Research Data Management (RDM)

Data organization refers to structuring data into a usable format, making it easier to find, analyze, and interpret. This involves classifying data, using logical file naming conventions, and creating a structured folder system, version control, metadata, etc. Consistency and logic are the top two reasons for researchers organize their data. The following sources contain more detailed information about organizing data:

• The Library of Congress has recommended formats for data and databases 
• NIH Organizing research data - PMC
• MIT File naming and folder hierarchy
• researchdata.org Organizing data
• University of Cambridge Organising your data

Metadata and documentation both help with understanding data, but they differ in their purpose and structure. Metadata is the data that describes the data and provides the necessary context needed for future use.  Documentation and metadata are key for understanding a dataset and promoting reproducibility and replicability.

Types of documentation:

• Metadata
• README files
• Codebooks
• Data Dictionaries
• Lab notebooks
• Version control

Essential metadata elements:

• Title: A meaningful title that describes the data's content. 
• Creator(s): The researchers or individuals responsible for creating the data. 
• Date Created: The date(s) the data was collected or created. 
• Data Format: The file types and software/hardware needed to access the data. 
• Description: A concise explanation of the data's content and purpose. 
• Subject/Keywords: Keywords that describe the topic of the data. 
• Coverage: Geographic or temporal information about the data. 
• Rights and Licensing: Information about who owns the data and any restrictions on its use. 
• Contact Information: Details for the person responsible for the data. 
• Funding Information: Details about grants or funding sources. 
• Data Dictionary: If applicable, a document that explains the variables, codes, and other elements within the dataset.  

Metadata standards

A metadata standard is a set of pre-defined guidelines that dictate the structure and format of metadata, ensuring consistency and interoperability when describing and managing data. Examples:

• Federal Geographic Data Committee provides geospatial metadata standards that describes maps, Geographic Information Systems (GIS) files, imagery, and other location-based data resources.
• GitHub Metadata Standards Directory a widely used, international standard for describing data from the social, behavioral, and economic sciences.
• Wikipedia Metadata standard lists more than 50 metadata standards.

Resources

Cold Spring Harbor Laboratory Metadata

• ICPSR TurboCurrator assists data depositors by providing recommendations for enhancing metadata quality before publication.
• Documentation of the USGS Oceanographic Time-Series Measurement Database
• Example readme file template this is an example of a generic readme template. It can be changed according to the dataset being described.
• Harvard University Documentation & Metadata
• ICPSR Guide to Social Science Data Preparation and Archiving
• NIH Common Data Elements (CDEs)
• NIH Data Management
• Readme file template for code-based data this readme file template is based on the CodeMeta metadata standard and can be used for any code-based dataset.
• Readme file template for experimental data this readme file template is based on the Core Scientific Metadata Model (CSMD) metadata standard and can be used for experimental datasets and facilities datasets.
• Understanding Metadata Official DDI Training Materials, this presentation -- created for a general audience -- covers the definitions, examples, roles, usage, management, and history of metadata.
• University of Cambridge Research Data Metadata