This dataset consists of three data folders including all related documents of the online survey conducted within the NESP 3.2.3 project (Tropical Water Quality Hub) and a survey format document representing how the survey was designed. Apart from participants’ demographic information, the survey consists of three sections: conjoint analysis, picture rating and open question. Correspondent outcome of these three sections are downloaded from Qualtrics website and used for three different data analysis processes.

Related data to the first section “conjoint analysis” is saved in the Conjoint analysis folder which contains two sub-folders. The first one includes a plan file of SAV. Format representing the design suggestion by SPSS orthogonal analysis for testing beauty factors and 9 photoshoped pictures used in the survey. The second (i.e. Final results) contains 1 SAV. file named “data1” which is the imported results of conjoint analysis section in SPSS, 1 SPS. file named “Syntax1” representing the code used to run conjoint analysis, 2 SAV. files as the output of conjoint analysis by SPSS, and 1 SPV file named “Final output” showing results of further data analysis by SPSS on the basis of utility and importance data.

Related data to the second section “Picture rating” is saved into Picture rating folder including two subfolders. One subfolder contains 2500 pictures of Great Barrier Reef used in the rating survey section. These pictures are organised by named and stored in two folders named as “Survey Part 1” and “Survey Part 2” which are correspondent with two parts of the rating survey sections. The other subfolder “Rating results” consist of one XLSX. file representing survey results downloaded from Qualtric website.

Finally, related data to the open question is saved in “Open question” folder. It contains one csv. file and one PDF. file recording participants’ answers to the open question as well as one PNG. file representing a screenshot of Leximancer analysis outcome.

Methods: This dataset resulted from the input and output of an online survey regarding how people assess the beauty of Great Barrier Reef. This survey was designed for multiple purposes including three main sections: (1) conjoint analysis (ranking 9 photoshopped pictures to determine the relative importance weights of beauty attributes), (2) picture rating (2500 pictures to be rated) and (3) open question on the factors that makes a picture of the Great Barrier Reef beautiful in participants’ opinion (determining beauty factors from tourist perspective). Pictures used in this survey were downloaded from public sources such as websites of the Tourism and Events Queensland and Tropical Tourism North Queensland as well as tourist sharing sources (i.e. Flickr). Flickr pictures were downloaded using the key words “Great Barrier Reef”. About 10,000 pictures were downloaded in August and September 2017. 2,500 pictures were then selected based on several research criteria: (1) underwater pictures of GBR, (2) without humans, (3) viewed from 1-2 metres from objects and (4) of high resolution.

The survey was created on Qualtrics website and launched on 4th October 2017 using Qualtrics survey service. Each participant rated 50 pictures randomly selected from the pool of 2500 survey pictures. 772 survey completions were recorded and 705 questionnaires were eligible for data analysis after filtering unqualified questionnaires. Conjoint analysis data was imported to IBM SPSS using SAV. format and the output was saved using SPV. format. Automatic aesthetic rating of 2500 Great Barrier Reef pictures –all these pictures are rated (1 – 10 scale) by at least 10 participants and this dataset was saved in a XLSX. file which is used to train and test an Artificial Intelligence (AI)-based system recognising and assessing the beauty of natural scenes. Answers of the open-question were saved in a XLSX. file and a PDF. file to be employed for theme analysis by Leximancer software.

Further information can be found in the following publication: Becken, S., Connolly R., Stantic B., Scott N., Mandal R., Le D., (2018), Monitoring aesthetic value of the Great Barrier Reef by using innovative technologies and artificial intelligence, Griffith Institute for Tourism Research Report No 15.

Format: The Online survey dataset includes one PDF file representing the survey format with all sections and questions. It also contains three subfolders, each has multiple files. The subfolder of Conjoint analysis contains an image of the 9 JPG. Pictures, 1 SAV. format file for the Orthoplan subroutine outcome and 5 outcome documents (i.e. 3 SAV. files, 1 SPS. file, 1 SPV. file). The subfolder of Picture rating contains a capture of the 2500 pictures used in the survey, 1 excel file for rating results. The subfolder of Open question includes 1 CSV. file, 1 PDF. file representing participants’ answers and one PNG. file for the analysis outcome.

