This resource comprises two Jupyter notebooks that includes the model code in python to train a random forest model to predict long-term seasonal nitrate and orthophosphate concentrations at each river reach in Great Britain. The input features considered are catchment descriptors and land cover matched to the reaches. The training data is obtained from the Environmental Agency Water Quality Archive, 2010-2020. This method provides an effective way to map water quality data from stations to the river network. A live demo of a web application to visualize the dataset can be viewed at https://moisture-wqmlviewer.datalabs.ceh.ac.uk/wqml_viewer Full details about this application can be found at https://doi.org/10.5285/ba208b6c-6f1a-43b1-867d-bc1adaff6445

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

This application is an implementation of a Fuzzy changepoint based approach to evaluate how well numerical models capture local scale temporal shifts in environmental time series. A changepoint in a time series represents a change in the statistical properties of the time series (either mean, variance or mean and variance in this case). These can often represent important local events of interest that numerical models should accurately capture. The application detects the locations of changepoints in two time series (typically one representing observations and one representing a model simulation) and estimates uncertainty on the changepoint locations using a bootstrap approach. The changepoint locations and associated confidence intervals are then converted to fuzzy numbers and fuzzy logic is used to evaluate how well the timing of any changepoints agree between the time series. The app returns individual similarity scores for each changepoint with higher scores representing a better performance of the numerical model at capturing local scale temporal changes seen in the observed record. To use this application, the user will upload a csv file containing the two time series to be compared. This work was supported by Engineering and Physical Sciences Research Council (EPSRC) Data Science for the Natural Environment (DSNE) project (EP/R01860X/1) and the Natural Environment Research Council (NERC) as part the UK-SCAPE programme (NE/R016429/1). Full details about this application can be found at https://doi.org/10.5285/49d04d55-90a7-4106-b8fe-2e75aba228e4

This model code provides an example to demonstrate a new application of the 'learnr' R package to help authors to make elements of their research analysis more readily reproducible to users. It turns a R Markdown document to guided, editable, isolated, executable, and resettable code sandboxes where users can readily experiment with altering the codes exposed Full details about this application can be found at https://doi.org/10.5285/df57b002-2a42-4a7d-854f-870dd867618c

This dataset contains the stochastic Rainfall and Weather GENerator (RWGEN) model and observational historical climate inputs for UK applications. The model simulates one or more stochastic realisations of any length for rainfall (mm), temperature (°C) and potential evapotranspiration (mm) at hourly or longer timesteps. RWGEN can be used for single site or spatial simulations of historical/reference or perturbed/future climate. The model version in this dataset is a snapshot of the RWGEN Github repository, which contains new releases and developments: https://github.com/rwgen1/rwgen. The observational climate inputs consist of historical hourly rainfall and daily weather time series for selected UK Met Office (UKMO) station locations. The historical time series are derived from the UKMO Met Office Integrated Data Archive System (MIDAS) Open datasets for the period 1853 to 2020. These time series can be used to train the RWGEN model for UK locations or catchments. Note that the data coverage is not consistent throughout the 1853-2020 period, with lower data availability prior to the mid-twentieth century. A user may also choose to use alternative data for model input. Full details about this application can be found at https://doi.org/10.5285/44c577d3-665f-40de-adce-74ecad7b304a

The R code "carbon_stock_calculations.R" estimates aboveground carbon stocks for 49 plots in 14 fragmented forest sites and 4 continuous forest sites in Sabah, Malaysian Borneo, using the vegetation dataset 'Vegetation and habitat data for fragmented and continuous forest sites in Sabah, Malaysian Borneo, 2017'. The 14 fragmented sites were all in Roundtable on Sustainable Palm Oil-certified oil palm plantations, and are hereafter termed 'conservation set-asides'. The code also estimates the aboveground carbon stocks of oil palm plantations for comparison. The R code "analyses_and_figures.R" runs analyses and makes figures of aboveground carbon stocks and associated plant diversity for these sites, as presented in Fleiss et al. (2020) This R code was created in order to investigate the following: (1) to establish the value of conservation set-asides for increasing oil palm plantation aboveground carbon stocks; (2) to establish whether set-asides with high aboveground carbon stocks can have co-benefits for plant diversity; (3) to compare the carbon stocks and vegetation structure of conservation set-asides with that of continuous forest, including assessing tree regeneration potential by examining variation in seedling density; (4) to examine potential drivers of variation in aboveground carbon stocks of conservation set-asides (topography, degree of fragmentation, and soil parameters); (5) to scale-up the estimates of the aboveground carbon stocks of conservation set-asides, in order to predict average carbon stocks of oil palm plantations with and without set-asides, and for varying coverage of set-asides across the plantation. Full details about this application can be found at https://doi.org/10.5285/9ff5cdca-b504-4994-8b07-5912ee6aff47

This dataset contains the material required to reproduce 3D volumetric data describing the energy density of photons within a simulated environment and heatmaps and journey lengths for ensembles of weighted walkers experiencing specific simulated environments. The dataset includes source code for snapshots of the Monte Carlo Radiative Transfer (MCRT) code used to run simulations, the weighted random walking code used to emulate the behaviour of animals experiencing the simulated environment, as well as inputs and configuration files for both codes. The MCRT software outputs 3D volumetric data describing the energy density of photons within the simulated environment. Then, the weighted random walk code takes 2D planes from this data and produces heatmaps and journey lengths for ensembles of weighted walkers experiencing these simulated environments. Full details about this application can be found at https://doi.org/10.5285/1b64b008-8c20-4dd4-bf54-bf1894767a56

[This application is embargoed until February 12, 2026]. This resource provides code to fit a transmission model of bovine tuberculosis spread to a population of wild badgers in Woodchester Park in the UK. The code produces Markov chain Monte Carlo samples from a model fitted to individual-level badger data from Woodchester Park. The badger data came from and can be requested from the Animal and Plant Health Agency. Example outputs are provided in the Supporting Information. This code was developed as part of a Natural Environment Research Council funded grant (number NE/V000616/1). Full details about this application can be found at https://doi.org/10.5285/fe0f6bd7-ffd2-4e21-8c84-493cf4f3080d

This resource contains the R code and core results of a study seeking to identify whether there are global patterns in whether larger or smaller bodied species are showing different population trends within communities. This used the global BioTIME database (including community time series from hundreds of studies of mostly covering the last 20-50 years) and several large trait databases to gain a very large sample size (12,956 assemblage time series from 144 studies, incorporating 2,109,593 observations of 10,286 species, of which 7,234 could be linked to at least one size trait). Data resources in this deposit include matched trait values for each species, several population trends of each species, and community level correlations between population trends and body-size trait values. Additionally, html files describing the R markdown code to produce the data resources are included. This resource does not contain the raw population and trait data, which are openly available from various sources that are listed in the supporting documentation. The matching between global databases required a large amount of initial cleaning and filtering steps. Although the data was subject to a number of checks, with tens of thousands of species, it was too large to check all alignments manually, and trait matches assume a single bodysize value for a species across its range. The original purpose was to generate a relative rank within a community, but caution is needed for more fine-grained analyses using this approach. Full details about this application can be found at https://doi.org/10.5285/1f7687de-d68e-4349-b4d3-b7d3a127a7df

This model code for object oriented data analysis of surface motion time series in peatland landscapes provides the procedure to assess peatland condition using object oriented data analysis. The model code assesses peatland condition according to which cluster each surface motion time series is assigned, based on key measures capturing differences between the time series. It can be run on any machine with R. Full details about this application can be found at https://doi.org/10.5285/dbdb9f19-c039-4a73-b590-e1acc7f79df4