4 Resources

We've created handy, though certainly incomplete, list of resources for data librarians. While many of these resources are geared torward new practitioners, you may find yourself references others frequently.

4.1 Portals / other

Table 4.1: Useful portals for data librarians.

title	key takeaways
Data Thesaurus of (library) data terms	A modern thesaurus of 70 terms you should know as a data librarian
Searchable LibGuides	Find librarian-created guides on any topic. Try searching for topics like: “data management”, “data literacy” “data reference”, “data repositories”, or even “data privacy”
Research Data Management	An ACRL lib-guide for all aspects of research data management. Includes various slide decks, worksheets and how-to guides. Includes data skills progression chart, which contains ideas for improving your skills.

4.2 Self-taught lesson

Table 4.2: Useful self-taught lessons for data librarians.

title	key takeaways
LIS 628 - Data Librarianship syllabus/materials	Syllabus from Data Librarianship class at Pratt. Includes various course materials, lesson plans, and quizzes. Handy navigation for diving into individual sections based on need or interest.
The Programming Historian	Peer-reviewed, open-access lessons on a variety of topics, tools, and methdologies for working with data (and other types of digital scholarship). The focus is on open-source tools and the lessons are written for non-specialists.
The Carpentries	Lesson plans for teaching foundational data science and coding skills to non-specialists. Lesson plans are meant for instructors to use, but can be used for self-teaching. The Carpentries organization is committed to open and inclusive pedagogy and technology. All lesson plans are open access, but you must speak with the Carpentries organization if you'd like to run or host a Carpentries-branded workshop.
Software Carpentry	NA
Data Carpentry	NA
Library Carpentry	NA

4.3 Books

Table 4.3: Useful books for data librarians.

title	key takeaways
Yau, N. (2011). Visualize This: The FlowingData Guide to Design, Visualization, and Statistics (1 edition). Indianapolis, Ind.: Wiley.	Practical guide to real-world visualizaiton, using a variety of primarily open source tools. Emphasis on the goal of telling stories with data.
Tufte, E. R. (2001). The Visual Display of Quantitative Information (2nd edition). Cheshire, Conn.: Graphics Pr.	Seminal work on princliples of graphical representation. While Tufte is strongly opinionated and his examples are primarily historical, his principles are important and his techniques are useful.
Bertin, J. (2010). Semiology of Graphics: Diagrams, Networks, Maps (1 edition). Redlands, Calif.: Esri Press.	Seminal work on princliples of graphical representation. An early attempt at a standardized set of rules for graphical representation, drawing from the worlds of grammar and topography. Particularly useful for readers interested in cartography and other complex graphic representations.
Few, S. (2012). Show Me the Numbers: Designing Tables and Graphs to Enlighten (Second edition). Burlingame, Calif.: Analytics Press.	Covers visual represenation of tables and graphs for effective communicaiton. Extremely practical and approachable, particularly suitable for those interested in business and community usage of data visualizations.
Data Visualization: A Practical Introduction (Healy)	This book covers both the 'why' and the 'how' of data visualization using the R programming language and the ggplot2 visualization library. This 'incomplete draft' is available freely online, and is a good introduction to the topics covered in the full book.
The Data Librarian's Handbook (Rice)	This book provides insights and advice for the newbie data librarian, with quite a bit of emphasis on research data management roles in academic libraries.
Working as a Data Librarian: A Practical Guide	This book serves as an introductory guide to general skills of a data librarian, and markets itself to students.
Databrarianship: The Academic Data Librarian in Theory and Practice	NA

4.4 Articles

Table 4.4: Useful articles for data librarians.

title	key takeaways
Data Librarian Competency Overview	Results from a study seeking to define the competencies and skills used in data librarianship. Includes Taxonomy of skills and expertise for data librarians
Data Visualization literacy: definitions, conceptual frameworks, exersizes and assessments	Constructs a topology of data visualization literacy and summary of (teachable) component skills.
Drucker, J. (2011). Humanities Approaches to Graphical Display. 5(1).	An argument for the development of visualization methods more fitting for humanities work, taking into account the co-dependent nature of observer and experience in humanities research, as well as the interpretative nature of this research.
Data Visualization Literacy: Investigating Data Interpretation Along the Novice—Expert Continuum. (n.d.). 8.	Presents results of a data literacy study meant to understand how expertise develops in this domain. While studies have been done on the development of reading and writing skills among students, this study seeks to fill the gap around reading and creating data visualizations. Implications for instruction are also discussed.
Nolan, D., & Perrett, J. (2015). Teaching and Learning Data Visualization: Ideas and Assignments. ArXiv:1503.00781 [Stat].	Calls for the early integration of data visualization activities into other disaplines, and provides assignment and exercise ideas. Advocates for the inclusion of data visualization as a topic of study throughout undergraduate curricula into graduate studies.
Wickham, H. (2014). Tidy Data. Journal of Statistical Software, 59(1), 1–23.	Discusses key role of data and processes for cleaning ahead of data visualization. Presents a framework that can be applied for efficiency and consistency in data cleaning work. While this article is intimately connected to the tidyverse group of packages in R (created in part by the author, Wickham), these principles can be applied in a variety of settings.
Borkin, M., Vo, A., Bylinskii, Z., Isola, P., Sunkavalli, S., Oliva, A., … others. (2013). What makes a visualization memorable? Visualization and Computer Graphics, IEEE Transactions On, 19(12), 2306–2315.	The largest scale study (as of 2013) of the memorability of a variety of visualizations. Finding include both the intuitive (color and "human-recognizable objects" increase memorability) and non-intuitive (unique visualization types are more memorable than common types).
Braun, S. (2019). Critically engaging with data visualization through an information literacy framework. Digital Humanities Quarterly, 012(4).	Explores an approach to critical data literacy that builds on common information literacy practices, such as critical engagement with the raw materials (raw data), the tools used to build visualizations, and the end-products (visualizations) of this process.