Posted on October 6, 2020
By Corinne Jones
Newsletter
October 6, 2020 Laboratory Informatics Weekly Update Volume 18, Issue 40 In This Issue Featured Books The Complete Guide to LIMS and Laboratory Informatics: 2020 Edition Featured Content 10/06/2020 - The True Value of Best-of-BreedBest-of-breed laboratory technology drives higher efficiency, productivity, and profitability compared to enterprise-wide system modules from vendors who do not maintain a targeted focus on only the lab. In this new video, learn how and why Sunquest’s wide-ranging technical and cross-discipline laboratory expertise make us the One delivering labs to new heights. [Learn More] 10/06/2020 - Running A Life Science R&D Business with Dr. Aaron Sato,…
Posted on October 1, 2020
By LabLynx
Journal articles
A growing trend in producing academic research is abiding by the FAIR principles, which state that produced research data be findable, accessible, interoperable, and re-usable. These, in theory, lend to the important concept of reproducibility. However, what about the software used to generate the data? Often that software is a home-grown solution, and the software and its developers are rarely cited in academic research. This does not lend to reproducibility. As such, researchers such as Davenport
et al. have written on the topic of improving research output reproducibility by addressing good software development and citation practices. In their 2020 paper published in
Data Science Journal, they present a brief essay on the topic, offering background and suggestions to researchers on how to improve research software development, use, and citation. They conclude that "[e]ncouraging the use of modern methods and professional training will improve the quality of research software" and by extension the reproducibility of research results themselves.
