Find research datasets worth reusing

opencc-by-4.0Sep 2020View details →

MoTiV: a Dataset of European User Mobility for Behavioral-Data

Mobility is a system involving several stakeholders. Therefore, it is relevant to characterize mobility behavior and preferences in a detailed way, to enable nuanced decisions. Current paradigms rely mostly on time saving, proposing to users solutions that include the shortest path. Even though the value of travel time can be extended beyond travel duration, no dataset to characterize mobility and value of travel time from different perspectives exists. This creates a gap between novel mobility paradigms and the characterization of user mobility. To enable the mining of user mobility under these new paradigms, in this paper, we present the MoTiV (Mobility and Time Value) dataset, which contains data about travelers and their journeys, collected from a mobile application, called Woorti. Each trip contains multi-faceted information: from the transport mode, through its evaluation, to the positive/negative experience factors. We also present a use case, which compares corresponding legs with different transport modes, studying experience factors that negatively impact users. We conclude by discussing other application domains and research opportunities enabled by the dataset.

Survey Study about Motivation for Participants in Citizen Science Projects

The survey study about motivation for participants in Citizen Science projects was designed within the ongoing H2020 project named <a href="https://actionproject.eu/">ACTION</a> (pArticipatory sCience Toolkit agaInst pollutiON). Volunteers participate to citizen science initiatives for multiple reasons: personal enjoyment, desire for improvement or achievement, establishment of personal relationships, care for the environment, etc. Studying motivation and investigating the factors influencing people participation to citizen science projects is an essential aspect in the analysis of citizen science communities. Understanding the reasons that foster people to engage can support the successful design and implementation of effective participant involvement tasks, as well as pave the way for long-term engagement. The goal of the study is to analyse the motivation to participate of a specific citizen science community and the structure of the survey proposed shuold be customised considering the topic of the activities. This research object describes the studies performed within the ACTION project to investigate motivations of different citizen science communities: the TESS Network (<a href="https://tess.stars4all.eu/">https://tess.stars4all.eu/</a>); the 6 ACTION pilots (<a href="https://actionproject.eu/citizen-science-pilots">https://actionproject.eu/citizen-science-pilots</a>) Mapping Mobility, Open Soil Atlas, Water Sentinels, Restart Data Workbench, Wow Nature, Walk Up Aniene. The surveys were designed and administered through Coney (<a href="https://coney.cefriel.com">https://coney.cefriel.com</a>) and made available as linked data exploiting the Survey Ontology (<a href="https://w3id.org/survey-ontology">https://w3id.org/survey-ontology</a>). The research object adopts the <a href="https://www.researchobject.org/ro-crate/1.0/">RO-Crate</a> specification. Files made available within the research object are: <ul> <li>*-procedure.ttl contains the RDF representation of the template structure of the conversational survey (questions, answers, etc.) using the <a href="https://w3id.org/survey-ontology">Survey Ontology</a></li> <li>*-mean-var-motivating-questions.csv contains the computed mean and average for each question considered (observable variables) comparing all the surveys performed</li> <li>*-mean-var-motivating-factor.csv contains the computed mean and average for each motivation factor considered (latent variables) comparing all the surveys performed</li> <li>*-correlation-factors-global-motivation.csv contains the correlation analysis between each motivation factor and the global motivation comparing all the surveys performed</li> </ul> The Research Object also references all the RO-Crates describing the different survey motivation studies in details.

