Explore our Services

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Statistical consulting

  • Data analysis and statistics
  • Experiment design
  • Method development
  • Machine learning
  • Data and text mining
  • Artificial intelligence
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Scientific programming

  • R, Python, Julia package development
  • Code review and optimization
  • Porting code to low-level languages
  • Parallel and distributed computing
  • In-database data science
  • GPU / FPGA programming
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Application Development and Integration

  • Desktop and web applications
  • Automation of analyses or predictive modeling
  • Data science APIs
  • Scientific data stores
  • Big data architecture
  • Data science tooling
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Data Analysis Hardware and Hosting

  • Custom data analysis hardware
  • Data science compute infrastructure
  • Data analysis platforms hosting
  • Data science APIs as a service
  • Managed services for scientific data stores

Discover our Products

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Architect

  • IDE for data science, state of the art
  • Comfort and productivity for the R, Python and Julia developer
  • Support of low-level languages (C, C++, FORTRAN)
  • Server version for teams and HPC environments
  • Fully open source, including all enterprise features
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ShinyProxy

  • Shiny app deployment for companies and large organizations
  • Highly scalable design using Docker infrastructure
  • Authentication and authorization, single-sign on deployments
  • Usage statistics and administrator views
  • Fully open source, including all enterprise features
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R Service Bus

  • Middleware for automation of R-based jobs
  • Rich set of supported protocols (REST, SOAP, e-mail protocols, etc.)
  • Integrated management of multiple R pools for distributed computing
  • Synchronous and asynchronous APIs, admin API
  • Supports plain R scripts and packages out of the box
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RDepot

  • Corporate management of R package repositories
  • RESTful APIs for package submission and repository generation
  • Authentication and authorization for actions on multiple repositories
  • Support of continuous integration infrastructure
  • Highly available repository set-up and full audit trails

Case studies

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From our blog

ShinyProxy 1.1.1 released!

on May 28, 2018

ShinyProxy is a novel, open source platform to deploy Shiny apps for the enterprise or larger organizations. Theming ShinyProxy 1.1.1 is in essence a maintenance release, but there is one new feature that has been on the wish list of our users for a long time: the possibility of theming the landing page of ShinyProxy which displays the overview of the Shiny apps. The standard display when using the ShinyProxy demo image from the Getting Started guide is a plain listing:

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ShinyProxy 1.1.0 released!

on March 25, 2018

ShinyProxy is a novel, open source platform to deploy Shiny apps for the enterprise or larger organizations. Scalability In our previous release (see this blog post) we announced our focus on scalability with support for Docker Swarm back-ends. With version 1.1.0 we moved to hyperscaling Shiny apps in the datacenter by adding support for Kubernetes. We have used it for customers that roll out internet-facing Shiny apps with high numbers of concurrent users and needs for automated deployment.

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Phaedra 1.0.2

on March 15, 2018

Phaedra is an open source platform for data capture and analysis of high-content screening data. With the release of Phaedra 1.0.2, we are taking another step towards our goal of unprecedented flexibility in supported setups, ranging from a single small Mac desktop to a cloud-based infrastructure with multiple servers and an array of mixed Windows/Mac/Linux clients. The initial release of Phaedra supported only the Windows platform. Update 1.0.1 introduced Phaedra on the Mac and Linux desktops, and allowed you to deploy a DataCapture server on Linux servers as well.

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Using an R ‘template’ package to enhance reproducible research or the 'R package syndrome'

on November 21, 2017

Motivation Have you ever had the feeling that the creation of your data analysis report(s) resulted in looking up, copy-pasting and reuse of code from previous analyses? This approach is time consuming and prone to errors. If you frequently analyze similar data(-types), e.g. from a standardized analysis workflow or different experiments on the same platform, the automation of your report creation via an R ‘template’ package might be a very useful and time-saving step.

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Technologies