Updated July 2022 by Xiaoli Ma
Metadata for Research Data, Best Practices
Metadata is information about data – information which may describe data’s provenance, contents, elements, formats, purposes, and more. Metadata is used to support discovery, interpretation, application, and preservation of data.
Formats: Data repositories usually require structured metadata, and may specify formats to use or elements to include. Following metadata best practices in your field will help colleagues access, understand, and repurpose your data. The Digital Curation Centre (DCC) provides information about discipline-specific metadata standards. General-purpose formats include the DataCite Metadata Schema, the Common European Research Information Format (CERIF), the Data Catalog Vocabulary (DCAT), the Data Documentation Initiative (DDI), and the Observations and Measurements schema for sampling features. The Libraries can be a resource for choosing and applying metadata formats.
Content: Describe the parameters, organization, and extent of your dataset. Use standardized terminology, and document these choices with a data dictionary or reference to an established vocabulary/ontology. These (and more) best practices are described in detail by the DataONE Organization. The Libraries can provide consultation and instruction on metadata best practices, and help devise a metadata plan for your projects.
UFDC: The University of Florida Digital Collections (UFDC)  hosts more than 300 outstanding digital collections, containing over 15 million pages, covering over 78 thousand subjects in rare books, manuscripts, antique maps, children's literature, newspapers, theses and dissertations, data sets, photographs, oral histories, and more for permanent access and preservation. Through UFDC, users have free and Open Access to full unique and rare materials held by the University of Florida and partner institutions. Researchers and scholars at UF are welcome to contribute their digital content to UFDC. For more about preparing Metadata for UFDC, please check the guide dedicated to this topic at https://guides.uflib.ufl.edu/ufdcmetadata
What is MAI (Machine Aided Indexing)?
Indexing connotes the processes of creating an index. It is derived from the Latin root “índicare,”
to point or indicate (Chakraborty and Chakrabarti 1983).
--Automated Indexing: The Key to Information Retrieval in the 21st Century, 2010
Automatic indexing, is the use of the machines to extract or assign index terms without human intervention.
---Automatic indexing, 1965
Some of these techniques or approaches are fully automated, while others are semi-automated or machine-aided.
---Automated Indexing: The Key to Information Retrieval in the 21st Century, 2010
More about MAI at the Libraries: https://ufdc.ufl.edu/IR00011738/00001/pdf/0