Science & research

Rooted in research.
Built for evidence.

OptiMod grew out of research at Leibniz University Hannover. Its development brings statistical methodology, active research collaboration and a commitment to reproducible evidence into one system.

Explore the research projectOur academic origins

From academic research to scientific software.

OptiMod began as a GRANAT summer-school project at Leibniz University Hannover and developed into an ITE spin-off. That academic connection continues through the Pharma Intelligence research collaboration.

Read the university’s account (PDF)

Collaborative research

Pharma Intelligence

The consortium investigates how AI-assisted clustering and statistical modelling can reveal relationships in pharmaceutical and patient-related data.

Project period
16 June 2025 – 15 June 2028
Consortium lead
OptiMod UG
Research partner
Leibniz University Hannover, Institute of Innovation Research, Technology Management & Entrepreneurship (ITE)
Industry partner
Soranos IT-Service GmbH
Project funding
European Regional Development Fund (ERDF / EFRE) and the State of Lower Saxony, through the EU-EFRE Research and Development in Enterprises programme.

The university contributes research in digitalisation and applied statistics. Pharma Intelligence is an ongoing research project focused on methodological development.

Read the university project record

Research relationships and their roles.

Leibniz University Hannover
Academic origins and research collaboration in Pharma Intelligence. Project record
BioIntelligence
OptiMod joined the hightech incubator after a multi-stage scientific and commercial assessment. BioIntelligence supports business development and mentoring. Portfolio profile
EU and Lower Saxony
Public funding of the Pharma Intelligence project. Funding record
NBank / HTI programme
NBank administers Lower Saxony’s hightech incubation programme. State programme record
University of Göttingen
Drini Morina’s personal academic and teaching affiliation. Lecturer profile

Recognition and exchange.

  • German preliminary-round winner

    The Waltz of Pioneers ViennaUP

  • Selected to present

    European DE-TECH Final Istanbul

  • Invited technology presentation

    Life Science Dialog Niedersachsen

  • Press feature

    Hannoversche Allgemeine Zeitung

Make scientific scrutiny routine.

Reliable findings require more than a model output. The data, method choice, assumptions and uncertainty must be open to examination, so another team can understand, question and build on the work.

OptiMod embeds statistical checks in the analysis workflow and records how a conclusion is reached. The Scientific Report connects the findings to methods, named tests, analytical decisions and limitations.

Why the record matters

In Nature’s 2016 survey, more than 70% of 1,576 researchers reported having tried and failed to reproduce another scientist’s experiments. The figure describes respondents’ experience. It illustrates why methods, assumptions, uncertainty and software versions matter alongside a result.

Read the Nature survey

Reproducibility also depends on retaining the data, executable settings or code, and software versions. Together with the Scientific Report, these form a record that others can revisit.

Explore a worked examplePublic data · Scientific Report & analysis code

This independently computed example illustrates the analytical record using public bike-sharing data.

Public data · Capital Bikeshare

Daily demand in light rain or snow

Estimated count ratio relative to clear weather

0.496 adjusted count ratio
95% confidence interval 0.399–0.617

Clear weather = 1.00. Estimates use all 731 days. The interval uses standard errors adjusted for residual correlation through seven daily lags.

Read the example report (PDF)

In the public bike-sharing data, light rain or snow was associated with about 50% lower expected demand than clear weather, after accounting for other variables. The uncertainty interval ranges from 38% to 60% lower.

The model estimates an exploratory association. The report identifies residual dependence, the small number of days in this weather category and the limits of a causal interpretation. Readers can revisit each choice using the underlying data and code.

Compare the candidate modelsDownload data and analysis code

The founders’ backgrounds.

Ylli Morina Co-founder & CEO

Ylli studied Computer Science at Georg-August University of Göttingen. He leads business development and strategic partnerships, bringing his technical background to product development.

Dr. rer. nat. Drini Morina Co-founder & CTO

Drini studied Statistics at Georg-August University of Göttingen. He is a researcher at ITE, Leibniz University Hannover, with a background in applied statistics and international economics, including studies at Fudan University. His research includes quantitative work on innovation, risk and returns. He is also listed as a lecturer in Göttingen’s LIPIT programme.

LUH staff profileGöttingen lecturer profilePLOS ONE publication

Sources and evidence.

University records, research publications and programme documentation describe OptiMod’s origins and collaborations. The supplementary worked example includes its public data source and an independently computed analysis.

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