TESS Network Motivation Survey

The TESS Network motivation study was conducted within the ongoing H2020 project named <a href="https://actionproject.eu/">ACTION</a> (pArticipatory sCience Toolkit agaInst pollutiON) on citizen science. Volunteers participate to citizen science initiatives for multiple reasons: personal enjoyment, desire for improvement or achievement, establishment of personal relationships, care for the environment, etc. Studying motivation and investigating the factors influencing people participation to citizen science projects is an essential aspect in the analysis of citizen science communities. Understanding the reasons that foster people to engage can support the successful design and implementation of effective participant involvement tasks, as well as pave the way for long-term engagement. The goal of the study is to analyse the motivation to participate of a specific citizen science community focused on fighting light pollution: the network of around 120 hosts of the <a href="https://tess.stars4all.eu/">TESS photometers</a>. Volunteers of this network accepted to host and install sensors to monitor sky brightness in order to collect data for measuring the level of light pollution in many areas of the Earth. The volunteers are very diverse: professional astronomers, amateur astronomers, light pollution fighters, astronomical outreach (museum, planetarium, dark sky association, etc.), astro-tourism actors, public administrations and others. The TESS Network Survey motivation study is part of the study about motivation in citizen science projects conducted within the ACTION project (<a href="https://doi.org/10.5281/zenodo.5753092">https://doi.org/10.5281/zenodo.5753092</a>). The survey was designed and administered using the <a href="https://coney.cefriel.com/">Coney</a> toolkit. The research object adopts the <a href="https://www.researchobject.org/ro-crate/1.0/">RO-Crate</a> specification. Files made available within the research object are: <ul> <li>*-procedure.ttl contains the RDF representation of the structure of the conversational survey (questions, answers, etc.) using the <a href="https://w3id.org/survey-ontology">Survey Ontology</a></li> <li>*-results.ttl contains the RDF representation of the answers collected using the <a href="https://w3id.org/survey-ontology">Survey Ontology</a></li> <li>*-survey.tll contains a comprehensive RDF representation of the survey data using the <a href="https://w3id.org/survey-ontology">Survey Ontology</a></li> <li>*-results.csv contains the CSV of the collected answers</li> <li>*-script.R is the R script developed to analyse the collected answers</li> <li>*-mean-var-motivating-questions.csv contains the computed mean and average for each question considered (observable variables)</li> <li>*-mean-var-motivating-factor.csv contains the computed mean and average for each motivation factor considered (latent variables)</li> <li>*-correlation-factors-global-motivation.csv contains the correlation analysis between each motivation factor and the global motivation </li> </ul> A <a href="https://doi.org/10.5281/zenodo.4066914">poster</a> and a <a href="https://doi.org/10.22323/2.20060203">paper</a> describing the study are additional resources referenced by the research object.

Restart Data Workbench Motivation Survey

The Restart Data Workbench motivation study was conducted within the ongoing H2020 project named <a href="https://actionproject.eu/">ACTION</a> (pArticipatory sCience Toolkit agaInst pollutiON) on citizen science. Volunteers participate to citizen science initiatives for multiple reasons: personal enjoyment, desire for improvement or achievement, establishment of personal relationships, care for the environment, etc. Studying motivation and investigating the factors influencing people participation to citizen science projects is an essential aspect in the analysis of citizen science communities. Understanding the reasons that foster people to engage can support the successful design and implementation of effective participant involvement tasks, as well as pave the way for long-term engagement. The goal of the study is to analyse the motivation to participate of a specific citizen science community focused on fighting soil pollution through waste reduction/management in the Restart Data Workbench pilot supported by the ACTION project. More info on the pilot available at <a href="https://actionproject.eu/citizen-science-pilots/restart-data-workbench/">https://actionproject.eu/citizen-science-pilots/restart-data-workbench/</a>. The Restart Data Workbench motivation study is part of the study about motivation in citizen science projects conducted within the ACTION project (<a href="https://doi.org/10.5281/zenodo.5753092">https://doi.org/10.5281/zenodo.5753092</a>). The survey was designed and administered using the <a href="https://coney.cefriel.com/">Coney</a> toolkit. The research object adopts the <a href="https://www.researchobject.org/ro-crate/1.0/">RO-Crate</a> specification. Files made available within the research object are: <ul> <li>*-procedure.ttl contains the RDF representation of the structure of the conversational survey (questions, answers, etc.) using the <a href="https://w3id.org/survey-ontology">Survey Ontology</a></li> <li>*-results.ttl contains the RDF representation of the answers collected using the <a href="https://w3id.org/survey-ontology">Survey Ontology</a></li> <li>*-survey.tll contains a comprehensive RDF representation of the survey data using the <a href="https://w3id.org/survey-ontology">Survey Ontology</a></li> <li>*-results.csv contains the CSV of the collected answers</li> <li>*-script.R is the R script developed to analyse the collected answers</li> <li>*-mean-var-motivating-questions.csv contains the computed mean and average for each question considered (observable variables)</li> <li>*-mean-var-motivating-factor.csv contains the computed mean and average for each motivation factor considered (latent variables)</li> <li>*-correlation-factors-global-motivation.csv contains the correlation analysis between each motivation factor and the global motivation </li> </ul>

