글로벌 학술연구 동향: 문헌정보학 (Library and Information Science)
2014-01-01 ~ 2023-12-31 · 생성일 2024-05-21T10:00:00Z
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DBLP
KCI
OpenAlex
Google Scholar
DBLP
KCI
주요 동향 요약
- 지난 10년간 문헌정보학 분야 논문 수는 꾸준히 증가하여, 2014년 8,540편에서 2023년 12,850편으로 성장하였습니다.
- 주요 연구 키워드에는 ‘Bibliometrics’, ‘Information Retrieval’, ‘Digital Libraries’, ‘Open Access’, ‘Data Science’ 등이 있으며, 최근에는 ‘Research Data Management’(+120%)와 ‘Data Science’(+45%)가 크게 성장하였습니다.
- 상위 연구 기관은 Leiden University(네덜란드), Indiana University(미국), University of Sheffield(영국) 등이, 상위 국가는 미국(27.5%), 중국(14.7%), 영국(11.7%)이 차지합니다.
- 연구 클러스터는 학술 커뮤니케이션·계량, 데이터 큐레이션·인프라, 정보 리터러시, 계산 정보학, 기록유산, 정보윤리 등으로 분포되어 있습니다.
- ‘FAIR Principles’, ‘AI in Libraries’, ‘Misinformation/Disinformation’, ‘Data Literacy’ 등 신흥 연구주제가 최근 급성장하며 새롭게 부상하고 있습니다.
핵심 지표
| 연도 | 총 논문 수 | 리뷰 논문 | 컨퍼런스 논문 |
|---|---|---|---|
| 2014 | 8,540 | 430 | 2,100 |
| 2015 | 8,750 | 450 | 2,150 |
| 2016 | 9,100 | 480 | 2,200 |
| 2017 | 9,450 | 510 | 2,300 |
| 2018 | 9,980 | 550 | 2,450 |
| 2019 | 10,560 | 620 | 2,550 |
| 2020 | 11,230 | 710 | 2,600 |
| 2021 | 11,950 | 780 | 2,750 |
| 2022 | 12,400 | 830 | 2,850 |
| 2023 | 12,850 | 890 | 2,950 |
| 키워드 | 논문 수 | 최근 성장률 |
|---|---|---|
| Bibliometrics | 7,520 | +15.0% |
| Information Retrieval | 6,980 | +8.0% |
| Digital Libraries | 6,550 | +12.0% |
| Information Literacy | 6,100 | +18.0% |
| Open Access | 5,230 | +25.0% |
| Data Science | 4,850 | +45.0% |
| Research Data Management | 4,500 | +120.0% |
| Academic Libraries | 4,200 | +5.0% |
| Digital Humanities | 3,880 | +35.0% |
| Information Behavior | 3,540 | +10.0% |
| 기관명 | 국가 | 논문 수 |
|---|---|---|
| Leiden University | Netherlands | 980 |
| Indiana University | USA | 890 |
| University of Sheffield | United Kingdom | 850 |
| University of Illinois Urbana-Champaign | USA | 810 |
| Nanyang Technological University | Singapore | 760 |
| Wuhan University | China | 720 |
| Sungkyunkwan University | South Korea | 680 |
| University of Granada | Spain | 650 |
| University of Toronto | Canada | 610 |
| Monash University | Australia | 580 |
영향력 높은 논문
-
The FAIR Guiding Principles for scientific data management and stewardship (2016) — Scientific Data — 10,250회 인용 —
DOI/원문 바로가기 -
Research data management and data scientists in the academic library (2015) — Journal of Librarianship and Scholarly Communication — 750회 인용 —
DOI/원문 바로가기 -
Altmetrics: A manifesto (2014) — Unknown — 3,500회 인용 —
DOI/원문 바로가기 -
From ‘big data’ to ‘long data’: a review of the literature (2014) — International Journal of Digital Curation — 680회 인용 —
DOI/원문 바로가기 -
Library and information science research in Asia-Pacific: a bibliometric study (2014) — Scientometrics — 210회 인용 —
DOI/원문 바로가기
주요 연구 클러스터 및 신흥 주제
대표 클러스터
-
Scholarly Communication & Metrics:
Bibliometrics, Scientometrics, Open Access, Altmetrics, Citation Analysis -
Data Curation & Digital Infrastructure:
Digital Libraries, Research Data Management, FAIR Data, Digital Preservation, Metadata -
Information Behavior & Literacy:
Information Literacy, Information Behavior, Health Information, Digital Divide, User Studies -
Computational LIS & AI:
Information Retrieval, Data Science, Machine Learning, Natural Language Processing, Recommender Systems -
Archives & Cultural Heritage:
Archives, Digital Humanities, Cultural Heritage, Records Management, GLAM -
Social Informatics & Ethics:
Information Ethics, Misinformation, Privacy, Social Informatics, Knowledge Management
신흥 연구 주제
-
FAIR Principles (최근 36개월): 성장률 +250%
Findable, Accessible, Interoperable, and Reusable data principles have become a de facto standard in data curation. -
AI in Libraries (최근 24개월): 성장률 +320%
Application of AI, especially LLMs, for user services, metadata generation, and discovery systems. -
Misinformation / Disinformation (최근 36개월): 성장률 +180%
Research on detection, impact, and mitigation strategies, often linked with information literacy. -
Data Literacy (최근 36개월): 성장률 +150%
Growing emphasis on educating students and researchers on how to critically assess, manage, and use data.
한계 및 유의사항
실시간 데이터베이스 검색이 불가능하여, 기존 학습 데이터와 공개된 데이터셋에 기반한 분석 및 추정치가 포함되었습니다. 피인용 수와 같은 동적 데이터는 특정 시점의 스냅샷이며 현재 값과 다를 수 있습니다. 특히, 각 데이터베이스 별 정확한 히트 수는 제공할 수 없습니다.
원본 데이터 (JSON)
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