Recommendations for repositories and scientific gateways from a neuroscience perspective

Digital services such as repositories and science gateways have become key resources for the neuroscience community, but users often have a hard time orienting themselves in the service landscape to find the best fit for their particular needs. INCF (International Neuroinformatics Coordinating Facility) has developed a set of recommendations and associated criteria for choosing or setting up and running a repository or scientific gateway, intended for the neuroscience community, with a FAIR neuroscience perspective. These recommendations have neurosciences as their primary use case but are often general. Considering the perspectives of researchers and providers of repositories as well as scientific gateways, the recommendations harmonize and complement existing work on criteria for repositories and best practices. The recommendations cover a range of important areas including accessibility, licensing, community responsibility and technical and financial sustainability of a service.

[1]  Maryann E. Martone,et al.  RRIDs: A Simple Step toward Improving Reproducibility through Rigor and Transparency of Experimental Methods , 2016, Neuron.

[2]  Incf Secretariat,et al.  INCF Workshop Report: Towards neuroscience-centered selection criteria for data repositories and scientific gateways , 2021 .

[3]  Barbara McGillivray,et al.  The citation advantage of linking publications to research data , 2019, PloS one.

[4]  Satrajit S. Ghosh,et al.  The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments , 2016, Scientific Data.

[5]  Daniel Garijo,et al.  Nine Best Practices for Research Software Registries and Repositories: A Concise Guide , 2020, ArXiv.

[6]  Jordan Matelsky,et al.  A community-developed open-source computational ecosystem for big neuro data , 2018, Nature Methods.

[7]  Oliver Rübel,et al.  NWB:N 2.0: An Accessible Data Standard for Neurophysiology , 2019, bioRxiv.

[8]  Michael L. Hines,et al.  NeuroML: A Language for Describing Data Driven Models of Neurons and Networks with a High Degree of Biological Detail , 2010, PLoS Comput. Biol..

[9]  Satrajit S. Ghosh,et al.  Toward Community-Driven Big Open Brain Science: Open Big Data and Tools for Structure, Function, and Genetics. , 2020, Annual review of neuroscience.

[10]  Wei Mun Chan,et al.  Data Repository Selection: Criteria That Matter , 2020 .

[11]  A connectomic study of a petascale fragment of human cerebral cortex , 2021 .

[12]  Roberto Di Cosmo,et al.  Referencing Source Code Artifacts: A Separate Concern in Software Citation , 2020, Computing in Science & Engineering.