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Environmental Science
Data Analysis for Environmental Science
Global warming, climate change, carbon footprints, pandemics: environmental concerns are a talking point for everyone and gaining more exposure than ever.
Environmental science studies the earth, its resources, and the effects of human actions on the environment. As a collective, we need to create a more sustainable relationship with the environment. To this end, we must increase our ability to predict climate variations, better manage natural resources, and prevent changes in the infectivity and virulence of organisms.
The grand challenge of environmental scientists is to understand the complex environmental systems that humans depend on and advance our knowledge of the natural world and its various intersections with society.
VSNi are here to help.
How Can Data Analysis and Evaluation Improve Environmental Science Research?
For over 20 years VSNi has developed a range of data and analytics software to support scientists, researchers and breeders within the biosciences. Designed by statisticians with non-statisticians in mind, our products are ideal for users looking for exceptional decision-making tools that can accommodate increasingly large and complex datasets.
Our data analysis software provides all the tools you need to bring viable results from your environmental data analyses. You can:
- Analyse large, complex datasets easily and efficiently
- Assist in defining factors that affect your research
- Define key traits to help you discover and create new methods of productivity
- Discover patterns in vast sets of data
- Define environmental variables
- Determine risk factors
- Investigate physical and biological changes
- Assist in increasing produce
- Analyse quantitative and qualitative data
- Integrate environmental and climatic data
With thousands of analyses, procedures and directives, the following is a small sample of some of the possibilities:
- Design experiments and plan environmental monitoring systems
- Analysis of variance to provide detail on GxE (Genotype by Environment) interactions
- REML estimation of effects within multi-environment variety trials
- Analyse univariate and multivariate data based on linear, generalized linear, mixed linear and hierarchical generalized linear models.
- Model environmental effects on growth and abundance using GLMM
- Cluster analysis to provide similarities of species composition
- Classification and regression trees
- Interpret GxE interactions with partial least squares
- Spatial analysis and mapping using kriging and co-kriging.
Whether you are looking to manage, conserve, control or research our data analysis software has a vast range of statistical techniques to suit your needs from the design of experiments through to advanced regression. Our role is to support you in achieving your goals and as such promise an unparalleled level of support to assist with your work in environmental science.
As the Indian proverb goes, “Only when the last tree has died and the last river been poisoned and the last fish been caught will we realise we cannot eat money.
Our Environmental Science Software Solutions
VSNI create easy-to-use statistical software to help your environmental science research thrive. We also have fully documented user guides, knowledge bases and video tutorials, making VSNi’s products easy to learn – but should you need more assistance you can even get help directly from the team who developed the software (us!).
Genstat
Genstat can make predictions from large environmental data sets using data mining techniques, such as support vector machines, regression trees, random regression forests, and neural networks. Genstat provides multivariate methods for analysing and interpreting biological and environmental data, including principal components analysis, multi-dimensional scaling, correspondence analysis, canonical correlation analysis, discriminant analysis and cluster analysis.
Moreover, Genstat provides tools to generate robust and efficient experimental designs, including randomised block, split-plot, row-column and cyclic designs for field trials, laboratory experiments, etc.
AsReml
ASReml is powerful statistical software specially designed for mixed models using Residual Maximum Likelihood (REML). ASReml offers comprehensive linear mixed model facilities for analysing spatial and repeated measures data. ASReml can be used to fit multi-environmental trial analyses with complex variance structures, allowing for better use and understanding of environmental interaction.
You can choose either ASReml-SA as a stand-alone tool, or ASReml-R to work in an R environment. Linear mixed-effects models provide a rich and flexible tool for the analysis of many data sets commonly arising in environmental sciences. ASReml is used in research worldwide for its reliability, speed and efficiency when making sense of large, often messy data sets.
Genstat can make predictions from large environmental data sets using data mining techniques, such as support vector machines, regression trees, random regression forests, and neural networks. Genstat provides multivariate methods for analysing and interpreting biological and environmental data, including principal components analysis, multi-dimensional scaling, correspondence analysis, canonical correlation analysis, discriminant analysis and cluster analysis.
Moreover, Genstat provides tools to generate robust and efficient experimental designs, including randomised block, split-plot, row-column and cyclic designs for field trials, laboratory experiments, etc.
ASReml is powerful statistical software specially designed for mixed models using Residual Maximum Likelihood (REML). ASReml offers comprehensive linear mixed model facilities for analysing spatial and repeated measures data. ASReml can be used to fit multi-environmental trial analyses with complex variance structures, allowing for better use and understanding of environmental interaction.
You can choose either ASReml-SA as a stand-alone tool, or ASReml-R to work in an R environment. Linear mixed-effects models provide a rich and flexible tool for the analysis of many data sets commonly arising in environmental sciences. ASReml is used in research worldwide for its reliability, speed and efficiency when making sense of large, often messy data sets.
Case Studies
Our analytics software and consulting services are chosen by seed, plant, aqua and animal breeding companies worldwide to support and inform the development of new varieties, strains, stocks and breeds.
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Need a statistics tool for environmental science?
If you are looking for data science to develop your environmental science research, submit your details below and we’ll be in touch.