Data science from a library and information science perspective

Data Science Curriculum in the iField

Many disciplines, including the broad Field of Information (iField), have been offering Data Science (DS) programs. There have been significant efforts exploring an individual discipline's identity and unique contributions to the broader DS education landscape. To advance DS education in the iField, the iSchool Data Science Curriculum Committee (iDSCC) was formed and charged with building and recommending a DS education framework for iSchools. This paper reports on the research process and findings of a series of studies to address important questions: What is the iField identity in the multidisciplinary DS education landscape? What is the status of DS education in iField schools? What knowledge and skills should be included in the core curriculum for iField DS education? What are the jobs available for DS graduates from the iField? What are the differences between graduate-level and undergraduate-level DS education? Answers to these questions will not only distinguish an iField approach to DS education but also define critical components of DS curriculum. The results will inform individual DS programs in the iField to develop curriculum to support undergraduate and graduate DS education in their local context.

Data mining topics in the discipline of library and information science: analysis of influential terms and Dirichlet multinomial regression topic model

Purpose

The purpose of this study is to explore to which extent data mining research would be associated with the library and information science (LIS) discipline. This study aims to identify data mining related subject terms and topics in representative LIS scholarly publications.

Design/methodology/approach

A large set of bibliographic records over 38,000 was collected from a scholarly database representing the fields of LIS and the data mining, respectively. A multitude of text mining techniques were applied to investigate prevailing subject terms and research topics, such as influential term analysis and Dirichlet multinomial regression topic modeling.

Findings

The findings of this study revealed the relationship between the LIS and data mining research domains. Various data mining method terms were observed in recent LIS publications, such as machine learning, artificial intelligence and neural networks. The topic modeling result identified prevailing data mining related research topics in LIS, such as machine learning, deep learning, big data and among others. In addition, this study investigated the trends of popular topics in LIS over time in the recent decade.

Originality/value

This investigation is one of a few studies that empirically investigated the relationships between the LIS and data mining research domains. Multiple text mining techniques were employed to delineate to which extent the two research domains would be associated with each other based on both at the term-level and topic-level analysis. Methodologically, the study identified influential terms in each domain using multiple feature selection indices. In addition, Dirichlet multinomial regression was applied to explore LIS topics in relation to data mining.

What Does Information Science Offer for Data Science Research?: A Review of Data and Information Ethics Literature

This paper reviews literature pertaining to the development of data science as a discipline, current issues with data bias and ethics, and the role that the discipline of information science may play in addressing these concerns. Information science research and researchers have much to offer for data science, owing to their background as transdisciplinary scholars who apply human-centered and social-behavioral perspectives to issues within natural science disciplines. Information science researchers have already contributed to a humanistic approach to data ethics within the literature and an emphasis on data science within information schools all but ensures that this literature will continue to grow in coming decades. This review article serves as a reference for the history, current progress, and potential future directions of data ethics research within the corpus of information science literature.

Data science education programmes in Middle Eastern institutions: A survey study

In response to the current trends in dealing with data in academia, various research institutions and commercial entities around the world are building new programmes to fill the gaps in workforce demand in specific disciplines, including data curation, big data, data management, data science and data analytics. Thus, the aim of the present study was to reveal the reality of data science education in the Middle East and to determine the opportunities and challenges for teaching data science in the region. Thirteen countries in the Middle East were offering 48 data science programmes at the time of the study. The results reveal that these data science programmes significantly use the words ‘data’ and ‘analytics’ in their names. With regard to the academic affiliations of the data science programmes, the study found that they are offered in a variety of schools, especially computer science, information technology and business. Moreover, the study found that computer science is the dominant trend in the programmes. Data science programmes have a significant overlap with other programmes, especially statistics and computer science, because of the interdisciplinary nature of this field. Data science schools in the Middle East differ in terms of their programme titles, programme descriptions, course catalogues, curriculum structures and course objectives. Broadly, this study may be useful for those who are seeking to establish a data science programme or to strengthen data science curricula at both the undergraduate and postgraduate levels.

Librarian’s perception on data librarianship core concepts: a survey of motivational factors, challenges, skills and appropriate trainings platforms

Purpose

Data librarianship, or data-driven librarianship, is the combination of information science, data science and e-science fields and is gaining gradual importance in the library and information science (LIS) profession. Hence, this study investigates the data librarianship core concepts (motivational factors, challenges, skills and appropriate training platforms) to learn and successfully launch data librarianship services.

Design/methodology/approach

A survey method was used and the data were collected through online questionnaire. Purposive sampling method was applied and 132 responses were received with 76 respondents from the public and 56 from the private sector universities of Pakistan. The statistical package for social sciences (SPSS version 25) was used, and descriptive and inferential statistics were applied to analyzed the data.

