Common Errors in Ecological Data Sharing

Kervin, Karina; Michener, William K.; Cook, Robert B.

doi:10.7191/jeslib.2013.1024

Cited by 18 publications

(23 citation statements)

References 10 publications

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“…For example, a review of peer-reviews of data papers (i.e. data and metadata) published in the Ecological Society of America's Data Papers from 2004-2012 indicated that most authors did not provide metadata that was sufficient to support interpretation and re-use of the data (Kervin et al, 2013). Similarly, a recent survey of managers of environmental and ecological data repositories demonstrated that data contributors frequently made errors with respect to how data were organized (83% of the time) and documented (79% of the time) (Kervin et al, 2014).…”

Section: Future Of Data Sharingmentioning

confidence: 98%

“…In 2005, the Ecological Society of America began publishing peer-reviewed Data Papers that consisted of ecological data and detailed metadata that were accompanied by an abstract in the journal Ecology (Kervin et al, 2013). BioMed Central established GigaScience in 2012 to support the publication of biomedical and life sciences data, including ecological data (http://www.…”

Section: The Role Of Publishersmentioning

confidence: 99%

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Ecological data sharing

Michener

2015

Ecological Informatics

188

153

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Data sharing is the practice of making data available for use by others. Ecologists are increasingly generating and sharing an immense volume of data. Such data may serve to augment existing data collections and can be used for synthesis efforts such as meta-analysis, for parameterizing models, and for verifying research results (i.e., study reproducibility). Large volumes of ecological data may be readily available through institutions or data repositories that are the most comprehensive available and can serve as the core of ecological analysis. Ecological data are also employed outside the research context and are used for decision-making, natural resource management, education, and other purposes. Data sharing has a long history in many domains such as oceanography and the biodiversity sciences (e.g., taxonomic data and museum specimens), but has emerged relatively recently in the ecological sciences. A review of several of the large international and national ecological research programs that have emerged since the mid-1900s highlights the initial failures and more recent successes as well as the underlying causes-from a near absence of effective policies to the emergence of community and data sharing policies coupled with the development and adoption of data and metadata standards and enabling tools. Sociocultural change and the move towards more open science have evolved more rapidly over the past two decades in response to new requirements set forth by governmental organizations, publishers and professional societies. As the scientific culture has changed so has the cyberinfrastructure landscape. The introduction of community-based data repositories, data and metadata standards, software tools, persistent identifiers, and federated search and discovery have all helped promulgate data sharing. Nevertheless, there are many challenges and opportunities especially as we move towards more open science. Cyberinfrastructure challenges include a paucity of easy-to-use metadata management systems, significant difficulties in assessing data quality and provenance, and an absence of analytical and visualization approaches that facilitate data integration and harmonization. Challenges and opportunities abound in the sociocultural arena where funders, researchers, and publishers all have a stake in clarifying policies, roles and responsibilities, as well as in incentivizing data sharing. A set of best practices and examples of software tools are presented that can enable research transparency, reproducibility and new knowledge by facilitating idea generation, research planning, data management and the dissemination of data and results.

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Section: Future Of Data Sharingmentioning

confidence: 98%

Section: The Role Of Publishersmentioning

confidence: 99%

Ecological data sharing

Michener

2015

Ecological Informatics

188

153

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“…Our story is only one potential path because there are many ways to upgrade scientific practiceswhether collaborating only with your 'future self ' or as a team-and they depend on the shared commitment of individuals, institutions and publishers 6,16,17 . We do not review the important, ongoing work regarding data management architecture and archiving 8,18 , work flows 11,[19][20][21] , sharing and publishing data [22][23][24][25] and code [25][26][27] , or how to tackle reproducibility and openness in science [28][29][30][31][32] . Instead, we focus on our experience, because it required changing the way we had always worked, which was extraordinarily intimidating.…”

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confidence: 99%

Our path to better science in less time using open data science tools

Best²,

et al. 2017

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“…The outstanding questions are 1) whether this reporting approach will ultimate result in improved availability of data with accompanying high quality metadata, and 2) what the tradeoffs are in terms of person-hours and who bears that cost-the data originator or dedicated data team personnel. We prioritized reporting formats in FRAMES to maximize reporting efficiency because although improving, the generally low quantity of shared data and poor quality of metadata is problematic in the earth sciences (Tenopir et al, 2011;Kervin et al, 2013;Michener, 2015).…”

Section: Lessons Learned and Future Developmentmentioning

confidence: 99%

“…Metadata are essential to describe the different approaches taken to obtain, process, and report diverse ecohydrological and biogeochemical observations and the resulting data products (Michener et al, 1997;Michener, 2006;Papale et al, 2012;Kervin et al, 2013). Metadata allow for interpretation and integration of heterogeneous data obtained from different measurement approaches across disparate study sites, which occur even in well-organized science projects.…”

Section: Introductionmentioning

confidence: 99%

A metadata reporting framework (FRAMES) for synthesis of ecohydrological observations

Christianson

Varadharajan

Christoffersen

et al. 2017

Ecological Informatics

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A B S T R A C TMetadata describe the ancillary information needed for data preservation and independent interpretation, comparison across heterogeneous datasets, and quality assessment and quality control (QA/QC). Environmental observations are vastly diverse in type and structure, can be taken across a wide range of spatiotemporal scales in a variety of measurement settings and approaches, and saved in multiple formats. Thus, well-organized, consistent metadata are required to produce usable data products from diverse environmental observations collected across field sites. However, existing metadata reporting protocols do not support the complex data synthesis and model-data integration needs of interdisciplinary earth system research. We developed a metadata reporting framework (FRAMES) to enable management and synthesis of observational data that are essential in advancing a predictive understanding of earth systems. FRAMES utilizes best practices for data and metadata organization enabling consistent data reporting and compatibility with a variety of standardized data protocols. We used an iterative scientist-centered design process to develop FRAMES, resulting in a data reporting format that incorporates existing field practices to maximize data-entry efficiency. Thus, FRAMES has a modular organization that streamlines metadata reporting and can be expanded to incorporate additional data types. With FRAMES's multi-scale measurement position hierarchy, data can be reported at observed spatial resolutions and then easily aggregated and linked across measurement types to support model-data integration. FRAMES is in early use by both data originators (persons generating data) and consumers (persons using data and metadata). In this paper, we describe FRAMES, identify lessons learned, and discuss areas of future development.

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Common Errors in Ecological Data Sharing

Cited by 18 publications

References 10 publications

Ecological data sharing

Ecological data sharing

Our path to better science in less time using open data science tools

A metadata reporting framework (FRAMES) for synthesis of ecohydrological observations

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