Find research datasets worth reusing

opencc-by-4.0Oct 2019View details →

Performance of users with Cerebral Palsy playing GABLE Games together with their results to the Left/Right Dynamic balance tool

This dataset contains data generated by users of GABLE platform. The data shows the performance of some users with Cerebral Palsy playing GABLE Games together with their results to the Left/Right Dynamic balance tool. More information about GABLE project can be found at: www.projectgable.eu

opencc-by-4.0Nov 2023View details →

Towards standardising the collection of game statistics in Europe: a dataset

This dataset is part of the article:Title : Towards standardising the collection of game statistics in Europe: a case studyJournal: European Journal of Wildlife Research. DOI : 10.1007/s10344-023-01746-3Two different data sets have been incorporated :  1) Data collected from a questionnaire to regional governmental hunting agencies (mainland Spain)<a href="https://zenodo.org/api/records/10080464/draft/files/QuestionnaireData_DOI_10.1007_s10344-023-01746-3.csv/content">QuestionnaireData_DOI_10.1007_s10344-023-01746-3.csv</a>Metadata with variable vocabulary and descriptors has been included<a href="https://zenodo.org/api/records/10080464/draft/files/QuestionnaireMetadata_DOI_10.1007_s10344-023-01746-3.docx/content">QuestionnaireMetadata_DOI_10.1007_s10344-023-01746-3.docx</a>2) Characterisation of each of the Autonomous Communities, including information on economic and human resources and the volume or coverage of hunting resources available in each of the regional administrations. <a href="https://zenodo.org/api/records/10080464/draft/files/SocioEconomicData_DOI_10.1007_s10344-023-01746-3.csv/content">SocioEconomicData_DOI_10.1007_s10344-023-01746-3.csv</a>Metadata with variable vocabulary and descriptors has been included<a href="https://zenodo.org/api/records/10080464/draft/files/SocioEconomicMetadata_DOI_10.1007_s10344-023-01746-3.docx/content">SocioEconomicMetadata_DOI_10.1007_s10344-023-01746-3.docx</a> Please remember to cite correctly the doi associated to this databases 10.5281/zenodo.10080464

opencc-by-4.0Jan 2020View details →

Dataset of the manuscript "Are Serious Games an Alternative to Personality Questionnaires? Initial Analysis of a Gamified Assessment"

The present database belongs to the manuscript titled "Are Serious Games an Alternative to Personality Questionnaires? Initial Analysis of a Gamified Assessment". The study has been peformed in English, but the research is conducted in Spanish.

opencc-by-4.0Mar 2022View details →

Data for: What's in a game: Video game visual-spatial demand location exhibits a double dissociation with reading speed

The aggregate data in these datasets were used in analyses for "What’s in a game: Video game visual-spatial demand location exhibits a double dissociation with reading speed".

