Better research, built together.

“We have one giant script with everything in it. How can we break it into logical modules?”

Turn one long analysis script into smaller functions with clear inputs, outputs, and tests.

“Different machines give different results. Can you help us pin our Python environment?”

Define shareable environments so collaborators can run the same analysis on different machines.

“We want to avoid reintroducing old bugs. How do we start adding basic tests in R?”

Add a first layer of practical checks around statistical functions and analysis steps.

“Our code runs, but it is incredibly slow. Can you help us find what is dragging it down?”

Use profiling tools to find bottlenecks before deciding whether to optimize or parallelize.

“We have three different forks with conflicting changes. How do we bring them back together?”

Plan a careful merge strategy for forks, duplicated scripts, and shared lab utilities.

“We would like to switch to Python. Can you help us translate our Matlab codebase?”

Map old analysis code to open Python tools while preserving scientific intent and outputs.

“We keep getting NA-related errors when reshaping our data. Can you help us trace the bug?”

Walk through missing values, factor levels, indexing mistakes, and reshaping bugs together.

“Can we distribute these loops across cores on a normal PC, or do we need a cluster?”

Speed up desktop workflows with multiprocessing, joblib, or vectorized library calls.

“We still use Python 2.7. Could you help us wrap it in Docker so collaborators can run it?”

Keep an older but still important codebase runnable on modern systems.

“Could a nightly job label suspicious runs and check basic data integrity for us?”

Create checks that flag missing files, suspicious outputs, or failed quality metrics.

“Our file structure is a mess. Can you help us make a layout collaborators can understand?”

Design a folder structure that supports current analysis and future handover.

“We need metadata for our recordings but do not know where to start. What is a minimal template?”

Capture acquisition parameters, subject information, and processing notes while they are fresh.

“We are about to submit a paper. Can you guide us through publishing the data?”

Choose a repository, clean up folders, add documentation, and prepare a data release.

“We are not sure how to license our code and data. Can you help us choose?”

Discuss open licenses for datasets, scripts, and derived outputs in collaborative projects.

“We want to adopt NWB, but how should we handle our custom metadata?”

Plan conversion from older proprietary formats into reusable, documented data structures.

“We get new recordings daily. How do we streamline merges and avoid duplicates?”

Append new data to a master dataset without duplicating or reprocessing old records.

“We have random seeds in different places. How do we make our final figures reproducible?”

Connect final plots to versioned data, stable seeds, and rerunnable scripts.

“We are ready to publish our data but do not know what belongs in the README.”

Write practical documentation so collaborators and reviewers know what is in a release.

“How do we trace which raw file generated which final figure?”

Create manifests that connect raw files, cleaned datasets, parameters, and final figures.

“We need a basic policy so everyone in the lab handles data consistently. Can you help?”

Create lightweight group conventions for storage, naming, archiving, and handover.

“We see a strong 50 Hz artifact in our signals. Where should we start troubleshooting?”

Reason through grounding, shielding, and acquisition setup issues that affect signal quality.

“We want all cameras to share timestamps so we can merge footage reliably. What works?”

Design timestamped multi-camera setups that can be aligned during analysis.

“We need to integrate real-time sensor logs with our experiment. What would you try?”

Integrate multiple data streams with a shared time base during live experiments.

“What are the best practices for preprocessing calcium imaging data before motion correction?”

Plan file conversion, downsampling, filtering, motion correction, and segmentation checks.

“How can we build a predictive model from trial data for mouse behavior analysis?”

Structure trial data, smooth signals, and validate predictive models with interpretable diagnostics.

“What alternative statistical models can we use for asymmetric periodic neural data?”

Compare model choices for asymmetric or circular neural response patterns.

“How do we construct an inception loop pipeline for generating MEIs from neural data?”

Structure a deep learning workflow for neural response prediction and MEI generation.

“Can someone check our scripts for readability before we turn them in?”

Use code review as a learning moment around clarity, structure, and maintainability.

“I am leaving soon. Can you help me make sure the next person can pick this up?”

Package scripts, data notes, and project context before a student or postdoc leaves.

“Can you help us set up automatic testing so we catch errors before merging?”

Run tests automatically when a shared repository changes.