These datasets contain cleaned data survey results from the October 2021-January 2022 survey titled "The Impact of COVID-19 on Technical Services Units". This data was gathered from a Qualtrics survey, which was anonymized to prevent Qualtrics from gathering identifiable information from respondents. These specific iterations of data reflect cleaning and standardization so that data can be analyzed using Python. Ultimately, the three files reflect the removal of survey begin/end times, other data auto-recorded by Qualtrics, blank rows, blank responses after question four (the first section of the survey), and non-United States responses. Note that State names for "What state is your library located in?" (Q36) were also standardized beginning in Impact_of_COVID_on_Tech_Services_Clean_3.csv to aid in data analysis. In this step, state abbreviations were spelled out and spelling errors were corrected.

This dataset contains the raw data survey results from the October 2021-January 2022 survey titled "The Impact of COVID-19 on Technical Services Units". This data was gathered from a Qualtrics survey, which was anonymized to prevent Qualtrics from gathering identifiable information from respondents. The specific iteration of this data does not reflect the removal of survey begin/end times, or other data auto-recorded by Qualtrics, nor has it removed blank rows or non-United States responses.

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

Fertilizers and deicers are common materials for property maintenance in the Midwest, however, their application contributes to negative environmental impacts when applied incorrectly. While fertilizer use is well researched, deicer use on private properties is not. This research aims to ascertain whether patterns of fertilizer use are different from those of deicer use in Hamilton County, Ohio, and determine what factors influence a resident’s decision to use these materials. Survey data were collected from 110 single-family households (38.9% response rate). Respondents are motivated by property appearance to apply fertilizers. Deicer use stems from safety concerns. Respondents were significantly more likely to consider the environmental impact of fertilizers than deicers. Respondents felt that using deicers is a more neighborly practice while using fertilizers reflects more positively on them in their neighborhood. This information can be used to develop outreach programs to reduce the environmental impacts of fertilizers and deicers. Methods A survey was designed to gauge respondents' perceptions and usage of fertilizers and deicers. Questions included in the survey asked respondents about the frequency with which residents use fertilizer and deicer, perceptions and knowledge of these materials, and demographic information (e.g., age, income, education, gender). Previous studies which focused on individuals’ uses of fertilizers, deicers, and other lawn management practices were used as a guide for designing questions for this survey. A random sample of 300 single family homes in Hamilton County was selected to receive the survey materials using ArcGIS Pro 2.9.2 and parcel data downloaded from the Hamilton County Community Planning Maps and GIS website in May of 2022. The surveys, as well as a $2 bill incentive, were distributed and collected using the Drop-off Pick-up (DOPU) method. Each survey packet contained a cover letter and printed cover sheet entitled “Research Consent Form” which informed potential participants about their rights as a survey participant. The cover sheet specified that answering the questions on the survey was completely voluntary and that the data participants provided would be anonymized and presented in aggregate form so that no one individual or household could be identified. No participant under 18 years of age was recruited and the cover letter stated that “Participation in this research is restricted to persons 18 years of age or older”. Lastly, the consent form provided contact information for the researchers and our Research Ethics and Integrity Office. Placing the fully or partially completed survey for the researchers to retrieve was understood as providing informed consent. The survey instrument, consent form, and recruitment mechanism were approved by the Research Ethics and Integrity Office at Miami University (project # 04247e). The dates of recruitment of participants, distribution and collection of survey materials took place from the June 1st to August 20th, 2022. Completed survey responses were recorded using Qualtrics. ArcGIS Pro was used to classify land cover and area for each household selected for surveying. The land covers on each parcel were digitized and divided into the following categories: lawn, building, driveway, sidewalk, patio, and pool. The various land cover classifications and their surface area for each parcel were used to calculate suggested fertilizers and deicer amounts for each household. These suggested amounts were compared to the amounts self-reported by respondents in the surveys.

