This section of the Data Driven Discovery Initiative provides resources to explore recommended competencies, knowledge, skills, and dispositions for data librarians.
Data librarian and open science competencies have been developed, through an evidence-based approach in collaboration with MLA. Links to other library organization resources about competencies in health science librarianship will also be presented.
Recent & Relevant Scholarship
Search for Data competencies and librarians (Google Scholar)
Highlighted NLM Resources
Blog: Living on the Data Fringes: Making Sense of Competencies - NNLM Region 4
Federer L, Foster ED, Glusker A, Henderson M, Read K, Zhao S. (2020). The Medical Library Association Data Services Competency: a framework for data science and open science skills development. Here is the MLA website version of the data competencies
Ohaji IK, Chawner B, Yoong P. (2019). The role of a data librarian in academic and research libraries
Prado JC, Marzal MÁ. (2013). Incorporating data literacy into information literacy programs: Core competencies and contents.
Health science librarian professional competencies (2007)
ALA/PLA data competencies at the Data 101, Data Geek and Data Expert Level
Applications in the Field
Carlson J, Johnston L. (2015). Data information literacy: Librarians, data, and the education of a new generation of researchers. Purdue University Press; Open Textbook.
Watts J, Sare L, Hubbard DE (2020). Collaborative data literacy education for research labs: a case study at a large research university.
For librarians that are interested in an actual list of skills and competencies, this supplemental list of competencies extracted from the literature that accompanies the Federer et al. (2020) article is a place to start thinking about your data librarian persona.
To think broader than the library, here is a list of post-doctoral set of research competencies that overlap data competencies
The Association of Research Libraries (ARL) has provided definitions and guidance on competencies for data management, as well as, scholarly communication and open science.
One of the most important aspects of learning about data is learning how to use tools to help you collect data, wrangle and clean data and analyze data. In addition to data management tools, there are also toolkits and management tools that will help you become a more effective data librarian. Find resources here to build awareness of the tools available and to help you develop the skills you need to use those tools.
Recent & Relevant Scholarship
Google Scholar search for scholarly articles at the intersection of data tools and data librarianship
Google Scholar search for open data science tools and libraries
Google Scholar Search for data tools and the health sciences
SEA Library Carpentry Workshop: that links to information on using Git, Open Refine and the Unix Shell
Robinson DC, Hand JA, Madsen MB, McKelvey KR. (2018). The Dat Project, an open and decentralized research data tool.
Pawlik A, van Gelder CWG, Nenadic A, Palagi PM, Korpelainen E, Lijnzaad P, … Goble C. (2017). Developing a strategy for computational lab skills training through software and Data Carpentry: experiences from the ELIXIR pilot
General sites to learn data tools and data management best practices:
- The Carpentries: Library Carpentry https://librarycarpentry.org
- Software Carpentry https://software-carpentry.org/ and
- Data Carpentry https://datacarpentry.org/
Learn about specific tools:
- The DMPtool - for creating a data management plan
- Manage Data
- Clean and Curate Data
- Bitbucket to test as well as store data
- Analyze and Present Data
If you are interested in learning R for data analysis, I would recommend you check out these open resources to R programming. I am a qualitative researcher and I have been reading about how R can be used to analyze qualitative data as well as quantitative data. I found a few great resources R for Data Science and the R graphics cookbook (2nd ed) that I am working through.