Title |
Development and use of Ontologies Inside the Neuroscience Information Framework: A Practical Approach
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Published in |
Frontiers in Genetics, January 2012
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DOI | 10.3389/fgene.2012.00111 |
Pubmed ID | |
Authors |
Fahim T. Imam, Stephen D. Larson, Anita Bandrowski, Jeffery S. Grethe, Amarnath Gupta, Maryann E. Martone |
Abstract |
An initiative of the NIH Blueprint for neuroscience research, the Neuroscience Information Framework (NIF) project advances neuroscience by enabling discovery and access to public research data and tools worldwide through an open source, semantically enhanced search portal. One of the critical components for the overall NIF system, the NIF Standardized Ontologies (NIFSTD), provides an extensive collection of standard neuroscience concepts along with their synonyms and relationships. The knowledge models defined in the NIFSTD ontologies enable an effective concept-based search over heterogeneous types of web-accessible information entities in NIF's production system. NIFSTD covers major domains in neuroscience, including diseases, brain anatomy, cell types, sub-cellular anatomy, small molecules, techniques, and resource descriptors. Since the first production release in 2008, NIF has grown significantly in content and functionality, particularly with respect to the ontologies and ontology-based services that drive the NIF system. We present here on the structure, design principles, community engagement, and the current state of NIFSTD ontologies. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 2 | 50% |
United Kingdom | 1 | 25% |
Unknown | 1 | 25% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 3 | 75% |
Scientists | 1 | 25% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 4 | 7% |
Brazil | 2 | 3% |
Mexico | 1 | 2% |
Finland | 1 | 2% |
Spain | 1 | 2% |
Russia | 1 | 2% |
Unknown | 50 | 83% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 20 | 33% |
Student > Ph. D. Student | 14 | 23% |
Student > Doctoral Student | 5 | 8% |
Professor | 4 | 7% |
Other | 4 | 7% |
Other | 9 | 15% |
Unknown | 4 | 7% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 17 | 28% |
Agricultural and Biological Sciences | 14 | 23% |
Biochemistry, Genetics and Molecular Biology | 4 | 7% |
Neuroscience | 4 | 7% |
Medicine and Dentistry | 4 | 7% |
Other | 12 | 20% |
Unknown | 5 | 8% |