At Codas Lab we firmly believe in the power of collaboration between high-level research teams. And that’s where our maxim comes from:
Let’s do Science together.
We know that we are talking about a complex world and that numerous doubts may arise around it. On this page we show you a list of frequently asked questions and answers about our philosophy, operation and main objectives.
We hope they are of help to you:
You can send your request to work with us from the Contact section.
Our work may consist of a Collaboration or a Service. We will establish with you the optimal way after carefully analyzing each case.
The Collaboration will be carried out through co-authorship and between both parties we will establish its characteristics taking into account the needs of the research and the costs linked to it.
Instead, the Service will be provided by Codas Lab as an external service (without co-authorship) and according to a personalized quote.
Yes. We collaborate with research teams from all over the world.
You can send your quote request from the Contact section.
Yes. All our services are developed according to the characteristics and needs of each investigation.
Yes. We use Matlab, Python and Scripting on Linux, although we also develop our own tools.
Yes. One of our main services is Consulting on experimental design from the —statistical— point of view of the data.
Yes. We use advanced computational techniques to maximize statistical power.
Yes. We provide customized solutions based on techniques from the literature or designed from scratch.
Yes. Biomarker localization is one of our specialties.
Yes. Managing large data sets is one of our specialties.
Yes. The loss of values is a complex problem, but we know how to manage it.
Yes. Machine Learning is one of our main lines of research, although our experience focuses on pattern recognition, visualization and statistical inference.
We help you establish your needs (for example, if your goal is to extract new information from your data, it may not be the best option).
No. Deep Learning is not a suitable technique for data interpretation.
If you have not found the information you were looking for or need to delve deeper into any aspect, do not hesitate to contact us.
