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The Funtional Requirements for Bibliographic Records (FRBR) document's main part defines the primary and secondary entities which became famous as FRBR models. Years later Tom Delsey created a mapping between the 12 functions and the individual MARC elements.
Tom Delsey (2002) Functional analysis of the MARC 21 bibliographic and holdings formats. Tech. report, Library of Congress, 2002. Prepared for the Network Development and MARC Standards Office Library of Congress. Second Revision: September 17, 2003. https://www.loc.gov/marc/marc-functional-analysis/original_source/analysis.pdf.
This page shows how these functions are supported by the records. The horizontal axis show the strength of the support: something on the left means that support is low so only small portion of the fields support a function are available in the records, something on the right means the support is strength. The bars represents a range of values. The vertical axis shows the number of records having values in the same range.
It is experimental because it turned out, that the the mapping covers about 2000 elements (fields, subfields, indicatiors etc.), however on an average record there are max several hundred elements, which results that even in the best record has about 10-15% of the totality of the elements supporting a given function. So the tool doesn't shows you exact numbers, and the scale is not 0-100 but 0-[best score] which is different for every catalogue.
These scores are calculated for each continuing resources (type of record (LDR/6) is language material ('a') and bibliographic level (LDR/7) is serial component part ('b'), integrating resource ('i') or serial ('s')).
The calculation is based on a slightly modified version of the method published by Jamie Carlstone in the following paper:
Jamie Carlstone (2017) Scoring the Quality of E-Serials MARC Records Using Java, Serials Review, 43:3-4, pp. 271-277, DOI: 10.1080/00987913.2017.1350525 URL: https://www.tandfonline.com/doi/full/10.1080/00987913.2017.1350525
These scores are the implementation of the following paper:
Kelly Thompson and Stacie Traill (2017) Implementation of the scoring algorithm described in Leveraging Python to improve ebook metadata selection, ingest, and management, Code4Lib Journal, Issue 38, 2017-10-18. http://journal.code4lib.org/articles/12828
Their approach to calculate the quality of ebook records comming from different data sources.
These charts show how the field frequency patterns. Each chart shows a line which is the function of field frequency: on the x axis you can see the subfields ordered by the frequency (how many time a given subfield occured in the whole catalogue). They are ordered by frequency from the most frequent top 1% to the least frequent 1% subfields. The Y axis represents the cumulative occurrence (from 0% to 100%).
This experimental website is part of a research project called Measuring Metadata Quality conducted by Péter Király. You can read more about the research at pkiraly.github.io.
This is an open source project. You can find the code at:
Credits
Thanks for Johann Rolschewski and Phú for their help in collecting the list of published library catalog, Jakob Voß for the Avram specification and for his help in exporting MARC schema to Avram, Carsten Klee for the MARCspec. I would like to thank the early users of the software, Patrick Hochstenbach (Gent), Osma Suominen and Tuomo Virolainen (FNL), Kokas Károly and Bernátsky László (SZTE), Sören Auer and Berrit Genat (TIB), Shelley Doljack, Darsi L Rueda, and Philip E. Schreur (Stanford), Marian Lefferts (CERL), Alex Jahnke and Maike Kittelmann (SUB) who provided data, suggestions or other kinds of feedback, Justin Christoffersen for language assistance. Special thanks to Reinhold Heuvelmann (DNB) for terminological and language suggestions.
I would like to thank the experts I have consulted regarding to subject analysis: Rudolf Ungváry (retired, Hungarian National Library, HU), Gerard Coen (DANS and ISKO-NL, NL), Andreas Ledl (BARTOC and Uni Basel, CH), Anna Kasprzik (ZBW, DE), Jakob Voß (GBV, DE), Uma Balakrishnan (GBV, DE), Yann Y. Nicolas (ABES, FR), Michael Franke-Maier (Freie Universität Berlin, DE), Gerhard Lauer (Uni Basel, CH).