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

This is covid-19 questionnaire data

SPSS Data GAI Evaluations

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.

The SPSS file includes the raw data as well as the generated variables. The word file explains the SPSS file and provides information on the data analyses. The data is NOT available for public use.

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.

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

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)

The shared dataset includes study variables and covariates in the study entitled 'Substance use, psychiatric sypmtoms, personal mastery, and social suport among COVID-19 long haulers: A compensatory model'.

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: MDCDatacitationReuse2021Codebook.pdf
  Codebook
• Filename: MDCDataCitationReuse2021surveydata.csv
  Dataset format in csv
• Filename: MDCDataCitationReuse2021surveydata.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: 94

Number of cases/rows: 2,492

Missing data codes: 999 Not asked

Refer to MDCDatacitationReuse2021Codebook.pdf for detailed variable information.

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

This dataset contains survey responses from 658 vaccinated USA MTurk workers who completed measures of: (a) pandemic fatigue and psychological distress (physical symptoms, trauma symptoms); (b) delays in receiving medical care due to COVID-19 restrictions; (c) vaccine-related behavior and beliefs (type of vaccine and vaccine hesitancy), and (d) COVID-19 preventive health behaviors. Several predictor variables were also collected including: (a) demographic variables; (b) health risk factors for COVID-19; (c) perceived susceptibility to disease and intolerance of uncertainty; (d) attitudes, subjective norms and perceived behavioral control about COVID-19 vaccine from the Theory of Planned Behavior; (e) compassion for self and others; (f) psychological flexibility and inflexibility; (g) Buddhist mindfulness insight (impermanence, acceptance of suffering, nonself attachment, mindfulness); and (h) cultural orientation and authoritarianism. The surveys were completed between August 28th and October 18th of 2021. The data permit evaluation of relationships among COVID-19 fatigue and distress; COVID-19 vaccine related behaviors and beliefs; COVID-19 preventive health behaviors; COVID-19 susceptibility and intolerance of uncertainty; and the role of compassion, psychological flexibility, mindfulness, cultural orientation, and authoritarianism as possible moderators of COVID-19 fatigue, distress, and vaccine beliefs.

Dataset with parents' quantitative responses collected within the impact study of the project Parental Engagement and Relationships (PEAR) in Early Childhood (EC).

The file (.sav) can be opened using IBM SPSS Software. The file is named using the following naming convention: Project acronym_Date (YYYYMMDD)_Study_Type of data_Type of participant_Version number of the file.

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 890925.

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

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

This dataset accompanies the research titled "Leadership Models and the Promotion of Innovation: Case Study in the Banking and Fintech Sector in Kosovo". It includes responses collected via a structured questionnaire aimed at examining the relationship between leadership styles and the promotion of innovation within Kosovo's banking and fintech sectors.

The dataset captures responses using a 5-point Likert scale, covering variables such as transformational, transactional, and laissez-faire leadership, and measures of process, product, and organizational innovation.

The dataset is in SPSS (.sav) format and is suitable for analysis using statistical techniques including descriptive statistics, correlation, and regression.

This data is shared to promote reproducibility, transparency, and further research in leadership and innovation within emerging economies.

SPSS data for study entitle "determinants of quality of life among cancer patients:A national study". In the case of using this data must get permission from principal investigator of this study.Prof.Dr.Sherina Mohd Sidik (sherina@upm.edu.my)

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. The purpose of this study was to produce knowledge about how to prevent at-risk youth from joining gangs and reduce delinquency among active gang members. The study evaluated a modification of Functional Family Therapy, a model program from the Blueprints for Healthy Youth Development initiative, to assess its effectiveness for reducing gang membership and delinquency in a gang-involved population. The collection contains 5 SPSS data files and 4 SPSS syntax files: adolpre_archive.sav (129 cases, 190 variables), adolpost_archive.sav (119 cases, 301 variables), Fidelity.archive.sav (66 cases, 25 variables), parentpre_archive.sav (129 cases, 157 variables), and parentpost_archive.sav {116 cases, 220 variables).

SPSS primary data

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. This study examined the validity, reliability, equity and cost of nine juvenile justice risk assessment instruments. It was designed to provide a comprehensive examination of how several risk assessments perform in practice. This study posed the following questions: Is each risk assessment instrument sufficiently reliable (i.e., inter-rater reliability) to ensure that decisions regarding level of risk and identified service needs are consistent across the organization? What specific risk assessment items are associated with less reliability? What items are rated reliably by staff? Is each risk assessment instrument valid? Specifically, what degree of discrimination is attained between assigned risk levels? Could the instrument be improved by adding or deleting specific factors and/or altering cut-off scores? Is each risk assessment instrument valid for population subgroups: White/Caucasian, Black/African American, Hispanic/Latino, females, probationers, and youth in aftercare status? Could equity be improved by adding or deleting specific factors or altering cut-off scores? What costs are associated with each risk assessment instrument? The study collection includes 31 SPSS data files all_jais_combined.sav (n=1,141; 6 variables) ar_fire_final_file_ojjdp-ICPSR.sav (n=119; 205 variables) AR_yls_irr_FINAL-ICPSR.sav (n=155; 136 variables) azaoc_FINALFILE-ICPSR.sav (n=7,589; 438 variables) AZAOC_irr_FINAL-ICPSR.sav (n=458; 101 variables) AZDJC_FINAL_FILE-ICPSR.sav (n=1,265; 1,290 variables) AZDJC_irr_FINAL-ICPSR.sav (n=55; 120 variables) COMMITMENT_FINAL_SAMPLE2-ICPSR.sav (n=11,154; 719 variables) FinalDJJReleasesWithRecid_BothYears2-ICPSR.sav (n=90,818; 31 variables) FIRE_NE_COMM_FINAL_FILE_OJJDP-ICPSR.sav (n=597; 174 variables) fire_ne_probation_final-ICPSR.sav (n=1,077; 237 variables) FL_irr_FINAL-ICPSR.sav (n=519; 140 variables) GA_irr_FINAL-ICPSR.sav (n=509; 263 variables) gafire_boyscommunityALL_FINAL-ICPSR.sav (n=5,009; 781 variables) gafire_communityALLforretrofit2-ICPSR.sav (n=6,943; 666 variables) gafire_finalsampforanalysis_all-ICPSR.sav (n=7,412; 642 variables) gafire_finalsampforanalysis_girls-ICPSR.sav (n=2,005; 768 variables) jais_boys_wk_1-ICPSR.sav (n=1,989; 484 variables) jais_girls_wk_1-ICPSR.sav (n=745; 484 variables) NE_irr_FINAL-ICPSR.sav (n=727; 160 variables) OR_irr_FINAL-ICPSR.sav (n=477; 112 variables) ORE_FIRE_final-ICPSR.sav (n=12,370; 340 variables) PROBATION_FINAL_BOYS_ALL-ICPSR.sav (n=20,621; 837 variables) PROBATION_FINAL_GIRLS_ALL-ICPSR.sav (n=6,748; 849 variables) va_boyssample-ICPSR.sav (n=1,106; 1,273 variables) va_final_sample_fullscreen-ICPSR.sav (n=1,439; 1,237 variables) va_girlssample-ICPSR.sav (n=333; 1,256 variables) VA_irr_expert_FINAL-ICPSR.sav (n=10; 308 variables) VA_irr_worker_FINAL-ICPSR.sav (n=685; 308 variables) vafinalsample-ICPSR.sav (n=1,919; 1,200 variables) workersurveyfinal-ICPSR.sav (n=400; 69 variables)