Anonymous case study

Recurring scientific collaborations, 2024-2026

How research groups with experimental data already available turned raw results into defensible reports, reusable pipelines and material ready for technical review.

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Client Research groups, master's and PhD students, postdoctoral researchers and scientists
Areas Analytical chemistry, materials, metabolomics, sensors, Python, machine learning, deep learning, computer vision, image analysis, web development and iOS/Android apps
Period 2024-2026, sustained collaboration
Format Recurring consulting, mentoring, training and methodological co-design
01

The problem

The experimental phase was solved. The bottleneck came afterwards: turning raw data into publishable results, defensible reports, reproducible figures and pipelines that did not depend on a single person in the group.

The cases included materials characterization, GC-MS metabolomics, sensor validation, AI-assisted research project management and methodological support for international proposals.

02

The approach

In each collaboration, we started from what the group already had: data, protocols and publication goals. First, we organized and verified the data; then we documented the analysis and key decisions in Markdown; finally, we transferred the workflow to the team, with instructions to repeat and adapt it without depending on DataQuorum.

Verifiable data

Tables, figures and quantitative criteria comparable across batches.

Reproducible documentation

Markdown, PDF, HTML, Jupyter and Git depending on the deliverable.

Real transfer

The group receives the pipeline and understands how to run, audit and adapt it.

03

Implementation

Collaboration
Deliverables
Verifiable result
Premium technical reports
Quantitative verification, figures and Markdown/PDF report
Officialized reports reusable across batches
GC-MS analytical pipeline
Preprocessing, alignment, identification and multivariate analysis
Executable pipeline and doctoral training
Instrumental validation
Bilingual reports, acceptance criteria and technical review
Final reports with documented criteria
AI for scientific management
Training in agents, skills, MCP and human-in-the-loop workflows
Team able to apply AI without breaking its workflow
Methodological co-design
Technical support in experimental design and proposal structure
Proposals submitted to national and international calls
04

Results

  • Officialized technical reports with quantitative verification and reusable structure.
  • End-to-end executable GC-MS pipeline transferred to the group.
  • Sustained doctoral mentoring with increasing autonomy in individual projects.
  • Training in AI applied to research with reusable modules and exercises.
  • Less time lost reinventing templates, figures and internal review criteria.
05

Transferable lessons

The report format is part of the method.
Markdown, Git and reproducible figures are a defensible minimum stack.
Quantitative verification works as preventive peer review.
AI creates value when it solves a real bottleneck.
Academic collaborations move best in short cycles with verifiable deliverables.
Next step

If your group has a similar bottleneck, start by diagnosing it

In 30 minutes we review the objective, available data, constraints and delivery format. Then I will tell you whether it fits better as Express Mentoring, a Pilot Project or a Research Collaboration.

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