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Uploading Evidence & Field Values

Overview​

Once you've configured your Evidence Requests, Expected Fields, and Model calculations, it's time to submit the actual data:

  • Evidence - Files containing supporting documentation
  • Expected Field Values - Data values that feed into your model calculations

File Naming Best Practices​

Before uploading evidence files, follow these naming conventions:

Rename Final Documents​

Rename final document versions to clearly reflect the facility name and document purpose.

Examples:

  • ✅ Puro Project Description [Facility Name].docx
  • ✅ LCA_Report_FacilityA_2024.pdf
  • ✅ Batch_Production_Data_Q4_2024.xlsx
  • ❌ Puro Project Description.docx (too generic)
  • ❌ document1.pdf (unclear purpose)

Keep Names Concise and Specific​

  • Include facility name
  • Include document type/purpose
  • Include time period if relevant
  • Avoid special characters
  • Use consistent format across all files

Uploading Evidence​

Evidence files are uploaded to Evidence Requests using multipart/form-data.

Uploading Evidence to a Standard Request​

Create evidence
curl -X 'POST' "/v0/evidence-requests/{id}/evidence" \
  -H "Accept: application/json" \
  -H "Authorization: Bearer $BEARER" \
  -d '{ \
    "file": "LCA_Report_Facility_A_2024.pdf", \
    "evidenceTemplateId": "template-uuid-if-applicable" \
  }'

Parameters​

  • file (required) - The file to upload
  • evidenceTemplateId (optional) - UUID of the evidence template if the file follows a specific template structure

Response​

The response includes:

id
required
string
evidenceRequestId
required
string
evidenceTemplateId
required
string or null
validationErrors
required
Array of objects
  • id - The evidence ID
  • evidenceRequestId - The request it belongs to
  • evidenceTemplateId - Template used (if applicable)
  • validationErrors - Any validation errors found in the submission
Validation

If you're uploading a file that matches an evidence template, the system will validate the file structure against the template and return any errors in the validationErrors array.

Creating Expected Field Values​

Expected Field Values are the actual data points that populate your Expected Fields.

Creating a Numeric Value​

Create expected field value
curl -X 'POST' "/v0/evidence-requests/{id}/values" \
  -H "Accept: application/json" \
  -H "Authorization: Bearer $BEARER" \
  -d '{ \
    "type": "NUMERIC", \
    "expectedFieldId": "field-uuid", \
    "value": "1250.5", \
    "batchItemId": null \
  }'

Creating a Text Value​

Create expected field value
curl -X 'POST' "/v0/evidence-requests/{id}/values" \
  -H "Accept: application/json" \
  -H "Authorization: Bearer $BEARER" \
  -d '{ \
    "type": "TEXT", \
    "expectedFieldId": "field-uuid", \
    "value": "Supplier XYZ Corp", \
    "batchItemId": null \
  }'

Creating a Date Value​

Create expected field value
curl -X 'POST' "/v0/evidence-requests/{id}/values" \
  -H "Accept: application/json" \
  -H "Authorization: Bearer $BEARER" \
  -d '{ \
    "type": "DATE", \
    "expectedFieldId": "field-uuid", \
    "value": "2024-06-15T10:30:00Z", \
    "batchItemId": null \
  }'

Creating a Boolean Value​

Create expected field value
curl -X 'POST' "/v0/evidence-requests/{id}/values" \
  -H "Accept: application/json" \
  -H "Authorization: Bearer $BEARER" \
  -d '{ \
    "type": "BOOLEAN", \
    "expectedFieldId": "field-uuid", \
    "value": true, \
    "batchItemId": null \
  }'

Creating Batch-Specific Values​

For batch evidence requests, include the batchItemId:

Create expected field value
curl -X 'POST' "/v0/evidence-requests/{id}/values" \
  -H "Accept: application/json" \
  -H "Authorization: Bearer $BEARER" \
  -d '{ \
    "type": "NUMERIC", \
    "expectedFieldId": "field-uuid", \
    "value": "500.75", \
    "batchItemId": "batch-uuid" \
  }'

Listing Expected Field Values​

To view all field values:

Get expected field values
curl -X 'GET' "/v0/expected-field-values" \
  -H "Accept: application/json" \
  -H "Authorization: Bearer $BEARER"

