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2,481 results for “gaming”
Data from: A high-performance brain-computer interface for finger decoding and quadcopter game control in an individual with paralysis
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Enhancing pay quality: Development of a physical environment rating scale for children's folk games in chinese neighborhoods
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Data from: Winter game crop plots for gamebirds retain hedgerow breeding songbirds in an improved grassland landscape
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Data from: Luck, skill, and depth of competition in games and social hierarchies
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The color communication game: how categorical understanding of colors can be shown without considering color naming data
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The name of the game: Palaeoproteomics and radiocarbon dates further refine the presence and dispersal of caprines in Eastern and Southern Africa
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Are Game Engines Software Frameworks? A Three-perspective Study
<p>Dataset for the paper: "Are Game Engines Software Frameworks? A Three-perspective Study"</p>
Beating the shell game with barcodes: Diversity of meiofaunal Caecidae snails (Truncatelloidea, Caenogastropoda) from the Central America
<p>Single gene alignments of COI, 16S rRNA and 28s rRNA sequence data and combined alignments for COI, and16S rRNA as well as COI, 16S rRNA and 28s rRNA sequence data used for phylogenetic inference and species delimitation in the publication Beating the shell game with barcodes: Diversity of meiofaunal Caecidae snails (Truncatelloidea, Caenogastropoda) from the Central America. Original indicates the alignments before gblocks and manual adjustments were used to exclude ambiguous sites.</p>
Dataset Game Anak
<p>Dataset ini merupakan data yang kami kumpulkan dari 32 game yang dijadikan sampel sebagai bahan untuk pembuatan paper Etika Profesi terkait Game Anak yang ada di Google Play Store </p>
ERC Locus Ludi. Play and Games in Antiquity. The Reception of the Alea iacta est Motif
<p>Katarzyna Marciniak, Warsaw is the PI of the ERC project Our Mythical Childhood. she is well known for her work on reception of antiquity in present youth culture as well as on ancient literature. Her talk: From Rubicon to the Children’s Room, or the Reception of the Alea iacta est Motif in Youth Culture, was given at the International Conference, Play and Games in Antiquity. Definition, Transmission, Reception, September 17-19, 2018, Swiss Museum of Games.</p> <p>Movie/Music: <a href="https://www.edwanmusic.com/">https://www.edwanmusic.com/</a></p> <p>More about: www.locusludi.ch</p>
Analyzing App Store Comments and Quality Attributes for Defining an Inspection Checklist for Mobile Educational Games
<p>To evaluate educational games, several techniques have been proposed considering different quality attributes. However, there are still several educational games for the mobile context that have low scores in the app stores. These stores allow users to make comments to evaluate the applications, as this data can be useful for the development team that aims to meet users' expectations. The analysis of comments made by users can help identify which attributes impact the use of mobile educational games. In this paper, an inspection checklist is proposed to evaluate mobile educational games. To complement the attributes identified in the analysis of comments, attributes from existing techniques for evaluating mobile educational games were also considered. The final evaluation form contains a total of 82 attributes distributed in evaluation categories, such as: user interface, mobility, pedagogy, gameplay, among others. To evaluate the proposed technique, an evaluation was carried out with the checklist in two mobile educational games available in the Google Play Store, different from those used to define the technique. The initial results indicate that the proposed checklist allows the identification of problems pointed out by users in the comments left in the app store.</p> <p> </p> <p><a href="https://zenodo.org/api/files/8ca1af4d-6f7c-440f-a272-9366de2ad3ef/SBES%202020%20-%20Ideias%20Inovadoras%20e%20Resultados%20Emergentes%20-%20Analisando%20atributos%20de%20qualidade%20e%20coment%C3%A1rios%20de%20lojas%20de%20aplicativos%20para%20a%20defini%C3%A7%C3%A3o%20de%20uma%20t%C3%A9cnica%20de%20inspe%C3%A7%C3%A3o%20de%20jogos%20educacionais%20m%C3%B3veis.mp4">SBES 2020 - Ideias Inovadoras e Resultados Emergentes</a></p>
The Con Espressione Game Dataset
