Kristin Vanderbilt

The NPSdataverse is a suite of R packages developed to create, document, publish, and access data and metadata in open and machine-readable formats. NPSdataverse is modeled off of the tidyverse concept of several packages built with a common goal (Wickham et al., 2019). The NPSdataverse supports Ecological Metadata Language (EML) metadata and .csv data files. Some of the constituent R packages (EML and EMLassemblyline) are general-use and aimed at authoring EML documents. Other R packages (QCkit, EMLeditor, DPchecker and NPSutils) are designed and maintained by the National Park Service (NPS). Although many functions within the NPSdataverse packages are NPS-specific (particularly some API calls), whenever possible the functions are written so that they can also be used by the general public. Scientists conducting permitted research in NPS units can utilize the NPSdataverse to efficiently and consistently meet the data delivery requirements of their permits. Additionally, the packages will be useful for data management plans in a wide variety of grant proposals and for anyone that needs to create open data and machine-readable metadata. The ability to swiftly and easily author, edit, and check Ecological Metadata Language (EML) metadata in a reproducible fashion will be useful for data publication at any number of repositories or data journals. Finally, a scripted interface for downloading NPS data and leveraging metadata while loading it into R or other platforms for subsequent analyses and visualizations will be useful to researchers in the government, academia, and industry as well as the public.

The Environmental Data Initiative (EDI) is a trustworthy, stable data repository, and data management support organization for the environmental scientist. In a bottom‐up community process, EDI was built with the premise that freely and easily available data are necessary to advance the understanding of complex environmental processes and change, to improve transparency of research results, and to democratize ecological research. EDI provides tools and support that allow the environmental researcher to easily integrate data publishing into the research workflow. Almost ten years since going into production, we analyze metadata to provide a general description of EDI's collection of data and its data management philosophy and placement in the repository landscape. We discuss how comprehensive metadata and the repository infrastructure lead to highly findable, accessible, interoperable, and reusable (FAIR) data by evaluating compliance with specific community proposed FAIR criteria. Finally, we review measures and patterns of data (re)use, assuring that EDI is fulfilling its stated premise.

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.

In this era of open data and reproducible science, graduate students need to learn where and how to publish their data and to be conversant with the challenges inherent when re-using someone else’s data. The Environmental Data Initiative partnered with UNM Libraries and Florida Coastal Everglades LTER to organize a 1-credit, semester-long distributed graduate seminar to learn if this approach could be an effective mechanism for transmitting such information. Each week during the Spring 2021 semester, an informatics specialist spoke remotely to students at University of New Mexico, Florida International University, and University of Wisconsin-Madison on topics ranging from FAIR principles to data security, team science to data provenance. Students prepared for the lecture with one or more readings, and in-class exercises reinforced the material covered. Student assignments included writing quality metadata for their own data and archiving their data in the EDI Repository. The capstone writing assignment, a data management plan for their own research project, allowed the students to integrate much of what they had learned. Student response to this class was positive, and students indicated that they learned a lot of immediately useful information without the course being a significant time-sink. The low registration numbers at UNM and FIU (6 and 7 students, respectively), however, where the seminar was not required, suggest a need to better inform both students and their advisors of the opportunity and the value provided by the training. Instructors also learned that it would be easier to create a cohesive flow to the course, without repetition, if the group of instructors took turns lecturing, rather than bringing in specialists on each subject. It was also apparent from student comments that many felt this information should be integrated, at an introductory level, into undergraduate classes or classes for new graduate students.

Science has become a data-intensive enterprise. Data sets are commonly being stored in public data repositories and are thus available for others to use in new, often unexpected ways. Such re-use of data sets can take the form of reproducing the original analysis, analyzing the data in new ways, or combining multiple data sets into new data sets that are analyzed still further. A scientist who re-uses a data set collected by another must be able to assess its trustworthiness. This chapter reviews the types of errors that are found in metadata referring to data collected manually, data collected by instruments (sensors), and data recovered from specimens in museum collections. It also summarizes methods used to screen these types of data for errors. It stresses the importance of ensuring that metadata associated with a data set thoroughly document the error prevention, detection, and correction methods applied to the data set prior to publication.

Many scientists around the world became interested in the U.S. Long Term Ecological Research (U.S. LTER) Network's research model during the 1990s and began to develop LTER and Long Term Socio‐ecological Research networks in their own countries. These local networks, including the U.S. LTER Network, were loosely federated in 1993 to form the International Long Term Ecological Research (ILTER) Network, a “network of networks.” Although the first 10 yr of ILTER Network activities were largely supported by funds from the U.S. National Science Foundation, the ILTER Network had transformed into a robust, self‐sustaining entity by 2006 following a two‐year strategic planning process. The goal of the ILTER Network is to improve understanding of how pressures such as climate change and land use affect global ecosystems in order to inform solutions to current and future environmental problems. To fulfill this mission, the ILTER Network fosters collaborations among member scientists to extend the scope of their research across disciplinary boundaries and across more of the ILTER's 600+ research sites. The ILTER Network also has many long‐term data sets that are freely available for use by students, scientists, and policymakers all over the world. In this collection of papers, we consider how the ILTER Network has been, and will be, leveraged by U.S. researchers to advance understanding of ecological and socio‐ecological systems around the globe.

The International Long Term Ecological Research (ILTER) Network was established in 1993 and is now composed of thirty‐eight national networks representing a diversity of ecosystems around the globe. Data generated by the ILTER Network are valuable for scientists addressing broad spatial and temporal scale research questions, but only if these data can be easily discovered, accessed, and understood. Challenges to publishing ILTER data have included unequal distribution among networks of information management expertise, user‐friendly tools, and resources. Language and translation have also been issues. Despite these significant obstacles, ILTER information managers have formed grassroots partnerships and collaborated to provide information management training, adopt a common metadata standard, develop information management tools useful throughout the network, and organize scientist/information manager workshops that encourage scientists to share and integrate data. Throughout this article, we share lessons learned from the successes of these grassroots international partnerships to inform others who wish to collaborate internationally on projects that depend on data sharing entailing similar management challenges.