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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
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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]
Self-check
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
Cites
Why Most Published Research Findings Are False
Ioannidis, J. P. A. (2005) · PLoS Medicine · doi:10.1371/journal.pmed.0020124 · Cited by 10,505
Estimating the reproducibility of psychological science
Open Science Collaboration (2015) · Science · doi:10.1126/science.aac4716 · Cited by 8,515
Power failure: why small sample size undermines the reliability of neuroscience
Button, K. S., Ioannidis, J. P. A., Mokrysz, C., Nosek, B. A., Flint, J., Robinson, E. S. J., & Munafò, M. R. (2013) · Nature Reviews Neuroscience · doi:10.1038/nrn3475 · Cited by 7,641 · cited 2×
“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…”
BackgroundThe WU-Minn Human Connectome Project: An overview
Van Essen, D. C., Smith, S. M., Barch, D. M., Behrens, T. E. J., Yacoub, E., & Ugurbil, K. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.041 · Cited by 5,651 · cited 1×
“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).”
BackgroundThe Algorithmic Foundations of Differential Privacy
Dwork, C., & Roth, A. (2014) · Foundations and Trends® in Theoretical Computer Science · doi:10.1561/0400000042 · Cited by 5,557
Correspondence of the brain's functional architecture during activation and rest
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“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).”
BackgroundWhy Most Published Research Findings Are False
Ioannidis, J. P. A. (2005) · CHANCE · doi:10.1080/09332480.2005.10722754 · Cited by 4,702 · cited 1×
“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).”
BackgroundThe Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods
Jack, C. R., Bernstein, M. A., Fox, N. C., Thompson, P., Alexander, G., Harvey, D., Borowski, B., Britson, P. J., L. Whitwell, J., Ward, C., et al. (2008) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.21049 · Cited by 4,347 · cited 1×
“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.”
BackgroundToward discovery science of human brain function
Biswal, B. B., Mennes, M., Zuo, X.-N., Gohel, S., Kelly, C., Smith, S. M., Beckmann, C. F., Adelstein, J. S., Buckner, R. L., Colcombe, S., et al. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0911855107 · Cited by 2,970 · cited 2×
“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…”
BackgroundDi Martino, A., Yan, C.-G., Li, Q., Denio, E., Castellanos, F. X., Alaerts, K., Anderson, J. S., Assaf, M., Bookheimer, S. Y., Dapretto, M., et al. (2014) · Molecular Psychiatry · doi:10.1038/mp.2013.78 · Cited by 2,780
Functional connectome fingerprinting: Identifying individuals using patterns of brain connectivity
Finn, E. S., Shen, X., Scheinost, D., Rosenberg, M. D., Huang, J., Chun, M. M., Papademetris, X., & Constable, R. T. (2015) · Nature Neuroscience · doi:10.1038/nn.4135 · Cited by 2,747 · cited 1×
“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).”
Background(2014) · Journal of Korean Medical Association · doi:10.5124/jkma.2014.57.11.899 · Cited by 2,667
The Human Connectome Project: A data acquisition perspective
Essen, D. C. V., Uǧurbil, K., Auerbach, E. J., Barch, D. M., Behrens, T. E. J., Bucholz, R. D., Chang, A., Chen, L., Corbetta, M., Curtiss, S. W., et al. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.02.018 · Cited by 2,638 · cited 4×
“It is possible for researchers to obtain some study data, that is, Human Connectome Project data, via an encrypted hard drive that is mailed.”
BackgroundInfluentialThe pathophysiology of cigarette smoking and cardiovascular disease
Ambrose, J. A., & Barua, R. S. (2004) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2003.12.047 · Cited by 2,394 · cited 1×
“Just two widely known examples include the links between smoking and cardiovascular disease (Ambrose & Barua, 2004) and cancer (O'Keeffe et al., 2018); and the links between low folate during pregnancy and the increased risk of neural tube defects (Blom, Shaw, Den Heijer, & Finnell, 2006).”
