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.

Includes the raw data, a filter variable to remove straight-lining responses, calculated scales, and saved standardized residuals from the ANCOVA models. In this file, “text_only” refers to the “no on-screen presenter” treatment. (SAV)

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...

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.

The key objective of the research was to empirically examine the validity of immersive multisensory VR (food) environments for modeling real-world food cue responses [FCR]. We conducted a lab-based "proof-of-principle" study to systematically compare individuals’ FCRs (i.e., self-reported craving and salivation levels) between a unisensory (vision only) VR environment, multisensory (vision + olfaction) VR environment, and a comparable real-life setting. Furthermore, we investigated possible mechanisms (i.e., presence) underlying FCR-enhancing effects of a multisensory VR environment. Research Questions: To what extent do individuals’ psychological (i.e., subjective craving) and physiological (i.e., salivary volume) responses to food cues (versus non-food cues) differ between a unisensory (vision only) VR environment versus a multisensory (vision + olfaction) VR environment versus a comparable real-life setting? What mechanisms underlie (indirect) FCR-enhancing effects of the multisensory VR environment? This lab-based study had a within-subjects cue exposure paradigm with a 2 (Stimuli Type: Non-food vs Food) by 3 (Exposure Mode: Unisensory VR vs Multisensory VR vs Real-life) factorial design. In total, there were 6 experimental conditions. Participants attended one screening session (~10 minutes) and one lab-based test session (~45 minutes) at the university, consisting of the six experimental conditions in a (pseudo)randomized order. During the test session, participants performed a cue exposure task in both virtual and real-life conditions, and answered a series of questionnaires. Data files: SMELLETECHNOLOGY_Dataset_FINAL– cleaned dataset for all participants in all experimental conditions CodingScheme_AdditionalExplanation_Dataset – additional explanation for some variable labels and values in the final dataset The following are raw data files (Excel/csv) from the Qualtrics questionnaires during lab test sessions, grouped per randomization order: RAOrder_1_QualtricsSurvey RAOrder_2_QualtricsSurvey RAOrder_3_QualtricsSurvey RAOrder_4_QualtricsSurvey RAOrder_5_QualtricsSurvey RAOrder_6_QualtricsSurvey The following are raw data files (Excel/csv) from the screening session Screening_QualtricsFile SniffinSticks_AnswerExcelSheet Supplemental material InformedConsent: Informed consent form (in English) TestSession_Questionnaires: All study questionnaires used (during the lab test session) Structure data package Data files Data files folder > Cleaned datasets > SMELLETECHNOLOGY_Dataset_FINAL CodingScheme_AdditionalExplanation_Dataset Data files folder > Raw Qualtrics data files > Lab test session > RAOrder_1_QualtricsSurvey RAOrder_2_QualtricsSurvey RAOrder_3_QualtricsSurvey RAOrder_4_QualtricsSurvey RAOrder_5_QualtricsSurvey RAOrder_6_QualtricsSurvey Data files folder > Raw Qualtrics data files > Screening session > Screening_QualtricsFile SniffinSticks_AnswerExcelSheet Supplementary Materials Supplemental Materials folder > InformedConsent TestSession_Questionnaires Ethical clearance: This study was approved by the Ethical Review Board of the Tilburg School of Humanities and Digital Sciences (Tilburg University; file number: REDC 2023.62). Preregistration: The hypotheses, study design, and analysis plan were preregistered (https://osf.io/6hjax) Production date: Begin date: 15-01-2024 to End date: 31-03-2024 Method: Lab-based experiment, consisting of a cue exposure task and series of questionnaires (Qualtrics) Universe 70 healthy university students (59% female; MAge = 20.71 years) from diverse educational tracks took part in the research. All participants were fluent English speakers and screened on certain eligibility criteria. See pre-registration for specific inclusion and exclusion criteria. Country / Nation: The Netherlands. The spreadsheet files (one-sheet xlsx files) are also present in their preferred file format csv. Preferred formats are file formats of which DANS – based on international agreements – is confident that they will offer the best long-term guarantees in terms of usability, accessibility and sustainability. For more information on preferred file formats, see https://dans.knaw.nl/en/file-formats/. Data cannot be used for commercial purposes (see license).

