---
license: cc-by-4.0
language:
- en
pretty_name: upper_waiakea_passive_recordings
task_categories: [audio-classification] # ex: image-classification, see key list at https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/src/pipelines.ts
modalities: [Audio]
tags:
- biology
- audio
- bioacoustics
- animals
- CV
- Hawaiian birds
size_categories: 1K<n<10K # ex: n<1K, 1K<n<10K, 10K<n<100K, 100K<n<1M, ...
---

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# Dataset Card for upper_waiakea_passive_recordings

<!-- Provide a quick summary of what the dataset is or can be used for. --> 

## 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
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- **Homepage:** 
- **Repository:** [related project repo]
- **Paper:** 


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[More Information Needed]

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### 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](https://github.com/imageomics) and [ABC Global Center](https://www.abcresearchcenter.org/) 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 is 1 hour long

### 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.
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## 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.
<!-- Motivation for the creation of this dataset. For instance, what you intended to study and why that required curation of a new dataset (or if it's newly collected data and why the data was collected (intended use)), etc. -->

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

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#### 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.
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## Considerations for Using the Data
These data are unlabelled, unprocessed, and may still contain significant noise. Employing source separation or audio preprocessing methods may be beneficial to downstream analyses. Also note, humans or vehicles may be audible in some recorders due to the presence of cars, helicopters, or people. 
<!--
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<!-- ### Bias, Risks, and Limitations -->
[More Information Needed]
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[More Information Needed]
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## Licensing Information
This dataset is available to share and adapt for any use under the [CC BY 4.0](https://creativecommons.org/licenses/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. 

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The data (images and text) contain a variety of licensing restrictions mostly within the CC family. Each image and text in this dataset is provided under the least restrictive terms allowed by its licensing requirements as provided to us (i.e, we impose no additional restrictions past those specified by licenses in the license file).

EOL images contain a variety of licenses ranging from [CC0](https://creativecommons.org/publicdomain/zero/1.0/) to [CC BY-NC-SA](https://creativecommons.org/licenses/by-nc-sa/4.0/).
For license and citation information by image, see our [license file](https://huggingface.co/datasets/imageomics/treeoflife-10m/blob/main/metadata/licenses.csv).

This dataset (the compilation) has been marked as dedicated to the public domain by applying the [CC0 Public Domain Waiver](https://creativecommons.org/publicdomain/zero/1.0/). However, images may be licensed under different terms (as noted above).
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## Citation
[More Information Needed]

**BibTeX:**
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  doi      = {<DOI>}
}
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## Acknowledgements

This work was supported by both the [Imageomics Institute](https://imageomics.org) and the [AI and Biodiversity Change (ABC) Global Center](https://abcresearchcenter.org/). The Imageomics Institute is funded by the US National Science Foundation's Harnessing the Data Revolution (HDR) program under [Award #2118240](https://www.nsf.gov/awardsearch/showAward?AWD_ID=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](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2330423&HistoricalAwards=false) and Natural Sciences and Engineering Research Council of Canada under [Award No. 585136](https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=782440). 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.

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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.

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## Dataset Card Authors 
Namrata Banerji, Jacob Beattie, Hikaru Keebler, and Kate Nepovinnykh

[More Information Needed]

## Dataset Card Contact
banerji.8@osu.edu
beattie.74@osu.edu
nepove@rpi.edu
keebler.8@osu.edu


[More Information Needed--optional]
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