The SIMBAD astronomical database is a dataset published in Astronomy and Astrophysics Supplement Series (2000). On theSindex it has a DataRank of 10.7, placing it in the top 20% of the data-sharing corpus. It has been cited 2,300 times, with 196 citing works in its 1-hop citation network. Its calibrated FAIR score is 49/100.
Simbad is the reference database for identification and bibliography ofastronomical objects. It contains identifications, `basic data', bibliography,and selected observational measurements for several million astronomicalobjects. Simbad is developed and maintained by CDS, Strasbourg. Building thedatabase contents is achieved with the help of several contributing institutes.Scanning the bibliography is the result of the collaboration of CDS withbibliographers in Observatoire de Paris (DASGAL), Institut d'Astrophysique deParis, and Observatoire de Bordeaux. When selecting catalogues and tables forinclusion, priority is given to optimal multi-wavelength coverage of thedatabase, and to support of research developments linked to large projects. Inparallel, the systematic scanning of the bibliography reflects the diversityand general trends of astronomical research. A WWW interface to Simbad is available at: http://simbad.u-strasbg.fr/Simbad
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Calibrated FAIR score — a parallel quality metric, independent of the DataRank citation score. See the full evaluation →
Base Score Contribution
1.2
From this paper's citation signal
Citation Network Contribution
9.5
From 196 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 11% comes from its base citations and 89% from the citation network (196 citing papers contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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