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Technical Consulting for Neuroscience Research

Academic research groups with proper technical support demonstrate measurably higher productivity, better reproducibility, and more successful grant outcomes. The right expertise at the right time can transform research bottlenecks into competitive advantages.

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Consulting Services

We offer flexible, short-term consulting to help neuroscience researchers overcome technical challenges. Whether you need help with data analysis, workflow automation, or research infrastructure, we provide expert guidance tailored to your needs.

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Scientific Data Analysis

End-to-end support for experimental neuroscience: from reliable data acquisition and synchronization to robust preprocessing, analysis, and reproducible publication. We focus on data quality and validity—not just code—so that scientific results are trustworthy.

Reducing 50 Hz Noise: "We see a strong 50 Hz artifact in our signals—could you guide us on potential grounding or shielding fixes?"
Multi-Camera Sync Setup: "We want all cameras to share timestamps so we can merge footage reliably—can you point us to best practices?"
Real-Time Data Logging Integration: "We need a system to integrate real-time sensor logs—what would be your approach?"
Preprocessing Calcium Imaging Data: "What are the best practices for preprocessing calcium imaging data for motion correction?"
Reproducible Figures (fUSI / Imaging): "What steps are involved in converting Matlab imaging code to Python for reproducible figures?"
Predictive Modeling of Behavior: "How can we build a predictive model from trial data for mouse behavior analysis?"
Preprocessing Calcium Imaging Data: "What are the best practices for preprocessing calcium imaging data for motion correction?"
Workshop: Handling Large Imaging Datasets: "We have 200 GB of 2P imaging files—could your workshop show us strategies to handle them on a normal computer?"
Modeling Periodic Neural Data: "What alternative statistical models can we use to analyze asymmetric periodic data?"
Inception Loop for MEI Generation: "How do we construct an inception loop pipeline for generating MEIs from neural data?"
Wald's Test Implementation in R: "Can you help troubleshoot an R implementation of Wald's test for our dataset?"
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Research Data Management

Help with organizing, documenting, licensing, and sharing data throughout the research lifecycle—from first raw files to published repositories.

Organizing Project Folder Structures: "Our file structure is a mess—can you help us make a layout that works for both current and future collaborators?"
Metadata Templates for Raw Data: "We need metadata for our recordings but don’t know where to start—can you help us define a minimal template?"
Preparing for Data Publication: "We’re about to submit a paper—can you guide us through publishing the data so it meets journal requirements?"
Choosing a License for Code and Data: "We’re not sure how to license our code and data—can you help us choose something open but appropriate?"
NWB File Standardization: "We’d like to adopt NWB but not sure how to handle our custom metadata—could you point us to a conversion approach?"
Incremental Data Merging: "We have a master CSV but get new recordings daily—how do we streamline merges to avoid duplicates?"
Automated Documentation Build: "We’d like clickable docs for each function—could you recommend a quick approach to auto-generate these from docstrings?"
Ensuring Reproducible Figures: "We have random seeds in different places—how do we unify them so figure outputs are reproducible run-to-run?"
Auto-Archiving Final Results: "We keep forgetting to back up final results—can we automate a push to our data share after each successful run?"
Motion Correction Metrics Logging: "Is there a way to store per-run alignment stats so we know if the images drift too much?"
Version Control for CaImAn Refactor: "We modified CaImAn for our pipeline—how do we keep track of changes while pulling official updates?"
Prep for a Reproducible Paper: "We’re about to submit; could you help finalize our methods code and ensure everything re-runs smoothly?"
Assigning DOIs to Final Datasets: "How can we publish our final results with a DOI so people can cite them directly?"
Reproducible Analysis with Timestamps: "We want to ensure others can repeat our results even if some steps take hours—what’s the best practice?"
Drafting a README for Publication: "We’re ready to publish our data but don’t know what to include in the README—can you help draft one?"
Versioning Intermediate Datasets: "We reprocess our data every few weeks—how can we track changes in a structured way without overwriting everything?"
Mapping Raw to Final Data: "How do we trace which raw file generated which final figure when there are multiple preprocessing scripts involved?"
Setting Up a Lab Data Policy: "We’d like a basic policy so everyone in the lab handles data consistently—can you help us write one?"
Automating Data Integrity Checks: "How can we make sure our uploaded datasets haven’t silently changed or gotten corrupted over time?"
Linking Code to Specific Datasets: "Our code works only with certain data versions—how do we make that obvious and reproducible?"
Postdoc Handoff Checklist: "I’m leaving soon—can you help me make sure everything is ready for the next person to pick up?"
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Pipeline Automation

Help with coding best practices, structuring reusable scripts, unit testing, dependency management, and tool selection in Python, R, and Matlab.

