king_2026_urban_meadows

11,376 rated or chosen profiles from 474 respondents, in up to 6 tasks of 4 profiles varying on 5 attributes.

About this table

DescriptionChoice experiment on how grassy areas in urban parks could be managed as wildflower meadows: 474 park visitors in Leeds, Bristol and Edinburgh chose among options A, B, C and the current situation D in six tasks, described by plant species, native share, pollinator quality, flower appearance and a yearly council tax increase.
ReferenceKing, P., Pett, T. J., Goddard, M. A., Lundhede, T. H., Austen, G. E., Davies, Z. G., & Dallimer, M. (2026). Understanding public preferences for urban wildflower meadows: data and replication code [Data set]. Zenodo.
DOI10.1016/j.jenvman.2026.131156
LicenceMIT License
Source datahttps://doi.org/10.5281/zenodo.23374961

Notes

These notes describe the source data, which the IRW reproduces as released. All notes are listed on the Data Notes page.

Size and shape

Respondents474
Rows (one per respondent, task and profile)11,376
Tasks per respondent (most)6
Profiles per task4
Attributes5
Tasks with no profile chosen0

Design

Topicclimate energy environment
A profile representspolicy
CountryGB
Language shownen
Language of stored levelsen
Randomization restrictionsunknown
Task orderrecorded in the deposit
Profile positionrecorded in the deposit

Outcomes:

choicePlease consider each table as a separate set of choices. Which ONE of the following options would you choose? (A / B / C / D) [choice, forced]

Columns

attr_appearanceattr_costattr_nativeattr_plantsattr_pollinatorschoicecov_age_groupcov_citycov_education_codecov_employment_codecov_gendercov_income_midpointcov_meadow_typecov_parkcov_survey_weightidprofiletasktrial_card

The layout (one row per respondent, task and profile; choice/rating outcomes; attr_ columns holding each attribute level as displayed) is defined in the IRW conjoint standard. What the outcomes asked is under Design above.

Get the data

Download CSVno account neededBrowse on Redivisexplore and queryCroissant metadataHugging Face, Kaggle, OpenML

Or load it directly in R or Python (both need a free Redivis account; the first call opens a browser to sign in):

# R
install.packages("remotes")
remotes::install_github("itemresponsewarehouse/Rpkg")
library(irw)
df <- irw_fetch("king_2026_urban_meadows", source = "conj")
# Python
pip install irw

import irw
df = irw.fetch("king_2026_urban_meadows", source="conj")

How to cite

@dataset{King2026,
  author = {King, P. and Pett, T. J. and Goddard, M. A. and Lundhede, T. H. and Austen, G. E. and Davies, Z. G. and Dallimer, M.},
  title = {Understanding public preferences for urban wildflower meadows: data and replication code},
  year = {2026},
  publisher = {Zenodo},
  doi = {10.5281/zenodo.23374961},
  url = {https://doi.org/10.5281/zenodo.23374961}
}

Version and provenance

IRW versionv584
Redivis datasetirw_conjoint_2 v1.4
Redivis dataset DOI10.57761/hk97-wv30
Manifest pin for this IRW versionv1.4
Metadata sourceirw_meta v44.0