CloudLIMS @ISBER 2018 Annual Meeting & Exhibits, Dallas, USA
CloudLIMS, a bronze sponsor of ISBER 2018 Annual Meeting & Exhibits, is excited to exhibit at the annual meeting, to be held in Hyatt Regency Dallas Hotel, Texas, United States, from May 20-24, 2018. This year, we are showcasing our Laboratory Information Management System (LIMS), CloudLIMS, at booth 105.…
Important Factors in LIMS Selection
The basic function of a LIMS is to track samples and store sample workflow related information from the time it enters a laboratory until it is disposed. In this blog, we highlight some important factors to be considered while evaluating a LIMS solution for your laboratory.Laboratory Informatics Weekly Update – Volume 16, Issue 19
May 9, 2018 Laboratory Informatics Weekly Update Volume 16, Issue 19 In This Issue Upcoming Webinars 05/15/2018 - Webinar: Roundtable on Open Source and Open Standards in Imaging Informatics05/23/2018 - Webinar: Lab Informatics Project Leadership: Doing the right things05/24/2018 - Webinar: A Guide for Laboratory Systems Management, Part 6: Instrument Data Systems Journal Articles Unmet needs for analyzing biological big data: A survey of 704 NSF principal investigatorsIn this brief education article published in PLOS Computational Biology, Barone et al. present the results of a survey of funded National Science Foundation (NSF) Biological Sciences Directorate principal investigators and how/if…Unmet needs for analyzing biological big data: A survey of 704 NSF principal investigators
In this brief education article published in PLOS Computational Biology, Barone et al. present the results of a survey of funded National Science Foundation (NSF) Biological Sciences Directorate principal investigators and how/if their computational needs were being met. Citing several other past surveys and reports, the authors describe the state of cyberinfrastructure needs as they understood them before their survey. Then they present their results. "Training on integration of multiple data types (89%), on data management and metadata (78%), and on scaling analysis to cloud/HPC (71%) were the three greatest unmet needs," they conclude, also noting that while hardware isn't a bottleneck, a "growing gap between the accumulation of big data and researchers’ knowledge about how to use it effectively" is concerning.
Building on Success and Aiming for the Clouds
We want to share a number of breakthrough developments that have kept us busy in the last months, in parallel with the hard work on the release of our next SLIMS version: SLIMS Cloud and the SLIMS Store.21 CFR Part 11: 20 Years and Counting
SampleManager LIMS™ Matrix Components
Evaluating Legacy LIMS Part 2: LIMS Migration
New Case Study From Autoscribe Informatics Highlights Use Of Matrix LIMS Over 20 Years
Laboratory Informatics Weekly Update – Volume 16, Issue 18
May 2, 2018 Laboratory Informatics Weekly Update Volume 16, Issue 18 In This Issue Upcoming Webinars 05/08/2018 - Webinar: Using AI and IoT to Accelerate Research05/15/2018 - Webinar: Roundtable on Open Source and Open Standards in Imaging Informatics05/23/2018 - Webinar: Lab Informatics Project Leadership: Doing the right things05/24/2018 - Webinar: A Guide for Laboratory Systems Management, Part 6: Instrument Data Systems Journal Articles Big data as a driver for clinical decision support systems: A learning health systems perspectiveWhat happens when you combine clinical big data tools and data with clinical decision support systems (CDSS)? In this 2018 journal article…Big data as a driver for clinical decision support systems: A learning health systems perspective
What happens when you combine clinical big data tools and data with clinical decision support systems (CDSS)? In this 2018 journal article published in Frontiers in Digital Humanities, Dagliati et al. report two such effective implementations affecting diabetes and arrhythmogenic disease research. Through the lens of the "learning healthcare system cycle," the authors walk through the benefits of big data tools to clinical decision support and then provide their examples of live use. They conclude that through the use of big data and CDDS, "when information is properly organized and displayed, it may highlight clinical patterns not previously considered ... [which] generates new reasoning cycles where explanatory assumptions can be formed and evaluated."