opencc-by-sa-4.0Dec 2017View details →

Humans display a reduced set of consistent behavioral phenotypes in dyadic games

Socially relevant situations that involve strategic interactions are widespread among animals and humans alike. To study these situations, theoretical and experimental research has adopted a game theoretical perspective, generating valuable insights about human behavior. However, most of the results reported so far have been obtained from a population perspective and considered one specific conflicting situation at a time. This makes it difficult to extract conclusions about the consistency of individuals’ behavior when facing different situations and to define a comprehensive classification of the strategies underlying the observed behaviors. We present the results of a lab-in-the-field experiment in which subjects face four different dyadic games, with the aim of establishing general behavioral rules dictating individuals’ actions. By analyzing our data with an unsupervised clustering algorithm, we find that all the subjects conform, with a large degree of consistency, to a limited number of behavioral phenotypes (envious, optimist, pessimist, and trustful), with only a small fraction of undefined subjects. We also discuss the possible connections to existing interpretations based on a priori theoretical approaches. Our findings provide a relevant contribution to the experimental and theoretical efforts toward the identification of basic behavioral phenotypes in a wider set of contexts without aprioristic assumptions regarding the rules or strategies behind actions. From this perspective, our work contributes to a fact-based approach to the study of human behavior in strategic situations, which could be applied to simulating societies, policy-making scenario building, and even a variety of business applications.   The data from the "dr Brain" experiment is organized in two separated files: drbrain_users.csv  and drbrain_decisions.csv. 1.)   drbrain_users.csv contains information about the participants of the experiment (or users). There is one row per user, with the following information about each one of them: User_ID: unique ID number to identify the user. Age: user's age Gender: user's gender Experiment_number: Number of the experiment the user participated in. For organizational reasons, our research actually was made 45 experiments (or replicas) run over a period of 2 days, each one run with differnt users. A user was only allowed to participate in one experiment. Each experiment included between 10-25 users typically, and they played around 13-18 game rounds, typically. Each round and each couple of users played in different games (that is, different values of S, Sucker's payoff, and T, Temptation to defect, while the values of P=5 , Punishment, and R=10, Reward, were always fixed). Earnings: number of points the user obtained in total, over all rounds. 2.)   drbrain_decisions.csv  contains the information of the all game rounds for all experiments and all users. User_ID: unique ID number to identify the user.     Experiment_number: Number of the experiment the user participated in. Round_number: Number of the round within a given experiment. S: Value for the "Sucker's payoff" in the game of that round. T: Value for the "Temptation to defect" in the game of that round.     Game: Name of the game corresponding to those values of S and T for that round Action: Action chosen by the user (C: cooperate, D: defect) Opponent_ID: ID number of the user's opponent in that round.  Opponent_Action: Action (C or D) chosen by the user's opponent in that round. -------- For more details, see our research article: Humans display a reduced set of consistent behavioral phenotypes in dyadic games. Julia Poncela-Casasnovas, Mario Gutiérrez-Roig, Carlos Gracia-Lázaro, Julian Vicens, Jesús Gómez-Gardeñes, Josep Perelló, Yamir Moreno, Jordi Duch and Angel Sánchez. Science Advances Vol. 2, no. 8, 2016. DOI: 10.1126/sciadv.1600451 http://advances.sciencemag.org/content/2/8/e1600451

opencc-by-4.0Jun 2024View details →

Player Experience in Video Game Character Analysis: A Study of Female Characters

<h3>Overview</h3> This dataset is part of the study titled "Player Experience in Video Game Character Analysis: A Study of Female Characters", conducted at Mapúa University. The research aims to integrate player experience into an existing framework for video game character analysis.  <h3>Content</h3> The dataset includes: <ul> <li>A partial transcript of 5 semi-structured interviews with the key informants. Originally, 8 interviews were conducted, but the audio/video recordings for 3 interviews were lost and thus their transcripts are not available.</li> <li>Significant codes presented in tabulated form.</li> </ul> <h3>Data Collection Method</h3> Data were collected through in-depth interviews conducted via Facebook Messenger and Discord from March to April 2024. Participants were various video game players from different backgrounds and age groups, ranging from 20 to 40 years old. Due to technical issues, the recordings of 3 interviews were lost, resulting in only 5 available transcripts.  <h3>Data Processing and Analysis</h3> The 5 available interviews were transcribed verbatim. Data were analyzed using thematic analysis, involving initial coding, theme development, and refinement. <h3>Usage data</h3> The dataset is organized into several sections within a single Word document (.docx). This word document has headings for navigation and a definition of terms. <h3>Limitations</h3> The dataset only includes 5 out of 8 due to technical difficulties encountered after the recording of the interview. This may impact the comprehensiveness of the findings. <h3>Contextual Reference</h3> The manuscript associated with this dataset heavily references the works "A Structural Model for Player-Characters as Semiotic Constructs." (DOI: https://doi.org/10.26503/TODIGRA.V2I2.37) and "Object, me, symbiote, other: A social typology of player-avatar relationships." (DOI:https://doi.org/10.5210/FM.V20I2.5433) which explore the foundational frameworks on video game character analysis.  For any further information or clarifications, please contact wbdg2000@gmail.com

