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+---
+name: Weird Data
+about: Report unexpected, suspicious or incorrect data in a planning data
+title: '[Weird Data] <dataset> - <brief description>'
+labels: 'weird-data'
+assignees: ''
+
+---
+
+### What did you find?
+_Describe the weird data — what looks wrong, unexpected, or suspicious._
+
+### Where is it?
+_Dataset name, organisation, endpoint URL, or resource hash if known._
+
+### How did you find it?
+_e.g. manual review, automated check, user report, pipeline output_
+
+### What were you expecting?
+_What should the data look like?_
+
+### What does it actually look like?
+_Paste an example row, screenshot, or link to the data._
+
+### Impact
+- [ ] P0 critical - major data inconsistency affecting many records
+- [ ] P1 high - affects core dataset functions or published outputs
+- [ ] P2 medium - isolated issue, limited downstream impact
+- [ ] P3 low - cosmetic or edge case
+
+### Investigation checklist
+
+- [ ] Find the entry in GitHub config — check git blame for any recent changes
+- [ ] Find the endpoint the data is collected from and try accessing it directly
+- [ ] Find the resources collected from this endpoint and check the resource log for errors
+- [ ] Run the endpoint through the manage service — check whether the converted/transformed output looks correct
+- [ ] Check the entities created from this data — are they being created as expected?
+- [ ] Run `debug_resource_transformation.ipynb` (digital-land-python) with the latest resource hash to inspect what is being transformed
+- [ ] Run the collection task with only this endpoint to isolate the issue (Tricky)
+- [ ] Check S3 logs across each pipeline phase (Transform, Append) to see where data may be dropping off
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