October 31st 2025 Data Archive Review & Publication

Hey guys! Let's dive into the data archives for October 31st, 2025. This article summarizes the results from the latest run of the pudl-archiver and outlines the steps needed to review, validate, and publish these archives. We'll walk through everything from checking for changes and validation failures to addressing any other issues that pop up. Our goal is to ensure the data is accurate, up-to-date, and ready for use. Let’s get started!

Summary of Results

You can find all the juicy details in the job run logs and results here. This link will take you directly to the GitHub Actions run where you can see the nitty-gritty of what happened during the archiving process. It's like peeking behind the curtain to understand how everything went down.

Why This Matters

Understanding these results is crucial for maintaining data integrity and ensuring our datasets are reliable. We want to make sure that when someone uses this data, they can trust its accuracy. By carefully reviewing these logs, we can catch any potential issues early on and address them proactively. This ensures we're providing the highest quality data possible, which, in turn, supports better analysis and decision-making. Think of it as quality control for data – essential for maintaining the value and usability of our archives.

Review and Publish Archives

For each archive listed below, your mission, should you choose to accept it, is to check the run status in the GitHub archiver run. If the validation tests give us the thumbs-up, it’s time for a manual review. If everything looks good, publish it! If there are no changes detected, we can delete the draft. However, if changes are spotted, we need to put on our detective hats and follow the guidelines in step 3 of README.md to review the archive thoroughly before publishing the new version. Once published, give it a confirmation shout-out with a note on the status (e.g., "v1 published," "no changes detected, draft deleted") or, if needed, create a follow-up sub-issue.

Step-by-Step Review Process

1. Check the Run Status: Head over to the GitHub archiver run and see how each archive fared during the automated checks. Did it pass with flying colors, or did it stumble a bit?
2. Validation Tests: A passing validation test is like a green light, but it doesn't mean we can skip the manual review. It just means the initial checks didn't find any glaring issues.
3. Manual Review: This is where your expertise comes in. Download the archive, poke around, and make sure everything looks as it should. We're talking about checking data integrity, format consistency, and overall quality.
4. Changes Detected? If you spot changes, don't panic! It just means the data has been updated. Follow the guidelines in README.md to ensure these changes are accurate and properly integrated.
5. Publish or Delete: If everything looks good, hit that publish button and share the updated archive with the world. If there were no changes, deleting the draft keeps things tidy.
6. Confirmation Note: After publishing, leave a note confirming the status. This helps keep everyone in the loop and provides a record of our progress.

By following this process diligently, we ensure that our archives are not only up-to-date but also reliable and trustworthy. It's like giving our data a seal of approval, letting users know it's ready for action.

Changed Archives

The following archives have successfully run and have new data. This is exciting because it means we have fresh insights to share! However, with great new data comes great responsibility. Each archive needs your keen eyes for review before we give it the green light for publication. Let's make sure everything is in tip-top shape.

Why Reviewing Changed Archives is Crucial

When archives show changes, it signifies that the underlying data has been updated. This could be new information, corrections to existing entries, or modifications to the dataset's structure. While updates are generally positive, they also introduce the possibility of errors or inconsistencies. Think of it like a new coat of paint on a house – it looks great, but you want to make sure there are no drips or missed spots.

By meticulously reviewing these archives, we ensure that the changes are accurate, consistent, and aligned with our data standards. This process involves:

• Verifying the Source: Confirming that the data source is reliable and that the changes are expected.
• Checking Data Integrity: Ensuring that the new data integrates seamlessly with the existing dataset without introducing errors or corrupting previous entries.
• Validating Data Quality: Assessing the accuracy, completeness, and consistency of the updated information.
• Reviewing Metadata: Updating any relevant metadata to reflect the changes in the archive.

This thorough review process acts as a safeguard, ensuring that our published archives remain trustworthy and valuable resources for users. It's like a final quality check before sending a product out the door, guaranteeing that it meets our high standards.

Validation Failures

For each run that stumbled due to validation test failures (you'll spot these in the GHA logs), we need to add it to our task list. Think of this as our to-do list for data troubleshooting. We'll grab the run summary JSON by diving into the "Upload run summaries" tab in the GHA run for each dataset and following the link. Then, the real fun begins: investigating the validation failure.

Diving Deep into Validation Failures

Validation failures are like little red flags waving at us, signaling that something might not be quite right with the data. These failures occur when the automated tests detect discrepancies, inconsistencies, or errors in the archive. It's our job to figure out what's causing these flags to pop up and take the necessary steps to resolve them.

Here's a breakdown of how we tackle validation failures:

1. Download the Run Summary JSON: This JSON file contains a detailed report of the validation tests, including any errors or warnings that were triggered. It's like a diagnostic report that gives us clues about what went wrong.
2. Investigate the Failure: We need to put on our detective hats and dig into the specifics of the failure. This might involve examining the data, reviewing the test logs, and comparing the current results with previous runs.
3. Determine the Cause: Is the failure due to a data issue, such as incorrect formatting, missing values, or unexpected changes in size? Or is it a problem with the validation tests themselves?
4. Take Action: Depending on the cause, we'll either address the data issue or adjust the validation tests. This might involve cleaning the data, fixing bugs in our code, or updating the test criteria.

Sometimes, a validation failure might seem alarming at first but turns out to be harmless after manual review. For example, if a file doubles in size because it now includes data from Q2, and the new data looks as expected, we can approve the archive and leave a note explaining our decision. However, if the failure is blocking, such as an incorrect file format or a 200% change in dataset size, we'll need to create an issue to resolve it before proceeding.

By thoroughly investigating validation failures, we ensure that our archives meet our quality standards and that users can rely on the accuracy of the data. It's like performing a health check on our data, making sure it's fit and ready for use.

Other Failures

Now, for the curveballs! If a run fails for reasons beyond validation (think underlying data changes or code hiccups), we need to create an issue detailing the failure and map out the steps to fix it. It's like being a data firefighter, putting out unexpected blazes to keep everything running smoothly.

Addressing the Unexpected

Sometimes, things go wrong in ways that our automated validation tests can't catch. This could be due to changes in the upstream data sources, unexpected behavior in our code, or even external factors like network issues. When these