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Zhang Y, Wu D, Hagen L, Song IY, Mostafa J, Oh S, Anderson T, Shah C, Bishop BW, Hopfgartner F, Eckert K, Federer L, Saltz JS. Data Science Curriculum in the iField. J Assoc Inf Sci Technol 2023; 74:641-662. [PMID: 37192888 PMCID: PMC10181812 DOI: 10.1002/asi.24701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 07/05/2022] [Indexed: 11/12/2022]
Abstract
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.
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Affiliation(s)
- Yin Zhang
- School of Information, Kent State University, Kent, Oho, USA
| | - Dan Wu
- School of Information Management, Wuhan University, Wuhan, China
| | - Loni Hagen
- School of Information, University of South Florida, Tampa, Florida, USA
| | - Il-Yeol Song
- College of Computing & Informatics, Drexel University, Philadelphia, Pennsylvania, USA
| | - Javed Mostafa
- School of Information and Library Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Sam Oh
- Department of Library and Information Science and Data Science, Sungkyunkwan University, Seoul, South Korea
| | | | - Chirag Shah
- Information School, University of Washington, Seattle, Washington, USA
| | - Bradley Wade Bishop
- School of Information Sciences, University of Tennessee, Knoxville, Tennessee, USA
| | | | - Kai Eckert
- Stuttgart Media University, Stuttgart, Germany
| | - Lisa Federer
- National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA
| | - Jeffrey S. Saltz
- School of Information Studies, Syracuse University, Syracuse, New York, USA
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Data mining topics in the discipline of library and information science: analysis of influential terms and Dirichlet multinomial regression topic model. ASLIB J INFORM MANAG 2022. [DOI: 10.1108/ajim-05-2022-0260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
PurposeThe 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/approachA 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.FindingsThe 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/valueThis 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.
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Gaitanou P, Andreou I, Sicilia MA, Garoufallou E. Linked data for libraries: Creating a global knowledge space, a systematic literature review. J Inf Sci 2022. [DOI: 10.1177/01655515221084645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
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.
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Affiliation(s)
| | - Ioanna Andreou
- Athens College, Hellenic American Educational Foundation, Greece
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Martins DL, Lemos DLDS, Oliveira LFR, Siqueira J, Carmo D, Medeiros VN. Information organization and representation in digital cultural heritage in Brazil: Systematic mapping of information infrastructure in digital collections for data science applications. J Assoc Inf Sci Technol 2022. [DOI: 10.1002/asi.24650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
| | | | | | - Joyce Siqueira
- Faculty of Information Science University of Brasília (UnB) Brasília Brazil
| | - Danielle Carmo
- Faculty of Information Science University of Brasília (UnB) Brasília Brazil
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