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6 results for “OMOP2OBO”

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zenodo44/100

Overview of OMOP2OBO

This figure provides an overview of the OMOP2OBO algorithm. As shown in the figure, the algorithm consists of five steps as indicated by the red-labeled grey boxes: (1) Query OMOP CDM. This step processes a table of OMOP concepts including identifiers, source codes, labels, synonyms, and concept ancestors. (2) Process OBO Foundry Ontologies. During this step, one or more OBO Foundry ontologies are downloaded and current classes, database cross-references, labels, and synonyms are extracted. (3) Map OMOP Standard Vocabulary Concepts to OBO Foundry Ontology Concepts. This step obtains three types of mappings (as indicated by the white boxes) and relies on publicly available resources including OHDSI Ananke and the UMLS Metathesaurus. First, exact-string matches between OMOP and OBO concept labels, definitions, and synonyms are obtained. Exact matches between OMOP standard concept codes and source codes and OBO ontology database cross-references are also obtained. Then, a scoring metric, like cosine similarity, is applied to embeddings learned from labels and synonyms for all OMOP and OBO concepts and concept ancestors. Finally, for all concepts not automatically mapped, this step also accepts manual mappings. (4) Synthesize and Process Mapping Results. This steps processes and synthesizes the mappings from the prior steps providing a mapping category (i.e., string constructed by combining: (i) the approach used to create it (i.e., “automatic”, “manual”, “cosine similarity”, or “unmapped”), (ii) cardinality (i.e., one-to-one or one-to-many), and (iii) level (i.e., concept or ancestor)) and mapping evidence (i.e., pipe-delimited free-text phrases that explain what fields were used to construct the mapping) for each mapping. (5) Output Mappings. The final step outputs the mappings as a flat-file, SQL database dump, or RDF/XML file. Acronyms: OBO (Open Biological and Biomedical Ontology); OHDSI (Observational Health Data Sciences and Informatics); OMOP (Observational Medical Outcomes Partnership); UMLS (Unified Medical Language System).

