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: Analysis script (SPSS Amos format) used for model 2 for all field studies

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: Analysis script (SPSS format) used on the data of all field studies

Discover the booming data science software market! Explore its impressive growth trajectory, key drivers, restraints, and leading players like IBM SPSS, Tableau, and SAS. This comprehensive analysis projects a massive market expansion, outlining trends and opportunities in predictive analytics, machine learning, and AI.

Data availability. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence.

• SPSS DATA. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence (spss data.sav). The data presented in this file contains the data imported wiyh the Software IBM SPSS Statistics, versión 28.0.1.1(15).
• EXCEL DATA. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence (spss data.sav). The data presented in this file contains the data imported wiyh the Software IBM SPSS Statistics, versión 28.0.1.1(15).
• Data of Project factorial.xlsx (The data presented in this file contains the results of the statistical analysis carried out with the Software Microsoft Excel).
• Data Project reliability.xlsx (The data presented in this file contains the results of the statistical analysis carried out with the Software Microsoft Excel).
• FIGURES. Multivariate data analysis. Validation of an instrument for the evaluation of teaching digital competence (Figure 1.jpeg, Figure 2.jpeg, Figure 3 and Figure 4.jpeg).

Datasets for the Exeter Cascade Project 26-50

This is an Annotation for Transparent Inquiry (ATI) data project. The annotated article can be viewed on the Publisher's Website. Data Generation The research project engages a story about perceptions of fairness in criminal justice decisions. The specific focus involves a debate between ProPublica, a news organization, and Northpointe, the owner of a popular risk tool called COMPAS. ProPublica wrote that COMPAS was racist against blacks, while Northpointe posted online a reply rejecting such a finding. These two documents were the obvious foci of the qualitative analysis because of the further media attention they attracted, the confusion their competing conclusions caused readers, and the power both companies wield in public circles. There were no barriers to retrieval as both documents have been publicly available on their corporate websites. This public access was one of the motivators for choosing them as it meant that they were also easily attainable by the general public, thus extending the documents’ reach and impact. Additional materials from ProPublica relating to the main debate were also freely downloadable from its website and a third party, open source platform. Access to secondary source materials comprising additional writings from Northpointe representatives that could assist in understanding Northpointe’s main document, though, was more limited. Because of a claim of trade secrets on its tool and the underlying algorithm, it was more difficult to reach Northpointe’s other reports. Nonetheless, largely because its clients are governmental bodies with transparency and accountability obligations, some of Northpointe-associated reports were retrievable from third parties who had obtained them, largely through Freedom of Information Act queries. Together, the primary and (retrievable) secondary sources allowed for a triangulation of themes, arguments, and conclusions. The quantitative component uses a dataset of over 7,000 individuals with information that was collected and compiled by ProPublica and made available to the public on github. ProPublica’s gathering the data directly from criminal justice officials via Freedom of Information Act requests rendered the dataset in the public domain, and thus no confidentiality issues are present. The dataset was loaded into SPSS v. 25 for data analysis. Data Analysis The qualitative enquiry used critical discourse analysis, which investigates ways in which parties in their communications attempt to create, legitimate, rationalize, and control mutual understandings of important issues. Each of the two main discourse documents was parsed on its own merit. Yet the project was also intertextual in studying how the discourses correspond with each other and to other relevant writings by the same authors. Several more specific types of discursive strategies were of interest in attracting further critical examination: Testing claims and rationalizations that appear to serve the speaker’s self-interest Examining conclusions and determining whether sufficient evidence supported them Revealing contradictions and/or inconsistencies within the same text and intertextually Assessing strategies underlying justifications and rationalizations used to promote a party’s assertions and arguments Noticing strategic deployment of lexical phrasings, syntax, and rhetoric Judging sincerity of voice and the objective consideration of alternative perspectives Of equal importance in a critical discourse analysis is consideration of what is not addressed, that is to uncover facts and/or topics missing from the communication. For this project, this included parsing issues that were either briefly mentioned and then neglected, asserted yet the significance left unstated, or not suggested at all. This task required understanding common practices in the algorithmic data science literature. The paper could have been completed with just the critical discourse analysis. However, because one of the salient findings from it highlighted that the discourses overlooked numerous definitions of algorithmic fairness, the call to fill this gap seemed obvious. Then, the availability of the same dataset used by the parties in conflict, made this opportunity more appealing. Calculating additional algorithmic equity equations would not thereby be troubled by irregularities because of diverse sample sets. New variables were created as relevant to calculate algorithmic fairness equations. In addition to using various SPSS Analyze functions (e.g., regression, crosstabs, means), online statistical calculators were useful to compute z-test comparisons of proportions and t-test comparisons of means. Logic of Annotation Annotations were employed to fulfil a variety of functions, including supplementing the main text with context, observations, counter-points, analysis, and source attributions. These fall under a few categories. Space considerations. Critical discourse analysis offers a rich method...

