{"id":13689509,"url":"https://github.com/neurodata/m2g","last_synced_at":"2025-04-30T12:44:42.100Z","repository":{"id":40954139,"uuid":"50844757","full_name":"neurodata/m2g","owner":"neurodata","description":"NeuroData's MRI to Graphs (m2g) - connectome estimation package and pipeline","archived":false,"fork":false,"pushed_at":"2024-04-13T18:13:37.000Z","size":93299,"stargazers_count":64,"open_issues_count":29,"forks_count":37,"subscribers_count":10,"default_branch":"deploy","last_synced_at":"2025-04-13T22:50:53.051Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"https://docs.neurodata.io/m2g/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/neurodata.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2016-02-01T14:30:12.000Z","updated_at":"2024-11-13T07:16:49.000Z","dependencies_parsed_at":"2023-01-31T05:01:07.387Z","dependency_job_id":"c9aa052c-5662-44f3-a495-6a24bd01c1fd","html_url":"https://github.com/neurodata/m2g","commit_stats":{"total_commits":3382,"total_committers":37,"mean_commits":91.4054054054054,"dds":0.6002365464222353,"last_synced_commit":"95d271f748e7d35b8d7feaa1b0112f471be8e514"},"previous_names":["neurodata/ndmg"],"tags_count":57,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/neurodata%2Fm2g","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/neurodata%2Fm2g/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/neurodata%2Fm2g/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/neurodata%2Fm2g/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/neurodata","download_url":"https://codeload.github.com/neurodata/m2g/tar.gz/refs/heads/deploy","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":251703113,"owners_count":21630168,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-08-02T15:01:50.742Z","updated_at":"2025-04-30T12:44:42.082Z","avatar_url":"https://github.com/neurodata.png","language":"Python","funding_links":[],"categories":["Python"],"sub_categories":[],"readme":"# m2g\n\n![Downloads shield](https://img.shields.io/pypi/dm/m2g.svg)\n[![PyPI](https://img.shields.io/pypi/v/m2g.svg)](https://pypi.python.org/pypi/m2g)\n[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.595684.svg)](https://doi.org/10.5281/zenodo.595684)\n[![Code Climate](https://codeclimate.com/github/neurodata/ndmg/badges/gpa.svg)](https://codeclimate.com/github/neurodata/ndmg)\n[![DockerHub](https://img.shields.io/docker/pulls/neurodata/m2g.svg)](https://hub.docker.com/r/neurodata/m2g)\n\nNeuroData's MR Graphs package, **m2g**, is a turn-key pipeline which uses structural and diffusion MRI data to estimate multi-resolution connectomes reliably and scalably.\n\n# Contents\n\n- [Overview](#overview)\n- [Documentation](#documentation)\n- [System Requirements](#system-requirements)\n- [Installation Guide](#installation-guide)\n- [Usage](#usage)\n- [License](#license)\n- [Issues](#issues)\n- [Citing `m2g`](#citing-m2g)\n\n# Overview\n\nThe **m2g** pipeline has been developed as a beginner-friendly solution for human connectome estimation by providing robust and reliable estimates of connectivity across a wide range of datasets. The pipelines are explained and derivatives analyzed in our pre-print, available on [BiorXiv](https://www.biorxiv.org/content/10.1101/2021.11.01.466686v1.full).\n\n# Documentation\n\nCheck out some [resources](http://m2g.io) on our website, or our [function reference](https://ndmg.neurodata.io/) for more information about **m2g**.\n\n# System Requirements\n\n## Hardware Requirements\n\n**m2g** pipelines requires only a standard computer with enough RAM (\u003c 16 GB).\n\n## Software Requirements\n\nThe **m2g** pipeline:\n\n- was developed and tested primarily on Mac OS (10,11), Ubuntu (16, 18, 20), and CentOS (5, 6);\n- made to work on Python 3.7-3.10;\n- is wrapped in a [Docker container](https://hub.docker.com/r/neurodata/m2g);\n- has install instructions via a Dockerfile;\n- requires no non-standard hardware to run;\n- has key features built upon FSL, AFNI, INDI, Dipy, Nibabel, Nilearn, Networkx, Numpy, Scipy, Scikit-Learn, and others\n  - For Python package version numbers, see [requirements.txt](requirements.txt)\n  - For binaries required to install AFNI, FSL, INDI, ICA_AROMA, see the [Dockerfile](Dockerfile)\n- takes approximately 1-core, \u003c 16-GB of RAM, and 1-2 hours to run for most datasets (varies based on data).\n\n# Installation\n\nInstructions can be found within our documentation: https://docs.neurodata.io/m2g/install.html\n\n# Usage\n\nInstructions can be found within our [documentation](https://docs.neurodata.io/m2g/usage.html) and a demo can be found [here](https://docs.neurodata.io/m2g/usage.html#demo).\n\n# License\n\nThis project is covered under the [Polyform License](https://github.com/neurodata/m2g/blob/deploy/LICENSE).\n\n# Issues\n\nIf you're having trouble, notice a bug, or want to contribute (such as a fix to the bug you may have just found) feel free to open a git issue or pull request. Enjoy!\n\n# Citing `m2g`\n\nIf you find `m2g` useful in your work, please cite the package via the [m2g paper](https://www.biorxiv.org/content/10.1101/2021.11.01.466686)\n\n\u003e Chung, J., Lawrence, R., Loftus, A., Kiar, G., Bridgeford, E. W., Roncal, W. G., Chandrashekhar, V., ... \u0026 Consortium for Reliability and Reproducibility (CoRR). (2024). A low-resource reliable pipeline to democratize multi-modal connectome estimation and analysis. bioRxiv, 2024-04.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fneurodata%2Fm2g","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fneurodata%2Fm2g","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fneurodata%2Fm2g/lists"}