<p><strong>Con Espressione Game Dataset</strong></p> <p>A piece of music can be expressively performed, or interpreted, in a variety of ways. With the help of an online questionnaire, the <a href="http://con-espressione.cp.jku.at"><em>Con Espressione</em> Game</a>, we collected some 1,500 descriptions of expressive character relating to 45 performances of 9 excerpts from classical piano pieces, played by different famous pianists. More specifically, listeners were asked to describe, using freely chosen words (preferably: adjectives), how they perceive the expressive character of the different performances. The aim of this research is to find the dimensions of musical expression (in Western classical piano music) that can be attributed to a performance, as perceived and described in natural language by listeners.</p> <p>The <em>Con Espressione</em> Game was launched on the 3rd of April 2018.</p> <p><strong>Dataset structure</strong></p> <p><strong>Listeners’ Descriptions of Expressive performance</strong></p> <ul> <li><code>piece_performer_data.csv</code>: A comma separated file (CSV) containing information about the pieces in the dataset. Strings are delimited with <code>"</code>. The columns in this file are: <ol> <li><code>music_id</code>: An integer ID for each performance in the dataset.</li> <li><code>performer_name</code>: (Last) name of the performer.</li> <li><code>piece_name</code>: (Short) name of the piece.</li> <li><code>performance_name</code>: Name of the the performance. All files in different modalities (alignments, MIDI, loudness features, etc) corresponding to a single performance will have the same name (but possibly different extensions).</li> <li><code>composer</code>: Name of the composer of the piece.</li> <li><code>piece</code>: Full name of the piece.</li> <li><code>album</code>: Name of the album.</li> <li><code>performer_name_full</code>: Full name of the performer.</li> <li><code>year_of_CD_issue</code>: Year of the issue of the CD.</li> <li><code>track_number</code>: Number of the track in the CD.</li> <li><code>length_of_excerpt_seconds</code>: Length of the excerpt in seconds.</li> <li><code>start_of_excerpt_seconds</code>: Start of the excerpt in its corresponding track (in seconds).</li> <li><code>end_of_excerpt_seconds</code>: End of the excerpt in its corresponding track (in seconds).</li> </ol> </li> <li><code>con_espressione_game_answers.csv</code>: This is the main file of the dataset which contains listener’s descriptions of expressive character. This CSV file contains the following columns: <ol> <li><code>answer_id</code>: An integer representing the ID of the answer. Each answer gets a unique ID.</li> <li><code>participant_id</code>: An integer representing the ID of a participant. Answers with the same ID come from the same participant.</li> <li><code>music_id</code>: An integer representing the ID of the performance. This is the same as the <code>music_id</code> in <code>piece_performer_data.csv</code> described above.</li> <li><code>answer</code>: (cleaned/formatted) participant description. All answers have been written as lower-case, typos were corrected, spaces replaced by underscores (<code>_</code>) and individual terms are separated by commas. See <code>cleanup_rules.txt</code> for a more detailed description of how the answers were formatted.</li> <li><code>original_answer</code>: Raw answers provided by the participants.</li> <li><code>timestamp</code>: Timestamp of the answer.</li> <li><code>favorite</code>: A boolean (0 or 1) indicating if this performance of the piece is the participant’s favorite.</li> <li><code>translated_to_english</code>. Raw translation (from German, Russian, Spanish and Italian).</li> <li><code>performer</code>. (Last) name of the performer. See <code>piece_performer_data.csv</code> described above.</li> <li><code>piece_name</code>. (Short) name of the piece. See <code>piece_performer_data.csv</code> described above.</li> <li><code>performance_name</code>. Name of the performance. See <code>piece_performer_data.csv</code> described above.</li> </ol> </li> <li><code>participant_profiles.csv</code>. A CSV file containing musical background information of the participants. Empty cells mean that the participant did not provide an answer. This file contains the following columns: <ol> <li><code>participant_id</code>: An integer representing the ID of a participant.</li> <li><code>music_education_years</code>: (Self reported) number of years of musical education of the participants</li> <li><code>listening_to_classical_music</code>: Answers to the question “How often do you listen to classical music?”. The possible answers are: <ul> <li>1: Never</li> <li>2: Very rarely</li> <li>3: Rarely</li> <li>4: Occasionally</li> <li>5: Frequently</li> <li>6: Very frequently</li> </ul> </li> <li><code>registration_date</code>: Date and time of registration of the participant.</li> <li><code>playing_piano</code>: Answer to the question “Do you play the piano?”