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Open datasets for the Exeter Cascade Project 1-25.

Abstract

The main objective of the HEIS survey is to obtain detailed data on household expenditure and income, linked to various demographic and socio-economic variables, to enable computation of poverty indices and determine the characteristics of the poor and prepare poverty maps. Therefore, to achieve these goals, the sample had to be representative on the sub-district level. The raw survey data provided by the Statistical Office was cleaned and harmonized by the Economic Research Forum, in the context of a major research project to develop and expand knowledge on equity and inequality in the Arab region. The main focus of the project is to measure the magnitude and direction of change in inequality and to understand the complex contributing social, political and economic forces influencing its levels. However, the measurement and analysis of the magnitude and direction of change in this inequality cannot be consistently carried out without harmonized and comparable micro-level data on income and expenditures. Therefore, one important component of this research project is securing and harmonizing household surveys from as many countries in the region as possible, adhering to international statistics on household living standards distribution. Once the dataset has been compiled, the Economic Research Forum makes it available, subject to confidentiality agreements, to all researchers and institutions concerned with data collection and issues of inequality.

Data collected through the survey helped in achieving the following objectives: 1. Provide data weights that reflect the relative importance of consumer expenditure items used in the preparation of the consumer price index 2. Study the consumer expenditure pattern prevailing in the society and the impact of demographic and socio-economic variables on those patterns 3. Calculate the average annual income of the household and the individual, and assess the relationship between income and different economic and social factors, such as profession and educational level of the head of the household and other indicators 4. Study the distribution of individuals and households by income and expenditure categories and analyze the factors associated with it 5. Provide the necessary data for the national accounts related to overall consumption and income of the household sector 6. Provide the necessary income data to serve in calculating poverty indices and identifying the poor characteristics as well as drawing poverty maps 7. Provide the data necessary for the formulation, follow-up and evaluation of economic and social development programs, including those addressed to eradicate poverty

Geographic coverage

National

Analysis unit

• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The Household Expenditure and Income survey sample for 2010, was designed to serve the basic objectives of the survey through providing a relatively large sample in each sub-district to enable drawing a poverty map in Jordan. The General Census of Population and Housing in 2004 provided a detailed framework for housing and households for different administrative levels in the country. Jordan is administratively divided into 12 governorates, each governorate is composed of a number of districts, each district (Liwa) includes one or more sub-district (Qada). In each sub-district, there are a number of communities (cities and villages). Each community was divided into a number of blocks. Where in each block, the number of houses ranged between 60 and 100 houses. Nomads, persons living in collective dwellings such as hotels, hospitals and prison were excluded from the survey framework.

A two stage stratified cluster sampling technique was used. In the first stage, a cluster sample proportional to the size was uniformly selected, where the number of households in each cluster was considered the weight of the cluster. At the second stage, a sample of 8 households was selected from each cluster, in addition to another 4 households selected as a backup for the basic sample, using a systematic sampling technique. Those 4 households were sampled to be used during the first visit to the block in case the visit to the original household selected is not possible for any reason. For the purposes of this survey, each sub-district was considered a separate stratum to ensure the possibility of producing results on the sub-district level. In this respect, the survey framework adopted that provided by the General Census of Population and Housing Census in dividing the sample strata. To estimate the sample size, the coefficient of variation and the design effect of the expenditure variable provided in the Household Expenditure and Income Survey for the year 2008 was calculated for each sub-district. These results were used to estimate the sample size on the sub-district level so that the coefficient of variation for the expenditure variable in each sub-district is less than 10%, at a minimum, of the number of clusters in the same sub-district (6 clusters). This is to ensure adequate presentation of clusters in different administrative areas to enable drawing an indicative poverty map.

