Organism

**Overview**
This data package contains the entire concept structure (sematic type, concepts and replationship) of UMLS Metathesaurus for the biomedical domain "Organism".

**Description**
The UMLS, or Unified Medical Language System, is a set of files and software that brings together many health and biomedical vocabularies and standards to enable interoperability between computer systems. There are three UMLS Knowledge Sources:
- The Metathesaurus, which contains over one million biomedical concepts from over 100 source vocabularies
- The Semantic Network, which defines 133 broad categories and fifty-four relationships between categories for labeling the biomedical domain
The SPECIALIST Lexicon & Lexical Tools, which provide lexical information and programs for language processing The Metathesaurus is a large, multi-purpose, and multi-lingual vocabulary database that contains information about biomedical and health related concepts, their various names, and the relationships among them. The Metathesaurus is built from the electronic versions of many different thesauri, classifications, code sets, and lists of controlled terms used in patient care, health services billing, public health statistics, indexing and cataloging biomedical literature, and/or basic, clinical, and health services research. The Metathesaurus contains over five million terms, or names, organized by meaning into concepts and assigned a unique identifier. The Metathesaurus is not a vocabulary. It contains many vocabularies that are standards and helps to create mappings between these vocabularies. The Semantic Network consists of:
- Semantic types (high level categories)
- Semantic relationships (relationships between semantic types) The Semantic Network can be used to categorize any medical vocabulary. There are 133 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types. Semantic types and semantic relationships create a network that represents the biomedical domain. Semantic types and relationships help with interpreting the meaning that has been assigned to the Metathesaurus concept.

**Benefits**
- The purpose of the umls is to improve the ability of computer programs to "understand" the biomedical meaning in user inquiries and to use this understanding to retrieve and integrate relevant machine-readable information for users.
- one powerful use of the umls is linking health information, medical terms, drug names, and billing codes across different computer systems.
- linking terms and codes between your doctor, your pharmacy, and your insurance company.
- patient care coordination among several departments within a hospital.
- timely access to accurate and up-to-date information improves decision making and ultimately the quality of patient care and research.
- the umls has many other uses, including search engine retrieval, data mining, public health statistics reporting, and terminology research.

**License Information**
The use of John Snow Labs datasets is free for personal and research purposes. For commercial use please subscribe to the [Data Library](https://www.johnsnowlabs.com/marketplace/) on John Snow Labs website. The subscription will allow you to use all John Snow Labs datasets and data packages for commercial purposes.

