In a rapidly changing world, macro-scale ecologists are asking increasingly complex questions about ecological systems and their spatiotemporal dynamics across systems and scales. Macro-scale ecology is challenged by data variety and the scale disconnect between questions of interest to global change ecologists, macroecologists, paleoecologists, and ecological forecasters versus the local scales of collection for most expert ecological observations. In ecology, an emergent solution is community-curated data resources (CCDRs), defined here as sociotechnological institutions that coalesce around scientific communities of practice, with a common IT platform and framework for modeling, sharing, and governing data, and with a science-centered mission that emphasizes data reuse and on-going improvements to living data. Here we define and explore the concept of CCDRs, identify common characteristics and needs, illustrated with examples drawn from the Neotoma Paleoecology Database and allied resources from the emerging network of CCDRs in ecology, paleobiology, and paleoclimatology.
Results/Conclusions
Multiple CCDRs are emerging in ecology, each gathering and curating a distinct kind of knowledge. Examples include experimental networks (DroughtNet, NutNet, ITEX), observational monitoring networks (Ameriflux, Fluxnet, TEAM), trait databases (BIEN, TRY), species distributional databases (VertNet, GBIF), and paleoecological databases (Neotoma, PBDB). CCDR are sociotechnological because 1) they require close partnerships among scientists, data modelers, and software developers and 2) new data models and informatics capabilities are needed to support scientific missions of large-scale insights from local-scale data networks. Scientific governance sets community standards for fair data use, metadata norms, data acquisition priorities, and driving questions. Data modelers and developers are charged with developing innovative new systems for finding, sharing, and analyzing ecological data in their many forms. CCDRs vary in governance model and data systems, but ideally should achieve the following goals: 1) FAIR principles that data be open, findable, accessible, interoperable, and reusable, 2) Transparent governance, with clear roles and pathways for on-boarding interested scientists, and 3) an emphasis on living data that closely support the rapid evolution of new scientific research questions and analytical methods. CCDRs shorten the time to science, support ecological forecasting, and enable large-scale data analytics from distributed networks of local-scale data.