The BRAIN Initiative, led by the US National Institutes of Health (NIH), launched in 2013 (https://braininitiative.nih.gov/). The program involves 10 NIH institutes and numerous additional federal and non-federal affiliates. The BRAIN Initiative’s core strategy is to build and sustain research partnerships between academics and industry, geared toward an overall objective of ‘neurotechnology’ development for scientific discovery, medical diagnosis, and therapy (BRAIN 2025: A Scientific Vision, 2014).

Data sharing—sharing scientific data with other researchers before or as soon as possible after publication of related findings—has been an integral component of the BRAIN Initiative from its inception. Since 2017, the BRAIN Initiative has supported the development and maintenance of a distributed network of ‘data archives’, what we call a data-sharing ‘ecosystem’, which at present is composed of seven archives (Table 1). These differ in the data they host as well as in various other characteristics, such as upload and download procedures, data access levels and rules (tiers), and associated tools. The BRAIN Initiative’s 2019 Data Sharing Policy (NOT-MH-19-010) directs researchers receiving BRAIN Initiative funding to submit the data generated by their projects, once it has been de-identified to industry standards, to one of these archives every 6 months, at which point it will remain private until the first associated paper is published or the end of the award period, whichever comes first. This timeline is consistent with that in the current NIH-wide Data Management and Sharing (DMS) policy (NOT-OD-21-013), which came into effect in January of 2023 and presumably will drive further data flow into BRAIN Initiative-supported archives. The NIH, and some BRAIN Initiative archives, encourage investigators to seek as broad consent as possible for sharing data derived from humans, in accordance with IRB approval and barring any concerns regarding the possibility of data re-identification (Bannier et al., 2021; Markiewicz et al., 2021).

In this paper, we provide an overview and analysis of the BRAIN Initiative data-sharing ecosystem, focusing on aspects germane to how data are shared and reused. We then discuss challenges, benefits, and future opportunities arising from this distributed data-sharing ecosystem, focusing on issues largely specific to sharing human data. Except where closely related to interoperability or functions that sharply distinguish the archives from one another, we do not discuss built-in analytical tools and instead direct readers to the cited literature.

For the technical details, we reviewed publicly available documents, websites, and articles (i.e., Delorme et al., 2022; Duncan et al., 2023; Hider et al., 2022; Markiewicz et al., 2021; Rübel et al., 2022; Subash et al., 2023). From June 2021 through June 2023, we also downloaded and uploaded test datasets, where possible. Finally, for challenges, benefits, and future opportunities, we conducted semi-structured interviews (N = 34) with diverse stakeholders including BRAIN Initiative and non-BRAIN Initiative-funded scientists, data stewards, law and ethics scholars and policymakers, patient advocates, and industry representatives (see Materials and methods). Our first objective is to provide a guide to the BRAIN Initiative data-sharing ecosystem for readers interested in sharing and reusing neuroscience, and especially human neuroscience, data. Second, we hope to support the development of empirically informed policy and practice options aimed at making neuroscience data more findable, accessible, interoperable, and reusable (FAIR) (Hendriks et al., 2022; Jwa and Poldrack, 2022; Poline et al., 2022; Sandström et al., 2022; Wilkinson et al., 2016).

The BRAIN Initiative’s data-sharing ecosystem illustrates a range of possible responses to recurrent issues in database design and organization (Jwa and Poldrack, 2022; Lin et al., 2020; Institute of Medicine (US), 2013; Subash et al., 2023). The distributed BRAIN ecosystem might on the surface seem to be a natural reflection of the diversity of neuroscience data. Yet, as noted above, the NIMH follows a centralized approach (https://nda.nih.gov/) (Lee and Majumder, 2022). The HBP also has a centralized repository, EBRAINS (https://ebrains.eu/) (Amunts et al., 2019). Moreover, there has been a tendency to centralize data in other fields, such as in genomics (Cook-Deegan et al., 2017; Cook-Deegan and McGuire, 2017). At least since the conclusion of the Human Genome Project, when DNA sequences were funneled into separate regional archives (Maxson Jones et al., 2018; Stevens, 2018), most complex human genomic data in the US has been channeled into dbGaP or (more recently for large datasets) the Genomic Analysis, Visualization, and Informatics Lab-space (AnVIL) (https://www.genome.gov/Funded-Programs-Projects/Computational-Genomics-and-Data-Science-Program/Genomic-Analysis-Visualization-Informatics-Lab-space-AnVIL). Thus, the degree of database centralization for any project is a policy choice, not an inevitability, and one that must rely on a balance of competing interests, goals, and continual reassessment of challenges, benefits, and opportunities.