It should be noted that in addition to the standard non response rate assumed, higher rates were expected in areas where poor households are concentrated in major cities. Therefore, those were taken into consideration during the sampling design phase, and a higher number of households were selected from those areas, aiming at well covering all regions where poverty spreads.

Mode of data collection

Face-to-face [f2f]

Research instrument

• General form
• Expenditure on food commodities form
• Expenditure on non-food commodities form

Cleaning operations

Raw Data: - Organizing forms/questionnaires: A compatible archive system was used to classify the forms according to different rounds throughout the year. A registry was prepared to indicate different stages of the process of data checking, coding and entry till forms were back to the archive system. - Data office checking: This phase was achieved concurrently with the data collection phase in the field where questionnaires completed in the field were immediately sent to data office checking phase. - Data coding: A team was trained to work on the data coding phase, which in this survey is only limited to education specialization, profession and economic activity. In this respect, international classifications were used, while for the rest of the questions, coding was predefined during the design phase. - Data entry/validation: A team consisting of system analysts, programmers and data entry personnel were working on the data at this stage. System analysts and programmers started by identifying the survey framework and questionnaire fields to help build computerized data entry forms. A set of validation rules were added to the entry form to ensure accuracy of data entered. A team was then trained to complete the data entry process. Forms prepared for data entry were provided by the archive department to ensure forms are correctly extracted and put back in the archive system. A data validation process was run on the data to ensure the data entered is free of errors. - Results tabulation and dissemination: After the completion of all data processing operations, ORACLE was used to tabulate the survey final results. Those results were further checked using similar outputs from SPSS to ensure that tabulations produced were correct. A check was also run on each table to guarantee consistency of figures presented, together with required editing for tables' titles and report formatting.

Harmonized Data: - The Statistical Package for Social Science (SPSS) was used to clean and harmonize the datasets. - The harmonization process started with cleaning all raw data files received from the Statistical Office. - Cleaned data files were then merged to produce one data file on the individual level containing all variables subject to harmonization. - A country-specific program was generated for each dataset to generate/compute/recode/rename/format/label harmonized variables. - A post-harmonization cleaning process was run on the data. - Harmonized data was saved on the household as well as the individual level, in SPSS and converted to STATA format.

Improvement projects (IPs) are a fundamental element in any quality management system from any organization. In Higher Education Institutions (HEIs), IPs are constantly implemented to maintain excellence in academic and administrative processes. In this study, we propose a model for IP implementation that is based on the Baldrige Performance Excellence Program (BPEP). As a part of the model, we propose a series of research hypotheses to be tested. The data used to test the hypotheses were gathered from a questionnaire that was developed after an extensive literature review. The survey was administered to Mexican public HEIs, and more than 700 responses were collected. The data were assessed in terms of convergent and discriminant validity, obtaining satisfactory results. To test the proposed relationships between the model constructs, we utilized Structural Equation Modeling (SEM) using the software IBM SPSS Amos. The analysis confirmed the statistical validity of both the model and the hypotheses. In conclusion, our model for IP implementation is a useful tool for HEIs that seek to attain excellence in their processes through IPs.

These files include the data set used in the analysis of data and a record of the process of analysing the data in SPSS.

This dataset consists of three data folders for the eye-tracking experiment conducted within the NESP 3.2.3 project (Tropical Water Quality Hub): Folder (1) The folder of Eye-tracking videos contains 66 Tobii recordings of participants’ eye movements on screen, Folder (2) The Heatmaps folder includes 21 heatmaps created by Tobii eye-tracking software on the basis of 66 participants’ data and Folder (3) The input folder has 21 original pictures used in eye-tracking experiment. Moreover, The dataset also includes 1 excel file representing eye-tracking data extracted from Tobii software and participant interview results, 1 SPV. file as the input of SPSS data analysis process and 1 SPV. file as the output of data analysis process.

