Creating Bibliometric Database for Croatian Clinicians Research
"Learn about the process of data retrieval, cleaning, and merging to create a comprehensive database for bibliometric analyses of publications by Croatian clinicians. Funding, tools used, and the significance of affiliations are highlighted in this study from 2005 to 2022."
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DATA RETRIEVAL, DATA CLEANING AND DATA MERGING: creating a database for bibliometric analyses Romana Jadrijevi 1,2, Antonija Mijatovi 3, Ivana Babi 2, Ana Maru i 2,3 1University of Split School of Medicine, TRIBE PhD program, Split, Croatia 2University Hospital of Split, Department of Research, Split, Croatia 3University of Split School of Medicine, Center for Evidence-based Medicine, Split, Croatia PUBMET2024, Zadar 13 September 2024 Correspondence: rjadrijevic@kbsplit.hr
WHAT, WHO, WHERE, WHEN what Aim: create a database of all publications by Croatian clinicians regardless of the research area Purpose: study potential effects of Croatia s European Union (EU) membership on those publications Focus on publications by clinicians rather than publications in clinical medicine using affiliations instead of research areas who This research was funded by the Croatian Science Foundation, grant number IP-2019-04-4882 where WoS, Scopus Pubmed Excel when Data exctraction: April 2023 Data time range: 2005-2022
WHAT Identification list of registered healthcare subjects list of all possible names for all types of medical subjects, excluding medical faculties truncation and grouping adapting and conducting the search separately for each database. Retrieval download in sections via built-in export functions raw data deposited copies made and combined into a single Excel document for each database separately Deduplication Python automate parts of cleaning process random controls after each step most distinguishable identifier common to both datasets was the DOI number. Excel sorting function removed all records without a DOI Python to identify and remove duplicates found in Scopus
WHAT Merging decide which data columns to keep which columns could be combined Excel s Merge Tables function unique dataset Cleaning trial and error check each record manually checked 21,651 records, excluded 3,531 for not meeting the inclusion criteria Missing data most important type was the publication month for our future analyses automated this process for 989 records using DOIs and PubMed export, manually extracted the 1,530 remaining. majority of missing publication month data originated from Scopus records for publications from Croatian journals.
WHY The most comprehensive bibliographic database of the research Croatian clinicians published in the period from 2005 to 2022, after including the records without DOI Affiliations may be the only currently viable way to gain insight into the research that specific types of institutions conduct Help guide researchers performing any type of bibliometric analysis, and in any research field
THANK YOU! Romana Jadrijevi 1,2, Antonija Mijatovi 3, Ivana Babi 2, Ana Maru i 2,3 1University of Split School of Medicine, TRIBE PhD program, Split, Croatia 2University Hospital of Split, Department of Research, Split, Croatia 3University of Split School of Medicine, Center for Evidence-based Medicine, Split, Croatia Correspondence: rjadrijevic@kbsplit.hr
