Virginia Tech
2 files

Replication Data for Neary et al 2022 - Recognizing post-castration pain in piglets

Version 2 2022-06-23, 13:19
Version 1 2022-06-21, 15:14
posted on 2022-06-23, 13:19 authored by Jessica NearyJessica Neary, Leonie JacobsLeonie Jacobs, Nathaniel PorterNathaniel Porter

 This data was collected using an online Qualtrics survey (SAP, Provo, UT, USA) distributed (August-September 2020) with the aim to receive responses from experienced swine industry respondents (“industry”) and respondents from the general public without swine industry work experience (“public”) at a 1:1 ratio. Industry respondents were recruited via Facebook and by direct email to industry stakeholders and university faculty within the authors’ network. Facebook posts were not sponsored and were not distributed in any Facebook groups. Facebook users, swine industry contacts and university faculty were invited to disseminate the survey to others with a relevant background, including farm owners, operators, technicians and veterinarians. Simultaneously, public respondents were recruited through Amazon Mechanical Turk (Amazon Web Services, Seattle WA, USA) and received a monetary compensation for their time through the website. Industry respondents did not receive compensation. Inclusion criteria required respondents to be over the age of 18 and living in the U.S. Responses were entered anonymously.

We received 129 complete survey responses. Five were omitted because respondents did not live in the U.S., and 5 were omitted because respondents failed the attention check question. Survey respondents were categorized as either ‘public respondents’ which was defined as having no professional swine industry experience, or as ‘industry respondents’ which was defined as having any professional, paid swine industry experience. We included 119 completed surveys in the data analysis, 66 from the public respondents (55%) and 53 from industry respondents (45%).

Respondents completed a short training module on classifying piglet grimace levels (indicators of pain) based on three facial features using the Piglet Grimace Scale (Viscardi & Turner, 2018). They were then asked to score 12 images on the scale and complete a series of questions about their demographic background, industry experience, and knowledge of swine agricultural practices.

The primary anonymized data is found in pgs_mturk_working_data.csv. with additional data from coding of the images by 4 experts in gold_standard_csv.csv. Together, these form the basis for the 2022 publication "Recognizing post-castration pain in piglets: a survey of swine industry stakeholders and the general public" in Frontiers in Veterinary Medicine. The instrument and summary demographic statistics are in Survey_Instrument_and_Demographics_Table.docx.

R Code used to produce the analyses reported in the paper is in pgs_data_prep_and_analysis_anonymized.R. Additional output data used for further analysis can be found  in the remaining 2 csv files, as well as labelled_tables.xlsx.


USDA National Institute of Food and Agriculture, Hatch Multistate project 1024623.



University Libraries, Virginia Tech


  • English (US)


United States

Corresponding Author E-mail Address

Files/Folders in Dataset and Description of Files

README - description and metadata contains: [gold_standard_csv.csv] - Expert coding of PGS images [HTHM_vectors_for_CF4.csv] - Correlation vectors for heterotrait-heteromethod combinations in Mutlitrait-Multimethod analysis [HTMM_vectors_for_CF4.csv] - Correlation vectors for heterotrait-monomethod combinations in Mutlitrait-Multimethod (MTMM) analysis [labelled_tables.xlsx] - Clean and labelled tables created from R output and MTMM correlations [pgs_data_prep_and_analysis_anonymized.R] - R code used to clean and combine data and produce analysis for paper [pgs_mturk_working_data.csv] - Survey data from industry and public samples [Survey_Instrument_and_Demographics_Table.docx] - Original survey questions/instrument and summary tables comparing demographics in the survey to US Census Bureau data

Usage metrics




    Ref. manager