BackgroundRobust De-anonymization of Large Sparse Datasets
Narayanan, A., & Shmatikov, V. (2008) · 2008 IEEE Symposium on Security and Privacy (sp 2008) · doi:10.1109/sp.2008.33 · Cited by 2,372
Available, N. (2000) · JAMA · doi:10.1001/jama.284.23.3043 · Cited by 2,344
The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites
Casey, B. J., Cannonier, T., Conley, M. I., Cohen, A. O., Barch, D. M., Heitzeg, M. M., Soules, M. E., Teslovich, T., Dellarco, D. V., Garavan, H., et al. (2018) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2018.03.001 · Cited by 2,167 · cited 3×
“…Alzheimer's Disease Neuroimaging Initiative (Mueller et al., 2005), Longitudinal Study of Adolescent Brain Cognitive Development or the ABCD Study (Casey et al., 2018), MIND Clinical Imaging Consortium (Gollub et al., 2013), COBRE (Aine et al., 2017), Pediatric Imaging, Neurocognition, and…”
BackgroundMultimodal population brain imaging in the UK Biobank prospective epidemiological study
Miller, K. L., Alfaro-Almagro, F., Bangerter, N. K., Thomas, D. L., Yacoub, E., Xu, J., Bartsch, A. J., Jbabdi, S., Sotiropoulos, S. N., Andersson, J. L. R., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4393 · Cited by 2,020
Gorgolewski, K. J., Auer, T., Calhoun, V. D., Craddock, R. C., Das, S., Duff, E. P., Flandin, G., Ghosh, S. S., Glatard, T., Halchenko, Y. O., et al. (2016) · Scientific Data · doi:10.1038/sdata.2016.44 · Cited by 1,775
Self-citationThe Alzheimer's Disease Neuroimaging Initiative
Mueller, S. G., Weiner, M. W., Thal, L. J., Petersen, R. C., Jack, C., Jagust, W., Trojanowski, J. Q., Toga, A. W., & Beckett, L. (2005) · Neuroimaging Clinics of North America · doi:10.1016/j.nic.2005.09.008 · Cited by 1,525
Emmers, E. (2019) · SGEM International Multidisciplinary Scientific Conferences on Social Sciences and Arts · doi:10.5593/sgemsocial2019v/1.1 · Cited by 1,317
Homer, N., Szelinger, S., Redman, M., Duggan, D., Tembe, W., Muehling, J., Pearson, J. V., Stephan, D. A., Nelson, S. F., & Craig, D. W. (2008) · PLoS Genetics · doi:10.1371/journal.pgen.1000167 · Cited by 1,221 · cited 1×
“…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 Emam, 2011; Homer et al., 2008).”
MethodEarly brain development in infants at high risk for autism spectrum disorder
The IBIS Network, Hazlett, H. C., Gu, H., Munsell, B. C., Kim, S. H., Styner, M., Wolff, J. J., Elison, J. T., Swanson, M. R., Zhu, H., et al. (2017) · Nature · doi:10.1038/nature21369 · Cited by 1,128 · cited 2×
“From the United States the most common include the Human Connectome Project (Van Essen et al., 2012; Van Essen et al., 2013), Baby Connectome Project (Howell et al., 2019), Alzheimer's Disease Neuroimaging Initiative (Mueller et al., 2005), Longitudinal Study of Adolescent Brain Cognitive Development or the ABCD Study…”
BackgroundMRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites
Esteban, O., Birman, D., Schaer, M., Koyejo, O. O., Poldrack, R. A., & Gorgolewski, K. J. (2017) · PLOS ONE · doi:10.1371/journal.pone.0184661 · Cited by 920
Yan, C.-G., Chen, X., Li, L., Castellanos, F. X., Bai, T.-J., Bo, Q.-J., Cao, J., Chen, G.-M., Chen, N.-X., Chen, W., et al. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1900390116 · Cited by 895 · cited 1×
“…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 al., 2017).”
The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data
the Alzheimer’s Disease Neuroimaging Initiative, E. C., Thompson, P. M., Stein, J. L., Medland, S. E., Hibar, D. P., Vasquez, A. A., Renteria, M. E., Toro, R., Jahanshad, N., Schumann, G., et al. (2014) · Brain Imaging and Behavior · doi:10.1007/s11682-013-9269-5 · Cited by 838 · cited 12×
“One well known and successful approach for data sharing within a collaborative network involves the ENIGMA consortium (Thompson et al., 2014).”
