Experimental Design and Statistics
Experimental design is the design of all information-gathering exercises in order to assess the effect of some process or intervention (the "treatment") on some objects (the "experimental units"). Treatment effects are evaluated using a variety of statistical procedures. Statistics are also used for the description and analysis of relationships among multiple variables in systems that are not experimentally manipulated. In this context, statistics describe patterns in data, such as variation in species composition among communities. Postgraduate students are encouraged to obtain statistical advice BEFORE conducting research in order to ensure that statistical assumptions are upheld for their experimental design, and that sufficient samples have been collected to assess the statistical significance of treatment effects.
Data management broadly captures questions about data that include bioinformatics and ecoinformatics. This category addresses issues related to the discovery, retrieval, integration and storage of ecological, environmental, phylogenetic, genetic and proteomic data. An important area of research is the integration of existing databases to allow the testing of new ecological and evolutionary hypotheses. Use other categories for specific questions about data analysis or modelling.
The R category is for general questions about the R statistical computing. For more specific questions (e.g., about graphing), please use sub-categories.
GIS and Remote Sensing
Discussion about this forum, its organization, how it works, and how we can improve it.
Mathematical modelling refers to a set of procedures used to understand, describe, and predict natural phenomena, and to evaluate quantitative hypotheses. Examples include modelling the dynamics of population size based on assumptions about birth and death rates, but applications in biology and ecology are everywhere. Modelling techniques range from simple compartment-flow models that can be implemented in Excel through to more complex techniques including ordinary and partial differential equations, difference equations, matrix models, optimisation, and computer simulations. There are also model inversion techniques, where you use data to infer how your model should be structured.
For questions about version control or version control systems, such as SVN or GIT. Versions control is the management of changes to documents, computer programs, large web sites, and other collections of information. Changes can be compared, restored, and with some types of files, merged.
A place to put bits of code/analyses/etc that you want to highlight. A nice graph, an analyses that you got help with from here, reproducible research, etc.
Request code review in this category.