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Loading contentAstronomy has become a data-intensive science. Behind every survey result is a stack of open-source software, elastic computing, and shared data infrastructure — the scientific Python ecosystem, cloud science platforms, and the Virtual Observatory that let a researcher find, access, and analyse petabytes of data without ever leaving the browser.
The open-source tools astronomers compute with — the scientific Python ecosystem, Astropy and SunPy, Jupyter notebooks, Astroquery, scientific visualisation, and the discipline of research software engineering.
6 entriesThe machines and platforms that do the heavy lifting — high-performance and GPU computing, cloud and distributed computing, the science platforms that bring analysis to the data, and containerised reproducible environments.
6 entriesThe ideas that organise data-intensive astronomy — scientific workflows, data provenance, the astronomical query languages, big-data astronomy in the petabyte era, and the integrated virtual research environment.
6 entriesThe community-developed core package for astronomy in Python, providing the shared building blocks the field relies on: physical units and constants, celestial coordinate transformations, time scales, cosmological calculations, and reading and writing of FITS and other data formats. A large ecosystem of affiliated packages builds on its foundation.
An Astropy-affiliated package that lets researchers query the major astronomical archives and databases directly from code. Instead of clicking through web forms, a scientist can pull catalogues and images from MAST, VizieR, SIMBAD, and dozens of other services programmatically, making analyses scriptable and repeatable.
Interactive documents that weave together live code, results, plots, and narrative text in one place. Notebooks have become the default medium for exploratory analysis, teaching, and sharing reproducible research in astronomy, and they sit at the heart of the cloud-based science platforms now serving the largest surveys.
The tools and craft of turning data into images that reveal structure — from the ubiquitous Matplotlib plots to interactive and three-dimensional renderings of simulations and surveys. Visualisation is both how astronomers explore their data and how they communicate discoveries.
The community package for solar physics in Python, the solar counterpart to Astropy. It provides the data structures, coordinate frames, and access to solar observatory archives that let researchers analyse images and time series of the Sun within the same open scientific-Python ecosystem.
The open-source foundation of modern astronomical computing: the Python language together with NumPy for array mathematics, SciPy for scientific algorithms, and pandas for tabular data. Free and community-built, it has become the common language in which most astronomical analysis is now written.
Each software package, computing infrastructure, and astroinformatics concept is a first-class knowledge-graph entity resolved through the Scientific Data Engine, reusing the Virtual Observatory, the TAP protocol, the FITS standard, the archives, the open-science practices, the machine-learning methods, and the Rubin, LSST, SKA and Gaia facilities already in the graph. Curated from NASA, STScI, and NOIRLab. Only well-established practice is stated. See source quality.