Water Sentinels Motivation Survey

The Water Sentinels motivation study was conducted within the ongoing H2020 project named <a href="https://actionproject.eu/">ACTION</a> (pArticipatory sCience Toolkit agaInst pollutiON) on citizen science. Volunteers participate to citizen science initiatives for multiple reasons: personal enjoyment, desire for improvement or achievement, establishment of personal relationships, care for the environment, etc. Studying motivation and investigating the factors influencing people participation to citizen science projects is an essential aspect in the analysis of citizen science communities. Understanding the reasons that foster people to engage can support the successful design and implementation of effective participant involvement tasks, as well as pave the way for long-term engagement. The goal of the study is to analyse the motivation to participate of a specific citizen science community focused on fighting water pollution in the Water Sentinels pilot supported by the ACTION project. More info on the pilot available at <a href="https://actionproject.eu/citizen-science-pilots/water-sentinels/">https://actionproject.eu/citizen-science-pilots/water-sentinels/</a>. The Water Sentinels motivation study is part of the study about motivation in citizen science projects conducted within the ACTION project (<a href="https://doi.org/10.5281/zenodo.5753092">https://doi.org/10.5281/zenodo.5753092</a>). The survey was designed using the <a href="https://coney.cefriel.com/">Coney</a> toolkit and administered using <a href="https://www.google.com/forms/about/">Google Forms</a>. The research object adopts the <a href="https://www.researchobject.org/ro-crate/1.0/">RO-Crate</a> specification. Files made available within the research object are: <ul> <li>*-procedure.ttl contains the RDF representation of the structure of the conversational survey (questions, answers, etc.) using the <a href="https://w3id.org/survey-ontology">Survey Ontology</a></li> <li>*-results.ttl contains the RDF representation of the answers collected using the <a href="https://w3id.org/survey-ontology">Survey Ontology</a></li> <li>*-survey.tll contains a comprehensive RDF representation of the survey data using the <a href="https://w3id.org/survey-ontology">Survey Ontology</a></li> <li>*-results.csv contains the CSV of the collected answers</li> <li>*-results-google-forms.csv contains the CSV of the collected answers exported from Google Forms</li> <li>*-script.R is the R script developed to analyse the collected answers</li> <li>*-mean-var-motivating-questions.csv contains the computed mean and average for each question considered (observable variables)</li> <li>*-mean-var-motivating-factor.csv contains the computed mean and average for each motivation factor considered (latent variables)</li> <li>*-correlation-factors-global-motivation.csv contains the correlation analysis between each motivation factor and the global motivation </li> </ul>

Walk Up Aniene Motivation Survey

The Walk Up Aniene motivation study was conducted within the ongoing H2020 project named <a href="https://actionproject.eu/">ACTION</a> (pArticipatory sCience Toolkit agaInst pollutiON) on citizen science. Volunteers participate to citizen science initiatives for multiple reasons: personal enjoyment, desire for improvement or achievement, establishment of personal relationships, care for the environment, etc. Studying motivation and investigating the factors influencing people participation to citizen science projects is an essential aspect in the analysis of citizen science communities. Understanding the reasons that foster people to engage can support the successful design and implementation of effective participant involvement tasks, as well as pave the way for long-term engagement. The goal of the study is to analyse the motivation to participate of a specific citizen science community focused on fighting soil and water pollution in the Walk Up Aniene pilot supported by the ACTION project. More info on the pilot available at <a href="https://actionproject.eu/citizen-science-pilots/walk-up-aniene/">https://actionproject.eu/citizen-science-pilots/walk-up-aniene/</a>. The Walk Up Aniene motivation study is part of the study about motivation in citizen science projects conducted within the ACTION project (<a href="https://doi.org/10.5281/zenodo.5753092">https://doi.org/10.5281/zenodo.5753092</a>). The survey was designed and administered using the <a href="https://coney.cefriel.com/">Coney</a> toolkit. The research object adopts the <a href="https://www.researchobject.org/ro-crate/1.0/">RO-Crate</a> specification. Files made available within the research object are: <ul> <li>*-procedure.ttl contains the RDF representation of the structure of the conversational survey (questions, answers, etc.) using the <a href="https://w3id.org/survey-ontology">Survey Ontology</a></li> <li>*-results.ttl contains the RDF representation of the answers collected using the <a href="https://w3id.org/survey-ontology">Survey Ontology</a></li> <li>*-survey.tll contains a comprehensive RDF representation of the survey data using the <a href="https://w3id.org/survey-ontology">Survey Ontology</a></li> <li>*-results.csv contains the CSV of the collected answers</li> <li>*-script.R is the R script developed to analyse the collected answers</li> <li>*-mean-var-motivating-questions.csv contains the computed mean and average for each question considered (observable variables)</li> <li>*-mean-var-motivating-factor.csv contains the computed mean and average for each motivation factor considered (latent variables)</li> <li>*-correlation-factors-global-motivation.csv contains the correlation analysis between each motivation factor and the global motivation </li> </ul>

