Looking for information on the NIH Data Management and Sharing Plan 2023 requirements?
Consult our Guide with UVA-focused guidance on preparing your plan. 

Research Data Management

Conducting research involves working with data and involves processes from start to finish, including naming files, preparing and cleaning your data, performing analyses, documenting your work, and more. Below are selected resources help improve your workflows through better data management practices.

File Naming

• File names should be:
  • Human readable - you should understand the content of the file from its name alone
  • Machine readable - don't use spaces (_ or - are preferable), special characters or accents
  • Use default ordering - start with a leading zero (01, 02, etc) for numbered files, use the YYYYMMDD date format
• Have a plan for version control, either by including version numbers in the file names or using an automated system l

File Organization

• Always save raw data in a separate folder
• Make sure folder names are descriptive and consistent
• You can create a folder structure based on data type, processing stage, or any other system that makes sense to you and your collaborators

File Formats

• Be sure to save data in an open format for sharing and long-term preservation
• Check the Library of Congress Recommended Formats list to see what is recommended for your file type

Resources:

• MIT Libraries File Naming Handouts
• UK Data Service Guide to Data Organization
• Data One Primer on Data Management

Following a few basic recommendations when working with research data in spreadsheets can save you time when it comes to analyzing your data. Consider these practices adapted from Data Organization in Spreadsheets, Karl W. Broman & Kara H. Woo.

Basic Spreadsheet Practices

Getting Started

• Make backups
• No calculations in the raw data files
• Make it a rectangle and fill in each cell

Inputting Data

• Be consistent
• What to do with empty cells? Consider options as described here: DataONE - Data Entry and Manipulation
• Use data validation to avoid errors. For Excel, see: Excel Data Validation Guide | ExcelJet

Tidy Data

• Data Science for the Biomedical Sciences
  • Every column is a variable
  • Every row is an observation
  • Every cell is a single value
• Do not use font color or highlighting as data - consider an additional column with a "flag" value, as described above in  Exercise 3

Sharing

• Save the data in plain text files

Additional Resources:

Data Science for the Biomedical Sciences - Spreadsheets
Data Carpentry Spreadsheet Lesson
DataONE Data Entry and Manipulation (creating files, missing values, data validation)
Data Organization in Spreadsheets, Karl W. Broman & Kara H. Woo

Documentation:

Describing your Project

CESSDA has a useful guide for creating project-level documentation:

1. For what purpose was the data created
2. What does the dataset contain?
3. How was the data collected?
4. Who collected the data and when?
5. How was the data processed?
6. What possible manipulations were done to the data?
7. What were the quality assurance procedures?
8. How can the data be accessed?

Describing your Dataset(s)

Nice overview on Readme, Data Dictionaries, Codebooks with examples (Iowa)

Readme File

A readme is typically a plain-text file that provides information about a datafile to help facilitate use and re-use of the data. Typical elements to a readme include the following (adapted from Guide to Writing "readme" Style Metadata). Using one of the templates below can help ensure you create a useful readme file.

Readme Content: General Information

• Dataset title
• Creator name and contact information
• Date(s) of data collection
• Location of data collection
• Keywords to describe data topic

Readme Content: Data and Files

• Descriptive file names for each file, and for each, a description of what data is contained
• Date the file was created
• List of variables for each dataset, including full names and descriptions of each
• Definitions of any codes or symbols, including those for missing data

Readme Content: Methods

• Methods for data collection or generation
• Methods used for data processing

Additional Readme Resources

• Sample Readme file from deposited dataset "Data and R code to support: Estimating densities of zebra mussels (Dreissena polymorpha) in early invasions using distance sampling"
• LibraData (UVA's Research Data Repository) Readme template 
• Guide to Writing "readme" Style Metadata (including a template) (Cornell)

Data Dictionary

• How to Make A Data Dictionary (OSF)
• Describing Your Data: Data Dictionaries (Smithsonian)  

Codebook

• Nice, simple codebook example (Kent State) (part of a good SPSS tutorial on Creating a Codebook)

Metadata and Standards

Disciplinary Metadata (Digital Curation Centre) - links to information about metadata standards by discipline/field

• List of Metadata Standards
• List of Metadata Use Cases to look over for examples - e.g. ICPSR - Inter-university Consortium for Political and Social Research - a huge repository of datasets that have been professionally curated (described) with all metadata conforming to the DDI standard.

Selecting a Data Repository

Considerations

An effective way to make your data accessible is to store it in a repository. In this case, a data repository refers to a storage service that offers a mechanism for managing and storing digital content, where users can upload final datasets to make them accessible and discoverable.

Benefits of digital repositories include:

• Raise the impact of your research by allowing you to make data accessible to other researchers and scholars
• Keep your data safe and readable in the long-term
• Meet funder or publisher requirements

NIH Data Management and Sharing Requirements

Get assistance with writing your plan for the new NIH Data Management and Sharing Policy from our Guide.