opencc-by-4.0Aug 2022View details →

zenodo44/100

N3C-Formatted OMOP2OBO Mappings

OMOP2OBO Mappings - N3C OMOP to OBO Working group This repository stores OMOP2OBO mappings which have been processed for use within the National COVID Cohort Collaborative (N3C) Enclave. The version of the mappings stored in this repository have been specifically formatted for use within the N3C Enclave. N3C OMOP to OBO Working Group: https://covid.cd2h.org/ontology   Accessing the N3C-Formatted Mappings  You can access the three OMOP2OBO HPO mapping files in the Enclave from the Knowledge store using the following link: <a href="https://unite.nih.gov/workspace/compass/view/ri.compass.main.folder.1719efcf-9a87-484f-9a67-be6a29598567">https://unite.nih.gov/workspace/compass/view/ri.compass.main.folder.1719efcf-9a87-484f-9a67-be6a29598567</a>. The mapping set includes three files, but you only need to merge the following two files with existing data in the Enclave in order to be able to create the concept sets: <ul> <li>OMOP2OBO_v2.0.0_N3C_Enclave_CSV_concept_set_expression_items.csv</li> <li>OMOP2OBO_v2.0.0_N3C_Enclave_CSV_concept_set_version.csv</li> </ul>   The first file OMOP2OBO_v2.0.0_N3C_Enclave_CSV_concept_set_expression_items.csv, contains columns for the OMOP concept ids and codes as well as specifies information like whether or not the OMOP concept’s descendants should be included when deriving the concept sets (defaults to FALSE). The other file OMOP2OBO_v2.0.0_N3C_Enclave_CSV_concept_set_version.csv, contains details on the mapping’s label (i.e., the HPO curie and label in the concept_set_id field) and its provenance/evidence (the specific column to access for this information is called intention).   Creating Concept Sets Merge these files together on the column named codeset_id and then join them with existing Enclave tables like concept and condition_occurrence to populate the actual concept sets. The name of the concept set can be obtained from the OMOP2OBO_v2.0.0_N3C_Enclave_CSV_concept_set_version.csv file and is stored as a string in the column called concept_set_id. Although not ideal (but is the best way to approach this currently given what fields are available in the Enclave), to get the HPO CURIE and label will require applying a regex to this column. An example mapping is shown below (highlighting some of the most useful columns): <pre><code>codeset_id: 900000000 concept_set_id: [OMOP2OBO] hp_0002031-abnormal_esophagus_morphology concept: 23868 code: 69771008 codeSystem: SNOMED includeDescendants: False intention: Mixed - This mapping was created using the OMOP2OBO mapping algorithm (https://github.com/callahantiff/OMOP2OBO). The Mapping Category and Evidence supporting the mappings are provided below, by OMOP concept: 23868 ******* Mapping Category: Automatic Exact - Concept ------------------------------------------------ Mapping Provenance ------------------ OBO_DbXref-OMOP_ANCESTOR_SOURCE_CODE:snomed_69771008 | OBO_DbXref-OMOP_CONCEPT_SOURCE_CODE:snomed_69771008 | CONCEPT_SIMILARITY:HP_0002031_0.713</code></pre>   Release Notes - v2.0.0 Preparation In order to import data into the Enclave, the following items are needed: <ul> <li>Obtain API Token, which will be included in the authorization header (stored as GitHub Secret)</li> <li>Obtain username hash from the Enclave</li> <li><a href="https://zenodo.org/record/7250177">OMOP2OBO Mappings (v1.5.0)</a></li> </ul> Data <ul> <li>Concept Set Container (concept_set_container): CreateNewConceptSet</li> <li>Concept Set Version (code_sets): CreateNewDraftOMOPConceptSetVersion</li> <li>Concept Set Expression Items (concept_set_version_item): addCodeAsVersionExpression</li> </ul> Script <ul> <li><a href="https://github.com/callahantiff/OMOP2OBO/blob/master/applications/N3C/n3c_mapping_conversion.py">n3c_mapping_conversion.py</a></li> </ul> Generated Output Need to have the codeset_id filled from self-generation (ideally, from a conserved range) prior to beginning any of the API steps. The current list of assigned identifiers is stored in the file named omop2obo_enclave_codeset_id_dict_v2.0.0.json. Note that in order to accommodate the 1:Many mappings the codeset ids were re-generated and rather than being ampped to HPO concepts, they are mapped to SNOMED-CT concepts. This creates a cleaner mapping and will easily scale to future mapping builds.   To be consistent with OMOP tools, specifically Atlas, we have also created Atlas-formatted json files for each mapping, which are stored in the zipped directory named atlas_json_files_v2.0.0.zip. Note that as mentioned above, to enable the representation of 1:Many mappings the filenames are no longer named after HPO concepts they are now named with the OMOP concept_id and label and additional fields have been added within the JSON files that includes the HPO ids, labels, mapping category, mapping logic, and mapping evidence.   File 1: concept_set_container <ul> <li>Generated Data: OMOP2OBO_v2.0.0_N3C_Enclave_CSV_concept_set_container.csv</li> <li>Columns: <ul> <li>concept_set_id</li> <li>concept_set_name</li> <li>intention</li> <li>assigned_informatician</li> <li>assigned_sme</li> <li>project_id</li> <li>status</li> <li>stage</li> <li>n3c_reviewer</li> <li>alias</li> <li>archived</li> <li>created_by</li> <li>created_at</li> </ul> </li> </ul>   File 2: concept_set_expression_items <ul> <li>Generated Data: OMOP2OBO_v2.0.0_N3C_Enclave_CSV_concept_set_expression_items.csv   </li> <li>Columns: <ul> <li>codeset_id</li> <li>concept_id</li> <li>code</li> <li>codeSystem</li> <li>ontology_id</li> <li>ontology_label</li> <li>mapping_category</li> <li>mapping_logic</li> <li>mapping_evidence</li> <li>isExcluded</li> <li>includeDescendants</li> <li>includeMapped</li> <li>item_id</li> <li>annotation</li> <li>created_by</li> <li>created_at</li> </ul> </li> </ul>   File 3: concept_set_version <ul> <li>Generated Data: OMOP2OBO_v2.0.0_N3C_Enclave_CSV_concept_set_version.csv   </li> <li>Columns: <ul> <li>codeset_id</li> <li>concept_set_id</li> <li>concept_set_version_title</li> <li>project</li> <li>source_application</li> <li>source_application_version</li> <li>created_at</li> <li>atlas_json</li> <li>most_recent_version</li> <li>comments</li> <li>intention</li> <li>limitations</li> <li>issues</li> <li>update_message</li> <li>status</li> <li>has_review</li> <li>reviewed_by</li> <li>created_by</li> <li>provenance</li> <li>atlas_json_resource_url</li> <li>parent_version_id</li> <li>is_draft</li> </ul> </li> </ul>   Generated Output: <ul> <li>OMOP2OBO_v2.0.0_N3C_Enclave_CSV_concept_set_container.csv</li> <li>OMOP2OBO_v2.0.0_N3C_Enclave_CSV_concept_set_expression_items.csv</li> <li>OMOP2OBO_v2.0.0_N3C_Enclave_CSV_concept_set_version.csv</li> <li>atlas_json_files_v2.0.0.zip</li> <li>omop2obo_enclave_codeset_id_dict_v2.0.0.json</li> </ul>