Researchers at Utah State University created a short survey instrument to gather information about the views and concerns of Utah residents related to water issues. This survey was designed to give the public a chance to share their perceptions and concerns about water supply, water quality, and other related issues. While finding out what the ‘average citizen’ feels about key water issues was one goal of the project, the most interesting and important results are found in exploring ways in which perspectives about water vary across the population based on where people live and their demographic background (gender, age, education, etc.). This survey helps bring a voice to groups of citizens typically not represented in water policy debates. The findings have been and continue to be shared with water managers and decision makers who are planning for local and state water system sustainability.

This survey effort is also a key outreach and education component of the iUTAH project. High school groups, college and university classes, and others are invited to collaborate with iUTAH faculty to conduct public intercept surveys. Co-collection and analysis of survey data provides a hands-on learning opportunity about the principles of social science research. This effort helps increase awareness about the complexity of water issues in Utah, and the methods through which scientists learn about the public’s thoughts and concerns. Between July 2014 and April 2016, the survey has been implemented with collaborating students and faculty from the University of Utah, Utah Valley University, Weber State University, Salt Lake Community College, Southern Utah University, Dixie State University, and Snow College.

The survey involved using a structured protocol to randomly approach adults entering grocery stores in communities across the state, and inviting them to complete a 3-minute questionnaire about thier perceptions and concerns about water issues in Utah. The survey was self-administered on an iPad tablet and uploaded to a web server using the Qualtrics Offline App.

The project generated responses from over 7,000 adults, with a response rate of just over 42% . Comparisons of the respondents with census data suggest that they are largely representative of the communities where data were collected and of the state's adult population.

The data are anonymous and are available as a public dataset here. The data also served as the basis for the development of an open-source web-based survey data viewer that can be found at: http://data.iutahepscor.org/surveys/ and were also reported in Jones et al. (2016). We encourage users to use the viewer to explore the survey results.

The files below include a document describing in detail the method/protocol used in the study, and copies of field materials we used to implement the project. We also include copies of the full dataset and a codebook in various formats.

Trophy hunting is one of the most contentious issues in recent biodiversity conservation discourse, eliciting opposition and support for the practice. Ethical concerns are often at the heart of the debate. To investigate moral views about trophy hunting, we conducted an online survey of randomly selected scholars worldwide who had published on biodiversity conservation (n = 2,315). Scholars expressed divergent views on the moral acceptability of trophy hunting as a conservation practice. Moral convictions were significantly related to the perspectives The most important factor in predicting the moral views of the respondents was the consequences of trophy hunting for local human communities. The results also indicated that utilitarian (versus deontological) decision-making in conservation, ecological consequences of trophy hunting, and animal welfare issues contribute to the divergent views. The findings emphasize the need for interdisciplinary work on ethical issues concerning animal rights and welfare in conservation, as well as providing robust and comprehensive evidence on the consequences of trophy hunting for local communities. We caution that polarization among conservation scholars may negatively affect conservation efforts. Based on the literature and our findings, we provide some recommendations to narrow the gap and consider different management options.