Data Dictionary:

Card 1: Picture design option number 1 suggested by SPSS orthogonal analysis. Importance value: The relative importance weight of each beauty attribute calculated by SPSS conjoint analysis. Utility: Score reflecting influential valence and degree of each beauty attribute on beauty score. Syntax: Code used to run conjoint analysis by SPSS Leximancer: Specialised software for qualitative data analysis. Concept map: A map showing the relationship between concepts identified Q1_1: Beauty score of the picture Q1_1 by the correspondent participant (i.e. survey part 1) Q2.1_1: Beauty score of the picture Q2.1_1 by the correspondent participant (i.e. survey part 2) Conjoint _1: Ranking of the picture 1 designed for conjoint analysis by the correspondent participant

References: Becken, S., Connolly R., Stantic B., Scott N., Mandal R., Le D., (2018), Monitoring aesthetic value of the Great Barrier Reef by using innovative technologies and artificial intelligence, Griffith Institute for Tourism Research Report No 15.

Data Location:

This dataset is filed in the eAtlas enduring data repository at: data esp3\3.2.3_Aesthetic-value-GBR

Online survey software developers have seen robust revenue growth over the past five years, driven by heightened demand for real-time feedback amid economic turbulence. Companies across retail, healthcare and the public sector turned to online survey platforms to gauge shifting customer sentiment and employee satisfaction, resulting in a 17.9% surge in revenue in 2022. Research and development (R&D) spending soared as businesses sought product differentiation, while public agencies, like the US Department of Veterans Affairs, adopted survey tools for large-scale feedback. Despite controlling a collective four-fifths of the market, major companies Qualtrics and Momentive Global have remained unprofitable, with heavy R&D expenses and stock-based compensation driving persistent losses. These losses attracted private equity interest, culminating in major acquisitions by Silver Lake and STG in 2023. Revenue has surged at a CAGR of 7.6% to an estimated $2.4 billion over the five years through 2025. Innovation has become central to the online survey software industry, reshaping user experience and competition. Artificial intelligence now allows users to automate question generation, reduce bias and analyze respondents' sentiments. Features like Typeform's jumps and interactive formats have boosted completion rates. As clients expect more from their chosen platform, developers have doubled down on expensive AI enhancements and analytics tools to stay competitive. However, these advancements are costly to develop and maintain. While innovation drives revenue and market relevance, it has also stifled profitability by inflating operational costs and intensifying the need for continuous upgrades. The next five years will likely bring slower revenue growth for online survey software developers as corporate profit slumps and businesses scrutinize discretionary spending. Still, economic uncertainty will maintain demand for survey insights, especially in areas like workforce management and product development. Companies will expand their plan options, offering affordable versions for cost-conscious buyers and premium packages featuring personalized, AI-driven analytics for larger enterprises. Stricter data privacy laws will force platforms to bolster security and transparency. Revenue is set to climb at a CAGR of 2.6% to an estimated $2.7 billion through the end of 2030.

These two syntax files were used to convert the SPSS data output from the Qualtrics survey tool into the 17 cleansed and anonymised RAAAP-2 datasets form the 2019 international survey of research managers and administrators. The first creates and interim cleansed and anonymised datafile, the latter splits these into separate datasets to ensure anonymisation. Errata (16/6/23): v13 of the main Data Cleansing file has an error (two variables were missing value labels). This file has now been replaced with v14, and the Main Dataset has also been updated with the new data.

This survey dataset examines COVID-19-related knowledge, attitudes, and adoption of prevention behaviors. The survey was conducted among non-random sample of 464 Connecticut residents in the U.S in the early stage of social distancing and shutdown from March 23 to March 29, 2020. The questionnaires were developed by using Qualtrics software. Participants were purposively recruited. Participants could choose a hyperlink for self-administration of the survey online or were interviewed over the phone or other means of communication and record their answers online. Data was transferred from Qualtrics to SPSS Version 26.0 for analysis. Data were analyzed using frequencies, percentages, means, and standard deviations.