High white-tailed deer abundance in the United States represents an ecological and human health threat. Reducing deer populations by lethal means and facilitating return of large predators are two potential, but controversial, management options. We used an online questionnaire to measure perspectives on deer management and predator return among a stratified sample of New York State residents. We found widespread acceptance (>70%) for reducing deer populations using lethal means if doing so would reduce Lyme disease, increase forest regeneration, protect native plants and animals, and improve road safety. Acceptance for shooting more deer was unaffected by ethnicity but strongest among respondents who were older, identified as hunters or conservationists, owned more land, and considered health and safety while answering our questionnaire. Respondents who identified as animal protectionists were least accepting. Restoring regionally extirpated wolves and cougars had limited acceptance (< 30%) but was strongest among those who identified as hunters or conservationists. Contrary to commonly held beliefs, preferences for deer management or predator restoration did not differ among urban and rural respondents. This common ground needs to be reflected in deer management in the state due to legal obligations to represent interests of all residents. Methods This dataset contains data from an online questionnaire we used to assess perspectives of New York State residents on deer management and potential return of large predators. Qualtrics LLC (www.qualtrics.com) recruited 1,206 adults (aged 18 or older) living in New York State who answered our questionnaire from 6 - 28 June 2022. To reduce sampling error and increase external validity, we stratified our sample to approximate the population of New York State in terms of age, ethnicity, and gender identity according to the most recent American Community Survey statistics (U.S. Census Bureau, 2020). We oversampled from rural areas to permit more powerful rural-urban comparisons. Respondents reported beliefs about who should participate in deer management; how acceptable it would be for people who shoot deer to use meat and other parts in various ways; how acceptable it would be for land managers to allow shooting more deer if doing so would help achieve various ecological and socioeconomic objectives; and how acceptable if would be for wolves and cougars to return to New York, either by natural recolonization or deliberate reintroduction, in order to help manage deer. We recorded responses using seven-point Likert-type items with the additional option of “I don’t know”. Individuals indicated relevance of ethical concerns when responding to previous blocks using four-point ordinal scales. Respondents described their perceptions and experiences with deer using a combination of ordinal and seven-point Likert scales. Respondents provided additional demographic and social identity information. To discover potential distinguishing characteristics of individuals who perceived shooting more deer generally to be more or less acceptable, we created a composite score of their responses to 11 items on deer management. We first converted the seven-point Likert scale to a numerical scale (strongly disagree = 1, disagree = 2, somewhat disagree = 3, neither agree nor disagree = 4, somewhat agree = 5, agree = 6, strongly agree = 7), and calculated the mean of these values across items for each respondent, excluding “I don’t know” responses. Following this method, we also created composite scores for responses to questions on whether wolves and cougars should be allowed to return or be reintroduced, and whether respondents would welcome them to their local area. The composite deer and predator scores served as our response variables in analyses, with respondents' answers to other survey questions as the predictor variables.

