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Article: Reshaping the future of academic library: Service model for holistic research data support
| Title | Reshaping the future of academic library: Service model for holistic research data support |
|---|---|
| Authors | |
| Issue Date | 1-Jan-2026 |
| Publisher | Elsevier |
| Citation | Journal of Academic Librarianship, 2026, v. 52 How to Cite? |
| Abstract | The increasingly data-intensive nature of modern research, particularly with the proliferation of artificial intelligence (AI), presents both a challenge and an opportunity for academic libraries. To address the evolving needs of the research community, we conducted a quantitative survey at the University of Hong Kong to identify gaps in existing data support services. Our findings indicate a significant demand for hands-on, technical assistance in data analysis, visualization, and cleaning, areas where researchers currently rely heavily on self-taught skills and peers. A substantial portion of the community is also actively engaged in AI-related projects, underscoring a need for advanced, specialized support. In response, this paper proposes a new service model, the “Data Clinic,” designed to function as a one-stop hub for both advisory and technical data support. By empowering librarians with advanced skills and fostering a collaborative, data-first mindset, this model aims to transform the library into a proactive partner in the research process, thereby enhancing research capabilities and affirming its indispensable role in the data-driven future. |
| Persistent Identifier | http://hdl.handle.net/10722/368334 |
| ISSN | 2023 Impact Factor: 2.5 2023 SCImago Journal Rankings: 0.858 |
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Ku, Kam Ming | - |
| dc.contributor.author | Mahboobani, Vanessa Ramesh | - |
| dc.contributor.author | Qiu, Yifei Vivian | - |
| dc.contributor.author | Yang, Tao | - |
| dc.contributor.author | Woo, Esther Mei Wa | - |
| dc.date.accessioned | 2025-12-24T00:37:41Z | - |
| dc.date.available | 2025-12-24T00:37:41Z | - |
| dc.date.issued | 2026-01-01 | - |
| dc.identifier.citation | Journal of Academic Librarianship, 2026, v. 52 | - |
| dc.identifier.issn | 0099-1333 | - |
| dc.identifier.uri | http://hdl.handle.net/10722/368334 | - |
| dc.description.abstract | <p>The increasingly data-intensive nature of modern research, particularly with the proliferation of artificial intelligence (AI), presents both a challenge and an opportunity for academic libraries. To address the evolving needs of the research community, we conducted a quantitative survey at the University of Hong Kong to identify gaps in existing data support services. Our findings indicate a significant demand for hands-on, technical assistance in data analysis, visualization, and cleaning, areas where researchers currently rely heavily on self-taught skills and peers. A substantial portion of the community is also actively engaged in AI-related projects, underscoring a need for advanced, specialized support. In response, this paper proposes a new service model, the “Data Clinic,” designed to function as a one-stop hub for both advisory and technical data support. By empowering librarians with advanced skills and fostering a collaborative, data-first mindset, this model aims to transform the library into a proactive partner in the research process, thereby enhancing research capabilities and affirming its indispensable role in the data-driven future.<br></p> | - |
| dc.language | eng | - |
| dc.publisher | Elsevier | - |
| dc.relation.ispartof | Journal of Academic Librarianship | - |
| dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
| dc.title | Reshaping the future of academic library: Service model for holistic research data support | - |
| dc.type | Article | - |
| dc.description.nature | published_or_final_version | - |
| dc.identifier.doi | 10.1016/j.acalib.2025.103180 | - |
| dc.identifier.volume | 52 | - |
| dc.identifier.eissn | 1879-1999 | - |
| dc.identifier.issnl | 0099-1333 | - |
