Merge branch 'master' of https://github.com/datacommonsorg/data into …

…floods

.gitignore

@@ -10,4 +10,7 @@ __pycache__/
 **/tmp/
 dc_generated/
 # Ignore the data folder under the un_census/enhanced_tmcf/ directory.
-*/*/*/data/*
+*/*/*/data/*
+lib/
+bin/
+pyvenv.cfg

.gitmodules

@@ -0,0 +1,6 @@
+[submodule "scripts/un/sdg/sdg-dataset"]
+	path = scripts/un/sdg/sdg-dataset
+	url = https://code.officialstatistics.org/undata2/data-commons/sdg-dataset.git
+[submodule "scripts/un/sdg/sssom-mappings"]
+	path = scripts/un/sdg/sssom-mappings
+	url = https://code.officialstatistics.org/undata2/sssom-mappings.git

LICENSE-CC4

@@ -0,0 +1,7 @@
+Copyright 2023 Google LLC
+
+The non-source code materials in this project are licensed under:
+Creative Commons - Attribution CC-BY 4.0
+
+For the full license text, please visit:
+https://creativecommons.org/licenses/by/4.0/legalcode

README.md

@@ -249,7 +249,6 @@ To disable yapf for some lines,
 
 For general questions or issues about importing data into Data Commons, please
 open an issue on our [issues](https://github.com/datacommonsorg/data/issues)
-page. For all other questions, please send an email to
-`[email protected]`.
+page. For all other questions, please [share feedback on this form](https://docs.google.com/forms/d/e/1FAIpQLScJTtNlIItT-uSPXI98WT6yNlavF-kf5JS0jMrCvJ9TPLmelg/viewform).
 
 **Note** - This is not an officially supported Google product.

docs/README.md

@@ -1,63 +1,42 @@
 # Adding a Dataset to Data Commons
 
-This document summarizes the steps involved in adding a dataset to Data Commons
-(DC).  As a prerequisite, please ensure that the DC core team
-([email protected]) has approved the addition of the dataset.
+This document summarizes the steps involved in adding a dataset to Data Commons (DC).
 
-## Background
+## Prerequisites
 
-The following documents provide a background on the data model, format and workflow:
+* Ensure that the Data Commons team has approved the addition of the dataset. Please [suggest a dataset here](https://docs.google.com/forms/d/e/1FAIpQLSf_kZ13bmzXvgEbim0OXeAVsTQYsIhN8_o9ekdbjKoeFjfvRA/viewform).
+* Review the following documents to get a background on the data model, format and workflow:
+  * [Summary of data model](https://schema.org/docs/datamodel.html) (DC inherits schema from schema.org)
+  * [How statistics is represented in DC](representing_statistics.md)
+  * [MCF Format](mcf_format.md)
+  * [Life of a dataset](life_of_a_dataset.md)
 
-1. [Summary of data model](https://schema.org/docs/datamodel.html) (DC inherits schema from schema.org)
-1. [How statistics is represented in DC](representing_statistics.md)
-1. [MCF Format](mcf_format.md)
-1. [Life of a dataset](life_of_a_dataset.md)
+## Design location and schema mapping
 
-## Designing location and schema mapping
+Data Commons is a single graph that reconciles references to the same entities and concepts across datasets. This linking happens at the time of importing datasets. To get started:
 
-Data Commons is a single graph that reconciles references to the same entities
-and concepts across datasets. This linking happens at the time of importing
-datasets.
+* Identify how the locations/places/entities and variables/properties in the dataset will get mapped.
+* For locations/places, use the following (in preferred order): global identifiers (like FIPS), geo info (lat/lng, geo boundary), qualified names.
+* For variables, find already existing schema in Data Commons (from existing statistical variables [here](https://datacommons.org/tools/statvar)), or add new StatisticalVariable nodes along with core schema (new Class, Property, Enumeration nodes) as necessary.
 
-As part of the first step, we identify how the locations/places/entities and
-variables/properties in the dataset will get mapped. 
+This process typically happens in collaboration with the DC core team, and we recommend that you put together a short import document ([Template](https://docs.google.com/document/d/1RUOD3VLZFBmyjZzBnwQBKB9TxNE7NhD4g9WX6gUZCQU/), [Example1](https://docs.google.com/document/d/e/2PACX-1vScfoVm36L7x1p4Bqh82JmDmsumhqiPz_w6zX7wzy0nX8kDLxMJw44hOBgB6CDd2o0kYKekdgNWIR1f/pub), [Example2](https://docs.google.com/document/d/e/2PACX-1vS9R0eZO-AhQ19jQcLyOyYODn3dF8wGjytro0nFTjp4MsoFvsAgD7mayppcseLvNSCO6Ac4-8b2SXe4/pub))
 
