Archive for January, 2015
The University of Connecticut has developed this site dedicated to the ornamental attributes, appropriate use and identification of landscape plants. This site is geared toward the teaching of landscape plants and contains valuable information for students, homeowners and plant professionals.
At the core of this site are plant information pages that contain text, photographs, illustrations and latin name pronounciations. The plants listed in this resource are meant to create an awareness of the great variety of ornamental plants that will grow in USDA hardiness zone 6 or colder, and to encourage people to think about planting a greater variety of ornamentals. Users should be aware that some plants listed are not readily available and may actually be difficult to find.
The interactive Plant Selector is available to allow users to search the University of Connecticut Plant Database to find trees, shrubs and vines which meet specific landscape situations and express particular ornamental traits.
The majority of images on the UConn Plant Database have been provided by Dr. Mark H. Brand, University of Connecticut. Others who have contributed photographs include: Dr. Edward Corbett, University of Connecticut; Dr. Glenn Dryer, Connecticut College Arboretum; Kimberly A. Mason, University of Connecticut; Jonathan M. Lehrer, University of Connecticut; Michael Harvey, Bartlett Arboretum; Dr. Mark Starrett, University of Vermont; Margaret Taylor, Storrs, CT; and the American Conifer Society.
Url : http://hort.uconn.edu/
The plantgdb.org web resource is currently being managed as part of an NSF-funded project (IOS-1126267) to develop robust genome annotation methods, tools, and standard training sets for the plethora of plant genomes currently or soon to be sequenced. Read more about the project here.
The scale of sequence and other data accumulation in plant genomics is outstripping our ability effectively to annotate those genomes. The goal of this project is to develop novel, highly automated, scalable, comprehensive, and accurate approaches to genome annotation to address this problem. Project deliverables include (1) software that implements the novel prediction algorithms, (2) visualization and data access portals, and (3) a cyberinfrastructure environment implementation of the developed tools for distributed computing, sharing of protocols, and analysis provenance recording. In the long run, the project seeks to explore the extent to which genomic biology can transition from a largely descriptive to a highly predictive science driven by quantitative measurements, with algorithms and computation as the domain-adapted language. Read more about project goals and approaches.