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83 results for “Chess”
Chess Game Dataset
<p>This is the dataset for all of the chess enthusiasts and chess.com members. It has been created via the chess.com API.</p><p>Sourced from: <a href="https://www.kaggle.com/datasets/adityajha1504/chesscom-user-games-60000-games">60,000+ Chess Game Dataset</a></p>
XES Chess Pieces Production
<p><strong>Nine Rooks (2023-04-28): Lathe Machining (Aluminium), Robotic Handling, Close To Production Measurement</strong></p> <p>This dataset has been created by researchers from the Technical University of Munich, Chair for Information Systems and Business Process Management (i17), Boltzmannstraße 3, 85748 Garching b. München. The dataset has been created through <a href="http://cpee.org">https://cpee.org</a>.</p> <p>The data set contains raw data and refined and aggregated data in the XES SensorStream format <a href="https://arxiv.org/abs/2206.11392">https://arxiv.org/abs/2206.11392</a>.</p> <p>The dataset contains data from the following sources:</p> <ol> <li>EMCO MT45 Lathe: standard internal sensors + additional custom power measurement (better quality than internal sensors). All data collected from this service can, to the best of our knowledge, be freely distributed and used for all purposes (e.g., analysis).</li> <li>Keyence LS-7000 High-speed, High-accuracy Optical Digital Micrometer: the part is moved through the measurement beam. On data collection we restricted the accuracy to 0.01 mm.</li> <li>ABB IRB-2600 Industrial Robot: movement coordinates and pneumatic valve states (gripper) are collected.</li> </ol> <p>The data is collected for nine manufactured parts: some parts are good, other parts are wrapped in chips from the turning process (see picture).</p>
OCEL Chess Pieces Production
<p>This dataset has been created by transforming the dataset </p> <p><a href="https://doi.org/10.5281/zenodo.7419655">https://doi.org/10.5281/zenodo.7419655</a></p> <p>to an object-centric setting, i.e., the recorded event data is stored in the OCEL event log format (see https://ocel-standard.org/). </p> <p> </p>
Chess piece dataset for image classification
<p>Chess piece dataset for image classification. Contains 4 different chess sets, 3 used for training and the remainder for validation purposes. Each chess piece from each set has been photograph by a static bird's eyes camera from each of the 64 squares that form a chess board. This way, each piece is seen from all its different angles. </p>
BRAIN Journal-Computational Intelligence in a Human Brain Model-Figure 4. Name and role of the Chess Pieces
<p>In assigning the brain function to the computational processing units the strategy of the chess game will be pursued: 1 king – consciousness, mind, resolving undefined situations, undetermined risk analysis, feedback: 1 queen – implementation strategy, thinking, learning; 2 rooks – initial knowledge memory and learning memory; 2 bishops – good or updated, time or emergency decision; 2 knights – rules, open schemes, fixed processes, templates; 8 pawns – interfaces with own senses and actions. Double chess pieces will be assigned in the model with initial knowledge (‘ marked) that can be updated as a learning experience to a second set (“ marked).</p>
BRAIN Journal-Computational Intelligence in a Human Brain Model-Figure 3. Assigning ` the "SAH" Human Brain Model the role of the Chess Pieces
<p>The components are not topically subordinated to each other but in a strong interoperability and used for outputs reflected as result of thinking, actions to receiving information from the sensor of the interfaces, movement or speaking. </p>
BRAIN Journal-Computational Intelligence in a Human Brain Model-Figure 1. Being Brain and Chess Game Strategy - similarities
<p>Finally, the following similar reactions between a chess player and a human being must be mentioned and considered. The power of reason for every being, human brain, or chess game player lies in similarities and has three main directions (see Figure 1)</p>
Data & Sample Chess
<p>Data & Sample Chess is a game-like figure to enable all project partners of the Horizon 2020 project "MEET" to mark for each work package, which task-related data or sample type has to be provided to other work packages. This figure was used within an interactive session during a DMP-workshop held in May 2019 in Zagreb.</p> <p>The outcomes of this game-like session were summarized and combined into one general figure to identify the internal data paths and dependencies between work packages.</p>
ChessRender360: High-Fidelity Rendered Chess Dataset with Multi-Modal Annotations
