Cellara, LLC, releases CultureTrax, Productivity Software Designed for the Unique Complexities of Stem Cell Research

Productivity software enables tracking, sharing and collaborating around stem cell research to accelerate scientific discoveries and breakthroughs.

Madison, WI, June 3, 2019 — Cellara, LLC, a software-as-a-service company, announced today the commercial release of CultureTrax software V2.0. It is the first productivity software designed to meet the unique needs of stem cell scientists.

Controlled Study Measures the Impact of Software on Reproducing Stem Cell Protocols

Controlled Study Measures the Impact of Software on Reproducing Stem Cell Protocols

In this NIH-sponsored study, 27 skill-matched scientists performed a protocol they had not previously seen. The results underscore the challenges involved in translating a published protocol into a plan with enough detail and accuracy to be successfully executed and highlight that software specifically designed to support stem cell culture shows promise for improving reproducibility.

Independent Study Concludes Software Improves the Transfer and Reproducibility of Cell Culture Methods

Independent Study Concludes Software Improves the Transfer and Reproducibility of Cell Culture Methods

To improve reproducibility between researchers and increase laboratory efficiency, we examined a cloud-based software application, CultureTrax ®. Through direct access via a laboratory tablet, we performed a 14-day cardiomyocyte differentiation using a digital standard that was shared electronically.

The software establishes a novel method of experimental transfer and execution by providing daily instructions and recipes for complex cell culture protocols.

Support Discovery through Good Data Management: the FAIR Principles

Support Discovery through Good Data Management: the FAIR Principles
Support Discovery through Good Data Management: the FAIR Principles

Support Discovery through Good Data Management: the FAIR Principles

Article Introductory Summary:

In 2018 the NIH published a Strategic Plan for Data Science, which endorses this concept of the “FAIR” principles, an initiative focused on overcoming obstacles for data discovery and research reuse, which states that data should be Findable, Accessible, Interoperable and Reusable both for machines and people.

The Economics of Reproducibility in Preclinical Research

The Economics of Reproducibility in Preclinical Research

Article Introductory Summary:

Low reproducibility rates within life science research undermine cumulative knowledge production and contribute to both delays and costs of therapeutic drug development. An analysis of past studies indicates that the cumulative prevalence of irreproducible preclinical research exceeds 50% in the United States alone. This article discusses the main causes of low reproducibility: study design, biological reagents and reference materials, laboratory protocols, and data analysis and reporting and outlines a framework for solutions and a plan for long-term improvements in reproducibility rates that will help to accelerate the discovery of life-saving therapies and cures.

Never Waste a Good Crisis: Confronting Reproducibility in Translational Research

Never Waste a Good Crisis: Confronting Reproducibility in Translational Research

Article Introductory Summary:

The lack of reproducibility of preclinical experimentation has implications for sustaining trust in and ensuring the viability and funding of the academic research enterprise. Here problematic behaviors and practices are identified, and solutions are suggested to enhance reproducibility in translational research.