# jupyter-naas/awesome-notebooks

[Legacy] Data & AI Notebook templates catalog organized by tools, following the IMO (input, model, output) framework for easy usage and discovery..

Repository: https://github.com/jupyter-naas/awesome-notebooks
Canonical: https://ross.abutalabs.com/products/awesome-notebooks
Homepage: https://naas.ai/search
Language: Jupyter Notebook
License: BSD-3-Clause
License Family: permissive
Topics: templates, opensource, naas, notebooks-templates, jupyter, jupyter-notebook, jupyterlab, python, awesome, awesome-list, hacktoberfest, hacktoberfest2023
Last push: 2024-10-21T10:10:48+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2163, "days_push": 681, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3014, forks 489 (observed 2026-08-28T04:07:37.878898+00:00)

## What it is
A curated catalog of production-ready Jupyter Notebook templates organized by tool and structured with an Input-Model-Output (IMO) framework. It serves as a template library for building data products like dashboards and automation/AI engines.

## Use cases
- find jupyter notebook templates for data analysis
- build a dashboard from a notebook template
- automate tasks with ready-made python notebooks
- learn how to structure notebooks with input model output
- connect to third-party apis from a notebook template
- kickstart a data product with reusable notebook components

## When to choose
- you want pre-built, production-ready notebook templates instead of starting from scratch
- you need examples of integrating Jupyter with third-party tools via APIs
- you want a consistent IMO structure for organizing data workflows

## When to avoid
- you need actively maintained or updated templates, as the repository is marked legacy
- you want a software library or package rather than a collection of notebooks
- you require templates without any data science setup skills

## Facets
- artifact type: dataset
- maturity: maintenance
- function: developer-tools, data-science, data-visualization, workflow-automation, machine-learning
- domain: data-science, developer-tools, tutorials, awesome-lists
- platform: python, jvm, cross-platform
- tags: jupyter-notebooks, notebook-templates, awesome-list, imo-framework, data-products, legacy, automation

## Member repositories
- jupyter-naas/awesome-notebooks (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:37.878898+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:47:01.713215+00:00, confidence not recorded.
  - readme: https://github.com/jupyter-naas/awesome-notebooks (fetched 2026-08-28T04:07:37.878898+00:00, sha 8f69ebe1662c)
  - homepage: https://naas.ai/search (fetched 2026-08-29T09:45:22.894386+00:00, sha 7488b4795722)
- Data as of 2026-08-30T08:39:29.467469+00:00.