Data was collected from two different studies conducted for the undergraduate student E-learning fall semester 2020 experience at the University of Science and Technology Fujairah (USTF) in the United Arab Emirates. The two studies were conducted using an online questionnaire via Google Forms. Students were invited to participate voluntarily by email. They were asked to answer questions regarding their distance learning experience during their 2020 academic year.

General information: The data sets contain information on how often materials of studies available through GESIS: Data Archive for the Social Sciences were downloaded and/or ordered through one of the archive´s plattforms/services between 2004 and 2017.

Sources and plattforms: Study materials are accessible through various GESIS plattforms and services: Data Catalogue (DBK), histat, datorium, data service (and others).

Years available: - Data Catalogue: 2012-2017 - data service: 2006-2017 - datorium: 2014-2017 - histat: 2004-2017

Data sets: Data set ZA6899_Datasets_only_all_sources contains information on how often data files such as those with dta- (Stata) or sav- (SPSS) extension have been downloaded. Identification of data files is handled semi-automatically (depending on the plattform/serice). Multiple downloads of one file by the same user (identified through IP-address or username for registered users) on the same days are only counted as one download.

Data set ZA6899_Doc_and_Data_all_sources contains information on how often study materials have been downloaded. Multiple downloads of any file of the same study by the same user (identified through IP-address or username for registered users) on the same days are only counted as one download.

Both data sets are available in three formats: csv (quoted, semicolon-separated), dta (Stata v13, labeled) and sav (SPSS, labeled). All formats contain identical information.

Variables: Variables/columns in both data sets are identical. za_nr ´Archive study number´ version ´GESIS Archiv Version´ doi ´Digital Object Identifier´ StudyNo ´Study number of respective study´ Title ´English study title´ Title_DE ´German study title´ Access ´Access category (0, A, B, C, D, E)´ PubYear ´Publication year of last version of the study´ inZACAT ´Study is currently also available via ZACAT´ inHISTAT ´Study is currently also available via HISTAT´ inDownloads ´There are currently data files available for download for this study in DBK or datorium´ Total ´All downloads combined´ downloads_2004 ´downloads/orders from all sources combined in 2004´ [up to ...] downloads_2017 ´downloads/orders from all sources combined in 2017´ d_2004_dbk ´downloads from source dbk in 2004´ [up to ...] d_2017_dbk ´downloads from source dbk in 2017´ d_2004_histat ´downloads from source histat in 2004´ [up to ...] d_2017_histat ´downloads from source histat in 2017´ d_2004_dataservice ´downloads/orders from source dataservice in 2004´ [up to ...] d_2017_dataservice ´downloads/orders from source dataservice in 2017´

More information is available within the codebook.

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

Citizen Science (CS) projects play a crucial role in engaging citizens in conservation efforts. While implicitly mostly considered as an outcome of CS participation, citizens may also have a certain attitude toward engagement in CS when starting to participate in a CS project. Moreover, there is a lack of CS studies that consider changes over longer periods of time. Therefore, this research presents two-wave data from four field studies of a CS project about urban wildlife ecology using cross-lagged panel analyses. We investigated the influence of attitudes toward engagement in CS on self-related, ecology-related, and motivation-related outcomes. We found that positive attitudes toward engagement in CS at the beginning of the CS project had positive influences on participants’ psychological ownership and pride in their participation, their attitudes toward and enthusiasm about wildlife, and their internal and external motivation two months later. We discuss the implications for CS research and practice. Dataset for: Greving, H., Bruckermann, T., Schumann, A., Stillfried, M., Börner, K., Hagen, R., Kimmig, S. E., Brandt, M., & Kimmerle, J. (2023). Attitudes Toward Engagement in Citizen Science Increase Self-Related, Ecology-Related, and Motivation-Related Outcomes in an Urban Wildlife Project. BioScience, 73(3), 206–219. https://doi.org/10.1093/biosci/biad003: Data (SPSS format) collected for all field studies

Percentage of total trial spend viewing each of the 9 locations.

Raw data file.xlsx and.sav format of raw data of the study that is available by Excel and SPSS software.

