Research internship
ConsoneAI / DioScor
A short research internship involving toxicology data mapping and structured data preparation in a dose-toxicity platform context.
- MapDose, endpoint, context
- StructureReusable data fields
Quick scan
- Role
- Research intern supporting toxicology data mapping and structured data preparation.
- Timeframe
- Six-week internship
- Context
- Dose-toxicity platform work kept high level because of proprietary boundaries.
- Work mode
- Support-based research and data-preparation work.
- Outputs
- Mapping notes, structured non-public data outputs and a poster presentation.
- Why it matters
- Shows early data discipline in messy biological evidence contexts.
Context
The six-week research internship was set around a dose-toxicity platform. The value of the work was learning how messy biological and toxicological evidence has to be structured before it can support analysis and have its chemical and biological properties propagated.
My role
I supported toxicology data mapping and structured data-preparation work, keeping the description here high level because the platform details were proprietary.
Approach
- Identify relevant toxicology and chemical data sources for extraction or comparison.
- Review dose-toxicity source fields and terminology differences.
- Organise compound, organism, route, dose-unit, test-type and effect-category information where available.
- Clean missing values, split combined categories and document limitations.
- Map fields to high-level information needs.
Outputs
- Internal mapping notes.
- Organised non-public data outputs.
- Dose-toxicity summaries.
- Poster presentation at the Anglia Ruskin PGR Conference 2024
What this shows
This was an early step from foundational biomedical science into data-enabled research: Python programming, careful extraction, cleaning, mapping and communication.