Transform your data into breakthroughs

  • Capture:  all research data and metadata as it is generated
  • Aggregate:  data across your ecosystem from all teams and repositories
  • Explore:  semantically classified, linked data that provides the full context and provenance for every sample
  • Analyze:  data as it flows through the system to create real-time streams of information
  • Communicate:  raw data and analyses in the moment they will be most useful

The challenge of scientific data

The life science research industry is faced with an enormous data challenge. The cost of automation and sequencing has fallen 100x over the past decade, leading to explosive data growth. The pace of generation outstrips Moore's Law in some cases, and requires new approaches for collection, aggregation, and analysis.

Key barriers to utilizing rapidly growing data sets:

  • Discoverability
  • Access to data
  • Interoperability across systems
  • Ability to reuse data

Tooling that addresses these issues and allows effectively democratized access to research data and the information that results from analyzing that data is crucial. Such tools must enable FAIR data principles, which are geared toward enabling comprehensive data usage, in order for organizations to fully utilize internal data and to enrich it with public data sets.

Looking for help managing your data and implementing FAIR principles?

Is your organization ready for data that

costs

100x

less

flows

10,000x

faster

doubles

in 7

months

Why Snthesis?

Because scientists should focus generating and analysing data, not on finding and managing it.

Life science has historically lagged behind other fields in terms of technology adoption, particularly when it concerns the application of truly cutting edge tools to the management and usage of research. However, that historical paradigm is rapidly changing as an increasing number of organizations are realizing the incredible potential offered by applying the very latest in computational techniques to the cutting edge of biological science. Indeed, we are in the midst of a revolution, particularly when considering the growing commercial and defense impact of the life sciences and their intersection with massive data sets, modern data science, and machine learning.

That revolution is not evenly distributed, however, and the range of capitalization on the gains available from novel approaches to research tend to cluster around organizations with a deep understanding of science, software, and data science. However, each of those fields is growing so rapidly that it is nearly impossible for a single organization to stay abreast of all three domains. For incumbent companies seeking to remain at the forefront of their fields, there are few options in the marketplace for collaborative, relationship driven support for bringing the best in computation to bear on research problems.

Snthesis was founded precisely to address the significant needs arising from that fundamental gap. We build data aggregation, analysis, and information synthesis tools to empower revolutionary scientific research and the business operations necessary to sustain and capitalize on that research