GENERAL INFORMATION

Title of Dataset: A dataset from a survey investigating disciplinary differences in data citation

Date of data collection: January to March 2022

Collection instrument: SurveyMonkey

Funding: Alfred P. Sloan Foundation

SHARING/ACCESS INFORMATION

Licenses/restrictions placed on the data: These data are available under a CC BY 4.0 license

Links to publications that cite or use the data:

Gregory, K., Ninkov, A., Ripp, C., Peters, I., & Haustein, S. (2022). Surveying practices of data citation and reuse across disciplines. Proceedings of the 26th International Conference on Science and Technology Indicators. International Conference on Science and Technology Indicators, Granada, Spain. https://doi.org/10.5281/ZENODO.6951437

Gregory, K., Ninkov, A., Ripp, C., Roblin, E., Peters, I., & Haustein, S. (2023). Tracing data:
A survey investigating disciplinary differences in data citation. Zenodo. https://doi.org/10.5281/zenodo.7555266

DATA & FILE OVERVIEW

File List

• Filename: MDCDatacitationReuse2021Codebookv2.pdf
  Codebook
• Filename: MDCDataCitationReuse2021surveydatav2.csv
  Dataset format in csv
• Filename: MDCDataCitationReuse2021surveydatav2.sav
  Dataset format in SPSS
• Filename: MDCDataCitationReuseSurvey2021QNR.pdf
  Questionnaire

Additional related data collected that was not included in the current data package: Open ended questions asked to respondents

METHODOLOGICAL INFORMATION

Description of methods used for collection/generation of data:

The development of the questionnaire (Gregory et al., 2022) was centered around the creation of two main branches of questions for the primary groups of interest in our study: researchers that reuse data (33 questions in total) and researchers that do not reuse data (16 questions in total). The population of interest for this survey consists of researchers from all disciplines and countries, sampled from the corresponding authors of papers indexed in the Web of Science (WoS) between 2016 and 2020.

Received 3,632 responses, 2,509 of which were completed, representing a completion rate of 68.6%. Incomplete responses were excluded from the dataset. The final total contains 2,492 complete responses and an uncorrected response rate of 1.57%. Controlling for invalid emails, bounced emails and opt-outs (n=5,201) produced a response rate of 1.62%, similar to surveys using comparable recruitment methods (Gregory et al., 2020).

Methods for processing the data:

Results were downloaded from SurveyMonkey in CSV format and were prepared for analysis using Excel and SPSS by recoding ordinal and multiple choice questions and by removing missing values.

Instrument- or software-specific information needed to interpret the data:

The dataset is provided in SPSS format, which requires IBM SPSS Statistics. The dataset is also available in a coded format in CSV. The Codebook is required to interpret to values.

DATA-SPECIFIC INFORMATION FOR: MDCDataCitationReuse2021surveydata

Number of variables: 95

Number of cases/rows: 2,492

Missing data codes: 999 Not asked

Refer to MDCDatacitationReuse2021Codebook.pdf for detailed variable information.

Background

Despite the public health importance of documenting the burden of physical inactivity and weight gain, there is a paucity of such data in Kuwait during the lockdown for Covid-19 pandemic. Therefore, this survey was designed to estimate: the burden of poor eating habits particularly binge eating habits, fluctuations in weight and its predictors among the Kuwaiti public.

Methods

This cross-sectional survey was conducted from 2nd to 12th April 2020 among the general public in Kuwait. All data were collected through social media platform (WhatsApp groups), through convenience and snowball sampling methods. comprised of three sections: a) demographic characteristics of respondents, b) eating habits particularly binge eating, consumption of snacks and beverages c) subjective feelings of anxiety and d) weight before and during the pandemic. The dataset is in SPSS format.

