2 resultados para Data-Driven Behavior Modeling
em Digital Commons @ DU | University of Denver Research
Resumo:
The purpose of this study is multifaceted: 1) to describe eScience research in acomprehensive way; 2) to help library and information specialists understand the realm of eScience research and the information needs of the community and demonstrate the importance of LIS professionals within the eScience domain; 3) and to explore the current state of curricular content of ALA accredited MLS/MLIS programs to understand the extent to which they prepare new professionals within eScience librarianship. The literature review focuses heavily on eScientists and other data-driven researchers’ information service needs in addition to demonstrating how and why librarians and information specialists can and should fulfill these service gaps and information needs within eScience research. By looking at the current curriculum of American Library Association (ALA) accredited MLS/MLIS programs, we can identify potential gaps in knowledge and where to improve in order to prepare and train new MLS/MLIS graduates to fulfill the needs of eScientists. This investigation is meant to be informative and can be used as a tool for LIS programs to assess their curriculums in comparison to the needs of eScience and other data-driven and networked research. Finally, this investigation will provide awareness and insight into the services needed to support a thriving eScience and data-driven research community to the LIS profession.
Nesting In The Clouds: Evaluating And Predicting Sea Turtle Nesting Beach Parameters From Lidar Data
Resumo:
Humans' desire for knowledge regarding animal species and their interactions with the natural world have spurred centuries of studies. The relatively new development of remote sensing systems using satellite or aircraft-borne sensors has opened up a wide field of research, which unfortunately largely remains dependent on coarse-scale image spatial resolution, particularly for habitat modeling. For habitat-specialized species, such data may not be sufficient to successfully capture the nuances of their preferred areas. Of particular concern are those species for which topographic feature attributes are a main limiting factor for habitat use. Coarse spatial resolution data can smooth over details that may be essential for habitat characterization. Three studies focusing on sea turtle nesting beaches were completed to serve as an example of how topography can be a main deciding factor for certain species. Light Detection and Ranging (LiDAR) data were used to illustrate that fine spatial scale data can provide information not readily captured by either field work or coarser spatial scale sources. The variables extracted from the LiDAR data could successfully model nesting density for loggerhead (Caretta caretta), green (Chelonia mydas), and leatherback (Dermochelys coriacea) sea turtle species using morphological beach characteristics, highlight beach changes over time and their correlations with nesting success, and provide comparisons for nesting density models across large geographic areas. Comparisons between the LiDAR dataset and other digital elevation models (DEMs) confirmed that fine spatial scale data sources provide more similar habitat information than those with coarser spatial scales. Although these studies focused solely on sea turtles, the underlying principles are applicable for many other wildlife species whose range and behavior may be influenced by topographic features.