Item Response Warehouse / Tables / zhu_2026_llm_meteorology_performancezhu_2026_llm_meteorology_performance
1,740 responses from 348 respondents to 5 items.
About this table
Description Graduate students' ratings (1-5) of LLM tool performance across 5 aspects (accuracy problem-solving practicality innovation ease-of-use) in meteorology research Reference Zhu S, Li H (2026). The application of large language models in meteorology graduate research: current status impact and prospects. PLOS One. DOI 10.1371/journal.pone.0347933 Licence CC BY 4.0 Source data https://doi.org/10.1371/journal.pone.0347933.s001
Size and shape
Responses 1,740 Respondents 348 Items 5 Response categories 5 Responses per respondent 5 Responses per item 348 Density 1 Longitudinal FALSE
Classification
sample Educational measurement tool Survey/questionnaire item format Likert Scale/selected response primary language(s) chi
Get the data
Or load it directly in R or Python:
# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("zhu_2026_llm_meteorology_performance")
# Python
pip install irw
import irw
df = irw.fetch("zhu_2026_llm_meteorology_performance")
Version and provenance
IRW version v393 Redivis dataset item_response_warehouse_4 v7.0 Redivis dataset DOI 10.57761/cpj5-vm97 Manifest pin for this IRW version v7.0 Metadata source irw_meta v23.0
Part of the Item Response Warehouse , IRW v393. This page describes the table as released in item_response_warehouse_4 v7.0.