Mapping Mobility Motivation Survey

The Mapping Mobility motivation study was conducted within the ongoing H2020 project named <a href="https://actionproject.eu/">ACTION</a> (pArticipatory sCience Toolkit agaInst pollutiON) on citizen science. Volunteers participate to citizen science initiatives for multiple reasons: personal enjoyment, desire for improvement or achievement, establishment of personal relationships, care for the environment, etc. Studying motivation and investigating the factors influencing people participation to citizen science projects is an essential aspect in the analysis of citizen science communities. Understanding the reasons that foster people to engage can support the successful design and implementation of effective participant involvement tasks, as well as pave the way for long-term engagement. The goal of the study is to analyse the motivation to participate of a specific citizen science community focused on fighting air pollution in the Mapping Mobility pilot supported by the ACTION project. More info on the pilot available at <a href="https://actionproject.eu/citizen-science-pilots/mapping-mobility/">https://actionproject.eu/citizen-science-pilots/mapping-mobility/</a>. The Mapping Mobility motivation study is part of the study about motivation in citizen science projects conducted within the ACTION project (<a href="https://doi.org/10.5281/zenodo.5753092">https://doi.org/10.5281/zenodo.5753092</a>). The survey was designed and administered using the <a href="https://coney.cefriel.com/">Coney</a> toolkit. The research object adopts the <a href="https://www.researchobject.org/ro-crate/1.0/">RO-Crate</a> specification. Files made available within the research object are: <ul> <li>*-procedure.ttl contains the RDF representation of the structure of the conversational survey (questions, answers, etc.) using the <a href="https://w3id.org/survey-ontology">Survey Ontology</a></li> <li>*-results.ttl contains the RDF representation of the answers collected using the <a href="https://w3id.org/survey-ontology">Survey Ontology</a></li> <li>*-survey.tll contains a comprehensive RDF representation of the survey data using the <a href="https://w3id.org/survey-ontology">Survey Ontology</a></li> <li>*-results.csv contains the CSV of the answers collected</li> </ul>

Open Soil Atlas Motivation Survey

The Open Soil Atlas motivation study was conducted within the ongoing H2020 project named <a href="https://actionproject.eu/">ACTION</a> (pArticipatory sCience Toolkit agaInst pollutiON) on citizen science. Volunteers participate to citizen science initiatives for multiple reasons: personal enjoyment, desire for improvement or achievement, establishment of personal relationships, care for the environment, etc. Studying motivation and investigating the factors influencing people participation to citizen science projects is an essential aspect in the analysis of citizen science communities. Understanding the reasons that foster people to engage can support the successful design and implementation of effective participant involvement tasks, as well as pave the way for long-term engagement. The goal of the study is to analyse the motivation to participate of a specific citizen science community focused on fighting soil pollution in the Open Soil Atlas pilot supported by the ACTION project. More info on the pilot available at <a href="https://actionproject.eu/citizen-science-pilots/open-soil-atlas/">https://actionproject.eu/citizen-science-pilots/open-soil-atlas/</a>. The Open Soil Atlas motivation study is part of the study about motivation in citizen science projects conducted within the ACTION project (<a href="https://doi.org/10.5281/zenodo.5753092">https://doi.org/10.5281/zenodo.5753092</a>). The survey was designed and administered using the <a href="https://coney.cefriel.com/">Coney</a> toolkit. The research object adopts the <a href="https://www.researchobject.org/ro-crate/1.0/">RO-Crate</a> specification. Files made available within the research object are: <ul> <li>*-procedure.ttl contains the RDF representation of the structure of the conversational survey (questions, answers, etc.) using the <a href="https://w3id.org/survey-ontology">Survey Ontology</a></li> <li>*-results.ttl contains the RDF representation of the answers collected using the <a href="https://w3id.org/survey-ontology">Survey Ontology</a></li> <li>*-survey.tll contains a comprehensive RDF representation of the survey data using the <a href="https://w3id.org/survey-ontology">Survey Ontology</a></li> <li>*-results.csv contains the CSV of the collected answers</li> <li>*-script.R is the R script developed to analyse the collected answers</li> <li>*-mean-var-motivating-questions.csv contains the computed mean and average for each question considered (observable variables)</li> <li>*-mean-var-motivating-factor.csv contains the computed mean and average for each motivation factor considered (latent variables)</li> <li>*-correlation-factors-global-motivation.csv contains the correlation analysis between each motivation factor and the global motivation </li> </ul>

