Ecoinformatics: supporting ecology as a data-intensive science.
By W. Michener, Matthew B. Jones. Trends in Ecology & Evolution, 2012
457 citations recorded by the source corpus.
Ecology is evolving rapidly and increasingly changing into a more open, accountable, interdisciplinary, collaborative and data-intensive science. Discovering, integrating and analyzing massive amounts of heterogeneous data are central to ecology as researchers address complex questions at scales from the gene to the biosphere. Ecoinformatics offers tools and approaches for managing ecological data and transforming the data into information and knowledge. Here, we review the state-of-the-art and recent advances in ecoinformatics that can benefit ecologists and environmental scientists as they tackle increasingly challenging questions that require voluminous amounts of data across disciplines and scales of space and time. We also highlight the challenges and opportunities that remain.
Subfields: Ecological Modeling, Information Systems, Information Systems and Management
View Paper: https://www.semanticscholar.org/paper/7a1b9cc42e6fc611970b451fbef795e72cbea46d
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In Ecological Modeling
- G. Guillera‐Arroita
- J. Lahoz‐Monfort
- R. O’Hara
- M. Scherer‐Lorenzen
- E. García‐Berthou
- T. O'Brien
- W. Durka
- Daniel Pauly
In Information Systems
Citations in the corpus
- Global overview of the applications of the Ecopath with Ecosim modeling approach using the EcoBase models repository (2015, background): “similar path, and has turned into a ‘data-intensive science’ (Kelling et al., 2009; Michener and Jones, 2012).”
- Effective monitoring of freshwater fish (2019, background): “…infer future changes, policies and procedures that guarantee the quality of data capture, documentation, and preservation for long-term use is required and needs to be conceptualized in a data management plan (Michener, 2015; Michener & Jones, 2012; Rüegg et al., 2014; Sutter et al., 2015).”
- Designing forest biodiversity experiments: general considerations illustrated by a new large experiment in subtropical China (2014, methodology): “Therefore, building upon Ecological Metadata Language (Fegraus et al. 2005; Michener & Jones 2012), we developed a web application tailored for the needs of BEF-China that manages naming conventions within primary data (Nadrowski et al. 2013).”
- Data Integration for Large-Scale Models of Species Distributions. (2020, background): “The process of bringing together data has many labels, for example, data fusion [31], assimilation [31,32], combination [33], or integration [34,35].”
- Satellite remote sensing for applied ecologists: opportunities and challenges (2014, methodology): “To make SRS products more accessible, the sharing of new algorithms and work-flows that support the analysis and interpretation of SRS data also needs to be improved (Michener & Jones 2011).”