Findings

LIS professionals understand the importance of data-driven library services and perceive that such services are helpful in evolving the image of the library, helping with the establishment of institutional data repositories/data banks, developing data resources and services for library patrons and especially researchers, and receiving appreciation and acknowledgment from the higher authorities. The major challenges that emerged from the data were: missing data policies, limited training opportunities for data librarianship roles, no additional financial benefits, lack of infrastructure and systems, lack of organizational support for the initiation of data-driven services, and lack of skills, knowledge and expertise. Data librarianship is in its early stages in Pakistan, and consequently, the LIS professionals are lacking basic, advanced and technical data-driven skills.

Research limitations/implications

The policy, theoretical and practical implications describe an immediate need for framing data policies. Such policies will help the libraries or any other relevant entities to store the data and assign metadata and documentation in such a way that it is easy to retrieve and reusable for others.

Originality/value

This is the first study in Pakistan to investigate the perceptions of LIS professionals about data librarianship core concepts: motivational factors, challenges, skills and appropriate training platforms to grasp data-driven skills and successfully launch library services.

Linked data for libraries: Creating a global knowledge space, a systematic literature review

The Semantic Web in general and the Linked Open Data Initiative, in particular, are a growing movement for organisations to make their existing data available in a machine-readable format. Thus, institutions are highly encouraged to publish, share and interlink their data publicly. The more data are opened on the Web (Open Data), the more integrated sets of data will be connected in the Semantic Web (Linked Open Data). Within this context, libraries can complement their data by linking it to other, external data sources. The purpose of this article is to identify papers that refer to linked data in libraries, emphasising the ways that linked data empower libraries to put their knowledge in the context of the open-world, thus enhancing semantic technology innovations. The study considered papers published between 2008 and 2019 in English and presents the collected literature by grouping it according to the topic each paper refers to. The results show that libraries are facing a period of continuing change which present several challenges and indicate that they are moving towards developing new practices, policies and services.

A systematized review on data librarianship literature: Current services, challenges, skills, and motivational factors

Data librarianship is becoming more common as a means of developing and integrating data-driven library services. Consequently, the academic and research libraries’ traditional role in providing information support and training has been expanded to include support in all aspects of the research lifecycle. Hence, this study systematically reviews the data librarianship literature focusing on current data librarianship services, challenges, skills, and motivational factors. A systematic review was conducted following the PRISMA guidelines. A comprehensive search strategy was formulated to extract maximum relevant results. The bibliographic data were retrieved from the Scopus, Web of Science, Library, Information Science and Technology Abstracts (LISTA), and Library and Information Science Abstracts (LISA). Finally, 27 studies that fulfill the criteria were included in this study. The findings revealed that two main factors that contribute to the success or failure in this emerging data librarianship roles are skills, knowledge, and expertise; and limited support and advocacy from library leadership and higher authorities. One is on the part of library professionals who can develop the required skills, knowledge, and expertise and the other is on the part of library leadership. The library professionals are hesitant to embrace this new role due to non-additional benefits, no relevant job description, and lacking leadership support. Overall, the findings revealed that the data librarianship scope is dynamic and has been expanded, albeit the progress is slow. The theoretical, practical, policy, and social implications described that the data librarianship services tend to be improved, and the relevant skills, knowledge, and expertise should be developed. The policy initiatives need to be taken, improved, and expanded to advance technical services related to data librarianship.

Data science and its relationship to library and information science: a content analysis

Purpose

The purpose of this paper is to present the results of a study exploring the emerging field of data science from the library and information science (LIS) perspective.

Design/methodology/approach

Content analysis of research publications on data science was made of papers published in the Web of Science database to identify the main themes discussed in the publications from the LIS perspective.

Findings

A content analysis of 80 publications is presented. The articles belonged to the six broad categories: data science education and training; knowledge and skills of the data professional; the role of libraries and librarians in the data science movement; tools, techniques and applications of data science; data science from the knowledge management perspective; and data science from the perspective of health sciences. The category of tools, techniques and applications of data science was most addressed by the authors, followed by data science from the perspective of health sciences, data science education and training and knowledge and skills of the data professional. However, several publications fell into several categories because these topics were closely related.

Research limitations/implications

Only publication recorded in the Web of Science database and with the term “data science” in the topic area were analyzed. Therefore, several relevant studies are not discussed in this paper that either were related to other keywords such as “e-science”, “e-research”, “data service”, “data curation”, “research data management” or “scientific data management” or were not present in the Web of Science database.

Originality/value

The paper provides the first exploration by content analysis of the field of data science from the perspective of the LIS.

Quality of Open Research Data: Values, Convergences and Governance

This paper focuses on the characteristics of research data quality, and aims to cover the most important issues related to it, giving particular attention to its attributes and to data governance. The corporate word’s considerable interest in the quality of data is obvious in several thoughts and issues reported in business-related publications, even if there are apparent differences between values and approaches to data in corporate and in academic (research) environments. The paper also takes into consideration that addressing data quality would be unimaginable without considering big data.