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| <?xml version="1.0" encoding="utf-8" ?> | ||
| <!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.2 20190208//EN" | ||
| "JATS-publishing1.dtd"> | ||
| <article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" dtd-version="1.2" article-type="other"> | ||
| <front> | ||
| <journal-meta> | ||
| <journal-id></journal-id> | ||
| <journal-title-group> | ||
| <journal-title>Journal of Open Source Software</journal-title> | ||
| <abbrev-journal-title>JOSS</abbrev-journal-title> | ||
| </journal-title-group> | ||
| <issn publication-format="electronic">2475-9066</issn> | ||
| <publisher> | ||
| <publisher-name>Open Journals</publisher-name> | ||
| </publisher> | ||
| </journal-meta> | ||
| <article-meta> | ||
| <article-id pub-id-type="publisher-id">7406</article-id> | ||
| <article-id pub-id-type="doi">10.21105/joss.07406</article-id> | ||
| <title-group> | ||
| <article-title>ndbc-api: Accelerating oceanography and climate science | ||
| research with Python</article-title> | ||
| </title-group> | ||
| <contrib-group> | ||
| <contrib contrib-type="author"> | ||
| <contrib-id contrib-id-type="orcid">https://orcid.org/0000-0003-0469-353X</contrib-id> | ||
| <name> | ||
| <surname>Jellen</surname> | ||
| <given-names>Christopher David</given-names> | ||
| </name> | ||
| <xref ref-type="aff" rid="aff-1"/> | ||
| </contrib> | ||
| <aff id="aff-1"> | ||
| <institution-wrap> | ||
| <institution>United States Naval Academy, USA</institution> | ||
| </institution-wrap> | ||
| </aff> | ||
| </contrib-group> | ||
| <pub-date date-type="pub" publication-format="electronic" iso-8601-date="2024-09-02"> | ||
| <day>2</day> | ||
| <month>9</month> | ||
| <year>2024</year> | ||
| </pub-date> | ||
| <volume>9</volume> | ||
| <issue>104</issue> | ||
| <fpage>7406</fpage> | ||
| <permissions> | ||
| <copyright-statement>Authors of papers retain copyright and release the | ||
| work under a Creative Commons Attribution 4.0 International License (CC | ||
| BY 4.0)</copyright-statement> | ||
| <copyright-year>2024</copyright-year> | ||
| <copyright-holder>The article authors</copyright-holder> | ||
| <license license-type="open-access" xlink:href="https://creativecommons.org/licenses/by/4.0/"> | ||
| <license-p>Authors of papers retain copyright and release the work under | ||
| a Creative Commons Attribution 4.0 International License (CC BY | ||
| 4.0)</license-p> | ||
| </license> | ||
| </permissions> | ||
| <kwd-group kwd-group-type="author"> | ||
| <kwd>Python</kwd> | ||
| <kwd>oceanography</kwd> | ||
| <kwd>climate science</kwd> | ||
| <kwd>NDBC</kwd> | ||
| </kwd-group> | ||
| </article-meta> | ||
| </front> | ||
| <body> | ||
| <sec id="summary"> | ||
| <title>Summary</title> | ||
| <p>The National Data Buoy Center (NDBC) and its partners are an | ||
| essential source of marine meteorological and oceanographic data | ||
| (<xref alt="National Data Buoy Center (NDBC), 2023" rid="ref-NDBC_Data_Guide" ref-type="bibr">National | ||
| Data Buoy Center (NDBC), 2023</xref>). The | ||
| <monospace>ndbc-api</monospace> Python package is an open-source tool | ||
| designed to streamline the acquisition, synthesis, and analysis of | ||
| this data. It provides a Python API for accessing real-time and | ||
| historical observations from a network of buoys, coastal stations, and | ||
| deployments. This package simplifies the process of retrieving, | ||
| parsing, and organizing NDBC data. Traditional HTTP access methods to | ||
| station-specific text files poses a challenge when dealing with | ||
| multiple stations or extended time ranges. The | ||
| <monospace>ndbc-api</monospace> provides a Python API which retrieves, | ||
| parses, and merges this data, returning Python-native data structures. | ||
| This package empowers researchers and practitioners in oceanography, | ||
| meteorology, and related fields to efficiently integrate NDBC data | ||