BackgroundMethodInfluentialSelf-citationEstimating the success of re-identifications in incomplete datasets using generative models
Rocher, L., Hendrickx, J. M., & de Montjoye, Y.-A. (2019) · Nature Communications · doi:10.1038/s41467-019-10933-3 · Cited by 816
Best practices in data analysis and sharing in neuroimaging using MRI
Nichols, T. E., Das, S., Eickhoff, S. B., Evans, A. C., Glatard, T., Hanke, M., Kriegeskorte, N., Milham, M. P., Poldrack, R. A., Poline, J.-B., et al. (2017) · Nature Neuroscience · doi:10.1038/nn.4500 · Cited by 714 · cited 2×
“These initiatives offer greater transparency, with the opportunity for external research groups to reproduce and/or replicate findings (Nichols et al., 2017).”
Self-citationEnhancing studies of the connectome in autism using the autism brain imaging data exchange II
Martino, A. D., O’Connor, D. A., Chen, B., Alaerts, K., Anderson, J. S., Assaf, M., Balsters, J. H., Baxter, L. C., Beggiato, A., Bernaerts, S., et al. (2017) · Scientific Data · doi:10.1038/sdata.2017.10 · Cited by 713 · cited 1×
“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…”
BackgroundSelf-citationKelly, S., Jahanshad, N., Zalesky, A., Kochunov, P., Agartz, I., Alloza, C., Andreassen, O. A., Arango, C., Banaj, N., Bouix, S., et al. (2018) · Molecular Psychiatry · doi:10.1038/mp.2017.170 · Cited by 701
Self-citationNeural tube defects and folate: case far from closed
Blom, H. J., Shaw, G. M., den Heijer, M., & Finnell, R. H. (2006) · Nature Reviews Neuroscience · doi:10.1038/nrn1986 · Cited by 562
Neuroimaging of the Philadelphia Neurodevelopmental Cohort
Satterthwaite, T. D., Elliott, M. A., Ruparel, K., Loughead, J., Prabhakaran, K., Calkins, M. E., Hopson, R., Jackson, C., Keefe, J., Riley, M., et al. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2013.07.064 · Cited by 504 · cited 1×
“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…”
MethodMapping context and content: the BrainMap model
Fox, P. T., & Lancaster, J. L. (2002) · Nature reviews. Neuroscience · doi:10.1038/nrn789 · Cited by 473 · cited 1×
“This enables meta-analytic approaches to be performed by combining experiments and studies (Fox & Lancaster, 2002).”
MethodThe UNC/UMN Baby Connectome Project (BCP): An overview of the study design and protocol development
Howell, B., Styner, M. A., Gao, W., Yap, P., Wang, L., Baluyot, K., Yacoub, E. S., Chen, G., Potts, T., Salzwedel, A., et al. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.03.049 · Cited by 452 · cited 2×
“From the United States the most common include the Human Connectome Project (Van Essen et al., 2012; Van Essen et al., 2013), Baby Connectome Project (Howell et al., 2019), Alzheimer's Disease Neuroimaging Initiative (Mueller et al., 2005), Longitudinal Study of Adolescent Brain Cognitive Development or the ABCD Study…”
BackgroundAn open science resource for establishing reliability and reproducibility in functional connectomics
Zuo, X., Anderson, J. S., Bellec, P., Birn, R. M., Biswal, B. B., Blautzik, J., Breitner, J. C. S., Buckner, R. L., Calhoun, V. D., Castellanos, F. X., et al. (2014) · Scientific Data · doi:10.1038/sdata.2014.49 · Cited by 449 · cited 2×
“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…”
Self-citationMaking big data open: data sharing in neuroimaging
Poldrack, R. A., & Gorgolewski, K. J. (2014) · Nature Neuroscience · doi:10.1038/nn.3818 · Cited by 429 · cited 1×
“Within human subjects research, large data sharing or data pooling initiatives have been especially prominent in the fields of genetics, neuroimaging, and the combination of the two (Poldrack & Gorgolewski, 2014; Poline et al., 2012; Thompson et al., 2014).”
