The Multispecies Ovary Tissue Histology Electronic Repository will provide public access to digitized microscopic images of ovary tissues along with information that ensures image integrity and quality. Currently, there is no electronic repository of ovary histology slides that preserves these valuable research collections for future generations. This repository will provide a web-accessible, open resource for scientists, educators, and the public to stimulate collaboration and scientific research. Educators may use the slide images in a range of courses from reproductive biology to teaching computerized image analysis.
Reproduction is vital for sustaining all living organisms, and multiple strategies exist among different species. The long-term goals for this project are to increase reproductive science capacity and infrastructure; and to serve as a resource for educators. The tool builds upon existing openly available resources, e.g., the Open Science Framework, to foster data sharing and collaboration. Metadata about each image ensures image quality and provides additional details about the animal and experimental design. An initial set of species (i.e., hundreds of vertebrate species including non-human primates, other mammals, fishes, and amphibians) will be included with a long-term goal that scientists will contribute data from additional species. Value-added data segmentation results will be made available along with the procedures used to generate the results. Upon completion, The respository will facilitate comparative studies of ovarian development and folliculogenesis to better understand: reproductive strategies across species and inspire new ideas for ensuring the survival of threatened and endangered species; the similarities and differences between vertebrate species at the organ, tissue and cellular level; and mechanisms that can be encoded in predictive mathematical/computational models that can extract additional value from the existing data and may lead to the reduced use of experimental animals. Biology is increasingly dependent upon quantitative data analysis, and this project should inspire computational thinking in biology broadly, while developing specific skills in microscopy, computer programming, and data and image analysis. Research results may be obtained at: mother-db.org.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
