The cultivation of scientific data specialists

Si, Li; Zhuang, Xiaozhe; Wei, Xing; Guo, Weining

doi:10.1108/lht-06-2013-0070

Cited by 44 publications

(11 citation statements)

References 7 publications

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“…Carlson et al (2011 underline that expanding the scope of information literacy to include data management and curation is a logical development. Si et al (2013) state that datarelated services should be supported by professionals with excellent information literacy skills.…”

Section: Data Governance Data Quality and Data Literacymentioning

confidence: 99%

Data governance, data literacy and the management of data quality

Koltay

2016

IFLA Journal

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Data governance and data literacy are two important building blocks in the knowledge base of information professionals, involved in supporting dataintensive research, and both address data quality and research data management. Applying data governance to research data management processes and data literacy education helps in delineating decision domains and defining accountability for decision making. Adopting data governance is advantageous, because it is a service based on standardised, repeatable processes and is designed to enable the transparency of data-related processes and cost reduction. It is also useful, because it refers to rules, policies, standards; decision rights; accountabilities and methods of enforcement. Therefore, although it received more attention in corporate settings and some of the skills related to it are already possessed by librarians, knowledge on data governance is foundational for research data services, especially as it appears on all levels of research data services, and is applicable to big data. Keywords Data-intensive research, data librarian, data governance, data literacy, research data services On this background, a review of the literature was done in order to identify and examine significant constituents of the knowledge base that is crucial for information professionals, involved in supporting data-intensive research. The first constituent is data governance (DG), which is extensively dealt with mainly in the corporate (business) sector, and is explored in this paper with the belief that bringing it into the picture will enable better research data services. The second one is data literacy, about which there is a massive body of literature, among others in the form of review articles (MacMillan, 2014; Koltay, 2015a, Koltay 2015b). Data literacy is closely related to research data services that include research data management (RDM). As the concept of RDSs itself and data literacy education are still evolving, their relationship to data governance requires examination that may lead to some kind of synthesis. The management of data quality is also inspected in order to determine to what extent it plays the role of an interface between these two constituents. Accordingly, this writing is built on three core terms. Data governance can be defined as the exercise of decision-making and authority that comprises a system of decision rights and accountabilities that is based on agreed-upon models, which describe who can take what actions, when and under what circumstances, using what methods (DGI, 2015a). While the various definitions of data literacy will be discussed below, we define it here as the ability to process, sort, and filter vast quantities of information, which requires knowing how to search, how to filter and process, to produce and synthesize it (Johnson, 2012). This definitions is in accordance with the idea, expressed by Schneider (2013), that the boundaries between information in information literacy and data literacy are blurring, because these boundaries never...

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Section: Data Governance Data Quality and Data Literacymentioning

confidence: 99%

Data governance, data literacy and the management of data quality

Koltay

2016

IFLA Journal

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“…Subsets of RDS job postings have been examined via content analysis. Si, Zhuang, Xing, and Guo (2013) compared the core competencies and duties of scientific data specialists in 46 job postings to the current curricula in 38 LIS programs. They found that most LIS curricula train students in the basics of data curation, but more specialized areas were limited.…”

Section: Job Posting Analyses To Create a Landscape Of Data Professiomentioning

confidence: 99%

Making Job Postings More Equitable: Evidence Based Recommendations from an Analysis of Data Professionals Job Postings Between 2013-2018

Thielen

Neeser

2020

EBLIP

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Objective - Over the last decade, many academic libraries have hired data professionals to offer research data services. As these positions often require different types of experience than traditional librarian positions, there is an increased interest in hiring professionals from outside the typical library and information science (LIS) pipeline. More broadly, there has also been an increased interest in academic libraries and higher education to incorporate the principles and practices of diversity, equity, inclusion, and accessibility (DEI&A) into their work. These phenomena allow an opportunity to examine the growing area of data professionals and library hiring practices through the lens of DEI&A. Data was collected from 180 data professional job positions, including education, experiences, and skills, to better understand the evolving and complex landscape of data professionals and to provide evidence based recommendations regarding how the profession can enact meaningful and lasting change in the areas of DEI&A. Methods - The qualifications and responsibilities listed in data professional job postings from 2013 to 2018 were examined. Prior to analyzing the job postings, a codebook of 43 variables was developed. The 177 data professional job postings (corresponding to 180 positions) were independently analyzed, noting the presence of each variable, including the locations and the degrees of complexity sought. After coding, discrepancies were mutually resolved. Overall, the coding process had 94% intercoder agreement, which indicates a high level of agreement. Results - Over one-third of postings (n = 63, 35%) did not use the word “librarian” in the job title. Eighty-eight percent (n = 159) required a Master’s in LIS degree, but 67% (n = 119) also accepted an equivalent degree. Over half of the positions (n = 108, 60%) were also looking for an additional degree, most frequently a graduate degree. The median salary of the positions listing a quantitative value was $57,000; however, this value may not be accurate because only 26% of job positions (n = 47) gave a quantitative salary. From the research data management skills mentioned, general data management (n = 155, 86%), data repositories (n = 122, 68%), and data curation (n = 101, 56%) appeared most frequently. Libraries were also looking for traditional LIS skills and experiences, including instruction (n = 138, 77%), consultation (n = 121, 67%), and a public services perspective (n = 69, 38%). Conclusion - The results show that academic libraries are trying to recruit candidates from outside the traditional academic library pipeline. Research data activities (a non-traditional area for LIS) and traditional LIS areas were both frequently mentioned. Overall, these job positions should be written through a more intentional lens of DEI&A. This would help to make data professional positions more diverse and inclusive, while also helping academic libraries to reach their goal of recruiting outside of LIS. A set of concrete DEI&A recommendations are provided that are applicable for writing all library positions, so that readers can put these results into action and enact meaningful change within the profession.

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“…Además constatan la notable presencia de cursos donde se vincula el concepto de dato con health y con management. Por tanto, podemos considerar que la "datificación" es una tendencia de la red, ya apuntada también anteriormente por Li et al (2013).…”

Section: Panorama Actualunclassified

Informe. La red académica Information Schools: orígenes, evolución y futuribles

Cobarsí-Morales¹

2018

ThinKEPI

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Resumen: Las Information Schools (iSchools) son en la actualidad la red académica de referencia internacional en información. Desde su creación en 2005, unas 80 universidades se han afiliado a la red, a través de las respectivas facultades o departamentos temáticamente involucrados. Las facultades o departamentos académicos que forman parte de las iSchools toman como objeto de estudio la información, en estrecha relación con las personas y la tecnología. Se presenta una panorámica general de las iSchools que incluye: la historia de sus orígenes y evolución, una revisión actualizada de su actividad académica, y tendencias de futuro. Esta panorámica se traza a través de una revisión crítica de una selección de la bibliografía existente sobre este movimiento académico, que incluye documentos de opinión y debate académico, estudios científicos basados en la recolección y análisis de datos del conjunto de universidades de la red o muestras representativas, y una mirada a la iConference, congreso mundial anual organizado por esta red académica.

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The cultivation of scientific data specialists

Cited by 44 publications

References 7 publications

Data governance, data literacy and the management of data quality

Data governance, data literacy and the management of data quality

Making Job Postings More Equitable: Evidence Based Recommendations from an Analysis of Data Professionals Job Postings Between 2013-2018

Informe. La red académica Information Schools: orígenes, evolución y futuribles

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