BackgroundProtection Against Reconstruction and Its Applications in Private Federated LearningPreprint
Bhowmick, A., Duchi, J. C., Freudiger, J., Kapoor, G., & Rogers, R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.00984 · Cited by 420
Smoking as a risk factor for lung cancer in women and men: a systematic review and meta-analysis
O’Keeffe, L. M., Taylor, G. M. J., Huxley, R., Mitchell, P., Woodward, M., & Peters, S. A. E. (2018) · BMJ Open · doi:10.1136/bmjopen-2018-021611 · Cited by 360
Toward open sharing of task-based fMRI data: the OpenfMRI project
Poldrack, R. A., Barch, D. M., Mitchell, J. P., Wager, T. D., Wagner, A. D., Devlin, J. T., Cumba, C., Koyejo, S., & Milham, M. P. (2013) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2013.00012 · Cited by 354 · cited 1×
“An early example of an fully open approach is the OpenfMRI Project (Poldrack et al., 2013), which provided an open dissemination of task-based functional neuroimaging data.”
BackgroundGet real in individual participant data (IPD) meta‐analysis: a review of the methodology
Debray, T. P. A., Moons, K. G. M., van Valkenhoef, G., Efthimiou, O., Hummel, N., Groenwold, R. H. H., Reitsma, J. B., & on behalf of the GetReal methods review group (2015) · Research Synthesis Methods · doi:10.1002/jrsm.1160 · Cited by 348
Statistical Challenges in “Big Data” Human Neuroimaging
Smith, S. M., & Nichols, T. E. (2018) · Neuron · doi:10.1016/j.neuron.2017.12.018 · Cited by 333 · cited 1×
“…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).”
BackgroundMarkram, H. (2012) · Scientific American · doi:10.1038/scientificamerican0612-50 · Cited by 325 · cited 3×
“…et al., 2014), and Infant Brain Imaging Study (Hazlett et al., 2017); from the United Kingdom the UK Biobank has the goal to release neuroimaging data from 100,000 participants (Miller et al., 2016), and the EU Human Brain Project (Markram, 2012) has data sharing as a key element of the grant.”
BackgroundHarnessing reliability for neuroscience research
Zuo, X.-N., Xu, T., & Milham, M. P. (2019) · Nature Human Behaviour · doi:10.1038/s41562-019-0655-x · Cited by 310
What difference does quantity make? On the epistemology of Big Data in biology
Leonelli, S. (2014) · Big Data & Society · doi:10.1177/2053951714534395 · Cited by 293 · cited 1×
“Shared data needs to be carefully curated and described in ways that other researchers can use the data properly (Leonelli, 2014), which is above and beyond the standard work load.”
BackgroundData sharing in neuroimaging research
Poline, J., Breeze, J. L., Ghosh, S., Gorgolewski, K. J., Halchenko, Y. O., Hanke, M., Haselgrove, C., Helmer, K. G., Keator, D. B., Marcus, D. S., et al. (2012) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2012.00009 · Cited by 283 · cited 2×
“Within human subjects research, large data sharing or data pooling initiatives have been especially prominent in the fields of genetics, neuroimaging, and the combination of the two (Poldrack & Gorgolewski, 2014; Poline et al., 2012; Thompson et al., 2014).”