opencc-by-4.0Feb 2022View details →

zenodo40/100

OMOP2OBO: Semantic Integration of Standardized Clinical Terminologies to Power Translational Digital Medicine Across Health Systems (Recorded Introduction)

This entry contains the recorded introduction that was presented at the 2020 Observational Health Data Science Initiative Symposium (<a href="https://www.ohdsi.org/events/2020-ohdsi-symposium/">https://www.ohdsi.org/events/2020-ohdsi-symposium/</a>).

opencc-by-4.0Nov 2021View details →

zenodo40/100

OMOP2OBO Measurement Mappings

OMOP2OBO Measurement Mappings V1.0 The mappings in this repository were created between OMOP standard measurement concepts (i.e., LOINC) to the Human Phenotype Ontology (HPO), Chemical Entities of Biological Interest (CheBI), Vaccine Ontology (VO), National Center for Biotechnology Information Taxon Ontology (NCBITaxon), Protein Ontology (PRO), Cell Ontology (CL), and the Uber-anatomy Ontology (UBERON). For each measurement, all levels of the test result (results above, below, and within a reference range) were mapped, not only those deemed clinically relevant. Results outside of a reference range, but not currently deemed clinically relevant (as advised by the literature or consultation via domain expert), were annotated to the nearest relevant ontology concept ancestor. For example, when annotating the results of a test for Asparagus IgE Ab RAST class [Presence] in Serum (LOINC:15547-3), a result above a reference range would be annotated with an increased anti-plant-based food allergen IgE antibody level (HP:0410228). While a low level of this antibody may not be deemed clinically relevant, it is still outside of the provided reference range and thus was annotated to the nearest applicable concept ancestor, abnormal immunoglobulin level (HP:0010701). There is one exception to this rule: all measured drugs and toxins (entities not normally found in the human body) with normal results (results that were not outside of a given reference range) were annotated to the same HP concept as the clinically relevant result and logically negated. For example, Amphetamine [Presence] in Urine by Screen (LOINC:19343-3), a positive finding was mapped to a positive urine amphetamine test (HP:0500112) and a negative finding was mapped to a positive urine amphetamine test and logically negated (NOT HP:0500112). LOINC2HPO currently aligns LOINC to HP. The current work extends existing LOINC2HPO annotations to match the OMOP2OBO mappings in the following two ways: (1) annotations were updated if new and/or more specific concepts had been added to the HP; and (2) existing mappings were expanded to include the measurement substance (body fluids, tissues, and organs via Uberon), the entity being measured (chemicals, metabolites, or hormones via ChEBI; cell types via CL; and proteins and protein complexes via PR), and the species of the measured entities (organism taxonomy via NCBITaxon). Consistent with LOINC2HPO, all measurements lacking sufficient specimen detail (those measured in non-specific body substances) were annotated as “Unspecified Sample” and all measurements without a valid result type were annotated as “Not Mapped test Type”. All modifications to the original LOINC2HPO annotations were meticulously recorded in the mapping evidence field enabling users to easily identify when an original LOINC2HPO annotation had been updated. For this OMOP domain, the owl:complementOf (“not” and was used to model normal test results), owl:intersectionOf (“and”), and owl:unionOf (“or”) constructors were used to construct semantically expressive mappings. Mapping Details Mappings included in this set were generated