Accordingly, the neuroscience data-sharing ecosystem should be viewed as malleable, both inside and outside the BRAIN Initiative. The meetings that led to BossDB were part of broader, ongoing conversations about how data sharing could be promoted and realized in the digital age (Choudhury et al., 2014; Insel et al., 2003; Poldrack and Poline, 2015; Poline et al., 2012; Sejnowski et al., 2014; Van Horn and Gazzaniga, 2002; Van Horn and Toga, 2009; Teeters et al., 2008). Data sharing also has long been a priority among a subset of the neuroimaging (MRI) community, including within initiatives focused on disease research (Borghi and Van Gulick, 2018; Mennes et al., 2013; Poline et al., 2012). A few more specific examples include the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (Jones‐Davis and Buckholtz, 2015), SchizConnect for schizophrenia (Ambite et al., 2015), and the Image and Data Archive (IDA) within the LONI at USC, which “started managing neuroimaging data for multi-centered research studies in the late 1990’s” (https://ida.loni.usc.edu/login.jsp). For over two decades, these leaders within neuroimaging have promoted data sharing for similar reasons to the BRAIN Initiative, emphasizing the goals of improving data quality and promoting reproducibility.

Additionally, existing literature has examined some of the challenges, alongside the benefits, of data sharing in neuroscience generally (i.e., Milham et al., 2018; Poline et al., 2012; Wiener et al., 2016; Rahimzadeh et al., 2023). This work has emphasized that major barriers to sharing include establishing and maintaining paths of data and archive interoperability and stewardship, such as sustainability and informed consent. Several data-sharing projects and supporting institutions, some founded before the start of the BRAIN Initiative, also interact closely with the BRAIN Initiative. These include the Neurosciences Information Framework (NIF) (https://neuinfo.org/) and the INCF, founded in 2006 and 2005, respectively (Poline et al., 2022; Poline et al., 2012; Sandström et al., 2022). Following meetings involving academia, industry, government, and the non-profit sector, the IBI was formed in 2018 (https://www.internationalbraininitiative.org/about). Such collective efforts are making neuroscience data FAIRer, for instance by streamlining collaborations and promoting adherence to data-sharing guidelines.

In turn, and in time, the relative benefits and drawbacks of centralized versus federated models in neuroscience data sharing also will become clearer. For example, there is the question of the value of any one archive. How can archives demonstrate and enhance their contributions, helping funders to determine how best to use their resources?

Perhaps some archives should indeed cease to exist, if they are not utilized or are inefficient (OECD Global Science Forum, 2017). The NIH Strategic Plan for Data Science states the intention to support the development of “standard use, utility, and efficiency metrics [for archives] and review expectations for data resources and tools” (https://datascience.nih.gov/nih-strategic-plan-data-science). Relatedly, a 2021 analysis employing interviews with representatives of 13 NIH repositories identified three categories of metrics for evaluation of an archive’s value: user behavior, scientific contribution/impact, and repository operations (NIH Metrics & LifeCycle Working Group and Metrics for Repositories (MetRe) Working Group, 2021). The NIH’s 2020 guidance, for data producers looking to select a repository, also lists among desirable characteristics for an archive “the ability to measure attribution, citation, and reuse of data (i.e., through assignment of adequate metadata and unique PIDs)” (https://grants.nih.gov/grants/guide/notice-files/NOT-OD-21-016.html). This feature also could benefit data contributors, helping to establish the value of an individual’s data-sharing efforts within the field.

