Citation:Okada A. (2025). Upskill Map self-reported instrument CCBYSAContact emailale.okada@open.ac.ukDatabase URL:CONNECT: https://ordo.open.ac.uk/projects/CONNECT_-_Inclusive_open_schooling_with_future_oriented_science/125821METEOR: https://ordo.open.ac.uk/projects/METEOR/244817Information:This questionnaire is part of UK METEOR studies and CONNECT projectQuestionnaire designSemi-structured questionnaire including a combination of open-ended and closed-ended questions.Platform used for a coded questionnaire with feedback and open badgeQualtricsMultilanguage supportTargetlanguage (English) toensure that respondents can understand and respond to the questions in theirpreferred language.Questionnaire implementationLogic for sore, feedback and open badge implementedLanguage selectionQualtrics allows respondents to select their preferred language before starting the survey.Data generationThe questionnaire was distributed to the target audience researchersthrough teachers members of METEOR and CONNECT project who agreed to contribute to this researchData storageAs respondents submit their responses, Qualtrics stores the data securely in its database infrastructure. Each response is associated with the respondent's unique identifier and includes the language in which the survey was completed.Data analysisExploratory factorial analysis, descriptive analyses and thematic analysis to support mixed methodsExtra InformationCreator of the Instrument used to generate this database:Okada, A. UPSKILL.MAPThis database refers to CONNECT project:METEOR projecthttps://www.connect-science.net/https://www.meteorhorizon.euFunder:European Commission No. 872814. and 101178320Questionnaire and database location:https://doi.org/10.21954/ou.rd.30109057

These are anonymized responses to a survey of 389 members of the UK public on their perceptions towards different maps about the social determinants of health. It was originally collected as part of a study described in the article 'Do personal narratives make thematic maps more persuasive? Integrating concrete examples into maps of the social determinants of health', in the Cartography and Geographic Information Science journal.The responses were collected in September 2024 on Qualtrics, via the recruitment platform Prolific.Participants were shown information on three social determinants of health (public transport, air pollution, youth services). For each topic, they were randomly shown one of three maps with varying levels of personal narratives presented. The type of map shown to each respondent can be found in columns 'transport_condition', 'pollution_condition', and 'youth_condition'. Most of the other variables refer to perceptions about those issues. For example, 'severity_pollution' refers to whether they deem air pollution a severe issue facing the country. Other variables include demographic information, chart literacy measured by four questions, and self-assessed confidence with charts.

Database File Name:2025 METEOR transversal skills 40 researchers 25 Aug 2025 CCBYSADatabase Description:Reseachers' self-report perceptions reflecting about transversal skillsDatabase Citation:Okada A.(2025). Dataset upskill mapContact email:ale.okada@open.ac.ukDatabase URL:https://doi.org/10.21954/ou.rd.30108928Information:This database provides the views of 40 researchers who participated in UPSKILL studyhttps://ordo.open.ac.uk/projects/METEORDatabase Methodology used to generated dataQuestionnaire designSemi-structured questionnaire including a combination of open-ended and closed-ended questions.Platform used for a coded questionnaire with feedback and open badgeQualtrixMultilanguage supportTarget language (English)Questionnaire implementationLogic for sore, feedback and open badge implementedLanguage selectionQualtrics allows respondents to select their preferred language before starting the survey.Data generationThe questionnaire was distributed to the target audience researchers through OU post graduation schoolsData storageAs respondents submit their responses, Qualtrics stores the data securely in its database infrastructure. Each response is associated with the respondent's unique identifier and includes the language in which the survey was completed.Data analysisExploratory factorial analysis, descriptive analyses and thematic analysis to support mixed methodsExtra InformationCreator of the Instrument used to generate this database:Okada, A. Upskill map self-report instrumenthttps://www.meteorhorizon.euProject description:METEOR: Methodologies for Teamworking in Eco-Outwards ResearchFunder:European Commission (Grant No. 101178320)Questionnaire and database location:https://doi.org/10.21954/ou.rd.23566662Questionnaire citation:Okada A. (2025) UPSKILL. CCBYSAJournal Article using data presented in this database:https://oro.open.ac.uk/96439/Article Citation:Okada, A. et al.License:CCBYSAEthics Protocol:Approval:The methodology and instruments used in this study received approval from both global and local Ethics Committees, ensuring compliance with ethical standards and regulations.Consent Forms:Informed consent was obtained from all participants involved in the study. Teachers and respondents were provided with consent forms detailing the nature and purpose of the research, and they had the option to voluntarily participate. Additionally, parents were informed about the study by schools, and an opt-out approach was implemented, allowing them to decline their child's participation if desired.Voluntary Participation:Participation in the study was entirely voluntary, and participants had the option to withdraw at any stage without penalty or consequence.Confidentiality and Data Protection:Measures were implemented to ensure the confidentiality and privacy of participants' data. All information collected was treated with strict confidentiality and used only for research purposes.Ethics approval:OU HREC 2025-0889-4

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

Open access data to (partially) replicate the research paper "Information frictions, overconfidence, and learning: Experimental evidence from a floodplain", by Sofia Badini. This data consists of:

• BAG data: data about addresses in the Netherlands, derived from the Addresses and Buildings Key Registry (Basisregistraties Adressen en Gebouwen, or BAG) and acquired in .csv format via Geotoko in July 2022.
• ENW data: shapefiles representing those areas of the Southern regions of The Netherlands (Limburg and North Brabant) that were flooded in July 2021. These maps have been shared by the ENW (Expertise Netwerk Waterveiligheid), the association of Dutch flood protection specialists, via the 4TU.ResearchData repository of Delft University of Technology (see here).
• Risicokaart data: shapefiles of the flood maps developed for the European Floods Directive (ROR2) delivered at the end of 2019 via the Risicokaart website, obtained in October 2021 by contacting lbo@risicokaart.nl.
• (Synthetic) survey data: synthetic data created by the author. The real data, collected via the platform Qualtrics, will be shared once this paper is published.

The data files are CSV and can be opened as XL files.

The CVLZ measurement database (in the report) contains measurements, tailored to different types of truck configurations, loading equipment and accessories, of the operating envelope around a commercial vehicle. Three different data types were obtained from Oregon State Driving and Bicycling Simulator Laboratory for purpose of this report and they are as follow: 1) Pre-post survey data (all included in one excel file) consists of series of questions that were answered in an online Qualtrics survey by 48 participants to a) identify their demographic variables, and b) map their self-reported responses to their behavior while riding through the experiment so that results can be validated. 2) Speed data was collected based on the cyclist’s speed while riding through the scenarios. For each scenario, the average speed (m/sec) of 48 cyclists from 25 meter before the location of the commercial vehicle to 15 meter after was recorded. 3) Lateral position data was collected based on cyclist’s divergence from the center of the bike lane. The average lateral position (m) of 48 cyclists from 25 meter before the location of the commercial vehicle to 15 meter after for each independent variable level was recorded. Note that center of the bike lane was defined as 0 m making the left edge -0.92 m (travel lane side). 50 participants were recruited, two of them had a simulator sickness so they were excluded from the data and the analysis. Therefore, the data has no quality or consistency issues and it is ready to be used. The average values were calculated to easily apply the optimal statistical analysis for such data (speed and lateral position). As the experiment consists of 3x3x2 factorial design, each participant had to ride 18 scenarios; therefore, 864 observations were obtained and recorded in the excel file.