Interview data obtained by Gonzaga University students takes the following two forms: 1) Raw interview data are provided in a zip file with the 17 text file transcripts of interviews with personally identifiable information removed. An interview script and a summary file indicating the stakeholder group and state of residence of the experts is also included in the zip file 2) Processed interview data includes a zip file with the output from the mixed method analysis of transcripts in Dedoose software. The excel files include number and percentage of interview text coded to the defined themes. The survey data were compiled by the University of Idaho with assistance from Qualtrics, an experience management company. Prior to sharing these results, Qualtrics conducted a quality control check so it is assumed that any inconsistent data have already been removed. The data file provides the entirety of the survey responses in a spreadsheet format. Four different data types were obtained from Oregon State Driving and Bicycling Simulator Laboratory for purpose of this report and they are as follow: 1) Speed data from the Passenger Car Driving Simulator consists of subject number, average speed, and all the independent variables. Speed data were collected based on the driver’s speed while driving through a certain scenario (total of 16). For each scenario, the average speed (mph) of 29 drivers were recorded at every intersection (10 ft upstream of the stop sign). 2) Speed data from the Bicycling Simulator consists of subject number, average speed, and all the independent variables. Speed data were collected based on the bicyclist’s speed while driving through a certain scenario (total of 16). For each scenario, the average speed (mph) of 30 bicyclists were recorded at every intersection (20 ft upstream of the stop sign). 3) Eye tracking data of bicyclists consists of subject number, total fixation duration (TFD) in milliseconds, area of interest (AOI), and all the independent variables. TFD data were collected while a bicyclist maneuvers through a certain scenario (total of 16). For each scenario, the TFD for each AOI was recorded for 21 subjects at every intersection (along 100 ft upstream of the stop sign). AOI represent the area of interest that a driver fixates for a certain of time to generate the total fixation duration. 4) Eye tracking data of bicyclists consists of subject number, GSR in peaks per minute, and all the independent variables. GSR data were collected while a bicyclist maneuvers through a certain scenario (total of 16). For each scenario, the peaks per minute data was recorded for 22 subjects at every intersection (along 100 ft upstream of the stop sign). Peaks per minute represents the emotional arousal (i.e., something is scary, threating, joyful, etc.) that a driver generates when reacting to a particular event. 80 participants were recruited, 40 for the bicycling simulator and 39 for the driving simulator. Simulator sickness affected 9 participants and equipment failure affected 11 participants, so they were excluded from the data and the analysis, bringing the bicycling and car simulator participant number to 59 for full analysis. There are no quality or consistency issues with this data set. The average values were calculated to apply robust statistical analysis for such data (speed). As the experiment consists of 4x2x2 factorial design, each participant had to driver or ride through 16 scenarios; therefore, approximately 480 scenario observations for each simulator were obtained and recorded in the excel files. The interview data was collected between February 10th and March 17th, 2021. The survey data were collected between July 2nd and July 8th, 2021. The heavy vehicle driving simulator data was collected between Mar 29th and April 14th, 2022.

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.

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

This dataset contains all data and analysis scripts pertaining to the research conducted for the frontiers paper: "Using a conversational agent for thought recording as a cognitive therapy task: feasibility, content, and feedback." Following a literature review that we conducted in 2017 and 2018 on the technological state of the art of e-mental health for depression, we saw an opportunity to use technology in a more dialogical way than was being done to date. We therefore developed a conversational agent to support people in regularly recording their thoughts. This thought recording is a common technique in cognitive therapy. The cognitive approach to psychotherapy aims to change patients' maladaptive schemas, that is, overly negative views on themselves, the world, or the future. To obtain awareness of these views, they record their thought processes in situations that caused pathogenic emotional responses. We recruited 308 participants through Prolific, a crowd-sourcing platform for research participants. The participants interacted with our chatbot in two sessions, one practice session of two thought records based on scenarios and one actual session in which we asked to complete at least one personal thought record but as many additional ones as they wanted. We assessed the feasibility of completing the task with the agent, the content of the personal thought records, and whether the agent providing feedback on the content of the thought record (using natural language processing) had a positive e ect on the number of voluntarily completed thought records and participant's engagement in self-reection. We here deliver:

a natural language dataset: the thoughts delineated by participants in the scenario-based and open thought records the coding of all personal thought records on their content by two independent coders: all thought records of the second session were labeled with respect to their content on the DIAMONDS and on three additional categories (COVID, Achievement/Competence, and Comprehensibility) analyses to test the hypotheses related to whether the feedback of the agent can increasemotivation to complete thought records additional materials (scenarios, qualtrics surveys, data management plan) that could assist in the replication of the study.

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.

Dataset for research study "Gender Stereotypes in Impression Formation." Participants were randomly assigned to one of three conditions: stereotype-contradicting, stereotype-confirming, and neutral. All groups completed a mental imagery task that either contradicted, confirmed, or was neutral to gender stereotypes about physicians. The participants then completed a first impressions task, in which they chose between headshots of a man and a woman and decided who was most likely to be the physician. Participants’ judgements and response latency were recorded. Comparisons were made between the responses and response latency both across age and within the conditions across groups, as well as overall responses and response latency among the three groups. In addition, the descriptive data from the mental imagery task were analyzed.

