Discover Top Posts Tagged with #labdata

ETLCO’s oil laboratory services cover testing for crude, refined, and synthetic products. Our labs deliver real-time data to enhance performance, reduce risk, and meet global benchmarks. When quality matters, our results speak clearly. Access our testing solutions: https://etlco.com.sa

#OilTesting #CrudeToRefined #LabData #ETLCOOilLab #QualityDriven #GlobalBenchmarks

Density of Common Alcohols

Find the density values for different alcohols in both SI (g/cm³) and US (lb/ft³) units on this page.

Explore a comprehensive list of density values for common alcohols in both metric (g/cm³) and US customary (lb/ft³) units.

#AlcoholDensity #Chemistry #LabData #DataAnalysis #ChemicalEngineering #Research #ChemicalCompounds #DensityValues #AlcoholChemistry #OrganicChemistry

Dealing with Data

This fall I attended an event called “Lab Data to Machine Learning in 30 Seconds”, put on by Riffyn (@riffyn, https://riffyn.com/) and AWS, as part of the synbiobeta 2018 (@SynBioBeta) Synthetic Biology Week. It included a panel discussion lead by Riffyn’s CEO Tim Gardner and a panel of data science experts from Amyris, GSK, and Novartis (all three are listed on NASDAQ or NYSE). The focus of the panel was the rapid advance of machine learning and AI in biotech and pharmaceutical R&D; however, the discussion also ventured into other interesting areas like structuring and integration in data systems and the goals and challenges of dealing with data in the biotech industry.

In order to understand how data science had changed the directions and shaped the perspectives of the panelists, the panel explored some of the challenges that they were dealing with in their projects. Sometimes their challenges included acquiring a deeper grasp of where signals or noise came from in the data. Sometimes the challenges included strenuous efforts to 'wrangle’ and structure the data (possibly from thousands of data sources) in order to create new molecules and compounds. Most of the time, however, the challenges involved long and painful analysis of countless spreadsheets. It was also interesting to hear about the kind of goals the panelists had for the data. When data poured in quickly, the panelists wanted to find ways to motivate teams while minding timelines, and to define objectives and limits for the data gathering. Often though, the goals were centered around creating better data models (which are only as good as the data that is fed them); but there was also a longing for an easier system.

The panelists had learned a lot about working with data in their projects and about trying to automate data-related processes. For instance, sometimes the ‘secret sauce’ they were looking for was buried deep within the data, and could only be ‘discovered’ through painstaking data combing and company cross-learning. Sometimes to ensure that they had good data, they had to hire outside help. Sometimes there was excitement in joining data, which connected the scientists with the data’s impact. Other times, they had to mitigate the risk of using ‘biased’ data, since the way the data was trained could result in missed discoveries. In some cases, data that was valuable actually lived in the outliers, yet was still thrown away. Sifting through ‘garbage’ data had to be done in a large-scale automated way so that biased decisions would not be relied on.

They also talked in depth about the aspect of private data (known as “dark data”), which is the data that companies keep to themselves. But how can incentive be increased for companies to start to bring the different sources of data together? There was no simple answer for this! There is usually a lot of mistrust inside of the organizations themselves, and often, the data is not easily available or sharable, hidden away in numerous SharePoint drives and Excel sheets. Most of the time, there is simply too much ego and territoriality attached to the data to allow the data to be accessible by everyone. The scientists working on the data are not cross-training enough or learning about each others’ processes. The culture of sharing data and cross-company involvement definitely needs to change and it usually starts at the top of the organizations. One big thing that could help this happen faster is the development of standards for the data sharing process itself. Such standards have often driven industries to the next level by making it easier for different companies and products to work together; however, standards take a long time to develop because they require companies to ‘opt-in’ to them first.

In tuning into this panel, I also found that there was a genuine enjoyment from the panelists in working with data, especially when it led to discoveries. They even had names for themselves like “data detective” and “molecule artist”. When all of their data could be used, it was very gratifying, as was the ability to navigate the space of billions of predictions in their models. When their work lead to a big landmark finding, it was all worth it! After all, isn’t science all about making discoveries?

#labdata #data #science #data modeling

QNOPY empowers Project Managers with Chemistry Module for Laboratory Data

QNOPY empowers environmental Project Managers and scientists by releasing first of a kind chemistry module for managing environmental analytical data. Keeping up with the QNOPY’s philosophy, the chemistry module is offered as a SAAS service with little to no upfront costs.

It gives the project managers a simpler and cost effective option for managing their analytical data. The module offers the following:

Simplified Sample Planning

Generate COCs through the mobile App or the website

Link with Labs to automatically pull in the Analytical data

Instant access to the data from your phone or computer

View results and create time series charts

Apply screening criteria and generate report tables

Export data into EDD format to upload to another database system

You can upload all your historic data into the chemistry module and use the online QNOPY dashboards and tools to view and print the data.

Contact [email protected] to learn more or to set up a live demo.

#labdata #chemistry