The survey dataset for identifying Shiraz old silo’s new use which includes four components: 1. The survey instrument used to collect the data “SurveyInstrument_table.pdf”. The survey instrument contains 18 main closed-ended questions in a table format. Two of these, concern information on Silo’s decision-makers and proposed new use followed up after a short introduction of the questionnaire, and others 16 (each can identify 3 variables) are related to the level of appropriate opinions for ideal intervention in Façade, Openings, Materials and Floor heights of the building in four values: Feasibility, Reversibility, Compatibility and Social Benefits. 2. The raw survey data “SurveyData.rar”. This file contains an Excel.xlsx and a SPSS.sav file. The survey data file contains 50 variables (12 for each of the four values separated by colour) and data from each of the 632 respondents. Answering each question in the survey was mandatory, therefor there are no blanks or non-responses in the dataset. In the .sav file, all variables were assigned with numeric type and nominal measurement level. More details about each variable can be found in the Variable View tab of this file. Additional variables were created by grouping or consolidating categories within each survey question for simpler analysis. These variables are listed in the last columns of the .xlsx file. 3. The analysed survey data “AnalysedData.rar”. This file contains 6 “SPSS Statistics Output Documents” which demonstrate statistical tests and analysis such as mean, correlation, automatic linear regression, reliability, frequencies, and descriptives. 4. The codebook “Codebook.rar”. The detailed SPSS “Codebook.pdf” alongside the simplified codebook as “VariableInformation_table.pdf” provides a comprehensive guide to all 50 variables in the survey data, including numerical codes for survey questions and response options. They serve as valuable resources for understanding the dataset, presenting dictionary information, and providing descriptive statistics, such as counts and percentages for categorical variables.

The current, dramatic biodiversity decline is a serious problem. In order to deal efficientlywith it, stakeholders and society need to acknowledge and be aware of this problem. Thiscould be fostered by engaging the public at large in biodiversity research activities. One way to do so is to involve citizens in citizen science (CS) projects. These are projects in which researchers collaborate with volunteering citizens in scientific research projects. Yet, it remains unclear whether engaging in such projects has an impact on the citizens who participate. Previous research has so far presented mixed results about the improvement of citizens’ attitudes and knowledge, mostly because this research has focused only on certain aspects of CS projects. To address these limitations, we investigated the effectiveness of a CS project on urban bat ecology regarding citizens’ attitudes toward bats, knowledge about bats, and attitudes toward engagement in CS. We also examined whether the degree of citizens’ participation had an influence on the outcomes. We conducted four field studies in this CS project on urban bat ecology using an experimental pre-post-measurement design. To manipulate the degree of participation, we assessed the post-measurement in one group directly after data collection, while in a second group, we assessed it after data collection and data analysis at the end of the project. Across all studies, the results demonstrated that citizens’ attitudes toward bats improved over time, their content knowledge of urban bat ecology increased over time, and their attitudes toward engagement in CS improved over time. Citizens’ degree of participation did not influence these outcomes. Thus, our research illustrates the effectiveness of CS for increasing awareness for urban bat conservation independently of citizens’ degree of participation. We discuss the implications of our findings for the CS community. Dataset for: Greving, H.*, Bruckermann, T.*, Schumann, A., Straka, T. M., Lewanzik, D., Voigt-Heucke, S. L., Marggraf, L., Lorenz, J., Brandt, M., Voigt, C. C., Harms, U., & Kimmerle, J. (2022). Improving attitudes and knowledge in a citizen science project about urban bat ecology. Ecology and Society, 27(2), Article 24. *shared first-authorship. https://dx.doi.org/10.5751/es-13272-270224: Data (SPSS format) collected for field study 1-4

Dataset survey methods document and report. There is a dataset in R format plus an SPSS .sav file and an accompanying .sps syntax codefile. Running the syntax file on the .sav file should provide labels etc for the .sav file.

NATIONAL FACE - TO - FACE SURVEYS OF REPRESENTATIVE SAMPLES OF PARENTS OF ELEMENTARY SCHOOL CHILDREN IN 10 SOUTH EAST EUROPEAN COUNTRIES Center for Educational Policy Studies (CEPS) in cooperation with Open Society Institute Education Support Program

A SPSS file with data used in the statistical analysis. Covariates were excluded in the file due to restrictions of the ethical permission. However a complete file is provided for researchers after request at publication@ventorp.com. (SAV)