The data relates to the paper that analyses the determinants or factors that best explain student research skills and success in the honours research report module during the COVID-19 pandemic in 2021. The data used have been gathered through an online survey created on the Qualtrics software package. The research questions were developed from demographic factors and subject knowledge including assignments to supervisor influence and other factors in terms of experience or belonging that played a role (see anonymous link at https://unisa.qualtrics.com/jfe/form/SV_86OZZOdyA5sBurY. An SMS was sent to all students of the 2021 module group to make them aware of the survey. They were under no obligation to complete it and all information was regarded as anonymous. We received 39 responses. The raw data from the survey was processed through the SPSS statistical, software package. The data file contains the demographics, frequencies, descriptives, and open questions processed.The study reported in this paper employed the mixed methods approach comprising a quantitative and qualitative analysis. The quantitative and econometric analysis of the dependent variable, namely, the final marks for the research report and the independent variables that explain it. The results show significance in terms of the assignments and existing knowledge marks in terms of their bachelor's average mark. We extended the analysis to a qualitative and quantitative survey, which indicated that the mean statistical feedback was above average and therefore strongly agreed/agreed except for library use by the student. Students, therefore, need more guidance in terms of library use and the open questions showed a need for a research methods course in the future. Furthermore, supervision tends to be a significant determinant in all cases. It is also here where supervisors can use social media instruments such as WhatsApp and Facebook to inform students further. This study contributes as the first to investigate the preparation and research skills of students for master's and doctoral studies during the COVID-19 pandemic in an online environment.

Background: Living near, recreating in, and feeling psychologically connected to nature are all associated with better overall mental health. This study aims to better understand people’s feelings towards different types of natural and built green space environments in the highly urbanized ‘garden city’ of Singapore. The key research question addresses the matter of what types of green space elicit positive (Eudemonic) or negative (Apprehensive) affective responses. Type of environment (natural and built), frequency of experience (high and low) and childhood location (urban, suburban, rural) were tested for effects of Eudemonia and Apprehension. 288 adults and university students residing in Singapore completed a survey that asked them to report affective states in response to images of 10 locally different environment types and to complete measures of nature connectedness, childhood location, frequency of visit to natural/built environments, and dispositional anxiety, as well as demographic items for age and gender.

This data record contains:

• Qualtrics survey data in SPSS (.spss), tab delimited (.dat) and open document (.ods) format.
• Supplementary material in PDF format (.pdf) containing the Mean (sd) ratings of Apprehension (A, anxious, isolated, lonely) and Eudemonia (E, alive, awe, connected, contemplative, empathy, freedom, fun, refreshed, relaxed, serene, talkative) for 10 types of environment.

The Qualtrics survey included the following:

• Participant demographics:
  • Age in years (continuous)
  • Gender (categorical: Male, Female, Nonbinary)
• Categorisation of urban green space in Singapore:
  • 20 photographs of urban green spaces in Singapore (stimuli).
  • 10 categories of urban green spaces consisted of: beach, forest, grassy field, heritage street, modern city street, rooftop garden, river, town park, wetland, and woodland.
  • Two photographs that were best suited to each category according to participant responses (i.e., highest frequency of category selection) were used as stimuli for the study, with a total of 20 photographs selected.
• Experiential feeling states (Eudemonia & Apprehension) (interval) (20 x 14 items).
  • “Imagine yourself in the environment shown above. To what extent would you feel the following?”
  • Responses were recorded on a 7-point scale ranging from not at all (1) to extremely (7).
• Frequency of experience in green space (interval) (20 x 1 item).
  • “On average, how often do you visit or experience the type of environment as the one shown above?” Responses were recorded on a 5-point scale ranging from never (1) to very often (5).
• Childhood location (categorical) (1 item). “In what sort of location did you spend the majority of your childhood?” Urban (Modernised city, city-centre, many buildings with few trees, high traffic), Suburban (More greenery than city-centre but still developed, outside the main city area, neighbourhood towns, moderate traffic), Rural (Mostly greenery, few facilities, low traffic, “kampung” environment).
• Nature Connectedness Index (NCI) (interval) (6 items). "The next items will help us understand how you feel about nature and natural environments. Remember, this is not a test so there are no 'right' or 'wrong' answers. We want to understand how you feel about nature." The six items draw on five pathways to nature connectedness: emotion, beauty, contact, meaning and compassion. Participants respond using a 7-point scale ranging from completely agree (1) to completely disagree (7). Raw scores were transformed using a weighted points index ranging from zero to 100.
• Brief State-Trait Anxiety Inventory (STAIT-5) (interval) (5 items). “A number of statements which people have used to describe themselves are given below. Read each statement and then select the number at the end of the statement that indicates how you generally feel.” Responses are recorded on a 4-point scale ranging from not at all (1) to very much so (4).

