{"refrec":{"BRefID":232524,"RR":"<b>Candela, L.; Castelli, D.; Coro, G.; Pagano, P.; Sinibaldi, F.</b> (2016). Species distribution modeling in the cloud. <i>Concurr. Comput. Practice Experience 28(4)</i>: 1056-1079. <a href=\"https://dx.doi.org/10.1002/cpe.3030\" target=\"_blank\">https://dx.doi.org/10.1002/cpe.3030</a>","BEntID":224236,"PublicFlag":1,"CheckedFlag":1,"wosflag":1,"vabbflag":null,"RefStringPartII":". <i>Concurr. Comput. Practice Experience 28(4)</i>: 1056-1079. <a href=\"https://dx.doi.org/10.1002/cpe.3030\" target=\"_blank\">https://dx.doi.org/10.1002/cpe.3030</a>","DocTypID":8,"DocType":"Journal article","MarineFlag":0,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"Candela, L.; Castelli, D.; Coro, G.; Pagano, P.; Sinibaldi, F.","OrigTitleTranslFlag":0,"Authorstringtrunc":"Candela, L. <i>et al.</i>","Englishabstract":"Species distribution modeling is a process aiming at computationally predicting the distribution of species in geographic areas on the basis of environmental parameters including climate data. Such a quantitative approach has a lot of potentialities in many areas that include setting up conservation priorities, testing biogeographic hypotheses, and assessing the impact of accelerated land use. To further promote the diffusion of such an approach, it is fundamental to develop a flexible, comprehensive, and robust environment capable of enabling practitioners and communities of practice to produce species distribution models more efficiently. A promising way to build such an environment is offered by modern infrastructures promoting the sharing of resources, including hardware, software, data, and services. This paper describes an approach to species distribution modeling based on a Hybrid Data Infrastructure that can offer a rich array of data and data management services by leveraging other infrastructures (including Cloud). 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