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Abstract Species occurrence data are foundational for research, conservation, and science communication, but the limited availability and accessibility of reliable data represents a major obstacle, particularly for insects, which face mounting pressures. We presentBeeBDC, a newRpackage, and a global bee occurrence dataset to address this issue. We combined >18.3 million bee occurrence records from multiple public repositories (GBIF, SCAN, iDigBio, USGS, ALA) and smaller datasets, then standardised, flagged, deduplicated, and cleaned the data using the reproducibleBeeBDC R-workflow. Specifically, we harmonised species names (following established global taxonomy), country names, and collection dates and, we added record-level flags for a series of potential quality issues. These data are provided in two formats, “cleaned” and “flagged-but-uncleaned”. TheBeeBDCpackage with online documentation provides end users the ability to modify filtering parameters to address their research questions. By publishing reproducibleRworkflows and globally cleaned datasets, we can increase the accessibility and reliability of downstream analyses. This workflow can be implemented for other taxa to support research and conservation.
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Database interoperability, uncertainty quantification and reproducible workflows in the paleogeosciences
The paleogeosciences are becoming more and more interdisciplinary, and studies increasingly rely on large collections of data derived from multiple data repositories. Integrating diverse datasets from multiple sources into complex workflows increases the challenge of creating reproducible and open science, as data formats and tools are often noninteroperable, requiring manual manipulation of data into standardized formats, resulting in a disconnect in data provenance and confounding reproducibility. Here we present a notebook that demonstrates how the Linked PaleoData (LiPD) framework is used as an interchange format to allow data from multiple data sources to be integrated in a complex workflow using emerging packages in R for geochronological uncertainty quantification and abrupt change detection. Specifically, in this notebook, we use the neotoma2 and lipdR packages to access paleoecological data from the Neotoma Database, and paleoclimate data from compilations hosted on Lipdverse. Age uncertainties for datasets from both sources are then quantified using the geoChronR package, and those data, and their associated age uncertainties, are then investigated for abrupt changes using the actR package, with accompanying visualizations. The result is an integrated, reproducible workflow in R that demonstrates how this complex series of multisource data integration, analysis and visualization can be integrated into an efficient, open scientific narrative.
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- Award ID(s):
- 1929460
- PAR ID:
- 10337239
- Editor(s):
- McHenry, K; Schreiber, L
- Date Published:
- Journal Name:
- Earthcube Annual Meeting 2022
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.5281/zenodo.6780665
