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drops.dagstuhl.de | ||
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www.nature.com
To make machine-learning analyses in the life sciences more computationally reproducible, we propose standards based on data, model and code publication, programming best practices and workflow automation. By meeting these standards, the community of researchers applying machine-learning methods in the life sciences can ensure that their analyses are worthy of trust. |
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blogs.egu.eu
Contemporary science faces many challenges in publishing results that are reproducible. This is due to increased usage of data and digital technologies as well as heightened demands for scholarly communication. These challenges have led to widespread calls for more research transparency, accessibility, and reproducibility from the science community. This article presents current findings and solutions to these problems, including recent new software that makes writing submission-ready manuscripts for jou... |
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richardzach.org
The challenge The authoring platform of choice in many math-heavy disciplines is LaTeX. It produces typeset documents of excellent quality and handles formulas and mathematical diagrams extremely w... |
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essd.copernicus.org
[AI summary] This scientific review article presents an update to the HYDE 3.2 database, providing historical anthropogenic land use and global population estimates from 10,000 BCE to 2015 CE. |
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