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81 results for “gamify”
Dataset of the manuscript "Are Serious Games an Alternative to Personality Questionnaires? Initial Analysis of a Gamified Assessment"
<p>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.</p>
Gamifying Moodle with Badges and Progress Bars: A Case Study
<p>Dataset, questenaire and text analyses of a case study with a mixed methods approach about gamifiying Moodle with badges and progress bars in distance higher education. </p>
Enhancing Motivation in Software Engineering Education through Gamified Agile Project-based Learning
<p>Project-based learning (PBL), e.g., student software development projects, is an essential part of today's Software Engineering (SE) education. They allow students to work on real-world projects and gain practical experience as a team. However, several challenges arise in such projects, including learning new technologies and dealing with communication and coordination issues within the team. These factors can lead to a lack of motivation to contribute to the project and a decrease in productivity, potentially resulting in an insufficient project outcome. This paper aims to promote student motivation in PBL and increase team productivity by applying gamification. We conducted a user and requirements analysis to identify the needs of students and supervisors of such projects. Based on the insights, we designed and implemented DinoDev, a gamified project management tool that combines project management features with gamification elements. The DinoDev concept was evaluated in a student project, indicating increased motivation and team productivity. The findings are valuable for advancing research on using gamification in PBL and for lecturers to improve their students' motivation and team productivity in SE education.</p>
Crowds & Machines Next level: Meditteranean wheat classification labels from gamified crowd-sourcing
<p>Machine learning (and especially deep learning) algorithms need lots of training and validation datasets, which are often unavailable. Creating on-ground datasets is costly and time consuming. Within the European Space Agency funded project ‘Crowds & Machine – Next Level’ (by <a href="https://www.blackshore.eu">Blackshore B.V.</a>, <a href="https://www.52impact.nl">52impact B.V.</a> and <a href="https://hcss.nl">The Hague Centre for Strategic Studies</a>), we aimed to solve this issue by generating labelled data effectively using an innovative gamified crowdsourced-based method.</p> <p>The objective of the project ‘Crowds & Machines Next Level’ was to generate labelled data for the training and validation of machine learning algorithms to classify the crop wheat. We make those labelled datasets freely available as open data to organisations that use machine learning for their activities, mainly companies and knowledge institutes. As part of the project we developed example scripts (Jupyter notebooks) that enable organisations to use the crowdsourced generated data smoothly for their own machine learning systems. </p> <p>BlackShore has developed the online platform Cerberus to enable large scale generation of labelled datasets, which is deployed on twenty locations around the Mediterranean Sea to generate labelled datasets of wheat and other land cover classes (see table). Those different locations encompass a diversity of climate regions, harvest cultures and crop calendars, posing a challenge to the training of machine learning algorithms. Gamers click on hexagons plotted on top of very high resolution satellite imagery (captured during the harvest period in 2021), and by combining 3 different hexagon grids those clicks are converted into triangles. Each triangle has a number of clicks (by different users) per land cover category, which provides a measure of accuracy to the label.</p> <p>52impact developed example tutorials to use the data to train pixel-based (Random Forest) and segmentation-based (U-Net) machine learning models, using Sentinel-2 imagery (provided in the data folder), which can be forked here: <a href="https://bitbucket.org/52impact/crowds-machines">https://bitbucket.org/52impact/crowds-machines</a>.