Dataset

This dataset was created by Zahra Zolghadr

Contents

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

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

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

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

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

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

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

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

Data Dictionary:

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

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

Data Location:

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

This is covid-19 questionnaire data

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.

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.

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

Title of Dataset:

• [ ] Spss file for visual impairment among DM, Hawassa, Ethiopia ## Description of the Data and file structure
• [ ] a spss data for visual impairment among adult diabites mellitus, Hawassa, ethiopa were collected through direct interview and chart review with accourdance of ethical rules and regulation. ## Sharing/access Information
• [ ] All future access of the data set will be with the consent of the autor.and, all data were orginally collected from the study partcipants.

The article uses a dataset, which cannot be deposited online, but is freely available to registered users. The data of the British Household Panel Study can be requested via https://discover.ukdataservice.ac.uk/catalogue/?sn=5151. Here we provide a STATA do-file that will create the working file, recode the original data and run some robustness tests. The data was prepared in Stata and then saved as SPSS files .sav using Stattrans. This was necessary, as the main Markov Chain models of the paper were estimated using LatentGOLD, which only reads .sav files. Here we also provide the syntax files that were used for estimating these models.

SPSS Data sets for study 1 to 3

ABSTRACT

The Albero study analyzes the personal transitions of a cohort of high school students at the end of their studies. The data consist of (a) the longitudinal social network of the students, before (n = 69) and after (n = 57) finishing their studies; and (b) the longitudinal study of the personal networks of each of the participants in the research. The two observations of the complete social network are presented in two matrices in Excel format. For each respondent, two square matrices of 45 alters of their personal networks are provided, also in Excel format. For each respondent, both psychological sense of community and frequency of commuting is provided in a SAV file (SPSS). The database allows the combined analysis of social networks and personal networks of the same set of individuals.

INTRODUCTION

Ecological transitions are key moments in the life of an individual that occur as a result of a change of role or context. This is the case, for example, of the completion of high school studies, when young people start their university studies or try to enter the labor market. These transitions are turning points that carry a risk or an opportunity (Seidman & French, 2004). That is why they have received special attention in research and psychological practice, both from a developmental point of view and in the situational analysis of stress or in the implementation of preventive strategies.

The data we present in this article describe the ecological transition of a group of young people from Alcala de Guadaira, a town located about 16 kilometers from Seville. Specifically, in the “Albero” study we monitored the transition of a cohort of secondary school students at the end of the last pre-university academic year. It is a turning point in which most of them began a metropolitan lifestyle, with more displacements to the capital and a slight decrease in identification with the place of residence (Maya-Jariego, Holgado & Lubbers, 2018).

Normative transitions, such as the completion of studies, affect a group of individuals simultaneously, so they can be analyzed both individually and collectively. From an individual point of view, each student stops attending the institute, which is replaced by new interaction contexts. Consequently, the structure and composition of their personal networks are transformed. From a collective point of view, the network of friendships of the cohort of high school students enters into a gradual process of disintegration and fragmentation into subgroups (Maya-Jariego, Lubbers & Molina, 2019).

These two levels, individual and collective, were evaluated in the “Albero” study. One of the peculiarities of this database is that we combine the analysis of a complete social network with a survey of personal networks in the same set of individuals, with a longitudinal design before and after finishing high school. This allows combining the study of the multiple contexts in which each individual participates, assessed through the analysis of a sample of personal networks (Maya-Jariego, 2018), with the in-depth analysis of a specific context (the relationships between a promotion of students in the institute), through the analysis of the complete network of interactions. This potentially allows us to examine the covariation of the social network with the individual differences in the structure of personal networks.

PARTICIPANTS

The social network and personal networks of the students of the last two years of high school of an institute of Alcala de Guadaira (Seville) were analyzed. The longitudinal follow-up covered approximately a year and a half. The first wave was composed of 31 men (44.9%) and 38 women (55.1%) who live in Alcala de Guadaira, and who mostly expect to live in Alcala (36.2%) or in Seville (37.7%) in the future. In the second wave, information was obtained from 27 men (47.4%) and 30 women (52.6%).

