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#reproducible

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bffo

From #arXiv | From #FAIR to #CURE: Guidelines for Computational #Models of Biological #Systems | #OpenScience #FAIR #CURE #Credible #Understandable #Reproducible #Extensible #Biology #Bioinformatics 🧬 🖥️ 🧪 🔓
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arxiv.org/abs/2502.15597
arxiv.org/abs/2502.15597

arXiv.orgFrom FAIR to CURE: Guidelines for Computational Models of Biological SystemsGuidelines for managing scientific data have been established under the FAIR principles requiring that data be Findable, Accessible, Interoperable, and Reusable. In many scientific disciplines, especially computational biology, both data and models are key to progress. For this reason, and recognizing that such models are a very special type of 'data', we argue that computational models, especially mechanistic models prevalent in medicine, physiology and systems biology, deserve a complementary set of guidelines. We propose the CURE principles, emphasizing that models should be Credible, Understandable, Reproducible, and Extensible. We delve into each principle, discussing verification, validation, and uncertainty quantification for model credibility; the clarity of model descriptions and annotations for understandability; adherence to standards and open science practices for reproducibility; and the use of open standards and modular code for extensibility and reuse. We outline recommended and baseline requirements for each aspect of CURE, aiming to enhance the impact and trustworthiness of computational models, particularly in biomedical applications where credibility is paramount. Our perspective underscores the need for a more disciplined approach to modeling, aligning with emerging trends such as Digital Twins and emphasizing the importance of data and modeling standards for interoperability and reuse. Finally, we emphasize that given the non-trivial effort required to implement the guidelines, the community moves to automate as many of the guidelines as possible.
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Stefano Zacchiroli

This week I'm in #Montreal to present at #SANER2025 recent joint work with A. Gurioli and M. Gabrielli, on how to recognize AI-generated code. Key differences in our results wrt the state-of-the-art are: (1) a multilingual approach that supports 10 different programming languages, and (2) a fully #reproducible pipeline based on both #OpenWeight and #OpenData self-hosted LLMs. Talk info and #OpenAccess preprint at: conf.researchr.org/details/san

conf.researchr.orgIs This You, LLM? Recognizing AI-written Programs with Multilingual Code Stylometry (SANER 2025 - Research Papers) - SANER 2025The IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) is the premier event on the theory and practice of recovering information from existing software and systems. The event explores innovative methods to extract the many kinds of information that can be recovered from software, software engineering documents, and systems artifacts, and examines innovative ways of using this information in system renewal and program understanding. SANER promotes discussion and interaction among researchers and practitioners about the development of maintainable system ...
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futurile

The #guix survey also showed that our users are knowledgeable #Linux people. Almost 50% are experts/advanced and 47% are intermediate!

A lot of the attraction is the #nix value of declarative configuration and reproducibility. Users also identified that Scheme, Guile and Lisp are cool! Perhaps the overlap of two different communities! That was my path from #clojure and #ubuntu /#debian

See Q1 in the post:

guix.gnu.org/en/blog/2025/guix

Bar chart showing survey participants GNU/Linux knowledge. Most uses are experienced.
#scheme#emacs#guile
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