Voluntary engagement is crucial for committed participation in Citizen Science (CS) projects. So far, the CS literature has argued that psychological ownership (i.e., subjective feelings of owning or possessing an object or entity) facilitates engagement in CS projects and is beneficial for several outcomes, such as attitudes toward CS. We argue that, as ownership is a self-relevant experience, it should influence other self-focused outcomes, such as the self-conscious emotion of pride. Therefore, the research presented here investigated the interrelations between psychological ownership and pride in five two-month long, two-wave longitudinal field studies of a CS project on urban wildlife ecology using cross-lagged panel analyses. We hypothesized that ownership has a positive impact on pride and not vice versa, as pride may take some time to develop and may therefore be particularly relevant at the end of a project. We found that, across all field studies combined, ownership had indeed a positive, time-lagged influence on pride. Thus, when people voluntarily engage in an activity that feels like their own, they also subsequently feel proud, which can motivate further voluntary behavior.: Data (SPSS format) collected for field study 1-5

This folder contains the material of an experiment that compares MDD and Ccd in Unreal Engine Context.

The material in this folder* is organized in three subfolders: 01_EXPERIMENT, which contains the information used to run the experiment; 02_RESULTS, which contains the information collected in the experiment; and 03_STATISTICAL ANALYSIS, which contains the data set and the results of the statistical analysis performed.

The documentation contained in each of these folders is described below:

01_EXPERIMENT

11_ CdE_ApproveReport: pdf file of the Favorable report from the ethical and scientific committee regarding the execution of the experiment.

12_Forms&Exercises: This folder contains the Taks Sheet for each group and the Unreal project with each exercise to resolve.

13_SupportMaterial: This folder contains the support material that the subjects could use during the performance of the different tasks of the experiment.

14_SessionMaterial: This folder contains the material used by the instructors during the session, including the video tutorial.

15_CorrectionMaterial: This folder contains the solution to the tasks and the rubric to correct.

02_RESULTS

201_DATASET: Data files containing the values of variables and factors necessary for conducting the statistical analysis proposed in the study. They are included in both IBM SPSS Statistics (.sav) and Microsoft Excel (.xlsx) formats.

202_BOXPLOTS-HISTOGRAMS: Files that contain the execution and results of the boxplots for all the variables and factors considered. They are included in both IBM SPSS Statistics (.spv) and PDF Reader (.pdf) formats.

203_DATASET_LMM: Files that contain the dataset that includes the residuals of LMM executions. They are included in both IBM SPSS Statistics (.sav) and PDF Reader (.xls) formats.

202_DEMOGRAPHIC: Excel file with the demographic results.

203_DESCRIPTIVES: Files that contain the values of the main descriptive statistics and the results of the normality tests that correspond to all the variables measured in the study: the response variables and the independent variables or factors. They are included in both IBM SPSS Statistics (.spv) and PDF Reader (.pdf) formats.

204_LMM: Files that contain the execution and results of the LMM Type III test of fixed effects with unstructured repeated covariance for all the variables in the study with different statistical models. The files also includes the normality test of the residuals of LMM executions. They are included in both IBM SPSS Statistics (.spv) and PDF Reader (.pdf) formats.

205_LMM_model selection: File that contain information of the statistical models used in the statistical analysis and the comparative.

206_ DESSCRIPTIVES COHEN: Files that include the computations executed to determine the effect size of the factors in all the dependent variables. It is are included in (.exe) format.

*Note: The documentation that appears in this folder contains texts in Spanish, since this is the language in which the experiment is executed.

Data to measure perceived stress, burnout and resilience in Kashmiri healthcare workers and explore the relationship of burnout with various socio-demographic, work-related and pandemic related factors.

This folder contains the material of an experiment that compares MDD and CDD with respect to Bug Localization tasks.

The material in this folder* is organized in three subfolders: 01_EXPERIMENT, which contains the information used to run the experiment; 02_RESULTS, which contains the information collected in the experiment; and 03_STATISTICAL ANALYSIS, which contains the data set and the results of the statistical analysis performed.

The documentation contained in each of these folders is described below:

01_EXPERIMENT

11_ CdEUSJ_FavorableReport: pdf file of the Favorable report from the ethical and scientific committee regarding the execution of the experiment.

12_Forms&Tables: This folder contains the Taks Sheet for each group and the scenarios of each task and each group.

13_SupportMaterial: This folder contains the support material that the subjects could use during the performance of the different tasks of the experiment.

