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Error code: DatasetGenerationError
Exception: GatedRepoError
Message: 401 Client Error. (Request ID: Root=1-68c31f24-2aebd96f1017a4283c863d24;78e4e0d3-af58-4633-86d8-6ccf47b0da8e)
Cannot access gated repo for url https://huggingface.co/datasets/imageomics/thewilds_cameratraps/resolve/7e0178cefcd529d37d04186c81ec5ea9f082300f/TW01-CT02/SD02_20250630_20250703/images/NSCF0002_250630121802_0022.JPG.
Access to dataset imageomics/thewilds_cameratraps is restricted. You must have access to it and be authenticated to access it. Please log in.
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/utils/_http.py", line 409, in hf_raise_for_status
response.raise_for_status()
File "/src/services/worker/.venv/lib/python3.9/site-packages/requests/models.py", line 1024, in raise_for_status
raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 401 Client Error: Unauthorized for url: https://huggingface.co/datasets/imageomics/thewilds_cameratraps/resolve/7e0178cefcd529d37d04186c81ec5ea9f082300f/TW01-CT02/SD02_20250630_20250703/images/NSCF0002_250630121802_0022.JPG
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1586, in _prepare_split_single
writer.write(example, key)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 553, in write
self.write_examples_on_file()
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 511, in write_examples_on_file
self.write_batch(batch_examples=batch_examples)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 631, in write_batch
self.write_table(pa_table, writer_batch_size)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 646, in write_table
pa_table = embed_table_storage(pa_table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2248, in embed_table_storage
arrays = [
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2249, in <listcomp>
embed_array_storage(table[name], feature, token_per_repo_id=token_per_repo_id)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1795, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1795, in <listcomp>
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2124, in embed_array_storage
return feature.embed_storage(array, token_per_repo_id=token_per_repo_id)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/image.py", line 282, in embed_storage
[
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/image.py", line 283, in <listcomp>
(path_to_bytes(x["path"]) if x["bytes"] is None else x["bytes"]) if x is not None else None
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 310, in wrapper
return func(value) if value is not None else None
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/image.py", line 279, in path_to_bytes
return f.read()
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 813, in read_with_retries
out = read(*args, **kwargs)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 811, in track_read
out = f_read(*args, **kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/hf_file_system.py", line 1012, in read
return f.read()
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 811, in track_read
out = f_read(*args, **kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/hf_file_system.py", line 1076, in read
hf_raise_for_status(self.response)
File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/utils/_http.py", line 426, in hf_raise_for_status
raise _format(GatedRepoError, message, response) from e
huggingface_hub.errors.GatedRepoError: 401 Client Error. (Request ID: Root=1-68c31f24-5fc3c4e36e7e33366c9429ee;53acc688-8ace-4f7d-bff6-52d554c9a026)
Cannot access gated repo for url https://huggingface.co/datasets/imageomics/thewilds_cameratraps/resolve/7e0178cefcd529d37d04186c81ec5ea9f082300f/TW01-CT02/SD02_20250630_20250703/images/NSCF0002_250630121802_0022.JPG.
Access to dataset imageomics/thewilds_cameratraps is restricted. You must have access to it and be authenticated to access it. Please log in.