BackgroundThe Pediatric Imaging, Neurocognition, and Genetics (PING) Data Repository
Jernigan, T. L., Brown, T. T., Hagler, D. J., Akshoomoff, N. A., Bartsch, H., Newman, E. R., Thompson, W. K., Bloss, C. S., Murray, S. S., Schork, N. J., et al. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.04.057 · Cited by 281 · cited 2×
“From the United States the most common include the Human Connectome Project (Van Essen et al., 2012; Van Essen et al., 2013), Baby Connectome Project (Howell et al., 2019), Alzheimer's Disease Neuroimaging Initiative (Mueller et al., 2005), Longitudinal Study of Adolescent Brain Cognitive Development or the ABCD Study…”
BackgroundDifferential Privacy for Statistics: What we Know and What we Want to Learn
Dwork, C., & Smith, A. (2010) · Journal of Privacy and Confidentiality · doi:10.29012/jpc.v1i2.570 · Cited by 267
Hadzhiev, Y., Qureshi, H. K., Wheatley, L., Cooper, L., Jasiulewicz, A., Nguyen, H. V., Wragg, J. W., Poovathumkadavil, D., Conic, S., Bajan, S., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-08487-5 · Cited by 267
Local active information storage as a tool to understand distributed neural information processing
Wibral, M., Lizier, J. T., Vögler, S., Priesemann, V., & Galuske, R. A. W. (2014) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2014.00001 · Cited by 239
Identification of Anonymous MRI Research Participants with Face-Recognition Software
Schwarz, C. G., Kremers, W. K., Therneau, T. M., Sharp, R. R., Gunter, J. L., Vemuri, P., Arani, A., Spychalla, A. J., Kantarci, K., Knopman, D. S., et al. (2019) · New England Journal of Medicine · doi:10.1056/nejmc1908881 · Cited by 215 · cited 1×
“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…”
MethodGollub, R. L., Shoemaker, J. M., King, M. D., White, T. J. H., Ehrlich, S., Sponheim, S. R., Clark, V. P., Turner, J. A., Mueller, B. A., Magnotta, V. A., et al. (2013) · Neuroinformatics · doi:10.1007/s12021-013-9184-3 · Cited by 212 · cited 2×
“…al., 2005), Longitudinal Study of Adolescent Brain Cognitive Development or the ABCD Study (Casey et al., 2018), MIND Clinical Imaging Consortium (Gollub et al., 2013), COBRE (Aine et al., 2017), Pediatric Imaging, Neurocognition, and Genetics Study (Jernigan et al., 2016), Philadelphia…”
BackgroundSelf-citationMultimodal Neuroimaging in Schizophrenia: Description and Dissemination
Aine, C. J., Bockholt, H. J., Bustillo, J., Cañive, J. M., Caprihan, A., Gasparovic, C., Hanlon, F. M., Houck, J. M., Jung, R. E., Lauriello, J., et al. (2017) · Neuroinformatics · doi:10.1007/s12021-017-9338-9 · Cited by 196 · cited 2×
“…of Adolescent Brain Cognitive Development or the ABCD Study (Casey et al., 2018), MIND Clinical Imaging Consortium (Gollub et al., 2013), COBRE (Aine et al., 2017), Pediatric Imaging, Neurocognition, and Genetics Study (Jernigan et al., 2016), Philadelphia Neurodevelopmental Cohort…”
BackgroundSelf-citationInference in the age of big data: Future perspectives on neuroscience
Bzdok, D., & Yeo, B. T. T. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.04.061 · Cited by 195 · cited 1×
“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).”
BackgroundWolfson, M. C., Wallace, S. E., Masca, N. G. D., Rowe, G., Sheehan, N. A., Ferretti, V., Laflamme, P., Tobin, M. D., Macleod, J., Little, J., et al. (2010) · International Journal of Epidemiology · doi:10.1093/ije/dyq111 · Cited by 190 · cited 2×
“Approaches like data SHIELD (Wolfson et al., 2010) enable analysis of centralized pre-computed features and 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).”
BackgroundMethodPaediatric population neuroimaging and the Generation R Study: the second wave
White, T. J. H., Muetzel, R. L., Marroun, H. E., Blanken, L. M. E., Jansen, P. R., Bolhuis, K., Kocevska, D., Mous, S. E., Mulder, R. H., Jaddoe, V. W. V., et al. (2017) · European Journal of Epidemiology · doi:10.1007/s10654-017-0319-y · Cited by 178
Self-citationA technique for the deidentification of structural brain MR images
Bischoff‐Grethe, A., Özyurt, İ. B., Busa, E., Quinn, B. T., Fennema‐Notestine, C., Clark, C. P., Morris, S., Bondi, M. W., Jernigan, T. L., Dale, A. M., et al. (2007) · Human Brain Mapping · doi:10.1002/hbm.20312 · Cited by 163 · cited 1×
“In light of the possibility to re-identify individuals based on the facial reconstruction from high-resolution structural MRI data, there have been a number of software packages that are able to “de-face” MR images (Bischoff-Grethe et al., 2007; Milchenko & Marcus, 2013).”