-* For locations/places, we can use the following (in preferred order):  global
-identifiers (like FIPS), geo info (lat/lng, geo boundary), qualified names.
-The approach we use depends on how the locations appear in the dataset.
-
-* For variables, we either need to find already existing schema in Data Commons
-(from existing statistical variables
-[here](https://datacommons.org/tools/statvar)), or add new StatisticalVariable
-nodes along with core schema (new `Class`, `Property`, `Enumeration` nodes) as
-necessary.
-
-This process typically happens in collaboration with the DC core team, and we
-recommend that you put together a short import document.
-
-Links:
-* Suggested [import document template](https://docs.google.com/document/d/1RUOD3VLZFBmyjZzBnwQBKB9TxNE7NhD4g9WX6gUZCQU/)
-* [Example1](https://docs.google.com/document/d/e/2PACX-1vScfoVm36L7x1p4Bqh82JmDmsumhqiPz_w6zX7wzy0nX8kDLxMJw44hOBgB6CDd2o0kYKekdgNWIR1f/pub)
-* [Example2](https://docs.google.com/document/d/e/2PACX-1vS9R0eZO-AhQ19jQcLyOyYODn3dF8wGjytro0nFTjp4MsoFvsAgD7mayppcseLvNSCO6Ac4-8b2SXe4/pub)
-
-## Schema-less imports
-
-For datasets with complex schema or ones that we want to import quickly, we can start with a schema-less import, and iteratively add schema. The “schema-less” part of this framework means that the SV is not yet fully defined. This lets us get the dataset into Data Commons without blocking on schema definition.
-
-Links:
-* [Schema-less Import Guide](https://docs.datacommons.org/import_dataset/schema_less_guide.html)
-* [Import document template](https://docs.google.com/document/d/1GC7DTpxXo_3zreDRt7wFuURBfA1T275p-qx1N-VIdGM/)
-* [Example](https://docs.google.com/document/d/e/2PACX-1vS6ItxH7T_XvYuz4-xeO9LKoYlrXr-YkrwiclRWcdtYm11J8OQHUwDw4E66uaTQA7yTdwLXfrNBdKgz/pub)
+### Schema-less imports
+For datasets with complex schema or ones that we want to import quickly, we can start with a schema-less import, and iteratively add schema. The “schema-less” part of this framework means that the statistical variables are not yet fully defined. This lets us get the dataset into Data Commons without blocking on schema definition. To learn more, please review the following links:
+  * [Schema-less Import Guide](https://docs.datacommons.org/import_dataset/schema_less_guide.html)
+  * [Schema-less import document template](https://docs.google.com/document/d/1GC7DTpxXo_3zreDRt7wFuURBfA1T275p-qx1N-VIdGM/)
+  * [Example](https://docs.google.com/document/d/e/2PACX-1vS6ItxH7T_XvYuz4-xeO9LKoYlrXr-YkrwiclRWcdtYm11J8OQHUwDw4E66uaTQA7yTdwLXfrNBdKgz/pub)
 
 ## Preparing artifacts
 
-Once the entity and schema mapping have been finalized, you prepare the artifacts.  This includes:
+Once the entity and schema mapping have been finalized, you can now prepare the artifacts. This includes:
+  * StatisticalVariable MCF nodes (if any) checked into [schema repo](https://github.com/datacommonsorg/schema/tree/main/stat_vars)
+  * Template MCF and corresponding cleaned tabular files (typically CSV).
+  * Data cleaning code (along with README) checked into [data repo](https://github.com/datacommonsorg/data)
+  * Validation results for the artifacts (from running [`dc-import`](https://github.com/datacommonsorg/import#using-import-tool) tool)
+
+Note: you may also use the [DC Import Wizard](https://datacommons.org/import) to help generate artifacts for common dataset structures
 
-1. StatisticalVariable MCF nodes (if any) checked into [schema repo](https://github.com/datacommonsorg/schema/tree/main/stat_vars). These nodes may be written by hand when there are only a handful in number. Otherwise, these nodes can be generated via scripts.
-2. Template MCF and corresponding cleaned tabular files (typically CSV). Like StatisticalVariable MCF nodes, the Template MCF nodes can also be hand-written or script-generated depending on number of nodes.
-3. Data cleaning code (along with README) checked into [data repo](https://github.com/datacommonsorg/data)
-4. Validation results for the artifacts (from running [`dc-import`](https://github.com/datacommonsorg/import#using-import-tool) tool)
+## Review by DC team
 
-When all the artifacts are ready, please get it reviewed by the DC core team
-via github Pull Requests. More details on this step are in the [Life of a
-Dataset](life_of_a_dataset.md) document.
+When all the artifacts are ready, please get it reviewed by the DC core team via github Pull Requests made to the [data repo](https://github.com/datacommonsorg/data).