Evidence map›Paper›PMID 38450403›Full record

ArticleFrontiers in plant science2024

AgTC and AgETL: open-source tools to enhance data collection and management for plant science research.

Luis Vargas-Rojas, To-Chia Ting, Katherine M Rainey, Matthew Reynolds, Diane R Wang

Open access · goldAbstract read
In one paragraph

Article in Frontiers in plant science, 2024. 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
1.0field-weighted citation impact, top 21% of its field
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 citations in OpenAlex.

  1. Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026
    Review
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

5 authors at 2 institutions in 2 countries.

Luis Vargas-RojasDepartment of Agronomy, Purdue University, West Lafayette, IN, United States.
To-Chia TingDepartment of Agronomy, Purdue University, West Lafayette, IN, United States.
Katherine M RaineyDepartment of Agronomy, Purdue University, West Lafayette, IN, United States.
Matthew ReynoldsWheat Physiology Group, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico.
Diane R WangDepartment of Agronomy, Purdue University, West Lafayette, IN, United States.
Purdue University West Lafayette · USCentro Internacional de Mejoramiento de Maíz Y Trigo · MX

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Advancements in phenotyping technology have enabled plant science researchers to gather large volumes of information from their experiments, especially those that evaluate multiple genotypes. To fully leverage these complex and often heterogeneous data sets (i.e. those that differ in format and structure), scientists must invest considerable time in data processing, and data management has emerged as a considerable barrier for downstream application. Here, we propose a pipeline to enhance data collection, processing, and management from plant science studies comprising of two newly developed open-source programs. The first, called AgTC, is a series of programming functions that generates comma-separated values file templates to collect data in a standard format using either a lab-based computer or a mobile device. The second series of functions, AgETL, executes steps for an

Indexed as

data aggregationdatabasedata pipelinedata processingextract-transform-loadplant phenotyping

Identifiers

PMID38450403
PMCPMC10915008
OpenAlexW4392002314

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.