diff --git a/paper/src/chapters/auto/whoclicked_dataset_card.md b/paper/src/chapters/auto/whoclicked_dataset_card.md
new file mode 100644
index 0000000..507f082
--- /dev/null
+++ b/paper/src/chapters/auto/whoclicked_dataset_card.md
@@ -0,0 +1,125 @@
+---
+pretty_name: whoclickedit
+license: mit
+language:
+- en
+task_categories:
+- tabular-classification
+task_ids:
+- tabular-multi-class-classification
+tags:
+- e-commerce
+- dynamic-pricing
+- behavioral-telemetry
+- human-vs-agent
+- session-data
+size_categories:
+- 1K<n<10K
+---
+
+# Dataset Card for whoclickedit
+
+## Dataset Summary
+whoclickedit is an event-level behavioral dataset for human versus agent interaction analysis in dynamic pricing experiments.
+It merges interaction logs and price quote logs into one flat CSV (`whoclicked.csv`) with explicit labels for actor type.
+
+## Dataset Snapshot
+- Rows: `3838`
+- Columns: `42`
+- Time range (UTC): `2025-12-05T09:43:31.301000+00:00` to `2026-02-28T19:32:06.444000+00:00`
+- Unique sessions by actor:
+- `agent`: 7
+- `human`: 25
+- Rows by actor:
+- `agent`: 3076
+- `human`: 762
+- Rows by record type:
+- `price_log`: 3331
+- `interaction`: 507
+- Rows by actor x record type:
+- `agent` / `interaction`: 197
+- `agent` / `price_log`: 2879
+- `human` / `interaction`: 310
+- `human` / `price_log`: 452
+- Store modes:
+- `hotel`: 3592
+- `airline`: 196
+- `shop`: 50
+
+## Source and Processing
+Data is collected from two local roots in the PHANTOM project:
+- `experiments/collected_data` (human sessions)
+- `experiments/agents/collected_data` (agent sessions)
+
+Each session folder contains:
+- `int.json` (interaction events)
+- `price.json` (price quote logs)
+
+The ETL does the following:
+- Normalizes both Kafka-envelope and flat payload formats
+- Flattens nested metadata fields into `metadata_*` columns
+- Preserves all raw rows (no deduplication)
+- Adds labels:
+  - `actor_type` in `{human, agent}`
+  - `is_agent` in `{0, 1}`
+  - `record_type` in `{interaction, price_log}`
+
+## Data Fields
+Core fields used for modeling:
+- `actor_type`, `is_agent`, `record_type`
+- `sessionId`, `experimentId`, `storeMode`, `ts`
+- `eventName`, `page`, `productId`, `price`, `userAgent`
+
+Kafka provenance fields:
+- `kafka_partition_id`, `kafka_offset`, `kafka_timestamp_ms`, `kafka_compression`
+- `kafka_is_transactional`, `kafka_headers`, `kafka_key_*`, `kafka_value_*`
+
+Flattened metadata fields currently present:
+- `metadata_cabinClass`
+- `metadata_dateIndex`
+- `metadata_dwellTime`
+- `metadata_elementText`
+- `metadata_fareRule`
+- `metadata_flightType`
+- `metadata_itemCount`
+- `metadata_nights`
+- `metadata_price`
+- `metadata_referrer`
+- `metadata_roomType`
+- `metadata_total`
+- `metadata_type`
+
+Top interaction events:
+- `page_view`: 236
+- `learn_more_about_item`: 88
+- `view_item_page`: 85
+- `add_item_to_cart`: 46
+- `hover_over_title`: 23
+- `checkout_start`: 19
+- `hover_over_paragraph`: 6
+- `remove_item`: 4
+
+## Intended Uses
+- Human-vs-agent traffic classification
+- Session-level behavioral modeling
+- Dynamic pricing robustness analysis under agent-mediated reconnaissance
+
+## Out-of-Scope Uses
+- Identity inference or user-level profiling
+- Credit, employment, insurance, or legal decision making
+
+## Data Splits
+No official train/validation/test split is provided in the current release.
+Users should create time-aware or session-aware splits to avoid leakage.
+
+## Privacy and Sensitive Content
+- `userAgent` and referrer metadata can be quasi-identifying in small samples.
+- Use care before publishing derived artifacts that can re-identify participants.
+
+## Limitations
+- Data is generated in a controlled experiment platform, not a full production marketplace.
+- Agent traffic currently reflects the configured tasking and browser automation setup.
+- Coverage is stronger for `hotel` than `airline` in the current release.
+
+## Citation
+If you use this dataset, cite the PHANTOM thesis project and link this dataset page.