The leaders of a few archives and datasets, such as at OpenNeuro, have managed to track information about reuse and resulting publications over time, and have estimated the savings realized through data sharing in terms of numbers of clinic hours and research costs (Markiewicz et al., 2021; Milham et al., 2018). Markiewicz et al. found that from 2018 to 2021, data reused from OpenNeuro resulted in 165 publications, with the volume increasing sharply over that period. Areas of contribution ranged from basic neuroscience (i.e., anatomical changes in the brain distinctive to pain syndromes) to the development and refinement of software tools (i.e., improvement of fMRIPrep preprocessing workflow). Collectively, as of 2021, these publications had generated over 1329 citations. In terms of research savings—that is, the cost were the reused data to have been generated de novo—Markiewicz et al. estimated nearly $21 million, using conservative assumptions (Markiewicz et al., 2021).

Recent announcements also have explicitly signaled the NIH’s interest in funding evaluation metrics. Examples include: “Notice of Special Interest (NOSI): Support for existing data repositories to align with FAIR and TRUST principles and evaluate usage, utility, and impact” (NOT-OD-23-044); “Enhancement and Management of Established Biomedical Data Repositories and Knowledgebases (PAR-23-237)”; and “Early-stage Biomedical Data Repositories and Knowledgebases (PAR-23-236)”.

However, such data remain difficult to obtain and are few and far between. As uses and citation practices vary, rigorous bibliometric analyses have necessitated full-text searches in Google Scholar followed by manual review (as with the OpenNeuro example above). Publication counts also tend to be lagging indicators of the scientific utility of a dataset. Furthermore, consensus standards for measuring the impact of a given dataset, such as counting the publications resulting from reuse, do not yet exist, even as various groups, including within the NIH, are studying the problem. Proposals for new author-level metrics of scientific achievement that integrate data sharing (i.e., the ‘S-index’ or ‘SCIENCE-index Augmented’) also have not yet been widely adopted (Emanuele and Minoretti, 2023), and the development and adoption of standard archive-level metrics to evaluate usage, utility, and impact remain a work in progress.

In sum, at present, the value of any one archive within a distributed ecosystem is challenging to measure quantitatively. Furthermore, without adequate redundancy, the disappearance of any single archive could cause instabilities in the entire system. Some redundancy is built into the BRAIN Initiative data-sharing ecosystem, for instance in the ability to store electrophysiology data in DABI, DANDI, or OpenNeuro and the extensions of the BIDS standards to EEG, iEEG, and MEG (Holdgraf et al., 2019; Niso et al., 2018; Pernet et al., 2019) in 2018 and 2019. But the ecosystem will continue to evolve, and it will require a sustainability plan. As scientists, policymakers, data stewards, and other interested parties consider the future of this data-sharing infrastructure, some of the primary considerations will need to include balancing needs and priorities in the areas of flexibility, sensitivity to innovation, diversity of reach, and coordinated management and sustainability.

Data for this manuscript were generated using mixed methods, including interviews with stakeholders and a landscape analysis of relevant policies, guidance documents, and data repositories.

To understand how and when researchers are required to share data, relevant policies and guidelines developed by the NIH were reviewed, including the BRAIN Initiative’s Data Sharing Policy (NOT-MH-19-010) and the new NIH-wide DMS policy (NOT-OD-21-013).

We also explored how each of the seven BRAIN Initiative data archives is structured and how it functions. One team member (SI) created accounts for each of the archives to ascertain data upload procedures (including formatting), what data were available for download, and available tiers of access to datasets (i.e., open or controlled access). Additional information was generated by a review of the literature including technical articles describing the archives (i.e., Delorme et al., 2022; Duncan et al., 2023; Hider et al., 2022; Markiewicz et al., 2021; Rübel et al., 2022; Subash et al., 2023).

