How we can solve the problem of InfoObesity
Do you ever run into the problem where you have too much data? The term “InfoObesity” was coined to describe the avalanche of information and the dysfunctional environment created when:
- We have too much information
- Not enough tools to wrangle data into a usable format.
The benefit of query-able knowledge
Customers use Thermo Fisher™ Platform for Science™ software to collaborate, share insights, and generate query-able knowledge from structured data. Now, thanks to a new analytical engine and visualization tool Shiny, Platform for Science software customers can visually mine large data sets with the click of a button.
Get the most from structured data
Our newest Platform for Science software Shiny application can be used for data analysis including graphical mapping and visual data exploration. When combined with descriptive statistics, visualization provides a simple and effective way to summarize relationships, easily see differences, and detect outliers in data.
Seeing your data from 50,000 feet
Don’t forget the value in looking at the big picture. Treemapping is an extremely helpful way to visualize your data when a dataset is composed of many granular and disparate parts.
Look at the treemapping application below. You can easily probe the type and quantity of biologic entities displaying hierarchical data using nested rectangles. Thousands of samples can be organized and differentiated using color and size to model different sample attributes. This provides a simple and intelligent way to visualize the samples without one sample type masking another. This makes it easy to spot patterns and gain the insights you need to make intelligent decisions.
Interactive dashboards with a combination of charts, graphs, and infographics
Configurable on demand reporting options
New R Shiny applications in the Platform for Science
Thermo Fisher Scientific is an official RStudio reseller and we are creating a catalog of Shiny applications for Platform for Science software, designed to flexibly support the changing needs of modern laboratories, integrate data sources, and speed informatics deployments. Our existing apps cover a wide range of academic and biopharma use cases and we are enhancing these apps with data visualization and analytics to support macromolecule drug discovery, in vitro and in vivo data analysis, genomics and 3D sample rendering. These enhancements utilize the array of statistical techniques including supervised and unsupervised machine learning, logistic regression and multi-linear analysis provided by Shiny. For more information regarding our integration with RStudio and Shiny please read the blog, New Analytics Tools in the Platform for Science.