Open Access
Subscription Access
Open Access
Subscription Access
Library Carpentry: Towards a New Professional Dimension (Part I – Concepts and Case Studies)
Subscribe/Renew Journal
The domain of library and information science is always on the move and LIS professionals are ardent users of emerging technologies. This research work discusses an emerging possibility in the LIS domain, which applies data science principles and techniques in the bibliographic world. The concept is known as library carpentry and involves different data wrangling techniques to get insight of bibliographic datasets. The discussion starts with the basic concepts of library carpentry and systematically reveals the components and methods of library carpentry with the help of three case studies. The case studies represent a variety of actual problem solving projects by using open datasets and open source data wrangling software called Openrefine. The case study (I) deals with the application of library carpentry in e-book selection by taking into consideration socio-academic web space data, the case study (II) shows how is it possible to quickly get an overview of institutional contributions to open access domain by applying library carpentry methods and the case study (III) demonstrates the process of gender analysis with the help of a name-to-gender inference service and by applying data wrangling techniques. Each case study is supported by a comprehensive and representative dataset to support and promote real-life problem solving in processional sphere by applying library carpentry methods.
Keywords
Application Program Interface (API), Data Carpentry, Data Wrangling, General Refine Expression Language (GREL), JavaScript Object Notation (JSON), Openrefine
User
About The Authors
Information
- Atwood, T. et al (2019). Joining together to build more: The New England software carpentry Library consortium. Journal of EScience Librarianship, 8(1). https://doi.org/10.7191/jeslib.2019.1161.
- Baker, J. et al . (2016). Library carpentry: Software skills training for library professionals. LIBER Quarterly, 26(3): 141-162. https://doi.org/10.18352/lq.10176.
- Burton, M., Lyon, L., Erdmann, C. and Tijerina, B. (2018). Shifting to Data Savvy: The future of data science in libraries [Monograph]. University of Pittsburgh. http://d-scholarship.pitt.edu/33891/.
- Cope, J. and Baker, J. (2017). Library carpentry: Software skills training for library professionals. International Journal of Digital Curation, 12(2): 266-273. https://doi.org/10.2218/ijdc.v12i2.576.
- Deardorff, A. (2020). Assessing the impact of introductory programming workshops on the computational reproducibility of biomedical workflows. PLOS ONE, 15(7): e0230697. https://doi.org/10.1371/journal.pone.0230697. PMid:32639955 PMCid:PMC7343163.
- Dennis, T. (2017). Positioning libraries to support data science. https://doi.org/10.5281/zenodo.1009480.
- Dennis, T., Chodacki, J. and Schneider, J. (2017). Taking the carpentry model to librarians. Presented at the CNI Fall 2017 Meeting. https://doi.org/10.5281/zenodo.1209481.
- Mani, N. S., Cawley, M., Henley, A., Triumph, T. and Williams, J. M. (2021). Creating a data science framework: a model for academic research libraries. Journal of Library Administration, 1-22. https://doi.org/10.1080/01930826.2021.1883366.
- Verborgh, R. and Wilde, M. D. (2013). Using OpenRefine (Revised ed. edition). Packt Publishing.
- Virkus, S. and Garoufallou, E. (2020). Data science and its relationship to library and information science: A content analysis. Data Technologies and Applications, 54(5): 643-663. https://doi.org/10.1108/DTA-07-20200167.
- Wilkinson, M. D. et al (2016). The FAIR guiding principles for scientific data management and stewardship. Scientific Data, 3(1): 160018. https://doi.org/10.1038/sdata.2016.18. PMid:26978244 PMCid:PMC4792175
- Wouter, K. (2019). IFLA -- a concept framework for data science in libraries. A Concept paper. https://www.ifla.org/publications/node/92282.
Abstract Views: 406
PDF Views: 9