Modern research increasingly relies on network accessible data, execution, security, and information access digital services. These services often provide web based user interfaces and Application Programming Interfaces (APIs). By invoking APIs software developers can create increasingly advanced research enhancing digital services. For example, researchers can access Science Gateways and Portals using a web browser to do analysis, simulations, machine learning, and visualizations that seamlessly combines gateway functionality with remote API accessible digital services. XSEDE's Mission is to "Substantially enhance the productivity of a growing community of scholars, researchers, and engineers through access to advanced digital services that support open research; and coordinate and add significant value to the leading cyberinfrastructure resources funded by the NSF and other agencies.". The XSEDE Cyberinfrastructure Integration (XCI) team's mission is to "integrate, adapt, and disseminate software tools and related services across the national CI community... and to enable the creation of an integrated national cyberinfrastructure." XCI introduces two new secure XSEDE information access APIs and propose that the OAuth 2.0 API security they use can accelerate development of powerful research enhancing digital services by breaking down services-to-service interactions barriers.

Extending the innovative “Def Wy” procedures for modeling evolutionary network effects (Dow, Cross-Cult Res 41:336–363, 2007; Dow and Eff, Cross-Cult Res 43:134–151, 2009; Dow and Eff, Cross-Cult Res 43:206–229, 2009), a Complex Social Science http://intersci.ss.uci.edu (CoSSci) Gateway was developed to provide complex analyses of ethnographic, archaeological, historical, ecological, and biological datasets with easy open access. Analysis begins with dependent variable y with n observations and X independent and other variables, and imputes missing data for all variates. Several (n × n) W* matrices measure evolutionary network effects such as diffusion or phylogenetic ancestries. W* is row-normalized to sum to 1 and combined to obtain a W, multiplied by X as WX, and allowing X and y multiplication by W: $$ \overset{.}{W}y={\overset{.}{\alpha}}_0+{\overset{.}{\alpha}}_i\;\left(W{X}_{i=1,\;n}\right). $$ Wy measures the evolutionary autocorrelation portion of y discounting evolutionary effects of propinquity and phylogenetics. Tested for exogeneity (error terms uncorrelated with Wy or independent variables) the two-stage Ordinary Least Squares (OLS) results include measures of independent variable and deep evolutionary autocorrelation predictors. We show how these methods apply to a wide variety of problems in the social sciences to which ecological and biological variables will apply once contributed.

The NSF TeraGrid project has designed and constructed a federated integrated information service (IIS) to serve its capability publishing and discovery needs. This service has also proven helpful in automating TeraGrid's operational activities. We describe the requirements that motivated this work; IIS's system architecture, information architecture, and information content; processes that IIS currently supports; and how various layers of the system architecture are being used. We also review motivating use cases that have not yet been satisfied by IIS and outline approaches for future work.

Grids comprise an infrastructure that enables scientists to use a diverse set of distributed remote services and resources as part of complex scientific problem-solving processes. We analyze some of the challenges involved in deploying software and components transparently in Grids. We report on three practical solutions used by the Globus Project. Lessons learned from this experience lead us to believe that it is necessary to support a variety of software and component deployment strategies. These strategies are based on the hosting environment.

Grids comprise an infrastructure that enables scientists to use a diverse set of distributedly owned, and managed remote services or resources as part of complex scientific problem-solving processes. Due to the diversity of such a complex environment, it is a challenging problem to deploy and maintain components in a fashion that transparently supports the problem solving process. We analyze some of the challenges involved in deploying components in Grids and report on three practical solutions used by the Globus Research Project that are related to component deployment. Lessons learned from this experience lead us to believe that it is necessary to support a variety of component deployment strategies, and select one that is best suited for the commu-