Neuroscience data and tool sharing

Frontiers in Neuroinformatics, 2023

The effective sharing of health research data within the healthcare ecosystem can have tremendous impact on the advancement of disease understanding, prevention, treatment, and monitoring. By combining and reusing health research data, increasingly rich insights can be made about patients and populations that feed back into the health system resulting in more effective best practices and better patient outcomes. To achieve the promise of a learning health system, data needs to meet the FAIR principles of findability, accessibility, interoperability, and reusability. Since the inception of the Brain-CODE platform and services in 2012, the Ontario Brain Institute (OBI) has pioneered data sharing activities aligned with FAIR principles in neuroscience. Here, we describe how Brain-CODE has operationalized data sharing according to the FAIR principles. Findable-Brain-CODE offers an interactive and itemized approach for requesters to generate data cuts of interest that align with their research questions. Accessible-Brain-CODE offers multiple data access mechanisms. These mechanisms-that distinguish between metadata access, data access within a secure computing environment on Brain-CODE and data access via export will be discussed. Interoperable-Standardization happens at the data capture level and the data release stage to allow integration with similar data elements. Reusable-Brain-CODE implements several quality assurances measures and controls to maximize data value for reusability. We will highlight the successes and challenges of a FAIR-focused neuroinformatics platform that facilitates the widespread collection and sharing of neuroscience research data for learning health systems.

2021

As neuroscience projects increase in scale and cross international borders, different ethical principles, national and international laws, regulations, and policies for data sharing must be considered. These concerns are part of what is collectively called data governance. Whereas neuroscience data transcends borders, data governance is typically constrained within geopolitical boundaries. An international data governance framework and accompanying infrastructure can assist investigators, institutions, data repositories, and funders with navigating disparate policies. Here, we propose principles and operational considerations for how data governance in neuroscience can be navigated at an international scale, and highlight gaps, challenges, and opportunities in a global brain data ecosystem. We consider how to approach data governance in a way that balances data protection requirements and the need for open science so as to promote international collaboration, through federated const...

eLife, 2024

In this paper, we provide an overview and analysis of the BRAIN Initiative data-sharing ecosystem. First, we compare and contrast the characteristics of the seven BRAIN Initiative data archives germane to data sharing and reuse, namely data submission and access procedures and aspects of interoperability. Second, we discuss challenges, benefits, and future opportunities, focusing on issues largely specific to sharing human data and drawing on N = 34 interviews with diverse stakeholders. The BRAIN Initiative-funded archive ecosystem faces interoperability and data stewardship challenges, such as achieving and maintaining interoperability of data and archives and harmonizing research participants' informed consents for tiers of access for human data across multiple archives. Yet, a benefit of this distributed archive ecosystem is the ability of more specialized archives to adapt to the needs of particular research communities. Finally, the multiple archives offer ample raw material for network evolution in response to the needs of neuroscientists over time. Our first objective in this paper is to provide a guide to the BRAIN Initiative data-sharing ecosystem for readers interested in sharing and reusing neuroscience data. Second, our analysis supports the development of empirically informed policy and practice aimed at making neuroscience data more findable, accessible, interoperable, and reusable.

2002

There is significant interest amongst neuroscientists in sharing neuroscience data and analytical tools.

Roberto Caso, Rossana Ducato, Intellectual Property, Open Science and Research Biobanks, Trento LawTech Research Paper nr. 22, 2014

In biomedical research and translational medicine, the ancient war between exclusivity (private control over information) and access to information is proposing again on a new battlefield: research biobanks. The latter are becoming increasingly important (one of the ten ideas changing the world, according to Time magazine) since they allow to collect, store and distribute in a secure and professional way a critical mass of human biological samples for research purposes. Tissues and related data are fundamental for the development of the biomedical research and the emerging field of translational medicine: they represent the “raw material” for every kind of biomedical study. For this reason, it is crucial to understand the boundaries of Intellectual Property (IP) in this prickly context. In fact, both data sharing and collaborative research have become an imperative in contemporary open science, whose development depends inextricably on: the opportunities to access and use data, the possibility of sharing practices between communities, the cross-checking of information and results and, chiefly, interactions with experts in different fields of knowledge. Data sharing allows both to spread the costs of analytical results that researchers cannot achieve working individually and, if properly managed, to avoid the duplication of research. These advantages are crucial: access to a common pool of pre-competitive data and the possibility to endorse follow-on research projects are fundamental for the progress of biomedicine. This is why the "open movement" is also spreading in the biobank's field. After an overview of the complex interactions among the different stakeholders involved in the process of information and data production, as well as of the main obstacles to the promotion of data sharing (i.e., the appropriability of biological samples and information, the privacy of participants, the lack of interoperability), we will firstly clarify some blurring in language, in particular concerning concepts often mixed up, such as “open source” and “open access”. The aim is to understand whether and to what extent we can apply these concepts to the biomedical field. Afterwards, adopting a comparative perspective, we will analyze the main features of the open models – in particular, the Open Research Data model – which have been proposed in literature for the promotion of data sharing in the field of research biobanks. After such an analysis, we will suggest some recommendations in order to rebalance the clash between exclusivity - the paradigm characterizing the evolution of intellectual property over the last three centuries - and the actual needs for access to knowledge. We argue that the key factor in this balance may come from the right interaction between IP, social norms and contracts. In particular, we need to combine the incentives and the reward mechanisms characterizing scientific communities with data sharing imperative.