<p>ChessRender360 is a synthetically crafted dataset featuring 10,000 rendered chess positions. Designed for computer vision and machine learning research, this dataset provides a rich collection of RGB images, depth maps, instance masks, and semantic segmentation masks for each chess piece and board element.</p> <p>Each chess position is rendered in high resolution (2000x2000 pixels), capturing the intricate details of the board and pieces from various angles. The dataset includes:</p> <ul> <li><strong>RGB Images:</strong> High-quality rendered images of chess positions, showcasing a diverse range of board configurations.</li> <li><strong>Depth Maps:</strong> Accurate depth representations of the scene, capturing depth in the range of 20 cm to 120 cm. In the depth maps, black corresponds to a depth of 20 cm, and white corresponds to 120 cm, providing spatial information for each position.</li> <li><strong>Instance Masks:</strong> Unique instance masks for each chess piece, enabling precise identification and localization.</li> <li><strong>Semantic Segmentation Masks:</strong> Segmentation masks that differentiate between piece types and board elements, with distinct hue values assigned to each type.</li> <li><strong>Bounding Boxes:</strong> Each sample has an annotation <code>.json</code> file containing bounding boxes for each piece.</li> <li><strong>Board Corners:</strong> Same annotation <code>.json</code> contains positions of corners of the board in order: white left, white right, black left, black right.</li> <li><strong>FENs:</strong> A CSV file containing the FEN (Forsyth-Edwards Notation) for each chess position in the dataset, listed in order. This allows users to easily recognize and replicate the exact board position from any image.</li> </ul> <p><strong>Bounding Box Generation:</strong></p> <ul> <li>The dataset does not include predefined bounding boxes, but they can be easily generated from the provided semantic and instance masks. This allows for flexible bounding box creation tailored to specific research needs.</li> </ul> <p><strong>Rendering Details:</strong></p> <ul> <li><strong>3D Models and Materials:</strong> The dataset uses a consistent set of 3D models for all chess pieces, with three different material/color schemes applied across the dataset, along with random perturbations in material brightness, contrast and saturation to introduce visual variety.</li> <li><strong>Camera Angles:</strong> Camera angles are randomly selected, with yaw ranging from 0 to 360 degrees and pitch between 30 to 80 degrees, providing diverse perspectives of the chess positions.</li> <li><strong>Background Variability:</strong> The chessboard is randomly placed on different types of tables, with the floor material randomly sampled to create a variety of backgrounds.</li> <li><strong>Lighting:</strong> Lighting conditions are randomly generated, adding further diversity and realism to the rendered scenes.</li> </ul> <p><strong>Augmentation Potential:</strong></p> <ul> <li>The instance and semantic masks can be used to further augment the dataset. Researchers can selectively modify specific parts of the images—such as the board, background, or individual pieces—enabling the creation of new variations and enhancing the dataset's utility for model training and testing.</li> </ul> <p><strong>Color Mapping:</strong></p> <ul> <li>The semantic masks are color-coded using a hue-based system, where the board frame, squares, and each piece type are assigned specific hues. Instances of the same piece type are differentiated by varying the value component, with saturation consistently set to 1. A detailed <code>color_mapping.json</code> file is included, providing a comprehensive guide to interpreting the masks.</li> </ul> <p><strong>Applications:</strong> ChessRender360 is ideal for tasks such as object detection, instance segmentation, depth estimation, and scene understanding in synthetic environments. Researchers and developers can leverage this dataset for training and evaluating models in computer vision, robotics, and artificial intelligence.</p> <p><strong>Dataset Highlights:</strong></p> <ul> <li>10,000 uniquely rendered chess positions</li> <li>High-resolution images (2000x2000 pixels) with diverse visual characteristics</li> <li>Comprehensive annotations with RGB, depth (20 cm to 120 cm), instance, and semantic maps</li> <li>Side identification map for distinguishing white and black sides of the board</li> <li>FEN notation CSV file for easy position recognition and replication</li> <li>Variety introduced through different material schemes and lighting setups</li> <li>Randomized camera angles for enhanced perspective diversity</li> <li>Potential for further augmentation by modifying specific image components</li> <li>Detailed color mapping for easy interpretation of segmentation masks</li> <li>Suitable for a wide range of computer vision and AI applications</li> </ul> <p>ChessRender360 offers a rich and versatile dataset for advancing research and development in the field of computer vision, providing a synthetic yet highly realistic environment for model training and testing.</p> <p>For any questions, feedback, or collaboration opportunities, or if you are interested in custom artificial datasets, please contact me:</p> <ul> <li><strong>Name:</strong> Marko Kojić</li> <li><strong>LinkedIn:</strong> https://www.linkedin.com/in/mmkoya</li> </ul> <p>I welcome inquiries from researchers, developers, and organizations interested in utilizing or collaborating on artificial datasets.</p>
Figure 2. Aciconula acanthosoma Chess 1989 in Littoral caprellids (Crustacea: Amphipoda) from the Mexican Central Pacific coast, with the description of four new species
Figure 2. Aciconula acanthosoma Chess 1989. Lateral view. Scale bar: 1 mm.
