Posts about reproducibility infrastructure (old posts, page 4)

Startup unveils tools to improve trial reproducibility

Elemental Machines, which develops smart laboratory technology, launched a new suite of tools that that measure environmental variables such as temperature and humidity—both of which are not traditionally accounted for in scientific experiments. By “debugging” the lab environment, the company believes it can improve experimental reproducibility, therefore reducing the time and cost of marketing new drugs and therapies. Elemental Machines recently raised $2.5 million in seed capital to support the development of the new suite of tools, which is called the EM Suite.

Misfit Founders Raise $2.5M to 'Debug the Physical World' With New Startup

Elemental Machines, a venture based in Boston and San Francisco, has come out of stealth mode. The startup says it's raised $2.5 million in seed from investors including Founders Fund’s FF Angel, PayPal co-founder Max Levchin and Project 11 Ventures. And now it’s ready to change the way our world does science, providing the infrastructure that will ensure experiment reproducibility for researchers.

ReproZip Poster Accepted at FORCE2016

Fernando Chirigati and Remi Rampin's poster "Enhancing Scholarly Communication with ReproZip" was recently accepted at FORCE2016, a conference from FORCE11 a community of scholars, librarians, archivists, publishers and research funders that has arisen organically to help facilitate the change toward improved knowledge creation and sharing.

New Shotgun Mass Spec Workflow Could Improve Reproducibility of Protein Quantitation in DDA

Researchers at Sweden's Karolinska Institute and Royal Institute of Technology have developed a new data analysis workflow for shotgun mass spec that could help improve the technique's quantitative reproducibility. Detailed in a paper published this month in Molecular & Cellular Proteomics, the approach uses a new quality scoring system that allows for more reliable recovery of missing data points across multiple mass spec runs.

noWorkflow Demo Video Released

A video demonstrating noWorkflow, a non-intrusive tool that allows researchers to capture a variety of provenance information and utilize the analyses it supports, including graph-based visualization, differencing over provenance trails, and inference queries.