14_SessionMaterial: This folder contains the material used by the instructors during the session, including the video tutorial.

15_CorrecctionMaterial: This folder contains the solution to the tasks and the correction template.

02_RESULTS

21_RESULTS: Excel file with the data extracted from the Forms and the pre-processed results before the statistical analysis.

22_SubjectComments&FocusGroup. Pdf file with the analysis of the subjects' comments on the forms, and the transcription of the comments during the sessions and in the focus group.

03_STATISTICAL ANALYSIS

301_DATASET: Data files containing the values of variables and factors necessary for conducting the statistical analysis proposed in the study. They are included in both IBM SPSS Statistics (.sav) and Microsoft Excel (.xlsx) formats.

302_DEMOGRAPHIC: Excel file with the demographic results.

303_DESCRIPTIVES: Files that contain the values of the main descriptive statistics and the results of the normality tests that correspond to all the variables measured in the study: the response variables and the independent variables or factors. They are included in both IBM SPSS Statistics (.spv) and PDF Reader (.pdf) formats.

304_LMM: Files that contain the execution and results of the LMM Type III test of fixed effects with unstructured repeated covariance for all the variables in the study with different statistical models. They are included in both IBM SPSS Statistics (.spv) and PDF Reader (.pdf) formats.

305_DATASET_BugLocalization.xlsx_lmmRes: Files that contain the dataset that includes the residuals of LMM executions. They are included in both IBM SPSS Statistics (.sav) and PDF Reader (.xls) formats.

306_NORMALITYTEST: Files that contain the dataset that includes the normality test of the residuals of LMM executions. They are included in both IBM SPSS Statistics (.spv) and PDF Reader (.pdf) formats.

307_EFFECTSIZE-Cohend: Files that include the computations executed to determine the effect size of the factors in all the dependent variables. They are included in both IBM SPSS Statistics (.spv) and PDF Reader (.pdf) formats.

308_DATASET_BugLocalization.xlsx_lmm_student_Res: Files that contain the dataset that includes the residuals of LMM executions for non-experienced subjects (students) in the study. They are included in both IBM SPSS Statistics (.sav) and PDF Reader (.xls) formats.

309_LMM_Students: Files that contain the execution and results of the LMM tests (Type III test of fixed effects with unstructured repeated covariance) for non-experienced subjects (students) in the study for all the variables in the study with different statistical models, the normality analysis of the residuals and the computations executed to determine the effect size of the factors considered in each dependent variable. They are included in both IBM SPSS Statistics (.spv) and PDF Reader (.pdf) formats.

310_DATASET_BugLocalization.xlsx_lmm_professionals_Res.xlsx: Files that contain the dataset that includes the residuals of LMM executions for experienced subjects (professionals) in the study. They are included in both IBM SPSS Statistics (.sav) and PDF Reader (.xls) formats.

311_LMM_Profesionales: Files that contain the execution and results of the LMM tests (Type III test of fixed effects with unstructured repeated covariance) for the experienced subjects (professionals) in the study for all the variables in the study with different statistical models, the normality analysis of the residuals and the computations executed to determine the effect size of the factors considered in each dependent variable. They are included in both IBM SPSS Statistics (.spv) and PDF Reader (.pdf) formats.

312_Boxplots: Files that contain the execution and results of the boxplots by method (MDD/CDD) and by Experience (students/professionals). They are included in both IBM SPSS Statistics (.spv) and PDF Reader (.pdf) formats.

*Note: The documentation that appears in this folder contains texts in Spanish, since this is the language in which the experiment is executed.