**Included Datasets**
- [Amphibian Concepts and Types](https://johnsnowlabs.com/wp-json/datasets-api/v1/amphibian-concepts-and-types)
- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type "Amphibian". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.
- [Amphibian Relationships](https://johnsnowlabs.com/wp-json/datasets-api/v1/amphibian-relationships)
- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type "Amphibian". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.
- [Animal Concepts and Types](https://johnsnowlabs.com/wp-json/datasets-api/v1/animal-concepts-and-types)
- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type "Animal". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.
- [Animal Relationships](https://johnsnowlabs.com/wp-json/datasets-api/v1/animal-relationships)
- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type "Animal". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.
- [Archaeon Concepts and Types](https://johnsnowlabs.com/wp-json/datasets-api/v1/archaeon-concepts-and-types)
- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type "Archaeon". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.
- [Archaeon Relationships](https://johnsnowlabs.com/wp-json/datasets-api/v1/archaeon-relationships)
- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type "Archaeon". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.
- [Bacterium Concepts and Types](https://johnsnowlabs.com/wp-json/datasets-api/v1/bacterium-concepts-and-types)
- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type "Bacterium". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.
- [Bacterium Relationships](https://johnsnowlabs.com/wp-json/datasets-api/v1/bacterium-relationships)
- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type "Bacterium". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.
- [Bird Concepts and Types](https://johnsnowlabs.com/wp-json/datasets-api/v1/bird-concepts-and-types)
- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type "Bird". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.
- [Bird Relationships](https://johnsnowlabs.com/wp-json/datasets-api/v1/bird-relationships)
- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type "Bird". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.
- [Eukaryote Concepts and Types](https://johnsnowlabs.com/wp-json/datasets-api/v1/eukaryote-concepts-and-types)
- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type "Eukaryote". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.
- [Eukaryote Relationships](https://johnsnowlabs.com/wp-json/datasets-api/v1/eukaryote-relationships)
- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type "Eukaryote". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.
- [Fish Concepts and Types](https://johnsnowlabs.com/wp-json/datasets-api/v1/fish-concepts-and-types)
- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type "Fish". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.
- [Fish Relationships](https://johnsnowlabs.com/wp-json/datasets-api/v1/fish-relationships)
- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type "Fish". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.
- [Fungus Concepts and Types](https://johnsnowlabs.com/wp-json/datasets-api/v1/fungus-concepts-and-types)
- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type "Fungus". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.
- [Fungus Relationships](https://johnsnowlabs.com/wp-json/datasets-api/v1/fungus-relationships)
- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type "Fungus". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.
- [Human Concepts and Types](https://johnsnowlabs.com/wp-json/datasets-api/v1/human-concepts-and-types)
- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type "Human". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.
- [Human Relationships](https://johnsnowlabs.com/wp-json/datasets-api/v1/human-relationships)
- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type "Human". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.
- [Mammal Concepts and Types](https://johnsnowlabs.com/wp-json/datasets-api/v1/mammal-concepts-and-types)
- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type "Mammal". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.
- [Mammal Relationships](https://johnsnowlabs.com/wp-json/datasets-api/v1/mammal-relationships)
- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type "Mammal". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.
- [Organism Concepts and Types](https://johnsnowlabs.com/wp-json/datasets-api/v1/organism-concepts-and-types)
- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type "Organism". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.
- [Organism Relationships](https://johnsnowlabs.com/wp-json/datasets-api/v1/organism-relationships)
- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type "Organism". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.
- [Physical Object Concepts and Types](https://johnsnowlabs.com/wp-json/datasets-api/v1/physical-object-concepts-and-types)
- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type "Physical Object". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.
- [Physical Object Relationships](https://johnsnowlabs.com/wp-json/datasets-api/v1/physical-object-relationships)
- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type "Physical Object". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.
- [Plant Concepts and Types](https://johnsnowlabs.com/wp-json/datasets-api/v1/plant-concepts-and-types)
- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type "Plant". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.
- [Plant Relationships](https://johnsnowlabs.com/wp-json/datasets-api/v1/plant-relationships)
- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type "Plant". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.
- [Reptile Concepts and Types](https://johnsnowlabs.com/wp-json/datasets-api/v1/reptile-concepts-and-types)
- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type "Reptile". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.
- [Reptile Relationships](https://johnsnowlabs.com/wp-json/datasets-api/v1/reptile-relationships)
- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type "Reptile". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.
- [Vertebrate Concepts and Types](https://johnsnowlabs.com/wp-json/datasets-api/v1/vertebrate-concepts-and-types)
- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type "Vertebrate". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.
- [Vertebrate Relationships](https://johnsnowlabs.com/wp-json/datasets-api/v1/vertebrate-relationships)
- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type "Vertebrate". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.
- [Virus Concepts and Types](https://johnsnowlabs.com/wp-json/datasets-api/v1/virus-concepts-and-types)
- This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type "Virus". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.
- [Virus Relationships](https://johnsnowlabs.com/wp-json/datasets-api/v1/virus-relationships)
- This dataset provides the information on relationships between concepts or atoms known to the Metathesaurus for the semantic type "Virus". In the dataset, for asymmetrical relationships there is one row for each direction of the relationship.

**Data Engineering Overview**
**We deliver high-quality data**
- Each dataset goes through 3 levels of quality review
- 2 Manual reviews are done by domain experts
- Then, an automated set of 60+ validations enforces every datum matches metadata & defined constraints
- Data is normalized into one unified type system
- All dates, unites, codes, currencies look the same
- All null values are normalized to the same value
- All dataset and field names are SQL and Hive compliant
- Data and Metadata
- Data is available in both CSV and Apache Parquet format, optimized for high read performance on distributed Hadoop, Spark & MPP clusters
- Metadata is provided in the open Frictionless Data standard, and its every field is normalized & validated
- Data Updates
- Data updates support replace-on-update: outdated foreign keys are deprecated, not deleted

**Our data is curated and enriched by domain experts**

Each dataset is manually curated by our team of doctors, pharmacists, public health & medical billing experts:
- Field names, descriptions, and normalized values are chosen by people who actually understand their meaning
- Healthcare & life science experts add categories, search keywords, descriptions and more to each dataset
- Both manual and automated data enrichment supported for clinical codes, providers, drugs, and geo-locations
- The data is always kept up to date – even when the source requires manual effort to get updates
- Support for data subscribers is provided directly by the domain experts who curated the data sets
- Every data source’s license is manually verified to allow for royalty-free commercial use and redistribution.

**Need Help?**
If you have questions about our products, contact us at [[e-mail]](mailto:[e-mail]).