Open and reproducible science: from theory to equations, algorithms and plots [* Cross-Reference *]

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Stanislaus J. Schymanski; Jiří Kunčar, 2026, "Open and reproducible science: from theory to equations, algorithms and plots [* Cross-Reference *]", https://lore.list.lu/citation?persistentId=perma:LIST.NDTYL6, LIST Open Repository, V1
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<p>Scientific theory is commonly formulated in the form of mathematical equations and new theory is often derived from a set of pre-existing equations. Most of us have experienced difficulty in following mathematical derivations in scientific publications and even more so their transfer into numerical algorithms that eventually result in quantitative tests and data plots. The Python package Environmental Science using Symbolic Math (ESSM, https://github.com/environmentalscience/essm) offers an open and transparent way to (a) verify derivations in the literature, (b) ensure dimensional consistency of the equations, (c) perform symbolic derivations, and (d) transfer mathematical equations into numerical code, perform computations and (e) generate plots.</p><p>Here we present an example workflow using jupyter notebooks illustrating the capabilities of the package from (a) to (e), including recently added advanced features.</p> (2020-03-09)
***This entry has been automatically imported via OpenAlex by LIST harvest scripts. Please refer to https://doi.org/10.5194/egusphere-egu2020-9177 for the original and latest version of the publication*** (2026-07-01)
Subject Computer and Information Science; Mathematical Sciences; Physics; Social Sciences
Keyword Python (programming language), Workflow, Computer science, Consistency (knowledge bases), Computation, Mathematical theory, Code (set theory), Set (abstract data type), Applied mathematics, Algorithm, Mathematics, Algebra over a field, Theoretical computer science, Programming language, Database, Pure mathematics, Artificial intelligence
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