LibGuides: Data Services: Metadata

What is Metadata?	Metadata Elements	Library Support
Controlled Vocabularies	Standards & Schemas	Terminologies
Data Dictionary

What is Metadata?

Metadata is often defined as "data about data". It is also known as data documentation. Metadata is used to describe and document research data. Metadata facilitates searching for data in an archive or repository, and makes the data easily understood by anyone who wants to use the data. It describes the who, what, when, where, how and why about data.

There are three types of metadata; descriptive, structural, and administrative.

Descriptive Metadata supports discovery and identification.
Examples: title, creator, unique identifiers, subjects, keywords

Structural Metadata describes how a resource is organized.
Examples: Manifest of files in a dataset, table of contents, or schema of database tables

Administrative Metadata helps in managing the resource by describing technical aspects, rights management, and preservation information.
Technical Examples: file type, version information, how/when created
Rights Management Examples: licensing, use restrictions, privacy concerns
Preservation Examples: ownership, history of use, authenticity

Best Practices for Creating Metadata

Consult with a Data Librarian
Think from the perspective of the user of the resource
Keep metadata simple as possible to meet repository needs
Adopt/extend existing standards as practical and possible
Avoid extraneous punctuation and abbreviations
Keep a data dictionary

Understanding Metadata
This NISO Primer Series is a four-part series of documents that provide introductory guidance to
users of data (the other three documents cover research data). This primer discusses the latest
developments in metadata and the new tools, best practices, and resources now available.

Metadata Elements

Metadata records should include:

Overall purpose of research
People involved on the research team
Structure of the data files and how they relate to each other
Research design information
Data collection processes and methods
Data processing processes and methods
Spatial or temporal coverage of the datasets
Variables used in the dataset and how they related to each other
Types of instruments used to collect each variable and their calibration
Description of any codes used for missing values
Explanations of any derived values
Explanations of errors within the data files
Documentation of any outliers

Standards and Schemas

CDISC Foundational Standards
CDISC Foundational Standards are the basis of the complete suite of standards, supporting clinical and non-clinical research processes from end to end. Foundational Standards focus on the core principles for defining data standards and include models, domains and specifications for data representation.
Darwin Core (DWC)
The Darwin Core standard was originally conceived to facilitate the discovery, retrieval, and integration of information about modern biological specimens, their spatiotemporal occurrence, and their supporting evidence housed in collections (physical or digital). The Darwin Core today is broader in scope and more versatile. It is meant to provide a stable standard reference for sharing information on biological diversity.
Data Documentation Initiative (DDI)
The Data Documentation Initiative (DDI) is an international standard for describing the data produced by surveys and other observational methods in the social, behavioral, economic, and health sciences.
DCC Disciplinary Metadata
Metadata standards for biology, earth science, general research data, physical science, and social science & humanities.
Dublin Core
The Dublin Core Metadata Element Set is a vocabulary of fifteen properties for use in resource description.
Friend of a Friend
FOAF is a computer language defining a dictionary of people-related terms that can be used in structured data.
List of Metadata Standards
DDC list of Metadata Standards
Metadata Object Description Schema (MODS)
Metadata Object Description Schema (MODS) is a schema for a bibliographic element set that may be used for a variety of purposes, and particularly for library applications.
MetaData Standards FAQ
FAQ from FAIRsharing.org
W3C HCLS Dataset Description Standard
This document describes a consensus among participating stakeholders in the Health Care and the Life Sciences domain on the description of datasets using the Resource Description Framework (RDF). This specification meets key functional requirements, reuses existing vocabularies to the extent that it is possible, and addresses elements of data description, versioning, provenance, discovery, exchange, query, and retrieval.

Data Dictionary

A data dictionary is critical to making your research more reproducible because it allows others to understand your data. The purpose of a data dictionary is to explain what all the variable names and values in your spreadsheet really mean.

Data Dictionaries
Example data dictionaries provided by the USGS
The Data Documentation Initiative (DDI)
The Data Documentation Initiative (DDI) is an international standard for describing the data produced by surveys and other observational methods in the social, behavioral, economic, and health sciences.
How to make a data dictionary
Guidance on creating a data dictionary from Open Science Framework, explains how to name your variables and what values to use.
Video on creating data dictionaries
Data dictionaries are the ideal way to document spreadsheets and datasets with lots of variables, making it easier for you and others to use the data. Presented by University of Wisconsin Data Services

Controlled Vocabularies

Vocabulary are concepts and terms in a domain.

Controlled Vocabulary indicate the same meaning for words. It is a closed prescribed list of terms.

Taxonomy is controlled vocabulary that is arranged in a hierarchy. Terms are not usually defined and relationships between terms are not defined.

Thesaurus is a taxonomy that contains additional information about the use of terms. Example: MeSH

Ontology is a controlled vocabulary that defines the terms in a given domain or knowledgebase, and the relationships between those terms. Example: SNOMED

Polysemy are terms that means more than one concept.
Example- Leads [Hypertension leads to heart disease, an EKG lead, Lead poisoning]

Synonyms are terms that mean the same thing.
Example- Common cold [cold, upper respiratory infection, URI, pharyngitis, viral syndrome, bronchitis, rhinitis]

Use Cases for Controlled Vocabularies:

Information capture
Communication- transferring information
Knowledge organization- classification of diseases
Information Retrieval
Decision support- implementing decision support rules

Terminologies and Vocabularies

ICD 10 Data
ICD10Data.com is a free reference website designed for the fast lookup of all current American ICD-10-CM (diagnosis) and ICD-10-PCS (procedure) medical billing codes.
Linked Open Vocabularies (LOV)
A vocabulary in LOV gathers definitions of a set of classes and properties (together simply called terms of the vocabulary), useful to describe specific types of things, or things in a given domain or industry, or things at large but for a specific usage.
LOINC
The international standard for identifying health measurements, observations, and documents.
The OBO Foundry
The Open Biological and Biomedical Ontology (OBO) Foundry is a collective of ontology developers that are committed to collaboration and adherence to shared principles. The mission of the OBO Foundry is to develop a family of interoperable ontologies that are both logically well-formed and scientifically accurate.
RxTerm
RxTerms is a drug interface terminology derived from RxNorm for prescription writing or medication history recording (e.g. in e-prescribing systems, PHRs).
SNOMED CT
SNOMED CT is the most comprehensive, multilingual clinical healthcare terminology in the world.