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http://www.linked-neuron-data.org/
Neuroscience data and knowledge from multiple scales and multiple data sources that has been extracted, linked, and organized to support comprehensive understanding of the brain. The core is the CAS Brain Knowledge base, a very large scale brain knowledge base based on automatic knowledge extraction and integration from various data and knowledge sources. The LND platform provides services for neuron data and knowledge extraction, representation, integration, visualization, semantic search and reasoning over the linked neuron data. Currently, LND extracts and integrates semantic data and knowledge from the following resources: PubMed, INCF-CUMBO, Allen Reference Atlas, NIF, NeuroLex, MeSH, DBPedia/Wikipedia, etc.
Proper citation: Linked Neuron Data (RRID:SCR_003658) Copy
https://confluence.crbs.ucsd.edu/display/NIF/DRG
Gene expression data from published journal articles that test hypotheses relevant to neuroscience of addiction and addictive behavior. Data types include effects of particular drug, strain, or knock out on particular gene, in particular anatomical region. Focuses on gene expression data and exposes data from investigations using DNA microarrays, polymerase chain reaction, immunohistochemistry and in-situ hybridizations. Data are available for query through NIF interface.Data submissions are welcome.
Proper citation: Drug Related Gene Database (RRID:SCR_003330) Copy
http://www.chibi.ubc.ca/WhiteText/
Freely available corpus of manually annotated brain region mentions created to facilitate text mining of neuroscience literature. The corpus contains 1,377 abstracts with 18,242 brain region annotations. Interannotator agreement was evaluated for a subset of the documents, and was 90.7% and 96.7% for strict and lenient matching respectively. We observed a large vocabulary of over 6,000 unique brain region terms and 17,000 words. For automatic extraction of brain region mentions we evaluated simple dictionary methods and complex natural language processing techniques. The dictionary methods based on neuroanatomical lexicons recalled 36% of the mentions with 57% precision. The best performance was achieved using a conditional random field (CRF) with a rich feature set. Features were based on morphological, lexical, syntactic and contextual information. The CRF recalled 76% of mentions at 81% precision, by counting partial matches recall and precision increase to 86% and 92% respectively. We suspect a large amount of error is due to coordinating conjunctions, previously unseen words and brain regions of less commonly studied organisms. We found context windows, lemmatization and abbreviation expansion to be the most informative techniques. We encourage you to test new methods and applications of the dataset. Please contact us if you do, we would like to hear about and link to your work. The abstracts are from PubMed/Medline, specifically The Journal of Comparative Neurology.
Proper citation: Automated recognition of brain region mentions in neuroscience literature. (RRID:SCR_002731) Copy
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The NIDDK Information Network (dkNET) serves the needs of basic and clinical investigators by providing seamless access to large pools of data and research resources relevant to the mission of The National Institute of Diabetes Digestive and Kidney Diseases (NIDDK). dkNET is hosted at University of California San Diego, and is supported by NIH NIDDK grant U24DK097771.
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