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http://purl.bioontology.org/ontology/FB-BT

A structured controlled vocabulary of the anatomy of Drosophila melanogaster.

Proper citation: Drosophila Gross Anatomy Ontology (RRID:SCR_010311) Copy   

•   Source: SciCrunch Registry

http://ohnlp.org/index.php/ICEPO

An ontology distributed in OWL format which contains comprehensive terms describing ion channel electrophysiology. Terms from related ontologies, such as Cell Physiology Ontology (CPO), Cardiac Electrophysiology Ontology (CEPO), and Unit Ontology, were integrated into ICEPO.

Proper citation: ICEPO (RRID:SCR_014359) Copy   

•   Source: SciCrunch Registry

http://purl.bioontology.org/ontology/FIX

Two ontologies: methods and properties (but not objects, which are subject of the chemical ontology). The methods are applied to study the properties.

Proper citation: Physico-Chemical Methods and Properties (RRID:SCR_010407) Copy   

•   Source: SciCrunch Registry

http://purl.bioontology.org/ontology/SSO

Ontology that encodes agreement among experts about how Emergency Department (ED) chief complaints are grouped into syndromes of public health importance (consensus definitions).

Proper citation: Syndromic Surveillance Ontology (RRID:SCR_010409) Copy   

•   Source: SciCrunch Registry

http://purl.bioontology.org/ontology/ORDO

Ontology to provide a structured vocabulary for rare diseases capturing relationships between diseases, genes and other relevant features which will form a useful resource for the computational analysis of rare diseases. It derived from the Orphanet database (http://www.orpha.net) , a multilingual database dedicated to rare diseases populated from literature and validated by international experts. It integrates a nosology (classification of rare diseases), relationships (gene-disease relations, epiemological data) and connections with other terminologies (MeSH, SNOMED CT, UMLS, MedDRA), databases (OMIM, UniProtKB, HGNC, ensembl, Reactome, IUPHAR, Geantlas) or classifications (ICD10). The ontology will be maintained by Orphanet and further populated with new data. Orphanet classifications can be browsed in the OLS view. The Orphanet Rare Disease Ontology is updated monthly and follows the OBO guidelines on deprecation of terms. It constitutes the official ontology of rare diseases produced and maintained by Orphanet (INSERM, US14).

Proper citation: Orphanet Rare Disease Ontology (RRID:SCR_010402) Copy   

•   Source: SciCrunch Registry

http://purl.bioontology.org/ontology/PEO

Ontology that models provenance metadata associated with experiment protocols used in parasite research. The PEO extends the upper-level Provenir ontology (http://knoesis.wright.edu/provenir/provenir.owl) to represent parasite domain-specific provenance terms. The PEO (v 1.0) includes Proteome, Microarray, Gene Knockout, and Strain Creation experiment terms along with other terms that are used in pathway.

Proper citation: Parasite Experiment Ontology (RRID:SCR_010403) Copy   

•   Source: SciCrunch Registry

http://purl.bioontology.org/ontology/PHARE

Ontology that proposes concepts and roles to represent relationships of pharmacogenomics interest.

Proper citation: Pharmacogenomic Relationships Ontology (RRID:SCR_010406) Copy   

•   Source: SciCrunch Registry

http://neurolog.i3s.unice.fr/public_namespace/ontology

An ontology for neuroimaging or medical imaging studies based on DOLCE (Descriptive Ontology for Linguistic and Cognitive Engineering), as the foundational ontology. Detailed description from web: Our aim is the design of a common semantic model providing a unified view on all data and tools to be shared between NeuroLOG partners. For this purpose, we built a multi-layered and multi-components formal ontology. We chose a design framework that structures the ontology at different levels of abstraction while respecting common conceptualization choices. At the highest level is a top-level ontology that includes abstract concepts and relationships valid across domains. We adopted DOLCE (Descriptive Ontology for Linguistic and Cognitive Engineering), as the foundational ontology. We then added Core ontologies, which provide generic, basic and minimal concepts and relations in a specific domain. By minimal we mean that core ontologies should include only the most reusable and widely applicable categories. These kinds of ontologies are essential for sharing intended meaning between different domains. We adopted I& DA (Information and Discourse Acts), a core ontology initially built for classifying documents as a function of their content.We use it to model medical images, which we consider as types of documents. Participant Roles is the core ontology we use to describe the modes of image participation in data processing. I& DA and Participant Roles are built according to DOLCE ontological commitments. On the basis of these two layers, we constructed our Domain ontology dedicated to conceptualizing a specific domain, in this case neuroimaging. Obviously, large domains such as neuroimaging can be divided into sub-domains for the sake of modularization.

Proper citation: OntoNeuroLOG (RRID:SCR_008957) Copy   

•   Source: SciCrunch Registry

http://purl.bioontology.org/ontology/DDI

Ontology for the description of drug discovery investigations. DDI aims to follow to the OBO (Open Biomedical Ontologies) Foundry principles, uses relations laid down in the OBO Relation Ontology, and be compliant with Ontology for biomedical investigations (OBI).

Proper citation: Ontology for Drug Discovery Investigations (RRID:SCR_010383) Copy   

•   Source: SciCrunch Registry

http://purl.bioontology.org/ontology/OGSF

Application ontology to model / represent the notion of genetic susceptibility to a specific disease or an adverse event or a pathological biological process. It is developed using BFO2.0''s framwork. The ontology is under the domain of genetic epidemiology.

