BorgVR vs NAIIVE

BorgVR brings large 3D volume datasets to life on Apple devices. The app is designed for research, education, medicine, engineering, and any field where complex spatial data needs to be explored, presented, or reviewed together. Open local or remote datasets, import new data, and adjust the visualization directly in the app. Depending on your task, you can switch between rendering modes, change isovalues, edit transfer functions, set clipping planes, and control the view with rotation, panning, and zoom. BorgVR supports live collaboration with SharePlay. Multiple users can explore the same dataset together, synchronize visualization settings, and run a shared analysis or presentation session. Interaction remains tailored to each platform: mobile on iPhone and iPad, desktop-style on Mac, or spatial with Apple Vision Pro. The app is built for large datasets and uses a progressive, memory-efficient volume rendering system. This allows extensive data to be explored while only the required parts are loaded and displayed. Datasets can be stored locally or streamed from a server. Features: - Interactive visualization of large 3D volume datasets - Open local and remote datasets - Import your own data - Multiple rendering modes, including transfer function and isovalue visualization - Transfer function editor - Clipping for focused inspection - SharePlay collaboration across supported Apple devices - Local storage and progressive loading BorgVR combines powerful 3D visualization with a familiar Apple experience, making complex volume data easier to access, understand, and explore together.

NAIIVE is an experimental scientific visualization and analysis tool designed for atomistic simulations. ## Scientific Format Support Native support for widely used atomistic simulation formats, including: • LAMMPS (Large-scale Atomic/Molecular Massively Parallel Simulator) Dump files (including .gz compressed trajectories) • VASP (Vienna Ab initio Simulation Package) POSCAR, OUTCAR, CHGCAR • GAMESS (General Atomic and Molecular Electronic Structure System) Input and output log files • Machine-learning force field and extended formats DeepMD (deepmd/npy, deepmd/raw) extended XYZ (extxyz)