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/utils/_http.py", line 409, in hf_raise_for_status
response.raise_for_status()
File "/src/services/worker/.venv/lib/python3.9/site-packages/requests/models.py", line 1024, in raise_for_status
raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 401 Client Error: Unauthorized for url: https://huggingface.co/datasets/imageomics/thewilds_cameratraps/resolve/7e0178cefcd529d37d04186c81ec5ea9f082300f/TW01-CT02/SD02_20250630_20250703/images/NSCF0002_250630121802_0022.JPG
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1595, in _prepare_split_single
num_examples, num_bytes = writer.finalize()
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 658, in finalize
self.write_examples_on_file()
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 511, in write_examples_on_file
self.write_batch(batch_examples=batch_examples)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 631, in write_batch
self.write_table(pa_table, writer_batch_size)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 646, in write_table
pa_table = embed_table_storage(pa_table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2248, in embed_table_storage
arrays = [
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2249, in <listcomp>
embed_array_storage(table[name], feature, token_per_repo_id=token_per_repo_id)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1795, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1795, in <listcomp>
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2124, in embed_array_storage
return feature.embed_storage(array, token_per_repo_id=token_per_repo_id)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/image.py", line 282, in embed_storage
[
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/image.py", line 283, in <listcomp>
(path_to_bytes(x["path"]) if x["bytes"] is None else x["bytes"]) if x is not None else None
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 310, in wrapper
return func(value) if value is not None else None
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/image.py", line 279, in path_to_bytes
return f.read()
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 813, in read_with_retries
out = read(*args, **kwargs)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 811, in track_read
out = f_read(*args, **kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/hf_file_system.py", line 1012, in read
return f.read()
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 811, in track_read
out = f_read(*args, **kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/hf_file_system.py", line 1076, in read
hf_raise_for_status(self.response)
File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/utils/_http.py", line 426, in hf_raise_for_status
raise _format(GatedRepoError, message, response) from e
huggingface_hub.errors.GatedRepoError: 401 Client Error. (Request ID: Root=1-68c31f24-2aebd96f1017a4283c863d24;78e4e0d3-af58-4633-86d8-6ccf47b0da8e)
Cannot access gated repo for url https://huggingface.co/datasets/imageomics/thewilds_cameratraps/resolve/7e0178cefcd529d37d04186c81ec5ea9f082300f/TW01-CT02/SD02_20250630_20250703/images/NSCF0002_250630121802_0022.JPG.
Access to dataset imageomics/thewilds_cameratraps is restricted. You must have access to it and be authenticated to access it. Please log in.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1451, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 994, in stream_convert_to_parquet
builder._prepare_split(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1447, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1604, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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Dataset Card for The Wilds Camera Trap Data
This dataset contains images and video captured from camera traps deployed at The Wilds safari park in Ohio during Summer 2025. It supports ecological monitoring, animal behavior analysis, and biodiversity studies.
Dataset Details
This dataset was created to support wildlife monitoring research using camera traps. Data were collected using various camera trap models, with each camera recording photos and videos in the wild. The images are organized by site deployment session, providing valuable data for species detection, behavioral analysis, and biodiversity monitoring.
Supported Tasks and Leaderboards
- Image Classification: Species identification from camera trap images
- Object Detection: Animal detection and localization in natural environments
- Behavioral Analysis: Activity pattern studies and wildlife monitoring
- Biodiversity Assessment: Species richness and abundance estimation
[No benchmarks currently available]
Dataset Structure
The dataset is organized hierarchically by site and deployment session:
/dataset/
The_Wilds_Camera_Trap_Log2025-06-30_21_56_00.csv
The_Wilds_Camera_Trap_Log2025-07-04_20_18_08.csv
TW01-CT02/
SD02_20250630_20250703/
images/
NSCF0002_250630121802_0022.JPG
NSCF0002_250630121802_0023.JPG
NSCF0002_250630121803_0024.JPG
...
NSCF0002_250702065001_0437.JPG
NSCF0002_250702065001_0438.JPG
NSCF0002_250702065002_0439.JPG
metadata.txt
TW02-CT03/
SD03_20250630_20250703/
images/
DSCF0008.JPG
DSCF0009.JPG
DSCF0010.JPG
...
DSCF0060.AVI
DSCF0061.AVI
DSCF0062.AVI
metadata.txt
TW03-CT01/
SD01_20250630_20250703/
images/
100MEDIA/
NSCF0001_250630130551_0081.MP4
NSCF0001_250630130604_0082.JPG
...
NSCF0001_250630164902_0999.JPG
101MEDIA/
NSCF0001_250630164902_0001.JPG
NSCF0001_250630164902_0002.MP4
...
NSCF0001_250630205901_0999.JPG
102MEDIA/
NSCF0001_250630205901_0001.JPG
NSCF0001_250630205902_0002.JPG
NSCF0001_250630205902_0003.MP4
...
