Presented by. The Metadata [R]evolution: Transformative Opportunities September 18, 2013

Transcription

1 Presented by The Metadata [R]evolution: Transformative Opportunities September 18, 2013

2 Using VIVO, Scopus, and PubMed to disambiguate Weill Cornell authors Paul Albert Weill Cornell Medical College

3

4

5

6

7 Original approach for managing faculty publications: rely on researchers or their proxies to manually enter publications.

8 Does this work?

9

10

11

12

13 Researchers response to requesting copy of CV

14 Why don t our researchers care? Failing to rigorously maintain an accurate list of publications is a rational choice. Time spent on maintaining publications bears a perceived, but more often real, opportunity cost.

15 Revised approach for managing faculty publications: use data from Scopus and PubMed to maintain profiles for them

16 Our publication ingest workflow 1. Librarian formulates queries. Stores in Google Doc. Developer queries Scopus API and translates result into XML. Use DOI and PMID to lookup record in PubMed. 2. Combine metadata from both sources as a candidate for ingest. 3. If duplicate, disregard. If new, ingest. 4. Re-ingest temporal data such as citation count.

17 What is ingested from where? Scopus Full Author Names Article Title Journal Title DOI PMID (PubMed Identifier) Date of publication ISSN Citation count PubMed Abstract Medical Subject Headings (MeSH) Funding PubMed Central Identifier Status (e.g., in process) Second ISSN Language Journal abbreviation Publication type

18 A key consideration: will a publication ingest be institutioncentric or person-centric?

19

20

21 Query by institution Query by person Affiliation ID = Weill Author ID = x 1300 Easier to identify hits Easier for institutional reporting, especially year to year comparisons Assertions of co-author identity can be unclear More laborious need an internal source for people Often accounts for publications w/ no or incorrect affiliation Accounts for previous affiliations

22 Scopus commits two varieties of disambiguation errors Splitting - one person, multiple author IDs; relatively easy to recover from Lumping - multiple people, one author ID; relatively hard to recover from

23

24

25 How accurate is Scopus at author disambiguation c. 2013? Gold standard = librarian judgment Ideal one-to-one relation between Scopus author ID and person n=369 Splitting more than one author ID per person n=707 Both errors n=23 Lumping more than one person per author ID n=86

26 Two author disambiguation methods against a gold standard Name query Scopus From Johnson et al. Submitted. Automatic generation of investigator bibliographies for institutional research networking systems.

27 Special queries can compensate for lumping errors

28 Examples of special queries (AU-ID( )) AND (AF-ID( ) OR AF-ID( ) OR AF-ID( )) (AU-ID( )) AND (AF-ID( ) OR AF-ID( ) OR AF-ID( ) OR AF-ID( ) OR AF-ID( ) OR AF-ID( ) OR AF-ID( ) OR AF-ID( ) OR AF-ID( ) OR AF-ID( ) OR AF-ID( ) OR AF-ID( ))

29 How can VIVO data address pressing institutional needs in order to strengthen its viability?

30 NIH Open Access policy compliance WCMC authors who have received NIH funding but haven t deposited pre-prints in PubMed Central receive a nastygram personalized notice Mar Apr May Jun Jul Aug Sep

31 Co-author network and expertise of arbitrary group of faculty

32 Suggested publications in annual faculty review tool

33 Administrators are avid consumers of institutional data.

34 Administrators want reporting tools (especially about publications) that are: Have current data Easy to use Allow for sophisticated queries

35 VIVO Dashboard now under development

36 Expertise recommendation tool also under development

37 Acknowledgements Eliza Chan and Prakash Adekkanattu - developers at Weill Cornell Don Carpenter and Zeheng Wang - VIVO Dashboard developer Jie Lin - Expertise Recommendation Tool developer Drew Wright - publications help and NIH Access Policy compliance