This dataset is derived from modelled changes to the distributions of >12,700 terrestrial mammal and bird species under four different climate scenarios, projected to 2070. It contains national-level projections of species richness change under each climate scenario, based on species' modelled climatic niches, as well as projected range shifts in relation to political borders globally. Full details about this dataset can be found at https://doi.org/10.5285/5bf972a8-c9a3-4721-8089-552dfe3ff124

This dataset contains responses from an online choice experiment with associated socio-economic covariates on the topic of environmental land management schemes. Sample: 348 farmers based in the north of England in 2022. Full details about this dataset can be found at https://doi.org/10.5285/1409404f-564f-43c5-81dd-00339a674dc8

This dataset contains details of digital elevation models (DEM) and orthomosaic photographs (orthophotos) of seven 5 x 5 m erosion plots on Iron Tongue Hill, Tameside, Manchester. Plots were surveyed on ten occasions in 2018/2019 following a severe moorland wildfire (July 2018). Plots were surveyed using ground-based photogrammetry and Struture-from-Motion methods. The work was supported by the Natural Environment Research Council (Grant NE/S011560/1). Full details about this dataset can be found at https://doi.org/10.5285/756d3a73-ca93-456f-8c0a-e767fb9f82a8

The dataset contains parameter values that maximize revised Kling Gupta Efficiency (KGE’) between modelled and observed daily mean river flows when running one of 24 different hydrological models with one of 21 different climatic input datasets in one of 33 different catchments across the Citarum basin or 5 catchments across the Ciliwung basin, both in Java island, Indonesia. This dataset was created as part of a study on the advantages and disadvantages of using existing hydrological models, primarily developed for temperate and cold climates, in a tropical volcanic region. The hydrological models were based on those created for MARRMoT v1.2 (10.5194/gmd-12-2463-2019), recoded as sequential models in the R programming language. This work was supported by the Natural Environment Research Council (Grants NE/S00310X/1 and NE/S002790/1). Full details about this dataset can be found at https://doi.org/10.5285/f6cec7d4-edee-44b8-8f44-86d4f12ac72d

[This dataset is embargoed until June 1, 2023]. This dataset consists of a single orthophoto mosaic image of Irontongue Hill on Swineshaw Moor. The area of interest includes seven erosion plots (approximately 5 x 5 m) which were set up on 26/07/2018 to capture the state of the burnt moorland surface and monitor subsequent erosion and vegetation recovery. The area of interest is approximately 0.45 km2. Full details about this dataset can be found at https://doi.org/10.5285/aff5210d-27e9-4655-badb-4d16c3adeb17

Dataset contains Terrestrial laser scanner (TLS) and CT scan data collected during fieldwork on a small gravel-bed river. TLS data show the river bed surface topography collected at five intervals between September 2014 and October 2018. CT scan data show the 3D structure of sections of the river bed. CT data has been processed to segment the images into gravel grains and fine-grained matrix. Full details about this dataset can be found at https://doi.org/10.5285/b30b4d56-f0a9-43e8-aacc-09d9b5b1f9fc

This dataset contains details of sediment geochemistry, loss-on-ignition and sediment median particle size for terrestrial sediment samples collected across the stream network of Harehill and Swineshaw Moors, Stalybridge, Tameside, Manchester. Samples were collected on three occasions in 2018 following a severe moorland wildfire (July 2018). Sediment samples were collected using stream sediment traps which accumulated sediments between the dates of sampling. Sediment traps were emptied in the field and samples were returned to the laboratory for analysis. The work was supported by the Natural Environment Research Council (Grant NE/S011560/1). Full details about this dataset can be found at https://doi.org/10.5285/ad1bb542-e75c-423f-8421-b247b2f72ce6

Terrestrial laser scanner (TLS) and CT scan data collected during flume experiments on a gravel bed. TLS data show the bed surface topography before and after waterworking of the bed. CT scan data show the 3D structure of sections of the river bed after waterworking. Some CT data has been processed to segment the images into the individual gravel grains, and for some of these data a database of grain properties is also available. Full details about this nonGeographicDataset can be found at https://doi.org/10.5285/6749d033-cdf4-479b-ba85-015c3dbb476a

[This dataset is embargoed until June 1, 2023]. This dataset contains details of sediment geochemistry, loss-on-ignition and sediment median particle size for two short reservoir cores collected from two reservoirs (Cowbury Dale and Higher Swineshaw), Stalybridge Tameside, Manchester. Cores were collected in 2018 following a severe moorland wildfire (July 2018) in the two reservoir catchments. Cores were collected from the deepest part of the reservoir using gravity coring and sampled at 2.5 mm intervals for analysis. The work was supported by the Natural Environment Research Council (Grant NE/S011560/1). Full details about this dataset can be found at https://doi.org/10.5285/4f447446-5461-48b2-b154-ff7094176502

The spectacular botanical preservation and long occupation of Qasr Ibrim, Egypt make this site archaeobotanically matchless. 600 samples have been collected over 20 years covering a timespan of c. 1000 BC - AD 1800. The project has particularly focussed on the period AD 100-400 during which several new summer crops including sorghum, cotton, lablab and sesame first appear. These new crops are thought to be associated with the introduction of new irrigation technology, specifically a device known as the saqia, an ox-driven water wheel from which descends a conveyor belt to which pots are attached. It has never before been possible to examine this crucial change archaeologically and this project has allowed the investigation of when and how this great change happened. This has major implications for the history of agriculture in Africa and the Indian Ocean.