opencc-by-4.0Sep 2018View details →

Bicycle trips collected using Cyclists Geo-C geo-game

This is an experimental dataset for the bicycle trips recorded using and geo-game called "Cyclist Geo-C". It contains the geometry of the trips recorded by 60 participants from three European Cities: Münster, Germany; Castelló, Spain; Valletta, Malta. This dataset was collected and analysed for the PhD Thesis "Mobile Services for Green Living" part of the European Joint Doctorate in Geoinformatics and the <a href="http://geo-c.eu/">Geo-C </a>Project.  The dataset is composed of three subsets. <ol> <li>There is a point dataset called "trips_od.geojson" which contained the point geometries where each trip started and ended with attributes for latitude, longitude, altitude, and precision coordinates. Each point also had the timestamp which indicates the time when the user started or ended the trip.</li> <li>There is a line dataset called "segments.geojson" which contained the geometries of the straight lines connecting two locations of the participant. Each segment started from an initial point "pi" recorded at a "ti” and ended at the next point recorded by the user "pf” at time “tf”. The time difference between "ti” and “tf” was at most five minutes while the length of the segment was at most one kilometre. Each segment also had the participant and trip identifier, and the segment's sequence number within the trip For each of the trip segments, we calculated the distance and speed using the recorded coordinates and timestamps from "pi" and "pf" points. \(trip\_segment = f(p_i,p_f)\) and \(segment\_speed = \frac{distance(p_i,p_f)}{\Delta time(p_i,p_f)}\). Then we classified the segments according to the calculated distance as: “walking segment” when the calculated speed was less than 5 km/h;  “cycling segment” when the calculated speed was between 5 and 50 km/h; or “non-cycling segment” when the calculated speed was more than 50 Km/h.</li> <li>There was another line dataset called “trips_tags.geojson ” which contained the geometries of each of the trip paths. A trip was a line (also called polyline by GIS users) defined by the ordered sequence of trip segments. It started from origin point "pi" of the trip’s first segment and ended at the destination point "pf" of the trip's last segment. Each trip also had the participant's identification, trip's identification, the number of segments, start and end times.</li> </ol> In addition to the experimental dataset recorded by participants, our analysis used a secondary dataset to define a comparable framework for the three cities. The secondary dataset consisted of the existing bicycle paths in the cities of Münster and Castelló as well as the planned bicycle paths around Valletta. For the city of Münster, the source of the bicycle paths was the <a href="http://www.openstreetmap.org">OpenStreetMap</a> (we downloaded the line elements with the tags “bicycle=yes” and "cycleway=yes”). For the city of Castelló, we obtained the bicycle paths from the city transport authority, including the city of Valletta, we created a digital version of the national bicycle network plan. We estimated the number of trips "bikepaths_trips.geojson" and the number of segments "bikepaths_segments.geojson" at each bike path. Also, we provide the areas where participants faced frictions during the experiment which corresponded to low cycling speeds "frictions.geojson". Finally, we provide a visual reference of the dataset in "frictions_cities.pdf".

STEM4Youth: Games xAire

STEM4Youth: Games xAire Data collected during the performance STEM4Youth: Games xAire in Barcelona (Spain) during two performances, the first in CCCB on April 23th, 2018, and the second in Ciutadella on June 9th and 10th, 2018 The participants played a public goods game. There were 6 experimental stations, one per individual, spatially distributed so that participants could not see each other. Also, they were rigorously prevented from talking or signaling one another. To further guarantee that potential interactions among players did not influence the results of the experiment, the assignment of players’ partners was completely random. All of the participants played through Citizen Social Lab, a web interface specifically developed for the experiment.  The participants were shown a brief tutorial, but were not given any clue. They were informed that they had to make decisions under different conditions and against different opponents. We made sure the interface be the most simple and understandable to ensure the correct understanding of the tasks. Also, the interface was the same for everybody. We made sure to avoid the research be upsetting or harmful for the participants by presenting the experiment as a game and playful activity. One researcher closely monitored each session to guarantee the experimental protocol be strictly followed. Yet, the researchers provided help when required. All participants in the experiment signed an informed consent to participate and no association was ever made between their real names and the results, in agreement with the Spanish Law for Personal Data Protection. This procedure was approved by the Ethics Committee of Universitat de Barcelona, and all methods were performed in accordance with the relevant guidelines and regulations.

