This dataset comprises of geochemical, mineralogical and microbiological analyses of material collected on the southwestern margin of the Greenland Ice Sheet in 2016 and 2017. Stream water, melted ice and snow samples were collected and analysed for carbon, nitrogen, phosphorus, cation and anion concentrations, pH, conductivity, total dissolved solids (TDS), mineral phase and class abundances and Rare Earth Elements (REE). Microbial community composition was also analysed. In addition, the results of a nutrient incubation experiment are also presented.The data were collected as part of a project investigating drivers of glacial ice algal growth on the Greenland Ice Sheet. We acknowledge funding from UK Natural Environment Research Council Consortium Grant, Black and Bloom (NE/M020770/1, NE/M021025/1 and NE/S001670/1). LGB and SL acknowledge funding from the German Helmholtz Recruiting Initiative (award number: I-044-16-01). LGB, AMA, and MT were also supported through an ERC Synergy Grant (''Deep Purple'' grant # 856416) from the European Research Council (ERC)

In 1991 a nitrogen x phosphorus fertilisation experiment on dwarf shrub tundra close to Ny-Alesund, Svalbard was established. Treatments (0, 10, 50 kg N ha-1 yr-1; 0, 5 kg P ha-1 yr-1) were applied to Cassiope heath for 3 years and Dryas heath for 8 years. In 2011 the experiment was revisited to investigate the persistence of effects of fertilisation on species composition, vegetation nutrient status and ecosystem carbon stocks. The whole experiment has been led by Dr Sarah Woodin and colleagues, University of Aberdeen. The 2011 study, for which data are provided, was undertaken by Dr Lorna Street. Funded was provided by the NERC grant NE/I016899/1

Water quality modelled outputs for daily channel discharge and depth, nitrate-N, ammonium-N, suspended sediment concentration, total phosphorus, and soluble reactive phosphorus for the Mekong River basin. Baseline models for P and N were calibrated over 1998 to 2017. Predictions spanning 2018 to 2098 were modelled based on eight scenarios (2 climate x 2 socioeconomic x 2 population). Please see Whitehead et al (2019) https://doi.org/10.1016/j.scitotenv.2019.03.315 for details. Full details about this dataset can be found at https://doi.org/10.5285/710fc65c-87eb-4932-9d8e-dc8328742232