Proper citation: Ontology for Genetic Susceptibility Factor (RRID:SCR_010386) Copy   

•   Source: SciCrunch Registry

http://purl.bioontology.org/ontology/ACGT-MO

Ontology to represent the domain of cancer research and management in a computationally tractable manner.

Proper citation: Cancer Research and Management ACGT Master Ontology (RRID:SCR_006953) Copy   

•   Source: SciCrunch Registry

http://purl.bioontology.org/ontology/OntoVIP

Ontology that describes the content of the models used in medical image simulation developed in the context of the Virtual Imaging Platform project (VIP), a french project aiming at sharing medical image simulation resources. This ontology can be used to annotate such models in order to highlight the different entities that are present in the 3D scene to be imaged, i.e. anatomical structures, pathological structures, foreign bodies, contrast agents etc. The model allows also to associate to these entities information about their physical qualities, which are used in the medical image simulation process (to mimick physical phenomena involved in CT, MR, US and PET imaging). This ontology partly relies on the OntoNeuroLOG ontology (ONL-DP ONL-MR-DA), as well as PATO, RadLex, FMA and ChEBI.

Proper citation: Medical image simulation (RRID:SCR_010355) Copy   

•   Source: SciCrunch Registry

http://purl.bioontology.org/ontology/ONL-MSA

Ontology that is a module of the OntoNeuroLOG ontology that covers the field of mental state assessments, i.e. instruments, instrument variables, assessments, and resulting scores, developed in the context of the NeuroLOG project, a french project aiming at integrating distributed heterogeous resources in neuroimaging. It includes a generic domain core ontology, that provides a general model of such entities and a general taxonomy of behavioural, neurosychological and neuroclinical instruments, that can be easily extended to model any particular kind of instrument. It also includes such extensions for 8 relatively standard instruments, namely: (1) the Beck-depression-inventory-(BDI-II), (2) the Expanded-Disability-Status-Scale, (3) the Controlled-oral-word-association-test, (4) the Free-and-Cued-Selective-Reminding-Test-with-Immediate-Recall-16-item-version-(The-Grober-and-Buschke-test), (5) the Mini-Mental-State, (6) the Stroop-color-and-word-test, (7) the Trail-making-test-(TMT), (8) the Wechsler-Adult-Intelligence-Scale-third-edition, (9) the Clinical-Dementia-Rating-scale, (10) the Category-verbal-fluency, (11) the Rey-Osterrieth-Complex-Figure-Test-(CFT).

Proper citation: Mental State Assessment (RRID:SCR_010357) Copy   

•   Source: SciCrunch Registry

http://purl.bioontology.org/ontology/MSV

An ontology for metagenome sample metadata that mainly defines predicates.

Proper citation: Metagenome Sample Vocabulary (RRID:SCR_010358) Copy   

•   Source: SciCrunch Registry

https://community.brain-map.org/t/allen-human-reference-atlas-3d-2020-new/405

Parcellation of adult human brain in 3D, labeling every voxel with brain structure spanning 141 structures. These parcellations were drawn and adapted from prior 2D version of adult human brain atlas.

Proper citation: Allen Human Reference Atlas, 3D, 2020 (RRID:SCR_017764) Copy   

•   Source: SciCrunch Registry

http://tools.thermofisher.com/content/sfs/manuals/cms_040970.pdf

Automated PCR instrument for automated amplification of nucleic acids with Polymerase Chain Reaction. It has reaction volumes of up to 50 uL and sample temperature range of 4 to 99.9 C.

Proper citation: Thermo Fisher: Applied Biosystems: GeneAmp 9700 PCR Thermocycler System (RRID:SCR_018436) Copy   

•   Source: SciCrunch Registry

https://github.com/ttrogers/DecodingDynamic

Data, code, and notebooks for replicating analyses reported in Rogers et al., Evidence for deep, distributed and dynamic semantic code in human ventral anterior temporal cortex.

Proper citation: DecodingDynamic (RRID:SCR_021099) Copy   

•   Source: SciCrunch Registry

https://kimlab.io/brain-map/atlas/

Labels provide resource to isolate and identify mouse brain anatomical structures. Cell type specific transgenic mice and an MRI atlas were used to adjust and further segment the labels. Highly segmented anatomical labels in the adult mouse brain common coordinate framework.

Proper citation: Enhanced and Unified Anatomical Labeling for Common Mouse Brain Atlas (RRID:SCR_022816) Copy   

•   Source: SciCrunch Registry

https://drive.google.com/drive/folders/1K5oiXPcZDPT40irrZ_G2hZ86uNGA7CYZ?usp=sharing

Reference atlas for mice. Contains both average and annotation templates.

Proper citation: fMOST Atlas (RRID:SCR_022868) Copy   

•   Source: SciCrunch Registry

https://metacpan.org/dist/Bio-EUtilities

Software package which interacts with and retrieves data from NCBI's eUtils. This distribution encompasses low-level API for interacting with (and storing) information from NCBI's eUtils interface. See Bio::DB::EUtilities for the query API to retrieve data from NCBI, and Bio::Tools::EUtilities for the general class storage system. Note this may change to utilize the XML schema for each class at some point, though we will attempt to retain current functionality for backward compatibility unless this becomes problematic.

Proper citation: Bio-EUtilities (RRID:SCR_024064) Copy   

•   Source: SciCrunch Registry