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Data Management Resources for UNLV: Defining Data and Data Management

This guide provides both public and UNLV-specific resources for creating and implementing your data management plan.

What is Data Management and Objectives of this Libguide

Data management refers to the process of decision making and documenting of how data will be collected, structured, analyzed, stored, and shared.  As a research project progresses, the ways in which data files might be structured, stored, and shared may change depending on the most appropriate strategy for that phase of the research project.  Often, the plan for how data will be treated during each phase of the research project will be documented in some fashion.  This documentation can be valuable when plans change, colleagues enter or leave the research collaboration, or in the future when a data set is revisited years after the research project was completed.

A variety of resources are available that describe data management and the range of considerations that researchers should analyze during the planning phases of any research project.  Many of these resources are freely available to members of the public.  To see the list of resources available to UNLV researchers, visit the Data Management Planning tab. 

In other sections of this guide we define data, why you might need or want to create a data management plan, the recommended components of a data management plan, and provide helpful tips about storing, sharing and archiving your data.

Data and Data Formats Defined

Data are (and a single datum is) a basic form of information that may be used to make meaning through activities such as calculating, summarizing, or analyzing. (See the Merriam-Webster dictionary definition:  For the purposes of academic research, scholarship, and creative endeavors it is most useful to consider that data are whatever foundational bits that researchers use to create meaning. 

Data can be numbers, text, or other symbols. Below are examples of research data (from NCSU Libraries' Guide)

  • Documents (text, Word), spreadsheets
  • Laboratory notebooks, field notebooks, diaries
  • Questionnaires, transcripts, codebooks
  • Audiotapes, videotapes
  • Photographs, films
  • Protein or genetic sequences
  • Spectra
  • Test responses
  • Slides, artifacts, specimens, samples
  • Collection of digital objects acquired and generated during the process of research
  • Database contents (video, audio, text, images)
  • Models, algorithms, scripts
  • Contents of an application (input, outputlogfiles for analysis software, simulation software, schemas)
  • Methodologies and workflows
  • Standard operating procedures and protocols

Resources for UNLV Researchers

UNLV provides unique resources and assistance to our researchers.  Please do not hesitate to contact the groups below to explore how they can assist with your data management needs.

Data Sharing and Management Snafu in 3 Short Acts (5 minutes)

Humorous video about sharing data and why best practices are important.  From the authors: "A data management horror story by Karen Hanson, Alisa Surkis and Karen Yacobucci. This is what shouldn't happen when a researcher makes a data sharing request! Topics include storage, documentation, and file formats."

Rethinking Research Data - Kristin Briney TED Talk (15 min)

The United States spends billions of dollars every year to publicly support research that has resulted in critical innovations and new technologies. Unfortunately, the outcome of this work, published articles, only provides the story of the research and not the actual research itself. This often results in the publication of irreproducible studies or even falsified findings, and it requires significant resources to discern the good research from the bad. There is way to improve this process, however, and that is to publish both the article and the data supporting the research. Shared data helps researchers identify irreproducible results. Additionally, shared data can be reused in new ways to generate new innovations and technologies. We need researchers to “React Differently” with respect to their data to make the research process more efficient, transparent, and accountable to the public that funds them.

Kristin Briney is a Data Services Librarian at the University of Wisconsin-Milwaukee. She has a PhD in physical chemistry, a Masters in library and information studies, and currently works to help researchers manage their data better. She is the author of “Data Management for Researchers” and regular blogs about data best practices at

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