Adopting Cloud-based Software for Biospecimen Management
CloudLIMS.com has announced that The University of Sheffield has adopted CloudLIMS Lite, for managing their biorepository operations—by replacing their legacy spreadsheets—for recording biospecimen data.
The University of Sheffield is a globally reputed university renowned for its world-leading research. It is also a world top-100 university, that strives to solve society’s greatest challenges through its excellence, impact, unique teaching and distinctiveness for its research-led learning. With the implementation of CloudLIMS Lite, the Sheffield Biorepository is able to track biospecimens, their corresponding storage locations and associated data with ease. Using the cloud-based software, they are able to cut costs on IT infrastructure and personnel. Furthermore, they are able to streamline collaboration between members of the biorepository and external institutions.
Steven Haynes, Core Facility Manager at Genomics Facility, Sheffield Biorepository said: "With CloudLIMS Lite, we are able to load our legacy data of 200,000 samples rapidly using the software's universal uploader. The uploader directly maps our existing data to the data fields in CloudLIMS Lite. The data fields and our laboratory workflows are easily configurable, and allows for a quick, cost effective deployment!"
Arun Apte, Chief Executive Officer of CloudLIMS said: “The University of Sheffield selected CloudLIMS Lite after a rigorous assessment of their needs and available solutions. We came in at a time when the biobank had already tried out another LIMS solution which was unable to meet their needs. We not only met their requirements, we also delivered additional functionality that they were looking for. We look forward to supporting The University of Sheffield's vision of delivering life-changing discoveries."
A novel encryption method devised by MIT researchers secures data used in online neural networks and blends two conventional techniques — homomorphic encryption and garbled circuits — in a way that helps the networks run orders of magnitude faster than they do with conventional approaches.READ MORE