From April 13, 2022 to August 26, 2022, 100 Ontario dairy farms were visited where blood from 1,990 dairy heifers (median = 20 samples per farm, range = 17 to 20; Figure 1) and 100 bulk tank milk samples were collected. In addition, a questionnaire was completed at each of the dairy farms, where each question was read verbally to the farmers by a single graduate student with responses recorded in a Qualtrics form. An anonymized distribution of responses to the questionnaire is available here as a PDF and raw CSV file.

These are data resulting from and related to an effort to examine subjectivity in the process of performing quality control on water quality data measured by in situ sensors. Participants (n=27) included novices unfamiliar with and technicians experienced in quality control. Each participant performed quality control post processing on the same datasets: one calendar year (2014) of water temperature, pH, and specific conductance. Participants were provided with a consistent set of guidelines, field notes, and tools. Participants used ODMTools (https://github.com/ODM2/ODMToolsPython/) to perform the quality control exercise. This resource consists of: 1. Processed Results: Each file in this folder corresponds to one of the variables for which quality control was performed. Each row corresponds to a single time stamp and each column corresponds to the processed results generated by each participant. The first column corresponds to the original, raw data. 2. Survey Data: The files in this folder are related to an exit survey administered to participants upon completion of the exercise. It includes the survey questions (pdf), the full Qualtrics output (QualityControlSurvey.pdf), data and metadata files organized and encoded for display in the Survey Data Viewer (http://data.iutahepscor.org/surveys/survey/QCEXP) (QCExperimentSurveyDataFile.csv, QCExperimentSurveyMetadata.csv), and a file used to organize data for plots for the associated paper. 3. Field Record: Participants were provided this document, which gives information about the field maintenance activities relevant to performing QC. 4. Scripts: Each file in this folder corresponds to a script automatically generated by ODMTools while performing quality control. The files are organized by user ID and by variable. 5. Code and Analysis: Script used to generate the figures for this work in the associated paper. It is important to note that novice users correspond to IDs 1-22 and experienced users correspond to IDs 25-38. This folder also includes subsets of the data organized in supporting files used to generate Figure 6 (ExpGapVals.xlsx) and Table 5 (NoDataCount.xlsx).

The raw data, preprocessing script, preprocessed data file, and the main analyses for the project "Are new gender-neutral pronouns difficult to process in reading? The case of hen in Swedish". The psychopy data set includes measures of key press responses to comprehension questions recorded during the experiment. It also includes information on in what order the stimuli were displayed to the participant, and how much time they spent looking at the stimulus until they pressed a key to move on to the next stimulus. The questionnaire data set includes background information collected through the online questionnaire designing webpage Qualtrics (https://qualtrics.com) after the eye-tracking experiment. Data was collected between Sept 25th 2017 and March 8th 2018. The eye-tracking data set includes measures of reading behaviors such as fixation time on stimuli including gendered and gender-neutral pronouns. Instrument- or software-specific information needed to run the experiment and interpret the data: Psychopy (Peirce et al., 2019) for running the experiment (https://www.psychopy.org/). Python for opening and managing the related .py files (https://www.python.org).

BeGaze (SensoMotoric Instruments, 2014) for opening and managing the .idf files. Requires software license. These files were created using SMI's software iView for recording eye movements with the eye-tracker.[https://gazeintelligence.com/smi-software-download] Rstudio used with R (R Core Team, 2017): https://rstudio.com/products/rstudio/ More information can be found in the README files, and on our OSF page.

The replication package includes the complete survey structure and the email invitation (with the Qualtrics' embedded fields). The participant collection sheet used for the convenience sample is shared empty, to disclose the data that was collected; note that we cannot provide the completed sheet (which included name, email and affiliation of invited participants) because we are restricted by our Ethical Protocol to preserve the participant's identity. This is a problem known as the `privacy vs utility paradox' (Li et al., 2009), and its study was out of scope for this investigation.