Software/equipment used to create/collect the data: Qualtrics Online Survey Software through JCU licence

Software/equipment used to manipulate/analyse the data: SPSS, Microsoft Excel

Anonymised quantitative data collected for the thesis entitled, 'The wake-up call we keep ‘snoozing’: The role of sleep and circadian factors in adolescent anxiety'. Participants completed an online survey on Qualtrics which included questionnaires and behavioural tasks. This dataset is an SPSS file.

Multinomial logistic regression parameter estimates.

The RAAAP project surveyed Research Managers and Administrators from across the world, asking questions about why people became RMAs, why they stayed as RMAs, what skills they need for their jobs (soft and hard), what level of seniority they are, demographic information, and so on - overall up to 222 data points were collected from each respondent. This SPSS syntax file was developed to process the raw qualtrics data, including data cleansing and anonymising. The process is described in detail in the "RAAAP Data Cleansing Process" DOI:10.6084/m9.figshare.5948461

This data set contains the replication data and supplements for the article "Knowing, Doing, and Feeling: A three-year, mixed-methods study of undergraduates’ information literacy development." The survey data is from two samples: - cross-sectional sample (different students at the same point in time) - longitudinal sample (the same students and different points in time)Surveys were distributed via Qualtrics during the students' first and sixth semesters. Quantitative and qualitative data were collected and used to describe students' IL development over 3 years. Statistics from the quantitative data were analyzed in SPSS. The qualitative data was coded and analyzed thematically in NVivo. The qualitative, textual data is from semi-structured interviews with sixth-semester students in psychology at UiT, both focus groups and individual interviews. All data were collected as part of the contact author's PhD research on information literacy (IL) at UiT. The following files are included in this data set: 1. A README file which explains the quantitative data files. (2 file formats: .txt, .pdf)2. The consent form for participants (in Norwegian). (2 file formats: .txt, .pdf)3. Six data files with survey results from UiT psychology undergraduate students for the cross-sectional (n=209) and longitudinal (n=56) samples, in 3 formats (.dat, .csv, .sav). The data was collected in Qualtrics from fall 2019 to fall 2022. 4. Interview guide for 3 focus group interviews. File format: .txt5. Interview guides for 7 individual interviews - first round (n=4) and second round (n=3). File format: .txt 6. The 21-item IL test (Tromsø Information Literacy Test = TILT), in English and Norwegian. TILT is used for assessing students' knowledge of three aspects of IL: evaluating sources, using sources, and seeking information. The test is multiple choice, with four alternative answers for each item. This test is a "KNOW-measure," intended to measure what students know about information literacy. (2 file formats: .txt, .pdf)7. Survey questions related to interest - specifically students' interest in being or becoming information literate - in 3 parts (all in English and Norwegian): a) information and questions about the 4 phases of interest; b) interest questionnaire with 26 items in 7 subscales (Tromsø Interest Questionnaire - TRIQ); c) Survey questions about IL and interest, need, and intent. (2 file formats: .txt, .pdf)8. Information about the assignment-based measures used to measure what students do in practice when evaluating and using sources. Students were evaluated with these measures in their first and sixth semesters. (2 file formats: .txt, .pdf)9. The Norwegain Centre for Research Data's (NSD) 2019 assessment of the notification form for personal data for the PhD research project. In Norwegian. (Format: .pdf)

