Search Results for "dataset"
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Showing 66 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... -
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Evo 2
Genome modeling and design across all domains of life
...The project models DNA at single-nucleotide resolution and supports context windows of up to one million base pairs, which places it in a class of models built for very large genomic reasoning tasks. According to the repository, it uses the StripedHyena 2 architecture, was pretrained with Savanna, and was trained autoregressively on the OpenGenome2 dataset containing 8.8 trillion tokens. The codebase is focused on local inference and generation through the Vortex inference stack rather than serving as a full training framework alone, although it also points users to training and fine-tuning resources. It supports multiple ways of working with the model, including forward passes, embeddings, generation workflows, notebooks, hosted APIs, and self-hosted deployment through NVIDIA NIM. -
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CycleGAN and pix2pix in PyTorch
Image-to-Image Translation in PyTorch
CycleGAN and pix2pix in PyTorch repository is a PyTorch implementation of two influential image-to-image translation frameworks: CycleGAN (for unpaired translation) and pix2pix (for paired translation). This repo gives developers and researchers a convenient, modern (PyTorch-based) platform to train and test these methods — supporting both paired datasets (input to output) and unpaired datasets (domain-to-domain) with minimal changes. The code supports standard training and inference... -
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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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Kst is the fastest real-time large-dataset viewing and plotting tool available and has basic data analysis functionality. Kst contains many powerful built-in features and is expandable with plugins and extensions.
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ParDRe
Parallel tool to remove duplicate DNA reads
...Duplicate reads can be seen as identical or nearly identical sequences with some mismatches. This tool will let the users to avoid the analysis of not necessary reads, reducing the time of subsequent procedures with the dataset (e.g., assemblies, mappings, etc.). The tool is implemented with MPI in order to exploit the parallel capabilities of multicore clusters. It is faster than multithreaded counterparts (end of 2015) for the same number of cores and, thanks to the message-passing technology, it can be executed on clusters. There also exists a MapReduce counterpart of ParDRe, called MarDRe (see the link above). ... -
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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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Gemi
PCR primers / probes design from multiple & degenerate sequences
...Gemi accepts multiple aligned and long DNA and RNA sequences with degenerate nucleotide (non-A/C/G/T bases). Gemi can be used for quantitative, real-time and conventional PCR (qPCR, rt-PCR, etc.), and Sanger sequencing. Gemi can parse large dataset of sequences efficiently. Python source code is available upon request. Milestone: The tool reached about 3000 downloads sine 2012. Article Gemi: PCR primers prediction from multiple alignments. Comparative and functional genomics 2012 PMID: https://www.ncbi.nlm.nih.gov/pubmed/23316117 A great review on designing primer, Gemi, and other tools: Designing degenerate primers: Overview, challenges, and computational methods. ... -
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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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OpenNum
OpenNum lets you distribute solvers with a nice graphical interface
...OpenNum lets you create a graphical interface adapted to your solvers by simply editing an XML configuration file. More specifically, OpenNum lets you · to collect a hierarchical dataset, · to call any executable file and · to visualize scalar and vector fields, plot graphs or show simple plain text files. It also has other useful utilities specifically designed for numerical simulation packages: · it allows managing a centralized materials dataset; · it can read several finite element mesh formats and several field formats. -
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Pytorch Points 3D
Pytorch framework for doing deep learning on point clouds
...We aim to build a tool that can be used for benchmarking SOTA models, while also allowing practitioners to efficiently pursue research into point cloud analysis, with the end goal of building models which can be applied to real-life applications. Task driven implementation with dynamic model and dataset resolution from arguments. Core implementation of common components for point cloud deep learning - greatly simplifying the creation of new models. 4 Base Convolution base classes to simplify the implementation of new convolutions. Each base class supports a different data format. -
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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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MarDRe
MapReduce-based tool to remove duplicate DNA reads
MarDRe is a de novo MapReduce-based parallel tool to remove duplicate and near-duplicate DNA reads through the clustering of single-end and paired-end sequences from FASTQ/FASTA datasets. This tool allows bioinformatics to avoid the analysis of not necessary reads, reducing the time of subsequent procedures with the dataset. MarDRe is the Big Data counterpart of ParDRe (link above), which employs HPC technologies (i.e., hybrid MPI/multithreading) to reduce runtime on multicore systems. Instead, MarDRe takes advantage of the MapReduce programming model to significantly improve ParDRe performance on distributed systems, especially on cloud-based infrastructures. ...