It’s still open science week!
INCF facilitates open neuroscience by 1) developing, vetting, and promoting FAIR standards and best practices, and 2) providing training in how to implement these standards and best practices on your own research.
INCF has implemented a formal procedure for evaluating and endorsing community standards and best practices in support of the FAIR principles. The purpose is to make neuroscience more open and FAIR, to ensure that research funds and efforts are well invested, and that neuroscientific findings are robust and replicable.
Quality community standards are necessary to make FAIR resources and processes work, but too many neuroscience communities lack robust standards or have competing incompatible standards. The fast development of new techniques also means that there is a continuous need for new and updated standards, and that old standards need an active developer and user community that keeps them up to date.
By endorsing standards, INCF:
makes it easy to find the best, most reliable standard appropriate for your research
ensures recognition for community members investing their time and effort in standards
INCF solicits community feedback as a part of our Standards and Best Practices (SBPs) endorsement process. Feedback from the community ensures that those who will use the standards have a chance to make them as interoperable as possible with the systems, software, and resources that are currently being used in the field. It verifies that the SPBs will benefit the community, and allows for streamlining to make the standards more efficient and effective - producing the desired result of reproducibility with methods that minimize wasted time and effort.
Beyond serving as a platform for soliciting new SBPs, the review process provides a venue for the collection and combination of standards that are already in place. With help from the community, this process helps our SBPs to be high-quality, user-friendly, and consistent with the principles of FAIR and open neuroscience. Learn more about why your voice matters: bit.ly/SBPcommunity
To support adoption and uptake of the standards and best practices INCF endorses, we have developed the INCF TrainingSpace, an online hub that makes neuroscience educational materials more accessible to the global neuroscience community. TrainingSpace is developed in collaboration with INCF, HBP, SfN, FENS, IBRO, CONP, IEEE, BD2K, and the iNeuro Initiative. TrainingSpace provides users with access to:
multimedia educational content from courses, conference lectures, and laboratory exercises from some of the world’s leading neuroscience institutes and societies
study tracks to facilitate self-guided study
tutorials on tools and open science resources for neuroscience research
Neurostars, a Q&A forum that serves the INCF network and the global neuroscience community as a platform for knowledge exchange between neuroscience researchers at all levels of expertise, software developers, and infrastructure providers. Neurostars has been adopted by several other large neuroscience initiatives such as the Neuromatch Academy, Neuro Hackademy, and the Organization for Computational Neuroscience (OCNS).
a neuroscience encyclopedia, KnowledgeSpace, that provides users with access to over 1.000.000 publicly available datasets as well as links to literature references and scientific abstracts
Topics currently included in TrainingSpace are general neuroscience, clinical neuroscience, computational neuroscience, neuroethics, neuroinformatics, computer science, data science, and open science. All courses and conference lectures in TrainingSpace include a general description, topics covered, links to prerequisite courses if applicable, and links to software described in or required for the course, as well as links to the next lecture in the course or more advanced related courses. In addition to providing resources for students and researchers, TrainingSpace also provides resources for instructors, such as laboratory exercises, open science services, and access to publicly available datasets and models. Explore TrainingSpace.