title: Data-intensive science applied to broad-scale citizen science.
abstract: Identifying ecological patterns across broad spatial and temporal extents requires novel approaches and methods for acquiring, integrating and modeling massive quantities of diverse data. For example, a growing number of research projects engage continent-wide networks of volunteers ('citizen-scientists') to collect species occurrence data. Although these data are information rich, they present numerous challenges in project design, implementation and analysis, which include: developing data collection tools that maximize data quantity while maintaining high standards of data quality, and applying new analytical and visualization techniques that can accurately reveal patterns in these data. Here, we describe how advances in data-intensive science provide accurate estimates in species distributions at continental scales by identifying complex environmental associations.
authors.affiliation: Cornell Lab of Ornithology, Ithaca, NY 14850, USA.
authors.forename: Wesley M
authors.initials: WM
authors.lastname: Hochachka
authors.forename: Daniel
authors.initials: D
authors.lastname: Fink
authors.forename: Rebecca A
authors.initials: RA
authors.lastname: Hutchinson
authors.forename: Daniel
authors.initials: D
authors.lastname: Sheldon
authors.forename: Weng-Keen
authors.initials: WK
authors.lastname: Wong
authors.forename: Steve
authors.initials: S
authors.lastname: Kelling
mesh_terms: D025041:Animal Migration; D000818:Animals; D044822:Biodiversity; D003625:Data Collection / Q000379:methods; D004463:Ecology / Q000379:methods* / Q000639:trends; D046428:Hawks / Q000502:physiology; D008962:Models, Theoretical; D011156:Population Density; D013269:Stochastic Processes; D014481:United States