<br> </p> <table> <caption><strong>Overview of locations</strong></caption> <thead> <tr> <th scope="col">ID</th> <th scope="col">location_id</th> <th scope="col">Country</th> <th scope="col">Region</th> <th scope="col">Shape</th> <th scope="col">Harvest period</th> <th scope="col">VHR image date</th> <th scope="col">S-2 pre-harvest</th> <th scope="col">S-2 harvest</th> <th scope="col">S-2 post-harvest</th> </tr> </thead> <tbody> <tr> <td>01</td> <td>portugalAlentejo</td> <td>Portugal</td> <td>Alentejo</td> <td>01_Portugal_Alentejo_SELECTION</td> <td>10 Jul - 1 Aug</td> <td>07/07/2021</td> <td>14/05/2021</td> <td>13/07/2021</td> <td>22/08/2022</td> </tr> <tr> <td>02</td> <td>spainAndalusia</td> <td>Spain</td> <td>Andalusia</td> <td>02_Spain_Andalusia_SELECTION</td> <td>10 Jul - 1 Aug</td> <td>02/07/2021</td> <td>16/05/2021</td> <td>15/07/2021</td> <td>03/09/2021</td> </tr> <tr> <td>03</td> <td>spainAragon</td> <td>Spain</td> <td>Aragon</td> <td>03_Spain_Aragon_SELECTION</td> <td>10 Jul - 1 Aug</td> <td>26/10/2021</td> <td>20/05/2021</td> <td>19/07/2021</td> <td>05/09/2021</td> </tr> <tr> <td>04</td> <td>franceAude</td> <td>France</td> <td>Aude</td> <td>04_France_Aude_SELECTION</td> <td>1 Jul - 1 Oct</td> <td>22/09/2021</td> <td>12/05/2021</td> <td>10/08/2021</td> <td>18/11/2021</td> </tr> <tr> <td>05</td> <td>franceCamargue</td> <td>France</td> <td>Camargue</td> <td>05_France_Camargue_SELECTION</td> <td>1 Jul - 1 Oct</td> <td>07/10/2021</td> <td>12/05/2021</td> <td>10/08/2021</td> <td>18/11/2021</td> </tr> <tr> <td>06</td> <td>franceProvence</td> <td>France</td> <td>Provence</td> <td>06_France_Provence_SELECTION</td> <td>1 Jul - 1 Oct</td> <td>26/10/2021</td> <td>19/05/2021</td> <td>17/08/2021</td> <td>20/11/2021</td> </tr> <tr> <td>07_08</td> <td>italyMarche</td> <td>Italy</td> <td>Marche (East and West)</td> <td>07_08_Italy_Marche_SELECTION</td> <td>1 Jul - 1 Sept</td> <td>09/08/2021</td> <td>26/05/2021</td> <td>25/07/2021</td> <td>20/11/2021</td> </tr> <tr> <td>09</td> <td>italySardinia</td> <td>Italy</td> <td>Sardinia</td> <td>09_Italy_Sardinia_SELECTION</td> <td>1 Jul - 1 Sept</td> <td>31/08/2021</td> <td>26/05/2021</td> <td>22/07/2021</td> <td>10/10/2021</td> </tr> <tr> <td>10</td> <td>italySicily</td> <td>Italy</td> <td>Sicily</td> <td>10_Italy_Sicily_SELECTION</td> <td>1 Jul - 1 Sept</td> <td>19/09/2021</td> <td>22/05/2021</td> <td>26/07/2021</td> <td>10/10/2021</td> </tr> <tr> <td>11</td> <td>italyPugliaNorth</td> <td>Italy</td> <td>Puglia (North)</td> <td>11_Italy_PugliaNorth_SELECTION</td> <td>1 Jul - 1 Sept</td> <td>06/10/2021</td> <td>11/06/2021</td> <td>31/07/2021</td> <td>04/10/2021</td> </tr> <tr> <td>12</td> <td>italyPuglia</td> <td>Italy</td> <td>Puglia</td> <td>12_Italy_Puglia_SELECTION</td> <td>1 Jul - 1 Sept</td> <td>19/08/2021</td> <td>03/06/2021</td> <td>02/08/2021</td> <td>21/10/2021</td> </tr> <tr> <td>13</td> <td>greeceWest</td> <td>Greece</td> <td>West</td> <td>13_Greece_West_SELECTION</td> <td>1 Sept - 1 Nov</td> <td>02/09/2021</td> <td>27/07/2021</td> <td>05/10/2021</td> <td>14/12/2021</td> </tr> <tr> <td>14</td> <td>greeceThessaly</td> <td>Greece</td> <td>Thessaly</td> <td>14_Greece_Thessaly_SELECTION</td> <td>1 Sept - 1 Nov</td> <td>14/07/2021</td> <td>27/07/2021</td> <td>25/09/2021</td> <td>19/12/2021</td> </tr> <tr> <td>15</td> <td>greeceMacedoniaCentral</td> <td>Greece</td> <td>Macedonia (Central)</td> <td>15_Greece_MacedoniaCentral_SELECTION</td> <td>1 Jun - 1 Aug</td> <td>22/07/2021</td> <td>13/05/2021</td> <td>22/07/2021</td> <td>15/09/2021</td> </tr> <tr> <td>16</td> <td>greeceMacedoniaEast</td> <td>Greece</td> <td>Macedonia (East)</td> <td>16_Greece_MacedoniaEast_SELECTION</td> <td>1 Jun - 1 Aug</td> <td>05/08/2021</td> <td>25/05/2021</td> <td>29/07/2021</td> <td>27/10/2021</td> </tr> <tr> <td>17</td> <td>greeceRhodes</td> <td>Greece</td> <td>Rhodes</td> <td>17_Greece_Rhodes_SELECTION</td> <td>15 May - 1 Jul</td> <td>09/05/2021</td> <td>25/03/2021</td> <td>24/05/2021</td> <td>22/08/2021</td> </tr> <tr> <td>18</td> <td>cyprusLarnaca</td> <td>Cyprus</td> <td>Larnaca</td> <td>18_Cyprus_Larnaca_SELECTION</td> <td>15 May - 1 Jul</td> <td>05/06/2021</td> <td>19/03/2021</td> <td>07/06/2021</td> <td>21/08/2021</td> </tr> <tr> <td>19</td> <td>turkeyCyprus</td> <td>Cyprus (T)</td> <td>Farmagusta</td> <td>19_Turkey_Cyprus_SELECTION</td> <td>15 May - 1 Jul</td> <td>05/06/2021</td> <td>29/03/2021</td> <td>17/06/2021</td> <td>26/08/2021</td> </tr> <tr> <td>20</td> <td>egyptBehera</td> <td>Egypt</td> <td>Behera</td> <td>20_Egypt_Behera_SELECTION</td> <td>1 Apr - 1 Jul</td> <td>06/03/2021</td> <td>26/01/2021</td> <td>07/03/2021</td> <td>19/08/2021</td> </tr> </tbody> </table> <p>The following data is provided:</p> <ul> <li>Triangulated_data.zip: contains per region and per category a geopackage (gpkg) file containing triangular polygons with the number of clicks per polygon. The filename of the polygon files depends on the location and category. For example, a file that contains the triangles corresponding to Cattle in Alentejo, Portugal, is called: 01_Portugal_Alentejo_Cattle.gpkg</li> <li>Data.zip: all data necessary to run the Jupyter notebooks, i.e., location data, cropped Sentinel-2 satellite imagery (for training location IDs 01, 02, 12 and 15, and validation locations near IDs 02 and 15) and also the triangulated polygons.</li> <li>Models.zip: pre-trained random forest and U-Net models based on the data, which can be generated by the Jupyter notebooks.<br> </li> </ul>
