1093/jlb/lsad032 · Cited by 8
“21 While ‘big brain data’ collected and sold by EEG device manufacturers is anonymized, the possibility of subject re-identification becomes more likely when the personalized features of brain data are cross-referenced with other types of data.”
BackgroundPerfusion imaging by arterial spin labeling in migraine: A literature review
Schramm, S., Börner, C., Reichert, M., Hoffmann, G., Kaczmarz, S., Griessmair, M., Jung, K., Berndt, M. T., Zimmer, C., Baum, T. H., et al. (2024) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1177/0271678x241237733 · Cited by 11
Federation of Brain Age Estimation in Structural Neuroimaging Data
Basodi, S., Raja, R., Ray, B., Gazula, H., Liu, J., Verner, E., & Calhoun, V. D. (2021) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc46164.2021.9629865 · Cited by 8
“perform multiple experiments with Freesurfer ROI features extracted from UPENN-PNC sMRI dataset with six sites (one as main and the remaining five as local sites) using COINSTAC[5], [6], [19], a platform that enables decentralized analysis of (neuroimaging) data without the need to pool”
MethodSelf-citationBCE-PPDS: Blockchain-based cloud–edge collaborative privacy-preserving data sharing scheme for IoT
Wang, G., Liu, Q., Yu, Z., et al. (2025) · Future Generation Computer Systems · doi:10.1016/j.future.2025.107922 · Cited by 7
Snodgrass, S. J., Weber, K. A., II, Wesselink, E. O., Stanwell, P., & Elliott, J. M. (2024) · Journal of Clinical Medicine · doi:10.3390/jcm13154485 · Cited by 8
FedMedICL: Towards Holistic Evaluation of Distribution Shifts in Federated Medical Imaging
Alhamoud, K., Ghunaim, Y., Alfarra, M., Hartvigsen, T., Torr, P. H. S., Ghanem, B., Bibi, A., & Ghassemi, M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72117-5_36 · Cited by 10
Survey on Open Science Practices in Functional NeuroimagingPreprint
Paret, C., Unverhau, N., Feingold, F. W., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.11.26.470115 · Cited by 7
“because of a lack of incentives, the fear of misuse, and legal issues such as data protection and privacy issues (Houtkoop et al., 2018; Sayogo and Pardo, 2013; Schmidt et al., 2016; Tenopir et al., 2011; White et al., 2020).”
BackgroundBuhomoli, O., & Muneja, P. (2023) · Information Development · doi:10.1177/02666669231219796 · Cited by 9
Iyer, S., Jones, K. M., Robinson, J. O., Provenza, N. R., Duncan, D., Lázaro‐Muñoz, G., McGuire, A. L., Sheth, S. A., & Majumder, M. A. (2024) · eLife · doi:10.7554/elife.94000 · Cited by 8
“…of the technical and logistical challenges often associated with central servers, such as storage limitations and coarseness in responding to data-specific upload, download, and analysis issues ( Rootes-Murdy et al., 2022 ; The Global Alliance for Genomics and Health, 2016 ; White et al., 2022 ).”
The Spectrum of Data sharing Policies in Neuroimaging Data RepositoriesPreprint
Jwa, A. S., & Poldrack, R. A. (2021) · doi:10.31234/osf.io/cnuy7 · Cited by 6
International Data Governance for NeurosciencePreprint
Eke, D., Bernard, A., Bjaalie, J. G., Chavarriaga, R., Hanakawa, T., Hannan, A. J., Hill, S., Martone, M. E., McMahon, M. A. B., Ruebel, O., et al. (2021) · doi:10.31234/osf.io/esz9b · Cited by 6
A systematic comparison of VBM pipelines and their application to age predictionPreprint
Antonopoulos, G., More, S., Raimondo, F., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.23.525151 · Cited by 6
Chappidi, S., Belue, M. J., Harmon, S. A., Jagasia, S., Zhuge, Y., Taşçı, E., Türkbey, B., Singh, J., Camphausen, K. A., & Krauze, A. V. (2025) · PLOS Digital Health · doi:10.1371/journal.pdig.0000755 · Cited by 5
“However, brain scans require additional processing for anonymization/de-identification purposes, which represents a barrier to public data sharing [64].”
Functional Connectivity Biomarkers in Schizophrenia
Howell, A. M., & Anticevic, A. (2024) · Advances in neurobiology · doi:10.1007/978-3-031-69491-2_10 · Cited by 5
Translational network neuroscience: Nine roadblocks and possible solutions
Fekonja, L. S., Forkel, S. J., Aydogan, D. B., Lioumis, P., Cacciola, A., Lucas, C. W., Tournier, J., Vergani, F., Ritter, P., Schenk, R., et al. (2025) · Network Neuroscience · doi:10.1162/netn_a_00435 · Cited by 8
“While Europe ’ s GDPR broadly regulates all personal information, protections in other regions vary; for example, United States regulations split between the Common Rule and the Health Insurance Portability and Accountability Act (White, Blok, & Calhoun, 2022).”
