Search Results for "data modeling" - Page 11
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Showing 471 open source projects for "data modeling"
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openLCA
professional open source software for LCA
Software for sustainability assessment, highly modular; initially focused on Life Cycle Assessments. -
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Java Games and Graphics is meant to be a fun project. The goal here is to have fun with JAVA graphics and JAVA games. There is no specific goal as to what kind of graphics or games.This project features an RPG Role Playing, MUD, and Turn Based Combat gam
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Wind Path Visualization tool (WPV) is a simple model for tracking wind trajectories using measurement data (wind direction, wind speed, gas concentration) at a actual location. It has the following features.
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RoboSat
Semantic segmentation on aerial and satellite imagery
RoboSat is an end-to-end pipeline written in Python 3 for feature extraction from aerial and satellite imagery. Features can be anything visually distinguishable in the imagery for example: buildings, parking lots, roads, or cars. -
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GEOMS2
Geostatistics and geosciences modeling software
GEOMS2 is a geostatistics and geosciences modeling software. Provides interface for grid (mesh), point, surface and data (non-spatial) objects. It has a 3D viewer and 2D plots using the well known Python engines Mayavi and Matplotlib. It has several functions to manipulate your data as well as provide univariate and multivariate analysis. NOTE: The software is still an early beta. -
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Machine Learning Mindmap
A mindmap summarising Machine Learning concepts
...The project organizes a wide range of machine learning topics into an interconnected diagram that helps learners understand how concepts relate to one another across the broader field of artificial intelligence. The mind map covers fundamental areas such as data preprocessing, statistical analysis, supervised learning, unsupervised learning, reinforcement learning, and deep learning architectures. By arranging these concepts visually, the repository allows students and practitioners to quickly explore the relationships between algorithms, techniques, and modeling approaches used in modern machine learning workflows. ... -
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TenorSpace.js
Neural network 3D visualization framework
TensorSpace is a neural network 3D visualization framework built using TensorFlow.js, Three.js and Tween.js. TensorSpace provides Keras-like APIs to build deep learning layers, load pre-trained models, and generate a 3D visualization in the browser. From TensorSpace, it is intuitive to learn what the model structure is, how the model is trained and how the model predicts the results based on the intermediate information. After preprocessing the model, TensorSpace supports the visualization... -
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data-science-ipython-notebooks
Data science Python notebooks: Deep learning
Data Science IPython Notebooks is a broad, curated set of Jupyter notebooks covering Python, data wrangling, visualization, machine learning, deep learning, and big data tools. It aims to be a practical map of the ecosystem, showing hands-on examples with libraries such as NumPy, pandas, matplotlib, scikit-learn, and others. Many notebooks introduce concepts step by step, then apply them to real datasets so readers can see techniques in action. Advanced sections touch on neural networks and... -
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DS-Take-Home
Solution to the book A Collection of Data Science Take-Home Challenge
...The problems cover a broad set of applied data science topics including conversion rate analysis, fraud detection, employee retention modeling, marketing campaign evaluation, and recommendation-style problems. -
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PyMOL Molecular Graphics System
PyMOL is an OpenGL based molecular visualization system
The Open-Source PyMOL repository has been moved to github: https://github.com/schrodinger/pymol-open-source We still use the pymol-users mailing list here on sourceforge. Please subscribe for community support: https://pymol.org/maillist (Note: SourceForge email newsletter and special offers are optional and can be unchecked) The PyMOL community wiki has its own home: https://pymolwiki.org/ -
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ATLAS_mPBPK
Modeling and Simulation of mPBPK models
ATLAS mPBPK is a MATLAb-based tool for modeling and Simulation of minimal Physiology Based Pharmacokinetic (mPBPK) models of small and large molecules. The tool enables the users to perform: i) PK data visualization, ii) simulation, iii) parameter optimization, and iv) local sensitivity analysis (SA) of mPBPK models in a simple and efficient manner. -
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Learn_Data_Science_in_3_Months
This is the Curriculum for "Learn Data Science in 3 Months"
This project lays out a 12-week plan to go from basics to a portfolio-ready understanding of data science. It breaks the journey into clear stages: Python fundamentals, data wrangling, visualization, statistics, machine learning, and end-to-end projects. The schedule mixes learning and doing, encouraging you to build small deliverables each week—like notebooks, dashboards, and model demos—to reinforce skills. It also includes suggestions for datasets and problem domains so you aren’t stuck... -
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tcomb
Type checking and DDD for JavaScript
...Its main value is bringing lightweight, expressive runtime type modeling to JavaScript applications. -
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apache spark data pipeline osDQ
osDQ dedicated to create apache spark based data pipeline using JSON
This is an offshoot project of open source data quality (osDQ) project https://sourceforge.net/projects/dataquality/ This sub project will create apache spark based data pipeline where JSON based metadata (file) will be used to run data processing , data pipeline , data quality and data preparation and data modeling features for big data. This uses java API of apache spark. -
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xeogl
A WebGL-based 3D engine for technical visualization
xeogl is an open source JavaScript library from xeolabs for 3D model visualization on WebGL. It gives you tools to create interactive 3D worlds in your browser. Load models from formats like glTF or OBJ, or generate them programmatically. Navigate them with the camera and script them with Javascript, to build compelling 3D presentations. Load multiple models. Isolate/move/emphasize objects. Camera navigation controls. Camera animations (flyto, follow, path etc). Emphasis effects (wireframe,... -
