🏆 Finalist — NIH Data Sharing Index (“S-Index”) Challenge

Erinija Pranckeviciene

ORCID: 0000-0003-1365-3564
Biochemistry, Genetics and Molecular BiologyMedicineComputer Science

Pilot corpus only

This score is computed over theSindex pilot corpus and does not cover the full scientific literature. Scores are relative to papers we have ingested — papers, authors, and institutions outside the pilot are not represented. Methodology.

N/A
0Author DataRank · unranked

Indexed papers

1in pilot corpus
datarank_citation_only_1hop_v6· scope data_onlyMethodology
Why this DataRank?

An author's DataRank is the sum of the DataRanks of all 1 indexed paper attributed to them. A prolific author with many moderate-impact papers can outrank one with a single high-impact paper.

Author scores recompute whenever paper DataRanks are refreshed, so this number lags the underlying paper scores by at most one batch run.

Read the full methodology →

Top data-sharing exemplar

The highest-impact dataset this researcher has shared, ranked by DataRank — the single contribution doing the most to lift their data-sharing standing.

Global Suicide Mortality Rates (2000–2019): Clustering, Themes, and Causes Analyzed through Machine Learning and Bibliographic Data

International Journal of Environmental Research and Public Health(2024)10.3390/ijerph21091202
Top 59%
0.269DataRank
Top 59%5 citations

Judita Kasperiuniene, Erinija Pranckeviciene

Papers

Driven by 1 paper — median percentile 42. Top paper: “Global Suicide Mortality Rates (2000–2019): Clustering, Themes, and Causes Analyzed through Machine Learning and Bibliographic Data”.

Global Suicide Mortality Rates (2000–2019): Clustering, Themes, and Causes Analyzed through Machine Learning and Bibliographic Data

International Journal of Environmental Research and Public Health(2024)10.3390/ijerph21091202
Top 59%
0.269DataRank
Top 59%5 citations

Judita Kasperiuniene, Erinija Pranckeviciene