The OpenNeuro resource for sharing of neuroscience data.
Ontology highlight
ABSTRACT:
SUBMITTER: Markiewicz CJ
PROVIDER: S-EPMC8550750 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The launch of the US BRAIN and European Human Brain Projects coincides with growing international efforts toward transparency and increased access to publicly funded research in the neurosciences. The need for data-sharing standards and neuroinformatics infrastructure is more pressing than ever. However, 'big science' efforts are not the only drivers of data-sharing needs, as neuroscientists across the full spectrum of research grapple with the overwhelming volume of data being generated daily and a scientific environment that is increasingly focused on collaboration. In this commentary, we consider the issue of sharing of the richly diverse and heterogeneous small data sets produced by individual neuroscientists, so-called long-tail data. We consider the utility of these data, the diversity of repositories and options available for sharing such data, and emerging best practices. We provide use cases in which aggregating and mining diverse long-tail data convert numerous small data sources into big data for improved knowledge about neuroscience-related disorders.
| S-EPMC4728080 | biostudies-literature
Project description:As public availability of gene expression profiling data increases, it is natural to ask how these data can be used by neuroscientists. Here we review the public availability of high-throughput expression data in neuroscience and how it has been reused, and tools that have been developed to facilitate reuse. There is increasing interest in making expression data reuse a routine part of the neuroscience tool-kit, but there are a number of challenges. Data must become more readily available in public databases; efforts to encourage investigators to make data available are important, as is education on the benefits of public data release. Once released, data must be better-annotated. Techniques and tools for data reuse are also in need of improvement. Integration of expression profiling data with neuroscience-specific resources such as anatomical atlases will further increase the value of expression data.
| S-EPMC2980754 | biostudies-other
Project description:SummaryNeural morphology, the branching geometry of brain cells, is an essential cellular substrate of nervous system function and pathology. Despite the accelerating production of digital reconstructions of neural morphology, the public accessibility of data remains a core issue in neuroscience. Deficiencies in the availability of existing data create redundancy of research efforts and limit synergy. We carried out a comprehensive bibliometric analysis of neural morphology publications to quantify the impact of data sharing in the neuroscience community. Our findings demonstrate that sharing digital reconstructions of neural morphology via NeuroMorpho.Org leads to a significant increase of citations to the original article, thus directly benefiting authors. The rate of data reusage remains constant for at least 16 years after sharing (the whole period analyzed), altogether nearly doubling the peer-reviewed discoveries in the field. Furthermore, the recent availability of larger and more numerous datasets fostered integrative applications, which accrue on average twice the citations of re-analyses of individual datasets. We also released an open-source citation tracking web-service allowing researchers to monitor reusage of their datasets in independent peer-reviewed reports. These results and tools can facilitate the recognition of shared data reuse for merit evaluations and funding decisions.Availability and implementationThe application is available at: http://cng-nmo-dev3.orc.gmu.edu:8181/. The source code at https://github.com/HerveEmissah/nmo-authors-app and https://github.com/HerveEmissah/nmo-bibliometric-analysis.
| S-EPMC10733721 | biostudies-literature
Project description:Science is changing: the volume and complexity of data are increasing, the number of studies is growing and the goal of achieving reproducible results requires new solutions for scientific data management. In the field of neuroscience, the German National Research Data Infrastructure (NFDI-Neuro) initiative aims to develop sustainable solutions for research data management (RDM). To obtain an understanding of the present RDM situation in the neuroscience community, NFDI-Neuro conducted a comprehensive survey among the neuroscience community. Here, we report and analyze the results of the survey. We focused the survey and our analysis on current needs, challenges, and opinions about RDM. The German neuroscience community perceives barriers with respect to RDM and data sharing mainly linked to (1) lack of data and metadata standards, (2) lack of community adopted provenance tracking methods, (3) lack of secure and privacy preserving research infrastructure for sensitive data, (4) lack of RDM literacy, and (5) lack of resources (time, personnel, money) for proper RDM. However, an overwhelming majority of community members (91%) indicated that they would be willing to share their data with other researchers and are interested to increase their RDM skills. Taking advantage of this willingness and overcoming the existing barriers requires the systematic development of standards, tools, and infrastructure, the provision of training, education, and support, as well as additional resources for RDM to the research community and a constant dialogue with relevant stakeholders including policy makers to leverage of a culture change through adapted incentivization and regulation.
