"If replication is normal and routine, people wouldn’t see it as a threat," Brian Nosek, the executive director of the Center for Open Science which supports the Reproducibility Project, tellsScience News. "Publication is the currency of advancement, a key reward that turns into chances ...
We show that innocent changes to display settings such as brightness reduction can cause dangerously unexpected results. Understanding and reporting colorimetric data in scientic publications is important for two reasons: (1) reproducibility, and (2) model delity.VealeE. RichardKawashimaTakahikoOkada...
which makes them suitable as a long-term identifier of an individual. They also can be used to identify people who are deceased in the aftermath of a crime. The recovery of fingerprints from a scene is an important part of forensics in identifying a possible suspect. Blood is often found ...
our results evidenced the presence of animal welfarism in animal welfare science, a problem that seems not to be intrinsic to animal welfare science itself, but rather to the way research is frequently conceived, conducted, interpreted, summarized and applied. Therefore, it seems urgent to further...
is vital and underpins trust in science. however, many challenges exist in relation to reproducing research. at elsevier, we take our role as a steward of trust seriously; in this article, we explore reproducibility challenges and elsevier’s activities to address them. complex challenges in ...
the robust package and environment management system. Conda allows users to create isolated environments for their projects, each with its own set of dependencies and Python versions. This capability is invaluable for ensuring project reproducibility and avoiding conflicts between different projects’ requir...
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Yulia Marchenko (StataCorp) September 12, 2024 14 Professional statistical software development Professional statistical software development Reproducibility What is reproducibility? In science, we often think of reproducibility as the ability to obtain the same results repeatedly under the same conditions—...
What is Retrieval Augmented Generation, and why is the data you feed it so important? read more Large language models (LLMs) and search; it’s a FAIR game, read more Revolutionizing Life Sciences: The incredible impact of AI in Life Science [Part 1], read more Why use your ontology man...
4. Reproducibility: Achieving reproducible and consistent results in a machine learning workflow, given the same input, is a fundamental MLOps principle. It ensures that ML experiments and processes can be replicated reliably. 5. Deployment: Model deployment should be based on effective experiment tra...