STEM4Youth: Games xPalaioFaliro

STEM4Youth: Games xPalaioFaliro Data collected during the performance STEM4Youth: Games xPalaioFaliro in Palaio Faliro (Greece) on April 28, 2018.  The participants played a public goods game. There were 6 experimental stations, one per individual, spatially distributed so that participants could not see each other. Also, they were rigorously prevented from talking or signaling one another. To further guarantee that potential interactions among players did not influence the results of the experiment, the assignment of players’ partners was completely random. All of the participants played through Citizen Social Lab, a web interface specifically developed for the experiment.  The participants were shown a brief tutorial, but were not given any clue. They were informed that they had to make decisions under different conditions and against different opponents. We made sure the interface be the most simple and understandable to ensure the correct understanding of the tasks. Also, the interface was the same for everybody. We made sure to avoid the research be upsetting or harmful for the participants by presenting the experiment as a game and playful activity. One researcher closely monitored each session to guarantee the experimental protocol be strictly followed. Yet, the researchers provided help when required. All participants in the experiment signed an informed consent to participate and no association was ever made between their real names and the results, in agreement with the Spanish Law for Personal Data Protection. This procedure was approved by the Ethics Committee of Universitat de Barcelona, and all methods were performed in accordance with the relevant guidelines and regulations.

STEM4Youth: Games xBadalona

STEM4Youth: Games xBadalona Data collected during the performance STEM4Youth: Games xBadalona in Badalona on April 19th and 20th, 2017.  The participants played different dyadic behavioral games, snowdrift game, dictator's games with punishment and trust game. The participants played these dilemmas in different roles. There were 6 experimental stations, one per individual, spatially distributed so that participants could not see each other. Also, they were rigorously prevented from talking or signaling one another. To further guarantee that potential interactions among players did not influence the results of the experiment, the assignment of players’ partners was completely random. All of the participants played through Citizen Social Lab, a web interface specifically developed for the experiment.  The participants were shown a brief tutorial, but were not given any clue. They were informed that they had to make decisions under different conditions and against different opponents. We made sure the interface be the most simple and understandable to ensure the correct understanding of the tasks. Also, the interface was the same for everybody. We made sure to avoid the research be upsetting or harmful for the participants by presenting the experiment as a game and playful activity. One researcher closely monitored each session to guarantee the experimental protocol be strictly followed. Yet, the researchers provided help when required. All participants in the experiment signed an informed consent to participate and no association was ever made between their real names and the results, in agreement with the Spanish Law for Personal Data Protection. This procedure was approved by the Ethics Committee of Universitat de Barcelona, and all methods were performed in accordance with the relevant guidelines and regulations.

STEM4Youth: Games xBarcelona

STEM4Youth: Games Barcelona. Data collected during the performance STEM4Youth: Games xBarcelona in Barcelona on September 28th, 2017.  The participants played different dyadic behavioral games, snowdrift game, dictator's games with punishment and trust game. The participants played these dilemmas in different roles. There were 6 experimental stations, one per individual, spatially distributed so that participants could not see each other. Also, they were rigorously prevented from talking or signaling one another. To further guarantee that potential interactions among players did not influence the results of the experiment, the assignment of players’ partners was completely random. All of the participants played through Citizen Social Lab, a web interface specifically developed for the experiment.  The participants were shown a brief tutorial, but were not given any clue. They were informed that they had to make decisions under different conditions and against different opponents. We made sure the interface be the most simple and understandable to ensure the correct understanding of the tasks. Also, the interface was the same for everybody. We made sure to avoid the research be upsetting or harmful for the participants by presenting the experiment as a game and playful activity. One researcher closely monitored each session to guarantee the experimental protocol be strictly followed. Yet, the researchers provided help when required. All participants in the experiment signed an informed consent to participate and no association was ever made between their real names and the results, in agreement with the Spanish Law for Personal Data Protection. This procedure was approved by the Ethics Committee of Universitat de Barcelona, and all methods were performed in accordance with the relevant guidelines and regulations.