Methods: This dataset resulted from both input and output data of eye-tracking experiments. The input includes 21 underwater pictures of the Great Barrier Reef, selected from online searching with the keyword “Great Barrier Reef”. These pictures are imported to Tobii eye-tracking software to design the eye-tracking experiments. 66 participants were recruited using convenience sampling in this study. They were asked to sit in front of a screen-based eye-tracking equipment (i.e. Tobii T60 eye-tracker) after providing informed consent. Participants were free to look at each picture on screen as long as they wanted during which their eye movements were recorded. They also rated each picture on a 10-point beauty scale (1-Not beautiful at all, 10-Very beautiful) and a 10-point expectation scale (1-Not at all, 10-Very much). After the experiment, 40 subjects were also interviewed to identify the areas of interest (AOI) in each picture and to rate the beauty of these AOIs. Eye-tracking data was then extracted from Tobii eye-tracking device including participants’ eye-tracking recordings, heatmaps (i.e. images showing viewers’ attention focus) and raw eye-tracking measures (i.e. picture beauty, time to first fixation, fixation count, fixation duration and total visit time) using XLSX. download format. Raw eye-tracking data was then imported to IBM SPSS using SAV. format for data analysis which results in a SPV. output file.

Further information can be found in the following publication: Scott, N., Le, D, Becken, S., and Connelly, R. (2018 Submitted) Measuring perceived beauty of the Great Barrier Reef using eye tracking. Journal of Sustainable Tourism. 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 project dataset includes 132 eye-tracking videos of AVI. format, 21 heatmaps of PNG. format, 21 pictures of JPEG. format, 1 XLSX. format document representing raw eye-tracking measures and interview data, 1 SAV. format document as the input of data analysis and 1 SPV. format file showing data analysis results.

Data Dictionary:

Q1, Q2, Q3, Q4, Q5, Q6, Q7, Q8, Q9, Q10: Names of pictures used in the eye-tracking experiment 2. 3Q1, 3Q2, 3Q3, 3Q4, 3Q5, 3Q6, 3Q7, 3Q8, 3Q9, 3Q10, 3Q11: Names of pictures used in the eye-tracking experiment 3.

Raw Eye tracking Measurements excel spreadsheets:

Tab - Picture: INDEX: the 10-point scale showed to participants VALUE: meaning of the 10-point scale Q1.1: Beauty score Q1.2: Expectation score

Tab - Area of Interest (AOI)" TIME TO FIRST FIXATION_Q1: Time to first fixation in the picture Q1 (i.e. i.e. the average time from the beginning of the recording until the respective picture was first fixated upon) TOTAL FIXATION DURATION_Q1: Fixation duration in the picture Q1 (i.e. the average length of all fixations during all recordings in the whole picture). A longer fixation means that the object is more engaging in some way. FIXATION COUNT_Q1: Fixation count in the picture Q1 (i.e. the average number of fixations in the picture). TOTAL VISIT DURATION_Q1: Total time visit for the picture Q1 (i.e. the average time participants spent looking at a picture). TIME TO FIRST FIXATION_AOI1: Time to first fixation in the AOI identified in the picture Q1 (i.e. i.e. the average time from the beginning of the recording until the respective picture was first fixated upon) TOTAL FIXATION DURATION_AOI1: Fixation duration in the AOI identified in the picture Q1 (i.e. the average length of all fixations during all recordings in the whole picture). A longer fixation means that the object is more engaging in some way. FIXATION COUNT_AOI1: Fixation count in the AOI identified in the picture Q1 (i.e. the average number of fixations in the picture). TOTAL VISIT DURATION_AOI1: Total time visit for the AOI identified in the picture Q1 (i.e. the average time participants spent looking at a picture).

Tab - AOI interview: AOI IDENTIFIED: The AOI that is the most mentioned by participants NUMBER OF PARTICIPANTS: the number of participants who mentioned the AOI in the previous column. BEAUTY MEAN: The average beauty score of the correspondent AOI rated by 40 participants. AOI-1: The AOI identified by the correspondent participant. RATING: the beauty score associated to the AOI identified by the correspondent participant.

