The librarian and his new data-related practices and skills
DOI:
https://doi.org/10.62758/re.v2i2.111Keywords:
Data Librarian, Data Competence, Data Literacy, Information Professional’s SkillsAbstract
The structures that form the scientific foundation have changed due to the increasing use of data by society, which makes it essential to adopt new ways of dealing with the storage, access and preservation of data from scientific research. In this scenario, librarians play a fundamental role in helping scientists in their research. They can offer training in data management, mediation in best practices, and extraction of information from graphical representations. Therefore, the objective of this work is to identify skills and competencies in data to be developed by librarians to mediate information processes, and to verify if visual literacy is one of them. The methodology proposed for this work is qualitative, descriptive, exploratory, and bibliographic in nature, as it surveys the literature and makes use of theoretical material to identify the skills of the contemporary professional. As a result, it reports that experience with metadata, search, preservation, and knowledge management makes librarians able to manage research data and provide training for researchers so that they learn to access, critically interpret, manage, handle, and use data in an ethical manner. Among the skills and competencies surveyed, knowledge regarding data curation, metadata, repositories, collection, description, data literacy training, visualization, reference service, data management policies, and others were identified. It concludes that data librarians need to transform their traditional doing and that their mission needs to be to make data reusable, shareable, and preservable over time. Retraining shows itself as a viable option for information professionals to be prepared to support the new requirements of scientific research, such as data planning, management, curation, and visualization.
References
Borgman, C. L. (2010). Research Data: Who will share what, with whom, when, and why? In: Chinanorth American Library Conference, Beijing. DOI: https://doi.org/10.2139/ssrn.1714427
Börner, K., Bueckle, A. & Ginda, M. (2019). Data visualization literacy: Definitions, conceptual frameworks, exercises, and assessments. Proceedings of the National Academy of Sciences, 116(6), pp.1857-1864. DOI: https://doi.org/10.1073/pnas.1807180116
Buckland, M. K. (1991). Information as thing. Journal of the American Society for Information Science, 42(5), pp.351–360. https://doi.org/10.1002/(sici)1097-4571(199106)42:5%3C351::aid-asi5%3E3.0.co;2-3. DOI: https://doi.org/10.1002/(SICI)1097-4571(199106)42:5<351::AID-ASI5>3.0.CO;2-3
Calzada Prado, J. & Marzal, M. Á. (2013). Incorporating Data Literacy into Information Literacy Programs: Core Competencies and Contents. Libri, 63(2). https://doi.org/10.1515/libri-2013-0010. DOI: https://doi.org/10.1515/libri-2013-0010
Capurro, R. & Hjorland, B. (2007). O conceito de informação. Perspectivas em Ciência da Informação, 12(1), pp.148-207. https://doi.org/10.1590/s1413-99362007000100012. DOI: https://doi.org/10.1590/S1413-99362007000100012
Castells, M. (1999). A sociedade em rede. São Paulo: Paz e Terra.
Davenport, T. H. & Prusak, L. (1998). Working knowledge: How organizations manage what they know. Harvard Business Press.
Federer, L. (2018). Defining data librarianship: a survey of competencies, skills, and training. Journal of the Medical Library Association: JMLA, 106(3), pp.294. DOI: https://doi.org/10.5195/jmla.2018.306
Federer, L. M., Lu, Y. L. & Joubert, D. J. (2016). Data literacy training needs of biomedical researchers. Journal of the Medical Library Association: JMLA, 104(1), pp.52. DOI: https://doi.org/10.3163/1536-5050.104.1.008
Fontichiaro, K. & Oehrli, J. A. (2016). Why data literacy matters. Knowledge quest, 44(5), pp.21-27.
Hey, T., Tansley, S. & Tolle, K. M. (2009). Jim Gray on eScience: a transformed scientific method. In The fourth paradigm: data-intensive scientific discovery. Microsoft research.
Jones, B. (2018). 17 key traits of data literacy. Data Literacy | Learn the Language of Data. https://dataliteracy.com/data-literacy-fundamentals/.
Koltay, T. (2017). Data literacy for researchers and data librarians. Journal of Librarianship and Information Science, 49(1), pp.3-14. DOI: https://doi.org/10.1177/0961000615616450
Nielsen, H. J. & Hjørland, B. (2014). Curating research data: the potential roles of libraries and information professionals. Journal of Documentation, 70(2). DOI: https://doi.org/10.1108/JD-03-2013-0034
Panetta, K. (2021, 26 de agosto). A Data and Analytics Leader's Guide to Data Literacy. Gartner. https://www.gartner.com/smarterwithgartner/a-data-and-analytics-leaders-guide-to-data-literacy#:~:text=Gartner%20defines%20data%20literacy%20as,case,%20application%20and%20resulting%20value
Romanini, A. V. & Lima, R. L. (2018). A interpretação da cultura através dos dados: O BIG DATA a partir da epistemologia do sul. Revista Extraprensa, 11(2), pp.7-22. DOI: https://doi.org/10.11606/extraprensa2018.144512
Semeler, A. R. & Pinto, A. L. (2019). Os diferentes conceitos de dados de pesquisa na abordagem da biblioteconomia de dados. Ciência da Informação, 48(1). DOI: https://doi.org/10.60144/v4i.2023.95
Semeler, A. R., Pinto, A. L. & Rozados, H. B. F. (2019). Data science in data librarianship: core competencies of a data librarian. Journal of Librarianship and Information Science, 51(3), pp.771-780. DOI: https://doi.org/10.1177/0961000617742465
Shorish, Y. (2015). Data information literacy and undergraduates: A critical competency. College & Undergraduate Libraries, 22(1), pp.97-106. DOI: https://doi.org/10.1080/10691316.2015.1001246
Shreiner, T. L. (2017). Data Literacy for Social Studies: Examining the Role of Data Visualizations in K–12 Textbooks. Theory & Research in Social Education, 46(2), pp.194–231. https://doi.org/10.1080/00933104.2017.1400483. DOI: https://doi.org/10.1080/00933104.2017.1400483
Stanton, J. M. (2012, 16 de julho). Data Science: What’s in it for the New Librarian? Syracuse University. https://ischool.syr.edu/data-science-whats-in-it-for-the-new-librarian/.
The Economist. (2020, 20 de fevereiro). Are data more like oil or sunlight? https://www.economist.com/special-report/2020/02/20/are-data-more-like-oil-or-sunlight.
Vitorino, E. V. & Piantola, D. (2009). Competência informacional-bases históricas e conceituais: construindo significados. Ciência da Informação, 38(3), pp.130-141. DOI: https://doi.org/10.1590/S0100-19652009000300009
Wang, L. (2018). Twinning data science with information science in schools of library and information science. Journal of Documentation, 74(6). DOI: https://doi.org/10.1108/JD-02-2018-0036
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