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CFM-ID
Competitive Fragmentation Modeling for Metabolite Identification
Windows binaries and cross-platform source code for processing of EI-MS (GC-MS) and ESI-MS/MS (LC-MS) data for identification of metabolites. Includes tools for applying Competitive Fragmentation Modeling (CFM) to spectrum prediction and metabolite identification tasks, as well as a tool for fragment generation, and another for peak annotation. For further documenation see https://sourceforge.net/p/cfm-id/wiki/Home/ For related publications, and their associated supplementary data, please see https://sourceforge.net/p/cfm-id/wiki/Home/#related-publications There is also a web server supporting much of this functionality at http://cfmid.wishartlab.com For CFM3.0, please see http://cfmid3.wishartlab.com/. ... -
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OpenSeq2Seq
Toolkit for efficient experimentation with Speech Recognition
...Its core goal is to give researchers a flexible, modular framework for building and training encoder–decoder architectures while fully leveraging distributed and mixed-precision training. The toolkit includes ready-made models for neural machine translation, automatic speech recognition, speech synthesis, language modeling, and additional NLP tasks such as sentiment analysis. It supports multi-GPU and multi-node data-parallel training, and integrates with Horovod to scale out across large GPU clusters. Mixed-precision support (float16) is optimized for NVIDIA Volta and Turing GPUs, allowing significant speedups and memory savings without sacrificing model quality. ... -
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Incanter
Clojure-based, R-like statistical computing and graphics environment
Incanter is a Clojure-based, R-like statistical computing and visualization library running on the JVM. It integrates core numerical libraries like Parallel Colt and JFreeChart to deliver data manipulation, modeling, statistical tests, and charting in a REPL-friendly environment. Start by visiting the Incanter website for an overview, check out the documentation page for a listing of HOW-TOs and examples, and then download either an Incanter executable or a pre-built version of the latest build of Incanter, which includes all the necessary dependencies, and unpack the file (if you would like to build it from source, read Building Incanter). ... -
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MatlabFunc
Matlab codes for feature learning
MatlabFunc is a collection of MATLAB functions developed by the ZJULearning group to support various tasks in computer vision, machine learning, and numerical computation. The repository brings together a wide range of utility scripts, algorithms, and implementations that serve as building blocks for research and development. These functions cover areas such as matrix operations, optimization, data processing, and visualization, making them broadly applicable across different research... -
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JS-Data
Give your data the treatment it deserves
JSData is a framework-agnostic, datastore-agnostic ORM for Node.js and the Browser. Adapters allow JSData to connect to various data sources such as Firebase, MySql, RethinkDB, MongoDB, localStorage, Redis, a REST API, etc. With JSData you can re-use your data modeling code between environments, keep your data layer intact when transitioning between app frameworks, and work with a unified data API on the server and the client. JSData employs conventions for rapid development, but allows for endless customization in order to meet your particular needs. -
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IVCon-TL 3D scene conversion tool
Converts between many 3D data formats
IVCon-TL can read and write a variety of 3D graphics file formats, converting from one to the other. Supported formats include 3DS, ASE, BYU, DXF, GMOD, HRC, IV, OBJ, OFF, POV, SMF, STL/STLA/STLB, TEC, TRI/TRIA/TRIB, UCD, VLA, WRL/VRML, XGL and more. -
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Convolutional Recurrent Neural Network
Convolutional Recurrent Neural Network (CRNN) for image-based sequence
Convolutional Recurrent Neural Network provides an implementation of the Convolutional Recurrent Neural Network (CRNN) architecture, a deep learning model designed for image-based sequence recognition tasks such as optical character recognition and scene text recognition. The architecture combines convolutional neural networks for extracting visual features from images with recurrent neural networks that model sequential dependencies in the extracted features. This hybrid approach allows the... -
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BioNet-Finder
Network-based modeling and analysis for complex biological data
BioNet-Finder is a network-based genomic data modeling project, supported by the Multivariate Statistics lab of the Brain and Behavioural Science Department at University of Pavia (Pavia, Italy), to share data, methods, and code for network-based analysis of complex disorders. Our goal is to provide straightforward tools for data reduction, modeling, and interpretation, avoiding common issues, including data selection, database compositional biases, topological/hierarchical organization of data, genomic complexity, latent effects on the observed system, and output ranking. ... -
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Edward
A probabilistic programming language in TensorFlow
A library for probabilistic modeling, inference, and criticism. Edward is a Python library for probabilistic modeling, inference, and criticism. It is a testbed for fast experimentation and research with probabilistic models, ranging from classical hierarchical models on small data sets to complex deep probabilistic models on large data sets. Edward fuses three fields, Bayesian statistics and machine learning, deep learning, and probabilistic programming. ... -
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DataScienceR
a curated list of R tutorials for Data Science, NLP
The DataScienceR repository is a curated collection of tutorials, sample code, and project templates for learning data science using the R programming language. It includes an assortment of exercises, sample datasets, and instructional code that cover the core steps of a data science project: data ingestion, cleaning, exploratory analysis, modeling, evaluation, and visualization. Many of the modules demonstrate best practices in R, such as using the tidyverse, R Markdown, modular scripting, and reproducible workflows. ...
