3 datasets found
  1. u

    Uber

    • marine.usgs.gov
    Updated Jul 30, 2025
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    (2025). Uber [Dataset]. https://marine.usgs.gov/coastalchangehazardsportal/ui/info/item/uber
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    Dataset updated
    Jul 30, 2025
    Area covered
    Description

    This item is the root of the tree that represents the navigable items or 'enabled' items. Adding aggregations or data items to this will display them as top-level items on the portal home. This item is not displayed on the portal, so none of these fields need ever be edited.

  2. w

    Global Business Mapping Software Market Research Report: By Business...

    • wiseguyreports.com
    Updated Jul 19, 2024
    + more versions
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    wWiseguy Research Consultants Pvt Ltd (2024). Global Business Mapping Software Market Research Report: By Business Function (Finance and Accounting, Operations and Supply Chain, Human Resources, Marketing and Sales, Strategic Planning), By Deployment Type (Cloud-Based, On-Premise), By Industry Vertical (Manufacturing, Retail and Wholesale, Financial Services, Healthcare, Energy and Utilities), By Data Source (Internal Company Data, External Data Sources, Combination of Internal and External Data) and By Regional (North America, Europe, South America, Asia Pacific, Middle East and Africa) - Forecast to 2032. [Dataset]. https://www.wiseguyreports.com/reports/business-mapping-software-market
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    Dataset updated
    Jul 19, 2024
    Dataset authored and provided by
    wWiseguy Research Consultants Pvt Ltd
    License

    https://www.wiseguyreports.com/pages/privacy-policyhttps://www.wiseguyreports.com/pages/privacy-policy

    Time period covered
    Jan 7, 2024
    Area covered
    Global
    Description
    BASE YEAR2024
    HISTORICAL DATA2019 - 2024
    REPORT COVERAGERevenue Forecast, Competitive Landscape, Growth Factors, and Trends
    MARKET SIZE 20233.64(USD Billion)
    MARKET SIZE 20244.17(USD Billion)
    MARKET SIZE 203212.5(USD Billion)
    SEGMENTS COVEREDBusiness Function ,Deployment Type ,Industry Vertical ,Data Source ,Regional
    COUNTRIES COVEREDNorth America, Europe, APAC, South America, MEA
    KEY MARKET DYNAMICSIncreasing data accessibility Growing need for location intelligence Advancements in AI and ML Rise of cloudbased services Expansion of IoT
    MARKET FORECAST UNITSUSD Billion
    KEY COMPANIES PROFILEDOracle Maps ,SAP ,Venntive ,Microsoft Azure Maps ,Mapbox ,Carto ,Uber ,IBM ,Esri ,Quantum GIS ,Google Maps Platform ,Here Technologies ,Pitney Bowes ,TomTom ,Precisely
    MARKET FORECAST PERIOD2024 - 2032
    KEY MARKET OPPORTUNITIESAIdriven insights and location intelligence Cloudbased solutions for scalability and flexibility Realtime data analytics and visualization Predictive analytics for proactive decisionmaking Industryspecific solutions with tailored functionality
    COMPOUND ANNUAL GROWTH RATE (CAGR) 14.71% (2024 - 2032)
  3. OMOP2OBO Measurement Mappings -- WRONG DATA FILE UPLOADED IGNORE THIS...

    • zenodo.org
    bin
    Updated Mar 29, 2023
    + more versions
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    Tiffany J Callahan; Tiffany J Callahan; Nicole A Vasilevsky; Nicole A Vasilevsky; Tellen D Bennett; Tellen D Bennett; Blake Martin; James A Feinstein; James A Feinstein; William A Baumgartner; William A Baumgartner; Lawrence D Hunter; Lawrence D Hunter; Michael G Kahn; Michael G Kahn; Blake Martin (2023). OMOP2OBO Measurement Mappings -- WRONG DATA FILE UPLOADED IGNORE THIS VERSION [Dataset]. http://doi.org/10.5281/zenodo.6949693
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    binAvailable download formats
    Dataset updated
    Mar 29, 2023
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Tiffany J Callahan; Tiffany J Callahan; Nicole A Vasilevsky; Nicole A Vasilevsky; Tellen D Bennett; Tellen D Bennett; Blake Martin; James A Feinstein; James A Feinstein; William A Baumgartner; William A Baumgartner; Lawrence D Hunter; Lawrence D Hunter; Michael G Kahn; Michael G Kahn; Blake Martin
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Description

    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

    • Automatic One-to-One Concept: Exact label or synonym, dbXRef, or expert validated mapping @ concept-level; 1:1
    • Automatic One-to-One Ancestor: Exact label or synonym, dbXRef, or expert validated mapping @ concept ancestor-level; 1:1
    • Automatic One-to-Many Concept: Exact label or synonym, dbXRef, cosine similarity, or expert validated mapping @ concept-level; 1:Many
    • Automatic One-to-Many Ancestor: Exact label or synonym, dbXRef, cosine similarity, or expert validated mapping @ concept-level; 1:Many
    • Manual One-to-One: Hand mapping created using expert suggested resources; 1:1
    • Manual One-to-Many: Hand mapping created using expert suggested resources; 1:Many
    • Cosine Similarity: score suggested mapping -- manually verified
    • UnMapped: No suitable mapping or not mapped type

    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:

    Mapping CategoryHPOUBERONChEBICLPRNCBITaxon
    Automatic One-to-One Concept20198126812919286
    Automatic One-to-Many Concept49502400
    Automatic One-to-One Ancestor43426114955207
    Automatic Constructor - Ancestor 0112100
    Cosine Similarity11350160354556
    Manual10663319144618515902357
    Manual One-to-Many49111852818133196
    UnMapped18418452936882296982


    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

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Share
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Click to copy link
Link copied
Close
Cite
(2025). Uber [Dataset]. https://marine.usgs.gov/coastalchangehazardsportal/ui/info/item/uber

Uber

uber

Explore at:
Dataset updated
Jul 30, 2025
Area covered
Description

This item is the root of the tree that represents the navigable items or 'enabled' items. Adding aggregations or data items to this will display them as top-level items on the portal home. This item is not displayed on the portal, so none of these fields need ever be edited.

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