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CiteSeerx is an evolving scientific literature digital library and search engine that focuses primarily on the literature in computer and information science. CiteSeerx aims to improve the dissemination of scientific literature and to provide improvements in functionality, usability, availability, cost, comprehensiveness, efficiency, and timeliness in the access of scientific and scholarly knowledge. Rather than creating just another digital library, CiteSeerx attempts to provide resources such as algorithms, data, metadata, services, techniques, and software that can be used to promote other digital libraries. CiteSeerx has developed new methods and algorithms to index PostScript and PDF research articles on the Web.
The UCI Machine Learning Repository is a collection of databases, domain theories, and data generators that are used by the machine learning community for the empirical analysis of machine learning algorithms. It is used by students, educators, and researchers all over the world as a primary source of machine learning data sets. As an indication of the impact of the archive, it has been cited over 1000 times.
To understand the global surface energy budget is to understand climate. Because it is impractical to cover the earth with monitoring stations, the answer to global coverage lies in reliable satellite-based estimates. Efforts are underway at NASA and universities to develop algorithms to do this, but such projects are in their infancy. In concert with these ambitious efforts, accurate and precise ground-based measurements in differing climatic regions are essential to refine and verify the satellite-based estimates, as well as to support specialized research. To fill this niche, the Surface Radiation Budget Network (SURFRAD) was established in 1993 through the support of NOAA's Office of Global Programs.