Online Guide to Metadata for Effective Research Data Management

What is metadata, and what does it have to do with research data? Find here a description of metadata for research data management and ten questions/guidelines you can use in developing comprehensive metadata for your project.

Documenting Your Research Data online module by Data Services

Documenting your research data is a prerequisite for data sharing and your own use of your data in the future. Good documentation helps your data be discoverable, understood, and trusted by others. Please view our individual modules for the training “Documenting Your Research Data” to learn documentation best practices by subtopic (e.g., code, tabular data, medical data, geospatial data, and using documentation standards). Resources in these modules are also available in the Guide for Documenting Research Data. 

Research Data Documentation Workbook 

A spreadsheet tool to help organize and document a research project, its digital files, and derivative sets of files such as data associated with a publication or shared online.

Here are some resources for researchers to find data standards. Not all research fields have data standards. You can also check your funding agency's or data repository's website to find standards. Some standards are published as journal articles. 

• FAIRsharing Standards Registry: This standards registry includes standards from many different research fields. It is a standards registry of terminology artefacts, models/formats, reporting guidelines, and identifier schemas.
• RDA Metadata Standards Directory: A metadata standards directory created by Research Data Alliance (RDA). This is also a general metadata standards directory. You can find data standards from different disciplines. 
• DCC Metadata Standards list: Digital Curation Centre has a list of metadata standards across different research fields. 
• The Center for Expanded Data Annotation and Retrieval (CEDAR): CEDAR provides templates for researchers in the biomedical field to better document data in standardized ways. Scientists can also collaborate with each other to create new standards. 
• NIH Common Data Elements: A repository hosted by the National Institutes of Health (NIH) to help researchers share datasets using the templates created by NIH Institutes and Centers and other organizations. 
•  Observational Medical Outcomes Partnership (OMOP) by the Observational Health Data Science and Informatics (OHDSI) community
  • New Tool Transforms Data Collection for Clinical Research: An article that introduces OMOP model and how it can be applied to standardize clinical data.
  • OMOP on PMAP: Using OMOP data on Johns Hopkins' Precision Medicine Analytics Platform (PMAP) platform. 

A ReadMe Template by Cornell University

A ReadMe template, created by Cornell University, for documenting research data and projects. In addition, they offer guidance and best practices to write a good ReadMe file.

A ReadMe Generator

A simple editor allows researchers to quickly add and customize all the sections in their ReadMe. 

Geospatial Data Curation Toolkit 

This GitHub repository contains a collection of Python scripts and ArcGIS tools to help researchers prepare spatial data for archiving or sharing.

Documenting Your Research Data

Documenting your research data is a prerequisite for data sharing and your own use of your data in the future. Good documentation helps your data be discoverable, understood, and trusted by others. Please view our individual modules for the training “Documenting Your Research Data” to learn documentation best practices by subtopic (e.g., code, tabular data, using documentation standards). Resources in these modules are also available in the Guide for Documenting Research Data.