OpenRefine: An approachable tool for cleaning and harmonizing bibliographical data
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Scopus is a large and widely recognized multidisciplinary database and covers a large number of academic literature.
“The data for this bibliometrics study come from the Scopus database, which is a large and widely recognized multidisciplinary database and covers a large number of academic literature [11].”
From Mapping the Research Landscape: A Comprehensive Bibliometric Analysis of Self-Determination Theory in Higher Education (2024) · cites this paper as [11]
Cited by
Sorted by citations
Lin, C., Zhang, Y., Wang, L., & Wu, J. (2024) · Proceedings of the 2024 2nd International Conference on Information Education and Artificial Intelligence · doi:10.1145/3724504.3724576
- Authors
- Ahmi, A.
- Corresponding author
- Ahmi, Aidi
- ORCID
- Ahmi, Aidi
- Venue
- AIP Conference Proceedings
- Publisher
- AIP Publishing
- Published
- 2023
- Volume
- 2894
- Pages
- 030006
- ISSN
- 0094-243X (ISSN-L)
- OpenAlex
- W4386636320
- Cited by
- 61
- References
- 0
- Citations by year
- 2026: 22 · 2025: 23 · 2024: 15 · 2023: 1
- FWCI
- 33.51
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- AIP conference proceedings (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Data Quality and Management
- Physical Sciences › Computer Science › Information Systems › Library Science and Information Systems
- Physical Sciences › Computer Science › Information Systems › Research Data Management Practices
- Keywords
- OpenRefine, data cleaning, data harmonization, bibliographic data, metadata, data quality
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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