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Harmonizing ecological community survey data for reuse: an update

The idea of harmonizing data is not new, and for some research domains has been successful. Our body of long-term observations of organisms in ecological communities is growing, and many datasets have been used already in synthesis and meta analyses – but only after considerable effort to bring them into alignment.  A goal of EDI has been to develop recommendations for data harmonization, and to convert “raw data” in specific domains into a common data model to prepare them for analysis and accelerate synthesis or meta analyses.

Temporal, spatial and taxonomic coverage of datasets available in the ecocomDP model. Data source: Black, EDI; Gray, NEON. A) Temporal coverage (years), B) Temporal evenness (years), C) Spatial extent, D) group. An asterisk indicates that two groups (Tick, Mosquito) are specifically targeted by NEON. When these taxa occur in EDI datasets, they are plotted here with Arthropods

EDI recently finalized its data model for ecological community surveys, called “ecocomDP”, which is described in a recent open-access paper. EDI harmonization uses the workflow approach supported by EDI’s PASTA platform to reformat data without altering the original. An R package is available from CRAN  to assist with reformatting original tables and work with ecocomDP data. Development of both the model and the R package was collaborative, involving NEON and LTER scientists and data managers. Another result of that collaboration is that the NEON Network now exposes their community surveys in the ecocomDP model, via the R package, The figure shows temporal, spatial and taxonomic coverage of datasets available in the ecocomDP model from EDI and NEON.

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Integrating Long-Tail Data: How Far Are We?

EDI’s Kristin Vanderbilt and Corinna Gries co-edited a Special Issue of Ecological Informatics “Integrating Long-Tail Data: How Far Are We?” that explores how far the informatics community has come toward lessening the time researchers must spend integrating small, heterogeneous datasets prior to analyzing them.

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