STEM4Youth: Games xViladecans

STEM4Youth: Games Viladecans Data collected during the performance STEM4Youth: Games xViladecans in Viladecans on May 12th, 2017.  The participants played a public goods game. There were 6 experimental stations, one per individual, spatially distributed so that participants could not see each other. Also, they were rigorously prevented from talking or signaling one another. To further guarantee that potential interactions among players did not influence the results of the experiment, the assignment of players’ partners was completely random. All of the participants played through Citizen Social Lab, a web interface specifically developed for the experiment.  The participants were shown a brief tutorial, but were not given any clue. They were informed that they had to make decisions under different conditions and against different opponents. We made sure the interface be the most simple and understandable to ensure the correct understanding of the tasks. Also, the interface was the same for everybody. We made sure to avoid the research be upsetting or harmful for the participants by presenting the experiment as a game and playful activity. One researcher closely monitored each session to guarantee the experimental protocol be strictly followed. Yet, the researchers provided help when required. All participants in the experiment signed an informed consent to participate and no association was ever made between their real names and the results, in agreement with the Spanish Law for Personal Data Protection. This procedure was approved by the Ethics Committee of Universitat de Barcelona, and all methods were performed in accordance with the relevant guidelines and regulations.

opencc-by-4.0Jan 2024View details →

Greenhouse Game Study Data

Anonymous pre and post-student survey data on learning statistics by playing the Greenhouse game; available at https://dataspace.sites.grinnell.edu/greenhouse1.html. The data dictionary and the code used to clean and anonymize the data are also available.

OpenNeuro44/100

EEG: Continuous gameplay of an 8-bit style video game

Open the record for dataset details and reuse information.

openCC0Jan 2021View details →

opencc-by-4.0Dec 2020View details →

ClairCity Skylines Game Player Survey Data

This dataset contains the responses of players of the ClairCity Skylines Game to a post-game survey to evaluate the player experience and changes in knowledge / future actions. The dataset has been cleaned and verified to ensure no privacy or ethics issues.

opencc-by-4.0Dec 2020View details →

ClairCity Skylines Game Data

This dataset contains play data collected using the ClairCity Skylines Game App. The data collected was used to understand the public perception of different policies and crowd-source consensus on preferred policies to be implemented. The dataset has been checked and verified according to privacy and ethics.

opencc-by-4.0Dec 2016View details →

Paper Survey Table - Smart Mobility - role of mobile games

This table (CSV File) compiles the papers reviewed for the survey paper "Smart Mobility, the role of Mobile Games" presented at the 8th Serious Games Development & Applications (SGDA 2017) conference. It compiles 140 documents read and described to understand the role of mobile games in the promotion of smart mobility, especially based on urban cycling.  The images provide relevant insights of the survey and support the analysis of the survey paper they are briefly described as follows:  Chart 1: Number of papers classified by publisher and type of publication reported. Chart 2: Number of participants classified by type of publication reported. Chart 3: Number of papers classified by the method reported and the number of citations. Chart 4: Number of papers reporting cycling classified by type of motivation used for gamification strategies Chart 5: Number of papers reporting cycling classified by device and location technology reported The following is a short explanation of each of the columns of the table and its contents, self-explanatory titles are omitted. <ul> <li>Database: source or publisher of the paper</li> <li>Type: description of the kind of publication between conference paper, book chapter, or journal paper. </li> <li>Oldest Reference: year of publication of the oldest reference cited in the paper.</li> <li>Newest Reference: year of publication of the newest reference cited in the paper.</li> <li>Citation: Classification of the number of citations that the paper has. Values: none, 1-5, 5-10, +10.</li> <li>Participants: Classification of the number of participants that the paper reports. Values: none, 1-5, 5-10, +10. </li> <li>Method Description: Short description of the method reported by the paper.</li> <li>Reported Method: Classification of the reported method of the paper in three main categories. Design, Experiment / Test, Literature review, Survey.</li> <li>Gamification - Motivation: Classification of the sources of motivation reported. Values: Intrinsic Motivation, Extrinsic Motivation, Mixed.</li> <li>Gamification - Negative Issues: Filled when the paper is reporting the analysis of negative consequences of using gamification:</li> <li>Gamification: Classification of the kind of technique reported. Values: Gamified, not Gamified</li> <li>Device and Location Usage: Classification of the use of mobile devices, wearables and location technologies. Values: No device, Device enabled, Mobile and location enabled, Device and location enabled.</li> <li>App Name: The name of the application reported by the paper when it exists.</li> </ul>