Cureus, 2020

Importance: In today's climate of high healthcare costs and limited research resources, much attention has been given to inefficiency in research. Open access to research data has been proposed as a way to pool resources and make the most of research funding while also promoting transparency and scientific rigor. Objective: The clinical neurosciences stand to benefit greatly from the potential opportunities afforded by open data, and we sought to evaluate the current state of publicly available research findings and data sharing policies within the clinical neurosciences. Design: The Clarivate Analytics Web of Science journal citation reports for 2017 were used to sort journals in the category 'Clinical Neurosciences' by impact factor. The top 50 journals were selected and reviewed, but data was only collected from journals focused on original research (42/50). For each journal we reviewed the 10 most recent original research articles for 2016, 2017, and 2018 as designated by Scopus. Results: A data sharing policy existed for 60% (25/42) of the journals reviewed. Of the articles studied 41% (517/1255) contained source data, and the amount of articles with available source data increased from 2016 to 2018. Of all the articles reviewed, 49.4% (620/1255) were open access. Overall, 6.9% (87/1255) of articles had their source data accessible outside of the manuscript (e.g. registries, databases, etc.) and 8.9% (112/1255) addressed the availability of their source data within the publication itself. The availability of source data outside the manuscript and in-article discussion of source data availability both increased from 2016 to 2018. Only 3.9% (49/1255) of articles reviewed reported negative results for their primary outcome, and 7.6% (95/1255) of the articles could not be defined as primarily reporting positive or negative findings (characterization studies, census reporting, etc.). The distribution of negative versus positive results reported showed no significant trend over the years studied. Conclusion and Relevance: Our results demonstrate an opportunity for increased data sharing in neuroscience original research. These findings also suggest a trend towards increased adoption of open data sharing policies among journals and increased availability of unprocessed data in publications. This can increase the quality and speed at which new research is developed in the clinical neurosciences.

Frontiers in Neuroinformatics, 2019

Current discussions of the ethical aspects of big data are shaped by concerns regarding the social consequences of both the widespread adoption of machine learning and the ways in which biases in data can be replicated and perpetuated. We instead focus here on the ethical issues arising from the use of big data in international neuroscience collaborations. Neuroscience innovation relies upon neuroinformatics, large-scale data collection and analysis enabled by novel and emergent technologies. Each step of this work involves aspects of ethics, ranging from concerns for adherence to informed consent or animal protection principles and issues of data re-use at the stage of data collection, to data protection and privacy during data processing and analysis, and issues of attribution and intellectual property at the data-sharing and publication stages. Significant dilemmas and challenges with far-reaching implications are also inherent, including reconciling the ethical imperative for openness and validation with data protection compliance and considering future innovation trajectories or the potential for misuse of research results. Furthermore, these issues are subject to local interpretations within different ethical cultures applying diverse legal systems emphasising different aspects. Neuroscience big data require a concerted approach to research across boundaries, wherein ethical aspects are integrated within a transparent, dialogical data governance process. We address this by developing the concept of "responsible data governance," applying the principles of Responsible Research and Innovation (RRI) to the challenges presented by the governance of neuroscience big data in the Human Brain Project (HBP).

Neuroinformatics, 2014

Genetic data has provided valuable insights into disease cause and risk as well as drug discovery and development in neuroscience. For example, human genetics studies have provided insights into cognition ) and psychiatric disorders . The genetic basis of several inherited disorders such as Down's Syndrome and Tay-Sachs disease are well known, and other associations such as the role of APOE in Alzheimer's disease are still extensively studied. However, despite advances in understanding the human genome, there are concerns about the privacy of genetic data and potential discrimination resulting from its disclosure, and there has been incomplete oversight of genetic testing . At the same time, there have been increased efforts to share research data to enable scientific discovery and achieve cost efficiencies. It has become clear that no scientist can guarantee absolute privacy, and it is also increasingly recognized that research will work better if scientists have more information about the people they study and that being identifiable has some benefits (Angrist 2013). There are examples of pioneering efforts in neuroscience research. The fMRI Data Center is a leader in open-access data sharing in the functional neuroimaging community, overcoming logistical, cultural and funding barriers . Similarly, the INCF Task Force on Neuroimaging Datasharing has started work on tools to ease and automate sharing of raw, processed, and derived neuroimaging data and metadata ).

2014

The growth of data sharing initiatives for neuroimaging and genomics represents an exciting opportunity to confront the "small N" problem that plagues contemporary neuroimaging studies while further understanding the role genetic markers play in the function of the brain. When it is possible, open data sharing provides the most benefits. However, some data cannot be shared at all due to privacy concerns and/or risk of re-identification. Sharing other data sets is hampered by the proliferation of complex data use agreements (DUAs) which preclude truly automated data mining. These DUAs arise because of concerns about the privacy and confidentiality for subjects; though many do permit direct access to data, they often require a cumbersome approval process that can take months. An alternative approach is to only share data derivatives such as statistical summaries-the challenges here are to reformulate computational methods to quantify the privacy risks associated with sharing the results of those computations. For example, a derived map of gray matter is often as identifiable as a fingerprint. Thus alternative approaches to accessing data are needed. This paper reviews the relevant literature on differential privacy, a framework for measuring and tracking privacy loss in these settings, and demonstrates the feasibility of using this framework to calculate statistics on data distributed at many sites while still providing privacy.