STAR/mmCIF: An ontology for macromolecular structure is a research paper published in Bioinformatics (2000). On theSindex it has a DataRank of 0.601. It has been cited 54 times.
MotivationCrystallographers were motivated 10 years ago to develop a simple and consistent data representation for the exchange and archiving of data associated with the crystallographic experiment and the final structure. As this process evolved (and the data grew at near exponential rates) came the recognition that this representation should also facilitate the automated management of the data and, with the aid of additional software for verification and validation, provide improved consistency and accuracy and hence improved scientific inquiry. This realization led to a new Dictionary Definition Language (DDL) and an extensive dictionary based on this DDL for describing macromolecular structure. In broad terms this could be considered an ontology. An important feature in the development of the ontology was the endorsement and ongoing maintenance and support of the International Union of Crystallography (IUCr). While the description of macromolecular structure and the x-ray crystallographic experiment used to derive it represent explicit data, the ontology is extensible and applicable to other less well-characterized data domains.ResultsDetails of the DDL, the dictionaries that have been developed, and software for reading and using this ontology are presented.AvailabilityExtensive documentation, software tools and the DDL and dictionaries are available from http://ndbserver.rutgers.edu/mmcif and associated mirror sites.ContactBourne: [email protected] and Westbrook:[email protected]
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Base Score Contribution
0.601
From this paper's citation signal
Citation Network Contribution
0
Citation network not refreshed for this result
This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
Learn more about DataRank methodology →DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 100% comes from its base citations and 0% from the citation network.
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.