Tab - Analysis: REC: Recording PICTURE: Picture number BEAUTY: The average beauty score of the correspondent picture by 66 participants EXPECTATION: The average expectation score of the correspondent picture by 66 participants AOI BEAUTY: The average beauty score of the AOI identified in the correspondent picture by interviewed participants. PICTURE 1st TIME: The average time to first fixation in the correspondent picture (i.e. i.e. the average time from the beginning of the recording until the respective picture was first fixated upon) by 66 participants PFDURATION: The average fixation duration in the correspondent picture (i.e. the average length of all fixations during all recordings in the whole picture) by 66 participants PFCOUNT: The average fixation count in the correspondent picture (i.e. the average number of fixations in the picture) by 66 participants PTING VISIT: The average of total time visit for the correspondent picture (i.e. the average time participants spent looking at a picture) by 66 participants AOI 1stTIME: The average time to first fixation in the AOI identified in the correspondent picture (i.e. i.e. the average time from the beginning of the recording until the respective picture was first fixated upon) by 66 participants AOIFDURATION: The average fixation duration in the AOI identified in the correspondent picture (i.e. the average length of all fixations during all recordings in the whole picture) by 66 participants AOIFCOUNT: The average fixation count in the the AOI identified in correspondent picture (i.e. the average number of fixations in the picture) by 66 participants AOITIMEVISIT: The average of total time visit for the AOI identified in the correspondent picture (i.e. the average time participants spent looking at a picture) by 66 participants

References:

Scott, N., Le, D, Becken, S., and Connelly, R. (2018 Submitted) Measuring perceived beauty of the Great Barrier Reef using eye tracking. Journal of Sustainable Tourism.

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: Analysis script (TXT format) used on the data of all field studies

Abstract

The programme for the World Census of Agriculture 2000 is the eighth in the series for promoting a global approach to agricultural census taking. The first and second programmes were sponsored by the International Institute for Agriculture (IITA) in 1930 and 1940. Subsequent ones up to 1990 were promoted by the Food and Agriculture Organization of the United Nations(FAO). FAO recommends that each country should conduct at least one agricultural census in each census programme decade and its programme for the World Census of Agriculture 2000 for instance corresponds to agricultural census to be undertaken during the decade 1996 to 2005. Many countries do not have sufficient resources for conducting an agricultural census. It therefore became an acceptable practice since 1960 to conduct agricultural census on sample basis for those countries lacking the resources required for a complete enumeration.

In Nigeria's case, a combination of complete enumeration and sample enumeration is adopted whereby the rural (peasant) holdings are covered on sample basis while the modern holdings are covered on complete enumeration. The project named “National Agricultural Sample Census” derives from this practice. Nigeria through the National Agricultural Sample Census (NASC) participated in the 1970's, 1980's, 1990's programmes of the World Census of Agriculture. Nigeria failed to conduct the Agricultural Census in 2003/2004 because of lack of funding. The NBS regular annual agriculture surveys since 1996 had been epileptic and many years of backlog of data set are still unprocessed. The baseline agricultural data is yet to be updated while the annual regular surveys suffered set back. There is an urgent need by the governments (Federal, State, LGA), sector agencies, FAO and other International Organizations to come together to undertake the agricultural census exercise which is long overdue. The conduct of 2006/2008 National Agricultural Sample Census Survey is now on course with the pilot exercise carried out in the third quarter of 2007.

The National Agricultural Sample Census (NASC) 2006/08 is imperative to the strengthening of the weak agricultural data in Nigeria. The project is phased into three sub-projects for ease of implementation; the Pilot Survey, Modern Agricultural Holding and the Main Census. It commenced in the third quarter of 2006 and to terminate in the first quarter of 2008. The pilot survey was implemented collaboratively by National Bureau of Statistics.

The main objective of the pilot survey was to test the adequacy of the survey instruments, equipments and administration of questionnaires, data processing arrangement and report writing. The pilot survey conducted in July 2007 covered the two NBS survey system-the National Integrated Survey of Households (NISH) and National Integrated Survey of Establishment (NISE). The survey instruments were designed to be applied using the two survey systems while the use of Geographic Positioning System (GPS) was introduced as additional new tool for implementing the project.

