- zootracerUse consumer video equipment to trace animal movement.
- Distribution Modeller: Environmental Modelling at the Speed of ThoughtDistribution Modeller (temporary name only!) is CEES' end-to-end browser tool that lets the researcher to rapidly import data, supplement that data with environmental info from FetchClimate, specify an arbitrary model by point and click or in code, parameterize the model against the data using Filzbach, make and visualize predictions with a full propagation of parameter uncertainty – then package and share everytihng, in a way that is inspectable, repeatable, and modifiable.
- Species Distribution ModellingSpecies Distribution Modelling (SDM) aims to explain why species occur where they do, and why they do not occur anywhere else. For instance, why does an oak tree not occur further south in hotter and dryer regions, and why it may not occur further north in colder and wetter regions? This sort of information is amongst the most fundamental of all ecological knowledge and is of great societal importance. Distribution data can feed into almost all other biodiversity models, and can inform adaptive
- FilzbachFilzbach is a flexible, fast, robust, parameter estimation engine that allows you to parameterize arbitrary, non-linear models, of the kind that are necessary in biological sciences, against multiple, heterogeneous data sets. Filzbach allows for Bayesian parameter estimation, maximum likelihood analysis, priors, latents, hierarchies, error propagation and model selection, often with just a few lines of code.
- Dynamic Data DisplayVisualize your data over the web: add complex dynamic graphs and maps to your web application.
- FetchClimateRetrieve global environmental information with the click of a button or a few lines of code. FetchClimate is a fast, free, intelligent environmental information retrieval service that operates over the cloud to return only the environmental data you need. FetchClimate can be accessed either through a simple web interface or via a few lines of code inside any program.
- The probabilistic terrestrial carbon model: towards actionable predictionsClimate change is the greatest global challenge of the 21st century. Models that reliably forecast future climates associated with different policy scenarios are urgently needed. This project has developed a new model for the key source of uncertainty in earth system models: the terrestrial carbon climate feedback, using a methodology to account for all known sources of uncertainty and enable robust estimates of the confidence that can be placed in predictions and objective model refinement.
- Scientific DataSetSoftware for reading/writing and sharing multidimensional arrays of data
- Forest DynamicsForests contain two thirds of terrestrial biodiversity and store as much carbon and is currently in the atmosphere. We are combining new abstractions of forests, with various sources of data, via Bayesian statistics, to produce useful, predictive models of forest dynamics.
- Environmental Scenario Search EngineData mining tools to explore exponentially growing archives of environmental sciences.
- Matthew J. Smith, Paul I. Palmer, Drew W. Purves, Mark C. Vanderwel, Vassily Lyutsarev, Ben Calderhead, Lucas N. Joppa, Christopher M. Bishop, and Stephen Emmott, Changing how Earth System Modelling is done to provide more useful information for decision making, science and society., in Bulletin of the American Meteorological Society, American Meteorological Society, February 2014
- M. J. Smith, D. W. Purves, M. C. Vanderwel, V. Lyutsarev, and S. Emmott, The climate dependence of the terrestrial carbon cycle, including parameter and structural uncertainties, in Biogeosciences, vol. 10, pp. 583-606, European Geosciences Union, 29 January 2013
- Matthew J. Smith, Mark C. Vanderwel, Vassily Lyutsarev, Stephen Emmott, and Drew W. Purves, The climate dependence of the terrestrial carbon cycle; including parameter and structural uncertainties, in Biogeosciences Discussions, vol. 9, pp. 13439-13496, European Geosciences Union, 4 October 2012
- Mikhail Zhizhin, Alexey Poyda, Dmitry Mishin, Dmitry Medvedev, Eric Kihn, and Vassily Lyutsarev, Grid-based data mining with Environmental Scenario Search Engine, in Data mining techniques in grid computing environments, Wiley, 2009
- Y. Y. Li, K. Harrison, M. A. Parker, V. Lyutsarev, and A. Tsaregorodtsev, Extension of the DIRAC workload management system to allow use of distributed Windows resources, in J. Phys.: Conf. Ser., vol. 119, no. 062035, 2008
- Mikhail Zhizhin, Eric Kihn, Vassily Lyutsarev, Sergei Berezin, Alexey Poyda, Dmitry Mishin, Dmitry Medvedev, and Dmitry Voitsekhovsky, Environmental scenario search and visualization, in GIS '07: Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems, ACM, November 2007
- Mikhail Zhizhin, Eric Kihn, Robin Redmon, Alexey Poyda, Dmitry Mishin, Dmitry Medvedev, and Vassily Lyutsarev, Integrating and mining distributed environmental archives on Grids, in Concurrency and Computation: Practice and Experience, vol. 19, no. 16, pp. 2157 - 2170, November 2007
- Mikhail Zhizhin, Alexey Poyda, Dmitry Mishin, Dmitry Medvedev, Eric Kihn, and Vassily Lyutsarev, Scenario Search on the Grid of Environmental Data Sources, no. MSR-TR-2006-72, July 2006