Dataset - paper: Child eating behaviors, parental feeding practices and food shopping motivations during the COVID-19 lockdown in France

Dataset corresponding to a paper that has been published in Appetite (Philippe K, Chabanet C, Issanchou S, Monnery-Patris S. Child eating behaviors, parental feeding practices and food shopping motivations during the COVID-19 lockdown in France: (How) did they change? Appetite. 2021 Jun 1;161:105132. doi: 10.1016/j.appet.2021.105132. Epub 2021 Jan 23. PMID: 33493611; PMCID: PMC7825985). The objective of the study was to evaluate possible changes in eating behaviors in children aged 3–12 years, in parental eating and cooking behaviors, in parental feeding practices, and also in parental motivations when shopping for food during the lockdown, compared to the period before the lockdown. Information about the dataset and the corresponding documents can be found in the document "Metadata-paper-COVID.docx".

opencc-by-4.0Nov 2022View details →

Replication package for "Motivation in the Dynamics of European Youth Migration"

Replication package for the paper "Motivation in the Dynamics of European Youth Migration". The package contains the data and the SPSS and Stata code for the the analyses presented in the paper. The original data from which the variables are extracted was collected within the EU Horizon 2020 project YMOBILITY (2015-2018).

opencc-by-4.0Oct 2020View details →

Data from the "The Psychology of Professional and Student Actors: Creativity, Personality, and Motivation"

Data associated with: Dumas, D., Doherty, M., Organisciak, P. (2020) "The Psychology of Professional and Student Actors: Creativity, Personality, and Motivation". PLOS ONE. Description of work associated with this data: <blockquote> As a profession, acting is marked by a high-level of economic and social riskiness concomitantly with the possibility for artistic satisfaction and/or public admiration. Current understanding of the psychological attributes that distinguish professional actors is incomplete. Here, we compare samples of professional actors (n = 104), undergraduate student actors (n = 100), and non-acting adults (n = 92) on 26 psychological dimensions and use machine-learning methods to classify participants based on these attributes. Nearly all of the attributes measured here displayed significant univariate mean differences across the three groups, with the strongest effect sizes being on Creative Activities, Openness, and Extraversion. A cross-validated Least Absolute Shrinkage and Selection Operator (LASSO) classification model was capable of identifying actors (either professional or student) from non-actors with a 92% accuracy and was able to sort professional from student actors with a 96% accuracy when age was included in the model, and a 68% accuracy with only psychological attributes included. In these LASSO models, actors in general were distinguished by high levels of Openness, Assertiveness, and Elaboration, but professional actors were specifically marked by high levels of Originality, Volatility, and Literary Activities. </blockquote>

opencc-by-4.0Dec 2017View details →

Data and analysis supplement for: Functional imagery training versus motivational interviewing for weight loss: a randomised controlled trial of brief individual interventions for overweight and obesity.

