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databases and the Gene Ontology Consortium that provides a unified view of gene function, biological processes, phenotypes, and disease associations across multiple model organisms. The AGR aggregates and harmonizes data from member databases including MGI (mouse), RGD (rat), SGD (yeast), WormBase (C. elegans), FlyBase (D. melanogaster), ZFIN (zebrafish), and Xenbase (X. laevis and X. tropicalis).", "license": { "terms_of_use_url": "https://www.alliancegenome.org/privacy-warranty-licensing", "license_name": "CC0 1.0 Universal", "license_url": "https://creativecommons.org/publicdomain/zero/1.0/" }, "url": [ "All downloads: https://www.alliancegenome.org/downloads", "FMS (File Management System): https://fms.alliancegenome.org/download/" ], "version": "9.0.0" }, { "@type": "Dataset", "identifier": "bgee", "name": "BGee Ingest Guide", "description": "Bgee is a database for retrieval and comparison of gene expression patterns across multiple animal species. It provides an intuitive answer to the question 'where is a gene expressed?' and supports research in cancer and agriculture, as well as evolutionary biology.", "license": { "terms_of_use_url": "https://www.bgee.org/about/", "terms_of_use_description": "You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission.", "license_name": "CC0", "license_url": "https://creativecommons.org/publicdomain/zero/1.0/" }, "url": [ null ], "version": "15.2" }, { "@type": "Dataset", "identifier": "bindingdb", "name": "BindingDb", "description": "The Binding Database (\"BindingDb\") is a public, web-accessible database of measured binding affinities, focusing chiefly on the interactions of protein considered to be drug-targets with small, drug-like molecules. As of July 31, 2021, BindingDB contains thousands of dataset Entries, each with a DOI, containing millions of binding assay results for thousands of protein targets assayed against over a million distinct small molecule ligands.", "license": { "terms_of_use_description": "All data and download files in bindingDB are freely available under a 'Creative Commons BY 3.0' license." }, "url": [ "BindingDb bulk downloads - https://www.bindingdb.org/rwd/bind/downloads/" ], "version": "202606" }, { "@type": "Dataset", "identifier": "chembl", "name": "ChEMBL Reference Ingest Guide", "description": "ChEMBL is a manually curated database of bioactive molecules with drug-like properties. It brings together chemical, bioactivity and genomic data to aid the translation of genomic information into effective new drugs.", "license": { "license_name": "Creative Commons Attribution-ShareAlike 3.0 Unported license", "license_url": "https://creativecommons.org/licenses/by-sa/3.0/" }, "url": [ "https://ftp.ebi.ac.uk/pub/databases/chembl/ChEMBLdb/latest/" ], "version": "36" }, { "@type": "Dataset", "identifier": "cohd", "name": "Columbia Open Health Data", "description": "The COHD provides access to counts and patient prevalence (i.e., prevalence from electronic health records) of conditions, procedures, drug exposures, and patient demographics, and the co-occurrence frequencies between them. Count and frequency data were derived from the Columbia University Irving Medical Center's OHDSI database including inpatient and outpatient data. Counts are the number of patients with the concept, e.g., diagnosed with a condition, exposed to a drug, or who had a procedure. Frequencies are the number of patients with the concept divided by the total number of patients in the dataset. Clinical concepts (e.g., conditions, procedures, drugs) are coded by their standard concept ID in the OMOP Common Data Model. To protect patient privacy, all concepts and pairs of concepts where the count \u2264 10 were excluded, and counts were randomized by the Poisson distribution.", "license": { "terms_of_use_url": null, "terms_of_use_description": null, "license name": null, "license url": null }, "url": "https://stars.renci.org/var/data_services/cohd_2/cohd_nodes.jsonl", "version": "2024-11-25" }, { "@type": "Dataset", "identifier": "ctd", "name": "CTD Resource Ingest Guide", "description": "CTD is a robust, publicly available database that aims to advance understanding about how environmental exposures affect human health. It provides knowledge, manually curated from the literature, about chemicals and their relationship to other biological entities: chemical to