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In the metadata of digital environmental datasets, automated processing is hindered by the wide variety of representations for unit that may be human-readable, but may not be unambiguous or machine-interpretable, (e.g., grams per square meter, gm/m2, g/m2, gm-2, g/m^2, g.m-2, g m-2 and gramPerMeterSquared). Matching disparate representations of the same unit into a single unit concept from an ontology assists with interpretation and reuse by providing a linkage to a complete unit definitions with label, description, dimensions. Datasets with shared units can be identified during searches, and are more suitable for automating analyses and potential transformation. This dataset contains data and code associated with a project to map units in ecological metadata collected between 2013 and 2022 by DataONE, the Environmental Data Initiative and the U.S. National Ecological Observatory Network to the QUDT ontology using successive string transformations. Data entities include a) raw metadata as received (355,057 unit instances); b) integrated raw data; c) substitution tables for string transformations; d) resulting lookup table for 896 distinct units matched to QUDT units; e) associated R code used for QUDT matching plus a web service and R functions for adding annotation elements to Ecological Metadata Language metadata documents. Using these substitutions and code, 91% of unit instances in the raw metadata could be matched to QUDT. Data and results are discussed in “Porter JH, M O’Brien, M Frants, S Earl, M Martin, C Laney. (in review) Using a Units Ontology to Annotate Pre-Existing Metadata. Submitted to Scientific Data. 

Abstract For many ecologists, publishing data in a data repository is a new, unfamiliar task. To reduce the learning curve, the Environmental Data Initiative has developed user‐friendly software to make capturing and submitting data and metadata a simple process. In this article, we introduce ezEML and discuss use cases for researchers who publish data infrequently or information managers who regularly update multiple datasets. 

Summary Phenological response to global climate change can impact ecosystem functions. There are various data sources from which spatiotemporal and taxonomic phenological data may be obtained: mobilized herbaria, community science initiatives, observatory networks, and remote sensing. However, analyses conducted to date have generally relied on single sources of these data. Siloed treatment of data in analyses may be due to the lack of harmonization across different data sources that offer partially nonoverlapping information and are often complementary. Such treatment precludes a deeper understanding of phenological responses at varying macroecological scales. Here, we describe a detailed vision for the harmonization of phenological data, including the direct integration of disparate sources of phenological data using a common schema. Specifically, we highlight existing methods for data harmonization that can be applied to phenological data: data design patterns, metadata standards, and ontologies. We describe how harmonized data from multiple sources can be integrated into analyses using existing methods and discuss the use of automated extraction techniques. Data harmonization is not a new concept in ecology, but the harmonization of phenological data is overdue. We aim to highlight the need for better data harmonization, providing a roadmap for how harmonized phenological data may fill gaps while simultaneously being integrated into analyses.