This dataset consists of computer code transcripts for two proprietary flood risk models from a study as part of the NERC Rural Economy and Land Use (RELU) programme. This project was conceived in order to address the public controversies generated by the risk management strategies and forecasting technologies associated with diffuse environmental problems such as flooding and pollution. Environmental issues play an ever-increasing role in all of our daily lives. However, controversies surrounding many of these issues, and confusion surrounding the way in which they are reported, mean that sectors of the public risk becoming increasingly disengaged. To try to reverse this trend and regain public trust and engagement, this project aimed to develop a new approach to interdisciplinary environmental science, involving non-scientists throughout the process. Examining the relationship between science and policy, and in particular how to engage the public with scientific research findings, a major diffuse environmental management issue was chosen as a focus - flooding. As part of this approach, non-scientists were recruited alongside the investigators in forming Competency Groups - an experiment in democratising science. The Competency Groups were composed of researchers and laypeople for whom flooding is a matter of particular concern. The groups worked together to share different perspectives - on why flooding is a problem, on the role of science in addressing the problem, and on new ways of doing science together. We aimed to achieve four substantive contributions to knowledge: 1. To analyse how the knowledge claims and modelling technologies of hydrological science are developed and put into practice by policy makers and commercial organisations (such as insurance companies) in flood risk management. 2. To develop an integrated model for forecasting the in-river and floodplain effects of rural land management practices. 3. To experiment with a new approach to public engagement in the production of interdisciplinary environmental science, involving the use of Competency Groups. 4. To evaluate this new approach to doing public science differently and to identify lessons learnt that can be exported beyond this particular project to other fields of knowledge controversy. This dataset consists of computer code transcripts for two proprietary flood risk models. Flood risk and modelling interview transcripts from this study are available at the UK Data Archive under study number 6620 (see online resources). Further documentation for this study may be found through the RELU Knowledge Portal and the project's ESRC funding award web page (see online resources).

This is an application providing code for the non-parametric comparison of soil depth profiles, and testing for significant differences between soil depth profiles, using bootstrapped Loess (local) regressions (BLR). The BLR approach was developed to be able to compare and test for significant differences in potentially non-linear depth profiles of soil properties across land use transitions, which does not need to meet any parametric distribution assumptions, and is intended to be generally applicable regardless of specific contexts of land use and soil type. A small dataset is provided with the code to demonstrate the BLR approach and its outputs. The code was written using the R statistical programming language and provides two examples of the BLR approach. This application was created by the Centre for Ecology & Hydrology at Lancaster in 2015 during the ELUM (Ecosystem Land Use Modelling & Soil Carbon GHG Flux Trial) project, which was commissioned and funded by the Energy Technologies Institute (ETI). Full details about this application can be found at https://doi.org/10.5285/d4f92cd8-43e8-49e4-8f9e-efcc0e3b2478

[THIS APPLICATION HAS BEEN WITHDRAWN]. MultiMOVE is an R package that contains fitted niche models for almost 1500 plant species in Great Britain. This package allows the user to access these models, which have been fitted using multiple statistical techniques, to make predictions of species occurrence from specified environmental data. It also allows plotting of relationships between species' occurrence and individual covariates so the user can see what effect each environmental variable has on the specific species in question. The package is built under R 2.10.1 and depends on R packages 'leaps', 'earth', 'fields' and 'mgcv'. Full details about this application can be found at https://doi.org/10.5285/c4d0393e-ff0a-47da-84e0-09ca9182e6cb

This application is an implementation of the Ecological Risk due to Flow Alteration (ERFA) method in R language. This method assesses the potential impact of flow change on river ecosystems. Although the code was developed with a geographical focus on southeast Asia (example datasets are provided for the Mekong River Basin), it can be applied for any location where baseline and scenario monthly river flow time series are available. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. Full details about this application can be found at https://doi.org/10.5285/98ec8073-7ebd-44e5-aca4-ebcdefa9d044

MultiMOVE is an R package that contains fitted niche models for almost 1500 plant species in Great Britain. This package allows the user to access these models, which have been fitted using multiple statistical techniques, to make predictions of species occurrence from specified environmental data. It also allows plotting of relationships between species' occurrence and individual covariates so the user can see what effect each environmental variable has on the specific species in question. The package is built under R 3.1.2 and depends on R packages 'leaps', 'earth', 'fields', 'mgcv', 'stringr', 'gsubfn', 'randomForest' and 'nnet'. Full details about this application can be found at https://doi.org/10.5285/94ae1a5a-2a28-4315-8d4b-35ae964fc3b9