nipype-beginner-s-guide
importedsoftware/nipype-beginner-s-guide
Beginner's guide for Nipype
Machine-generated from the listed sources and not yet reviewed by a human.
- Category
- Software & Systems
- Subcategory
- unknown
- License
- BSD-3-Clause(osi)
- Status
- dormant
- Maturity
- deployed
- Organization
- unknown
- Country
- unknown
- Documentation
- unknown
- Tags
- neuroimaging · nipype · python · tutorial
- Regulatory
- unknown
Top contributors by commit count, from the project’s public repository. Avatars are served by their origin, not stored here. To be removed from this list, open an issue.
Computed from shared tags, weighted so a rare tag counts for more than a common one. These are suggestions, not curated relationships.
The Medical Image Analysis Laboratory Super-Resolution ToolKit (MIALSRTK) consists of a set of C++ and Python processing and workflow tools necessary to perform motion-robust super-resolution fetal…
- emr-spark-jupytertutorial
:notebook: Repository/Tutorial for initiallizing Jupyter Notebook and Spark cluster on Amazon EMR
- EpiMethodstutorial
A unique open textbook to teach the nuances of applying advanced epidemiological methods using real data.
- NGS-variants-trainingtutorial
GitHub for the SIB courses NGS - Genome variant analysis
- single-cell-trainingtutorial
SIB course on single cell transcriptomics by mostly using the Seurat pipeline
- acerta-abideneuroimaging
Deep learning using the ABIDE data
- api.github.com/repos/miykael/nipype-beginner-s-guideretrieved 2026-08-05 · via github-api
Machine-imported from GitHub search. Last push 2023-02-07, 37 stars, license reported as BSD-3-Clause. Category and schematic were assigned by keyword heuristics and are unreviewed.
Not yet verified by a human. Correct this record →
/v1/entries/32.json→ .entries["nipype-beginner-s-guide"]
Entries are sharded 64 ways by a stable hash of the id, so a consumer can find any record without an index.