The Land Manager Experience with Resilience dataset consists of internet-based survey responses from USDA Forest Service planners and managers which investigated beliefs and perceptions surrounding the meaning of resilience and the implementation of resilience-based forest management on national forest management units. The data consists of de-identified survey responses from 428 respondents recruited from a list of 2,213 USDA Forest Service planners listed as "Responsible Officials" and "ID Team" members on National Forest Environmental Impact Statements. The data are available as .csv and .sav (IBM SPSS Statistics 26 Data Document). The survey was administered using Qualtrics Survey software and distributed via email through the use of an anonymous link from January to April 2020. Including metadata, there are 81 columns. Responses are likert-scale and multiple choice formats. These data were collected and analyzed under Joint Fire Science Program (grant #16-3-01-10) funded project entitled "Integrating Social and Ecological Resilience into Forest Management Planning". For more information please visit https://www.firescience.gov.

The project aimed to understand whether young adults who take care of a loved-one (young adult caregivers; YACs) differ in their perceived life balance and psychosocial functioning as compared to young adults without care responsibilities (non-YACs). In addition, this project aimed to understand how YACs evaluated a tool to support informal careg

ivers. This tool (“Caregiver Balance”; https://balans.mantelzorg.nl) is specifically designed to support informal caregivers taking care of a loved-one in the palliative phase and could potentially be adapted to meet the needs of YACs.

In this project, we collected data of 74 YACs and 246 non-YACs. Both groups completed questionnaires, and the YACs engaged in a usability test. The questionnaire data was used to compare the perceived life balance and psychological functioning between YACs and non-YACs, aged 18-25 years, and studying in the Netherlands (study 1). Furthermore, we examined the relationship between positive aspects of caregiving and relational factors, in particular, relationship quality and collaborative coping among YACs (study 2). Finally, we conducted a usability study where we interviewed YACs to understand the needs and preferences towards a supportive web-based solution (study 3).

Table: Study details and associated files

    Number
    Study Name
    Study Aim
    Study Type
    Type of data
    Associated Files


    1
    Perceived life balance among young adult students: a comparison between caregivers and non-caregivers
    Compare the perceived life balance and psychological functions among student young adult caregivers aged 18-25 years (YACs) with young adult without care responsibilities
    Survey study
    Quantitative

ENTWINE_YACs_nonYACsSurvey_RawData

ENTWINE_PerceivedLifeBalanceSurvey_YACs_nonYACs_CleanedData

ENTWINE_ PerceivedLifeBalanceSurvey _Syntax

ENTWINE_YACs_nonYACsSurvey_codebook

    2
    Examining the relationship of positive aspects of caregiving with relational factors among young adult caregivers
    Examine the relationship of positive aspects of caregiving with relational factors, in particular, relationship quality and collaborative coping among a particular group of ICGs, young adult caregivers (YACs), aged 18-25 years.
    Survey study
    Quantitative

ENTWINE_YACs_nonYACsSurvey_RawData

ENTWINE_PositiveAspectsCaregiving_Survey_YACs_cleanedData

ENTWINE_PositiveAspectsCaregiving_Survey_Syntax

ENTWINE_YACs_nonYACsSurvey_codebook

    3
    Exploring the support needs of young adult caregivers, their issues, and preferences towards a web-based tool
    Explore (i) challenges and support needs of YACs in caregiving, (ii) their needs towards the content of the ‘MantelzorgBalans’ tool, and (iii) issues they encountered in using the tool and their preferences for adaptation of the tool.
    Usability study

Qualitative and Quantitative

ENTWINE_Needs_Web-basedTools_YACs_Interview_Usability_RawData [to be determined whether data can be shared]

ENTWINE_Needs_Web-basedTools_YACs_Questionnaires_RawData

Description of the files to be uploaded

Study 1: Perceived life balance among young adult students: a comparison between caregivers and non-caregivers

ENTWINE_YACs_nonYACsSurvey_RawData: SPSS file with the complete, raw, pseudonomyzed survey data. The following cleaned dataset ‘ENTWINE_PerceivedLifeBalanceSurvey_YACs_nonYACs_CleanedData’ was generated from this raw data.

