Search Results for "raw"
Sort By:
Showing 34 open source projects for "raw"
View related business solutions-
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.
-
AI-generated apps that pass security reviewRetool lets you generate dashboards, admin panels, and workflows directly on your data. Type something like “Build me a revenue dashboard on my Stripe data” and get a working app with security, permissions, and compliance built in from day one. Whether on our cloud or self-hosted, create the internal software your team needs without compromising enterprise standards or control.
-
1
ClickVisual
Log analytic and data visualize platform built on clickhouse
ClickVisual is a lightweight browser-based logs analytics and logs search platform for ClickHouse. -
2
CleanVision
Automatically find issues in image datasets
...The quality of machine learning models hinges on the quality of the data used to train them, but it is hard to manually identify all of the low-quality data in a big dataset. CleanVision helps you automatically identify common types of data issues lurking in image datasets. This package currently detects issues in the raw images themselves, making it a useful tool for any computer vision task such as: classification, segmentation, object detection, pose estimation, keypoint detection, generative modeling, etc. -
3
NCDatasets.jl
Load and create NetCDF files in Julia
NCDatasets allows one to read and create netCDF files. NetCDF data set and attribute list behave like Julia dictionaries and variables like Julia arrays. This package implements the CommonDataModel.jl interface, which means that the datasets can be accessed in the same way as GRIB files opened with GRIBDatasets.jl. -
4
Foxglove Studio
Robotics visualization and debugging
...Experience the world as your robot does. Visualize images and point clouds, overlay bounding boxes, add classification labels and planned movements, and drill down into your data with plots or raw message views. Upload recordings to your private data lake for easy storage, searching, and analysis. Stream recorded data directly into Foxglove Studio to get insights into your robots' behavior. We're long-time fans and beneficiaries of open source software. Join our community on Github and Slack to contribute bug reports, feature requests, or pull requests. -
Go from Code to Production URL in SecondsSkip the Kubernetes configs. Cloud Run handles HTTPS, scaling, and infrastructure automatically. Two million requests free per month.
-
5
visual-explainer
Agent skill + prompt templates that generate rich HTML pages
...The project includes prompt templates and automation logic that enable coding agents to generate visual summaries such as diff reviews, architecture overviews, plan audits, and structured data tables. Its primary goal is to bridge the readability gap between raw machine output and stakeholder-friendly documentation. By producing styled web pages instead of plain text logs, visual-explainer improves communication in engineering and AI workflows where clarity is critical. The tool is particularly useful in environments that rely on autonomous agents or CI pipelines that generate dense technical output. -
6
Diffgram
Training data (data labeling, annotation, workflow) for all data types
...Training Data is the art of supervising machines through data. This includes the activities of annotation, which produces structured data; ready to be consumed by a machine learning model. Annotation is required because raw media is considered to be unstructured and not usable without it. That’s why training data is required for many modern machine learning use cases including computer vision, natural language processing and speech recognition. -
7
CxxWrap
Package to make C++ libraries available in Julia
...This dynamic library is then loaded into Julia, where the Julia part of this package uses the data provided through a C interface to generate functions accessible from Julia. The functions are passed to Julia either as raw function pointers (for regular C++ functions that don't need argument or return type conversion) or std::functions (for lambda expressions and automatic conversion of arguments and return types). The Julia side of this package wraps all this into Julia methods automatically. -
8
F1 Race Replay
An interactive Formula 1 race visualisation and data analysis tool
...Users can scrub through time, jump between cars, and overlay performance graphs such as speed, sector times, and gap differentials to evaluate performance trends across laps. This deep dive capability turns passive viewing into active exploration, empowering enthusiasts and professionals to discover insights usually hidden in raw data. The viewer also supports annotations and bookmark capabilities so users can mark moments of interest for future review or comparison. -
9
ggstatsplot
Enhancing {ggplot2} plots with statistical analysis
{ggstatsplot} is an extension of {ggplot2} package for creating graphics with details from statistical tests included in the information-rich plots themselves. In a typical exploratory data analysis workflow, data visualization and statistical modeling are two different phases: visualization informs modeling, and modeling in its turn can suggest a different visualization method, and so on and so forth. Bayesian hypothesis-testing. The central idea of {ggstatsplot} is simple: combine these... -
Gemini 3 and 200+ AI Models on One PlatformBuild generative AI apps with Vertex AI. Switch between models without switching platforms.
