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 scholarlyengagement@tulane.edu to schedule a consultation.
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.
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 |
Metadata |
Data Curation and Reuse |
Data Preservation |
Data Analytics |
Data Visualization |
Ethics |
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. https://doi.org/10.1353/pla.2011.0022
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:
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 |
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:
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:
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:
Metadata
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:
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.
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.
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.
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 |
Mean |
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:
T-Test |
ANOVA |
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.
Ethics
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.
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.
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