automatically using OMOP2OBO or through the use of a Bag-of-words embedding model using TF-IDF. Cosine similarity is used to compute similarity scores between all pairwise combinations of OMOP and OBO concepts and ancestor concepts. To improve the efficiency of this process, the algorithm searches only the top 𝑛 most similar results and keeps the top 75th percentile among all pairs with scores >= 0.25. Manually created mappings are also included. Mapping Categories <ul> <li>Automatic One-to-One Concept: Exact label or synonym, dbXRef, or expert validated mapping @ concept-level; 1:1</li> <li>Automatic One-to-One Ancestor: Exact label or synonym, dbXRef, or expert validated mapping @ concept ancestor-level; 1:1</li> <li>Automatic One-to-Many Concept: Exact label or synonym, dbXRef, cosine similarity, or expert validated mapping @ concept-level; 1:Many</li> <li>Automatic One-to-Many Ancestor: Exact label or synonym, dbXRef, cosine similarity, or expert validated mapping @ concept-level; 1:Many</li> <li>Manual One-to-One: Hand mapping created using expert suggested resources; 1:1</li> <li>Manual One-to-Many: Hand mapping created using expert suggested resources; 1:Many</li> <li>Cosine Similarity: score suggested mapping -- manually verified</li> <li>UnMapped: No suitable mapping or not mapped type</li> </ul> Mapping Statistics Additional statistics have been provided for the mappings and are shown in the table below. This table presents the counts of OMOP concepts by mapping category and ontology: <table align="center"> <thead> <tr> <th scope="col">Mapping Category</th> <th scope="col">HPO</th> <th scope="col">UBERON</th> <th scope="col">ChEBI</th> <th scope="col">CL</th> <th scope="col">PR</th> <th scope="col">NCBITaxon</th> </tr> </thead> <tbody> <tr> <td>Automatic One-to-One Concept</td> <td>20</td> <td>1981</td> <td>268</td> <td>129</td> <td>19</td> <td>286</td> </tr> <tr> <td>Automatic One-to-Many Concept</td> <td>49</td> <td>5</td> <td>0</td> <td>24</td> <td>0</td> <td>0</td> </tr> <tr> <td>Automatic One-to-One Ancestor</td> <td>43</td> <td>426</td> <td>1149</td> <td>5</td> <td>5</td> <td>207</td> </tr> <tr> <td>Automatic Constructor - Ancestor  </td> <td>0</td> <td>1</td> <td>12</td> <td>1</td> <td>0</td> <td>0</td> </tr> <tr> <td>Cosine Similarity</td> <td>113</td> <td>50</td> <td>160</td> <td>35</td> <td>45</td> <td>56</td> </tr> <tr> <td>Manual</td> <td>10663</td> <td>319</td> <td>1446</td> <td>185</td> <td>1590</td> <td>2357</td> </tr> <tr> <td>Manual One-to-Many</td> <td>49</td> <td>1118</td> <td>528</td> <td>18</td> <td>133</td> <td>196</td> </tr> <tr> <td>UnMapped</td> <td>184</td> <td>184</td> <td>529</td> <td>3688</td> <td>2296</td> <td>982</td> </tr> </tbody> </table> Provenance and Versioning: The V1.0 deposited mappings were created by OMOP2OBO v1.0.0 on October 2022 using the OMOP Common Data Model V5.0 and OBO Foundry ontologies downloaded on September 14, 2020.  Caveats: Please note that these are the original mappings that were created for the preprint. They have not been updated to current versions of the ontologies. In our experience, this should result in very few errors, but we do suggest that you check the ontology concepts used against current versions of each ontology before using them.   Important Resources and Documentation <ul> <li>GitHub: <a href="https://github.com/callahantiff/OMOP2OBO">OMOP2OBO</a></li> <li>Project Wiki: <a href="https://github.com/callahantiff/OMOP2OBO/wiki">OMOP2OBO - wiki</a></li> <li>Zenodo Community: <a href="https://zenodo.org/communities/omop2obo">OMOP2OBO</a></li> <li>Preprint Manuscript: <a href="https://doi.org/10.5281/zenodo.5716421">10.5281/zenodo.5716421</a></li> </ul>

