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Data Literacy

This guide was created to support the data literacy efforts of faculty, staff, and students at Tulane Libraries. If you have any suggestions for this guide, reach out to

About This Guide

This library guide provides an overview of data literacy for those beginning teaching, learning, and/or research in this area. Visit the DATA LITERACY LIBRARY GUIDES page for library guides that describe each data literacy competency area.

If you are interested in offering data literacy instruction to your students, complete the following instruction request form below. Our librarians can offer instruction on basic data literacy topics such as determining appropriate collection method and data analysis. If you are a student looking to learn more about using data for your research or creative work, reach out to our team at to schedule a consultation.

Data Literacy Defined

We hear the word “data” all of the time.

It is collected, analyzed, shared, hacked, bought and sold. But what exactly is data? Watch this video to learn more about data and its various forms. 

Data Literacy 


The desire and ability to constructively engage in society through and about data


According to Carlson et al. (2011), data literacy encompasses the following competency areas. You can use these competencies when planning data literacy instruction. Some areas are linked to related library guides. Visit these links for more in-depth descriptions of the competency area.

Databases and Data Formats
Discovery and Acquisition of Data
Data Management and Organization
Data Conversion and Interoperability
Quality Assurance
Data Curation and Reuse
Data Preservation
Data Analytics
Data Visualization

The following sections of this page describes each competency and provides potential learning outcomes for data literacy instruction.

Carlson, Fosmire, M., Miller, C. ., & Nelson, M. S. (2011). Determining data information literacy needs: A study of students and research Faculty. Portal (Baltimore, Md.), 11(2), 629–657.

Databases and Data Formats

Understands the concept of relational databases, how to query those databases, and becomes familiar with standard data formats and types for their discipline. Understands which formats and data types are appropriate for different research questions

Potential Learning Objectives for Data Literacy Instruction: 

  • Differentiate between data types for statistical analysis (nominal, ordinal, discrete, continuous -- interval and ratio)
  • Define relational database concepts including, but not limited to:
Row Column
Table Record
One-to-one Data Modeling One-to-many Data Modeling
Many-to-many Data Modeling Primary and Foreign Keys
Query SQL
Data Type Referential Integrity
  • Determine data types appropriate to purpose of analysis
  • Query database using SQL commands SELECT and FROM
  • Query database using SQL commands to limit returned data: LIKE, WHERE, BETWEEN, IN, >, < <=,>=,=, !=
  • Query multiple tables using  LEFT JOIN, RIGHT JOIN, and FULL JOIN
  • Perform advanced Windows functions in SQL
  • Define and differentiate between data formats in Python, R, Java, and other data tools

Discovery and Acquisition of Data

Locates and utilizes disciplinary data repositories. Not only identifies appropriate data sources, but also imports data and converts it when necessary, so it can be used by downstream processing tools.

Potential Learning Objectives for Data Literacy Instruction:

  • Create instruments and protocols to collect qualitative data including but not limited to surveys, interviews, focus groups, etc.
  • Create instruments and protocols to collect quantitative data including but not limited to surveys, interviews, experiments, etc.
  • Access and navigate library-cataloged data sources
  • When permitted, download and store data hosted through the library's catalog
  • Identify and access discipline data repositories such as ICSPR
  • Identify open data sources 

Data Management and Organization

Understands the lifecycle of data, develops data management plans, and keeps track of the relation of subsets or processed data to the original data sets. Creates standard operating procedures for data management and documentation.

Potential Learning Outcomes for Data Literacy Instruction:

Data Conversion and Interoperability 

Becomes proficient in migrating data from one format to another. Understands the risks and potential loss or corruption of information caused by changing data formats. Understands the benefits of making data available in standard formats to facilitate downstream use.

Potential Learning Outcomes for Data Literacy Instruction:

  • Convert data to appropriate format for storage/preservation, analysis, and reporting
  • Identify standard/interoperable data formats for conversion based on data need
  • Identify consequences of improperly converted data

Quality Assurance

Recognizes and resolves any apparent artifacts, incompletion, or corruption of data sets. Utilizes metadata to facilitate understanding of potential problems with data sets.

Potential Learning Outcomes for Data Literacy Instruction:

  • Create and update procedures for checking data quality
  • Identify the consequences of not performing quality assurance
  • Identify the benefits of performing quality assurance 
  • Determine best practices for correcting quality assurance issues including data conversion, data deletion, and data normalization


Understands the rationale for metadata and proficiently annotates and describes data so it can be understood and used by self and others. Develops the ability to read and interpret metadata from external disciplinary sources. Understands the structure and purpose of ontologies in facilitating better sharing of data.
Potential Learning Outcomes for Data Literacy Instruction:

  • Determine appropriate metadata schema to use to ensure data interoperability and efficient data transfer
  • Document metadata in a way that is accessible to future data users
  • Identify the benefits of metadata and metadata documentation
  • Identify the consequences of inadequate metadata and metadata documentation
  • Determine when and how to create local (non-standard) metadata to meet the needs of data users
  • Understand the utility of standard metadata schemas and profiles including but not limited to Dublin Core and MODS
  • Understand the utility of discipline-specific metadata schemas and profiles including but not limited to DDI (Social Sciences), FGDC (Geospatial), VRA (Visual Resources), and Darwin Core (Biological Sciences).
  • Understand the utility of standard controlled vocabularies including but not limited to Library of Congress Authorities.
  • Understand the utility of discipline-specific controlled vocabularies including but not limited to AAT (Arts and Architecture), ICD (Health Sciences), and Getty (Sciences - Geography).