This submission provides the data and code for analyses reported in our publication.

opencc-by-4.0Jan 2023View details →

Dataset and codebook for the article by Gaume J, Bertholet N, McCambridge J, et al. Effect of a Novel Brief Motivational Intervention for Alcohol-Intoxicated Young Adults in the Emergency Department: A Randomized Clinical Trial. JAMA Netw Open. 2022;5(10):e2237563. doi: 10.1001/jamanetworkopen.2022.37563

Dataset and codebook for the article Gaume J, Bertholet N, McCambridge J, et al. Effect of a Novel Brief Motivational Intervention for Alcohol-Intoxicated Young Adults in the Emergency Department: A Randomized Clinical Trial. JAMA Netw Open. 2022;5(10):e2237563. doi: <a href="http://jamanetwork.com/article.aspx?doi=10.1001/jamanetworkopen.2022.37563">10.1001/jamanetworkopen.2022.37563</a> The dataset contains all data needed to reproduce the results in the above cited article. Variable description and labels can be found in the codebook. Please refer to the published article and supplemental online content for further information about the data and the study procedures.

opencc-by-4.0Aug 2023View details →

EnergyPROSPECTS Energy Citizenship Factsheet Series, Part 2: Motivations and objectives

This document is Part 2 of the EnergyPROSPECTS Factsheet Series. We have created the Series to publish the results of a mapping of energy citizenship in Europe, along with the first stage of our analysis of the respective data. The EnergyPROSPECTS consortium mapped 596 cases of energy citizenship between November 2020 and May 2021 using desk research, collecting data on many aspects of the cases. Although the analysis is a work in progress, we believe it is important to share our data and, through doing this, contribute to the understanding of energy citizenship in Europe. EnergyPROSPECTS (PROactive Strategies and Policies for Energy Citizenship Transformation), a H2020 project between 2021-2024, works with a critical understanding of energy citizenship that is grounded in state-of-the-art social sciences and humanities (SSH) insights.

opencc-by-4.0May 2020View details →

Application-Motivated, Holistic Benchmarking of a Full Quantum Computing Stack: Experimental Data

Full experimental dataset for the publication "Application-Motivated, Holistic Benchmarking of a Full Quantum Computing Stack". The archive `application_motivated_benchmarks.zip` contains the following files and directories: - uncompiled_log.csv Gives IDs for the uncompiled circuits initially generated for use in our experiments, along with the properties of the circuits. - properties_log.csv Gives IDs for device property files, along with the device and the time at which they were collected. - compiled_log.csv Gives the calculated figures of merits for the compiled and run circuits. Compiled circuits are identified by the ID of the uncompiled circuit, the compilation strategy used, and the device compiled onto. Device property IDs at the time of compilation and run are given. - circuits/ Contains a subdirectory for each uncompiled circuit. Each subdirectory has files of 2 forms.          - uncompiled.qasm is the uncompiled circuit.     - files of the form 'strategy'_'device'.qasm are the compiled circuits. - data/ Contains a subdirectory for each uncompiled circuit. Each subdirectory has files of 3 forms.     - prob_vector.csv contains the ideal output probability distribution.     - files of the form 'strategy'_'device'.csv contain the shot counts for       each compiled circuit when run on the real device.     - files of the form 'strategy'_'device'_simulated.csv contain the shot       counts for each compiled circuit when run using a classical simulator       with noise model build from device properties at the time of the real       run. - device_properties/ Contains json files detailing device properties for each device property ID.

opencc-by-4.0Dec 2020View details →

Intrinsische Motivation von Schülerinnen und Schülern beim Physical Computing im Informatikunterricht

Der KIM-Fragebogen wurde genutzt, um die intrinsische Motivation von Schülerinnen und Schülern bezüglich des Physical-Computing-Unterrichts zu erheben. Anschließend wurde der Fragebogen verwendet um zu untersuchen, welche Physical-Computing-Tätigkeiten besonders positiv auf die intrinsische Motivation wirken können. Zusätzlich wurde in offenen Fragen erhoben, welche Tätigkeiten die Schülerinnen und Schüler im Unterricht besonders mochten.

opencc-zeroMar 2016View details →

Combining internal and external motivations in multi-actor governance arrangements for biodiversity and ecosystem services

