Recommendations for developing, documenting, and distributing data products derived from NEON data
Abstract The National Ecological Observatory Network (NEON) provides over 180 distinct data products from 81 sites (47 terrestrial and 34 freshwater aquatic sites) within the United States and Puerto Rico. These data products include both field and remote sensing data collected using standardized pr...
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Ecosphere |
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| Online Access: | https://doi.org/10.1002/ecs2.70159 |
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| author | Jeff W. Atkins Kelly S. Aho Xuan Chen Andrew J. Elmore Rich Fiorella Wenqi Luo Danica Lombardozzi Claire Lunch Leah Manak Luis X. dePablo Allison N. Myers‐Pigg Sydne Record Tong Qiu Samuel Reed Benjamin Ruddell Brandon Strange Christa L. Torrens Kelsey Yule Andrew D. Richardson |
| author_facet | Jeff W. Atkins Kelly S. Aho Xuan Chen Andrew J. Elmore Rich Fiorella Wenqi Luo Danica Lombardozzi Claire Lunch Leah Manak Luis X. dePablo Allison N. Myers‐Pigg Sydne Record Tong Qiu Samuel Reed Benjamin Ruddell Brandon Strange Christa L. Torrens Kelsey Yule Andrew D. Richardson |
| author_sort | Jeff W. Atkins |
| collection | DOAJ |
| description | Abstract The National Ecological Observatory Network (NEON) provides over 180 distinct data products from 81 sites (47 terrestrial and 34 freshwater aquatic sites) within the United States and Puerto Rico. These data products include both field and remote sensing data collected using standardized protocols and sampling schema, with centralized quality assurance and quality control (QA/QC) provided by NEON staff. Such breadth of data creates opportunities for the research community to extend basic and applied research while also extending the impact and reach of NEON data through the creation of derived data products—higher level data products derived by the user community from NEON data. Derived data products are curated, documented, reproducibly‐generated datasets created by applying various processing steps to one or more lower level data products—including interpolation, extrapolation, integration, statistical analysis, modeling, or transformations. Derived data products directly benefit the research community and increase the impact of NEON data by broadening the size and diversity of the user base, decreasing the time and effort needed for working with NEON data, providing primary research foci through the development via the derivation process, and helping users address multidisciplinary questions. Creating derived data products also promotes personal career advancement to those involved through publications, citations, and future grant proposals. However, the creation of derived data products is a nontrivial task. Here we provide an overview of the process of creating derived data products while outlining the advantages, challenges, and major considerations. |
| format | Article |
| id | doaj-art-2f8e42983fd043fb91535d656dfb0f6c |
| institution | Kabale University |
| issn | 2150-8925 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Ecosphere |
| spelling | doaj-art-2f8e42983fd043fb91535d656dfb0f6c2025-01-30T01:44:38ZengWileyEcosphere2150-89252025-01-01161n/an/a10.1002/ecs2.70159Recommendations for developing, documenting, and distributing data products derived from NEON dataJeff W. Atkins0Kelly S. Aho1Xuan Chen2Andrew J. Elmore3Rich Fiorella4Wenqi Luo5Danica Lombardozzi6Claire Lunch7Leah Manak8Luis X. dePablo9Allison N. Myers‐Pigg10Sydne Record11Tong Qiu12Samuel Reed13Benjamin Ruddell14Brandon Strange15Christa L. Torrens16Kelsey Yule17Andrew D. Richardson18USDA Forest Service Southern Research Station New Ellenton South Carolina USADepartment of Earth & Environmental Sciences Michigan State University East Lansing Michigan USADepartment of Biological Sciences Salisbury University Salisbury Maryland USAAppalachian Laboratory University of Maryland Center for Environmental Science Frostburg Maryland USALos Alamos National Laboratory Los Alamos New Mexico USASchool of Environment and Sustainability University of Michigan Ann Arbor Michigan USANational Center for Atmospheric Research Boulder Colorado USANational Ecological Observatory Network Battelle Boulder Colorado USASchool of Informatics, Computing, and Cyber Systems Northern Arizona University Flagstaff Arizona USABiofrontiers Institute University of Colorado Boulder Colorado USAPacific Northwest National Laboratory Marine and Coastal Research Laboratory Sequim Washington USADepartment of Wildlife, Fisheries, and Conservation Biology University of Maine Orono Maine USANichoals School of the Environment Duke University Durham North Carolina USANatural Resources and Management University of Minnesota St. Paul Minnesota USASchool of Informatics, Computing, and Cyber Systems Northern Arizona University Flagstaff Arizona USASchool of Informatics, Computing, and Cyber Systems Northern Arizona University Flagstaff Arizona USAFlathead Lake Biological Station University of Montana Polson Montana USASchool of Life Sciences Arizona State University Tempe Arizona USASchool of Informatics, Computing, and Cyber Systems Northern Arizona University Flagstaff Arizona USAAbstract The National Ecological Observatory Network (NEON) provides over 180 distinct data products from 81 sites (47 terrestrial and 34 freshwater aquatic sites) within the United States and Puerto Rico. These data products include both field and remote sensing data collected using standardized protocols and sampling schema, with centralized quality assurance and quality control (QA/QC) provided by NEON staff. Such breadth of data creates opportunities for the research community to extend basic and applied research while also extending the impact and reach of NEON data through the creation of derived data products—higher level data products derived by the user community from NEON data. Derived data products are curated, documented, reproducibly‐generated datasets created by applying various processing steps to one or more lower level data products—including interpolation, extrapolation, integration, statistical analysis, modeling, or transformations. Derived data products directly benefit the research community and increase the impact of NEON data by broadening the size and diversity of the user base, decreasing the time and effort needed for working with NEON data, providing primary research foci through the development via the derivation process, and helping users address multidisciplinary questions. Creating derived data products also promotes personal career advancement to those involved through publications, citations, and future grant proposals. However, the creation of derived data products is a nontrivial task. Here we provide an overview of the process of creating derived data products while outlining the advantages, challenges, and major considerations.https://doi.org/10.1002/ecs2.70159community sciencedataderived data productsNEONobservatory scienceSpecial Feature: Harnessing the NEON Data Revolution |
| spellingShingle | Jeff W. Atkins Kelly S. Aho Xuan Chen Andrew J. Elmore Rich Fiorella Wenqi Luo Danica Lombardozzi Claire Lunch Leah Manak Luis X. dePablo Allison N. Myers‐Pigg Sydne Record Tong Qiu Samuel Reed Benjamin Ruddell Brandon Strange Christa L. Torrens Kelsey Yule Andrew D. Richardson Recommendations for developing, documenting, and distributing data products derived from NEON data Ecosphere community science data derived data products NEON observatory science Special Feature: Harnessing the NEON Data Revolution |
| title | Recommendations for developing, documenting, and distributing data products derived from NEON data |
| title_full | Recommendations for developing, documenting, and distributing data products derived from NEON data |
| title_fullStr | Recommendations for developing, documenting, and distributing data products derived from NEON data |
| title_full_unstemmed | Recommendations for developing, documenting, and distributing data products derived from NEON data |
| title_short | Recommendations for developing, documenting, and distributing data products derived from NEON data |
| title_sort | recommendations for developing documenting and distributing data products derived from neon data |
| topic | community science data derived data products NEON observatory science Special Feature: Harnessing the NEON Data Revolution |
| url | https://doi.org/10.1002/ecs2.70159 |
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