openmit-licenseSep 2020View details →

zenodo40/100

OMOP2OBO Drug Exposure Ingredient Mappings

OMOP2OBO Drug Exposure Ingredient Mappings V1.0 These mappings were created by the OMOP2OBO mapping algorithm (see links below).  OMOP2OBO - the first health system-wide, disease-agnostic mappings between standardized clinical terminologies and eight Open Biomedical Ontology (OBO) Foundry ontologies spanning diseases, phenotypes, anatomical entities, cell types, organisms, chemicals, vaccines, and proteins. These mappings are also the first to be explicitly created using standard terminologies in the Observational Medical Outcomes (OMOP) common data model (CDM), ensuring both semantic and clinical interoperability across a space of N conditions [and N relationships curated in these ontologies]. The mappings in this repository were created between OMOP standard drug exposure concepts at the ingredient-level (i.e., RxNorm) to the Chemical Entities of Biological Interest (ChEBI), the National Center for Biotechnology Information Taxon Ontology (NCBITaxon), the Protein Ontology (PRO), and the Vaccine Ontology (VO). All concepts were aligned to at least one ChEBI concept and the remaining ontologies (NCBITaxon, PR, and VO) were mapped by their drug class and/or type (e.g., biologics versus vaccines). For these OMOP domains, owl:intersectionOf (“and”), and owl:unionOf (“or”) constructors were used to construct semantically expressive mappings. Mapping Details Mappings included in this set were generated automatically using OMOP2OBO or through the use of a Bag-of-words embedding model using TF-IDF. Cosine similarity is used to compute similarity scores between all pairwise combinations of OMOP and OBO concepts and ancestor concepts. To improve the efficiency of this process, the algorithm searches only the top 𝑛 most similar results and keeps the top 75th percentile among all pairs with scores >= 0.25. Manually created mappings are also included. Mapping Categories <ul> <li>Automatic One-to-One Concept: Exact label or synonym, dbXRef, or expert validated mapping @ concept-level; 1:1</li> <li>Automatic One-to-One Ancestor: Exact label or synonym, dbXRef, or expert validated mapping @ concept ancestor-level; 1:1</li> <li>Automatic One-to-Many Concept: Exact label or synonym, dbXRef, cosine similarity, or expert validated mapping @ concept-level; 1:Many</li> <li>Automatic One-to-Many Ancestor: Exact label or synonym, dbXRef, cosine similarity, or expert validated mapping @ concept-level; 1:Many</li> <li>Manual One-to-One: Hand mapping created using expert suggested resources; 1:1</li> <li>Manual One-to-Many: Hand mapping created using expert suggested resources; 1:Many</li> <li>Cosine Similarity: score suggested mapping -- manually verified</li> <li>UnMapped: No suitable mapping or not mapped type</li> </ul> Mapping Statistics Additional statistics have been provided for the mappings and are shown in the table below. This table presents the counts of OMOP concepts by mapping category and ontology: <table align="center"> <thead> <tr> <th scope="col">Mapping category</th> <th scope="col">ChEBI</th> <th scope="col">NCBITaxon</th> <th scope="col">PRO</th> <th scope="col">VO</th> </tr> </thead> <tbody> <tr> <td>Automatic One-to-One Concept</td> <td>3151</td> <td>155</td> <td>43</td> <td>108</td> </tr> <tr> <td>Automatic One-to-Many Constructor</td> <td>404</td> <td>1</td> <td>1</td> <td>0</td> </tr> <tr> <td>Automatic One-to-One Ancestor</td> <td>147</td> <td>17</td> <td>20</td> <td>4</td> </tr> <tr> <td>Automatic One-to-Many Ancestor</td> <td>210</td> <td>3</td> <td>2</td> <td>2</td> </tr> <tr> <td>Cosine Similarity</td> <td>109</td> <td>4241</td> <td>18</td> <td>17</td> </tr> <tr> <td>Manual</td> <td>322</td> <td>230</td> <td>157</td> <td>21</td> </tr> <tr> <td>Manual One-to-Many</td> <td>72</td> <td>14</td> <td>8</td> <td>2</td> </tr> <tr> <td>UnMapped</td> <td>7392</td> <td>7146</td> <td>11558</td> <td>11653</td> </tr> </tbody> </table> Provenance and Versioning: The V1.0 deposited mappings were created by OMOP2OBO v1.0.0 on October 2022 using the OMOP Common Data Model V5.0 and OBO Foundry ontologies downloaded on September 14, 2020.  Caveats: Please note that these are the original mappings that were created for the preprint. They have not been updated to current versions of the ontologies. In our experience, this should result in very few errors, but we do suggest that you check the ontology concepts used against current versions of each ontology before using them.   Important Resources and Documentation <ul> <li>GitHub: <a href="https://github.com/callahantiff/OMOP2OBO">OMOP2OBO</a></li> <li>Project Wiki: <a href="https://github.com/callahantiff/OMOP2OBO/wiki">OMOP2OBO - wiki</a></li> <li>Zenodo Community: <a href="https://zenodo.org/communities/omop2obo">OMOP2OBO</a></li> <li>Preprint Manuscript: <a href="https://doi.org/10.5281/zenodo.5716421">10.5281/zenodo.5716421</a></li> </ul>