The Stakeholders workshop held at Kaduna on 21st-23rd May 2007 was one of the initial bench marks for the take off of the pilot survey. The pilot survey implementation started with the first level training (training of trainers) at the NBS headquarters between 13th - 15th June 2007. The second level training for all levels of field personnels was implemented at headquarters of the twelve (12) concerned states between 2nd - 6th July 2007. The field work of the pilot survey commenced on the 9th July and ended on the 13th of July 07. The IMPS and SPSS were the statistical packages used to develop the data entry programme.

Geographic coverage

State

Analysis unit

Household based of fish farmers

Universe

The survey covered all de jure household members (usual residents), who were into fish production

Kind of data

Census/enumeration data [cen]

Sampling procedure

The survey was carried out in 12 states falling under 6 geo-political zones. 2 states were covered in each geo-political zone. 2 local government areas per selected state were studied. 2 Rural enumeration areas per local government area were covered and 3 Fishing farming housing units were systematically selected and canvassed .

Sampling deviation

There was deviations from the original sample design

Mode of data collection

Face-to-face [f2f]

Research instrument

The NASC fishery questionnaire was divided into the following sections: - Holding identification: This is to identify the holder through HU serial number, HH serial number, and demographic characteristics. - Type of fishing sites used by holder. - Sources and quantities of fishing inputs. - Quantity of aquatic production by type. - Quantity sold and value of sale of aquatic products. - Funds committed to fishing by source and others

Cleaning operations

The data processing and analysis plan involved five main stages: training of data processing staff; manual editing and coding; development of data entry programme; data entry and editing and tabulation. Census and Surveys Processing System (CSPro) software were used for data entry, Statistical Package for Social Sciences (SPSS) and CSPro for editing and a combination of SPSS, Statistical Analysis Software (SAS) and EXCEL for table generation. The subject-matter specialists and computer personnel from the NBS and CBN implemented the data processing work. Tabulation Plans were equally developed by these officers for their areas and topics covered in the three-survey system used for the exercise. The data editing is in 2 phases namely manual editing before the data entry were done. This involved using editors at the various zones to manually edit and ensure consistency in the information on the questionnaire. The second editing is the computer editing, this is the cleaning of the already enterd data. The completed questionnaires were collated and edited manually (a) Office editing and coding were done by the editor using visual control of the questionnaire before data entry (b) Cspro was used to design the data entry template provided as external resource (c) Ten operator plus two suppervissor and two progammer were used (d) Ten machines were used for data entry (e) After data entry data entry supervisor runs fequency on each section to see that all the questionnaire were enterd

Response rate

Both Enumeration Area (EA) and Fish holders' level Response Rate was 100 per cent.

Sampling error estimates

No computation of sampling error

Data appraisal

The Quality Control measures were carried out during the survey, essentially to ensure quality of data

HIV is a major risk factor for active Tuberculosis (TB.) This raises patients’ risk of original infection, reinfection, and TB reactivation. Providing healthcare to KPLHIV in developing countries requires TB prevalence research. This study aims to determine the prevalence of TB and HIV co-infection and associated factors among KPLHIV. This is a retrospective cross-sectional study among KP’s living with HIV enrolled on care in One Stop Shop (OSS) of Heartland Alliance Ltd/GTE across six states in Nigeria. Data were analysed using IBM SPSS version 25.0. Secondary data analysis of client’s records from the RADET files of the KPCARE 1 project from 6 states was conducted. Means with standard deviations were computed for continuous variables like age, and frequency tables were generated for categorical variables. Chi-square tests and t-tests were used for the bivariate analysis of variables. All tests were done at a 5% level of statistical significance (p = 0.05).TB prevalence was 19.1% among KP’s living with HIV, with variations observed in age groups, geographic locations, target populations, marital status, educational backgrounds, clinical characteristics, and antiretroviral therapy (ART) history. KPs aged 51 and above exhibited the highest TB prevalence (21.0%), while those aged below 20 years had the lowest (18.2%). Jigawa KPs recorded the highest TB prevalence (38.4%), and Niger had the least (13.3%). TB was more prevalent among People who inject drugs (20.3%), divorced (32.3%), and those who attained Qur’anic education (29.7%). KPs who had to restart ART exhibited the highest TB prevalence (22.0%), whereas those who experienced Interruption in treatment (IIT) reported the lowest at 10.0%. Immune-suppressed KPs (CD4 counts < 200 cells/m3) had a higher TB prevalence of 26.6%. TB prevalence among KPs living with HIV varies greatly, underlining the need for targeted treatments, especially for high-risk categories, to improve HIV treatment outcomes and reduce TB prevalence.