DATE STRUCTURE AND ARCHIVES FORMAT

The data is organized in two longitudinal observations, with information on the complete social network of the cohort of students of the last year, the personal networks of each individual and complementary information on the sense of community and frequency of metropolitan movements, among other variables.

Social network

The file “Red_Social_t1.xlsx” is a valued matrix of 69 actors that gathers the relations of knowledge and friendship between the cohort of students of the last year of high school in the first observation. The file “Red_Social_t2.xlsx” is a valued matrix of 57 actors obtained 17 months after the first observation.

The data is organized in two longitudinal observations, with information on the complete social network of the cohort of students of the last year, the personal networks of each individual and complementary information on the sense of community and frequency of metropolitan movements, among other variables.

In order to generate each complete social network, the list of 77 students enrolled in the last year of high school was passed to the respondents, asking that in each case they indicate the type of relationship, according to the following values: 1, “his/her name sounds familiar"; 2, "I know him/her"; 3, "we talk from time to time"; 4, "we have good relationship"; and 5, "we are friends." The two resulting complete networks are represented in Figure 2. In the second observation, it is a comparatively less dense network, reflecting the gradual disintegration process that the student group has initiated.

Personal networks

Also in this case the information is organized in two observations. The compressed file “Redes_Personales_t1.csv” includes 69 folders, corresponding to personal networks. Each folder includes a valued matrix of 45 alters in CSV format. Likewise, in each case a graphic representation of the network obtained with Visone (Brandes and Wagner, 2004) is included. Relationship values range from 0 (do not know each other) to 2 (know each other very well).

Second, the compressed file “Redes_Personales_t2.csv” includes 57 folders, with the information equivalent to each respondent referred to the second observation, that is, 17 months after the first interview. The structure of the data is the same as in the first observation.

Sense of community and metropolitan displacements

The SPSS file “Albero.sav” collects the survey data, together with some information-summary of the network data related to each respondent. The 69 rows correspond to the 69 individuals interviewed, and the 118 columns to the variables related to each of them in T1 and T2, according to the following list:

• Socio-economic data.

• Data on habitual residence.

• Information on intercity journeys.

• Identity and sense of community.

• Personal network indicators.

• Social network indicators.

DATA ACCESS

Social networks and personal networks are available in CSV format. This allows its use directly with UCINET, Visone, Pajek or Gephi, among others, and they can be exported as Excel or text format files, to be used with other programs.

The visual representation of the personal networks of the respondents in both waves is available in the following album of the Graphic Gallery of Personal Networks on Flickr: <https://www.flickr.com/photos/25906481@N07/albums/72157667029974755>.

In previous work we analyzed the effects of personal networks on the longitudinal evolution of the socio-centric network. It also includes additional details about the instruments applied. In case of using the data, please quote the following reference:

• Maya-Jariego, I., Holgado, D. & Lubbers, M. J. (2018). Efectos de la estructura de las redes personales en la red sociocéntrica de una cohorte de estudiantes en transición de la enseñanza secundaria a la universidad. Universitas Psychologica, 17(1), 86-98. https://doi.org/10.11144/Javeriana.upsy17-1.eerp

The English version of this article can be downloaded from: https://tinyurl.com/yy9s2byl

CONCLUSION

The database of the “Albero” study allows us to explore the co-evolution of social networks and personal networks. In this way, we can examine the mutual dependence of individual trajectories and the structure of the relationships of the cohort of students as a whole. The complete social network corresponds to the same context of interaction: the secondary school. However, personal networks collect information from the different contexts in which the individual participates. The structural properties of personal networks may partly explain individual differences in the position of each student in the entire social network. In turn, the properties of the entire social network partly determine the structure of opportunities in which individual trajectories are displayed.