High-sensitivity detection of facial features on MRI brain scans with a convolutional networkPreprint
Bansal, S., Kori, A., Zulfikar, W. D., Wexler, J., Markiewicz, C. J., Feingold, F. W., Poldrack, R. A., & Estéban, O. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.04.25.441373 · Cited by 3
Pennsieve: A Collaborative Platform for Translational Neuroscience and Beyond
Goldblum, Z., Xu, Z., Shi, H., et al. (2025) · Scientific Data · doi:10.1038/s41597-025-06075-5 · Cited by 5
Personal Information Protection in Government Data Openness Using Decision Tree Model
Gao, B., Sun, J., & Wang, B. (2023) · Journal of Global Information Management · doi:10.4018/jgim.332815 · Cited by 4
Research data management practices of faculty members in Ghanaian universities
Arthur, B., & van der Walt, T. (2024) · Library Management · doi:10.1108/lm-04-2023-0030 · Cited by 4
Barriers and facilitators to research data sharing: a lifecycle perspective
He, Z., & Fang, W. (2024) · Journal of Documentation · doi:10.1108/jd-03-2024-0048 · Cited by 4
Pérez‐Edgar, K. E., Dozier, M., Saxe, R. R., & MacDuffie, K. E. (2024) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2024.101490 · Cited by 3
Image data sharing for patients born with a cleft lip: A call for action to the UK cleft community
Fell, M. J., Butterworth, S., Humphries, K., et al. (2022) · Journal of Plastic Reconstructive & Aesthetic Surgery · doi:10.1016/j.bjps.2022.06.082 · Cited by 4
Palacios-García, I., Mhuireach, G. Á., Grasso‐Cladera, A., et al. (2022) · doi:10.31234/osf.io/sa6be · Cited by 4
Shafiei, G., Esper, N. B., Hoffmann, M., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.24.639850 · Cited by 4
Reproducible brain PET data analysis: easier said than done
Naseri, M., Ramakrishnapillai, S., & Carmichael, O. T. (2024) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2024.1420315 · Cited by 4
Parsons, M. V., Skolnik, M., Mwobobia, J. M., Solomon, E. D., DuBois, J. M., & McIntosh, T. J. (2025) · PLoS ONE · doi:10.1371/journal.pone.0330367 · Cited by 3
“Additionally, clear and comprehensive informed consent processes are crucial to ensuring that participants are aware of and agree to the potential secondary uses of their data [81].”
Advancing image-based meta-analysis through systematic use of crowdsourced NeuroVault data
Peraza, J. A., Kent, J. D., Blair, R. W., Poline, J., Nichols, T. E., de la Vega, A. I., & Laird, A. R. (2025) · Scientific Reports · doi:10.1038/s41598-025-20328-8 · Cited by 3
Metadata
- Authors
- White, T., Blok, E., & Calhoun, V. D.
- Affiliations
- Erasmus MC (NL)
- Georgia Institute of Technology (US)
- Corresponding author
- White, Tonya
- Venue
- Human Brain Mapping
- Publisher
- Wiley
- Published
- 2020-07-04
- Volume
- 43
- Issue
- 1
- Pages
- 278-291
- ISSN
- 1065-9471 (ISSN-L)
- 1097-0193
- PMC
- PMC8675413
- OpenAlex
- W3040489328
- Semantic Scholar
- 03d93ddfd388eb371ee495eae6a403b4a431d225
- Cited by
- 176
- References
- 95
- Citations by year
- 2026: 25 · 2025: 38 · 2024: 44 · 2023: 24 · 2022: 22 · 2021: 19 · 2020: 4
- FWCI
- 2.53
- Type
- journal article
- Language
- en
- Update notices
- correction (2025-08-28)
- Open access
- gold
- doi.org
- Licence
- Locations
- Human Brain Mapping (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (publishedVersion, cc-by)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion, cc-by)
- RePub (Erasmus University Rotterdam) (submittedVersion, other-oa)
- Funders
- ZonMw (91211021)
- Topics
- Physical Sciences › Computer Science › Information Systems › Research Data Management Practices
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Ethics in Clinical Research
- Social Sciences › Sociology and Political Science › Data Analysis and Archiving
- Keywords
- neuroimaging data sharing, data privacy, data ownership, informed consent, General Data Protection Regulation, open science, federated learning, data pooling, research ethics
- MeSH terms
- Humans, Informed Consent, Privacy, Information Dissemination, Biomedical Research, Neuroimaging
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