| into their workflows, accelerating research in climate science and | ||
| oceanography.</p> | ||
| </sec> | ||
| <sec id="statement-of-need"> | ||
| <title>Statement of need</title> | ||
| <p>The National Oceanic and Atmospheric Association’s National Data | ||
| Buoy Center maintains marine monitoring and observation stations | ||
| around the world | ||
| (<xref alt="National Data Buoy Center (NDBC), 2024" rid="ref-NDBC_Active_Stations" ref-type="bibr">National | ||
| Data Buoy Center (NDBC), 2024</xref>). These stations report | ||
| atmospheric, oceanographic, and other meteorological data at regular | ||
| intervals to the NDBC | ||
| (<xref alt="National Data Buoy Center (NDBC), 2023" rid="ref-NDBC_Data_Guide" ref-type="bibr">National | ||
| Data Buoy Center (NDBC), 2023</xref>, | ||
| <xref alt="2024" rid="ref-NDBC_Active_Stations" ref-type="bibr">2024</xref>). | ||
| Measurements are made available over HTTP through the NDBC’s data | ||
| service. Measurements are typically distributed as quality-controlled | ||
| <monospace>utf-8</monospace> encoded, station-by-station, fixed-period | ||
| text files | ||
| (<xref alt="National Data Buoy Center (NDBC), 2023" rid="ref-NDBC_Data_Guide" ref-type="bibr">National | ||
| Data Buoy Center (NDBC), 2023</xref>). While the data collected and | ||
| maintained by the NDBC is critical to oceanography and climate science | ||
| researchers, the mode of access adds complexity to their workflows. | ||
| These challenges are particularly pronounced when working with | ||
| long-duration data, data from multiple stations, or data with a high | ||
| proportion of missing measurements.</p> | ||
| <p>The <monospace>ndbc-api</monospace> addresses these critical gaps | ||
| by providing a streamlined, programmatic interface to the NDBC’s data | ||
| service. By abstracting the complexities of file-based access, | ||
| handling of missing measurements, and cross-station joins, the | ||
| <monospace>ndbc-api</monospace> package lowers the barriers to | ||
| obtaining and using the NDBC’s global oceanographic and meteorological | ||
| data in scientific research. Researchers specify their stations, data | ||
| modalities (for example, <monospace>adcp</monospace> for acoustic | ||
| doppler current profiler measurements or <monospace>cwind</monospace> | ||
| for continuous winds data), and time ranges of interest, and the | ||
| package returns the processed NDBC data either as a Pandas | ||
| <monospace>DataFrame</monospace> object or as an xarray | ||
| <monospace>Dataset</monospace> object | ||
| (<xref alt="Hoyer & Hamman, 2017" rid="ref-hoyer2017xarray" ref-type="bibr">Hoyer | ||
| & Hamman, 2017</xref>; | ||
| <xref alt="Mckinney, 2010" rid="ref-pandas" ref-type="bibr">Mckinney, | ||
| 2010</xref>; | ||
| <xref alt="The pandas development team, 2020" rid="ref-reback2020pandas" ref-type="bibr">The | ||
| pandas development team, 2020</xref>). The package maps missing | ||
| measurements from their varied text-based identifiers such as | ||
| <monospace>99</monospace>, <monospace>999</monospace>, or | ||
| <monospace>MM</monospace>, into a single missing measurement | ||
| representation of <monospace>nan</monospace>. The challenge of | ||
| aligning and joining data across measurements and stations is | ||
| similarly handled before the final object is returned to the user. By | ||
| exposing station metadata and search functionality alongside data | ||
| retrieval methods, researchers are also able to identify the set of | ||
| stations, based on their NDBC identifier, that were active during a | ||
| given period, or within some radius of a given location. The | ||
| combination of efficient identification of the relevant stations, | ||
| retrieval of the desired data variables, and processing of the data | ||
| from the NDBC data service make the <monospace>ndbc-api</monospace> a | ||