| S-EPMC9933933 | biostudies-literature
Project description:PurposeData sharing between clinicians, laboratories, and patients is essential for improvements in genomic medicine, but obtaining consent for individual-level data sharing is often hindered by a lack of time and resources. To address this issue, the Clinical Genome Resource (ClinGen) developed tools to facilitate consent, including a one-page consent form and online supplemental video with information on key topics, such as risks and benefits of data sharing.MethodsTo determine whether the consent form and video accurately conveyed key data sharing concepts, we surveyed 5,162 members of the general public. We measured comprehension at baseline, after reading the form and watching the video. Additionally, we assessed participants' attitudes toward genomic data sharing.ResultsParticipants' performance on comprehension questions significantly improved over baseline after reading the form and continued to improve after watching the video.ConclusionResults suggest reading the form alone provided participants with important knowledge regarding broad data sharing, and watching the video allowed for broader comprehension. These materials are now available at http://www.clinicalgenome.org/share . These resources will provide patients a straightforward way to share their genetic and health information, and improve the scientific community's access to data generated through routine healthcare.
| S-EPMC6292744 | biostudies-literature
Project description:Technologically enabled sharing-economy networks are changing the way humans trade and collaborate. Here, using a novel 'Wi-Fi sharing' game, we explored determinants of human sharing strategy. Subjects (N = 1,950) participated in a networked game in which they could choose how to allocate a limited, but personally not usable, resource (representing unused Wi-Fi bandwidth) to immediate network neighbors. We first embedded N = 600 subjects into 30 networks, experimentally manipulating the range over which subjects could connect. We find that denser networks decrease any wealth inequality, but that this effect saturates. Individuals' benefit is shaped by their network position, with having many partners who in turn have few partners being especially beneficial. We propose a new, simplified "sharing centrality" metric for quantifying this. Further experiments (N = 1,200) confirm the robustness of the effect of network structure on sharing behavior. Our findings suggest the possibility of interventions to help more evenly distribute shared resources over networks.
| S-EPMC6403336 | biostudies-literature
Project description:With more and more demand from devices to use wireless communication networks, there has been an increased interest in resource sharing among operators, to give a better link quality. However, in the analysis of the benefits of resource sharing among these operators, the important factor of co-location is often overlooked. Indeed, often in wireless communication networks, different operators co-locate: they place their base stations at the same locations due to cost efficiency. We therefore use stochastic geometry to investigate the effect of co-location on the benefits of resource sharing. We develop an intricate relation between the co-location factor and the optimal radius to operate the network, which shows that indeed co-location is an important factor to take into account. We also investigate the limiting behavior of the expected gains of sharing, and find that for unequal operators, sharing may not always be beneficial when taking co-location into account.
| S-EPMC10889630 | biostudies-literature
Project description: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).
| S-EPMC6499198 | biostudies-literature
Project description:Rapid release of prepublication data has served the field of genomics well. Attendees at a workshop in Toronto recommend extending the practice to other biological data sets.
| S-EPMC3073843 | biostudies-literature
Project description:Scalable technologies to sequence the transcriptomes and epigenomes of single cells are transforming our understanding of cell types and cell states. The Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative Cell Census Network (BICCN) is applying these technologies at unprecedented scale to map the cell types in the mammalian brain. In an effort to increase data FAIRness (Findable, Accessible, Interoperable, Reusable), the NIH has established repositories to make data generated by the BICCN and related BRAIN Initiative projects accessible to the broader research community. Here, we describe the Neuroscience Multi-Omic Archive (NeMO Archive; nemoarchive.org), which serves as the primary repository for genomics data from the BRAIN Initiative. Working closely with other BRAIN Initiative researchers, we have organized these data into a continually expanding, curated repository, which contains transcriptomic and epigenomic data from over 50 million brain cells, including single-cell genomic data from all of the major regions of the adult and prenatal human and mouse brains, as well as substantial single-cell genomic data from non-human primates. We make available several tools for accessing these data, including a searchable web portal, a cloud-computing interface for large-scale data processing (implemented on Terra, terra.bio), and a visualization and analysis platform, NeMO Analytics (nemoanalytics.org).
| S-EPMC9825473 | biostudies-literature