During this years annual Microsoft Research TechFest, where Microsoft Research exposes compelling research projects to employees and guests, there are a couple Science related projects being highlighted. The Computational Ecology and Environmental Science (CEES) group at our Microsoft Research Cambridge lab is demoing some very interesting projects.
In 2012, Microsoft formed a unique partnership with the International Union for Conservation of Nature’s Red List of Threatened Species. Central to the partnership is creating the Red List Threat Mapping Tool — a spatial database application that enables experts and decision-makers around the world to find, map, explore, add, modify, and notate the various threats to any focal species. This SQL Server 2012 application enables visitors to query global biodiversity, protected area, and threat databases in real time. New software is being built to make it easy for anyone to construct these kinds of geo-data applications "at the speed of thought," without having to write a line of code. The software natively understands spatial data and spatial search, introduces a new, iterative search method, and produces databases that remain flexible, so that all aspects of the database and the application can be modified at any time.
Since 2007, the Computational Ecology and Environmental Science (CEES) group at Microsoft Research Cambridge has been pursuing the fundamental research needed to build predictive models of critical global environmental systems. Such predictions are needed urgently at a variety of scales—and to support effective decision-making, they must include uncertainty. In recent years, the philosophy of how to make such predictions has become clear: A “defensible modeling pipeline” is needed in which data and models are integrated in a Bayesian context and which is transparent and repeatable enough to stand up in court. The technology, though, is lagging far behind, making this pipeline impossible to build for all but the most technically savvy. Enter CEES Distribution Modeler, a browser app that enables users to visualize data, define a complex model, parameterize it using Bayesian methods, make predictions with uncertainty, and then share all that in a fully transparent and repeatable form.