-
10
AutoGluon
AutoGluon: AutoML for Image, Text, and Tabular Data
AutoGluon enables easy-to-use and easy-to-extend AutoML with a focus on automated stack ensembling, deep learning, and real-world applications spanning image, text, and tabular data. Intended for both ML beginners and experts, AutoGluon enables you to quickly prototype deep learning and classical ML solutions for your raw data with a few lines of code. Automatically utilize state-of-the-art techniques (where appropriate) without expert knowledge. Leverage automatic hyperparameter tuning, model selection/ensembling, architecture search, and data processing. Easily improve/tune your bespoke models and data pipelines, or customize AutoGluon for your use-case. ... -
11
Fondant
Production-ready data processing made easy and shareable
Fondant is a modular, pipeline-based framework designed to simplify the preparation of large-scale datasets for training machine learning models, especially foundation models. It offers an end-to-end system for ingesting raw data, applying transformations, filtering, and formatting outputs—all while remaining scalable and traceable. Fondant is designed with reproducibility in mind and supports containerized steps using Docker, making it easy to share and reuse data processing components. It’s built for use in research and production, empowering data scientists to streamline dataset curation and preprocessing workflows efficiently. -
12
Timesketch
Collaborative forensic timeline analysis
...Powerful search, aggregations, and saved views help you pivot quickly, highlight anomalies, and preserve investigative steps for later review. The system supports tagging, sketch notes, and story building so teams can annotate findings and share context without losing the raw data trail. Integrations with popular DFIR pipelines make ingestion repeatable, while role-based access and audit logs support enterprise workflows. By combining scale, collaboration, and reproducibility, Timesketch moves incident response beyond ad-hoc spreadsheets to a durable, team-oriented investigation record. -
13
Automated Tool for Optimized Modelling
Automated Tool for Optimized Modelling
...On the other hand, using multiple notebooks makes it harder to compare the results and to keep an overview. On top of that, refactoring the code for every test can be quite time-consuming. How many times have you conducted the same action to pre-process a raw dataset? How many times have you copy-and-pasted code from an old repository to re-use it in a new use case? ATOM is here to help solve these common issues. The package acts as a wrapper of the whole machine learning pipeline, helping the data scientist to rapidly find a good model for his problem. -
14
Ridgepole
Ridgepole is a tool to manage DB schema. It defines DB schema
Ridgepole is a database schema management tool that treats your schema as code by expressing it in a Ruby DSL and applying diffs to keep databases in sync. You describe the desired state in a “Schemafile” (tables, columns, indexes, constraints), and Ridgepole compares it with the live database to generate only the necessary changes. This diff-and-apply approach makes schema changes repeatable and reviewable, avoiding hand-written migrations for routine structural edits. It supports multiple... -
15
mzMatch is a Java collection of small commandline tools specific for metabolomics MS data analysis. The tools are built on top of the PeakML core library, providing mass spectrometry specific functionality and access to the PeakML file format.