NSCF0001_250701010702_0999.MP4
103MEDIA/
104MEDIA/
105MEDIA/
106MEDIA/
107MEDIA/
108MEDIA/
metadata.txt
TW04-CT04/
SD04_20250630_20250704/
101MEDIA/
DSCF0152.JPG
DSCF0153.JPG
DSCF0154.JPG
...
DSCF0833.JPG
DSCF0834.JPG
DSCF0835.MP4
metadata.txt
Data Instances
Each camera trap deployment folder (e.g., TW01-CT02/SD02_20250630_20250703/) contains:
- Images folder: Contains .jpg image files captured by motion detection
- Video files: .mp4 format videos in some deployments (stored in 101MEDIA for CT04)
- Metadata file:
metadata.txtwith deployment information and camera settings
Images vary in resolution and quality depending on camera model and environmental conditions. Videos are typically short clips (10 seconds) triggered by motion detection.
Data Fields
metadata.txt (found in each deployment folder):
- Camera Trap ID: Unique device identifier (e.g., CT01, CT02, CT03, CT04)
- Trap Model: Camera model name (e.g., GardePro T5NG, Trail Camera)
- Camera Serial Number: Manufacturer-assigned serial number
- Site ID: Location code where deployed (e.g., TW01, TW02, TW03, TW04)
- Deployment Location Description: Text description of deployment site
- GPS Coordinates: Latitude and longitude in decimal format
- Deployment Date and Time: Camera deployment timestamp (YYYY-MM-DD HH:MM format)
- Retrieval Date and Time: Camera retrieval timestamp (YYYY-MM-DD HH:MM format)
- Camera Configuration: Settings including photo/video mode, resolution, trigger sensitivity
- Maintenance Notes: Configuration changes or technical issues during deployment
- Observer: Name or initials of person completing metadata
CSV Log Files:
The_Wilds_Camera_Trap_Log2025-06-30_21_56_00.csv: Deployment log from June 30, 2025The_Wilds_Camera_Trap_Log2025-07-04_20_18_08.csv: Retrieval log from July 4, 2025
Data Splits
This dataset has no predefined training/validation/test splits. Data are organized by site (TW01-TW04) and deployment session. Users may create their own splits based on:
- Temporal splits: Using deployment dates
- Spatial splits: Using different site locations
- Camera-based splits: Using different camera trap IDs
Recommended approach depends on modeling goals and research questions.
Dataset Creation
Curation Rationale
This dataset supports behavioral ecology research, development of automated species detection models, and studies of animal activity patterns. Camera traps provide non-invasive monitoring of wildlife behavior and are essential tools for conservation biology and ecological research.
Source Data
Data Collection and Processing
Camera trap data were collected at The Wilds safari park during summer 2025 using GardePro and Trail Camera models. Cameras were strategically deployed at four sites (TW01-TW04) using four different camera units (CT01-CT04).
Cameras were configured to capture both photos and videos based on motion detection. The deployment period was from June 30 to July 3-4, 2025. Upon retrieval, data were organized into folders by site and deployment session, with each deployment containing an images folder and metadata file describing camera settings and deployment context.
Who are the source data producers?
The dataset was collected and curated by researchers and students from the Imageomics Institute and Ohio State University in collaboration with conservation staff at The Wilds safari park in Ohio.
Annotations
Annotation process
No species or behavioral annotations are currently provided with this initial dataset release. Manual and AI-assisted labeling efforts for species identification, behavioral classification, and bounding box annotations are planned for future versions.
Who are the annotators?
N/A - annotations will be added in future releases
Personal and Sensitive Information
The dataset includes GPS coordinates within The Wilds, a public conservation park in Ohio. Some images may contain endangered or sensitive species, though specific species identifications are not currently provided. Spatial coordinates have not been redacted as they fall within a public conservation area.
