Search Results for "dataset"
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Showing 48 open source projects for "dataset"
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Mathematics Dataset
This dataset code generates mathematical question and answer pairs
The Mathematics Dataset, developed by Google DeepMind, is a synthetic dataset designed to evaluate and train machine learning models on mathematical reasoning and symbolic manipulation. It generates question-and-answer pairs across a wide range of mathematical topics typically found in school-level curricula, testing a model’s ability to reason about algebra, arithmetic, calculus, probability, and more. -
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Open X-Embodiment
Unified open dataset enabling cross-embodiment learning for robotics
Open X-Embodiment is a large-scale collaborative initiative led by Google DeepMind to unify robotic learning datasets into a consistent and standardized format, simplifying access and usage across the robotics research community. Its primary goal is to make all available open-source robotic data interoperable by representing them using the RLDS (Reinforcement Learning Dataset Structure) episode format. This enables seamless integration for training, evaluation, and model development across diverse robotic tasks and embodiments. The dataset aggregates contributions from multiple open-source robotic projects, all harmonized under a single unified data schema. The repository also provides Colab notebooks for dataset visualization, batching, and model inference, along with pretrained model checkpoints such as RT-1-X, a multitask robotic transformer model trained on this data. -
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kepler.gl
Kepler.gl is a powerful open source geospatial analysis tool for data
...For information on how to embed kepler.gl in your app take a look at this step-by-step tutorial on vis.academy. Built with Deck.gl, Kepler.gl utilizes WebGL to render large datasets quickly and efficiently. You can easily drag and drop a dataset, add filters, apply scales, and do aggregation on the fly. Built on React & Redux, Kepler.gl can be embedded inside your own mapping applications. -
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AWS Data Wrangler
Pandas on AWS, easy integration with Athena, Glue, Redshift, etc.
An AWS Professional Service open-source python initiative that extends the power of Pandas library to AWS connecting DataFrames and AWS data-related services. Easy integration with Athena, Glue, Redshift, Timestream, OpenSearch, Neptune, QuickSight, Chime, CloudWatchLogs, DynamoDB, EMR, SecretManager, PostgreSQL, MySQL, SQLServer and S3 (Parquet, CSV, JSON, and EXCEL). Built on top of other open-source projects like Pandas, Apache Arrow and Boto3, it offers abstracted functions to execute... -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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PyTorch Geometric Temporal
Spatiotemporal Signal Processing with Neural Machine Learning Models
The library consists of various dynamic and temporal geometric deep learning, embedding, and Spatio-temporal regression methods from a variety of published research papers. Moreover, it comes with an easy-to-use dataset loader, train-test splitter and temporal snaphot iterator for dynamic and temporal graphs. The framework naturally provides GPU support. It also comes with a number of benchmark datasets from the epidemiological forecasting, sharing economy, energy production and web traffic management domains. Finally, you can also create your own datasets. ... -
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Surya
Implementation of the Surya Foundation Model for Heliophysics
Surya is an open‑source, AI‑based foundation model for heliophysics developed collaboratively by NASA (via the IMPACT AI team) and IBM. Named after the Sanskrit word for “sun,” Surya is trained on nine years of high‑resolution solar imagery from NASA’s Solar Dynamics Observatory (SDO). It is designed to forecast solar phenomena—such as flares, solar wind, irradiance, and active region behavior—by predicting future solar images with a sophisticated long–short vision transformer architecture,... -
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PetoronAI-Drug-Discovery
PetoronAI Drug Discovery Experiments
# PetoronAI Drug Discovery Benchmark https://github.com/01alekseev/PetoronAI PetoronAI was evaluated on the NCI-ALMANAC development dataset. • 2,225,137 experimental records • 602 experimentally measured drug pairs • Blind pair-level benchmark • 20×10 cross-validation • 99.5% validation coverage Validation: Correlation = 0.617327 MAE = 3.971799 Null model MAE = 4.656203 Sign accuracy = 64.69% Permutation p = 0.000100 Top HSA hypotheses: • Dactinomycin + Vinblastine sulfate (9.096) • Cabazitaxel + Vinblastine sulfate (8.224) • Mitoxantrone + Vinblastine sulfate (7.254) • Cabazitaxel + Mitoxantrone (7.128) • Dactinomycin + Daunorubicin HCl (6.965) Generated laboratory validation protocols (8×8 dose matrix, HSA, Bliss, Loewe, ZIP). ... -
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seaborn
Statistical data visualization in Python
...Its plotting functions operate on dataframes and arrays containing whole datasets and internally perform the necessary semantic mapping and statistical aggregation to produce informative plots. Its dataset-oriented, declarative API lets you focus on what the different elements of your plots mean, rather than on the details of how to draw them. Behind the scenes, seaborn uses matplotlib to draw its plots. For interactive work, it’s recommended to use a Jupyter/IPython interface in matplotlib mode, or else you’ll have to call matplotlib.pyplot.show() when you want to see the plot. -