Once user accounts were established at each archive, SI used an Excel spreadsheet to systematically capture defining characteristics of each archive, including similarities and areas of variation among them, as well as connections between them. Variables of interest included: privacy (defined as pathways of access to data, including whether accounts were required to upload, download, view, and/or analyze, and open/public and/or controlled access options); approaches for data backup and maintenance; data upload and download processes; accepted data types and files; analysis tools; and any external repository or resource interactions (i.e., links to access archive datasets from outside resources). In some cases, outreach was made to archive contacts through the websites’ Help or Contact Us functions. Communications with these members of the archives’ staff helped confirm understandings of how the archives worked, usually clarifying or corroborating information available from archive websites and publications.

Finally, we include data from semi-structured interviews (n = 34) with individuals who were recruited based on their work and expertise as BRAIN and non-BRAIN scientists (n = 9), data stewards (n = 4), law and ethics scholars and policymakers working in government and not-for-profit institutions (n = 10), patient advocates (n = 5), and industry representatives (n = 6). Interviews were conducted virtually as part of the BRAINshare project to explore perspectives on data sharing from human neuroscience studies, including barriers and facilitators of data sharing. Interviews were conducted in English and audio-recorded. Participants were offered a $50 electronic gift card upon completion of the interview. De-identified transcripts were analyzed using thematic analysis (Creswell and Poth, 2018). A codebook was developed deductively from the interview guide, which was revised with codes generated inductively. All transcripts were coded by a primary coder (KMJ, MAM, or AD), and a secondary coder (JOR, MAM, or AD) reviewed each transcript. Any discrepancies were discussed and resolved by consensus. For this manuscript, we reviewed data that were coded as related to credits, rewards, and incentives for sharing human neuroscience data, as well as technical aspects of sharing. Selected quotations represent salient issues identified. Dedoose, a web application for managing qualitative research, was used for the coding process (version 9.0.82, 2022, Los Angeles, CA: SocioCultural Research Consultants, LLC). This study was approved by the Baylor College of Medicine Institutional Review Board (protocol H-49969).

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Author details

1. Sudhanvan Iyer

   Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, United States

   Contribution

   Conceptualization, Resources, Formal analysis, Investigation, Visualization, Writing – original draft

   Contributed equally with

   Kathryn Maxson Jones

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0009-0003-0744-9441

2. Kathryn Maxson Jones

   1. Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, United States
   2. Department of History, Purdue University, West Lafayette, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Supervision, Validation, Investigation, Visualization, Methodology, Writing – review and editing

   Contributed equally with

   Sudhanvan Iyer

   For correspondence

   kmaxson@purdue.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8268-0422

3. Jill O Robinson

   Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Supervision, Validation, Investigation, Visualization, Methodology, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0487-304X

4. Nicole R Provenza

   Department of Neurosurgery, Baylor College of Medicine, Houston, United States

   Contribution

   Resources, Software, Formal analysis, Validation, Visualization, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-6952-5417

5. Dominique Duncan

   Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, United States

   Contribution

   Resources, Data curation, Validation, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-6154-9262

6. Gabriel Lázaro-Muñoz

   1. Center for Bioethics, Harvard Medical School, Boston, United States
   2. Department of Psychiatry, Massachusetts General Hospital, Boston, United States

   Contribution

   Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1933-9453

7. Amy L McGuire

   Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, United States

   Contribution

   Conceptualization, Supervision, Funding acquisition, Methodology, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7819-519X

8. Sameer A Sheth

   Department of Neurosurgery, Baylor College of Medicine, Houston, United States

   Contribution

   Conceptualization, Supervision, Funding acquisition, Methodology, Project administration, Writing – review and editing

   Competing interests

   Consultant for Boston Scientific, Zimmer Biomet, Neuropace, Sensoria Therapeutics, Koh Young, and Varian Medical; co-founder of Motif Neurotech

   "This ORCID iD identifies the author of this article:" 0000-0001-8770-8965

9. Mary A Majumder

   Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Supervision, Validation, Investigation, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5025-3618

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