ENTWINE_PerceivedLifeBalanceSurvey_YACs_nonYACs_CleanedData: SPSS file with the cleaned dataset having the following metadata -

Population: young adult caregivers and young adult non-caregivers aged 18-25 years studying in the Netherlands;

Number of participants: 320 participants in total (74 young adult caregivers and 246 young adult non-caregivers)

Time point of measurement: Data was collected from December 2020 till March 2022

Type of data: quantitative

Measurements included, topics covered: perceived life balance (based on the Occupational balance questionnaire [1]), burnout (Burnout Assessment Tool [2]), negative and positive affect (Positive and Negative Affect Schedule [3]), and life satisfaction (Satisfaction with Life Scale [4])

Short procedure conducted to receive data: online survey on Qualtrics platform

SPSS syntax file ‘ENTWINE_ PerceivedLifeBalanceSurvey _Syntax’ was used to clean and analyse ENTWINE_PerceivedLifeBalanceSurvey_YACs_nonYACs_CleanedData dataset

ENTWINE_YACs_nonYACsSurvey_codebook: Codebook having the variable names, variable labels, and the associated code values and code labels for ENTWINE_PerceivedLifeBalanceSurvey_YACs_nonYACs_CleanedData dataset

Study 2: Examining the relationship of positive aspects of caregiving with relational factors among young adult caregivers

ENTWINE_YACs_nonYACsSurvey_RawData: SPSS file with the complete, raw survey data. The following cleaned dataset ‘ENTWINE_PositiveAspectsCaregiving_Survey_YACs_cleanedData’ was generated from this raw data.

ENTWINE_PositiveAspectsCaregiving_Survey_YACs_cleanedData: SPSS file with the cleaned dataset having the following metadata -

Population: young adult caregivers aged 18-25 years studying in the Netherlands;

Number of participants: 74 young adult caregivers

Time point of measurement: Data was collected from December 2020 till March 2022

Type of data: quantitative

Measurements included, topics covered: positive aspects of caregiving (positive aspects of caregiving scale [5]), relationship quality (Relationship Assessment Scale [6]), collaborative coping (Perception of Collaboration Questionnaire [7] )

Short procedure conducted to receive data: online survey on Qualtrics platform.

SPSS syntax file ‘ENTWINE_PositiveAspectsCaregiving_Survey_Syntax’ was used to clean and analyse ‘ENTWINE_PositiveAspectsCaregiving_Survey_YACs_cleanedData’ dataset.

ENTWINE_YACs_nonYACsSurvey_codebook: Codebook having the variable names, variable labels, and the associated code values and code labels for ENTWINE_PositiveAspectsCaregiving_Survey_YACs_cleanedData dataset.

Study 3: Exploring the support needs of young adult caregivers, their issues, and preferences towards a web-based tool

ENTWINE_Needs_Web-basedTools_YACs_Interview_Usability_RawData: Pseudonymized word file including 13 transcripts having the qualitative data from interview and usability testing with the following metadata –

Population: young adult caregivers aged 18-25 years studying in the Netherlands; 13 participants in total

Time point of measurement: data was collected from October 2021 till February 2022

Type of data: qualitative and quantitative

Measurements included, topics covered: Caregiving challenges, support needs and barriers, usability needs, preferences and issues towards eHealth tool

Short procedure conducted to receive data: Online interviews

ENTWINE_Needs_Web-basedTools_YACs_Questionnaires_RawData: Excel sheet having the quantitative questionnaire raw data with the following metadata

Population: young adult caregivers aged 18-25 years studying in the Netherlands; 13 participants in total

Time point of measurement: data was collected from October 2021 till February 2022

Type of data: qualitative and quantitative

Measurements included, topics covered: User experience (user experience questionnaire [8]), satisfaction of using the web-based tool (After scenario questionnaire [9]), Intention of use and persuasive potential of the eHealth tool (persuasive potential questionnaire [10])

Short procedure conducted to receive data: Online questionnaire

Data collection details

All data was collected, processed, and archived in accordance with the General Data Protection Regulation (GDPR) and the FAIR (Findable, Accessible, Interoperable, Reusable) principles under the supervision of the Principal Investigator.

