Data from: Influence of different data cleaning solutions of point-occurrence records on downstream macroecological diversity models
Digital point-occurrence records from the Global Biodiversity Information Facility (GBIF) and other data providers enable a wide range of research in macroecology and biogeography. However, data errors may hamper immediate use. Manual data cleaning is time-consuming and often unfeasible, given that...
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ftzenodo:oai:zenodo.org:6834791 2024-09-15T18:41:31+00:00 Data from: Influence of different data cleaning solutions of point-occurrence records on downstream macroecological diversity models Fuehrding-Potschkat, Petra Ickert-Bond, Stefanie M. 2022-07-14 https://doi.org/10.5061/dryad.8pk0p2np4 unknown Zenodo https://zenodo.org/communities/dryad https://doi.org/10.5061/dryad.8pk0p2np4 oai:zenodo.org:6834791 info:eu-repo/semantics/openAccess Creative Commons Zero v1.0 Universal https://creativecommons.org/publicdomain/zero/1.0/legalcode Professor of Botany and Curator of the UA Museum Herbarium (ALA) FNA Regional Coordinator Alaska-Yukon info:eu-repo/semantics/other 2022 ftzenodo https://doi.org/10.5061/dryad.8pk0p2np4 2024-07-27T04:11:11Z Digital point-occurrence records from the Global Biodiversity Information Facility (GBIF) and other data providers enable a wide range of research in macroecology and biogeography. However, data errors may hamper immediate use. Manual data cleaning is time-consuming and often unfeasible, given that the databases may contain thousands or millions of records. Automated data cleaning pipelines are therefore of high importance. This study examined the extent to which cleaned data from six pipelines using data cleaning tools (e.g., the GBIF web application, different R packages) affect downstream species distribution models. In addition, we assessed how the pipeline data differ from expert data. From 13,889 North American Ephedra observations in GBIF, the pipelines removed 31.7% to 62.7% false-positives, invalid coordinates, and duplicates, leading to data sets that included between 9,484 (GBIF application) and 5,196 records (manual-guided filtering). The expert data consisted of 703 thoroughly handpicked records, comparable to data from field studies. Although differences in the record numbers were relatively large, stacked species distribution models (sSDM) from the pipelines and the expert data were strongly related (mean Pearson's r across the pipelines: 0.9986, versus the expert data: 0.9173). The ever-stronger correlations resulted from occurrence information that became increasingly condensed in the course of the workflow (from individual occurrences to collectivized occurrences in grid cells to predicted probabilities in the sSDMs). In sum, our results suggest that the R package-based pipelines reliably identified invalid coordinates. In contrast, the GBIF-filtered data still contained both spatial and taxonomic errors. However, major drawbacks emerge from the fact that no pipeline fully discovered misidentified specimens without the assistance of expert taxonomic knowledge. We conclude that application-filtered GBIF data will still need additional review to achieve higher spatial data quality. Achieving ... Other/Unknown Material Alaska Yukon Zenodo |
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Professor of Botany and Curator of the UA Museum Herbarium (ALA) FNA Regional Coordinator Alaska-Yukon |
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Professor of Botany and Curator of the UA Museum Herbarium (ALA) FNA Regional Coordinator Alaska-Yukon Fuehrding-Potschkat, Petra Ickert-Bond, Stefanie M. Data from: Influence of different data cleaning solutions of point-occurrence records on downstream macroecological diversity models |
topic_facet |
Professor of Botany and Curator of the UA Museum Herbarium (ALA) FNA Regional Coordinator Alaska-Yukon |
description |
Digital point-occurrence records from the Global Biodiversity Information Facility (GBIF) and other data providers enable a wide range of research in macroecology and biogeography. However, data errors may hamper immediate use. Manual data cleaning is time-consuming and often unfeasible, given that the databases may contain thousands or millions of records. Automated data cleaning pipelines are therefore of high importance. This study examined the extent to which cleaned data from six pipelines using data cleaning tools (e.g., the GBIF web application, different R packages) affect downstream species distribution models. In addition, we assessed how the pipeline data differ from expert data. From 13,889 North American Ephedra observations in GBIF, the pipelines removed 31.7% to 62.7% false-positives, invalid coordinates, and duplicates, leading to data sets that included between 9,484 (GBIF application) and 5,196 records (manual-guided filtering). The expert data consisted of 703 thoroughly handpicked records, comparable to data from field studies. Although differences in the record numbers were relatively large, stacked species distribution models (sSDM) from the pipelines and the expert data were strongly related (mean Pearson's r across the pipelines: 0.9986, versus the expert data: 0.9173). The ever-stronger correlations resulted from occurrence information that became increasingly condensed in the course of the workflow (from individual occurrences to collectivized occurrences in grid cells to predicted probabilities in the sSDMs). In sum, our results suggest that the R package-based pipelines reliably identified invalid coordinates. In contrast, the GBIF-filtered data still contained both spatial and taxonomic errors. However, major drawbacks emerge from the fact that no pipeline fully discovered misidentified specimens without the assistance of expert taxonomic knowledge. We conclude that application-filtered GBIF data will still need additional review to achieve higher spatial data quality. Achieving ... |
format |
Other/Unknown Material |
author |
Fuehrding-Potschkat, Petra Ickert-Bond, Stefanie M. |
author_facet |
Fuehrding-Potschkat, Petra Ickert-Bond, Stefanie M. |
author_sort |
Fuehrding-Potschkat, Petra |
title |
Data from: Influence of different data cleaning solutions of point-occurrence records on downstream macroecological diversity models |
title_short |
Data from: Influence of different data cleaning solutions of point-occurrence records on downstream macroecological diversity models |
title_full |
Data from: Influence of different data cleaning solutions of point-occurrence records on downstream macroecological diversity models |
title_fullStr |
Data from: Influence of different data cleaning solutions of point-occurrence records on downstream macroecological diversity models |
title_full_unstemmed |
Data from: Influence of different data cleaning solutions of point-occurrence records on downstream macroecological diversity models |
title_sort |
data from: influence of different data cleaning solutions of point-occurrence records on downstream macroecological diversity models |
publisher |
Zenodo |
publishDate |
2022 |
url |
https://doi.org/10.5061/dryad.8pk0p2np4 |
genre |
Alaska Yukon |
genre_facet |
Alaska Yukon |
op_relation |
https://zenodo.org/communities/dryad https://doi.org/10.5061/dryad.8pk0p2np4 oai:zenodo.org:6834791 |
op_rights |
info:eu-repo/semantics/openAccess Creative Commons Zero v1.0 Universal https://creativecommons.org/publicdomain/zero/1.0/legalcode |
op_doi |
https://doi.org/10.5061/dryad.8pk0p2np4 |
_version_ |
1810485914713980928 |