This dataset originates from a series of experimental studies titled “Tough on People, Tolerant to AI? Differential Effects of Human vs. AI Unfairness on Trust” The project investigates how individuals respond to unfair behavior (distributive, procedural, and interactional unfairness) enacted by artificial intelligence versus human agents, and how such behavior affects cognitive and affective trust.1 Experiment 1a: The Impact of AI vs. Human Distributive Unfairness on TrustOverview: This dataset comes from an experimental study aimed at examining how individuals respond in terms of cognitive and affective trust when distributive unfairness is enacted by either an artificial intelligence (AI) agent or a human decision-maker. Experiment 1a specifically focuses on the main effect of the “type of decision-maker” on trust.Data Generation and Processing: The data were collected through Credamo, an online survey platform. Initially, 98 responses were gathered from students at a university in China. Additional student participants were recruited via Credamo to supplement the sample. Attention check items were embedded in the questionnaire, and participants who failed were automatically excluded in real-time. Data collection continued until 202 valid responses were obtained. SPSS software was used for data cleaning and analysis.Data Structure and Format: The data file is named “Experiment1a.sav” and is in SPSS format. It contains 28 columns and 202 rows, where each row corresponds to one participant. Columns represent measured variables, including: grouping and randomization variables, one manipulation check item, four items measuring distributive fairness perception, six items on cognitive trust, five items on affective trust, three items for honesty checks, and four demographic variables (gender, age, education, and grade level). The final three columns contain computed means for distributive fairness, cognitive trust, and affective trust.Additional Information: No missing data are present. All variable names are labeled in English abbreviations to facilitate further analysis. The dataset can be directly opened in SPSS or exported to other formats.2 Experiment 1b: The Mediating Role of Perceived Ability and Benevolence (Distributive Unfairness)Overview: This dataset originates from an experimental study designed to replicate the findings of Experiment 1a and further examine the potential mediating role of perceived ability and perceived benevolence.Data Generation and Processing: Participants were recruited via the Credamo online platform. Attention check items were embedded in the survey to ensure data quality. Data were collected using a rolling recruitment method, with invalid responses removed in real time. A total of 228 valid responses were obtained.Data Structure and Format: The dataset is stored in a file named Experiment1b.sav in SPSS format and can be directly opened in SPSS software. It consists of 228 rows and 40 columns. Each row represents one participant’s data record, and each column corresponds to a different measured variable. Specifically, the dataset includes: random assignment and grouping variables; one manipulation check item; four items measuring perceived distributive fairness; six items on perceived ability; five items on perceived benevolence; six items on cognitive trust; five items on affective trust; three items for attention check; and three demographic variables (gender, age, and education). The last five columns contain the computed mean scores for perceived distributive fairness, ability, benevolence, cognitive trust, and affective trust.Additional Notes: There are no missing values in the dataset. All variables are labeled using standardized English abbreviations to facilitate reuse and secondary analysis. The file can be analyzed directly in SPSS or exported to other formats as needed.3 Experiment 2a: Differential Effects of AI vs. Human Procedural Unfairness on TrustOverview: This dataset originates from an experimental study aimed at examining whether individuals respond differently in terms of cognitive and affective trust when procedural unfairness is enacted by artificial intelligence versus human decision-makers. Experiment 2a focuses on the main effect of the decision agent on trust outcomes.Data Generation and Processing: Participants were recruited via the Credamo online survey platform from two universities located in different regions of China. A total of 227 responses were collected. After excluding those who failed the attention check items, 204 valid responses were retained for analysis. Data were processed and analyzed using SPSS software.Data Structure and Format: The dataset is stored in a file named Experiment2a.sav in SPSS format and can be directly opened in SPSS software. It contains 204 rows and 30 columns. Each row represents one participant’s response record, while each column corresponds to a specific variable. Variables include: random assignment and grouping; one manipulation check item; seven items measuring perceived procedural fairness; six items on cognitive trust; five items on affective trust; three attention check items; and three demographic variables (gender, age, and education). The final three columns contain computed average scores for procedural fairness, cognitive trust, and affective trust.Additional Notes: The dataset contains no missing values. All variables are labeled using standardized English abbreviations to facilitate reuse and secondary analysis. The file can be directly analyzed in SPSS or exported to other formats as needed.4 Experiment 2b: Mediating Role of Perceived Ability and Benevolence (Procedural Unfairness)Overview: This dataset comes from an experimental study designed to replicate the findings of Experiment 2a and to further examine the potential mediating roles of perceived ability and perceived benevolence in shaping trust responses under procedural unfairness.Data Generation and Processing: Participants were working adults recruited through the Credamo online platform. A rolling data collection strategy was used, where responses failing attention checks were excluded in real time. The final dataset includes 235 valid responses. All data were processed and analyzed using SPSS software.Data Structure and Format: The dataset is stored in a file named Experiment2b.sav, which is in SPSS format and can be directly opened using SPSS software. It contains 235 rows and 43 columns. Each row corresponds to a single participant, and each column represents a specific measured variable. These include: random assignment and group labels; one manipulation check item; seven items measuring procedural fairness; six items for perceived ability; five items for perceived benevolence; six items for cognitive trust; five items for affective trust; three attention check items; and three demographic variables (gender, age, education). The final five columns contain the computed average scores for procedural fairness, perceived ability, perceived benevolence, cognitive trust, and affective trust.Additional Notes: There are no missing values in the dataset. All variables are labeled using standardized English abbreviations to support future reuse and secondary analysis. The dataset can be directly analyzed in SPSS and easily converted into other formats if needed.5 Experiment 3a: Effects of AI vs. Human Interactional Unfairness on TrustOverview: This dataset comes from an experimental study that investigates how interactional unfairness, when enacted by either artificial intelligence or human decision-makers, influences individuals’ cognitive and affective trust. Experiment 3a focuses on the main effect of the “decision-maker type” under interactional unfairness conditions.Data Generation and Processing: Participants were college students recruited from two universities in different regions of China through the Credamo survey platform. After excluding responses that failed attention checks, a total of 203 valid cases were retained from an initial pool of 223 responses. All data were processed and analyzed using SPSS software.Data Structure and Format: The dataset is stored in the file named Experiment3a.sav, in SPSS format and compatible with SPSS software. It contains 203 rows and 27 columns. Each row represents a single participant, while each column corresponds to a specific measured variable. These include: random assignment and condition labels; one manipulation check item; four items measuring interactional fairness perception; six items for cognitive trust; five items for affective trust; three attention check items; and three demographic variables (gender, age, education). The final three columns contain computed average scores for interactional fairness, cognitive trust, and affective trust.Additional Notes: There are no missing values in the dataset. All variable names are provided using standardized English abbreviations to facilitate secondary analysis. The data can be directly analyzed using SPSS and exported to other formats as needed.6 Experiment 3b: The Mediating Role of Perceived Ability and Benevolence (Interactional Unfairness)Overview: This dataset comes from an experimental study designed to replicate the findings of Experiment 3a and further examine the potential mediating roles of perceived ability and perceived benevolence under conditions of interactional unfairness.Data Generation and Processing: Participants were working adults recruited via the Credamo platform. Attention check questions were embedded in the survey, and responses that failed these checks were excluded in real time. Data collection proceeded in a rolling manner until a total of 227 valid responses were obtained. All data were processed and analyzed using SPSS software.Data Structure and Format: The dataset is stored in the file named Experiment3b.sav, in SPSS format and compatible with SPSS software. It includes 227 rows and