Project Summary This dataset contains all qualitative and quantitative data collected in the first phase of the Pandemic Journaling Project (PJP). PJP is a combined journaling platform and interdisciplinary, mixed-methods research study developed by two anthropologists, with support from a team of colleagues and students across the social sciences, humanities, and health fields. PJP launched in Spring 2020 as the COVID-19 pandemic was emerging in the United States. PJP was created in order to “pre-design an archive” of COVID-19 narratives and experiences open to anyone around the world. The project is rooted in a commitment to democratizing knowledge production, in the spirit of “archival activism” and using methods of “grassroots collaborative ethnography” (Willen et al. 2022; Wurtz et al. 2022; Zhang et al 2020; see also Carney 2021). The motto on the PJP website encapsulates these commitments: “Usually, history is written only by the powerful. When the history of COVID-19 is written, let’s make sure that doesn’t happen.” (A version of this Project Summary with links to the PJP website and other relevant sites is included in the public documentation of the project at QDR.) In PJP’s first phase (PJP-1), the project provided a digital space where participants could create weekly journals of their COVID-19 experiences using a smartphone or computer. The platform was designed to be accessible to as wide a range of potential participants as possible. Anyone aged 15 or older, living anywhere in the world, could create journal entries using their choice of text, images, and/or audio recordings. The interface was accessible in English and Spanish, but participants could submit text and audio in any language. PJP-1 ran on a weekly basis from May 2020 to May 2022. Data Overview This Qualitative Data Repository (QDR) project contains all journal entries and closed-ended survey responses submitted during PJP-1, along with accompanying descriptive and explanatory materials. The dataset includes individual journal entries and accompanying quantitative survey responses from more than 1,800 participants in 55 countries. Of nearly 27,000 journal entries in total, over 2,700 included images and over 300 are audio files. All data were collected via the Qualtrics survey platform. PJP-1 was approved as a research study by the Institutional Review Board (IRB) at the University of Connecticut. Participants were introduced to the project in a variety of ways, including through the PJP website as well as professional networks, PJP’s social media accounts (on Facebook, Instagram, and Twitter) , and media coverage of the project. Participants provided a single piece of contact information — an email address or mobile phone number — which was used to distribute weekly invitations to participate. This contact information has been stripped from the dataset and will not be accessible to researchers. PJP uses a mixed-methods research approach and a dynamic cohort design. After enrolling in PJP-1 via the project’s website, participants received weekly invitations to contribute to their journals via their choice of email or SMS (text message). Each weekly invitation included a link to that week’s journaling prompts and accompanying survey questions. Participants could join at any point, and they could stop participating at any point as well. They also could stop participating and later restart. Retention was encouraged with a monthly raffle of three $100 gift cards. All individuals who had contributed that month were eligible. Regardless of when they joined, all participants received the project’s narrative prompts and accompanying survey questions in the same order. In Week 1, before contributing their first journal entries, participants were presented with a baseline survey that collected demographic information, including political leanings, as well as self-reported data about COVID-19 exposure and physical and mental health status. Some of these survey questions were repeated at periodic intervals in subsequent weeks, providing quantitative measures of change over time that can be analyzed in conjunction with participants' qualitative entries. Surveys employed validated questions where possible. The core of PJP-1 involved two weekly opportunities to create journal entries in the format of their choice (text, image, and/or audio). Each week, journalers received a link with an invitation to create one entry in response to a recurring narrative prompt (“How has the COVID-19 pandemic affected your life in the past week?”) and a second journal entry in response to their choice of two more tightly focused prompts. Typically the pair of prompts included one focusing on subjective experience (e.g., the impact of the pandemic on relationships, sense of social connectedness, or mental health) and another with an external focus (e.g., key sources of scientific information, trust in government, or COVID-19’s economic impact). Each week,...