The principal researcher and a team of experts (supervisors) in the field of health psychology and eHealth (University of Twente, The Netherlands) reviewed the scientific quality of the research. The studies were piloted and tested before starting the collection of the data. For the survey study, the researchers monitored the data collection weekly to ensure it was running smoothly.

The ethical review board, Centrale Ethische Toetsingscommissie of the University Medical Center Groningen, The Netherlands (CTc), granted approval for this research (Registration number: 202000623).

Participants digitally signed informed consent for participating in the study.

Terms of use

Interested persons can send a data request by contacting the principal investigator (Prof. dr. Mariët Hagedoorn, University Medical Center Groningen, the Netherlands mariet.hageboorn@umcg.nl).

Interested persons must provide the research plan (including the research question, methodology, and analysis plan) when requesting for the data.

The principal investigator reviews the research plan on its quality and fit with the data and informs the interested person(s).

(Pseudo)anonymous data of those participants who agreed on the reuse of their data is available on request for 15 years from the time of completion of the PhD project.

Data will be available in Excel or SPSS format alongside the variable codebook after the completion of this PhD project and publication of the study results.

References

1. Wagman P, Håkansson C. Introducing the Occupational Balance Questionnaire (OBQ). Scand J Occup Ther 2014;21(3):227–231. PMID:24649971

2. Schaufeli WB, Desart S, De Witte H. Burnout assessment tool (Bat)—development, validity, and reliability. Int J Environ Res Public Health 2020;17(24):1–21. PMID:33352940

3. Watson D, Clark LA, Tellegen A. Development and Validation of Brief Measures of Positive and Negative Affect: The

Dataset supporting data for a University of Southampton Doctorate in Clinical Psychology Thesis titled "Self-Esteem and Gaming Disorder" (both study 1 and study 2). The data was collected using Qualtrics and then transferred to SPSS. You need SPSS software in order to access the data. Also included are two participant information and consent forms that participants completed prior to study 1 or study 2. You need Microsoft Word software in order to access these files.

The aim of the study was to investigate the main arguments used for and against CCS in national newspapers in the Netherlands. Newspaper articles were analyses using Qualtrics and Atlas.ti, resulting in an SPSS dataset.

In this dataset, a cross-sectional survey design was used to collect data. A non-probability sampling techniques were used, which included both convenience and snowball methods to recruit participants.Qualtrics, an online survey tool, was used to collect data from participating individuals. The online survey contained a questionnaire that included all the measuring instruments. After the survey had been compiled and all instruments had been included in the questionnaire, an email explaining the purpose of the study, an informed consent letter, and a link to the Qualtrics survey were distributed to the veterinary professionals. Data was downloaded to Microsoft Excel once collection was completed. To conduct the data analysis, the SPSS program was used, and the data were coded, captured, and cleaned. A codebook is attached which clearly explains the different codes that were used in the dataset. In collecting data, the researcher did not disregard the regulations of the Protection of Personal Information Act (Act 4 of 2013) because this contact information is freely available to the public on SAVC’s website. The researcher only used this information to contact the professionals and enquire as to their willingness to participate voluntarily in the study.

This dataset contains the data collected during a lab study investigating the inherent emotional and sensory qualites of the pressure touch parameters: force, surface area and actuation speed. Two touch devices were used in the study: a McKibben sleeve and a motorized ribbon. The study is a 3x2 design were all parameters were presented at two different intensity levels. Participants wore noise-canceling headphones and a barrier prevented them from seeing their arm in the set-up.