BackgroundAssessment of the impact of shared brain imaging data on the scientific literature
Milham, M. P., Craddock, R. C., Son, J. J., Fleischmann, M., Clucas, J. C., Xu, H., Koo, B., Krishnakumar, A., Biswal, B. B., Castellanos, F. X., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-04976-1 · Cited by 147
PhenoMeNal: processing and analysis of metabolomics data in the cloud
Peters, K., Bradbury, J., Bergmann, S., Capuccini, M., Cascante, M., de Atauri, P., Ebbels, T. M. D., Foguet, C., Glen, R. C., Gonzalez-Beltran, A. N., et al. (2018) · GigaScience · doi:10.1093/gigascience/giy149 · Cited by 147
Obscuring Surface Anatomy in Volumetric Imaging Data
Milchenko, M., & Marcus, D. (2013) · Neuroinformatics · doi:10.1007/s12021-012-9160-3 · Cited by 145 · cited 1×
“In light of the possibility to re-identify individuals based on the facial reconstruction from high-resolution structural MRI data, there have been a number of software packages that are able to “de-face” MR images (Bischoff-Grethe et al., 2007; Milchenko & Marcus, 2013).”
BackgroundNeuroimaging Study Designs, Computational Analyses and Data Provenance Using the LONI Pipeline
Dinov, I. D., Lozev, K., Petrosyan, P., Liu, Z., Eggert, P., Pierce, J., Zamanyan, A., Chakrapani, S., Horn, J. D. V., Parker, D. S., et al. (2010) · PLoS ONE · doi:10.1371/journal.pone.0013070 · Cited by 136
Leonelli, S. (2016) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2016.0122 · Cited by 131 · cited 1×
“…dramatic progress over past century in the manner in which data has been used, there have also been considerable advances in the methods of study design, data collection, and data analyses and importantly, dramatic changes in the ethics of human subject data (Leonelli, 2016; Nichols et al., 2017).”
MethodClinical epidemiology in the era of big data: new opportunities, familiar challenges
Ehrenstein, V., Nielsen, H., Pedersen, A., Johnsen, S. P., & Pedersen, L. (2017) · Clinical Epidemiology · doi:10.2147/clep.s129779 · Cited by 110
Sharing the wealth: Neuroimaging data repositories
Eickhoff, S. B., Nichols, T. E., Horn, J. D. V., et al. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.10.079 · Cited by 108
Boedhoe, P. S. W., Heymans, M. W., Schmaal, L., Abe, Y., Alonso, P., Ameis, S. H., Anticevic, A., Arnold, P. D., Batistuzzo, M. C., Benedetti, F., et al. (2019) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2018.00102 · Cited by 105 · cited 3×
“…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…”
BackgroundMethodInfluentialPlis, S., Sarwate, A. D., Wood, D., Dieringer, C., Landis, D., Reed, C., Panta, S. R., Turner, J. A., Shoemaker, J. M., Carter, K. W., et al. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00365 · Cited by 102 · cited 9×
“, COINSTAC [Plis et al., 2016]) No No Yes High Med-low Yes Higher”
BackgroundMethodInfluentialSelf-citationO’Connor, D. A., Potler, N. V., Kovacs, M., Xu, T., Ai, L., Pellman, J., Vanderwal, T., Parra, L. C., Cohen, S. S., Ghosh, S., et al. (2017) · GigaScience · doi:10.1093/gigascience/giw011 · Cited by 100 · cited 2×
“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…”
In the Era of Precision Medicine and Big Data, Who Is Normal?
Manrai, A. K., Patel, C. J., & Ioannidis, J. P. A. (2018) · JAMA · doi:10.1001/jama.2018.2009 · Cited by 98
Zou, K. H., Greve, D. N., Wang, M., et al. (2005) · Radiology · doi:10.1148/radiol.2373041630 · Cited by 94
Responsible practices for data sharing.
Alter, G. C., & Gonzalez, R. (2018) · American Psychologist · doi:10.1037/amp0000258 · Cited by 93 · cited 2×
“In many cases, both in Europe and North America, it is the academic institutions that claim ownership of data from sponsored research projects (Alter & Gonzalez, 2018).”
