Open Notebook

Open Notebook is a community skill for creating and managing computational notebooks for reproducible research, covering notebook structure, code cell organization, output management, metadata annotation, and sharing workflows for open science practices.

What Is This?

Overview

Open Notebook provides tools for building well-structured computational notebooks that support reproducibility and sharing. It covers notebook structure that organizes content into logical sections with narrative text and executable code, code cell organization that sequences analysis steps with clear dependencies and documentation, output management that controls which results are displayed, saved, and exported from notebook executions, metadata annotation that records environment details and execution parameters for reproducibility, and sharing workflows that prepare notebooks for publication with cleaned outputs and dependency specifications. The skill enables researchers to maintain transparent computational records.

Who Should Use This

This skill serves researchers documenting computational experiments for publication, data scientists sharing analysis workflows with collaborators, and educators creating interactive teaching materials using notebooks.

Why Use It?

Problems It Solves

Unstructured notebooks become difficult to follow when cells are executed out of order or lack explanatory context. Notebooks without environment metadata fail to reproduce when dependencies change. Large output cells consume storage and clutter notebook files shared through version control. Notebooks shared without dependency information leave collaborators unable to run the analysis.

Core Highlights

Structure builder organizes notebooks into titled sections with narrative flow. Cell manager sequences code cells with clear dependencies and inline documentation. Output controller manages which results are displayed and persisted. Environment recorder captures dependency and parameter information for reproducibility.

How to Use It?

Basic Usage

from dataclasses import (
  dataclass, field)
from datetime import (
  datetime)
import json

@dataclass
class NotebookMeta:
  title: str
  author: str
  created: str = field(
    default_factory=
      lambda: datetime
        .now()
        .isoformat())
  python_version:
    str = ''
  packages:
    dict = field(
      default_factory=
        dict)

  def capture_env(self):
    import sys
    import pkg_resources
    self.python_version = (
      sys.version)
    self.packages = {
      p.project_name:
        p.version
      for p in
        pkg_resources
          .working_set}

  def to_dict(self):
    return {
      'title': self.title,
      'author':
        self.author,
      'created':
        self.created,
      'python':
        self.python_version,
      'packages':
        self.packages}

  def save(self, path):
    with open(path,
      'w') as f:
      json.dump(
        self.to_dict(),
        f, indent=2)

Real-World Examples

import nbformat

class NotebookValidator:
  def __init__(
    self,
    notebook_path: str
  ):
    with open(
      notebook_path) as f:
      self.nb = nbformat\
        .read(f, as_version=4)

  def check_structure(
    self
  ) -> list[str]:
    issues = []
    cells = self.nb.cells

    # Check first cell
    if (not cells
        or cells[0]
          .cell_type
          != 'markdown'):
      issues.append(
        'Missing title cell')

    # Check for outputs
    for i, cell in\
        enumerate(cells):
      if (cell.cell_type
          == 'code'
          and cell.outputs):
        total = sum(
          len(str(
            o.get(
              'text', '')))
          for o in
            cell.outputs)
        if total > 10000:
          issues.append(
            f'Cell {i}: '
            f'large output')

    # Execution order
    exec_counts = [
      c.execution_count
      for c in cells
      if c.cell_type
        == 'code'
      and c.execution_count
        is not None]
    if (exec_counts
        != sorted(
          exec_counts)):
      issues.append(
        'Out-of-order '
        'execution')

    return issues

  def report(self) -> dict:
    return {
      'cells':
        len(self.nb.cells),
      'code_cells': sum(
        1 for c in
          self.nb.cells
        if c.cell_type
          == 'code'),
      'issues': self
        .check_structure()}

Advanced Tips

Run notebooks from top to bottom in a clean kernel before sharing to verify that all cells execute in sequence without hidden state dependencies. Use nbstripout as a git filter to automatically remove outputs from notebooks before committing to version control. Include a requirements file alongside notebooks specifying exact package versions for environment reproduction.

When to Use It?

Use Cases

Prepare a research notebook for publication by validating structure, cleaning outputs, and recording environment metadata. Create a template notebook with standard sections for consistent analysis documentation across a research team. Validate that shared notebooks execute cleanly in a fresh environment before distribution.

Related Topics

Jupyter notebooks, reproducible research, computational notebooks, open science, notebook validation, and literate programming.

Important Notes

Requirements

Jupyter notebook environment or compatible notebook platform. nbformat library for programmatic notebook manipulation. Version control system for tracking notebook changes.

Usage Recommendations

Do: include a markdown cell at the top of every notebook with title, author, date, and purpose description. Clear all outputs and restart the kernel before final execution to verify reproducibility. Use relative file paths in notebooks so they work across different environments.

Don't: store sensitive data like credentials or API keys in notebook cells since these persist in notebook files. Use global variables to share state between distant cells since this creates hidden dependencies. Commit notebooks with large binary outputs to version control since this bloats repository size.

Limitations

Notebook format stores outputs inline with code creating large files when results include images or data frames. Cell execution order is not enforced by the notebook interface allowing hidden state dependencies. Merging notebook files in version control is difficult due to their JSON structure.