Data sharing and publishing in the field of neuroimaging is a research paper published in GigaScience (2012). On theSindex it has a DataRank of 1.9. It has been cited 31 times, with 31 citing works in its 1-hop citation network.
There is growing recognition of the importance of data sharing in the neurosciences, and in particular in the field of neuroimaging research, in order to best make use of the volumes of human subject data that have been acquired to date. However, a number of barriers, both practical and cultural, continue to impede the widespread practice of data sharing; these include: lack of standard infrastructure and tools for data sharing, uncertainty about how to organize and prepare the data for sharing, and researchers' fears about unattributed data use or missed opportunities for publication. A further challenge is how the scientific community should best describe and/or reference shared data that is used in secondary analyses. Finally, issues of human research subject protections and the ethical use of such data are an ongoing source of concern for neuroimaging researchers.One crucial issue is how producers of shared data can and should be acknowledged and how this important component of science will benefit individuals in their academic careers. While we encourage the field to make use of these opportunities for data publishing, it is critical that standards for metadata, provenance, and other descriptors are used. This commentary outlines the efforts of the International Neuroinformatics Coordinating Facility Task Force on Neuroimaging Datasharing to coordinate and establish such standards, as well as potential ways forward to relieve the issues that researchers who produce these massive, reusable community resources face when making the data rapidly and freely available to the public. Both the technical and human aspects of data sharing must be addressed if we are to go forward.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.520
From this paper's citation signal
Citation Network Contribution
1.3
From 26 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 28% comes from its base citations and 72% from the citation network (26 citing papers contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
Click a node to highlight its connections. Use scroll to zoom. Drag to pan.