In today’s competitive research environment, the need for librarians to be knowledgeable about all things digital is growing. Data-savvy librarians are able to better assist their patrons with the resources they need for their research, as well as extract useful insights from library data.
Data science as a discipline aims to provide solutions for managing the steeply growing amount of data in the world. Due to their educational background and inquisitive approach to information and knowledge, librarians are well-positioned to use data science in their work. Yet how prepared are they to work with data science? Areas discussed within this presentation are data science competencies, data librarianship as a profession and the three roles of data librarianship.
4. Statistics + computer science + domain-specific knowledge
+ data, lots of data!
What is data science?
“the art and science of acquiring knowledge through data” Ozdemir, 2016
22/01/2019
An Overview of Data Science Practices and
Competencies in Danish Academic Libraries
7. The 3 levels of data librarianship
1. Data-savvy librarians
2. Data librarians
3. Data science librarians
Know of data
Whose subject is data
Who work with data science
22/01/2019
An Overview of Data Science Practices and
Competencies in Danish Academic Libraries
8. 1995 “Cultural Network Denmark” digitalization project
1996 New Public Libraries Act
4 out of 5 libraries
offered Internet access
2000 New Library Law enforcing
a hybrid library structure
2011
“libraries mediate IT tools
and
quality data collections
that citizens can use to build their
own value-creating knowledge
products”
2015, 2016
First European DST4L
Trainings
Danish Union of Librarians
“Data Science” working group
est. 2017
22/01/2019
An Overview of Data Science Practices and
Competencies in Danish Academic Libraries
9. Departments:
• Digital Cultural
Heritage and
Media
• IT Development
and Infrastructure
UCPH Library Data Labs:
• Digital Social Science
Lab
• [HUM]Lab
• Health and Science
Data Lab
DTU Library:
• Research data
management
workshops
• Host of DST4L
trainings in 2015 and
2016
• “Smart library “ since
2017
39 major research libraries
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An Overview of Data Science Practices and
Competencies in Danish Academic Libraries
10. 47.1
29.4
5.9
17.6
31-40
41-50
>50
<30
Public
sector
Private
sector
Programming languages
• Python
• JavaScript
• HTML
• SQL
And what do they do?*
Data cleaning
• OpenRefine
Network analysis and visualization
• Gephi, VOS Viewer
Code editors
• Atom, Oxygen XML Editor
Data visualization
• Tableau, Plot.ly
Collaborative platforms
• GitHub
Web scrapers
• Netvizz, NCapture
Who works with data science in Danish academic libraries...
*Bibliotekarforbundet’s “Data Science” working group members22/01/2019
An Overview of Data Science Practices and
Competencies in Danish Academic Libraries
11. The 3 levels of data librarianship, real-life examples
1. Data-savvy librarians
2. Data librarians
3. Data science librarians
“I use the [DS] group to gather knowledge
and hopefully learn something so I can be
at the forefront of my researchers' needs in
the field”
“We are building a teaching offer that will cover
[data] harvesting, analysis and visualization”, “I
teach in NVivo” “[Data] support for colleagues”
“Through development projects”; “I try to
take tasks - such as data cleaning, export data
[...] and enrich and clean data”; “Data
management of unstructured data.”
22/01/2019
An Overview of Data Science Practices and
Competencies in Danish Academic Libraries
12. Challenges
hands-on learning opportunities?
skills gap
certification
lack of competence visibility within library
unclear professional path
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An Overview of Data Science Practices and
Competencies in Danish Academic Libraries
13. Then why do it?
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An Overview of Data Science Practices and
Competencies in Danish Academic Libraries
15. References
1. 365DataScience. (2017). Can I Become a Data Scientist: Research into 1,001 Data
Scientist Profiles. Retrieved June 28, 2018, from https://365datascience.com/research-
into-1001-data-scientist-profiles/#2
2. Affelt, A. L. (2015). The accidental data scientist, big data applications and
opportunities for librarians and information professionals. Medford, New Jersey:
Information Today, Inc.
3. Bern, P. H. (2005). You’re A What ??? Taking Stock of the Data Profession. Conference
Presentation of IASSIST 2005.
4. Burton, M., Lyon, L., Erdmann, C., & Tijerina, B. (2018). Shifting to Data Savvy : The
Future of Data Science In Libraries . Pittsburgh, PA
5. Danish Agency for Libraries and Media. (2011). The Public Libraries in the Knowledge
Society. Focus.
6. DEFF. (2009). The Future of Research and the Research Library: A Report to DEFF.
Denmark’s Electronic Research Library, 1–70.
7. Galluzzi, A. (2013). Libraries and public perceptions: A comparative analysis of the
European press. Methodological insights. JLIS.It https://doi.org/10.4403/jlis.it-8987
8. Gordon-Murnane L. (2012) Big data: A Big Opportunity for Librarians. Online. 36 (5):
30-34.
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An Overview of Data Science Practices and
Competencies in Danish Academic Libraries
What is data science? From a broad perspective, data science is “the art and science of acquiring knowledge through data.”(Ozdemir, 2016, p.4) A mix of methods from sciences such as statistics, computer science and domain-specific knowledge, data science takes proven-to-work methods and blends them in new ways in order to deal with today’s data.
The 4A s of data science… are also the roles of librarians
Stanton suggests that a data scientist will be most involved in what he calls “the four A’s” of data: data architecture, data acquisition, data analysis, and data archiving (Stanton, 2012). Due to the large amounts of data that data scientists work with, it is necessary for them to have the capacity to simplify, be critical about, and effectively communicate the results of their data analysis.
Sounds familiar? That is because these are some of the same roles that research librarians deal with in their daily work: simplifying and organizing large amounts of information, being critical of sources, and having the capacity to effectively communicate with patrons. But is this overlap of roles sufficient to allow librarians to take on a data scientist role?
Why and how librarians are well positioned to work with data science – Librarians bring the brains to the books
librarians are professionals whose skills bring solutions to some of the problems we face in today’s “Big Data” world. They “facilitate and enable data discovery and retrieval; add value to the data through cataloguing, indexing, and metadata; and [...] “provide for re-use [of data] over time through activities including authentication, archiving, management, preservation, and representation.” (Gordon-Murnane, 2012, p. 33) These are skills that librarians have been using to work with printed materials for decades. Furthermore, they do “not only educate the community on data and information literacy, but conduct their own research on how the scientific community can best rise to the data challenge.” (Haendel et al., 2012)
Modern research library roles + professional identities of research librarians
3 levels of data librarianship
Denmark library stats
Data science group organization
DST4L
Denmark Data Science competency stats
Data science group
3 levels of data librarianship – there’s something for everyone – and everyone in a library should get involved at least on the first level
Challenges
Advantages – value adding, time saving, reestablishing librarians as information experts in our society
I believe data science can provide libraries with a new role in today’s society. If librarians can demonstrate their value in building and interacting with a wide array of data products, it will boost libraries’ image as an information expert in our society.
This is how Danish academic librarians are using data science… how will you? Additional resources, and contact info
![22/01/2019
[data] challenge accepted:
An Overview of Data Science
Practices and Competencies in
Danish Academic Libraries
Alina Stoicescu, Cand.scient.bibl.](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)