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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

https://www.distillersr.com/products/distillersr-systematic-review-software

Literature review software by DistillerSR Inc. Automates management of literature collection, screening, and assessment using AI and intelligent workflows. From systematic literature review to rapid review to living review, makes any project simpler to manage and configure to produce transparent, audit-ready, and compliant results.

Proper citation: DistillerSR (RRID:SCR_023078) Copy   

•   Source: SciCrunch Registry

http://prime.psc.riken.jp/compms/msdial/main.html

Software tool for data independent MS/MS deconvolution for comprehensive metabolome analysis. Universal program for untargeted metabolomics that supports multiple instruments (GC/MS, GC/MS/MS, LC/MS, and LC/MS/MS) and MS vendors (Agilent, Bruker, LECO, Sciex, Shimadzu, Thermo, and Waters). Used for untargeted metabolomics and lipidomics supporting any type of chromatography/mass spectrometry methods.

Proper citation: MS-DIAL (RRID:SCR_023076) 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://sbi.postech.ac.kr/oasis/introduction/

A tool for various statistical tasks involved in analyzing survival data which provides a uniform platform to facilitate efficient statistical analyses of survival data in the aging field. The statistical features of OASIS include the calculation of Kaplan-Meier estimates, mean/median lifespan, mortality rate, Mantel-Cox Log-Rank test, Fishers exact test, weighted Log-Rank test, Kolmogorov-Smirnov test and Neymans smooth test. Moreover, OASIS generates survival and mortality curves that can be easily exported and modified by using common graphic softwares.

Proper citation: Online Application for Survival Analysis (OASIS) (RRID:SCR_014450) 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

https://bolin.su.se/data/

Provides open access to Climate and Earth System Data from scientists at the centre and their collaborators. Helps to make your data open, FAIR and visually appealing. Each dataset and source code in the Bolin Centre Database is assigned a unique DOI. This makes it easy to cite and find your data. If dataset has more than one version, each version will have its own DOI.

Proper citation: Bolin Centre Database (RRID:SCR_023142) Copy   

•   Source: SciCrunch Registry

https://www.braincommons.org/

Scalable cloud-based platform for computational discovery designed for the brain health community.The BRAINCommons empowers the global research community by providing access to multi-model data, state-of-the-art tools and a secure interoperable system for data sharing.

Proper citation: BRAIN Commons (RRID:SCR_023140) Copy   

•   Source: SciCrunch Registry

http://www.cbs.dtu.dk/services/SignalP/

Web application for prediction of the presence and location of signal peptide cleavage sites in amino acid sequences from different organisms. The method incorporates a prediction of cleavage sites and a signal peptide/non-signal peptide prediction based on a combination of several artificial neural networks.

Proper citation: SignalP (RRID:SCR_015644) Copy   

•   Source: SciCrunch Registry

https://researchdata.bbk.ac.uk

Repository allows all researchers at Birkbeck to upload data, and get DOI.Data in the Birkbeck Data Repository is stored on Arkivum server.This is a very secure storage space, which will allow our data to remain unchanged and accessible for many years after it is deposited.

Proper citation: Birkbeck Research Data (RRID:SCR_023139) Copy   

•   Source: SciCrunch Registry

https://dmportal.biodata.pt/

Portuguese distributed infrastructure for biological data and Portuguese node of ELIXIR.

Proper citation: BioData Management Portal (RRID:SCR_023138) Copy   

•   Source: SciCrunch Registry

http://archive.stsci.edu/

Astronomical data archive focused on optical, ultraviolet, and near infrared. Used for maximizing scientific accessibility and productivity of astronomical data. MAST hosts data from over dozen missions like Webb, Hubble, TESS, Kepler, and in the future Roman.

Proper citation: Barbara A. Mikulski Archive for Space Telescopes (RRID:SCR_023137) 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://www.evidenceontology.org

A controlled vocabulary that describes types of scientific evidence within the realm of biological research that can arise from laboratory experiments, computational methods, manual literature curation, and other means. Researchers can use these types of evidence to support assertions about research subjects that result from scientific research, such as scientific conclusions, gene annotations, or other statements of fact. ECO comprises two high-level classes, evidence and assertion method, where evidence is defined as a type of information that is used to support an assertion, and assertion method is defined as a means by which a statement is made about an entity. Together evidence and assertion method can be combined to describe both the support for an assertion and whether that assertion was made by a human being or a computer. However, ECO can not be used to make the assertion itself; for that, one would use another ontology, free text description, or other means. ECO was originally created around the year 2000 to support gene product annotation by the Gene Ontology. Today ECO is used by many groups concerned with provenance in scientific research. ECO is used in AmiGO 2

Proper citation: ECO (RRID:SCR_002477) Copy   

•   Source: SciCrunch Registry