Update README.md

README.md

@@ -43,4 +43,180 @@ language:
 tags:
 - crowdsourcing
 pretty_name: Crowdsourced VRU Annotations
----
+---
+
+# Crowdsourced VRU Annotations
+
+## Dataset Summary
+
+This dataset provides tabular annotations from two underlying datasets — **ECP** and **ZOD** — and is organized into two splits (`ecp` and `zod`).  
+It contains the results of crowdsourced annotation tasks focusing on **vulnerable road users (VRUs)**.
+
+The underlying examples are *image crops* showing bounding boxes of VRUs from the ECP and ZOD datasets. Each crop is referenced via a `crop_id`. The actual image pixels are **not** included in this repository; instead, we provide per-crop annotations and auxiliary metadata that reference these crops.
+
+---
+
+## Dataset at a glance
+
+- **Splits**: `ecp`, `zod`  
+- **Domain**: Vulnerable road users (pedestrians, cyclists, persons on mobility devices, etc.)  
+- **Data types**: tabular annotation rows (answers), auxiliary CSV metadata files (`ecp/boxes.csv`, `zod/attributes.csv`)
+
+---
+
+## Annotation tasks
+
+For each crop, annotators answered one or more binary questions (with an additional `can't solve` option):
+
+- **ECP tasks (6 questions)**  
+  - `human being`: Is this a human being?  
+  - `statue/mannequin`: Is this a statue or mannequin (i.e. not a real person)?  
+  - `reflection of a person`: Is this a reflection (e.g. in glass)?  
+  - `on wheels`: Is the person on wheels?  
+  - `on poster/picture/billboard`: Is the person shown on a poster/picture/billboard?  
+  - `on bike`: Is the person on a bike?
+
+- **ZOD tasks (1 question)**  
+  - `human being`: Is this a human being?
+
+Answers are chosen from `yes`, `no`, or `can't solve`. When `can't solve` is chosen the `answer` field is `None` / `NaN` and the boolean `cant_solve` flag is set.
+
+---
+
+## Annotation design
+
+- Annotations were collected from multiple human annotators. Each crop/question pair was answered multiple times (repeats).
+  - **ECP**: 5–12 repeats per task; an adaptive allocation protocol increases the number of annotators for tasks with higher disagreement.
+  - **ZOD**: fixed 11 repeats per task.
+- Tasks were grouped and issued in **work packages**:
+  - **ECP**: nominal size 20 tasks per work package, time limit 300 seconds.
+  - **ZOD**: nominal size 30 tasks per work package, time limit 540 seconds.
+- Due to concurrency and runtime behaviour, some submitted work packages may contain fewer tasks than the nominal size.
+- Each submitted work package receives a unique `wp_id` and a submission timestamp — these are present in the data.
+
+---
+
+## Dataset statistics
+
+- **ECP**
+  - Tasks per crop: 6 (see list above)
+  - Annotated crops: 32,711
+  - Repeats per task: 5–12 (adaptive)
+  - 1,035,840 individual answers
+
+- **ZOD**
+  - Tasks per crop: 1 (human being)
+  - Annotated crops: 16,000
+  - Repeats per task: 11
+  - 175,962 individual answers
+
+---
+
+## Tabular data format
+
+Both splits (`ecp` and `zod`) share the same annotation-row schema. Each row corresponds to one single annotator response to one task:
+
+- `id` (`str`): globally unique identifier for the row  
+- `crop_id` (`str`): identifier of the crop (the bounding-box image crop)  
+- `question` (`str`): identifier of the question asked (e.g., `human being`)  
+- `user_id` (`str`): identifier of the annotator (unique within the annotation system)  
+- `answer` (`Optional[str]`): submitted answer; `None` / `NaN` if `cant_solve` is true  
+- `cant_solve` (`bool`): whether the annotator marked the task as unsolvable  
+- `created_at` (`datetime64[ns, UTC+01:00]`): ISO8601 timestamp when the work package was processed (note: this is the processing/submission time for the work package, not the individual annotation start time)  
+- `duration_ms` (`int`): duration for the task in milliseconds  
+- `wp_id` (`str`): work package identifier the task belongs to
+
+---
+
+## Metadata files
+
+The repository also contains auxiliary metadata CSVs. These are provided as separate files so consumers can join/merge them with the annotation rows using `crop_id` if and when needed.
+
+### `ecp/boxes.csv`
+
+Schema:
+- `crop_id` (`str`): unique id of the crop (reference key)  
+- `image_path` (`str`): path of the original ECP image the crop belongs to  
+- `left`, `top`, `right`, `bottom` (`int`): bounding box coordinates (upper-left and bottom-right corners)
+
+### `zod/attributes.csv`
+
+Schema:
+- `crop_id` (`str`): unique id of the crop (reference key)  
+- `label` (`str`): original ZOD label for the object  
+- `attributes` (`json/object`): dictionary with object attributes (e.g., occlusion level) — unchanged from ZOD  
+- `left`, `top`, `width`, `height` (`float`): bounding box coordinates (upper-left corner, width, height)
+
+---
+
+## Usage example
+
+Load annotation tables (answers) via `datasets`:
+
+```python
+from datasets import load_dataset
+
+REPO_ID = "cklugmann/crowdsourced-vru-annotations"
+dataset = load_dataset(REPO_ID)
+
+# Example: load ECP answers into a pandas DataFrame
+df_answers_ecp = (
+    dataset["ecp"]
+    .to_pandas()
+    .set_index("id")
+)
+```
+
+Load ECP boxes metadata (without merging) using `hf_hub_download`:
+
+```python
+import pandas as pd
+from huggingface_hub import hf_hub_download
+
+df_boxes_ecp = (
+    pd.read_csv(hf_hub_download(
+        repo_id=REPO_ID,
+        repo_type="dataset",
+        filename="ecp/boxes.csv"
+    ))
+    .set_index("crop_id")
+)
+```
+
+(Analogously you can load `zod/attributes.csv` with `hf_hub_download`.)
+
+---
+
+## How we recommend working with the data
+
+- Use `load_dataset(...)` to fetch annotation rows as Hugging Face `Dataset` objects (one split per underlying source: `ecp`, `zod`).  
+- Use `hf_hub_download(...)` to fetch auxiliary CSVs (boxes/attributes) as raw files and load them into Pandas with `pd.read_csv(...)`.  
+- Perform any merging/joins yourself using `crop_id` so you keep the original annotation rows unchanged and control join semantics and filtering.
+
+---
+
+## License
+
+This dataset is released under **CC-BY-SA-4.0**.
+
+The dataset builds on:
+- **ECP dataset** — (Link to website)[https://eurocity-dataset.tudelft.nl/]
+- **ZOD dataset** — (Link to website)[https://zod.zenseact.com/]
+
+---
+
+## Citation
+
+If you use this dataset in your research, please consider citing:
+
+```bibtex
+@misc{liao2025minorityreportsbalancingcost,
+      title={Minority Reports: Balancing Cost and Quality in Ground Truth Data Annotation},
+      author={Hsuan Wei Liao and Christopher Klugmann and Daniel Kondermann and Rafid Mahmood},
+      year={2025},
+      eprint={2504.09341},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2504.09341},
+}
+```