Journal Sharing Requirements

• Science journals http://www.sciencemag.org/authors/science-journals-editorial-policies
• SpringerNature https://www.springernature.com/gp/authors/research-data-policy
• Nature journals https://www.nature.com/authors/policies/availability.html
• Wiley https://authorservices.wiley.com/author-resources/Journal-Authors/open-access/data-sharing-citation/index.html
• Sage https://us.sagepub.com/en-us/nam/journal/big-data-society#submission-guidelines

To make your data/supplements available, first make sure that they are appropriate for sharing (e.g. de-identified if needed), and properly organized and labeled. Typically uploading datasets or supplements are straightforward. 

More general considerations when deciding where to deposit your data

• Is the repository recommended by the publisher or funder?  If you are submitting your supplemental data in a journal article, you should check for the journal's data policy and data repositories specified therein
• Is the repository recognized within the research field and/or a discipline-specific repository? Or, if none is available in your field, do you need a generalist repository?
• Does the repository provide a Digital Object Identifier (DOI) or other means for your data to be cited? 
• Will the data be easy to find by other researches? Does the it metadata or other methods to describe your data?

Discipline-Specific Repositories

First, check funder or journal requirements for recommended or preferred repositor/ies 

Repository Directories and Lists

• NIH-supported Scientific Data Repositories provided by NIH
• re3data is a searchable global registry of research data repositories
• OpenDOAR global directory of Open Access repositories and their policies
• Nature's Scientific Data and PLOS journals list discipline-specific repositories and cross-disciplinary repositories

Sample Discipline-Specific Repositories

• Protein Data Bank - Worldwide repository of 3D structures of proteins, nucleic acids, and complex assemblies
• ArrayExpress - One of the major international repositories for high-throughput functional genomics data from both microarray and high throughput sequencing studies.
• Cardiovascular Research Grid - sharing cardiovascular data
• Mouse Genome Database
• Zebra Fish Model Organism

General and Cross-Disciplinary Repositories

UVA Data Repository

• LibraData (UVA’s data repository)

NIH-affiliated Repositories

In general, NIH does not endorse any particular repository. Overall, NIH encourages researchers to select the repository that is most appropriate for their data type and discipline. This list of NIH-supported repositories provides examples of suitable repositories.

General Data Repositories 

The Generalist Repository Ecosystem Initiative (GREI) includes seven established generalist repositories that will work together to establish consistent metadata, develop use cases for data sharing, train and educate researchers on FAIR data and the importance of data sharing, and more:

• Dryad - well-established repository led by a nonprofit organization that (note there are Data Publishing Charges)
• Dataverse - note that UVA's respository, LibraData, is a Dataverse
• Figshare - upload any file format; accepts scholarly output including figures, datasets, media, papers, posters, presentations and filesets 
• Mendeley Data - a free cloud-based service run by Elsevier
• Open Science Framework
• Vivli - a clinical research data sharing platform
• Zenodo - a free cloud-based service based on the European Organization for Nuclear Research (CERN's) data repository platform

University Policies:

• UVA Data Protection Standards
• UVA RES-002: Ownership, Retention, Safeguarding, Management, and Transfer of Research Records
• Additional UVA Data Policies

UVA-Contracted Cloud Storage:

• User responsibilites and data restrictions for UVA Box and OneDrive
• UVA Box
• UVA OneDrive

Additional UVA Storage Resources:

• Research Computing Data Storage
• ITS Storage, Hosting & Servers

Backing Up Your Data:

• Consider the rule of three:
  • Here (lab computer, personal computer)
  • Near (portable hard drive, flash drive)
  • Far (cloud storage, remote backup)
• CESSDA Guide to Backing Up Data

Archiving and Sharing:

• Institutional Data Repository
  • UVA LibraData
• List of Disciplinary Data Repositories
  • Re3Data
• Multidisciplinary Repositories
  • Figshare
  • Zenodo
  • Dryad
  • Open Science Framework

UVA Resources for Computing and Analysis:

• Research Computing
• ITS Software Gateway
• UVA Library Research Data Services + Sciences
• UVA Library Research Data Management Guide
• UVA Claude Moore Health Sciences Library Research Data Services

The Open Science Framework (OSF) is a free, open web-based platform to support your research and enable collaboration. Think of it as a site where you can manage your whole research project. UVA is an institutional member and you can log in with your NetBadge (select "Sign in via institution.")

See examples:

• OSF for a shared lab space, share lab standards and resources, and collate the work that is being done by individuals/groups within the lab
• template for researchers to use an OSF project as an Electronic Lab Notebook (Johns Hopkins University)

Project TIER seeks to enhance transparency and reproducibility of empirical research in the social sciences. Resources include: 

• The TIER Protocol (4.0) which provides a template for students to document statistical computation (e.g. folders for your input data, your analysis, your scripts, etc). A template exists in OSF for TIER.

Need more information on managing your research data? We are here to help:

University Library Research Data Services +  Sciences - contact at dmconsult@virginia.edu 

Health Sciences Library Research & Data Services - contact at hsl-rdas@virginia.edu