Institutional statement
Biological data are not just any data. Behind every row there is a patient, a pathogen or an ecosystem, behaving with a variability that generic data science is not equipped to read. Analysing them properly demands advanced statistics, computation at scale, and an ethical framework that takes responsibility for what is measured, what is modelled and what is concluded.
INSECAB is the independent European institute focused specifically on the intersection of advanced statistics, causal inference, computational science and the life sciences. We are not a consultancy adapting itself to the health sector. We are not a faculty bolting on a few programming modules. We are a structure built from that intersection outwards.
First pillar · Research institute
Our Scientific Institute works on active international projects: cross-border epidemiological modelling, causal inference on observational data, reproducible clinical data infrastructures, computational surveillance. Every tool we build comes out of a real scientific problem — not an academic exercise.
Second pillar · Specialist academy
Our programmes do not teach biostatistics in the abstract: they teach the biostatistics the team is applying today, on projects that are not yet in the textbooks. Every module feeds on INSECAB's active research — its questions, its methodological decisions, what it learns. Our partners' confidential data never leave the project; what comes into the classroom is the judgement with which they were analysed.
The result is an unusual model: research feeds the teaching, and the teaching feeds back into the research. A veterinarian learns causal inference by applying it to the same methodological challenges we face in our animal health projects. An epidemiologist gains access to computational pipelines built by the team on active European projects.
Under the scientific direction of Dr Marta María Dolcet Negre — an active researcher at the University of Salamanca and IBSAL — INSECAB trains professionals able to design studies, run reproducible analyses and publish without depending on third parties as a matter of course.
Because biological data are too important to be treated as just any data.