Countries
Institutions
Fields
Rankings
Compare

Search institutions, scholars, countries and publications

Sankore

Named after the University of Sankore in Timbuktu — one of the world’s oldest centres of learning. Every figure here is computed from work-level records in our own graph, never copied from a source’s summary count.

Built from

OpenAlex — works, authorships, subject classification
ROR — institutional identity
ORCID — researcher identity
Crossref — publisher confirmation

Browse

Countries
Institutions
Fields
Rankings
What we cover

Coverage is partial and says so. A country appears with its research output only once its works have been swept; until then the portal shows whatever the graph happens to hold for it and labels it as exactly that.

Predicting COVID‐19 Cases From Atmospheric Parameters Using Machine Learning Approach

2022GeoHealthJournalOpen access

10.1029/2021gh000509 Full text19 citations

Authors

3 authors across 3 institutions in 2 countries.

Samuel OgunjoFirst authorCorresponding
- Federal University of Technology (NG)
as printed: S. T. Ogunjo
Ibiyinka Fuwape
- Federal University of Technology (NG)
as printed: I. A. Fuwape
Babatunde RabiuSenior author
- National Space Research and Development Agency (NG)
as printed: A. B. Rabiu

Fields

Assignment is probabilistic — a work belongs to several fields in proportions.

COVID-19 epidemiological studieslevel 3 · 1.00
COVID-19 diagnosis using AIlevel 3 · 1.00
COVID-19 Pandemic Impactslevel 3 · 0.95

Sources

Several sources describing one work is the point — it means the record rests on more than one authority.

openalexseen September 9, 2026
2022 · GeoHealth · 19 citations
https://openalex.org/W4220655672

Predicting COVID‐19 Cases From Atmospheric Parameters Using Machine Learning Approach · Sankore