11,376 rated or chosen profiles from 474 respondents, in up to 6 tasks of 4 profiles varying on 5 attributes.
| Description | Choice 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. |
|---|---|
| Reference | King, 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. |
| DOI | 10.1016/j.jenvman.2026.131156 |
| Licence | MIT License |
| Source data | https://doi.org/10.5281/zenodo.23374961 |
These notes describe the source data, which the IRW reproduces as released. All notes are listed on the Data Notes page.
| Respondents | 474 |
|---|---|
| Rows (one per respondent, task and profile) | 11,376 |
| Tasks per respondent (most) | 6 |
| Profiles per task | 4 |
| Attributes | 5 |
| Tasks with no profile chosen | 0 |
| Topic | climate energy environment |
|---|---|
| A profile represents | policy |
| Country | GB |
| Language shown | en |
| Language of stored levels | en |
| Randomization restrictions | unknown |
| Task order | recorded in the deposit |
| Profile position | recorded in the deposit |
Outcomes:
| choice | Please consider each table as a separate set of choices. Which ONE of the following options would you choose? (A / B / C / D) [choice, forced] |
|---|
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.
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")
@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}
}
| IRW version | v584 |
|---|---|
| Redivis dataset | irw_conjoint_2 v1.4 |
| Redivis dataset DOI | 10.57761/hk97-wv30 |
| Manifest pin for this IRW version | v1.4 |
| Metadata source | irw_meta v44.0 |