Talk given to professionals from Research Impact field. The aim is to show why data cleansing is still needed, even with the automated treatment bibliometrics database goes through.

1. What Does It Take To Have
Precise Indicators?
Presented at
BRIC 2019
Held De Souza
held.bsouza@gmail.com
Observatoire des sciences et des technologies
Centre interuniversitaire de recherche sur la science et la technologie
Université du Québec à Montréal
May 15th to 16th 2019 1

2. Entity Level VS Metrics
Reliability
• A couple missed
papers have a greater
impact in a
researcher level than
in an institutional level
• So, which are the
most common
issues?
Country
Province/State/Region
City/Metropolitan area
Institutions
Researchers
2

3. Typical Scientific Paper
Authors
Institutional
Addresses
3

4. Data cleansing:
Authors name
• Homonyms may have merged into one
profile
• Multidisciplinary authors may have
publications split into multiple profiles
• The same author may have different
signatures
4

5. Source:Scopus,1May2019
5

6. Institutions name
• Can be written in several manners
Example:
Laval University Medical Center - CHUL
(Centre Hospitalier de l’Université Laval - CHUL)
6

7. Source: Scopus, 1 May 2019
7

8. Case Study:
Observatoire des sciences et des
technologies (OST)
8

9. OST
• non-profit organization
• provide bibliometric services to assess
research activity
• supports the development of basic and
applied research
www.ost.uqam.ca
9

10. 10

11. OST
Interface
for
institutional
addresses
harmonization
Interface
for
author’s
profile
composition
11

12. Author’s profile composition
Authors profile validated
Automatic composition of profiles
List of names sent by
the client
Authors matching in
Web of Science
(WoS)
Manual validation
12

13. 13

14. Observations
• Cases that makes data cleansing more
difficult:
– Homonyms
– Same author, different signatures
– Authors in multidisciplinary fields
– Young authors attached to multiple institutions
• Search of information online
• Lots of cases need a human eye for final
validation
14

15. Institutional Addresses
Harmonization
• Normalized Information:
– Institution name
– Geographical information: city, province and
country
– Journal title
• Institutions are assigned to a sector
(university, government, hospital,
industry) 15

16. Data from statost on 30th April 2019.
16

17. Link of Affiliated Institutions
Du secteur hospitalier
Centre Hospitalier de l'Université de Montréal (CHUM)
Institut de Recherches Cliniques de Montréal (IRCM)
Centre Dollard-Cormier
Institut Universitaire de Gériatrie de Montréal (IUGM)
Institut universitaire en santé mentale de Montréal (Hôpital Louis-H. Lafontaine) (IUSMM)
Hôpital Maisonneuve-Rosemont (HMR)
Hôpital Rivière-des-Prairies
Hôpital Sacré-Coeur de Montréal
Hôpital Ste-Justine
Institut du Cancer de Montréal (ICM)
Institut Philippe Pinel de Montréal
Institut de Cardiologie de Montreal (ICM)
Du secteur universitaire
HEC Montréal
École Polytechnique de Montréal
Autres (n. = 126 )
Example for University of Montréal:
Number of publications between 1980 and 2017:
Without affiliated institutions: 61 611
With affiliated institutions: 95 045 +54%
17

18. Observations
• Hard to investigate the extension of the automatic
treatment from the bibliometric databases
• Hard to predict the variations
– Misspelling
– Too much or too little information in the institutional name
field
– Use of acronyms
– Institution belonging to a hierarchy of institutions
– … 18

19. General notes
• Be skeptical about automated data
processing
• Know the limitations of your database
• Take an informative decision about
whether to perform some types of
analysis
• Consider the promotion of a standardized
form for the institutional name within your
institution 19

20. Thank you!
20
Q&A