Data were collected using online user surveys to assess participants' responses to textual information (words) in terms of participation, perception, and perseverance dimensions of information engagement (IE). The surveys were administered through the Qualtrics platform, ensuring efficient data collection and management. In this revised study, each participant was presented with 7 to 10 words randomly selected from the dataset, and the order of presentation was randomized to minimize potential biases. The use of multiple sets of words allowed for a more comprehensive investigation into the impact of phrasing on IE. A total of 80,500 observations were collected from 8,561 distinct participants, providing a substantial dataset for analysis. To ensure the validity of the findings, the survey design aimed to control for potential biases, such as selection bias and allocation bias. Chi-square analysis was conducted to assess the goodness of fit and ensure the representativeness of the samples. The analysis revealed that the composition of demographic groups who responded to each word sample was comparable to that of the overall population, indicating the reliability of the collected data. The measurement of perception, participation, and perseverance followed established scales and methodologies. Participants provided evaluations of the words' sensory appeal, attention-drawing capabilities, ease of understanding, and overall reward using a 5-point scale. The selection and retention rates were recorded to measure participants' active engagement and information retention, respectively. The comprehensive dataset and rigorous survey design provide a robust foundation for analyzing the impact of word choice on the dimensions of information engagement. The findings derived from this dataset will contribute to a deeper understanding of how word choice influences users' perceptions, participation, and perseverance, and inform strategies for effective communication and engagement in various domains.

The User Interview Tools market, valued at $1705 million in 2025, is projected to experience robust growth, driven by the increasing adoption of user-centered design methodologies across diverse industries. Companies are prioritizing user feedback to enhance product development, improve user experience (UX), and gain a competitive edge. The market's expansion is fueled by the rising demand for efficient and scalable tools that streamline the user interview process, from participant recruitment to data analysis. Key trends shaping this market include the integration of AI-powered analytics for automated insights extraction, the rise of remote user testing solutions catering to geographically dispersed teams, and the increasing focus on qualitative data analysis to complement quantitative data. While challenges remain, such as the need for specialized skills in conducting and analyzing user interviews, and the potential for data privacy concerns, the overall market outlook remains positive. The high adoption of agile development methodologies further contributes to the demand for rapid user feedback loops, solidifying the role of user interview tools. The competitive landscape is fragmented, with a mix of established players like Qualtrics and UserTesting alongside emerging innovative companies such as UXtweak and Loop11. These companies offer a range of features, including participant recruitment platforms, screen recording tools, usability testing software, and qualitative data analysis capabilities. The market is expected to see further consolidation through mergers and acquisitions as companies strive to expand their feature sets and market reach. The continued focus on improving accessibility and affordability of these tools will further drive market penetration, especially amongst smaller businesses and startups. Geographic expansion, particularly in rapidly developing economies, presents significant growth opportunities for market players.

INCLUDE was a mixed-methods cross-sectional observational study embedded in, and forming a discrete component of, the ongoing longitudinal ‘Improving the experience of Dementia and Enhancing Active Life’ (IDEAL) cohort.

INCLUDE comprised 172 people with dementia and 288 carers living in England and Wales. People with dementia residing in care homes could not be contacted to take part so only community dwelling people with dementia took part in INCLUDE. Interviews with people with dementia were carried out over the telephone or via videoconference. All responses were recorded by researchers using an online survey designed in Qualtrics. Carers were either interviewed in a similar way or self-completed an online form in their own time. Data were stored in Qualtrics.

Structured assessments with all participants were conducted remotely due to COVID-19 restrictions between September 21, 2020, and April 30, 2021. For people with dementia, interviews began with questions about health and healthcare during the pandemic, and subsequent sections covered perceptions of social connection and relationships, psychological health, ability to manage everyday life during the period, and overall perceptions of the capability to ‘live well.’ Carers were asked to provide informant reports and describe their own experiences. The questions for carers covered health, social networks, psychological well-being, and caregiving experiences.