gene/protein interactions plus chemical to disease and gene to disease relationships. These data are integrated with functional and pathway data to aid in the development of hypotheses about the mechanisms underlying environmentally influenced diseases. It also generates novel inferences by further analyzing the knowledge they curate/create - based on statistically significant connections with intermediate concept (e.g. Chemical X associated with Disease Y based on shared associations with a common set of genes).", "license": { "terms_of_use_url": "https://ctdbase.org/about/legal.jsp", "terms_of_use_description": "Bespoke terms describing specific conditions for use of CTD data. No formal license." }, "url": [ "CTD Bulk Downloads - http://ctdbase.org/downloads/ (this page includes file sizes and simple data dictionaries for each download)", "CTD Catalog - https://ctdbase.org/reports/ (a simple list of files, reports the number of rows in each file)" ], "version": "May_2026" }, { "@type": "Dataset", "identifier": "ctkp", "name": "Multiomics Clinical Trials Knowledge Provider (CTKP) Reference Ingest Guide", "description": "The Clinical Trials KP provides information on Clinical Trials, ultimately derived from researcher submissions to clinicaltrials.gov, via the Aggregate Analysis of Clinical Trials (AACT) database. Information on select trials includes the NCT Identifier of the trial, interventions used, diseases/conditions relevant to the trial, adverse events, etc. ", "license": { "terms_of_use_description": "Freely available" }, "url": [ "Direct download links for the latest version are given in the manifest file: https://github.com/multiomicsKP/clinical_trials_kp/blob/main/manifest.json" ], "version": "3.1.38" }, { "@type": "Dataset", "identifier": "dakp", "name": "Multiomics Drug Approvals Knowledge Provider (DAKP) Reference Ingest Guide", "description": "The Drug Approvals KP provides information on FDA-approved drugs and their approved indications, derived from DailyMed and FAERS data. Information includes drug-disease treatment relationships with FDA approval status, approval numbers, and supporting evidence from adverse event reports.", "license": { "terms_of_use_description": "Freely available" }, "url": [ "Direct download links for the latest version are given in the manifest file: https://github.com/multiomicsKP/drug_approvals_kp/blob/main/manifest.json" ], "version": "0.5.6" }, { "@type": "Dataset", "identifier": "dgidb", "name": "Drug-Gene Interaction Database (DGIdb) Reference Ingest Guide", "description": "The Drug-Gene Interaction Database (DGIdb) streamlines the search for druggable therapeutic targets through the aggregation, categorization, and curation of drug and gene data from publications and expert resources.", "license": { "terms_of_use_url": "https://dgidb.org/about/overview/data-accessibility", "terms_of_use_description": "According to the webpage on 'Data Accessibility', '[t]he data used in DGIdb is all open access and where possible made available as raw data dumps in the downloads section.' However, according to the 2024 paper, DGIdb 'aggregate[s] content from...sources with varying content licenses'; and the DGIdb downloads page says 'some of the data sources we import may have restrictions that prevent us from redistributing them'." }, "url": [ "Downloads page: https://dgidb.org/downloads" ], "version": "2024_12_06" }, { "@type": "Dataset", "identifier": "diseases", "name": "Jensen Lab DISEASES Database Reference Ingest Guide", "description": "The DISEASES database is a web resource that integrates knowledge on gene-disease associations. It generates de novo associations through automated text mining, and aggregates associations from external sources of manually curated knowledge and GWAS-based study results. The associations are assigned a confidence score to facilitate comparisons across data types and sources.", "license": { "license_name": "CC BY 4.0" }, "url": [ "https://diseases.jensenlab.org/Downloads" ], "version": "2026_06_12" }, { "@type": "Dataset", "identifier": "drug_rep_hub", "name": "Drug-Repurposing Hub Reference Ingest Guide", "description": "The Drug Repurposing Hub is a curated and annotated collection of FDA-approved drugs, clinical trial drugs, and selected pre-clinical tool compounds. Because the compounds are annotated and well characterized, the collection is a novel tool to explore known biological targets