Add notebook guidelines (#316)

docs/cell_census_notebook_guidelines.md

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+# Cell Census API notebook/vignette editorial guidelines
+
+API demonstration code that is part of the documentation should be deposited here: 
+
+- Python notebooks [`cell-census/api/python/notebooks`](https://github.com/chanzuckerberg/cell-census/tree/main/api/python/notebooks)
+- R vignettes [`cell-census/api/r/CellCensus/vignettes`](https://github.com/chanzuckerberg/cell-census/tree/main/api/r/CellCensus/vignettes)
+
+
+These assets are user-facing and are automatically rendered to the doc-sites, they are one of the primary means by which users get onboarded to the product. Thus the following guidelines need to be followed to ensure readability and a consistent experience.
+
+## Guidelines
+
+### Title
+
+* It must use the highest-level markdown header `#`.
+* Unless needed, it should not contain "Cell Census".
+* It should be concise, self-explanatory, and if possible indicate an action.
+
+Examples:
+
+:white_check_mark: `# Exploring all data from a tissue.`
+
+:white_check_mark: `# Query and extract slices of data.`
+
+:x: `# Census datasets presence` *not self-explanatory, has "Census" in it.*
+
+:x: `# Normalizing full-length gene sequencing data from the Cell Census` *has "Census" in it.*
+
+### Introduction
+
+Introductory text must be included right underneath the title.
+
+* It must provide a one paragraph summary of the notebook's goals.
+* It must not contain an explanation of the Cell Census.
+
+Examples: 
+
+:white_check_mark:
+
+> This notebook provides a demonstration for integrating two Cell Census datasets using scvi-tools. The goal is not to provide an exhaustive guide on proper integration, but to showcase what information in the Cell Census can inform data integration.
+
+:x: *it contains a long explanation of what the Cell Census is, and the goal is not clear*
+
+> The Cell Census is a versioned container for the single-cell data hosted at CELLxGENE Discover. The Cell Census utilizes SOMA powered by TileDB for storing, accessing, and efficiently filtering data.
+>
+>This notebook shows you how to learn about the Cell Census contents and how to query it.
+
+### Table of Contents 
+
+Immediately after the introduction a table of contents must be provided:
+
+* It must be placed under the bolded word "**Contents**" . 
+* It must contain an ordered list of the second-level headers (`##`) used for [Sections](#Sections).
+* If necessary it may contain sub-lists corresponding to lower-level headers (`###`, etc)
+
+Example:
+
+:white_check_mark:
+
+> **Contents**
+> 
+> 1. Learning about the lung data.
+> 2. Fetching all human lung data from the Cell Census.
+> 3. Obtaining QC metrics for this data slice.
+
+### Sections
+
+The rest of the notebook/vignette content must be organized within sections:
+
+* The section title must use the second-level markdown header `##`. **This is important as the python doc-site renders these in the sidebar and in the full view of all notebooks.**
+* The section title should be concise, self-explanatory, and if possible indicate an action.
+* The section's contents and presence or absence of sub-headers are left to the discretion of the writer.
+* The section's non-code content should be kept as succinct as possible.
+
+
+## Example notebook/vignette 
+
+```
+# Integrating data with SCVI.
+
+This notebook provides a demonstration for integrating two 
+Cell Census datasets using `scvi-tools`. The goal is not to 
+provide an exhaustive guide on proper integration, but to showcase 
+what information in the Cell Census can inform data integration.
+
+**Contents**
+
+1. Finding and fetching data from mouse liver.
+2. Gene-length normalization of Smart-Seq2 data.
+3. Integration with scvi-tools.
+   1. Inspecting data prior to integration.
+   2. Integration with batch defined as dataset_id.
+   3. Integration with batch defined as dataset_id + donor_id.
+   4. Integration with batch defined as dataset_id + donor_id + assay_ontology_term_id + suspension_type.
+
+## Finding and fetching data from mouse liver
+
+Let's load all modules needed for this notebook.
+
+\code
+  import cell_census
+  import scanpy as sc
+  import numpy as np
+  import scvi
+  from scipy.sparse import csr_matrix
+\code 
+
+Now we can open the Cell Census 
+
+\code 
+  census = cell_census.open_soma(census_version="latest")
+\code
+
+In this notebook we will use Tabula Muris Senis data 
+from the liver as it contains cells from both 10X 
+Genomics and Smart-Seq2 technologies.
+
+Let's query the datasets table of the Cell Census by 
+filtering on collection_name for "Tabula Muris Senis" 
+and dataset_title for "liver".
+
+[...]
+```