The longitudinal character and the combination of the personal networks of individuals with a common complete social network, make this database have unique characteristics. It may be of interest both for multi-level analysis and for the study of individual differences.

ACKNOWLEDGEMENTS

The fieldwork for this study was supported by the Complementary Actions of the Ministry of Education and Science (SEJ2005-25683), and was part of the project “Dynamics of actors and networks across levels: individuals,

The 2022 APS Employee Census was administered to all available Australian Public Service (APS) employees, running from 9 May to 10 June 2022. \r \r The Employee Census provides a comprehensive view of the APS and ensures no eligible respondents are omitted from the survey sample, removing sampling bias and reducing sample error. The Census' content is designed to establish the views of APS employees on workplace issues such as leadership, employee wellbeing, and job satisfaction.\r \r Overall, 120,662 APS employees responded to the Employee Census in 2022, a response rate of 83%.\r \r Please be aware that the very large number of respondents to the employee census means these files are over 200MB in size. Downloading and opening these files may take some time.\r \r TECHNICAL NOTES \r \r Three files are available for download.\r \r * 2022 APS Employee Census - Questionnaire: This contains the 2022 APS Employee Census questionnaire.\r \r * 2022 APS Employee Census - 5 point dataset.csv: This file contains individual responses to the 2022 APS Employee Census as clean, tabular data as required by data.gov.au. This will need to be used in conjunction with the above document.\r \r * 2022 APS Employee Census - 5 point dataset.sav: This file contains individual responses to the 2022 APS Employee Census for use with the SPSS software package. \r \r To protect the privacy and confidentiality of respondents to the 2022 APS Employee Census, the datasets provided on data.gov.au include responses to a limited number of demographic or other attribute questions.\r \r Full citation of this dataset should list the Australian Public Service Commission (APSC) as the author. \r \r A recommended short citation is: 2022 APS Employee Census data, Australian Public Service Commission. \r \r Any queries can be directed to research@apsc.gov.au.\r

Data for a Brief Report/Short Communication published in Body Image (2021). Details of the study are included below via the abstract from the manuscript. The dataset includes online experimental data from 167 women who were recruited via social media and institutional participant pools. The experiment was completed in Qualtrics.Women viewed either neutral travel images (control), body positivity posts with an average-sized model (e.g., ~ UK size 14), or body positivity posts with a larger model (e.g., UK size 18+); which images women viewed is show in the ‘condition’ variable in the data.The data includes the age range, height, weight, calculated BMI, and Instagram use of participants. After viewing the images, women responded to the Positive and Negative Affect Schedule (PANAS), a state version of the Body Satisfaction Scale (BSS), and reported their immediate social comparison with the images (SAC items). Women then selected a lunch for themselves from a hypothetical menu; these selections are detailed in the data, as are the total calories calculated from this and the proportion of their picks which were (provided as a percentage, and as a categorical variable [as used in the paper analyses]). Women also reported whether they were on a special diet (e.g., vegan or vegetarian), had food intolerances, when they last ate, and how hungry they were.

Women also completed trait measures of Body Appreciation (BAS-2) and social comparison (PACS-R). Women also were asked to comment on what they thought the experiment was about. Items and computed scales are included within the dataset.This item includes the dataset collected for the manuscript (in SPSS and CSV formats), the variable list for the CSV file (for users working with the CSV datafile; the variable list and details are contained within the .sav file for the SPSS version), and the SPSS syntax for our analyses (.sps). Also included are the information and consent form (collected via Qualtrics) and the questions as completed by participants (both in pdf format).Please note that the survey order in the PDF is not the same as in the datafiles; users should utilise the variable list (either in CSV or SPSS formats) to identify the items in the data.The SPSS syntax can be used to replicate the analyses reported in the Results section of the paper. Annotations within the syntax file guide the user through these.

A copy of SPSS Statistics is needed to open the .sav and .sps files.

