Below are selected links and resources to help you manage your research data.

File Naming

• Most important is to be descriptive and consistent
• You should be able to understand what is in the file without additional context (like the file creation date or the folder name)
• Other best practices:
  • Don't use spaces or special characters that aren't machine readable
  • Preferred date format: YYYYMMDD
  • Use leading zeros (e.g. 01, 02, 03) for better sorting
  • Indicate version numbers in file name if not using an automated version control system

File Organization

• As with file names, make sure folder names are also descriptive and consistent
• Always save raw data in a separate folder
• Can sort folders by date, 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 Handout
• UK Data Service Guide to Data Organization
• Library of Congress Recommended Formats
• 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
• Data 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.

University Policies:

• UVA Data Protection Standards
• Additional UVA Data Policies

UVA-Contracted Cloud Storage:

• 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

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