Evidence map›Paper›PMID 41014096›Full record

ArticleDatabase : the journal of biological databases and curation2025

VineColD: an integrative database for global historical tracing and real-time monitoring of grapevine cold hardiness.

Hongrui Wang, Jason P Londo

Abstract read
In one paragraph

Article in Database : the journal of biological databases and curation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

2 authors.

Hongrui WangSchool of Integrative Plant Science, Horticulture Section, Cornell University-Cornell AgriTech, 635 W North St, Geneva, NY 14456, United States.ORCID 0000-0003-4238-0884
Jason P LondoSchool of Integrative Plant Science, Horticulture Section, Cornell University-Cornell AgriTech, 635 W North St, Geneva, NY 14456, United States.

Funding

Cornell University School of Integrative Plant ScienceNew York Wine and Grape FoundationUSDA-NIFA Agriculture and Food Research Initiative 2023-68008-39274
6 · The paper itself

Abstract

Cold hardiness is a crucial physiological parameter that determines the survival of grapevines during the dormant season. Accurate modelling and large-scale prediction of grapevine cold hardiness are essential for assessing the potential geographic distribution of grapevine cultivation, quantifying the impact of climate change on grapevine habitats, and ensuring the sustainability of the grape and wine industries worldwide in the regions characterized by cool or cold dormant seasons. However, until now, no comprehensive database has been available. In this research, we combined advanced automated machine learning techniques with extensive historical and current weather data to create an integrative database for grapevine cold hardiness: VineColD (https://cornell-tree-fruit-physiology.shinyapps.io/VineColD/). We developed the NYUS.2.1 model, an automated machine learning-based system for predicting grapevine cold hardiness and applied it to global historical weather data from 17,985 curated weather stations between latitudes 30° and 55° in both hemispheres from 1960 to 2024, resulting in the development of an integrative grapevine cold hardiness database and monitoring system, VineColD. VineColD integrates both a global historical dataset and a daily updated regional cold hardiness system, offering a comprehensive resource to study grape cold hardiness for 54 grapevine cultivars. The platform provides multiple download options, from single-station data to complete datasets, and the interactive multifunctional R Shiny application facilitates data analysis and visualization. VineColD delivers critical insights into the impact of climate change on grapevine cultivation and supports a range of analytical functions, making it a valuable tool for grape growers and researchers.

Indexed as

Cold TemperatureDatabases, FactualVitisClimate ChangeMachine Learning

Identifiers

PMID41014096
PMCPMC12475903

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.