Dataset Card for upper_waiakea_passive_recordings

Dataset Details

Dataset Description

This is a dataset containing unlabelled, unprocessed passive acoustic recordings of Hawaiian birds in the Upper Waiākea Forst Reserve in Hawaii. This dataset is intended for use in unsupervised audio analysis methods, classification using existing models, and other machine learning and ecology research purposes.

Curated by: Namrata Banerji, Jacob Beattie, Hikaru Keebler, Kate Nepovinnykh
Repository: [https://github.com/Imageomics/amakiki-project]

Supported Tasks and Leaderboards

This dataset contains passive acoustic recordings collected as part of the Experiential Introduction to AI and Ecology course through the Imageomics Institute and ABC Global Center during January 2025.

This dataset is intended for use with unsupervised computer vision or acoustic machine learning models. No labels are provided, but recorder locations and recording timestamps are included, allowing for analysis of the relationship between ecological factors and variations in birdsong.

The dataset contains ~1623 hours of recording from 19 different recorders located in the Upper Waiākea Forest Reserve.

Dataset Structure

/kipuka_data/
    <recorder_id>/
          <recorder_id>_Summary.txt
          Data/
              <recorder_id>_YYYYMMDD_HHMMSS.wav
              ...
      <recorder_id>/
          ...
      ...
    kipuka_metadata.csv

Data Instances

All audio files are named (recorder_id)-YYYYMMDD-HHMMSS.wav inside a folder named after the recorder id. Each recording starts at the time listed in the filename. Most recordings are 1 hour long, but some may be shorter. Recordings were taken using a SongMeter Micro 2.

Data Fields

recorder_id,card_code,point_id,HDD Path,Deployment Date,Retrieval Date,Latitude,Longitude

kipuka_metadata.csv

recorder_id: Unique identifier for each recorder
card_code: Unique identifier for SD card used in each recorder
point_id: Unique identifier for each point where a recorder was placed
Deployment Date: Date the recorder was deployed
Retrieval Date: Date the recorder was retrieved.
Latitude: Latitude of recorder
Longitude: Longitude of recorder

Data Splits

Only one data split: data. If being used for training/testing/validation of models, splits must be made manually.

Dataset Creation

This dataset was compiled as part of the field component of the Experiential Introduction to AI and Ecology Course run by the Imageomics Institute and the AI and Biodiversity Change (ABC) Global Center. This field work was done on the island of Hawai'i January 15-30, 2025.

Curation Rationale

This dataset was created in order to study Hawaiian bird call variation across kipuka. Passive acoustic monitoring was done to capture Hawaiian bird calls across varying kipuka.

Source Data

These data were originally created by placing recorders in kipuka in the Upper Waiākea Forest Reserve on Hawaii island, recording bird calls.

Data Collection and Processing

Recorder locations were selected based on historic datasets (specifically data from Patrick Hart from UH Hilo). These data have significant overlap with historic data, while retaining only minimally sufficient overlap with recently collected data to allow for calibration between datasets.

Who are the source data producers?

These data are produced by members of the ABC Global Center.

Considerations for Using the Data

Bias, Risks, and Limitations

These data are unlabelled, unprocessed, and may still contain significant noise due to some recorder's proximity to the road or footpaths. Because of this, humans, cars, or helicopters may also be audible in some recordings.

Recommendations

Consider the impact that raw, unprocessed data may have on use cases fpr these data. Employing source separation or audio preprocessing methods may be beneficial to downstream analyses.

Licensing Information

This dataset is available to share and adapt for any use under the CC BY 4.0 license, provided appropriate credit is given. We ask that you cite this dataset if you make use of these data in any work or product.

Citation

[More Information Needed]

BibTeX:

Acknowledgements

This work was supported by both the Imageomics Institute and the AI and Biodiversity Change (ABC) Global Center. The Imageomics Institute 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). The ABC Global Center is funded by the US National Science Foundation under Award No. 2330423 and Natural Sciences and Engineering Research Council of Canada under Award No. 585136. 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 or Natural Sciences and Engineering Research Council of Canada.

This material is based in part upon work supported by the National Ecological Observatory Network (NEON), a program sponsored by the U.S. National Science Foundation (NSF) and operated under cooperative agreement by Battelle.

Dataset Card Authors

Namrata Banerji, Jacob Beattie, Hikaru Keebler, and Kate Nepovinnykh

Dataset Card Contact

[email protected] [email protected] [email protected] [email protected]