UCLA-Data-Literacy-Core-Competencies

Data Literacy Core Competencies

Ashley Peterson and Ibraheem Ali

Introduction

UCLA is home to a diverse array of resources that support learners, researchers, and instructors working with data and other computational methodologies. The Data Literacy Core Competencies are intended to represent and unify the existing best practices that are essential to digital scholarship and data literacy, thus ensuring a holistic learning experience across campus.
The competencies provide common ground for teaching and learning with an emphasis on critical inquiry and an ethical approach to the use and creation of data and data-driven research. They are intended to be an inclusive and responsive set of standards useful to learners, researchers, and instructors across disciplines. They are structured to address the needs of all users regardless of experience and fluency with data-driven research. These guidelines are meant to empower researchers to create data-intensive scholarship that is ethical and transparent.
The competencies are presented as actions a learner may take to answer a scaffolded set of guiding questions about a data-driven research process. This is to ensure that they are centered on the constantly evolving needs of learners as they engage with a dynamic research environment. This is a living document, and should change as often as needed to remain relevant and impactful.
The guiding questions and corresponding competencies are presented at three interrelated stages:

• Conceptual addresses what all learners need to understand in order to contextualize their research.
• Basic addresses the information a learner needs to attain essential skills and an awareness of resources, tools, and best practices.
• Advanced addresses the information an experienced learner needs in order to build on basic skills and effectively, critically, and ethically participate in the creation of data sets and data-driven research.

Guiding Questions and Core Competencies

When do I have a data need?

Conceptual

What is data?

• Determine if your research question can be answered by a few observable claims (i.e., statistics), or if it requires a larger body of information to establish trends or patterns (i.e., a data set).
• Determine if your work relies on quantitative information (measurements, counts, rankings) or qualitative information (texts, surveys, opinions, words).

  Basic

  How is data used to answer research questions in my discipline?

• Conduct a literature review to identify existing data sets that are used by studies relevant to your research question, as well as gaps in these datasets.
• Identify parameters for the data sets you will need to access. (e.g. date range, geographic location)

  Advanced

  When do I know if my research requires the creation of new data sets?

• Following a literature review, consider how your research question addresses existing gaps and how generating new data sets will help you answer that question.

How do I find, collect, and organize data from a variety of sources and perspectives?

Conceptual

How do I critically assess data resources?

• Establish the context within which a given data set was collected.
• Consider what and who might, or might not, be represented as part of the work and why that might be the case.

  Basic

  How do I find data sets based on my research needs?

• Locate one or more repositories relevant to your research needs.
• Identify the methods used to download data sets from a given repository.
• Understand data citation and attribution best practices.
• Identify the copyright license that defines how a data set can be used or reused.

  Advanced

  How do I effectively and ethically collect and organize new data that address my research question?

• Construct a plan to follow ethics and privacy guidelines governing data collection.(e.g. Institutional Review Board (IRB) approval for human subjects work, Institutional Animal Care and Use Committee (IACUC) for animal research)
• Recognize disciplinary standards for how data is analyzed, de-identified, or anonymized, when stored and shared.
• Examine assumptions that lead to gaps associated with your research and data collection.

How do I analyze and contextualize data?

Conceptual

How can I effectively interpret and interrogate existing data sets, and/or data sets I create, to answer my research question?

• Identify disciplinary best practices for analyzing your data to ensure reproducibility.
• Visualize data in order to extract additional information from the data set.

  Basic

  How can I clean, analyze, and visualize data?

• Analyze the structure of an existing data set, including unique information types, metadata, and variables in order to identify its intended use.
• Choose an analytical tool, or software, to analyze and visualize the information you collect. (Excel, SAS/SPSS, ArcGIS, R, etc.)

  Advanced

  How can I ensure that data sets I create are organized, interpretable, and reusable?

• Arrange the data you collect in a platform-agnostic file format. (e.g. CSV, plain text files)
• Collect enough information to establish a pattern using methods that are common in your field.
• Compare multiple analytical tools to determine the reproducibility of your analysis and visualization strategies.
• Prioritize accessibility and readability when creating your data sets.

How do I manage, share, and preserve data and data-driven research?

Conceptual

How do I ensure that my research is accessible to a diverse audience?

• Define an inclusive audience for research and research data in your discipline.
• Identify barriers to accessing information within your discipline.
• Prioritize best practices for ensuring privacy and the ethical handling of research subjects in your discipline.

  Basic

  How can I determine an appropriate format and platform for publishing my research data?

• Compare discipline-specific and general-purpose research data repositories.
• Identify an archive, or data repository, suitable for storing information that you collect.
• Determine whether repositories adhere to best practices for managing, archiving, and preserving digital assets.

  Advanced

  What are some diverse modes of data and data-driven research publishing? How can I meaningfully engage audiences with my research?

• Explore alternative publishing practices such as open access licensing, self-publishing, community-based models, and others that may be specific to your discipline.
• Consider scholarly publishing practices and platforms that enhance the discoverability, reproducibility, and preservation of your research. (e.g. permanent identifiers, version control, non-proprietary file types)

How can I reflect on my own data-driven research practice, and the data research landscape?

Conceptual

What is my community of practice and how do we handle our data, information, and communication mechanisms? How does my work enrich this community?

• Identify disciplinary practices with a particular focus on common data interpretation methodologies, data storage practices, and scholarly communication strategies.
• Contextualize your work within a disciplinary scholarly conversation, and how it might reinforce and/or challenge the norms of that conversation.

  Basic

  What is my community of practice doing well, and what are its blind spots? What are the gaps in the information, research, and data collected in this community? What are my own blind spots as a researcher?

• Building on your understanding of disciplinary norms, apply a critical lens to these practices.
• Identify voices that are well-represented, and those that are under-represented, in your discipline in terms of practitioners, research subjects, and audience.
• Assess how research in your discipline is supported institutionally and financially.
• Identify ethical consideration (your own and those of others) with regard to sources of financial support.
• Examine how your own personal background and academic training might reinforce implicit and structural bias. Consider how these biases might inform your research methodologies.

  Advanced

  How can I work to reform my community of practice?

• Consider how your research, and research practices, might challenge issues you have identified within your discipline.
• Identify voices of reform within, and outside of, your discipline. Consider how you might collaborate on existing or new efforts to change disciplinary practices.

Acknowledgements

We want to thank all members of the UCLA Library, especially within User Engagement and the Data Science Center, who provided feedback and guidance on this document. Special recognition goes to Doug Worsham, Leigh Phan, Tim Dennis, Zhiyuan Yao, Matthew Johnson, Zoe Borovsky, Anthony Caldwell, Allison Benedetti, Rikke Ogawa, and Nisha Mody.