Methods In November and December 2020, we conducted a web-based survey of biodiversity conservation scholars who had published in the scientific literature since 2010. We used the publications listed in the 'Web of Science – All Databases' as the sampling frame and searched for publications using the search term: 'biodiversity conservation' OR 'wildlife conservation' OR 'conservation biology' OR 'trophy hunting' in the 'topic' field. We obtained the authors' email addresses from the same database and sent individualized email invitations with a link to the Web survey hosted by the Qualtrics survey platform. Qualtrics only accepted one response per link, avoiding the possibility of a respondent sharing their link with unidentified respondents. Two additional follow-up invitations were sent within two weeks of the initial invitation to those who did not respond to the earlier invitation. The instructions in the invitation email and survey noted that it was limited to the authors who had published work in the area of biodiversity conservation. Additionally, at the beginning of the questionnaire, we asked the respondents if their work, study, or research was related to biodiversity conservation. Those who responded 'no' to this question were automatically excluded from the survey. We also asked the respondents to provide their opinions on trophy hunting in the context of the developing world. For clarity and consistency in the responses, the following definition of trophy hunting was provided to the respondents on multiple pages throughout the survey: “Trophy hunting is a type of selective recreational hunting of animals done to obtain their body parts as a representation of success or memorial,” with an emphasis on 'developing countries' (see Appendix S1). The Institutional Review Board of Texas A&M University approved the data collection protocols and the survey instrument (IRB2020-1228M). Of the 26,064 scholars who received the invitation, 3,794 responded (response rate: 14.5%), and 2,430 completed the questionnaire (completion rate: 64.0%). We used the authors’ contact information at the time of publication. Many had likely changed their institutional affiliation since publication (beginning in 2010). We cannot discern how many authors had changed their affiliations and, consequently, did not receive the invitation. Furthermore, we only sent invitations to authors whose email addresses were available through the database (all co-authors). After screening out responses from scholars whose work or research did not involve biodiversity conservation (n = 106) and those who did not answer our outcome variable (n = 9), we included 2,315 cases in the analyses.

Quality of life among patients with atrial fibrillation https://doi.org/10.5061/dryad.gtht76hsf | Element | Notes | | ------- | ----- | | Title * | Quality of life among patients with atrial fibrillation: A theoretically-guided cross-sectional study | | Creator * | Dr. Kathy L Rush | | Description * | The primary motivation behind the creation of this dataset is to bring attention to the notably reduced health-related quality of life (HRQoL) individuals suffering with atrial fibrillation (AF) experience when compared to both the general population and individuals with other heart-related conditions. Current research tends to concentrate on understanding how AF symptoms impact HRQoL, often overlooking the significance of individual characteristics determining HRQoL. To bridge this research gap, this study aims to establish an enhanced predictive model for HRQoL in individuals with AF. This model is based on an adapted HRQoL conceptual framework that takes into account both the influence of symptoms and the unique characteristics of each individual. | | Alternate Title | Quality of life among patients with atrial fibrillation | | Contact Name | Dr. Kathy L Rush | | Contact Email | kathy.rush@ubc.ca | | Contact Other | | | Update Frequency * | One time upload September 2023 | | Date Issued | September 2023 | | Date Created * | Data collection began November 2020 | | Start Date | 11/1/2020 | | End Date | 10/31/2021 | | Spatial Coverage | British Columbia | | Usage Considerations | This dataset is used to answer the associated research questions and fulfill the purpose of the study. We examined whether individual characteristics (overall mental health, perceived stress, sex, age, AF knowledge, household and recreational physical activity) incremented prediction of HRQoL and AF treatment satisfaction beyond AF symptom recency and overall health | ## Methodology Sample and Recruitment All patients of the clinic with an AF diagnosis who were over 18 years and could complete an online survey or had a family member who could assist, were eligible to participate. The clinic’s booking clerk sent a letter detailing the research study (by mail or email) to all patients with upcoming appointments during the recruitment period. The letter informed patients of the ongoing study and to expect a telephone initiation from a research team member regarding their eligibility and interest in the study. Patient contact information was then shared with the research team using secure file transfer. Subsequently a research assistant (a physician or a licensed practical nurse) who had no prior relationship with participants contacted patients by telephone. Recruitment began in November 2020 and continued for one year until a sample size of approximately 200 was achieved. A post hoc power analysis assuming a medium effect size estimated required sample size for modelling to be 114, indicating appropriate sample size had been achieved for analyses (Faul et al., 2007). Data Collection Study data were collected using an online survey hosted on Qualtrics (Qualtrics, Provo, UT). Prior to taking the survey, all participants gave electronic consent. Participants who finished the survey were eligible for a chance to win one of three $150 gift certificates through a random draw. Measures Overall Health: Participants were asked to rate their overall health on a scale ranging from 1 (poor) to 4 (excellent) (Ware et al., 1996). Overall mental health: Participants were asked to rate their overall mental health on a scale ranging from 1 (poor) to 4 (excellent) (Ahmad et al., 2014). Perceived stress (S. Cohen et al., 1983): The Perceived Stress Scale (PSS-10), a 10-item, 5-point scale, measures the degree to which situations in one's life are appraised as stressful, ability to control aspects of life, confidence in handling problems, or being unable to cope with demands. The PSS-10 previously had a reliability alpha of .78 and correlated in a predictable way with other measures of stress (S. Cohen et al., 1983) Socio-demographic characteristics: These included sex, age, marital status, race/ethnicity, education, and income. AF Knowledge (McCabe et al., 2020). The Knowledge about AF tool is a 28-item multiple choice-style questionnaire including questions about AF symptoms, treatment, medications, risk factors, and lifestyle. Participants are asked to choose one of 3 options for each question, only one of which is the correct response. The tool was developed using research on gaps in patient knowledge and patient values and management preferences. Knowledge scores are calculated as a percentage of correct answers, with higher numbers indicating higher knowledge. Four items were removed from the overall knowledge percent scores, as per McCabe et al. (McCabe et al., 2020) finding that these items had factor loadings below .45 and were not reliable predicto...

