Evidence map›Paper›PMID 42140982›Full record

ArticleScientific data2026

The Ireland BioClim dataset for observational and future climate projections.

Parvaneh Nowbakht, Paraic C Ryan, Jingyu Wang, Jenny Harmon O'Driscoll, Rosa Rogers, Mark Stewart, Enda O'Brien, Basanta Samal, Paul Nolan, Paul Holloway

Abstract read
In one paragraph

Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Parvaneh NowbakhtDepartment of Geography, University College Cork, Cork, Ireland. parvaneh.nowbakht@ucc.ie.ORCID http://orcid.org/0000-0003-4096-4632
Paraic C RyanSustainability Institute, University College Cork, Cork, Ireland.
Jingyu WangSustainability Institute, University College Cork, Cork, Ireland.
Jenny Harmon O'DriscollDiscipline of Civil, Structural and Environmental Engineering, School of Engineering, University College Cork, Cork, Ireland.
Rosa RogersSchool of English & Digital Humanities, University College Cork, Cork, Ireland.
Mark StewartSchool of Civil & Environmental Engineering, University of Technology Sydney, Sydney, Australia.
Enda O'BrienIrish Centre for High-End Computing, University of Galway, Galway, Ireland.
Basanta SamalIrish Centre for High-End Computing, University of Galway, Galway, Ireland.
Paul NolanIrish Centre for High-End Computing, University of Galway, Galway, Ireland.
Paul HollowayDepartment of Geography, University College Cork, Cork, Ireland.ORCID http://orcid.org/0000-0002-9773-4531

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Bioclimatic variables are essential indicators used across disciplines including ecology, geography, environmental science, and regional planning. While global datasets such as WorldClim and CHELSA provide climate information, the absence of regionally tailored datasets limits the ability to capture fine-scale climatic variability, which is critical for regional analysis. The Translate project addresses this gap by providing high-resolution (~1 km) observed and projected climate data for Ireland, enabling the generation of region-specific bioclimatic variables. The observational dataset was derived from long-term climate records using quality-controlled interpolation of national station data. An ensemble-based approach using ~200 Regional Climate Model (RCM) simulations was developed to generate percentile-based projections (10th, 50th, and 90th percentiles) across Representative Concentration Pathways (RCPs 2.6, 4.5, and 8.5) and multiple time periods. These percentiles represent a range of plausible climate futures and support uncertainty assessment. Three complementary approaches were applied: full-ensemble, period-specific, and scenario-specific outputs across RCPs and time periods. The observational and full-ensemble datasets each comprise 57 GeoTIFFs, with additional outputs provided for period- and scenario-specific analyses.

Identifiers

PMID42140982
PMCPMC13434107

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.