Data literacy for researchers and data librarians

Koltay, Tibor

doi:10.1177/0961000615616450

Cited by 126 publications

(106 citation statements)

References 61 publications

(79 reference statements)

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“…Notes 1. See Shields (2004), Carlson et al (2011), Hogenboom et al (2011, Calzada and Marzal (2013), Twidale et al (2013), Martin (2014), Bhargava and D'Ignazio (2015), MacMillan (2015), Koltay (2015aKoltay ( , 2015b, Herzog (2015) and Frank et al (2016). 2.…”

Section: Declaration Of Conflicting Interestsmentioning

confidence: 99%

Data infrastructure literacy

2018

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A recent report from the UN makes the case for ''global data literacy'' in order to realise the opportunities afforded by the ''data revolution''. Here and in many other contexts, data literacy is characterised in terms of a combination of numerical, statistical and technical capacities. In this article, we argue for an expansion of the concept to include not just competencies in reading and working with datasets but also the ability to account for, intervene around and participate in the wider socio-technical infrastructures through which data is created, stored and analysed -which we call ''data infrastructure literacy''. We illustrate this notion with examples of ''inventive data practice'' from previous and ongoing research on open data, online platforms, data journalism and data activism. Drawing on these perspectives, we argue that data literacy initiatives might cultivate sensibilities not only for data science but also for data sociology, data politics as well as wider public engagement with digital data infrastructures. The proposed notion of data infrastructure literacy is intended to make space for collective inquiry, experimentation, imagination and intervention around data in educational programmes and beyond, including how data infrastructures can be challenged, contested, reshaped and repurposed to align with interests and publics other than those originally intended.

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Section: Declaration Of Conflicting Interestsmentioning

confidence: 99%

Data infrastructure literacy

2018

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“…Even though reproducibility of underlying research has been undermined by the prioritization of citation counts and journal prestige [18], it has the potential to allow not only the verification of research results, but also may lead to the emergence of new research questions [19] Let us add that -in the hopefully not so distant future -data citation could become a regular motivating factor for researchers to share and publish their data [20].…”

Section: Research Data Management (Rdm)mentioning

confidence: 99%

Identifying new roles for academic libraries in supporting data-intensive research

Koltai

2020

Библиосфера

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Reacting to the appearance of data-intensive research prompts academic libraries to become service providers for scholars, who work with research data. Although this is an imperative for libraries worldwide, due to the differences between countries and institutions, the level of readiness to engage in related activities differs from country to country. While some of the related tasks are fairly novel, others heavily build on librarians’ traditional, well-known skills. To identify these tasks, as well as making an inventory of the required skills and abilities, this paper, based on a non-exhaustive review of the recent literature, presents both theoretical and practical issues. It is demonstrated that the most obvious directions of the service development in academic libraries to support data-intensive science are research data management, data curation, data literacy education for users, and data literacy education for librarians.

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“…However, more recently library and information science (LIS) literature has focused on the importance of the research data lifecycle, with particular emphasis on aspects such as research data management and data literacy (Koltay, 2017;Federer et al, 2016) As part of its strategic direction, the Council of Australian University Librarians (CAUL) (2019) is implementing a 'Digital Dexterity Framework'. Based on the work done by Jisc as described above, it outlines 'the skills and capabilities that students will need to succeed in the workforce of the future' (p1).…”

Section: Librarymentioning

confidence: 99%

“…In addition, the authors have identified a number of operational capabilities relevant to research teams: governance (Holewa et al, 2015), information (data) management (Federer et al, 2016;Holewa et al, 2015;Koltay, 2017;Reeves & Pearlman, 2017), technology infrastructure (Galanek & Brooks, 2018;Holewa et al, 2015), analytical techniques (Solis, 2016;Reeves & Pearlman, 2017), and process agility (Fitzgerald et al, 2014;Solis, 2016). Information management, technology infrastructure and governance (i.e.…”

Section: A Proposed Model Of Digital Capability For Researchmentioning

confidence: 99%

A Model for Librarians to Assess the Digital Capability of Research Teams

Wolski

Krahe

Richardson

2020

Journal of the Australian Library and Information Association

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This paper examines the issue of digital capability at the research team level, specifically their ability to adapt to an uncertain digital future. Within higher education, there is a driving imperative to adapt to the new digital environment not only in areas involved in learning and teaching but also among research teams. While libraries play an important role in developing the digital literacy of researchers as individuals, little attention has been paid to the digital capability of the research team. A review of the literature was undertaken of digital transformation frameworks and models across business, government and higher education. No applicable models or frameworks were found to be suitable for a research team environment. A digital capability model was developed, drawing upon the critical elements discussed in the literature. This model enables library staff to assess the digital capability of the research team, allowing them to not only tailor their services to specific research teams but also driving changes to overall service offerings to better meet the needs of researchers across the organisation. Using this model changes the nature of library staff's conversation with research team leaders.

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Data literacy for researchers and data librarians

Cited by 126 publications

References 61 publications

Data infrastructure literacy

Data infrastructure literacy

Identifying new roles for academic libraries in supporting data-intensive research

A Model for Librarians to Assess the Digital Capability of Research Teams

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