Considerations for Using the Data
Images and videos exhibit natural variation in quality due to weather conditions, lighting changes, motion blur, and camera settings. Some footage may be partially obstructed by vegetation, contain false triggers from wind-blown plants, or show camera malfunctions. The data represent a specific temporal window (summer 2025) and geographic location, which should be considered when generalizing findings.
Bias, Risks, and Limitations
- Sampling bias: Camera traps were deployed strategically at ecological hotspots rather than randomly, potentially overrepresenting certain species or behaviors
- Temporal limitations: Data represent only a 3-4 day deployment period in summer 2025, limiting seasonal representation
- Detection bias: Motion-triggered cameras may miss slow-moving species or have reduced detection rates for small animals
- Spatial bias: Limited to four sites within The Wilds, may not represent broader regional wildlife patterns
- Technical limitations: Variable image quality due to different camera models and environmental conditions
Recommendations
Users should consider the ecological and methodological context when analyzing this data. The dataset is best suited for proof-of-concept studies, algorithm development, and preliminary ecological analyses. For robust ecological conclusions, combination with additional seasonal data and broader spatial sampling would be beneficial.
Future dataset releases may include additional deployment periods, seasonal data, and species annotations to address current limitations.
Licensing Information
This dataset is dedicated to the public domain under a CC0 license for the benefit of scientific pursuits. Users are encouraged to cite the dataset and acknowledge contributors when using this data in research or applications.
Citation
BibTeX:
Data
@misc{thewilds_cameratraps_2025,
author = {Tanishka Wani and Vedant Patil and Rugved Katole and Bharath Pillai and Anirudh Potlapally and Ally Bonney and Jenna Kline},
title = {The Wilds Camera Trap Data},
year = {2025},
url = {https://huggingface.co/datasets/imageomics/thewilds_cameratraps},
publisher = {Hugging Face}
}
Paper
@article{kline2025smartwilds,
title={SmartWilds: Multimodal Wildlife Monitoring Dataset},
author={Kline, Jenna and Potlapally, Anirudh and Pillai, Bharath and Wani, Tanishka and Katole, Rugved and Patil, Vedant and Covey, Penelope and Steven, Samuel and Subramoni, Hari and Berger-Wolf, Tanya and Stewart, Christopher},
journal={arXiv preprint arXiv:2509.18894},
year={2025}
}
Acknowledgements
This project is supported by the AI Institute for Intelligent Cyberinfrastructure with Computational Learning in the Environment (ICICLE), which is funded by the US National Science Foundation under Award #2112606, and by the Imageomics Institute, which is funded by the US National Science Foundation's Harnessing the Data Revolution (HDR) program under Award #2118240 (Imageomics: A New Frontier of Biological Information Powered by Knowledge-Guided Machine Learning).
This work is also supported by the AI and Biodiversity Change (ABC) Global Center. The ABC Global Center is funded by the US National Science Foundation under Award No. 2330423 and the Natural Sciences and Engineering Research Council of Canada under Award No. 585136. This dataset draws on research supported by the Social Sciences and Humanities Research Council.
Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation, Natural Sciences and Engineering Research Council of Canada, or Social Sciences and Humanities Research Council.
Special thanks to Dan Beetem and The Wilds in Ohio for providing access to deployment sites and supporting this wildlife monitoring research. We thank the Columbus Zoo Aquarium for their support in facilitating this project. All data collection, including drone flights, was conducted under the supervision of the Director of Animal Management, with permission from The Wilds Animal Care and Use Committee.
Glossary
- Camera Trap: Motion-activated camera used for wildlife monitoring
- Deployment: Period when a camera trap is installed and actively recording
- Site ID: Geographic location identifier (TW01-TW04 in this dataset)
- Camera Trap ID: Individual camera device identifier (CT01-CT04 in this dataset)
- SD Card Session: Data collection period identified by SD card and date range
Dataset Card Authors
Tanishka Wani, Vedant Patil, Rugved Katole, Bharath Pillai, Anirudh Potlapally, Ally Bonney, and Jenna Kline
Dataset Card Contact
For questions about this dataset, please open a discussion on the Hugging Face dataset page or contact the Imageomics Institute.
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