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Computational Pipelines & Agentic AI for Scientific Research

We develop computational pipelines, agentic AI workflows, and no-code scientific tools for research organizations, research institutes, and life science teams working with complex biological and computational data.

From biological data analysis and reproducible computational workflows to specialized research agents that coordinate analysis, evidence, tools, and validation, DataLens.Tools helps research teams build structured computational systems around their scientific questions.

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New Launch

Biological Tabular Data Advisor

A scientific workflow advisor that helps biology researchers decide which statistical or machine learning approach fits their current experimental dataset.

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Not just AutoML, biology-aware guidance

Built for experimental biology, not generic business tables.

Upload a CSV file and the advisor inspects your dataset structure, detects biological patterns, and recommends a scientifically defensible analysis strategy. It is especially useful for fluorescence data, cell-line assays, imaging-derived features, dose-response experiments, and longitudinal measurements.

Detects structure

Time-series, wide-format tables, repeated measures, batch/plate columns, and measurement readouts.

Prevents bad ML

Flags leakage risks, inappropriate random splitting, small-sample overfitting, and temporal autocorrelation.

Recommends workflows

Suggests preprocessing, validation, visualization, mixed-effects models, and time-series analysis when needed.

Exports reports

Generate Markdown or JSON summaries for notebooks, lab records, documentation, or project planning.

Open the Biological ML Advisor → See More Tools

Computational Research Agents

Biology-aware AI agents for research workflows

DataLens.Tools designs computational agents for biology, bioinformatics, neuroscience, chemical ecology, and data-intensive research. These systems can coordinate scientific tasks, inspect structured data, use computational tools, organize evidence, and support reproducible analytical workflows.

We do not treat a research agent as a generic chatbot. Each system is designed around the scientific question, biological data structure, computational environment, validation requirements, and human review points of the organization using it.

Scientific workflow coordination

Break complex computational research tasks into inspectable, specialist steps.

Data & metadata auditing

Check experimental structure, repeated measures, leakage risks, and analysis readiness.

Tool-enabled analysis

Connect approved scripts, databases, statistical tools, and computational pipelines.

Human-reviewed decisions

Keep uncertainty, evidence, and approval points visible to researchers.

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Conceptual communication visualization. Client systems are developed around specific research workflows, data structures, validation requirements, tool access, and institutional safeguards.

Interactive advisor

Find a sensible first ML model for your biological dataset

Model Match asks a few questions about your samples, target, feature space, biological design, preprocessing, and validation plan. It returns a practical model shortlist with risks to check before trusting the result.

Step 1

Describe the dataset

Step 2

Review model fit

Step 3

Check analysis risks

Open Model Match Use the full ML advisor
Cover of the book Python & AI for Modern Biological Research by Taufia Hussain

📘 New Digital Book

Python & AI for Modern Biological Research

A practical guide for biologists to learn Python, data analysis, statistics, machine learning, imaging workflows, and AI for modern research.

Designed for students, researchers, and scientists who want a clear, hands-on path into computational biology without getting lost in overly technical material.

Python for biological data
Statistics made intuitive
Machine learning for biology
Imaging and time-series workflows
Download Free Sample Buy Full Book – €19

Instant access after payment. Secure checkout powered by Stripe.

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What is DataLens.Tools?

DataLens.Tools develops scientific software, computational pipelines and agentic AI systems for life science researchers, research organizations and research institutes. We combine biology-aware data analysis, reproducible computational workflows and specialized research agents to help teams move from complex data and scientific questions to structured, inspectable computational solutions.

  • Analyze wingbeat, fluorescence, IR, or grayscale image data
  • Automate repetitive lab workflows and calculations
  • Generate structured, publication-ready reports and summaries
  • Use AI-powered tools to extract insights from complex datasets

Whether you are working in neurobiology, imaging, or experimental biology, DataLens.Tools helps you move from raw data to clear results faster.

Personalized Data Analysis Services

Need help cleaning, analyzing, or visualizing your research data? Book a 1-on-1 consultation tailored to your project in biology, neuroscience, or imaging.

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Featured Tools

New Launch

Biological Tabular Data Advisor

Get biology-aware recommendations for ML/statistical workflows, validation, preprocessing, and visualization from your CSV dataset.

Open Tool →

Fluro Motion Corrector

Correct motion artifacts in fluorescence recordings and improve ΔF/F accuracy before analysis.

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FLIR IR Image Analyzer

Analyze and interpret thermal images from FLIR cameras with region statistics and automated reports.

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Neuroscience Tools (New!)

CalciumTrace: ΔF/F Extractor

Extract and visualize ΔF/F calcium imaging signals with automated baselining and export options.

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SpikeTrain Visualizer

Generate raster plots and PSTHs from spike trains — no coding needed, just upload your data.

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TrackViz: Behavior Heatmaps

Visualize movement paths, heatmaps, and velocity maps from behavioral tracking data in seconds.

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Learning Hub

Step-by-step tutorials for modern biology, bioinformatics, and data analysis workflows. Learn practical methods you can apply directly to your own datasets.

RNA-seq

Build Gene Regulatory Networks from RNA-seq Using GENIE3

Learn how to infer gene regulatory relationships from RNA-seq data using GENIE3 and translate expression data into biological network insight.

Open Tutorial →
Single-Cell

Single-Cell RNA-seq Quality Control & Cell Filtering

A practical guide to filtering low-quality cells, assessing QC metrics, and preparing single-cell RNA-seq data for downstream analysis.

Open Tutorial →
Explore Learning Hub

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