massimopizzol/aligned-datapackage

Using the frictionless framework to create data packages, resources, and schema for life cycle inventory data generated in the aligned project

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aligned-datapackage

Using the frictionless framework to create data packages, resources, and schema for Life Cycle Inventory (LCI) data generated in the ALIGNED project.

(Wait, I am not a programmer!)

You can still use the material in this repository. All files can be opened and edited manually using a text editor (Notepad, TextEdit, etc.). You can modify the schema.json, datapackage.json, and resource.json files manually and use them as a starting point for documenting your data.

About this repository

• Includes code and templates to share, report, and document LCI data.

• Is designed primarily for foreground LCI data, i.e. new primary data about the product and biosphere exchanges of a relatively restricted number of activities, ideally linked to a background database of secondary data.

• Is based on the frictionless standards for describing data. The python scripts use the frictionless framework and related libraries.

• Uses the LCI data format decribed in detail in this ALIGNED T1.2 LCI data template zenodo archive. This tabular format mirrors the nomenclature and fields used in the Brightway2 open LCA software framework.

• It is intended for both python and non-python users.

• Targets as primary audience the Life Cycle Assessment and Industrial Ecology communities.

• Allows to include GLAD metadata. GLAD is the Global LCA Data Access Network.

Structure of the repository

Folder aligned-schema:

• includes a base schema for the LCI data format, the same documented in the ALIGNED T1.2 LCI data template zenodo archive.

• In plain words, a schema is here intended as a description of each column of a table of data. This allows to understand the type of data and what is contained in the table of data.

• This schema includes only the recommended minimal number of fields to be used to share LCI data. Other fields can be added if needed.

• Once you have put your LCI data in table format, use this schema to document their structure.

Folder example-package:

• Is essentially a template for a datapackage of LCI data.
• Includes a datapackage.json file, a schema.json file, and the data (in this context called "resources", in practice these are .csv files) in a separate subfolder.
• It is thus compliant with the frictionless framework recommended structure
• The datapackage.jsonfile specifies which data are included in the package and provides metadata such as author, license, contributors, sources, and description.
• Use this folder as a template to organize and document the LCI data that you have.

Folder code-and-examples:

• Is primarily for python users.

• Uses pythorn libraries from the frictionless framework.

• Contains code that can be used to create descriptors for schema, datapackage, and resources.

• In particular create-resource-with-glad-metadata.py shows how to add GLAD metadata to a LCI tabular resource.

How to use

The suggested procedure is:

1. Download a copy of the example-package folder.

2. Prepare LCI data in tabular format using information in the zenodo archive or in the aligned-schema.json file, example tables available in the repository aligned-converter repository or in the example-package/data folder. Save the files in .csv format in the Data subfolder.

3. If needed, based on the tabular structure of the data modify manually the schema.json file or create one automatically as shown in create-schema.py

4. Modify manually the datapackage.json file or create one automatically as shown in create-datapackage-with-metadata.py and add minimal metadata such as name, description, licenses, contributors...

5. Optionally (but recommended!) create a resource descriptor for each data file using the GLAD metadata fields. See create-resource-with-glad-metadata.py

6. Share the data in a repository.

At this point you will have LCI data:

• Formatted in a way that can be imported in open source LCA software.

• Whose format is clearly documented in a schema, that can be used to validate the datasets and detect errors but also to properly read the data.

• Organized in files and directories with a standard structure.

• Shared and accesible to others (if in a public repository).

• That include the essential metadata as well as domain-specific GLAD metadata that are recognized by, and relevant for, the LCA community.

Contact & info

Massimo Pizzol

Funding

This work was carried out within Work Package 1: Shared modelling framework and learnings, Task 1.2: Framework for foreground life cycle inventory of bio-based sectors, of the ALIGNED project wwww.alignedproject.eu, Horizon Europe grant agreement N° 101059430 ¹.

Footnotes

1. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Executive Agency. Neither the European Union nor the granting authority can be held responsible for them. ↩