BackgroundMethodGorgolewski, K. J., Varoquaux, G. P., Rivera, G., Schwartz, Y., Sochat, V., Ghosh, S., Maumet, C., Nichols, T. E., Poline, J., Yarkoni, T., et al. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.04.016 · Cited by 85
Towards structured sharing of raw and derived neuroimaging data across existing resources
Keator, D. B., Helmer, K. G., Steffener, J., Turner, J. A., van Erp, T. G. M., Gadde, S., Ashish, N., Burns, G., & Nichols, B. N. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.094 · Cited by 79 · cited 2×
“Even just the analysis pipeline is incredibly complex, initiatives like the neuroimage data model are working to try to incorporate standardized provenance tracking into the major analysis packages (Dinov et al., 2010; Keator et al., 2013).”
BackgroundMethodMethods for the de-identification of electronic health records for genomic research
Emam, K. E. (2011) · Genome Medicine · doi:10.1186/gm239 · Cited by 78 · cited 1×
“…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 Emam, 2011; Homer et al., 2008).”
MethodResting‐state networks in 6‐to‐10 year old children
Muetzel, R. L., Blanken, L. M. E., Thijssen, S., van der Lugt, A., Jaddoe, V. W. V., Verhulst, F. C., Tiemeier, H. W., & White, T. J. H. (2016) · Human Brain Mapping · doi:10.1002/hbm.23309 · Cited by 77 · cited 1×
“The next level is to share unthresholded (Gorgolewski, Varoquaux, et al., 2016) or network maps (Muetzel et al., 2016), which allows for voxelwise or connectivity analyses to be done even for regions that did not achieve significance in the original study.”
BackgroundSelf-citationTowards an ontology for sharing medical images and regions of interest in neuroimaging
Temal, L., Dojat, M., Kassel, G., et al. (2008) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2008.03.002 · Cited by 69
Epidemiological Data Challenges: Planning for a More Robust Future Through Data Standards
Fairchild, G., Tasseff, B., Khalsa, H., et al. (2018) · Frontiers in Public Health · doi:10.3389/fpubh.2018.00336 · Cited by 66
Sarwate, A. D., Plis, S., Turner, J. A., Arbabshirani, M. R., & Calhoun, V. D. (2014) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2014.00035 · Cited by 63
Self-citationFastring, D., Mayfield-Johnson, S. L., Funchess, T. T., et al. (2018) · Aquila Digital Community (University of Southern Mississippi) · Cited by 63
White, T. J. H., Jansen, P. R., Muetzel, R. L., Sudre, G. P., Marroun, H. E., Tiemeier, H. W., Qiu, A., Shaw, P., Michael, A. M., & Verhulst, F. C. (2017) · Human Brain Mapping · doi:10.1002/hbm.23911 · Cited by 63 · cited 1×
“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).”
BackgroundSelf-citationOpen Humans: a platform for participant-centered research and personal data exploration
Greshake Tzovaras, B., Angrist, M., Arvai, K., Dulaney, M., Estrada-Galiñanes, V., Gunderson, B., Head, T., Lewis, D., Nov, O., Shaer, O., et al. (2019) · GigaScience · doi:10.1093/gigascience/giz076 · Cited by 56 · cited 3×
“Initiative such as “Open Humans” are highlighting the potential paradigm shift related to the ownership of personal data moving in the direction of the owners being those from whom the data were originally derived.”
BackgroundInfluentialCOINS Data Exchange: An open platform for compiling, curating, and disseminating neuroimaging data
Landis, D., Courtney, W., Dieringer, C., Kelly, R., King, M., Miller, B., Wang, R., Wood, D., Turner, J. A., & Calhoun, V. D. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.05.049 · Cited by 55 · cited 1×
“…field of bioinformatics, mechanisms are emerging that allow for data sharing without the data ever leaving the institution where the data collected (Landis et al., 2016), which can offer the opportunity for institutions with more restrictive policies to be able to engage in data sharing…”
BackgroundSelf-citationViPAR: a software platform for the Virtual Pooling and Analysis of Research Data.
