DOI: 10.13012/b2idb-2556310_v1
title: MapAffil 2018 dataset -- PubMed author affiliations mapped to cities and their geocodes worldwide with extracted disciplines, inferred GRIDs, and assigned ORCIDs

description: Prepared by Vetle Torvik 2021-05-07

The dataset comes as a single tab-delimited Latin-1 encoded file (only the City column uses non-ASCII characters).

• How was the dataset created?
The dataset is based on a snapshot of PubMed (which includes Medline and PubMed-not-Medline records) taken in December, 2018. (NLMs baseline 2018 plus updates throughout 2018). Affiliations are linked to a particular author on a particular article. Prior to 2014, NLM recorded the affiliation of the first author only. However, MapAffil 2018 covers some PubMed records lacking affiliations that were harvested elsewhere, from PMC (e.g., PMID 22427989), NIH grants (e.g., 1838378), and Microsoft Academic Graph and ADS (e.g. 5833220). Affiliations are pre-processed (e.g., transliterated into ASCII from UTF-8 and html) so they may differ (sometimes a lot; see PMID 27487542) from PubMed records. All affiliation strings where processed using the MapAffil procedure, to identify and disambiguate the most specific place-name, as described in:
Torvik VI. MapAffil: A bibliographic tool for mapping author affiliation strings to cities and their geocodes worldwide. D-Lib Magazine 2015; 21 (11/12). 10p

• Look for Fig. 4 in the following article for coverage statistics over time:
Palmblad, M., Torvik, V.I. Spatiotemporal analysis of tropical disease research combining Europe PMC and affiliation mapping web services. Trop Med Health 45, 33 (2017). https://doi.org/10.1186/s41182-017-0073-6
Expect to see big upticks in coverage of PMIDs around 1988 and for non-first authors in 2014.

• The code and back-end data is periodically updated and made available for query by PMID at http://abel.ischool.illinois.edu/cgi-bin/mapaffil/search.py

• What is the format of the dataset?
The dataset contains 52,931,957 rows (plus a header row). Each row (line) in the file has a unique PMID and author order, and contains the following eighteen columns, tab-delimited. All columns are ASCII, except city which contains Latin-1.

1. PMID: positive non-zero integer; int(10) unsigned
2. au_order: positive non-zero integer; smallint(4)
3. lastname: varchar(80)
4. firstname: varchar(80); NLM started including these in 2002 but many have been harvested from outside PubMed
5. initial_2: middle name initial
6. orcid: From 2019 ORCID Public Data File https://orcid.org/ and from PubMed XML
7. year: year of the publication
8. journal: name of journal that the publication is published
9. affiliation: author's affiliation??
10. disciplines: extracted from departments, divisions, schools, laboratories, centers, etc. that occur on at least unique 100 affiliations across the dataset, some with standardization (e.g., 1770799), English translations (e.g., 2314876), or spelling corrections (e.g., 1291843)
11. grid: inferred using a high-recall technique focused on educational institutions (but, for experimental purposes, includes a few select hospitals, national institutes/centers, international companies, governmental agencies, and 200+ other IDs [RINGGOLD, Wikidata, ISNI, VIAF, http] for institutions not in GRID). Based on 2019 GRID version https://www.grid.ac/
12. type: EDU, HOS, EDU-HOS, ORG, COM, GOV, MIL, UNK
13. city: varchar(200); typically 'city, state, country' but could include further subdivisions; unresolved ambiguities are concatenated by '|'
14. state: Australia, Canada and USA (which includes territories like PR, GU, AS, and post-codes like AE and AA)
15. country
16. lat: at most 3 decimals (only available when city is not a country or state)
17. lon: at most 3 decimals (only available when city is not a country or state)
18. fips: varchar(5); for USA only retrieved by lat-lon query to https://geo.fcc.gov/api/census/block/find