The second stream within the NESP 5.5 project was conducted using eye-tracking technology to examine possible differences between three participant groups in evaluating the aesthetic beauty of GBR underwater sceneries. This research continue the efforts initiated in the previous NESP 3.2.3 project to explore the power of eye-tracking as an objective measure of human aesthetic assessment of GBR underwater sceneries. By employing a sample of three social-cultural groups (non-indigenous Australians, Chinese and First Peoples), this research provides further empirical evidence for the effectiveness of eye-tracking in aesthetic research in a cross-cultural context. Data collected using eye tracking was stored in one Excel file of eye-tracking data exported from Tobii eye-tracking device and 20 heatmaps showing participants’ visual attention to 20 images of underwater GBR sceneries.

Methods:

Following the initial research conducted in the previous NESP 3.2.3 project, 93 participants of various socio-cultural backgrounds (non-indigenous Australians, First People Australians and Chinese) were recruited using convenience sampling in this study. Participants were asked to sit in front of a screen-based eye-tracking equipment (i.e. Tobii T60 eye-tracker) after providing informed consent. Participants were free to look at each picture on screen as long as they wanted during which their eye movements were recorded (similar to lab setting in NESP 3.2.3). They also rated each picture on a 10-point beauty scale (1-Not beautiful at all, 10-Very beautiful). Raw eye-tracking data was then imported to IBM SPSS using SAV. format for data analysis. Raw eye-tracking data was then extracted from Tobii eye-tracking device (i.e. picture beauty, time to first fixation, fixation count, fixation duration and total visit time) in Exel format. Twenty heatmaps in Png format generated from the eye-tracking software to show participants’ visual attention were also included. As an extension of the previous study conducted within the NESP 3.2.3 project, data collected was used to examine the influences of social-cultural differences in aesthetic assessment of GBR underwater sceneries.

Advanced technologies were used in combination with self-reporting measurements for a better understanding of socio-cultural differences and socio-cultural influences on aesthetic assessment among three groups. Eye-tracking provides a measure of visual attention, enabling researchers to explore further potential differences among three groups regarding their interest in viewing and assessing the GBR aesthetics. Previous research (NESP 3.2.3) demonstrated that eye-tracking measures of viewers' visual attention (i.e., fixation duration and fixation count) and aesthetic ratings are correlated, suggesting the usefulness of eye-tracking in aesthetic research. This study verifies the usefulness of eye-tracking in aesthetic research in a cross-cultural context. Participants were exposed to 20 images of underwater GBR scenery in random order which were used in the previous focus groups.

Further information can be found in the following publication: Le, D., Becken, S., & Whitford, M. (2020) A cross-cultural investigation of the great barrier Reef aesthetics using eye-tracking and face-reader technologies. Report to the National Environmental Science Program. Reef and Rainforest Research Centre Limited, Cairns. Published online at https://nesptropical.edu.au/wp-content/uploads/2020/09/NESP-TWQ-Project-5.5-Technical-Report-2.pdf

Format:

The eye-tracking folder contains one Excel file containing raw eye-tracking data and 20 heatmaps generated from eye-tracking software in Png format.

Data Dictionary:

• Beauty1: Name of the corresponding GBR underwater picture used in the eye-tracking experiment
• ABeauty1: Aesthetic evaluations of the corresponding picture (e.g., Beauty1)
• EBeauty1: Aesthetic emotion (i.e., pleasant) of the corresponding picture (e.g., Beauty1)
• FD_Beauty1: Fixation duration in the picture Beauty1 (i.e. the average length of all fixations during all recordings in the whole picture). A longer fixation means that the object is more engaging in some way. Measurement unit:
• AOIFD_Beauty1: Fixation duration in the central area of interest (AOI) in picture Beauty1 (i.e. the average length of all fixations during all recordings in the whole picture). A longer fixation means that the object is more engaging in some way.
• FC_Beauty1: Fixation count in the picture Beauty1 (i.e. the average number of fixations in the picture).
• AOIFC_Beauty1: Fixation count in the central area of interest (AOI) in picture Beauty1 (i.e. the average number of fixations in the picture).

