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# System prepended metadata

title: Survey
tags: [liber-dslib]

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tags: liber-dslib
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# Survey
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## What's With Data Science In Libraries?

LIBER Data Science in Libraries Working Group (DSLib) is working on a landscape analysis of data science in libraries and would like to invite representatives of libraries to take part in this initiative.  
   
It will take about 30 minutes to complete this questionnaire.  

It is possible that for the completion of this questionnaire, you will need to consult with your colleagues.

Before submitting, a draft of your responses will be kept for 7 days. You can return to the draft and finalise it later.
  
This survey results will be used to depict the current landscape data science in libraries. The results of the analysis will be presented only in a summarised form: in a DSLib workshop in Liber 2022 and a DSLib report.  
   
As part of the questionnaire, we ask your name, e-mail address, and organisation. Your name and e-mail address will be used only to contact you in case of additional questions. The names of organisations in conjunction with specific activities will be made publicly available.  
   
The survey will be closed on May 12, 2022 6 pm (CEST).  
   
In case of questions or feedback, contact Linda Sīle-Shriram: linda.sile@uantwerpen.be

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**Tell us who you are!**

**Q1 Full name**

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**Q2 Organisation**

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**Q3 E-mail address**

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**Describe data science activities in your library**  
   
**What do we mean by data science?**  
   
For the purpose of this survey, we define data science as a set of computational methods for the identification of novel and actionable insights from data. Computational methods used in data science include, but are not limited to, descriptive and inferential statistics, visualisation, text mining, image processing and computer vision, machine learning, and data engineering. Data science in libraries is the use of data science methods in the delivery and/or improvement of library services and the delivery of data science training or services.  
   
**Categorizing data science activities**  
   
To describe data science activities in a library, we make use of four groups of activities where data science methods can be used:   	Collections as Data 	Library Intelligence 	Research Support 	Research Intelligence  In your answers, be as specific as possible. Name concrete use examples and data science methods.  
 
**Q4 Which categories apply to your data science activities? (multiple options possible)**

▢ **Collections as Data**
Data science activities that facilitate the use of library collections in computationally-driven research and teaching. This can include activities that ensure that data from library collections are high-quality, rich with information, reliable, suitable for analysis, and easily accessible for computational interactions.  Examples: data pipelines that are created to enhance the quality of data, machine learning and computer vision techniques used to: generate data, discover resources, identify and extract rich metadata and/or full-text from documents.    (4) 

▢ **Library Intelligence**
Data science activities geared towards the improvement of traditional library services and support for decision-making by library management.   Examples are data-driven item suggestions for library patrons, the application of machine learning techniques in the management of library material flows, the use of library loan data analytics in collection management, and automated library analytics for day-to-day planning and annual reports.    (5) 

▢ **Research Support**
Data science activities to support researchers through the research lifecycle. This can cover areas such as research data management, research data/software engineering, digital humanities, and (digital) information skills.   Examples include data management planning, research data/software engineering, ensuring FAIRness (findability, accessibility, interoperability, reusability) of data, data curation and preservation, working with Linked Open Data and digital corpora, the use of data science methods in (automated) systematic searches and reviews of literature.    (6) 

▢ **Research Intelligence**
Data science activities in compiling and visualizing data for decisions and benchmarking within the scientific community. Given the scale of the data available, Research Intelligence often requires the implementation of data pipelines and dashboard tools.   Examples of data collected are metadata of publications and other research outputs, and data related to these outputs such as citations. An integral part of RI is also the continuous development of analysis workflows, for instance combining traditional citation metrics with alternative metrics such as policy citations.    (7) 

**Q5 Describe the current data science activities in your library**

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**Q6 Describe how are the data science activities organized.**

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**Q7 Describe how are the data science activities funded**

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**Q8 Which department(s) or team(s) carry out data science activities in your library?**
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**Q9 Describe the staff who is responsible for data science activities.**

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**Q10 With whom does the library collaborate in relation to data science activities?**

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**Q11 How are the ethical and legal aspects in relation to data science addressed within the library?**

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**Q12 What library data science activities are planned in the future?**

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**Q13 Anything else to add?**
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**Clicking the button below will submit your responses**

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