This database refers to the data collected by the European University Association (EUA) for its Open Access Survey 2017-2018, which gathered responses from universities and higher education institutions across Europe. The full report published by the association is available at https://eua.eu/resources/publications/826:2017-2018-eua-open-access-survey-results.html.

The data included in this database refers only to those universities and higher education institutions that accepted their data to be available in open access (n=266). All information that could lead to the identification of individual universities and higher education institutions was removed from the database. The following files are available:

• Questionnaire
• Database in the following formats: .sav (IBM SPSS Statistics), .xlsx (Microsoft Excel) and .csv
• Codebook: includes information on all the variables and their coding.

Aharoni et al. study data in both csv and SPSS format. See codebook for coding value/label assignments. Dataset for: Aharoni, E., Simpson, D., Nahmias, E., & Gollwitzer, M. (2022). A painful message: Testing the effects of suffering and understanding on punishment judgments. Zeitschrift für Psychologie, 230(2), 138–151. https://doi.org/10.1027/2151-2604/a000460: data - horizontal format

The dataset is available in both SPSS (.sav) and CSV formats and comprises 445 observations collected through surveys administered to tourists. The objective of the survey was to assess the relationship between the presence of plastic waste on Galapagos beaches and its economic impact on tourism. Variable definitions and SPSS syntax used for analysis are included in the accompanying codes file. Additionally, the XGBoost folder contains the dataset in CSV format along with R scripts used to implement the XGBoost algorithm.