and pathways, and a powerful way to discover new biological insights, better understand disease mechanisms, and discover new activities of existing molecules. ", "license": { "license_name": "The Repurposing Hub compound metadata (including compound names, structures, targets, mechanisms, and indications) are made available under the permissive CC-BY 4.0 license. Restrictions: The Drug Repurposing Hub data is provided for non-commercial use only.", "license_url": "https://repo-hub.broadinstitute.org/repurposing#about" }, "url": [ "https://repo-hub.broadinstitute.org/repurposing#download-data" ], "version": "2025-08-19" }, { "@type": "Dataset", "identifier": "drugcentral", "name": "DrugCentral Reference Ingest Guide", "description": "DrugCentral is an open-access online drug information repository. It covers over 4950 drugs, incorporating structural, physicochemical, and pharmacological details to support drug discovery, development, and repositioning.", "license": { "terms_of_use_url": "https://drugcentral.org/privacy", "terms_of_use_description": "DrugCentral's webpage says DrugCentral is available under the Creative Commons license. In the 2023 papers, DrugCentral is described as available 'free of charge, without registration' (https://pmc.ncbi.nlm.nih.gov/articles/PMC10692006/#notes3) thru its website, public instance, a full database dump, and a Docker container (https://pmc.ncbi.nlm.nih.gov/articles/PMC9825566/#SEC6).", "license name": "CC BY-SA" }, "url": [ "Download and database access info: https://drugcentral.org/download" ], "version": "2023_11_01" }, { "@type": "Dataset", "identifier": "gene2phenotype", "name": "EBI Gene2Phenotype Reference Ingest Guide", "description": "EBI's Gene2Phenotype dataset contains high-quality gene-disease associations curated by UK disease domain experts and consultant clinical geneticists. It integrates data on genes, their variants, and related disorders. It is constructed by experts reviewing published literature, and it is primarily an inclusion list to allow targeted filtering of genome-wide data for diagnostic purposes. Each entry associates a gene with a disease, including a confidence level, allelic requirement and molecular mechanism.", "license": { "terms_of_use_description": "Unsure, but likely uses EMBL-EBI terms of use (linked in website footer). Currently don't see a formal standard license. Various resource webpages (About -> The G2P Project, Downloads, Publications) say that all data is 'freely available' and to please cite the date accessed/data version and Thorman et al 2019", "terms_of_use_url": "https://www.ebi.ac.uk/about/terms-of-use/#general" }, "url": [ "Latest data is provided at https://www.ebi.ac.uk/gene2phenotype/download (downloads created on-the-fly)", "Archived static releases provided on the FTP site at https://ftp.ebi.ac.uk/pub/databases/gene2phenotype/G2P_data_downloads/" ], "version": "2026_06_21" }, { "@type": "Dataset", "identifier": "geneticskp", "name": "GeneticsKP Reference Ingest Guide", "description": "GeneticsKP provides gene-disease associations derived from GWAS data using MAGMA analysis. It integrates genetic evidence from genome-wide association studies to identify genes associated with various diseases and traits.", "license": { "terms_of_use_description": "Freely available" }, "url": [ "Data is provided as a checked-in tar.gz file in the translator-ingests repository" ], "version": "2026-06-21" }, { "@type": "Dataset", "identifier": "go_cam", "name": "Gene Ontology Causal Activity Models (GO-CAM) Reference Ingest Guide", "description": "GO-CAM (Gene Ontology Causal Activity Models) is a framework that extends standard GO annotations by connecting molecular functions, biological processes, and cellular components into causally linked pathways. GO-CAMs provide explicit causal connections between gene products and their activities within specific biological contexts, enabling more detailed representation of biological mechanisms than traditional GO annotations.", "license": { "license_name": "CC BY 4.0 - Creative Commons Attribution 4.0 International License" }, "url": [ "GO-CAMs are downloaded model by model, via kghub-downloader that takes an index file that shows all possible gocams by identifier, and then iterates one by one through the identifiers, downloading each gocam.", "Index: https://s3.amazonaws.com/provider-to-model.json", "URL pattern: https://live-go-cam.geneontology.io/product/yaml/go-cam/[id].json" ], "version": "2026-05-19" }, { "@type": "Dataset", "identifier": "goa", "name": "GO Annotations (GOA) Reference Ingest Guide", "description": "GO Annotations connect genes to a Gene Ontology term that describes a molecular function it enables, a biological process in which it participates, or a cellular component in which it is located. Most are produced through rigorous manual curation of the literature, although some are based on automated pipelines that assign GO terms based on things like orthology or sequence similarity.", "license": { "terms_of_use_url": "https://geneontology.org/docs/go-citation-policy/", "license_name": "CC BY 4.0", "license_url": "https://creativecommons.org/licenses/by/4.0/legalcode#s3a1" }, "url": [ "All downloads: https://geneontology.org/docs/download-go-annotations/", "Commonly studied organisms: https://current.geneontology.org/products/pages/downloads.html" ], "version": "2026-05-19" }, { "@type": "Dataset", "identifier": "gtopdb", "name": "The IUPHAR/BPS Guide to PHARMACOLOGY (GtoPdb) Reference Ingest Guide", "description": "The IUPHAR/BPS Guide to PHARMACOLOGY (GtoPdb; https://www.guidetopharmacology.org) is an open-access, expert-curated, online database that provides succinct overviews and key references for pharmacological targets and their recommended experimental ligands. It includes over 3039 protein targets and 12,163 ligand molecules, including approved drugs, small molecules, peptides and antibodies. GtoPdb) was established and developed by the International Union of Basic and Clinical Pharmacology (IUPHAR) and the British Pharmacological Society (BPS). It originated from the IUPHAR-DB, a former resource focused on receptors and channels that was first compiled in 2003 and from the BPS\u2018 Guide to Receptors and Channels\u2019, a compendium of a wider range of targets than those originally covered in IUPHAR-DB", "license": { "license_name": "Creative Commons Attribution 4.0 International", "license_url": "https://creativecommons.org/licenses/by/4.0/legalcode" }, "url": [ "gtoPdb Downloads: https://www.guidetopharmacology.org/download.jsp (this page includes file sizes and simple data dictionaries for each download)" ], "version": "2026.2" }, { "@type": "Dataset", "identifier": "hpoa", "name": "Human Phenotype Ontology Annotations", "description": "The [Human Phenotype Ontology (HPO)](https://hpo.jax.org/) provides a standard vocabulary of phenotypic abnormalities encountered in human disease. Each term in the HPO describes a phenotypic abnormality, such as Atrial septal defect. The HPO is currently being developed using the medical literature, Orphanet, DECIPHER, and OMIM. HPO currently contains over 18,000 terms and over 156,000 annotations to hereditary diseases. The HPO project and others have developed software for phenotype-driven differential diagnostics, genomic diagnostics, and translational research.\nThe Human Phenotype Ontology group curates and assembles over 115,000 HPO-related annotations (\"HPOA\") to hereditary diseases using the HPO ontology. Here we create Biolink associations between diseases and phenotypic features, together with their evidence, and age of onset and frequency (if known). Disease annotations here are also cross-referenced to the MONarch Disease Ontology (MONDO) (https://mondo.monarchinitiative.org/).\nThere are four HPOA ingests ('disease-to-phenotype' (includes capture of disease modes of inheritance, 'gene-to-phenotype' and 'gene-to-disease') that parse out records from the HPO Phenotype Annotation File (http://purl.obolibrary.org/obo/hp/hpoa/phenotype.hpoa).", "license": { "terms_of_use_url": "https://hpo.jax.org/license", "terms_of_use_description": "Bespoke terms describing specific conditions for use of HPOA data. No formal license." }, "url": [ "https://hpo.jax.org/data/annotations" ], "version": "2026-06-06" }, { "@type": "Dataset", "identifier": "icees", "name": "ICEES (Integrated Clinical and Environmental Exposures Service)", "description": "ICEES KG is the KG version of the Integrated Clinical and Environmental Exposures Service (ICEES), which provides regulatory-compliant open access to clinical data (e.g., electronic health records, survey data, clinical research data) that have been integrated at the patient level with environmental exposures data (e.g., airborne pollutants, landfills, concentrated animal feeding