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Add anchored CreditClassInfo datasets along with proposed updates (#35)
* add anchored datasets (v0) * add anchored datasets (v0 & v1) * drop v0 * rename to v0 * updates to BT01 * updates to C01 * use xxx for properties that warrent discussion * update xxx from note field * remove more inline notes * remove notes from C05 * drop comment * add csv of untracked fields * update csv * transpose table * truncate csv * update csv * create jsonld validation script, start refactoring Impact * add known fields csv * update validate script * update validate script * update validation script * add .vscode/launch.json for step debugging * add back astro launch.json * add validate output * cleanup C05 meatadata * update credit classes to incorporate missing metadata * add primary impacts and cobenefits for C01 and C03 * update C05 typeo * rm unneeded csvs * mv bufferPoolAccounts to hasBufferPoolAccounts for C05 * last updates to CreditClassInfo datasets * updates to Pauls ProjectInfo schema * fix PR bugs (incorrect url for managedUnderProgram values, missing eligible activity for C02 * rm validate_output file * update schema:url for consistent usage of marketplace URLs, and set as json-ld @id * fix typeo * typeo in Verra program URL * add start & end date slots to ProjectInfo
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‎.vscode/launch.json‎

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"name": "Development server",
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"request": "launch",
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"type": "node-terminal"
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},
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{
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"name": "Python Debugger: Current File",
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"type": "debugpy",
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"request": "launch",
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"program": "${file}",
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"console": "integratedTerminal",
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"args": ["data/anchored/v0/classes/BT01.jsonld"],
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"cwd": "${workspaceFolder}/schema",
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"justMyCode": false
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}
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]
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}

‎schema/data/anchored/create_csv_known_fields.py‎

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import os
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import json
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import pandas as pd
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# Define the folder containing the JSON-LD files
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folder_path = 'v0/classes' # update with your folder path
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# Dictionary to store data where keys are file names (without extension) and values are dicts of key-value pairs
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data = {}
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# Iterate over all files in the folder
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for filename in os.listdir(folder_path):
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if filename.endswith('.jsonld'):
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file_id = os.path.splitext(filename)[0] # Remove extension, e.g. "C01" from "C01.jsonld"
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file_path = os.path.join(folder_path, filename)
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with open(file_path, 'r', encoding='utf-8') as f:
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try:
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json_data = json.load(f)
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except json.JSONDecodeError as e:
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print(f"Error decoding JSON in file {filename}: {e}")
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continue
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# Filter out key-value pairs that start with 'xxx:'
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filtered = {}
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for key, value in json_data.items():
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if not key.startswith("xxx:"):
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# # If the value is not a string, replace it with a placeholder
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# if isinstance(value, list):
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# filtered[key] = str(value)[:15]
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# elif isinstance(value, dict):
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# filtered[key] = str(value)[:15]
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# else:
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# filtered[key] = value
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filtered[key] = value
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data[file_id] = filtered
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# Create a set of all keys found across all files
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all_keys = set()
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for file_dict in data.values():
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all_keys.update(file_dict.keys())
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# Build a DataFrame where index is the keys (row names) and columns are file identifiers
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df = pd.DataFrame(index=sorted(all_keys))
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for file_id, kv in data.items():
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# Create a Series for this file, aligning it with the DataFrame index
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s = pd.Series(kv)
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df[file_id] = s
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# Save the DataFrame to CSV
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output_csv = 'known_fields.csv'
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df.to_csv(output_csv)
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print(f"CSV output written to {output_csv}")

