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.

Reproducibility: A tragedy of errors

Mistakes in peer-reviewed papers are easy to find but hard to fix, report David B. Allison and colleagues: "In the course of assembling weekly lists of articles in our field, we began noticing more peer-reviewed articles containing what we call substantial or invalidating errors. These involve factual mistakes or veer substantially from clearly accepted procedures in ways that, if corrected, might alter a paper's conclusions."

GBSI Doubles Down on Research Reproducibility at Annual BioPolicy Summit and Webcast in Washington, DC, February 9th

The Summit will also introduce GBSI’s Reproducibility2020, an action plan for the biomedical research community to significantly improve the quality of research by 2020 targeting: 1) improved validation and standardization of biological reagents; 2) better tools and technologies to expand open access for reporting and sharing protocols and data; and 3) increased training that emphasizes rigorous study design and practice.

Reproducibility from a Mostly Selfish Point of View

A talk given by Noam Ross: "Why was, as the title suggests, primarily focused on the benefits of reproducibility to us, and I proceeded from avoiding negatives (risk avoidance) to creating positives (more impact). In How I tried to be very high-level, talking about major concepts in reproducibility, and then talking generally about the tools that I have used for each, emphasizing that they may not be the right tools for everyone. Then we had a discussion about the most promising areas and tools to start with."

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.