operations, socio-economic indicators).", "license": { "terms_of_use_url": "https://github.com/NCATSTranslator/Translator-All/wiki/Exposures-Provider-ICEES-and-ICEES-KG-Terms-and-Conditions-of-Use", "terms_of_use_description": "UNC Health is permitted to provide under 45 C.F.R. \u00c2\u00a7 164.502(d)(2)" }, "url": [ "https://stars.renci.org/var/plater/bl-4.2.1/icees-kg/8-20-2024/ - Automat ICEES Plater loaded KGX jsonl files, plus meta_knowledge_graph.json" ], "version": "2024-08-20" }, { "@type": "Dataset", "identifier": "intact", "name": "IntAct Reference Ingest Guide", "description": "IntAct is a curated, open data resource of molecular interactions hosted by EMBL-EBI. It stores experimentally determined molecular interactions, primarily protein\u2013protein interactions, derived from literature curation and direct user submissions. IntAct uses detailed controlled vocabularies (e.g., PSI-MI) to describe experimental details such as interaction type, detection method, and participant roles, enabling consistent representation and downstream reuse of fine-grained interaction evidence.", "license": "", "url": [ { "IntAct main site": "https://www.ebi.ac.uk/intact/" }, { "IntAct Download / FTP or bulk access": "https://ftp.ebi.ac.uk/pub/databases/intact/current/psimitab/intact.zip" } ], "version": "2026_01_14" }, { "@type": "Dataset", "identifier": "ncbi_gene", "name": "NCBI Gene Reference Ingest Guide", "description": "\"The NCBI Gene integrates information from a wide range of species. A record may include nomenclature, Reference Sequences (RefSeqs), maps, pathways, variations, phenotypes, and links to genome-, phenotype-, and locus-specific resources worldwide.\"", "license": { "terms_of_use_url": "https://www.ncbi.nlm.nih.gov/home/about/policies/", "license_name": "NCBI Public Domain", "license_url": "https://www.ncbi.nlm.nih.gov/home/about/policies/" }, "url": [ "NCBI FTP Site: https://ftp.ncbi.nih.gov/gene/DATA/" ], "version": "2026_06_18" }, { "@type": "Dataset", "identifier": "panther", "name": "Panther Gene Orthology and Annotation RIG", "description": "The PANTHER (Protein ANalysis THrough Evolutionary Relationships) Classification System was designed to classify proteins (and their genes) in order to facilitate high-throughput analysis. The core of PANTHER is a comprehensive, annotated library of gene family phylogenetic trees. All nodes in the tree have persistent identifiers that are maintained between versions of PANTHER, providing a stable substrate for annotations of protein properties like subfamily and function.", "license": { "terms_of_use_url": "http://www.pantherdb.org/tou.jsp" }, "url": [ "http://data.pantherdb.org/ftp/" ], "version": "19.0" }, { "@type": "Dataset", "identifier": "pathbank", "name": "PathBank Reference Ingest Guide (RIG)", "description": "PathBank is a comprehensive, visually rich pathway database (>110k pathways) providing machine-readable exports (PWML, BioPAX, SBML, SBGN) and CSV summaries for pathway metadata and links.", "license": { "terms_of_use_url": "https://pathbank.org/about", "license_name": "See PathBank About/Downloads for reuse and citation guidance" }, "url": [ "All downloads: https://pathbank.org/downloads" ], "version": "2019-09-13" }, { "@type": "Dataset", "identifier": "semmeddb", "name": "SemMedDB Minimal Reference Ingest Guide (RIG)", "description": "Literature-derived semantic predications extracted by SemRep from PubMed; this RIG documents a post-processed RTX-KG2 edges subset rather than a raw MySQL ingest.", "license": "", "url": [ "SemMedDB (original NLM source behind SemRep; not the file this ingest reads directly): https://lhncbc.nlm.nih.gov/temp/SemRep_SemMedDB_SKR/SemMedDB_download.html", "RTX-KG2 public SemMedDB edges slice (gzipped JSONL \u2014 this is what translator-ingests downloads): https://rtx-kg2-public.s3.us-west-2.amazonaws.com/kg2.10.3-semmeddb-edges.jsonl.gz RTX-KG2 has already projected SemMedDB predications into Biolink-shaped edges (predicate, publications, publications_info, kg2_ids, optional qualifiers, domain_range_exclusion). This pipeline transforms that slice with Koza; it does not ingest raw SemMedDB MySQL dumps." ], "version": "semmeddb-2023-kg2.10.3" }, { "@type": "Dataset", "identifier": "sider", "name": "SIDER Reference Ingest Guide", "description": "SIDER contains information on marketed drugs and their recorded adverse drug reactions. The information is extracted from