LibGuides: Scholarly Communication : Data Management

Research Data Management

Research Data Management is the continuous process of documenting, organizing, analyzing, storing, and sharing of data created during the research life cycle. Many funders now require that researchers publicly share their data on an open access repository. Storing data on a repository also gives researchers to the ability to link their data to publications or presentations, which increases transparency and reproducibility. Storing datasets on a repository provides long term preservation of research data.

Many repositories allow users to use a Creative Commons license to more easily share their data (see Creative Commons and Author Rights section). The repositories listed below allow researchers to use a Creative Commons license and track number of downloads. These repositories also offer a suggested citation and DOI (unique digital object identifier) so that data can be found and referenced. Some repositories have additional features which are described below.

Data Repositories

figshare This repository includes data from the sciences and humanities. Researchers can embargo their data, share links on social media, and can upload up to 5 GB of data.
Harvard Dataverse This repository focuses largely on social sciences. Links to datasets can be shared on social media, and researchers can upload up to 10 GB per dataset.
Mendeley Data Mendeley Data also allows researchers to place an embargo on their data. A researcher can upload up to 10GB per dataset.
Registry of Research Data Repositories re3data is a global registry of research data repositories. The registry covers research data repositories from different academic disciplines. Use this resource to search and find data repositories and basic information about individual ones.
Subject Specific From Nature This site provides a list of subject specific data repositories and can be a great place to start if none of the above repositories meet your needs.
Zenodo Zenodo is funded by CERN, Open AIRE, and the European Union, and is open to any science field. This repository allows embargoed data, and researchers can upload up to 50 GB per dataset.

Data Management Practices

Good data management helps verify and advance your work. Creating a solid data management plan will help you keep track of data, make your data citable, and help other researchers in your field. Good

ensures integrity of data
ensures that data is findable and usable when grad students leave projects over the years
makes the data of a project readily understandable to people outside the project
enables the sharing of data within and across disciplines
makes it easier to archive and preserve data in the long term
encourages data citation to increase the impact of the research

*Adapted from Data Management Guide at UTA Libraries

Best Practices

Data Organization in Spreadsheets by Karl W. Broman & Kara H. Woo Spreadsheets are widely used software tools for data entry, storage, analysis, and visualization. Focusing on the data entry and storage aspects, this article offers practical recommendations for organizing spreadsheet data to reduce errors and ease later analyses. The basic principles are: be consistent, write dates like YYYY-MM-DD, do not leave any cells empty, put just one thing in a cell, organize the data as a single rectangle (with subjects as rows and variables as columns, and with a single header row), create a data dictionary, do not include calculations in the raw data files, do not use font color or highlighting as data, choose good names for things, make backups, use data validation to avoid data entry errors, and save the data in plain text files.
Managing and Sharing Data: Best Practices for Researchers by Van den Eynden, V., Corti, L., Woollard, M., Bishop, L.& Horton, L. (2011).