Open source is not only the most efficient paradigm for scalability and collaboration, it facilitates verification and reproducibility.

We do not only advocate open source principles, but actively consult scientific developers on open source licensing and development practices.

Scientific community is blooming with bright minds doing great research and sharing powerful software solutions and data collections.

Instead of (re)inventing a new "better" wheel we instead develop products to harness, improve, and integrate existing solutions.

Scientific software is developed by enthusiasts, who neither have facilities nor funds to support large-scale promotion or reliable distribution.

We help to promote and distribute software, data, and related documentation while paying special attention to such aspects as intellectual property and due acknowledgment.

Research developers are often not programmers by training. As a result, scientific tools are often insufficiently tested and inherently broken.

We work on exercising existing and establishing new unit-, regression, integration and validity testing to guarantee robust and correct operation. Reproducible bogus results are useless!

Never was a buzz word (yet), but it is one of the critical drivers summarizing reliability, accessibility etc. Only when software and data are conveniently available we can talk about wide-scale adoption and reproducibility.

We work to make software and data available to the community, so that everyone could harness available resources regardless of their original complexity or size.

Neuroscience community is vast, but scientific projects often pursued in isolation and without interaction with other scientific communities.

We participate in many initiatives and projects that cross borders among different communities and disciplines, and in return make bridges among previously disconnected research groups.

We share our own code and data openly, run and support public data archives, and develop the data standards that let labs share and combine their studies.

Much research data never leaves the lab that collected it. Results are then hard to check, and questions that need larger or more diverse samples go unanswered.

We work across data standards, such as BIDS, NWB, and DICOM, and across research initiatives, so that data, tools, and conventions from one community can be used in another.

Standards and initiatives often grow up separately. The same data then has to be converted again for each one, and solutions found in one community rarely reach the next.

We are building tools and practices that keep data, code, recordings, and other research materials findable and recoverable, with copies in more than one place.

A paper is only part of the work. The data, code, and notes behind it are easily lost when a service shuts down, a link breaks, or a project ends.

We are making research software and services that groups can run on their own computers, such as NeuroDebian, Debian Med, DANDI, and ORINOCO Lite; con/serve collects how to run them.

Many research groups depend on tools and services they do not control. When a provider changes its terms or shuts down, a group can lose the services its work depends on, and sometimes its data.

We turn published methods into turnkey software that researchers can install, inspect, and run on their own data. PyMVPA, our framework for machine learning analyses of brain imaging data, was one of the first to do this for neuroscience.

Many new methods are described in papers but never released as usable software, so other researchers cannot try them.

We are developing ways to analyze brain activity and behavior recorded during rich experiences, such as watching a film. Hyperalignment, which aligns such responses across people, was first implemented in PyMVPA; this work builds on Psychoinformatics' work on naturalistic studies.

Films combine images, sound, and events that unfold over time. Most analysis methods were made for simple, controlled tasks, so much of what such recordings contain goes unused.