Preview of Pittcon 2016 LIMS & Informatics Events
News Feb 17, 2016
The Pittsburgh Conference, aka Pittcon, is the premier conference and exposition on laboratory equipment and chemical analyses in North America. This year it returns to the Georgia World Congress Center in Atlanta, GA from March 6-10.
As always, there are LIMS and Informatics symposia, contributed sessions, short courses, and workshops to bring you up to date on the latest in LIMS, informatics, laboratory automation and laboratory regulations spread throughout the week. The information below also includes details about lab management activities that involve informatics solutions, from laboratory safety to laboratory improvement projects.
There are several short courses of particular interest that address LIMS this year. These include Siri Segalstad’s one-day short course on LIMS on Monday, March 7; Kurt Robak’s half-day session covering Laboratory Workflow Reengineering for a LIMS or ELN Implementation on Tuesday, March 8; and, Howard Rosenberg’s one-day course on LIMS and ELN: How to Select, Plan and Implement the Right Software Solutions for Your Laboratory on Tuesday, March 8.
To assist you in making the rounds of LIMS and Informatics events and exhibitors, this article highlights the current details.
At a Glance: LIMS and Laboratory Informatics Sessions
- R&D to QC: Bridging the Gap (Sunday Organized Contributed Session)
- Laboratory Safety (Sunday Conferee Networking Session)
- Data Analysis and Manipulation (Monday Oral Session)
- Scientific Management in a Service Oriented World (Monday Conferee Networking Session)
- LIMSLive@Pittcon: Best Practices & Lessons Learned from the Lab (Tuesday Contributed Session)
- Choosing the Best Laboratory Improvement Project (Tuesday Conferee Networking Session)
- LIMS – No One Size Fits All (Wednesday Oral Session)
- Analytical Information Markup Language (AnIML) Data Standards (Wednesday Workshop)
• Big Data in Analytical Sciences: Challenges & Solutions (Wednesday Symposia)
- Bioinformatics: Metabolite Identification and Quantification (Thursday Symposia)
SUNDAY, MARCH 6
R&D to QC: Bridging the Gap, Organized Contributed Session, Room B312, 1:30-3:00
Organizer: Justin Shearer, Dow AgroSciences
This session will chronicle some of the considerations that many research and development scientists utilize when developing methods that will transfer to quality control laboratories in the agricultural chemistry and other regulated industries. Perspectives from Research and Development and Quality Control will be discussed.
Laboratory Safety, Conferee Networking Session, Room A407, 1:30-3:00
Organizer: James Kaufman, Laboratory Safety Institute
The laboratory safety networking session will provide an opportunity for interested participants to share ideas and discuss current topics in lab safety. The topics will include: (1) How to create more effective lab safety programs, (2) how to comply with laboratory regulations, (2) how to convince others that lab safety is important and that you are serious about it, and (4) How to prepare for laboratory emergencies. The emphasis will be placed on the simple and inexpensive things on can do (without a purchase order or requisition).
MONDAY, MARCH 7
Data Analysis and Manipulation, Oral Session, Room B301, 8:30-10:40
Eight sessions cover a range of data analysis topics from “Quantitative Evaluation of Spectral Interferences in Atomic Emission Spectroscopy” to “Lifecycle of Analytical Methods: Development of Equivalent Dissolution Methods for Immediate-release Oral Dosage Forms Post-approval.”
Scientific Management in a Service Oriented World, Conferee Networking Session, Room A408, 3:30-5:00
Clean HTML - AAA + Case
Remove tag attributes
Remove inline styles
Remove classes and IDs
Remove all tags
Remove successive s
Remove empty tags
Remove tags with one
Remove span tags
Replace table tags with structured
Encode special characters
Set new lines and text indents
With machine learning systems now being used to determine everything from stock prices to medical diagnoses, it's never been more important to look at how they arrive at decisions. A new approach out of MIT demonstrates that the main culprit is not just the algorithms themselves, but how the data itself is collected.