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--- |
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task_categories: |
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- tabular-classification |
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tags: |
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- chemistry |
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- climate |
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size_categories: |
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- n<1K |
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language: |
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- en |
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license: other |
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license_name: nist |
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license_link: https://github.com/mahynski/pychemauth/blob/main/LICENSE.md |
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--- |
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# Summary |
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This dataset was originally reported in Mahynski et al. (2021) and used in the Mahynski et al. (2022) publication. |
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See these papers for a full description of the dataset's origin and processing. |
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From Mahynski2021: |
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> The multi-entity, long-term [Seabird Tissue Archival and Monitoring Project (STAMP)](https://www.nist.gov/programs-projects/seabird-tissue-archival-and-monitoring-project-stamp) has collected |
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eggs from various avian species throughout the North Pacifc Ocean for over 20 years to create a geospatial |
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and temporal record of environmental conditions. Over 2,500 samples are currently archived at the [NIST |
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Biorepository](https://www.nist.gov/programs-projects/nist-biorepository) at [Hollings Marine Laboratory](https://www.nist.gov/mml/hollings-marine-laboratory/hml-overview) in Charleston, South Carolina. Longitudinal monitoring |
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efforts of this nature provide invaluable data for assessment of both wildlife and human exposures as these |
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species often consume prey (e.g., fish) similar to, and from sources (e.g., oceanic) comparable to, human |
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populations nearby. In some areas, seabird eggs also comprise a signifcant part of subsistence diets |
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providing nutrition for indigenous peoples. Chemometric profles and related health implications are known |
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to differ across species. Eggs, however, can be diffcult to assign to a species unless the bird is observed |
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on the nest from which the sample was collected due to similar appearance within a genus and sympatric |
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nesting behavior. This represents a large point of uncertainty for both wildlife managers and exposure |
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researchers alike. |
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> |
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> Here we have curated analytical data for eggs collected from 1999 to 2010 on a subset of species and |
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analytes that were measured regularly and reasonably systematically. Included in this publication are 487 |
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samples analyzed for 174 ubiquitous environmental contaminants such as brominated diphenyl ethers |
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(BDEs), mercury, organochlorine pesticides, and polychlorinated biphenyls (PCBs). Data were collated to |
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form a dataset useful in chemometric and related analyses of the marine ecosystem in the North Pacifc |
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Ocean. |
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The data here was obtained from [github.com/mahynski/stamp-dataset-1999-2010](https://github.com/mahynski/stamp-dataset-1999-2010) using the [PyChemAuth package](https://github.com/mahynski/pychemauth). |
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Here are some summary statistics of the seabirds in the dataset. |
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<img src="colony_name.png" width=400 align="left" /> |
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<img src="common_name.png" width=400 align="left" /> |
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<img src="collection_year.png" width=400 align="left" /> |
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# Citation |
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~~~bibtex |
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@article{Mahynski2021, |
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author = {Nathan A. Mahynski and Jared M. Ragland and Stacy S. Schuur and Rebecca Pugh and Vincent K. Shen}, |
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doi = {10.6028/jres.126.028}, |
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journal = {Journal of Research of the National Institute of Standards and Technology}, |
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title = {Seabird Tissue Archival and Monitoring Project (STAMP) Data from 1999-2010}, |
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url = {https://dx.doi.org/10.6028/jres.126.028}, |
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volume = {126}, |
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number = {126028}, |
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year = {2021}, |
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} |
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~~~ |
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~~~bibtex |
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@article{Mahynski2022, |
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title={Building interpretable machine learning models to identify chemometric trends in seabirds of the north pacific ocean}, |
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author={Nathan A. Mahynski and Jared M. Ragland and Stacy S. Schuur and Vincent K. Shen}, |
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journal={Environmental Science \& Technology}, |
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volume={56}, |
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number={20}, |
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pages={14361--14374}, |
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year={2022}, |
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publisher={ACS Publications} |
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} |
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~~~ |
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# Access |
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See [HuggingFace documentation](https://huggingface.co/docs/datasets/load_hub#load-a-dataset-from-the-hub) on loading a dataset from the hub. |
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Briefly, you can access this dataset using the huggingface api like this: |
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~~~python |
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from huggingface_hub import hf_hub_download |
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import pandas as pd |
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dataset = pd.read_csv( |
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hf_hub_download( |
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repo_id="mahynski/stamp2010", |
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filename="train.csv", |
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repo_type="dataset", |
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token="hf_*" # Enter your own token here |
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) |
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) |
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~~~ |
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or using [datasets](https://github.com/huggingface/datasets): |
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~~~python |
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from datasets import load_dataset |
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ds = load_dataset( |
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"mahynski/stamp2010", |
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token="hf_*" # Enter your own token here |
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) |
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df = ds['train'].to_pandas() |
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~~~ |