Manuscript abstract:

Body Positivity (or ‘BoPo’) social media content may be beneficial for women’s mood and body image, but concerns have been raised that it may reduce motivation for healthy behaviours. This study examines differences in women’s mood, body satisfaction, and hypothetical food choices after viewing BoPo posts (featuring average or larger women) or a neutral travel control. Women (N = 167, 81.8% aged 18-29) were randomly assigned in an online experiment to one of three conditions (BoPo-average, BoPo-larger, or Travel/Control) and viewed three Instagram posts for two minutes, before reporting their mood and body satisfaction, and selecting a meal from a hypothetical menu. Women who viewed the BoPo posts featuring average-size women reported more positive mood than the control group; women who viewed posts featuring larger women did not. There were no effects of condition on negative mood or body satisfaction. Women did not make less healthy food choices than the control in either BoPo condition; women who viewed the BoPo images of larger women showed a stronger association between hunger and calories selected. These findings suggest that concerns over BoPo promoting unhealthy behaviours may be misplaced, but further research is needed regarding women’s responses to different body sizes.

The article uses a dataset, which cannot be deposited online, but is freely available to registered users. The data of the British Household Panel Study can be requested via https://discover.ukdataservice.ac.uk/catalogue/?sn=5151. Here we provide a STATA do-file that will create the working file, recode the original data and run some robustness tests. The data was prepared in Stata and then saved as SPSS files .sav using Stattrans. This was necessary, as the main cross-lagged latent class models of the paper were estimated using LatentGOLD, which only reads .sav files. Here we also provide the syntax files that were used for estimating these models.

The SPSS data file (RES-062-23-1831 FBS data for ESRC archive.sav) contains 215 variables entered either directly from Farm Management Survey (FMS) Field Books or derived from calculations using field book data and supplementary information (such as price indices). The file ‘RES-062-23-1831 SPSS data handbook.xlsx’ lists all of the variables (both in alphabetical order and the order they appear in in the SPSS file) and includes additional explanatory notes for each variable. Data cleaning was undertaken by looking for logically inconsistent relationships between various variables, querying and checking of anomalous results during data analysis and double checking a number of entries with the original field books. The data file contains information on 168 farm holdings in Devon, Dorset and Cornwall from 1939 to 1984. The file contains 4,987 cases. Each case in the SPSS file relates to a specific field book for a specific year for a particular farm. The 168 farms selected for inclusion in the SPSS dataset represent a proportion of all of the farms in the University of Exeter FMS archive. Farms were purposively selected, initially on grounds of longevity in the FMS sample and then to achieve coverage of a cross-section of farming situations in the counties of Devon, Dorset and Cornwall.

The objectives of this project were to produce a detailed survey of agricultural change, and technical change in particular, over the period 1935 – 1985, and to shed light on how and when changes on individual farms were brought about. These objectives were realised, as detailed in the project end of award report. We should note that there was no requirement at the time of the awarding of the grant to produce a pathways to impact plan, and impact beyond these objectives was not the central focus of the project. As an historical project its impact beyond its contribution to the field of knowledge in this area was always bound to be limited. We did, however, identify groups of beneficiaries and we have worked to engage with these audiences to discuss our findings and to broaden knowledge and cultural understanding, and this work is outlined below. In particular we were keen to discuss our findings with rural historians, focusing on but not restricting ourselves to individuals and groups in the area studied, and to this end we undertook engagement with publics including relevant societies and other organisations, and this engagement conintues. Crucially, the PI and Co-Is lead numerous other funded research projects and the findings and knowledge gained from this project help to set the context for and feed into each of those. The policy work of the PI in particular is informed by broad historical contexts and knowledge about the implementation of and response to technological change provided by work on this project is vital in this regard.limited. We did, however, identify groups of beneficiaries and we have worked to engage with these audiences to discuss our findings and to broaden knowledge and cultural understanding, and this work is outlined below. In particular we were keen to discuss our findings with rural historians, focusing on but not restricting ourselves to individuals and groups in the area studied, and to this end we undertook engagement with publics including relevant societies and other organisations, and this engagement conintues. Crucially, the PI and Co-Is lead numerous other funded research projects and the findings and knowledge gained from this project help to set the context for and feed into each of those. The policy work of the PI in particular is informed by broad historical contexts and knowledge about the implementation of and response to technological change provided by work on this project is vital in this regard.