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Habfuzz
A command-line tool for data-driven fuzzy modelling
Input 1 - A training dataset (multiple observations) of up to four variables (predictors) against one (response variable) Input 2 - A test dataset (multiple observations) of the same four variables with unknown response variable Output - Calculation of the response variable for each test observation using fuzzy logic or fuzzy rule-based Bayesian algorithms HABFUZZ is a habitat model, which can be used in ecohydraulic modelling applications for the calculation of the instream habitat suitability in various discharge scenarios in a simulated river reach. ... -
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Watershed Districts
Creates voting districts using watershed boundaries.
Uses the USGS Watershed Boundary Dataset and the 2010 US Census data to create representational voting districts with equivalent population. -
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coronavirus
The coronavirus dataset
The coronavirus package gives a tidy format dataset of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic. Relevant and updated information about the virus, such as summary of new cases by country and total number of cases by region can be retrieved from this package. The raw data is pulled and arranged by the Johns Hopkins University Center for Systems Science and Engineering, which is gathered from various leading sources including the World Health Organization, China CDC, US CDC, European Centre for Disease Prevention and Control, and Australia Government Department of Health. -
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sRNAWorkbench
The UEA sRNA Workbench
A suite of tools for analysing small RNA (sRNA) data from Next Generation Sequencing devices. Including expression profiling of known mirco RNA (miRNA), identification of novel miRNA in deep-sequencing data and identification of other interesting landmarks within high-throughput genetic data -
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MaChIAto Example Files
The example files of MaChIAto
MaChIAto (Microhomology-associated Chromosomal Integration/editing Analysis tools); a comprehensive analysis software that can precisely classify, deeply analyze, correctly align, and thoroughly review the targeted amplicon sequencing analysis data obtained by various CRISPR experiments, including template-free gene knock-out, short homology-based gene knock-in, and even a new-class CRISPR methodology, Prime Editing. In this repository, we provide the example files of MaChIAto. You can... -
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Objectron
A dataset of short, object-centric video clips
The dataset consists of 15K annotated video clips supplemented with over 4M annotated images in the following categories: bikes, books, bottles, cameras, cereal boxes, chairs, cups, laptops, and shoes. In addition, to ensure geo-diversity, our dataset is collected from 10 countries across five continents. Along with the dataset, we are also sharing a 3D object detection solution for four categories of objects — shoes, chairs, mugs, and cameras. -
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Chuck Coleman's SAS Codes
SAS Codes
...Directories under "Files" with descriptions: NESUG 2014: "A Fast, High-Precision Implementation of the Univariate One-Parameter Box-Cox Transformation Using the Golden Section Search in SAS/IML®" SESUG 2017: "Decomposing the R-squared of a Regression Using the Shapley value in SAS®" SESUG 2019: "Conditionally Executing Data Steps and Statements Based on the Presence of Variables in a SAS® Dataset" GASP 2020: "Online Winsorization of the Survey of Construction’s Price Estimates" My SESUG 2015 materials can also be found in my project "constrainingarrays" at https://sourceforge.net/projects/constrainingarrays/. -
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...It constitutes the Supporting Information for a scientific article entitled "Application of blended physical-virtual experiments and flipped instruction in a fundamental fluid mechanics course: a design-based research study" submitted to Computer Applications in Engineering Education. The educational material contained in this dataset is shared under the GNU General Public License v3.0. Please read the licence terms from the following link: https://www.gnu.org/licenses/quick-guide-gplv3.html
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Calis-p
Estimates delta13C of species in a microbiome from proteome data
Calis-p (The CALgary approach to ISotopes in proteomics) is a java application to estimate isotopic composition (e.g. delta13C or delta15N) of individual species in a microbial community from a proteomic dataset. Calis-p 2.0 handles both natural isotope abundances and data from labelling experiments such as stable isotope probing (SIP). It requires a mzIdent (or target spectrum match) and mzML files as the input and requires about 1 min per mzML file with 10 threads and needs <10 Gb of RAM. It has been tested with data from various nano liquid chromatography/Orbitrap platforms. ... -
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lottie vectors
Create, display and process 2D vectors in a 3D window.