| valuable tool for oceanography and climate science researchers.</p> | ||
| </sec> | ||
| <sec id="acknowledgements"> | ||
| <title>Acknowledgements</title> | ||
| <p>The authors would like to acknowledge the invaluable contributions | ||
| of the National Data Buoy Center (NDBC), their teams, and their | ||
| network of partners. Their efforts in deploying and maintaining the | ||
| buoys and marine observation stations, collecting and | ||
| quality-controlling oceanographic data, and making their measurements | ||
| readily available through their data service are fundamental to | ||
| advancing our understanding of earth systems. The | ||
| <monospace>ndbc-api</monospace> Python package would not be possible | ||
| without their commitment to open data access and their unwavering | ||
| support for the scientific and operational communities. We extend our | ||
| sincere appreciation to everyone involved in this vital endeavor.</p> | ||
| </sec> | ||
| </body> | ||
| <back> | ||
| <ref-list> | ||
| <title></title> | ||
| <ref id="ref-NDBC_Data_Guide"> | ||
| <element-citation> | ||
| <person-group person-group-type="author"> | ||
| <string-name>National Data Buoy Center (NDBC)</string-name> | ||
| </person-group> | ||
| <article-title>NDBC web data guide</article-title> | ||
| <publisher-name>https://www.ndbc.noaa.gov/docs/ndbc_web_data_guide.pdf</publisher-name> | ||
| <year iso-8601-date="2023">2023</year> | ||
| </element-citation> | ||
| </ref> | ||
| <ref id="ref-NDBC_Active_Stations"> | ||
| <element-citation> | ||
| <person-group person-group-type="author"> | ||
| <string-name>National Data Buoy Center (NDBC)</string-name> | ||
| </person-group> | ||
| <article-title>NDBC active stations</article-title> | ||
| <publisher-name>https://www.ndbc.noaa.gov/wstat.shtml</publisher-name> | ||
| <year iso-8601-date="2024">2024</year> | ||
| </element-citation> | ||
| </ref> | ||
| <ref id="ref-pandas"> | ||
| <element-citation publication-type="article-journal"> | ||
| <person-group person-group-type="author"> | ||
| <name><surname>Mckinney</surname><given-names>Wes</given-names></name> | ||
| </person-group> | ||
| <article-title>Data Structures for Statistical Computing in Python</article-title> | ||
| <source>Proceedings of the 9th Python in Science Conference</source> | ||
| <year iso-8601-date="2010-01">2010</year><month>01</month> | ||
| <volume>445</volume> | ||
| <issue>1</issue> | ||
| <pub-id pub-id-type="doi">10.25080/majora-92bf1922-00a</pub-id> | ||
| <fpage>56</fpage> | ||
| <lpage>61</lpage> | ||
| </element-citation> | ||
| </ref> | ||
| <ref id="ref-reback2020pandas"> | ||
| <element-citation publication-type="software"> | ||
| <person-group person-group-type="author"> | ||
| <string-name>The pandas development team</string-name> | ||
| </person-group> | ||
| <article-title>pandas-dev/pandas: Pandas</article-title> | ||
| <publisher-name>Zenodo</publisher-name> | ||
| <year iso-8601-date="2020-02">2020</year><month>02</month> | ||
| <uri>https://doi.org/10.5281/zenodo.3509134</uri> | ||
| <pub-id pub-id-type="doi">10.5281/zenodo.3509134</pub-id> | ||
| </element-citation> | ||
| </ref> | ||
| <ref id="ref-hoyer2017xarray"> | ||
| <element-citation publication-type="article-journal"> | ||
| <person-group person-group-type="author"> | ||
| <name><surname>Hoyer</surname><given-names>S.</given-names></name> | ||
| <name><surname>Hamman</surname><given-names>J.</given-names></name> | ||
| </person-group> | ||
| <article-title>Xarray: N-D labeled arrays and datasets in Python</article-title> | ||
| <source>Journal of Open Research Software</source> | ||
| <publisher-name>Ubiquity Press</publisher-name> | ||
| <year iso-8601-date="2017">2017</year> | ||
| <volume>5</volume> | ||
| <issue>1</issue> | ||
| <uri>https://doi.org/10.5334/jors.148</uri> | ||
| <pub-id pub-id-type="doi">10.5334/jors.148</pub-id> | ||
| </element-citation> | ||
| </ref> | ||
| </ref-list> | ||
| </back> | ||
| </article> |