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 sought to apply current and advanced Y-STR DNA technology in forensic laboratories to a large in vivo population of proxy-couples, to provide groundwork for future inquiry about the conditions affecting DNA recovery in the living patient, to determine timing for evidence collection, and to attempt to identify variables influencing DNA recovery. The objective of this research was to create the evidence base supporting or limiting the expansion of the 72-hour period for evidence collection. Another objective was to identify conditions that might influence the recovery of DNA, and therefore influence policies related to sample collection from the complex post-coital environment. The collection includes 6 SPSS data files: AlleleRecovery Jun 2014 Allrec.sav (n=70; 34 variables) AlleleRecovery Jun 2014 Used for descriptve analysis.sav (n=66; 58 variables) Condom_collections-baseline-d9-Jun2014 Allrec without open-ended-ICPSR.sav (n=70; 66 variables) DNADemogFemalesJun2014- without open-ended AllRec-ICPSR.sav (n=73; 67 variables) DNADemogFemalesJun2014- without open-ended -For analysis with group variables-ICPSR.sav (n=66; 73 variables) DNADemogMalesJun2014- without open-ended AllRec-ICPSR.sav (n=73; 46 variables) and 1 SAS data file (dnalong.sas7bdat (n=264; 7 variables)). Data from a focus group of subject matter experts which convened to identify themes from their practice are not included with this collection.

The 2008 National Survey of Drinking and Driving Attitudes and Behaviors was composed of a single questionnaire administered to a sample of randomly selected individuals 16 and older, with ages 16 through 24 over-sampled. The respondents were asked about their drinking behavior, their drinking and driving behavior, use of designated drivers, their hosting events in which drinking occurred, risks they perceive associated with drinking and driving, experience with anti-DWI enforcement activity, and their attitudes concerning major intervention strategies.The survey was administered from September 10, 2008 to December 22, 2008. A total of 6,999 respondents completed the survey, including 5,392 landline interviews and 1,607 cell phone interviews. The total number of completed interviews for each of the four Census regions (Northeast, Midwest, South, and West) was 1,409, 1,654, 2,390, and 1,546, respectively.

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

Data set from PLOS ONE Article Published Entitled: Western Lowland Gorillas Signal Selectively Using Odor

We sought to assess the measurement properties of items aimed at operationalizing participation in care by next of kin, applied in nursing homes.

Data file 1: SPSS file (.sav) with sex, age category, contact person, 37 items aimed to capture participationin care. Data file 2: Excel (.xlsx) transformation tables from raw total scores, to linear logits, to linearised total scors with the same range as the original scores. Data file 3: File (.rum) for Rasch model analysis, prepared for RUMM2030.

To address Toronto's 2012 budget gap of $774 million, City Council has launched a review of all of its services and implemented a multi-year financial planning process. This data set contains the responses to the multiple- choice questions on the Core Services Review Public Consultation Feedback Form from members of the public. Approximately 13,000 responses were received (full and partial). The consultation was held between May 11 and June 17, 2011. As a public consultation, respondents chose to participate, and chose which questions to answer. This produced a self-selected sample of respondents. The majority of the responses were from City of Toronto residents. There were some responses from GTA residents. City staff reviewed the data and removed personal information and input violating city policies (for example, contravenes the City's current anti-discrimination policy or confidentiality policy). The .SAV file may be viewed with Statistics software such as SPSS or SAS.