Open Source JavaScript Data Management Systems for Mac

Over 1,000,000 page views per month. Jmol/JSmol is a molecular viewer for 3D chemical structures that runs in four independent modes: an HTML5-only web application utilizing jQuery, a Java applet, a stand-alone Java program (Jmol.jar), and a "headless" server-side component (JmolData.jar). Jmol can read many file types, including PDB, CIF, SDF, MOL, PyMOL PSE files, and Spartan files, as well as output from Gaussian, GAMESS, MOPAC, VASP, CRYSTAL, CASTEP, QuantumEspresso, VMD, and many other quantum chemistry programs. Files can be transferred directly from several databases, including RCSB, EDS, NCI, PubChem, and MaterialsProject. Multiple files can be loaded and compared. A rich scripting language and a well-developed web API allow easy customization of the user interface. Features include interactive animation and linear morphing. Jmol interfaces well with JSpecView for spectroscopy, JSME for 2D->3D conversion, POV-Ray for images, and CAD programs for 3D printing (VRML export).

Command Line by Jeroen Janssens, published by O’Reilly Media in October 2021. Obtain, scrub, explore, and model data with Unix Power Tools. This repository contains the full text, data, and scripts used in the second edition of the book Data Science at the Command Line by Jeroen Janssens. This thoroughly revised guide demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist. You’ll learn how to combine small yet powerful command-line tools to quickly obtain, scrub, explore, and model your data. To get you started, author Jeroen Janssens provides a Docker image packed with over 100 Unix power tools, useful whether you work with Windows, macOS, or Linux. You’ll quickly discover why the command line is an agile, scalable, and extensible technology. Even if you’re comfortable processing data with Python or R, you’ll learn how to greatly improve your data science workflow by leveraging the command line’s power.

The Data Science Specialization Courses repository is a collection of materials that support the Johns Hopkins University Data Science Specialization on Coursera. It contains the source code and resources used throughout the specialization’s courses, covering a broad range of data science concepts and techniques. The repository is designed as a shared space for code examples, datasets, and instructional materials, helping learners follow along with lectures and assignments. It spans essential topics such as R programming, data cleaning, exploratory data analysis, statistical inference, regression models, machine learning, and practical data science projects. By providing centralized resources, the repo makes it easier for students to practice concepts and replicate examples from the curriculum. It also offers a structured view of how multiple disciplines—programming, statistics, and applied data analysis—come together in a professional workflow.

Webbased groupware written in php,mysql. Supports log,events,project management, calendar, todolist, contact management, cvs (file management). Next version includes a rss feed

A derivative of the project 'learning patterns for the design and depolyment of mathematical games' (http://lp.noe-kaleidoscope.org/), this is an on-line, html (AJAX) based editor & browser for FreeMind (http://freemind.sourceforge.net/) mindmaps.

A viewer for large user models. VlUM visualises an RDF description of a user model graph.