To get a specific field value:

Get expected field value
curl -X 'GET' "/v0/expected-field-values/{id}" \
  -H "Accept: application/json" \
  -H "Authorization: Bearer $BEARER"

Updating Expected Field Values​

To update an existing field value:

Update expected field value
curl -X 'PATCH' "/v0/expected-field-values/{id}" \
  -H "Accept: application/json" \
  -H "Authorization: Bearer $BEARER" \
  -d '{ \
    "type": "NUMERIC", \
    "value": "1500.25" \
  }'
Type Required

When updating a field value, you must include the type parameter matching the value's original type.

Deleting Expected Field Values​

To delete a field value:

Delete expected field value
curl -X 'DELETE' "/v0/expected-field-values/{id}" \
  -H "Accept: application/json" \
  -H "Authorization: Bearer $BEARER"

Creating Evidence Export Jobs​

To export all evidence you've submitted:

Creating an Export Job​

Create evidence export job
curl -X 'POST' "/v0/evidence/export" \
  -H "Accept: application/json" \
  -H "Authorization: Bearer $BEARER" \
  -d '{ \
    "merge": false, \
    "evidenceTemplateIds": [ \
      "template-uuid-1" \
    ], \
    "monitoringPeriodIds": [ \
      "monitoring-period-uuid" \
    ], \
    "facilityIds": [ \
      "facility-uuid" \
    ], \
    "evidenceRequestStatuses": [] \
  }'

Parameters​

  • merge (optional, default: false) - If true, merges all evidence files with the same template into a single file
  • evidenceTemplateIds (optional) - Filter by specific templates
  • monitoringPeriodIds (optional) - Filter by monitoring periods
  • facilityIds (optional) - Filter by facilities
  • evidenceRequestStatuses (optional) - Filter by request status

Checking Export Status​

Get evidence export job
curl -X 'GET' "/v0/evidence/export/{id}" \
  -H "Accept: application/json" \
  -H "Authorization: Bearer $BEARER"

The response includes:

  • id - Job ID
  • status - PENDING, COMPLETED, or FAILED
  • signedUrl - Download URL when status is COMPLETED

Data Submission Workflow​

1. Prepare Your Data​

  • Organize files with clear, consistent naming
  • Ensure data matches Expected Field definitions
  • Validate against any templates
  • Remove any confidential information from public documents

2. Upload Evidence Files​

  • Submit files to appropriate Evidence Requests
  • Link to templates where applicable
  • Review any validation errors

3. Submit Field Values​

  • Enter values for all required Expected Fields
  • Include batch IDs for batch evidence requests
  • Verify values are within defined constraints

4. Review and Validate​

  • Check that all required evidence is submitted
  • Verify field values are accurate
  • Ensure calculations are producing expected results
  • Review field and formula computed values

5. Update Evidence Request Status​

Once all data is submitted for an evidence request:

Update evidence request
curl -X 'PATCH' "/v0/evidence-requests/{id}" \
  -H "Accept: application/json" \
  -H "Authorization: Bearer $BEARER" \
  -d '{ \
    "status": "READY_FOR_REVIEW" \
  }'

Best Practices​

  1. Upload incrementally - Don't wait until the last minute to upload all evidence
  2. Validate as you go - Check for validation errors after each upload
  3. Keep backups - Maintain copies of all uploaded files
  4. Use templates - Leverage evidence templates for structured data
  5. Check calculations - Verify that field values produce correct model outputs
  6. Document deviations - Include notes for any unusual or missing data
  7. Review before submission - Double-check everything before marking ready for review

Common Issues​

Validation Errors​

If you receive validation errors:

  • Review the template structure requirements
  • Check data types match expectations
  • Verify all required columns are present
  • Ensure values are within defined ranges

Missing Data​

If data is unavailable:

  • Document the reason in a separate file
  • Consider skipping the evidence request with a reason
  • Contact your Puro representative if uncertain

File Size Limits​

  • Be aware of any file size restrictions
  • Compress large files if possible
  • Split very large datasets into multiple files

Next Steps​

Once all evidence and field values are submitted and validated, you're ready to perform a final consistency check and submit your Audit Package.