These extended supplementary materials go with the article:

Witzel & Dewis (2022) Why Bananas Look Yellow: The Dominant Hue of Object Colours. Vision Research.

A. SURVEYS

A pdf-printout for each of the three Qualtrics surveys illustrates details of the procedure. The layout may have been slightly different in Qualtrics (e.g., wide screen vs portrait display). Also note that the second and third surveys feature a few questions that were unrelated to the dominant-hue study (identifying a grey image).

B. STIMULI

The images used in Experiments 1-3, and the animated images used as cues to colour changes in Experiment 3 are packed in zip-files.

C. CODE

The Matlab code "onehue_maker.m" is a function that implements the dominant-hue algorithm to produce one-hue images like those in the experiments. To try out the program, the photo of the banana and the mask identifying its background are also uploaded (= first and second input to the function). The purpose of the mask is to remove the background colour from the dominant-hue computations.

D. DATA

The uploaded data is not completely raw but has been polished in the following ways:

Pilot data has been removed (i.e., meaningless data from us and our students to try out, check and polish the survey).

Incomplete runs have been removed (i.e., when participants quitted before completing the whole survey).

Data irrelevant to this study have been removed (date and time; grey-identification task [see above]).

There are 3 sheets with data and three sheets with stimulus specifications for each of the three experiments. The stimulus specifications include the measures used in the analyses in "Other Factors" in the Discussion of Experiment 3.

Columns in the Data sheets are:

Participant information: recruit (soc med = social media; UG pool = undergraduate students, prolific = https://www.prolific.co/); coldef = Colour deficiencies (1 Yes, 2 No according to test, 3 No without test, 4 Don't know); sex (1 male, 2 female, 3 other); age (in years), and duration (in minutes).

Main data: Column labels are composed of the following elements, separated by an underscore (_):

The first 3-5 letters of the object name: ban = banana, car = carrot, cher = cherry, dress = #theDress, fro = frog, gra = grapes, lem = lemon, let = lettuce, ora = orange, pig, ros = rose, shoe = #theShoe, stra = strawberry, zuc = zucchini/courgette.

A symbol indicating the stimulus condition: 1 = One-Hue, m = Minus-Hue Rotation, p = Plus-Hue Rotation.

A number identifying the measure: 1 = responded position; 2 = accuracy of the response (1 = correct); 3 = response time (in sec), 4 (Experiment 2-3) = confidence rating (between 0 and 100), 5 (Experiment 3) = cue confidence (cf. Figure 11.a).

For inverted colours (Experiment 3), the column label starts with an "i" (for inverted).

Practice Trials: Start with the prefix ex (for example) followed by an underscore (_) and the ID of the object; otherwise, data as in main trials.

Catch Trials (Experiment 2-3): Start with object name "d" for disk, otherwise, data as in main trials.

Eidolon Guesses (Experiment 2): Start with "guess" followed by the object ID (see main trials) followed by a number indicating the measure: 1 = response (yes/no), 2 = confidence (if positive response). In case of a positive response, the text entries are save in the variables starting with guess_txt.

