aidweather
Open sourceThe library and command-line tool. Install it from PyPI.
pip install aidweather
Things we built that are open
Weather and solar data, ready for your research.
AidWeather is an open-source Python library and CLI that turns NASA POWER data into clean, cached, analysis-ready tables — for a point, a transect, or a whole region.
AidWeather Report
DemoOur product: a standalone weather-history report for your site, built from NASA POWER data. Choose a location and we will send you a sample — your feedback is welcome.
AidWeather is built and maintained by AidBio and released as open source. We also develop internal tools for client projects — get in touch to learn more.
Applied intelligence
Small teams. Serious output.
From field trials and climate-linked agronomic signal, to omics interpretation, biomarker discovery, and scientific reporting, AidBio delivers work that survives contact with a real lab, a real season, or a real peer review.
Pipeline focus
ML + Data
Prediction, automation, and reporting workflows
Domain fit
Agri + Bio + Pharma
Agritech, biotech, pharma R&D, omics, translational analysis
Delivery mode
Scoped
Clear milestones and maintainable code hand-off
Philosophy
Rigorous
No black-box hype without scientific accountability
Core capabilities
Targeted intelligence for technically demanding domains.
AidBio bridges deep biological domain knowledge with modern machine learning and data-engineering practice — producing systems that hold up in front of a principal investigator, a peer reviewer, or an agronomist who's seen too many black boxes.
Agritech & Climate Analytics
Crop-linked and geospatial analytics for field trials, yield-oriented investigation, environmental signal extraction, and weather-aware modeling — built for the realities of seasons, sites, and imperfect sensors.
Omics, Bioinformatics & Scientific Data Systems
Support for multi-omics interpretation, biomarker-oriented workflows, experimental data consolidation, and reproducible analysis architecture — for academic labs building toward publication and pharma teams building toward a decision.
Machine Learning & Predictive Modeling
Supervised learning, model selection, validation, and interpretation for field, laboratory, and translational settings. Built with strong attention to data quality, honest evaluation, and downstream usability.
Agentic AI Workflows
Domain-aware assistants, structured reporting flows, retrieval pipelines, and lightweight automation that augment small scientific teams without creating infrastructure sprawl.
Selected work
Tools and papers, at a glance.
The open-source tools, research software and papers behind our work.
Longer reads
For when you want the longer version.
A few longer pieces, for when a paragraph on this page isn't enough.
Founder story · Mechanism
Before prediction, understand regulation.
How a single regulatory protein in a lab plant became a lesson in signals that interact rather than act alone.
Founder story · Data
Complexity eventually exceeds manual reasoning.
Gene-activity tables, weather time series, field records — different data, the same problem: finding the real pattern without losing what was measured.
Founder story · Software
An analysis answers today's dataset. Software answers the next one too.
Why one-off scripts stopped being enough, and what replaced them: installable tools that record where their numbers came from.
Open-source docs
Agroclimatic & solar data integration from NASA POWER
What AidWeather actually does under the hood: request handling, spatial sampling, caching, and the CLI — from the project's own documentation.
Engagement model
Rigorous process. Zero ambiguity.
Every engagement is scoped tightly, so your team spends time on the science and the decision — not on decoding a vague deliverable.
01
Define & Align
Clarify the business question, scientific constraints, dataset realities, and decision context before implementation starts.
02
Interrogate Data
Standardize, clean, audit, and explore the data to isolate signal from experimental and operational noise.
03
Build & Validate
Develop reproducible, maintainable pipelines for prediction, interpretation, or structured automation.
04
Ship & Transfer
Deliver documented code, practical outputs, and a hand-off your internal team can actually own.
The founder
Scientific depth meets practical implementation.
AidBio is led by Cleverson Matiolli, PhD — a quantitative biologist and machine-learning practitioner with experience spanning academic research, applied R&D, and computational problem-solving across life sciences and agritech.
The operating philosophy is straightforward: honest modeling, reproducible architecture, and deliverables that remain useful after the presentation ends.
Ready to turn your lab, trial, or pipeline data into a decision?
Whether you're an academic lab, a pharma R&D group, or an agritech company, AidBio engages through well-scoped projects that move from messy data to a decision you can act on.
contact@aidbio.org