Research and Data Services

Data Support, Training, and Software

Research Data Management

Introduction

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 and Organization

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

Resources:

Spreadsheets and Tidy Data

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

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

Data Documentation and Description

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

Data Dictionary

Codebook

Metadata and Standards

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

UVA Research Data Resources

University Policies:

UVA-Contracted Cloud Storage:

Additional UVA Storage Resources:

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:

UVA Resources for Computing and Analysis:

Additional RDM Resources

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. 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.
  • The DRESS Protocol is a set of standards for replication documentation that embodies the same principles that underlie the TIER Protocol but is tailored to suit the purposes of professional researchers

Questions?

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

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