openmit-licenseSep 2020View details →

zenodo40/100

OMOP2OBO Condition Occurrence Mappings

OMOP2OBO Condition Occurrence Mappings V1.0 These mappings were created by the OMOP2OBO mapping algorithm (see links below).  OMOP2OBO - the first health system-wide, disease-agnostic mappings between standardized clinical terminologies and eight Open Biomedical Ontology (OBO) Foundry ontologies spanning diseases, phenotypes, anatomical entities, cell types, organisms, chemicals, vaccines, and proteins. These mappings are also the first to be explicitly created using standard terminologies in the Observational Medical Outcomes (OMOP) common data model (CDM), ensuring both semantic and clinical interoperability across a space of N conditions (and N relationships curated in these ontologies). The mappings in this repository were created between OMOP standard condition occurrence concepts (i.e., SNOMED CT) to the Human Phenotype Ontology (HPO) and the (Mondo). The National Library of Medicine's Unified Medical Language System (UMLS) Semantic Types are first used to filter out all concepts that did not have a biological origin (accidents, injuries, external complications, and findings without clear interpretations). Then, the Semantic Type was used to prioritize the mapping of HPO concepts to findings and symptoms and Mondo to Semantic Types indicative of disease. For these OMOP domains, owl:intersectionOf (“and”), and owl:unionOf (“or”) constructors were used to construct semantically expressive mappings. Mapping Details Mappings included in this set were generated automatically using OMOP2OBO or through the use of a Bag-of-words embedding model using TF-IDF. Cosine similarity is used to compute similarity scores between all pairwise combinations of OMOP and OBO concepts and ancestor concepts. To improve the efficiency of this process, the algorithm searches only the top 𝑛 most similar results and keeps the top 75th percentile among all pairs with scores >= 0.25. Manually created mappings are also included. Mapping Categories <ul> <li>Automatic One-to-One Concept: Exact label or synonym, dbXRef, or expert validated mapping @ concept-level; 1:1</li> <li>Automatic One-to-One Ancestor: Exact label or synonym, dbXRef, or expert validated mapping @ concept ancestor-level; 1:1</li> <li>Automatic One-to-Many Concept: Exact label or synonym, dbXRef, cosine similarity, or expert validated mapping @ concept-level; 1:Many</li> <li>Automatic One-to-Many Ancestor: Exact label or synonym, dbXRef, cosine similarity, or expert validated mapping @ concept-level; 1:Many</li> <li>Manual One-to-One: Hand mapping created using expert suggested resources; 1:1</li> <li>Manual One-to-Many: Hand mapping created using expert suggested resources; 1:Many</li> <li>Cosine Similarity: score suggested mapping -- manually verified</li> <li>UnMapped: No suitable mapping or not mapped type</li> </ul> Mapping Statistics Additional statistics have been provided for the mappings and are shown in the table below. This table presents the counts of OMOP concepts by mapping category and ontology: <table align="center"> <thead> <tr> <th scope="col">Mapping Category</th> <th scope="col">HPO</th> <th scope="col">Mondo</th> </tr> </thead> <tbody> <tr> <td>Automatic One-to-One Concept</td> <td>4767</td> <td>9097</td> </tr> <tr> <td>Automatic One-to-Many Concept</td> <td>150</td> <td>885</td> </tr> <tr> <td>Cosine Similarity</td> <td>1375</td> <td>667</td> </tr> <tr> <td>Automatic One-to-One Ancestor</td> <td>13595</td> <td>8911</td> </tr> <tr> <td>Automatic One-to-Many Ancestor  </td> <td>38080</td> <td>40224</td> </tr> <tr> <td>Manual</td> <td>5131</td> <td>755</td> </tr> <tr> <td>Manual One-to-Many</td> <td>10326</td> <td>2835</td> </tr> <tr> <td>Unmapped</td> <td>36301</td> <td>46345</td> </tr> </tbody> </table> Provenance and Versioning: The V1.0 deposited mappings were created by OMOP2OBO v1.0.0 on October 2022 using the OMOP Common Data Model V5.0 and OBO Foundry ontologies downloaded on September 14, 2020.  Caveats: The deposited files only contain the mappings that were generated automatically by the algorithm. The manually generated mappings will be deposited with the official preprint manuscript. Please note that these are the original mappings that were created for the preprint. They have not been updated to current versions of the ontologies. In our experience, this should result in very few errors, but we do suggest that you check the ontology concepts used against current versions of each ontology before using them.   Important Resources and Documentation <ul> <li>GitHub: <a href="https://github.com/callahantiff/OMOP2OBO">OMOP2OBO</a></li> <li>Project Wiki: <a href="https://github.com/callahantiff/OMOP2OBO/wiki">OMOP2OBO - wiki</a></li> <li>Zenodo Community: <a href="https://zenodo.org/communities/omop2obo">OMOP2OBO</a></li> <li>Preprint Manuscript: <a href="https://doi.org/10.5281/zenodo.5716421">10.5281/zenodo.5716421</a></li> </ul>

openmit-licenseSep 2020View details →

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ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

DANDI Archive for NWB datasets

DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

International Brain Laboratory public data

The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

OpenNeuro

OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record