7 papers · ranked by Valyu relevance
Samantha Durdy, Cameron J. Hargreaves, Mark Dennison, Benjamin Wagg + 5 more
The discovery of new materials often requires collaboration between experimental and computational chemists. Web based platforms allow more flexibility in this collaboration by giving access to computational tools without the need for access to computational researchers. We present Liverpool Materials Discovery Server…
Eric Deutsch, Luis Mendoza, David Shteynberg, Michael Hoopmann + 3 more
The Trans-Proteomic Pipeline mass spectrometry data analysis suite has been in continual development and refinement since its first tools PeptideProphet and ProteinProphet were published twenty years ago. The current release provides a large complement of tools for spectrum processing, spectrum searching, search…
Rebecca Brunk, Kriti Shukla, Bryant Hutson, Yue Wang + 7 more
Genomic sequencing and other big biological data is unquestionably of paramount value, however the success in recruiting highly skilled individuals with diverse backgrounds has been limited. A main reason for this deficiency could be due to the lack of educational resources and early exposure to the field. With the…
Kamal Choudhary
SLMat is a serverless, browser-based toolkit that revolutionizes computational materials science by offering a scalable and efficient alternative to traditional server-based platforms like Google Colab. By eliminating the need for server management and providing persistent setups, SLMat enhances productivity and…
Authors not listed
Automation of experiments in cloud laboratories promises to revolutionize scientific research by enabling remote experimentation and improving reproducibility. However, maintaining quality control without constant human oversight remains a critical challenge. Here, we present a novel machine learning framework for…
Raphaël Robidas, Claude Legault
Computational chemistry is an increasingly active field due to the improvement of computing resources and theoretical tools. However, its use remains usually limited to technically-inclined users due to the technical challenges of preparing, launching and analyzing calculations. In this context, we have developed…
Authors not listed
Machine learning models are transforming data-driven research across scientific disciplines, yet their deployment as accessible and reliable web services remains a significant challenge. We introduce the NERDD framework, a scalable, maintainable, and secure microservices platform designed to support the sustainable…