...This could be a common math symbol or greek letter, start/end signposts, or a number of the position either from the begining or after a reset. After all the information has been loaded for your dataset. Either passed over the command line, or as a text file. Lottie Vectors displays inside a figure that you can resize, rotate and turn 2D positions into a 3D picture. -
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Charting tools, backtesting tools, and data visualization tools for the most popular cryptocurrencies.
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PyCosmic is a tool to detect and clean single images from the disturbing cosmic ray hits. It was designed and tested specifically for CALIFA and other fiber-fed integral-field spectroscopy dataset. It is written in Python and can be executed from the command line. New release is version v0.5. Updated to be compatible with numpy version 1.12. New release is version v0.4. It's now compatible with astropy for fits I/O and computation time is reduced by a factor of 2.
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metasort
A metagenome assembler by reducing microbial community
...MetaSort provides a sorted mini-metagenome approach based on flow cytometry and single-cell sequencing methodologies, and employs new computational algorithms to efficiently recover high-quality genomes from the sorted mini-metagenome by the complementary of the original metagenome. Through extensive evaluations on simulated dataset, salivary and gut microbiomes, we demonstrated that metaSort has an excellent and unbiased performance on genome recovery and assembly. -
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3DimViewer
3DimViewer is a lightweight 3D viewer of medical DICOM datasets.
3DimViewer is a lightweight 3D viewer of medical DICOM datasets that is distributed as open source software. The viewer is multiplatform software written in C++ that runs on Windows, Mac OS X and Linux systems. 3DimViewer source codes are available on Bitbucket (https://bitbucket.org/3dimlab/3dimviewer). Video tutorials can be found on YouTube (https://www.youtube.com/playlist?list=PLCvwUmKRw9ZO4YexLKEXmw_2jPo-kXVRn). -
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Density-ratio based clustering
Discovering clusters with varying densities
...One approach is to modify a density-based clustering algorithm to do density-ratio based clustering by using its density estimator to compute density-ratio. The other approach involves rescaling the given dataset only. An existing density-based clustering algorithm, which is applied to the rescaled dataset, can find all clusters with varying densities that would otherwise impossible had the same algorithm been applied to the unscaled dataset. Reference: Zhu, Y., Ting, K. M., & Carman, M. J. (2016). Density-ratio based clustering for discovering clusters with varying densities. ... -
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Yggdrasill
3D Evolutionary Tree Visualization
...Currently available features include procedural tree generation of evolutionary data provided by the Tree of Life (ToL) Web Project (http://tolweb.org/tree/), full three dimensional camera controls as well as a web search, an extinct species overlay and more. Later updates will include the full ToL dataset, improve tree generation and overall accessability, as well as adding access to other datasets. Special thanks go to the Tree of Life Web Project and all its contributors for providing the data and to Unity Technologies for free access to Unity 5. -
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For the process of RDF dataset transformation the R2R Framework specifies a mapping language and an implementation in form of a a Java API. More infos at: http://www4.wiwiss.fu-berlin.de/bizer/r2r/