Similar labels are used for other pictures, including Beauty 2,3,4; Human 1,3,5,6; Medium 1,2,3,4; Restoration 1,2,3,8 and Ugly 1,2,3,4.

Further information can be found in the following publication: Le, D., Becken, S., & Whitford, M. (2020) A cross-cultural investigation of the great barrier Reef aesthetics using eye-tracking and face-reader technologies. Report to the National Environmental Science Program. Reef and Rainforest Research Centre Limited, Cairns. Published online at https://nesptropical.edu.au/wp-content/uploads/2020/09/NESP-TWQ-Project-5.5-Technical-Report-2.pdf

References:

Murray, N., Marchesotti, M. & Perronnin, F (2012). AVA: A Large-Scale Database for Aesthetic Visual Analysis. Available (09/10/17) http://refbase.cvc.uab.es/files/MMP2012a.pdf

Data Location:

This dataset is filed in the eAtlas enduring data repository at: data\custodian\2019-2022-NESP-TWQ-5\5.5_Measuring-aesthetics

Data was collected using a questionnaire administered before and several months following an art/science-based intervention. The intervention's primary aim was to improve attitudes to the adder (Vipera berus), Britain's only venomous snake. Three schools participated, two were part of the intervention, and the other acted as a control school. Questionnaires were summed by hand and the totals for attitudes towards adders, and connectedness to nature were inputted into Excel. Statistical analysis was completed on SPSS v20.

The GAPs Data Repository provides a comprehensive overview of available qualitative and quantitative data on national return regimes, now accessible through an advanced web interface at https://data.returnmigration.eu/.

This updated guideline outlines the complete process, starting from the initial data collection for the return migration data repository to the development of a comprehensive web-based platform. Through iterative development, participatory approaches, and rigorous quality checks, we have ensured a systematic representation of return migration data at both national and comparative levels.

The Repository organizes data into five main categories, covering diverse aspects and offering a holistic view of return regimes: country profiles, legislation, infrastructure, international cooperation, and descriptive statistics. These categories, further divided into subcategories, are based on insights from a literature review, existing datasets, and empirical data collection from 14 countries. The selection of categories prioritizes relevance for understanding return and readmission policies and practices, data accessibility, reliability, clarity, and comparability. Raw data is meticulously collected by the national experts.

The transition to a web-based interface builds upon the Repository’s original structure, which was initially developed using REDCap (Research Electronic Data Capture). It is a secure web application for building and managing online surveys and databases.The REDCAP ensures systematic data entries and store them on Uppsala University’s servers while significantly improving accessibility and usability as well as data security. It also enables users to export any or all data from the Project when granted full data export privileges. Data can be exported in various ways and formats, including Microsoft Excel, SAS, Stata, R, or SPSS for analysis. At this stage, the Data Repository design team also converted tailored records of available data into public reports accessible to anyone with a unique URL, without the need to log in to REDCap or obtain permission to access the GAPs Project Data Repository. Public reports can be used to share information with stakeholders or external partners without granting them access to the Project or requiring them to set up a personal account. Currently, all public report links inserted in this report are also available on the Repository’s webpage, allowing users to export original data.

This report also includes a detailed codebook to help users understand the structure, variables, and methodologies used in data collection and organization. This addition ensures transparency and provides a comprehensive framework for researchers and practitioners to effectively interpret the data.

The GAPs Data Repository is committed to providing accessible, well-organized, and reliable data by moving to a centralized web platform and incorporating advanced visuals. This Repository aims to contribute inputs for research, policy analysis, and evidence-based decision-making in the return and readmission field.

Explore the GAPs Data Repository at https://data.returnmigration.eu/.

It includes the data, the Python project, the SPSS project, and the project tutorial that support the findings of the study (Assessing the coordination of the transportation service network and the population mobility network in mega-urban agglomerations: A deviation-based framework).