. The possible answers are <ul> <li>1: No</li> <li>2: A little bit</li> <li>3: Quite well</li> <li>4: Very well</li> </ul> </li> </ol> </li> <li> <p><code>cleanup_rules.txt</code>: Rules for cleaning/formatting the terms in the participant’s answers.</p> </li> <li> <p><code>translations_GERMAN.txt</code>: How the translations from German to English were made.</p> </li> </ul> <p><strong>Metadata</strong></p> <p>Related meta data is stored in the <code>MetaData</code> folder.</p> <ul> <li><code>Alignments</code>. This folders contains the manually-corrected score-to-performance alignments for each of the pieces in the dataset. Each of these alignments is a text file.</li> <li><code>ApproximateMIDI</code>. This folder contains reconstructed MIDI performances created from the alignments and the loudness curves. The onset time and offset times of the notes were determined from the alignment times and the MIDI velocity was computed from the loudness curves.</li> <li><code>Match</code>. This folder contains score-to-performance alignments in Matchfile format.</li> <li><code>Scores_MuseScore</code>. Manually encoded sheet music in MuseScore format (.mscz)</li> <li><code>Scores_MusicXML</code>. Sheet music in MusicXML format.</li> <li><code>Scores_pdf</code>. Images of the sheet music in pdf format.</li> </ul> <p><strong>Audio Features</strong></p> <p>Audio features computed from the audio files. These features are located in the <code>AudioFeatures</code> folder.</p> <ul> <li><code>Loudness</code>: Text files containing loudness curves in dB of the audio files. These curves were computed using code provided by Olivier Lartillot. Each of these files contains the following columns: <ul> <li><code>performance_time_(seconds)</code>: Performance time in seconds.</li> <li><code>loudness_(db)</code>: Loudness curve in dB.</li> <li><code>smooth_loudness_(db)</code>: Smoothed loudness curve.</li> </ul> </li> <li><code>Spectrograms</code>. Numpy files (<code>.npy</code>) containing magnitude spectrograms (as Numpy arrays). The shape of each array is (149 frequency bands, number of frames of the performance). The spectrograms were computed from the audio files with the following parameters: <ul> <li>Sample rate (<code>sr</code>): 22050 samples per second</li> <li>Window length: 2048</li> <li>Frames per Second (<code>fps</code>): 31.3 fps</li> <li>Hop size: <code>sample_rate</code> // <code>fps</code> = 704</li> <li>Filterbank: log scaled filterbank with 24 bands per octave and min frequency 20 Hz</li> </ul> </li> </ul> <p><strong>MIDI Performances</strong></p> <p>Since the dataset consists of commercial recordings, we cannot include the audio files in the dataset. We can, however, share the 2 synthesized MIDI performances used in the <em>Con Espressione</em> game (for Bach’s Prelude in C and the second movement of Mozart’s Sonata in C K 545) in mp3 format. These performances can be found in the <code>MIDIPerformances</code> folder.</p>
Game Boy - Nintendo + Tetris
Game Boy free to use Support our project please: https://www.patreon.com/museudocomputador Source: Objaverse 1.0 / Sketchfab
Game Ready Dragon Armor
This armor is part of my [Hack 'n' Slash Online Combat](http://tiny.cc/dttdgz) plugin on Unreal Engine Marketplace, take a look there you might like it. Anyway... I just made the armor free as a sample, I hope you enjoy Source: Objaverse 1.0 / Sketchfab
Tabuleiro de Jogo | Medieval Game Board
Este é um tabuleiro do jogo, encontrado na calçada da antiga Rua do Bispo, no Castelo de Montemor-o-Novo. This is a game board, found at the old Bishop's Street, at the Montemor-o-Novo's Castle, in Portugal. Source: Objaverse 1.0 / Sketchfab
Low Poly Bordeaux 1 Game Ready
**Low Poly Bordeaux 1 Game Ready** * 2048x2048 Pixel **PNG** Texture. * **PBR**:BaseColor, Ambient Oclusion, Metalness, Roughness and Normalmap. * **Retopology and UV**:Blender. * **Texturing**:SubstancePainter. * **Source 3D Model Website**:[](http://)threedscans.com/lincoln/bordeaux-alter-cast/ Source: Objaverse 1.0 / Sketchfab
Wagon game asset
Hey, Here is a model of western wagon for game asset or other! I hope it will be useful and if it is, don't hesitate to show me the result (it will make me happy) Source: Objaverse 1.0 / Sketchfab
Japanese Torii gate Game Asset
This time Torii Gate, my main reference were photos I took during my last trip to Japan. This is part of bigger enviro Im working on right now. Source: Objaverse 1.0 / Sketchfab
Pre-Columbian Stone Game Ball
This is a stone ball. Used in a game. Model made by David John Smith. Source: Objaverse 1.0 / Sketchfab
Medieval Fork (Game Ready)
227 poly medieval fork with 1024x1024 pbr textures. Textures created using Substance Painter. CC0 - no credit needed. Source: Objaverse 1.0 / Sketchfab
ScienceDex guides
Understand access before you commit
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.