Learner Perceptions on Gamifying Active Video Watching Platforms (supplementary material)
<p>This repository contains supplementary material for "Learner Perceptions on Gamifying Active Video Watching Platforms":</p> <ul> <li>Questionnaire.pdf: PDF file containing the survey questions on motivation, experience and perception on gamification.</li> <li>Perception_on_gamification.xlsx: Raw dataset of the survey.</li> </ul>
A Gamified, Social Media Inspired Personalized Normative Feedback Alcohol Intervention for Sexual Minority Women
ClinicalTrials.gov study NCT03884478. IPD Sharing: YES. Countries: 1. Publications: 2.
A Clinical Trial of a Gamified Attention Bias Modification Training in Anxious Youth
ClinicalTrials.gov study NCT03283930. IPD Sharing: Not stated. Countries: 1. Publications: 5.
Artifacts for Bachelor's Thesis: "Development and Integration of an Area Generator for the Gamify-IT Project"
<p>The project files and evaluation questionnaires and results for the Bachelor's Thesis "Development and Integration of an Area Generator for the Gamify-IT Project".</p> <p>The project files are part of the Gamify-IT project and have only been expanded in this work. </p> <p>The evaluation questionnaires and results are partially in German. </p>
Questionnaire and Results for the "Gamify-IT - A Web-Based Gaming Platform for Software Engineering Education" Paper
<p>This archive contains the questions and results of our questionnaire to evaluate the prototype Gamify-IT e-learning platform. The questionnaire contains 18 questions and statements to evaluate the students' experience with the platform. We conducted the evaluation with students of our programming introductory course at the University of Stuttgart. 20 students provided their feedback. Results are anonymized.</p>
Gamified Flipped Class Room and Nursing Students' Skills Competency and Confidence
ClinicalTrials.gov study NCT04859192. IPD Sharing: NO. Countries: 1. Publications: 1.
Improving Breast Health Knowledge Among Women Using a Gamified Metaverse-Based Platform
ClinicalTrials.gov study NCT06930898. IPD Sharing: UNDECIDED. Countries: 2. Publications: 1.
Cueing-assisted Gamified Augmented-reality Gait-and-balance Rehabilitation at Home for People With Parkinson's Disease
ClinicalTrials.gov study NCT06590987. IPD Sharing: UNDECIDED. Countries: 1. Publications: 7.
Gamified Rehab vs Take-home Packet Rehab for Non-specific Low Back Pain
ClinicalTrials.gov study NCT05573932. IPD Sharing: NO. Countries: 1. Publications: 1.
Gamified Family-based Health Exercise Intervention to Improve Adherence to 24-h Movement Behaviors Recommendations in Children.
ClinicalTrials.gov study NCT05741879. IPD Sharing: NO. Countries: 1. Publications: 2.
Gamifying Patient's Personal Data Validation and Completion in a Personal Health Record
ClinicalTrials.gov study NCT02970461. IPD Sharing: UNDECIDED. Countries: 1. Publications: 10.
Gamified Learning for Pressure Injury Prevention
ClinicalTrials.gov study NCT07028892. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.
Augmentation of Depression Treatment by Gamified Network Retraining
ClinicalTrials.gov study NCT04400162. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Transcranial Direct Current Stimulation and Gamified Rehabilitation for Upper Limb Function in Pediatric Brain Damage
ClinicalTrials.gov study NCT06214364. IPD Sharing: YES. Countries: 1. Publications: 9.
The Study Estimates the Longitudinal Impact of a Gamified Health Education App on Students' Health and Learning Outcomes
ClinicalTrials.gov study NCT05458141. IPD Sharing: NO. Countries: 1. Publications: 1.
Using Gamified Elements to Increase Daily Step Count in University Students in Taiwan A RCT
ClinicalTrials.gov study NCT06818071. IPD Sharing: UNDECIDED. Countries: 1. Publications: 6.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.