From 1 - 5 / 5
  • 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

  • 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

  • This R application is an implementation of state tagging approach for improved quality assurance of environmental data. The application returns state-dependent prediction intervals on input data. The states are determined based on clustering of auxiliary inputs (such as meteorological data) made on the same day. The method provides contextual information to assess the quality of observational data and is applicable to any point-based, daily time series observational data. To use this application, the user will need to input two separate csv files: one for state variables and the other for observations. This work was supported by the Natural Environment Research Council award number NE/R016429/1 as part of the UK-SCAPE programme delivering National Capability. Full details about this application can be found at

  • Data comprise the collection label details of museums specimens for five bumblebee species (Bombus hortorum, B. muscorum, B. lapidarius, B. pascuorum and B. sylvarum) from five UK museums (Natural History Museum (London), National Museums Scotland, Oxford University Museum of Natural History, Tullie House Museum and Art Gallery, and World Museum (Liverpool)). The details include species, collector, date collected, location, and caste. The location for each specimen was geotagged using Google Maps’ Geocoding application programming interface. Each specimen had its left and right forewing landmarked, with the wing shapes aligned using a Procrustes alignment, and Procrustes distance between the wings calculated. The data came from a digitisation program as part of a NERC funded Standard Grant awarded to R. Gill (NE/P012574/1) and I. Barnes (NE/P012914/1). Full details about this dataset can be found at

  • This dataset contains information about predicted future erosion hazards to electricity transmission towers at a site in the Mersey River valley. River channel change and floodplain erosion rates were simulated under 6 hypothetical flow scenarios, covering the years 2018 to 2050. These scenarios include: “baseline” where we assumed the 32 years of flow from 2018 to 2050 matched the preceding 32-year period; and “plus 10, 20, 30, 40 & 50%” where we assumed daily averaged flow magnitudes increased by 10, 20, 30, 40 or 50%, depending on the scenario. Simulations were run using the CAESAR-Lisflood landscape evolution model. Input files that were used to drive the simulations include a 15-metre resolution DEM covering a ~4.5 km long reach of river valley, and daily-averaged flow inputs (m3 s-1). Landscape changes over time were extracted at the locations of each electricity transmission tower, with the severity of erosion used to judge the relative risks of each tower from future climate change. The work was supported by the Natural Environment Research Council (Grant NE/S01697X/1) as part of the project: ‘Erosion Hazards in River Catchments: Making Critical Infrastructure More Climate Resilient’. Full details about this dataset can be found at