In addition, people with dementia and carers who were both willing and able to participate in a semi-structured interview were identified, and interviews were conducted remotely between November 2020 and January 2022. Three sets of interviews were conducted, totalling 51 interviews. In the first set, eighteen interviews were conducted representing 21 participants: 8 were with people with dementia, 7 were with carers and 3 were joint interviews, i.e., the person with dementia and carer were interviewed together. In the second set, fourteen interviews were conducted representing 15 participants: 7 with people with dementia, 6 with carers, and 1 joint interview. For the third set, participants had previously been interviewed either for one of the two sets of INCLUDE in-depth interviews or for an earlier IDEAL sub-study conducted at the start of the pandemic, the IDEAL COVID-19 Dementia Initiative (IDEAL-CDI). In the third set, nineteen interviews were conducted: 9 with people with dementia and 10 with carers. Of the people with dementia interviewed in this third set, 6 participants were from INCLUDE set 1 interviews, 2 from INCLUDE set 2 interviews and 1 from IDEAL-CDI. Of the carers interviewed in this third set, 6 participants were from INCLUDE set 1 interviews, 3 from INCLUDE set 2 interviews and 1 from IDEAL-CDI. It must be noted that the IDEAL-CDI interviews are not archived, and the data is not currently available.INCLUDE was a mixed-methods, cross-sectional observational study embedded in, and forming a discrete component of, the ongoing, longitudinal ‘Improving the experience of Dementia and Enhancing Active Life’ (IDEAL) cohort study. The 10-year IDEAL programme centres on a longitudinal cohort study of people with dementia and family carers (hereafter ‘carers’) across Great Britain. The IDEAL programme has two phases, IDEAL (2014 – 2019) and IDEAL-2 (2018 – 2023). IDEAL was funded by the Economic and Social Research Council and National Institute for Health Research and IDEAL-2 was funded by Alzheimer’s Society as a Centre of Excellence. INCLUDE added a COVID-19-specific data-collection module to the planned follow-ups of the cohort in IDEAL-2. The study was co-ordinated by the Centre for Research in Ageing and Cognitive Health (REACH) at the University of Exeter Medical School.

Involvement of people with dementia and carers is central to the IDEAL programme. The ALWAYs (‘Action on Living Well: Asking You’) group of people with dementia and carers was set up to ensure meaningful involvement. The involvement of people with dementia and carers ensured that the study processes, materials, and emerging outcomes were clear and relevant. The ALWAYs group advised on the design and content of the INCLUDE surveys.

INCLUDE aimed to identify the impact of COVID-19 and resulting physical distancing measures on PwD and their carers. The goals of INCLUDE were as follows: 1. To identify the impact of COVID-19 on people with dementia and carers. 2. To understand reciprocal dyadic influences. 3. To build on this evidence to create resources to support the social, mental and physical health, and relationships of community-dwelling people with dementia and carers and provide guidance to health, social care, and voluntary sector staff.

The global healthcare survey software market is experiencing robust growth, driven by increasing demand for patient satisfaction measurement, enhanced healthcare quality, and the need for efficient data collection in clinical research. The market size in 2025 is estimated at $2.5 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This growth is fueled by several key trends, including the rising adoption of cloud-based solutions, integration with Electronic Health Records (EHR) systems, and the increasing use of mobile-first survey platforms to reach a wider range of patients and healthcare professionals. Furthermore, regulatory compliance requirements and the need for data-driven decision-making within healthcare organizations are significant drivers. The market is segmented by deployment type (cloud, on-premises), survey type (patient satisfaction, employee engagement, clinical research), and end-user (hospitals, clinics, pharmaceutical companies). While competition is fierce among established players like Qualtrics, SurveyMonkey, and QuestionPro, smaller, specialized companies are also gaining traction by offering niche solutions tailored to specific healthcare needs. Challenges include data security and privacy concerns, integration complexities with existing healthcare IT infrastructures, and the need for user-friendly interfaces for diverse user groups. The projected market size in 2033, based on the 15% CAGR, is estimated to exceed $8 billion. This substantial growth reflects the continuing digital transformation within the healthcare sector and the increasing recognition of the value of data-driven insights for improving patient care, operational efficiency, and research outcomes. Key players are investing heavily in research and development to enhance their platforms with advanced analytics capabilities, AI-powered features, and better integration possibilities. The focus on personalized medicine and proactive healthcare further strengthens the market's growth potential, as these approaches rely heavily on the collection and analysis of patient data via sophisticated survey tools. The market's success hinges on addressing the inherent challenges related to data privacy and regulatory compliance while continually enhancing user experience and analytical capabilities.