Data Curation and Reuse

For more information on Data Curation and Reuse, visit the Data Curation library guide:

 Overview - Data Curation - Library Guides at Tulane University

Recognizes that data may have value beyond the original purpose, to validate research or for use by others. Understands that curating data is a complex, often costly endeavor that is nonetheless vital to community-driven e-research. Recognizes that data must be prepared for its eventual curation at its creation and throughout its lifecycle. Articulates the planning and actions needed to enable data curation.

Potential Learning Outcomes for Data Literacy Instruction:

Cultures of Practice

Recognizes the practices, values, and norms of his/her chosen field, discipline, or sub-discipline as they relate to managing, sharing, curating, and preserving data. Recognizes relevant data standards of his/her field (metadata, quality, formatting, etc.) and understands how these standards are applied.

Potential Learning Outcomes for Data Literacy Instruction:

Data Preservation

For more information on Data Preservation, visit the Data Curation library guide:

Overview - Data Curation - Library Guides at Tulane University

Recognizes the benefits and costs of data preservation. Understands the technology, resource, and organizational components of preserving data. Utilizes best practices in preservation appropriate to the value and reproducibility of data.

Potential Learning Outcomes for Data Literacy Instruction:

Data Analytics

Becomes familiar with the basic analysis tools of the discipline. Uses appropriate workflow management tools to automate repetitive analysis of data.

Potential Learning Outcomes for Data Literacy Instruction:

 Determine appropriate statistical process to use based on analysis need

- Generate descriptive statistical indicators for a variable or data set including the following:

Central Tendency Variability

Range and Interquartile Ranges

Median Variance
Mode Standard Deviation

- Use normalization procedures to prepare data for advanced analysis

- Understand the difference between parametric and non-parametric measures

- Generate inferential statistical indicators for a variable or data set including the following:

Wilcoxon tests
Kruskal-Wallis H
Pearson's r
Spearman's r
Chi Square
Linear Regression
Multiple Linear Regression
Logistic Regression

- Understand the difference between various tools used for data analysis including Excel, SPSS, SAS, Tableau, Python, R, and other tools.

Data Visualization

Proficiently uses basic visualization tools of discipline. Avoids misleading or ambiguous representations when presenting data. Understands the advantages of different types of visualization, for example, maps, graphs, animations, or videos, when displaying data.

Potential Learning Outcomes for Data Literacy Instruction:


Develops an understanding of intellectual property, privacy and confidentiality issues, and the ethos of the discipline when it comes to sharing data. Appropriately acknowledges data from external sources.

Potential Learning Outcomes for Data Literacy Instruction:

Critical and ethical data engagement must occur at each point in the data lifecycle.  The following data lifecycle models will help you think through the actions required to turn your data into insights for both short- and long-term use. Remember that each data use is iterative, so you may find yourself moving backward and forward through the data lifecycle. Visit the website of each organization to learn more about data literacy efforts occurring across the world

DataONE Data Lifecycle

  • Plan: description of the data that will be compiled, and how the data will be managed and made accessible throughout its lifetime
  • Collect: observations are made either by hand or with sensors or other instruments and the data are placed a into digital form
  • Assure: the quality of the data are assured through checks and inspections
  • Describe: data are accurately and thoroughly described using the appropriate metadata standards
  • Preserve: data are submitted to an appropriate long-term archive (i.e. data center)
  • Discover: potentially useful data are located and obtained, along with the relevant information about the data (metadata)
  • Integrate: data from disparate sources are combined to form one homogeneous set of data that can be readily analyzed
  • Analyze: data are analyzed

Harvard Medical School (Longwood) Biomedical Data Lifecycle

  • Plan & Design:   Plan processes from onboarding to project closure and data resources

  • Collect & Create:   Organization and integration of data sets and collection processes

  • Analyze & Collaborate:   Processing and analyzing data should be collaborative and documented

  • Store & Manage:   Each stage of the Biomedical Data Lifecycle revolves around the management of data storage

  • Evaluate & Archive:   Identify essential research records and evaluate for retention

  • Share & Disseminate:   Establishing and supporting the reach and impact of your data

  • Access & Reuse:   Ensuring the broad utility of your research data efforts for other researchers

Geospatial Data Lifecycle - Federal Geographic Data Committee

  • Business Requirements:  Needs of the organization making use of the data 
  • Define:  Characterization of data requirements based upon business-driven user needs
  • Inventory/ Evaluate:  The creation and publication of a detailed list of data assets and data gaps (both internal and external) as they relate to business-driven user needs
  • Obtain: The collection, purchase, conversion, transformation, sharing, exchange, or creation of geospatial data that were selected to meet the business needs is identified
  • Access: Making data produced known and retrievable to the community through documentation and discovery mechanisms so the users can meet their business require-ments
  • Maintain: Ongoing processes and procedures to ensure that the data meet business requirements
  • Use/Evaluate: The ongoing assessment, validation, and potential enhancement of data to meet user needs and business requirements
  • Archive: Required retention of data and the data’s retirement into long-term storage
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