Store your data as recomputable documents — so any published result can be re-run, verified and preserved.
A dedicated resource for Computational scientists, research software engineers, PhD students and lab groups in physics, chemistry and bioinformatics who run simulations and data analysis (molecular dynamics, NumPy/HDF5 pipelines) and need their published results to be recomputable and their data to stay usable for decades — plus the wider open-source scientific Python community..
From the blog
Archiving Data Services: A Practical Guide
Archiving data services are systems and providers that move research data from active storage to independently managed long-term storage with integrity che
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Data Science for Python: A Practical Guide
Data science for Python is the practice of using the Python scientific stack (NumPy, pandas, SciPy, Matplotlib, scikit-learn, and Jupyter) to load, clean,
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Best Data Archiving Tools Compared for Research (2026)
Data archiving involves moving data that is no longer actively used to a separate, less expensive storage tier where it is retained long-term for complianc
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Molecular Dynamics Repository: A Practical Guide
A Molecular Dynamics Repository is a version-controlled host for simulation code, input datasets, force field parameters, and analysis scripts, and the eco
Read more → NumPy Data Processing: A Practical Guide
NumPy data processing is the practice of loading, reshaping, cleaning, and reducing numerical arrays with the NumPy library, whose core ndarray object stor
Read more → NumPy for Data Scientists: A Practical Guide
NumPy for data scientists is the basic Python library for numerical computing: it provides the ndarray, a fixed-type contiguous block of memory that suppor
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