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.

Data-Driven Insights Into Vitex doniana Distribution: A Machine Learning Approach With Selected Abiotic Features

2025Cureus Journal of Computer Science.JournalOpen access

10.7759/s44389-025-04581-6 Full text1 citations

Authors

5 authors across 2 institutions in 2 countries.

Peter E UgegeFirst author
- Forestry Research Institute of Nigeria (NG)
Peter Edemewe UgegeFirst author
- Forestry Research Institute of Nigeria (NG)
as printed: Peter E Ugege
Peace A Ukoha
- Forestry Research Institute of Nigeria (NG)
Alaba O. Ugbogu
- Université d'Abomey-Calavi (BJ)
- Forestry Research Institute of Nigeria (NG)
as printed: Omokafe A Ugbogu
Jean Cossi GangloSenior author
- Forestry Research Institute of Nigeria (NG)
as printed: Jean C Ganglo

Fields

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

Species Distribution and Climate Changelevel 3 · 1.00
Remote Sensing in Agriculturelevel 3 · 0.97
Genetic diversity and population structurelevel 3 · 0.97

Sources

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

openalexseen September 24, 2026
2025 · Cureus Journal of Computer Science. · 1 citations
https://openalex.org/W4411058041

Data-Driven Insights Into Vitex doniana Distribution: A Machine Learning Approach With Selected Abiotic Features · Sankore