Columns in the stimulus sheets are:

DomHue: Angle of the dominant hue (cf. Figure 3); as principal components are relative to the average, the angle is relative to the average, not the origin.

pole1 and pole2: Poles of the dominant hue direction. "pole1_rgb" provides corresponding RGBs for illustration (cf. Figure 1).

ChromaRescaled: Rescale Factor (see Experiment 3).

MaxChr: Maximum chroma of the colour distribution in CIELUV.

M: Average chromaticities (u*, v*) of the colour distribution.

pc: Coefficients of the first principal component for u* and v*.

latent & expl: Absolute and relative explained variance, respectively; second column corresponds to orthogonal variance.

hueM & hueSD: Average and standard deviation of the hue of the colour distribution (cf. Figure 3).

rot_minus, rot_plus: The hue rotations in the rotated-hue condition (constant minus or plus 5, except for #theShoe).

oog_1hue, oog_plus, oog_minus: The proportion of out-of-gamut values.

oogdist_1hue, oogdist_minus, oogdist_plut: Average difference between clipped and original images (in CIELUV).

Mshift_1hue, Mshift_minus, Mshift_plus: Average and standard deviation of chromaticity shift due to the experimental manipulation (cf. Figure 5 and Table S1).

Mhueshift_1hue, Mhueshift_minus, Mhueshift_plus: Average and standard deviation of hue shift in CIELUV (cf. Figure S4.d-f and Table S2).

Lab_shift_1hue, Lab_shift_minus, Lab_shift_plus: Average and standard deviation of chromaticity shift in CIELAB (cf. Figure S4.a-c and Table S1).

Lab_hueshift_1hue, Lab_hueshift_minus, Lab_hueshift_plus: Average and standard deviation of hue shift in CIELAB (cf. Figure S4.g-i and Table S2).

Lab_Mhue: Hue of the average colour in CIELAB

Lab_hueM & Lab_hueSD0: Average and standard deviation of the CIELAB hue distribution.

huehist0: CIELUV hue histogram; each entry corresponds to the frequencies for 72 bins of 5-deg (cf. Figure 3); the zero indicates that the hue is relative to the origin, not to the average chromaticity.

We anonymously surveyed members and non-members of the LGBTQIA+ community of conservation students and professionals in North America to explore participants’ lived experiences in conservation regarding safety, belonging, and inclusion. Our 737 responses included 10% that identified as genderqueer, gender nonconforming, questioning, nonspecific, genderfluid, transgender woman, agender, transgender man, two spirit Indigenous, or intersex (hereafter gender expansive), and 29% bisexual, queer, lesbian, gay, asexual, pansexual, omnisexual, questioning, or non-heterosexual (hereafter queer+). Data also include results of a non-response survey of 157 individuals who chose not to complete our the full survey, but answered basic demographic questions to determine non-response bias., Responses were solicited from an email list that included natural resource, conservation, ecology, wildlife, and fisheries departments from public and private universities; 4-year colleges; 2-year colleges; professional schools; technical, vocational, or trade schools; Hispanic-serving institutions; historically Black colleges and universities; tribal colleges, and women’s colleges. To include perspectives from non-academic settings and to target LGBTIQA+ individuals, we included listserv members of the “Out in the Field'' LGBTQIA+ and ally working group of the Wildlife Society as part of our survey population. We distributed a Qualtrics suvey and consent letter to ask respondents about their feelings and experiences of safety, belonging, and inclusion working in the field of conservation., Data were analyzed in R version 4.2.2. , # LGBTQIA+ experiences in conservation survey data

https://doi.org/10.5061/dryad.rfj6q57gr

Survey data from 737 conservation students and professionals describing their lived experience and feelings on inclusion, safety, and belonging while working in the field of conservation. Data were used to describe lessened feelings of inclusion, safety, and belonging among LGBTQIA+ conservation professionals compared to non-LGBTQIA+ professionals. We also include a file of 157 individuals who did not respond to the main survey, but responded to a short survey of demographic questions to quantify non-response bias. Location data and extended text response data have been removed to protect survey respondents' anonymity.

Description of the data and file structure

Data are an anonymous output from a Qualtrics survey. Location information has been removed for further anonymity. Includes basic demographic information and quantitative ratings of feelings...