These files provide the original survey data of the paper on motivations for biodiversity conservation in Europe. This paper analyses the possibility of building a mutually supportive dynamics between internally and externally motivated behaviour for biodiversity conservation and ecosystem services provision. To this purpose a face to face survey amongst 169 key actors of 34 highly successful and prominent biodiversity arrangements in seven EU countries was conducted. The main finding of the paper is the feasibility of combining inherently intrinsically motivated behaviours (providing enjoyment, pleasure from experimentation and learning, aesthetic satisfaction) and internalized extrinsic motivations (related to the identification with the collective goals of conservation policy) through a common set of governance features. Successful initiatives that combine internal and external motivations share the following features: inclusive decision making processes, a broad monitoring by “peers” beyond the core staff of the initiatives, and a context that is supportive for the building of autonomous actor competences. These findings are in line with the psycho-sociological theory of motivation, which shows the importance of a psycho-social context leading to a subjective perception of autonomy and a sense of competence of the actors.

opencc-by-4.0Mar 2017View details →

Data supplementing the article Schomaker, J., Walper, D., Wittmann, B.C., & Einhäuser, W. (2017). Attention in natural scenes: Affective-motivational factors guide gaze independently of visual salience. Vision Research, 133, 161-175.

These data supplement the article Schomaker, J., Walper, D., Wittmann, B.C., & Einhäuser, W. (2017). Attention in natural scenes: Affective-motivational factors guide gaze independently of visual salience. Vision Research, 133, 161-175. Use is free for academic purposes, provided the aforementioned article is appropriately cited. The directory contains the following files stimuli.tar.gz - stimuli used in this study; note that this is based on the MONS database, but some deviations from the final version of the database do exist. ratings.mat contains the variables arousal - mean arousal rating valence - mean valence rating valence2 - squared mean valence rating (after subtracting midpoint) motivationalValue - mean motivation rating motivaionalValue2 - squared mean motivation rating (after subtracting midpoint) All variables are 104x3, where the first dimension is the stimulus number, and the second dimension the motivation ground truth (aversive, neutral, appetitive) Experiment 1 fixationsExperiment1.mat contains the variables fixationX, fixationY, fixationDuration, fixaitonOnset, fixationInitial, which contain for each fixation horizontal and vertical coordinate, the duration, the time of the onset relative to the trial onset and whether it is the initial fixation. All variables have dimensions 16x104x3x50, where the first dimension is the observer, the second the scene, the third the condition and the forth a counter of fixations. Whenever there are less than 50 fixations the remainder are filled with NaN. boundingBoxesExperiment1.mat contains for each critical object the bounding box coordinates x,y of upper left corner and width and height as variables boundingBoxX, boundingBoxY, boundingBoxW, boundingBoxH respectively. Note that this is relative to the eyetracker coordinates of experiment 1 (full display 1024x768, presentation in the center) and will therefore not match the coordinates of the images in the archive or the bounding box coordinates of experiment 2. Dimensions are 104x3, the dimensions representing scene number and condition, respectively. figure2.m uses these data to computes figure 2 of the article from these data dataForExperiment1.Rdata contains the data frame data, which contains for each fixation the values of the predictors used in the model of table 1. This is computed from the matlab data listed above in addition to the peak values of the AWS salience in the object. table1.R computes and prints the models for table 1 Experiment 2 fixationsExperiment2.mat contains fixation data for experiment 2. Variable names as in experiment 1. Dimensions are 18x99x3x3x50, where the first dimension is the observer, the second the image number, the third the visual condition, the third the motivational condition and the fifth the fixation count. Since only one visual condition was shown to each observer per motivational condition, there is an additional variable 'hasData', which is 1 if the image was presented to the observer in this condition and 0 otherwise. Since fixations can be outside the image and will therefore be excluded, there is also an additional variable fixationNumber to keep a correct count of the fixation number in the trial. boundingBoxesExperiment2.mat contains bounding box data for experiment 2 in image (and fixation) coordinates. Notation as for experiment 1, but coordinates refer to image and eyetracking coordinates used for experiment 2 and therefore can differ occasionally. figure3and4.m generates figures 3 and 4 of the article from these data files. dataForExperiment2.Rdata contains the data frame data, which contains for each fixation the values of the predictors used in the model of tables 2 amd 3. This is computed from the matlab data listed above in addition to the peak values of the AWS salience in the object. The fields imgMot and imgVis contain the motivational ground truth and the salience manipulation, respectively. table2.R uses the Rdata file to compute the models for table 2 of the article and print summary results table3.R uses the Rdata file to compute the models for table 3 of the article and print summary results. Note that the computation can take substantial time; results might deviate slightly depending on the exact version of R and its libraries used.