Carter, K. W., Carter, K., Francis, R., Bresnahan, M., Gissler, M., Grønborg, T., Gross, R., Gunnes, N., Hammond, G., Hornig, M., et al. (2016) · International Journal of Epidemiology · doi:10.1093/ije/dyv193 · Cited by 51
Extracting Intrinsic Functional Networks with Feature-Based Group Independent Component Analysis
Calhoun, V. D., & Allen, E. A. (2012) · Psychometrika · doi:10.1007/s11336-012-9291-3 · Cited by 50
Self-citationRe-identification of home addresses from spatial locations anonymized by Gaussian skew
Cassa, C. A., Wieland, S. C., & Mandl, K. D. (2008) · International Journal of Health Geographics · doi:10.1186/1476-072x-7-45 · Cited by 45
on behalf of the MAGNIMS Study Group and Alzheimer’s Disease Neuroimaging Initiative, de Sitter, A., Visser, M., Brouwer, I., Cover, K. S., van Schijndel, R. A., Eijgelaar, R. S., Müller, D. M. J., Ropele, S., Kappos, L., et al. (2019) · European Radiology · doi:10.1007/s00330-019-06459-3 · Cited by 44
Fishbein, E. A. (1991) · Academic Medicine · doi:10.1097/00001888-199103000-00002 · Cited by 35 · cited 1×
“Whoever owns the data has control over the data, its dissemination, and the timing of dissemination (Fishbein, 1991).”
BackgroundWhite, T. J. H. (2019) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1016/j.jaac.2019.02.010 · Cited by 35 · cited 1×
“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).”
BackgroundSelf-citationO’Connor, D. A., Potler, N. V., Kovacs, M., et al. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/078881 · Cited by 34
A Pragmatic Approach for Reproducible Research With Sensitive Data
Shepherd, B. E., Peratikos, M. B., Rebeiro, P. F., Duda, S. N., & McGowan, C. C. (2017) · American Journal of Epidemiology · doi:10.1093/aje/kwx066 · Cited by 29
The quest for standards in medical imaging
Bernard Gibaud, A. (2010) · European Journal of Radiology · doi:10.1016/j.ejrad.2010.05.003 · Cited by 24 · cited 1×
“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.”
BackgroundDe-anonymization Attacks on Neuroimaging Datasets
Ravindra, V., & Grama, A. Y. (2021) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3448016.3457234 · Cited by 22
A spectrum of sharing: maximization of information content for brain imaging data
Calhoun, V. D. (2015) · GigaScience · doi:10.1186/s13742-014-0042-5 · Cited by 11
Self-citationOpen Humans: A platform for participant-centered research and personal data explorationPreprint
Tzovaras, B. G., Angrist, M. H., Arvai, K. J., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/469189 · Cited by 10
Cleary, M. L., Jackson, D. W., & Walter, G. (2013) · Journal of Clinical Nursing · doi:10.1111/jocn.12140 · Cited by 4
COINS Tools for Automated NIMH Data Archive Submissions
Bn, M., Wang, R., Kelly, R., et al. (2017) · Cited by 1
Self-citationThe UNC/UMN Baby Connectome Project (BCP): An overview of the study design and protocol development
Salzwedel, A., Piven, J., Yap, P.-T., et al. (2021) · Carolina Digital Repository (University of North Carolina at Chapel Hill) · doi:10.17615/fppy-e402 · Cited by 1
Flagged first, then by citations
Research works printed but not linked: 4
- GDPR, 2016, Reform of EU data protection rules—European Commission
- Morris E. D., 2019, Why we need guidelines for brain scan data, WIRED
- Rosenberg M., 2018, How trump consultants explotied the Facebook data of millions, The New York Times, 8, 35
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Cited by
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
Alsamhi, S. H., Myrzashova, R., Hawbani, A., Kumar, S., Srivastava, S., Zhao, L., Wei, X., Guizan, M., & Curry, E. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3367249 · Cited by 131
“However, centralized data-sharing systems have limitations, such as data privacy and security issues [5], interoperability issues [6], and single points of failure [7].”
BackgroundOnciul, R., Tataru, C.-I., Dumitru, A. V., Crivoi, C., Serban, M., Covache-Busuioc, R.-A., Radoi, M. P., & Toader, C. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14020550 · Cited by 113
“For example, models trained primarily on data from Western populations may struggle to generalize to patients from other regions or cultural backgrounds [263].”
Surianarayanan, C., Lawrence, J. J., Chelliah, P. R., Prakash, E., & Hewage, C. T. E. R. (2023) · Sensors · doi:10.3390/s23063062 · Cited by 127
The neuroplastic brain: current breakthroughs and emerging frontiers
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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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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