‎schema/data/anchored/create_csv_unknown_fields.py‎

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import os
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import json
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import pandas as pd
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# Define the folder containing the JSON-LD files
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folder_path = 'v0/classes' # update with your folder path
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# Dictionary to store data where keys are file names (without extension) and values are dicts of key-value pairs
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data = {}
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# Iterate over all files in the folder
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for filename in os.listdir(folder_path):
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if filename.endswith('.jsonld'):
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file_id = os.path.splitext(filename)[0] # Remove extension, e.g. "C01" from "C01.jsonld"
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file_path = os.path.join(folder_path, filename)
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with open(file_path, 'r', encoding='utf-8') as f:
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try:
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json_data = json.load(f)
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except json.JSONDecodeError as e:
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print(f"Error decoding JSON in file {filename}: {e}")
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continue
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# Filter out key-value pairs that start with 'xxx:'
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filtered = {}
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for key, value in json_data.items():
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if key.startswith("xxx:"):
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# # If the value is not a string, replace it with a placeholder
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# if isinstance(value, list):
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# filtered[key] = str(value)[:15]
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# elif isinstance(value, dict):
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# filtered[key] = str(value)[:15]
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# else:
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# filtered[key] = value
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filtered[key] = value
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data[file_id] = filtered
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# Create a set of all keys found across all files
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all_keys = set()
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for file_dict in data.values():
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all_keys.update(file_dict.keys())
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# Build a DataFrame where index is the keys (row names) and columns are file identifiers
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df = pd.DataFrame(index=sorted(all_keys))
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for file_id, kv in data.items():
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# Create a Series for this file, aligning it with the DataFrame index
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s = pd.Series(kv)
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df[file_id] = s
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# Save the DataFrame to CSV
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output_csv = 'output.csv'
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df.to_csv(output_csv)
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print(f"CSV output written to {output_csv}")

‎schema/data/anchored/known_fields.csv‎

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‎schema/data/anchored/v0/classes/BT01.jsonld‎

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{
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"@type": "rfs:CreditClassInfo",
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"@context": {
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"rfs": "https://framework.regen.network/schema/",
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"rft": "https://framework.regen.network/taxonomy/",
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"schema": "http://schema.org/"
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},
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"schema:url": "https://app.regen.network/credit-classes/BT01",
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"schema:name": "Terrasos Biodiversity Unit",
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"schema:description": "The Biodioversity Credits protocol outlines a strategic framework for the development of projects that guarantee quantifiable gains in biodiversity, all while providing robust financial and legal assurances to secure their sustainability and enduring impact. This pioneering approach to biodiversity conservation and resource management emphasizes the selection of projects distinguished by their significant ecological value within a performance-based model. \n This protocol streamlines the process of registration, quantification, and issuance of Biodiversity Credits (VBC) for conservation projects that meet the eligibility criteria. It is designed to resonate with both individuals and organizations committed to contributing to the preservation of threatened ecosystems and biodiversity, ultimately delivering long-term sustainability and socio-economic benefits.",
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"rfs:hasPrimaryImpact": {
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"rfs:hasImpactType": "IMPROVED_BIODIVERSITY",
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"rfs:supportsSDG": [
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"SDG_15"
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],
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"schema:name": "Improved Biodiversity"
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},
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"rfs:hasCoBenefits": [
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{
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"rfs:hasImpactType": "IMPROVED_BIODIVERSITY",
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"rfs:supportsSDG": [
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"SDG_13"
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],
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"schema:name": "Climate"
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},
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{
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"rfs:hasImpactType": "IMPROVED_WILDLIFE_HABITAT",
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"schema:name": "Improved Wildlife Habitat"
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}
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],
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"rfs:managedUnderProgram": {
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"schema:url": "https://registry-program-guide.regen.network",
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"schema:name": "Regen Registry Program"
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},
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"rfs:hasCreditingPeriod": "P30Y",
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"rfs:eligibleEnvironmentTypes": [
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"TROPICAL_FOREST"
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],
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"rfs:eligibleActivities": [
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"CONSERVATION",
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"AFFORESTATION",
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"ECOSYSTEM_RESTORATION"
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],
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"rfs:hasCreditProtocol": [
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{
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"schema:url": "https://www.registry.regen.network/crediting-protocols/terrasos-biodiversity-unit",
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"schema:name": "Terrasos Protocol for Issuing Voluntary Biodiversity Credits",
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"schema:version": "V3.0"
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},
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{
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"schema:url": "https://www.registry.regen.network/crediting-protocols/terrasos-biodiversity-unit",
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"schema:name": "Terrasos Protocol for Issuing Voluntary Biodiversity Credits",
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"schema:version": "V4.0"
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}
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]
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}

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