public documents and package inserts. The current version of the database contains data on 1,430 drugs and 5,868 adverse drug reactions.", "license": "Creative Commons Attribution-Noncommercial-Share Alike 4.0 License", "url": [ "http://sideeffects.embl.de/download/" ], "version": "4.1" }, { "@type": "Dataset", "identifier": "signor", "name": "SIGnaling Network Open Resource (Signor) Reference Ingest Guide", "description": "SIGNOR 3.0, https://signor.uniroma2.it, is a public repository that captures causal information and represents it according to an 'activity-flow' model. SIGNOR provides freely-accessible static maps of causal interactions that can be tailored, pruned and refined to build dynamic and predictive models. Each signaling relationship is annotated with an effect (up/down-regulation) and with the mechanism (e.g. binding, phosphorylation, transcriptional activation, etc.) causing the regulation of the target entity. Since its latest release, SIGNOR has undergone a significant upgrade including: (i) a new website that offers an improved user experience and novel advanced search and graph tools; (ii) a significant content growth adding up to a total of approx. 33,000 manually-annotated causal relationships between more than 8900 biological entities; (iii) an increase in the number of manually annotated pathways, currently including pathways deregulated by SARS-CoV-2 infection or involved in neurodevelopment synaptic transmission and metabolism, among others; (iv) additional features such as new model to represent metabolic reactions and a new confidence score assigned to each interaction.", "license": { "license_name": "Creative Commons Attribution 4.0 International", "license_url": "https://creativecommons.org/licenses/by/4.0/legalcode" }, "url": [ "Signor 3.0 Downloads: https://signor.uniroma2.it/downloads.php (this page includes file sizes and simple data dictionaries for each download)" ], "version": "2026_March" }, { "@type": "Dataset", "identifier": "tmkp", "name": "Text Mining Knowledge Provider (TMKP)", "description": "The Text Mining Knowledge Provider (TMKP) is a Translator knowledge provider that extracts\nbiomedical assertions from scientific literature using advanced text mining techniques.\nIt processes PubMed abstracts and full-text articles to identify relationships between\nbiological entities such as chemicals, genes, diseases, and phenotypes. The system uses\nnatural language processing and machine learning approaches to extract high-confidence\nassertions about how these entities interact.\n", "license": { "terms_of_use_url": null, "terms_of_use_description": "Open access data provided through the Translator consortium" }, "url": [ "https://storage.googleapis.com/translator-text-workflow-dev-public/" ], "version": "tmkp-2024-09-07" }, { "@type": "Dataset", "identifier": "ttd", "name": "Therapeutic Target Database (TTD) Reference Ingest Guide", "description": "TTD is a database providing information about the known and explored therapeutic protein and nucleic acid targets, the targeted disease, pathway information and the corresponding drugs directed at each of these targets.", "license": { "terms_of_use_description": "No formal license. Andrew Su has emailed developers and gotten a positive response for use with 'proper attribution'. Papers describe resource as 'freely accessible to all users'." }, "url": [ "Downloads page: https://db.idrblab.net/ttd/full-data-download" ], "version": "2024_03_30" }, { "@type": "Dataset", "identifier": "ubergraph", "name": "Ubergraph Ontological Hierarchy Reference Ingest Guide", "description": "Ubergraph is a unified OWL ontology integrating multiple biomedical ontologies including GO, UBERON, CL, CHEBI, HP, and others. It provides both redundant (with full inference closure for subclass relations) and non-redundant graph representations. This ingest uses the redundant version which contains complete transitive, reflexive subclass relations for ontological hierarchy reasoning.", "license": { "license_name": "CC0 1.0 Universal (CC0 1.0) Public Domain Dedication", "license_url": "https://raw.githubusercontent.com/INCATools/ubergraph/master/LICENSE.txt" }, "url": [ "Ubergraph Downloads: https://ubergraph.apps.renci.org/downloads/current/", "redundant-graph-table.tgz contains complete inference closure", "nonredundant-graph-table.tgz contains direct assertions only" ], "version": "2026-05-31" } ] }