Participants indicated the affect of a stimulus with the emojigrid and chose which emotional and sensory qualities they found appropriate from a check-all-that-apply list. Their answers were logged via qualtrics and can be found in a .xlsx file the original coding that corresponds with the qualtrics questionaire can be found in the spss.sav. The exact forces measured for the study with the motorized ribbon for each participant and in what order can be found in .txt files in the Experience_profiling_stim_data.zip.

Survey responses exported from Qualtrics XM platform used for data analysis. The objectives of the study were to evaluate the user experience of current Wisdom Panel customers and evaluate their motivations to pursue canine genetic services, their perceptions of the services and test(s) used, and their response to the canine genetic test results. The file format provided is for Excel. Data analysis was completed using SPSS version 28. Please contact the author directly with any questions about the data.

Can a name (i.e., verbal context) change how we react to and perceive an object? This question has been addressed several times for chemosensory objects, but never for touch. To address this, two studies were run. In each, we allocated participants to a Positive, Neutral or Negative Group, and asked them to touch the same four objects, twice – first, named by the experimenter according to their Group-name, and second, named by the participant. Participants were timed as they touched and rated the objects on pleasantness and disgust. Negative-named objects were touched for shorter durations, and rated more negatively, than neutral-named objects, and positive-named objects were touched for the longest and rated most positively. In the second presentation, most objects (>90%) were named by participants in accord with their assigned Group-names. The similarity of these findings to chemosensory verbal context effects and their mechanistic basis is discussed. Methods Two lab based experiments - data collected via Qualtrics, Time-duration recordings, and Video-data. Data processed on SPSS. All methods and analyses are detailed in the MS.

This dataset consists of 3 files:

File 1: Qualitative data enclosed in Microsoft Excel file. Data collection includes one to one interviews with Western Australian migrants.

Content analysis: interview utterances were analysed for content, classified into categories, and coded and entered in Microsoft Excel file columns.

File 2: Qualitative data enclosed in Microsoft Word file. Data collection includes one to one interviews with Western Australian migrants.

Content analysis: interview utterances were analysed for content, classified into categories, and coded and entered in Microsoft Word file columns.

File 3: Quantitative data enclosed in Qualtrics software which includes public librarians’ responses to a Web survey. SPSS Tables and Figures imported from the Qualtrics Software raw data into a Word file.

One prediction derived from the disease avoidance account of disgust is that proximal disgust cues (smells, tastes, touches), should elicit this emotion more intensely than distal disgust cues (sights, sounds). If correct, then memories of disgusting experiences should involve smelling, tasting or touching to a greater degree, than seeing or hearing. Two surveys were conducted on university students to test this idea, drawing upon their naturalistic experiences. Survey one (N = 127) asked participants to detail their most memorable disgusting, fear-provoking, morally-repulsive, and yucky/gross experience, with each recollection self-rated for sensory involvement. Survey two (N = 89) employed the same task, but this time participants recollected their most common disgusting, fear-provoking, morally-repulsive, and yucky/gross experience in the preceding week. For core disgusts, the proximal and distal sensory cues contributed equally for most memorable disgust experiences, but proximal ex..., Data was collected on Qualtrics, and analysed via SPSS and R (for graphical purposes only). Only deidentifiable data is uploaded., , # Differential involvement of the senses in disgust memories

This survey examines which senses are most involved in participants' most memorable (across their lives) and most-common (in their past week), disgusting, yucky/gross, fearful and morally repulsive experiences.

We had two main aims: (1) Core disgust experiences (disgust, and yucky/gross) will have more involvement of proximal senses (smell, taste, passive touch, active touch) compared to the distal senses (sound, sight). To achieve this aim we compare whether any proximal sense was involved more than any distal sense (i.e., maximum distal vs. maximum proximal). (2) Core disgust experiences (disgust, and yucky/gross) will have more involvement of proximal vs distal sensory involvement, relative to other forms of disgust and emotions (morally repulsive and fear**). To test this we compare the difference in proximal and distal sensory involvement (proximal sensory minus distal sensory), across the different affective stat...