-
16
Orbit
ORBIT : Operating Business Intelligence Tool
ORBIT : Operating Business Intelligence Tool Making Data Accessible Through Centralized Database Access ORBIT is a business intelligence tool designed to make data accessible to a broad audience within your company by centralizing access to databases. With this application, users can easily create reports, perform interactive analyses, and extract insights from raw data. The application simplifies data handling by providing easy-to-use features for non-technical users while maintaining flexibility for advanced queries. -
17
Feathr
A scalable, unified data and AI engineering platform for enterprise
Feathr is a data and AI engineering platform that is widely used in production at LinkedIn for many years and was open sourced in 2022. It is currently a project under LF AI & Data Foundation. Define data and feature transformations based on raw data sources (batch and streaming) using Pythonic APIs. Register transformations by names and get transformed data(features) for various use cases including AI modeling, compliance, go-to-market and more. Share transformations and data(features) across team and company. Feathr is particularly useful in AI modeling where it automatically computes your feature transformations and joins them to your training data, using point-in-time-correct semantics to avoid data leakage, and supports materializing and deploying your features for use online in production. -
18
HiPlot
HiPlot makes understanding high dimensional data easy
...Because it renders as self-contained HTML, you can embed the visualization in notebooks, export it, or serve it as a lightweight web app for teammates. HiPlot also offers summary statistics, correlation hints, and outlier highlighting to surface patterns that aren’t obvious from raw tables. -
19
Luminance HDR
Complete solution for HDR photography
...Its graphical user interface, based on Qt5, runs on a variety of platforms, such as Microsoft Windows, Mac OS X 10.9 and later and several Unix flavors (Linux, FreeBSD and others). Input images can be supplied in multiple formats, from JPEG to RAW files. In the same way, output can be saved in many different formats as well, from JPEG to TIFF (both 8 bit and 16 bit per channel), enabling all the power of your post processing tools. -
20
ImagingAnalysis
Direct tissue-level image quantification package for Mathematica
ImagingAnalysis is a Mathematica package that performs grid-based analysis of time-lapse imaging data saved in a sequence of TIFF files. This package requires Mathematica 7.0. Revised on 14 May 2017: Bugs are fixed and incompatibility issues are resolved. The current version runs on Mathematica 11. -
21
Krona
Hierarchical data browser
Krona allows hierarchical data to be explored with zoomable HTML5 pie charts. Krona charts can be created using an Excel template or Krona Tools, which includes support for several bioinformatics tools and raw data formats. -
22
Zika-RNAseq-Pipeline
An open RNA-Seq data analysis pipeline tutorial
RNA-seq analysis is becoming a standard method for global gene expression profiling. However, open and standard pipelines to perform RNA-seq analysis by non-experts remain challenging due to the large size of the raw data files and the hardware requirements for running the alignment step. Here we introduce a reproducible open source RNA-seq pipeline delivered as an IPython notebook and a Docker image. The pipeline uses state-of-the-art tools and can run on various platforms with minimal configuration overhead. The pipeline enables the extraction of knowledge from typical RNA-seq studies by generating interactive principal component analysis (PCA) and hierarchical clustering (HC) plots, performing enrichment analyses against over 90 gene set libraries, and obtaining lists of small molecules that are predicted to either mimic or reverse the observed changes in mRNA expression. -
23
Py3DN
Morphometric analysis and visualization of the 3D structure of neurons
...Above all, it provides a flexible environment where new types of analyses can be easily set up allowing total freedom to test new hypotheses. The application was developed in Python and uses Blender (an open source program) to produce detailed 3D representations of raw and processed data. Py3DN is described in the paper: Aguiar P et al, "Versatile morphometric analysis and visualization of the three-dimensional structure of neurons", Neuroinformatics, 2013, 11(4):393-403 doi: 10.1007/s12021-013-9188-z -
24
QuickNXS
Polarized ToF reflectivity raw data analysis tool
Data evaluation tool for the magnetism reflectometer at the spallation neutron source (BL-4A@SNS). Reads raw nexus files (HDF5) of histogrammed or event mode data to create reflectivity curves and 2D Q-maps. -
25
OVATION Prime
Seasonal model of the diffuse, monoenergetic, proton, and wave aurora.
...Documentation includes a model description and instructions for running the software. The README is available here: http://sourceforge.net/p/ovation-prime/code/HEAD/tree/trunk/README.pdf?format=raw A web interface for running the model for historical periods is available here: http://sd-www.jhuapl.edu/Aurora/ovation_prime/prime_display.html Realtime versions are running here: http://www.ngdc.noaa.gov/stp/ovation_prime/ http://www.swpc.noaa.gov/ovation/