Modularizing a Matlab Toolbox: "We have one giant script with everything in it—how can we break this into logical modules with clear inputs and outputs?"
Conda Environments for Reproducibility: "Different machines give different results—can you help us freeze our Python environment across systems?"
Unit Testing in R with `testthat`: "We want to avoid reintroducing old bugs—how do we start adding basic tests in our R codebase?"
Profiling Slow Python Code: "Our code runs but it’s incredibly slow—can you help us find what’s dragging it down?"
Merging Forked Codebases: "We have three different forks with conflicting changes—could you guide us on a merge strategy and test coverage?"
Testing a Classification Library: "We have a 2k-line script with no tests—could you show us how to modularize and adopt pytest?"
Inscopix Data Extraction: ""
Migrating Matlab to Python: "We’d like to switch to Python—can you help us translate our Matlab codebase and find replacements for built-ins?"
Container Registry Setup: "We’d like to track container versions so different projects can pull consistent environments—how do we do that?"
Debugging Index Errors in R: "We keep getting NA-related errors when reshaping our data—can you help us walk through the bug?"
Local Parallel Processing: "Our script hogs one CPU—can we spawn tasks in parallel or use joblib so it’ll finish faster?"
One-on-One Debug Coaching: "Sometimes our array out-of-bounds—can we do a quick screen-share so you can see how we’re indexing the data?"
Dockerizing a Python 2.7 Analysis: "We keep using Python 2.7—could you help us wrap it in Docker so others can run it on modern systems?"
Automatic QA Checks: "We’d like a nightly job to label suspicious runs—any straightforward approach for auto-checking data integrity?"
Property-Based Testing for File Parsers: "Our file parser sometimes misreads the last bytes—how do we test edge cases systematically?"
Parallel Speedups on Desktop: "Can we distribute these loops across cores on a normal PC, or do we need a cluster for that?"
File Naming Overhaul: "We have old experiment data labeled 'Run1,' 'Exp3_1,' etc.—how can we reorganize them in a consistent manner?"
Packaging a Python Toolbox: "We want collaborators to just ‘pip install’ our code—could you help us with the packaging steps?"
Using Git for Matlab Projects: "We never used Git before—can you show us how to track changes in our Matlab project?"
Code Reviews for Student Projects: "Can someone check our scripts for readability and structure before we turn them in?"
Using Virtualenv in Python: "My packages keep clashing—how can I isolate them per project?"
Matlab Function Refactoring: "We keep duplicating code and manually editing paths—can you help us clean that up?"
Intro to Python Class Design: "Our script is too long—can we use classes to clean it up?"
Automated Documentation in R: "We forget what our functions do—can we generate docs automatically from comments?"
Packaging Matlab Code for Sharing: "How do we share our Matlab tool without requiring people to copy a dozen files by hand?"
Jupyter Notebooks as Teaching Tools: "We want to use Jupyter to teach analysis workflows—can you help set it up for interactivity?"
CI Testing for a Python Pipeline: "We work together on one repo—can you help us set up automatic testing so we catch errors early?"
Script Templates for New Experiments: "Each project starts from scratch—can you help us build reusable script templates?"
Switching to VSCode from Matlab IDE: "We’re trying VSCode—how do we set it up for both Matlab and Python?"
Crash Course: R Markdown Reports: "Can you show us how to make PDFs from our R code with embedded plots and inline results?"
MATLAB to Python Conversion: "We rely on older MATLAB code for final plots—how do we replicate these in Python for an open-source release?"
In-Depth Code Reviews: "We’d love a second opinion on code clarity—could you do a review of our main analysis function?"
Translating to a New Framework: "We used our own script for years—could you walk us through a well-maintained library for sorting spikes?"
Unifying Data Scripting Approaches: "We all code differently—could you lead a session showing how to structure a typical dataset cleanup script?"

Our Approach to Consulting

Collaborative Problem-Solving for Research

We specialize in hands-on, collaborative consulting that builds both solutions and skills. Our approach emphasizes pair programming, knowledge transfer, and sustainable practices that empower your team long after the project ends.

Increased Research Velocity

Resolve technical blockers quickly, allowing researchers to focus on scientific questions rather than implementation details.

Competitive Grant Applications

Demonstrate robust methodologies, data management plans, and reproducible workflows that reviewers expect.

Sustainable Research Infrastructure

Build systems and skills that benefit multiple projects and can be maintained by your team long-term.

Getting Short-Term iBOTS Consulting is Easy!

1
Email Request

Let us know what interests you! Just send a message to any of our team members, and we'll help you arrange a meeting!

2
Online Consultation Meeting

What problem are you facing, what are your goals, and what's your current situation? Together, we'll select a strategy and schedule that best-fits your situation.

3
Pair Programming Sessions

Learning is best done together! We'll meet 1-2 times per week, either online or in-person, to do intensive work on the project.

4
Project Review

Celebrate our successes! At the end of the last session, we'll review the outcome and discuss next steps.

Let's Get in Touch!

Nicholas A. Del Grosso
Nicholas A. Del Grosso

delgrosso.nick@uni-bonn.de

About Nicholas
Sangeetha Nandakumar
Sangeetha Nandakumar

nandakum@uni-bonn.de

About Sangeetha
Ole Bialas
Ole Bialas

bialas@uni-bonn.de

About Ole
Atle E. Rimehaug
Atle E. Rimehaug

rimehaug@uni-bonn.de

About Atle
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