Chess Piece
Material: [Ivory](http://vocab.getty.edu/page/aat/300131099) or [Animal Bone](http://vocab.getty.edu/page/aat/300417720). Accession no: HCM 207. Current location: [The Hunt Museum](https://www.huntmuseum.com/), [Limerick](https://www.geonames.org/7778675/limerick-city.html). A chess piece shaped like an animal, perhaps a horse. It dates from the [7th Century AD](http://n2t.net/ark:/99152/p08m57h8zmq). [More](https://www.huntmuseum.com/explore/item/9b822dd9-23e5-39fe-9668-431096eef83b/?s%3Dchess&pos=2). Source: Objaverse 1.0 / Sketchfab
Queen From The Lewis Chess Pieces - Optimised
"A queen from the Lewis chessmen" (https://skfb.ly/66Gty) by The British Museum is licensed under CC Attribution-NonCommercial-ShareAlike (http://creativecommons.org/licenses/by-nc-sa/4.0/). I used [rapidcompact.com](http://rapidcompact.com) optimize this excellent 3D scan, choosing default settings and a 3mb target filesize. Object info > Chess-piece; walrus ivory; queen wearing floriated crown, veil, mantle and tunic; right hand placed on cheek; seated in chair ornamented on back with adjacent leaf scrolls; cloth hanging over top of back of chair. > 1150-1175 (circa) > Height: 94 millimetres Width: 40.71 millimetres Depth: 30.48 millimetres > COL: [MCM865]>(http://www.britishmuseum.org/research/collection_online/collection_obect_details.aspx?partId=1&objectId=408) > Read more on the [British Museum Blog](http://british.museumblog.org/check-it-out-the-lewis-chessmen-at-the-british-museum/) > 115 photographs taken by Emma Palmer, Daniel Pett, Naomi Speakman and Lucy Ellis (a group training exercise.) Source: Objaverse 1.0 / Sketchfab
Chess Piece (knight)
Chess piece handed down through my family, unsure exactly how old it is but at least 4 generations. My first upload and my first attempt at photogrammetry, so excuse the holes. Source: Objaverse 1.0 / Sketchfab
Chess King
This is just a king from chess that I made to replace the real one I lost but I'm sorta proud of it so here it is Source: Objaverse 1.0 / Sketchfab
Chess Piece (Handcarved in India)
This is the King piece... This is a test upload. The rest will follow soon. Source: Objaverse 1.0 / Sketchfab
King - Concept Chess
Vietnamese Feudalism Concept Chess The powerful Tiger and the royal Dragon Source: Objaverse 1.0 / Sketchfab
WorkflowsExercise-Chess Dataset
<p>Chess Game Dataset (Lichess) from kaggle. Download link: https://www.kaggle.com/datasnaek/chess/download</p>
WorkflowsDLC-Chess Dataset
<p>Chess Game Dataset (Lichess) from kaggle. Download link: https://www.kaggle.com/datasnaek/chess/download</p>
Shakkinappula - Chess Piece
TMK23146:PU071:010 Puinen shakkinappula Katedralskolanin kaivauksilta 2014-2015. Nappula todennäköisesti ajoittuu 1300-luvulle. A wooden chess piece from the excavation at Katedralskolan in 2014-2015. The piece is likely from the 14th century. Source: Objaverse 1.0 / Sketchfab
Chess Board
Chess is a two-player strategy board game played on a chessboard, a checkered gameboard with 64 squares arranged in an 8×8 grid.[1] The game is played by millions of people worldwide. Chess is believed to have originated in India sometime before the 7th century. The game was derived from the Indian game chaturanga, which is also the likely ancestor of the Eastern strategy games xiangqi, janggi, and shogi. Chess reached Europe by the 9th century, due to the Umayyad conquest of Hispania. The pieces assumed their current powers in Spain in the late 15th century; the rules were standardized in the 19th century. I have a Patreon Join now! :https://www.patreon.com/user?u=14434838 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.