Important Links

Important Links & Resources¶

This directory brings together key platforms, portals, datasets, software packages, and academic references for Connectome 2026–2027.

1. Competition Platforms & Community¶

• Competition Handbook & Website: https://clematisresearch.github.io/connectome/

• Competition Repository: https://tinyurl.com/Connectome2026-27Repository

• Clematis Discord Community: https://tinyurl.com/discordclematis (Join for announcements, Q&A, office hours, and submitting video links)

• Official Mailing List & Updates: https://tinyurl.com/Connectome2026-27Mail

• YouTube Channel: @clematisresearch

• Instagram: @clematisresearch

• LinkedIn: Clematis Research Hub

• Email Inquiries: clematis.reh@gmail.com

Project Submission Forms¶

• Track 1 (Flies) Written Report Submission: Google Form for Track 1

• Track 2 (Humans) Written Report Submission: Google Form for Track 2

• Video Submissions: Post your video link on the Clematis Discord in the #Connectome2026-2027 channel.

2. Track 1: Fly Connectomics Resources¶

Web Exploration Tools & Viewers¶

• Male CNS Cell Type Explorer: https://reiserlab.github.io/celltype-explorer-drosophila-male-cns/
  Search, filter, and inspect cell types across the adult male fly brain and VNC.

• neuPrint Web Portal: https://neuprint.janelia.org (Dataset: male-cns:v1.0)
  Interactive web interface for querying synaptic connectivity, ROIs, and neuron partners.

• neuPrint Brain Regions / Neuropil ROI Directory: Neuropil Acronyms & ROI Explorer
  Full reference list of standard neuropil abbreviations (e.g., EB, FB, NO, MB, AL, OL).

• Neuroglancer 3D Visualization (Male CNS v1.0): Pre-loaded Male CNS Neuroglancer Viewer
  3D volumetric and skeleton visualizer for reconstructed neurons and brain regions.

• Virtual Fly Brain (VFB): https://www.virtualflybrain.org/
  Integrated knowledgebase linking fly neuroanatomy, cell types, driver lines, and published literature.

• Janelia FlyEM Project Team: https://www.janelia.org/project-team/flyem
  Home of high-resolution electron microscopy connectome reconstructions.

Python Packages & API Documentation¶

• neuprint-python Documentation: neuPrint Python Quickstart Guide

• neuprint-python GitHub Repository: connectome-neuprint/neuprint-python

• Plotly Python Library: Plotly Express Documentation
  Interactive graphing library for scatter plots, heatmaps, and network graphs.

• Navis (Neuron Analysis and Visualization in Python): Navis Documentation
  Python library for 3D neuron skeleton transformations, morphology analysis, and plotting.

Key Literature & References¶

• Male CNS Connectome Preprint: Berg et al., 2025 (bioRxiv) — The connectome of the adult male Drosophila Central Nervous System.

• Hemibrain Connectome Paper: Scheffer et al., 2020 (eLife) — A connectome and analysis of the dense connectome of the Drosophila brain.

• Whole-Brain Drosophila Connectome: Dorkenwald et al., 2024 (Nature) — Neuronal wiring diagram of an adult brain.

• Standard Insect Brain Nomenclature: Ito et al., 2014 (Neuron) — A systematic nomenclature for the insect brain.

• Characterizing Neuronal Cell Types: Zeng, 2022 (Cell) — What is a cell type and how do we characterize it?

• Adult Drosophila Brain Structure & Function Review: Current Research in Insect Science (2025) — The Drosophila adult brain: short overview of structure, function, and resources.

3. Track 2: Human Neuroimaging & Macroscale Connectomics Resources¶

Datasets, Parcellations, & Key Papers¶

• LEMON Dataset Paper: Babayan et al., 2019 (Scientific Data) — A mind-brain-body dataset of MRI, EEG, cognition, emotion, and peripheral physiology in young and old humans.

• OpenNeuro LEMON Dataset Portal: OpenNeuro ds000221
  Direct access to raw and preprocessed functional/structural MRI datasets.

• Schaefer Cortical Parcellation Paper: Schaefer et al., 2018 (Cerebral Cortex) — Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI.

• Yeo 7 & 17 Resting-State Networks Paper: Yeo et al., 2011 (Journal of Neurophysiology) — The organization of the human cerebral cortex estimated by intrinsic functional connectivity.

• Complex Brain Networks & Graph Theory in Neuroscience: Bullmore & Sporns, 2009 (Nature Reviews Neuroscience) — Complex brain networks: graph theoretical analysis of structural and functional systems.

• Graph Theory Methods: Applications in Brain Networks: Sporns, 2018 (Dialogues in Clinical Neuroscience) — Graph theory methods: applications in brain networks.

Python Neuroimaging & Graph Analysis Packages¶

• Nilearn Documentation: https://nilearn.github.io/stable/index.html
  Fast and easy statistical learning on neuroimaging data (fMRI, structural MRI, connectomes).

• NiBabel Documentation: https://nipy.org/nibabel/
  Access a catalog of neuroimaging file formats (NIfTI, GIFTI, CIFTI).

• NetworkX Documentation: https://networkx.org/documentation/stable/
  Creation, manipulation, and study of the structure, dynamics, and functions of complex networks.

4. Scientific Computing & Data Science Foundations¶

• Pandas Documentation: https://pandas.pydata.org/docs/ — Data structures and data analysis tools.

• NumPy Documentation: https://numpy.org/doc/stable/ — Fundamental package for scientific computing with arrays.

• SciPy Documentation: https://docs.scipy.org/doc/scipy/ — Algorithms for scientific and statistical computing.

• Scikit-learn Documentation: https://scikit-learn.org/stable/ — Machine learning and predictive data analysis in Python.

• Matplotlib Documentation: https://matplotlib.org/ & Seaborn Documentation: https://seaborn.pydata.org/ — Comprehensive 2D data visualization libraries.

5. Free Online Programming & Neuroscience Courses¶

• CS50’s Introduction to Programming with Python (Harvard): https://cs50.harvard.edu/python/
  Gentle, thorough, and highly rated introduction to Python programming.

• Python Data Science Handbook (Jake VanderPlas): Online Book Access
  Free textbook covering NumPy, Pandas, Matplotlib, and Scikit-Learn.

• Data Carpentry (Python for Social Scientists): https://datacarpentry.github.io/python-socialsci/
  Practical data workflows and analysis for beginners.

Kaggle Learn Micro-Courses¶

• Pandas: https://www.kaggle.com/learn/pandas
  Extracting, indexing, grouping, and transforming tabular datasets.

• Data Visualization: https://www.kaggle.com/learn/data-visualization
  Creating bar charts, scatter plots, heatmaps, and distributions with Seaborn and Matplotlib.

• Intro to Machine Learning: https://www.kaggle.com/learn/intro-to-machine-learning
  Exploring data, model validation, decision trees, and random forests.

• Intermediate Machine Learning: https://www.kaggle.com/learn/intermediate-machine-learning
  Handling missing values, categorical variables, cross-validation, data leakage, and XGBoost.

• Machine Learning Explainability: https://www.kaggle.com/learn/machine-learning-explainability
  Permutation importance, partial dependence plots, and SHAP values.

• Feature Engineering: https://www.kaggle.com/learn/feature-engineering
  Mutual information, creating features, clustering, and PCA.