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  • Concentration and accumulation palaeoenvironmental proxy data derived from a 6.3m sedimentary core drilled at Marcacocha, a present-day wetland (formerly a small lake) located at 3355m above sea-level in the Cordillera Oriental of the Peruvian Andes. Multi-proxy analysis of the sediments at decadal to sub-decadal temporal resolution has provided detailed datasets that include sedimentology, palynology, geochemistry, plant macrofossils, diatoms and oribatid mite remains (Chepstow-Lusty et al., 2003, 2007, 2009; Sterken et al., 2006). Select data are presented here relating to the uppermost 1.9m of the sequence (ca. the last 1200 years). The data relate specifically to: Chepstow-Lusty, A., Frogley, M.R., Baker, A.S. Comparison of Sporormiella dung fungal spores and oribatid mites as indicators of large herbivore presence: evidence from the Cuzco region of Peru. J. Arch. Sci. https://doi.org/10.1016/j.jas.2018.12.006 Chepstow-Lusty, A., Bennett, K., Fjeldså, J., Kendall, A., Galiano, W., Tupayachi Herrera, A., 1998. Tracing 4000 years of environmental history in the Cuzco area, Peru, from the pollen record. Mt. Res. Dev. 18, 159–172. Chepstow-Lusty, A., Frogley, M.R., Bauer, B.S., Bush, M.B., Tupayachi Herrera, A., 2003. A late Holocene record of arid events from the Cuzco region, Peru. J. Quat. Sci. 18, 491–502. Chepstow-Lusty, A., Frogley, M.R., Bauer, B.S., Leng, M., Cundy, A., Boessenkool, K.P., Gioda, A., 2007. Evaluating socio-economic change in the Andes using oribatid mite abundances as indicators of domestic animal densities. J. Arch. Sci. 34, 1178–1186. Chepstow-Lusty, A.J., Frogley, M.R., Bauer, B., Leng, M.J., Boessenkool, K.P., Carcaillet, C., Ali, A.A., Gioda, A., 2009. Putting the rise of the Inca empire within a climatic and land management context. Clim. Past 5, 1–14. Sterken, M., Sabbe, K., Chepstow-Lusty, A., Frogley, M., Vanhoutte, K., Verleyen, E., Cundy, A., Vyverman, W., 2006. Climate and land-use changes in the Cuzco region (Cordillera Oriental, South East Peru) during the last 1200 years: a diatom based reconstruction. Arch. Hydrobiol. 165, 289–312.

  • A very incomplete dataset of surface lakes in Antarctica. Data have been prepared from various map and remotely sensed datasets.

  • A very incomplete dataset of surface lakes in Antarctica. Data have been prepared from various map and remotely sensed datasets.

  • A very incomplete dataset of surface lakes in Antarctica. Data have been prepared from various map and remotely sensed datasets. This dataset has been generalised from the high resolution version.

  • Surface waters and shallow groundwater samples were collected by completely filling 30 mL polyethylene bottles, which were then sealed with electrical tape to minimise the risk of evaporative loss. Rainwater samples were integrated samples of total monthly rainfall collected in a specially-adapted rainfall collector following IAEA protocols (IAEA http://www-naweb.iaea.org/napc/ih/documents/userupdate/sampling.pdf [accessed 22 June 2012). Mexico, State of Yucatan. Yaal Chac (lake) (lake centre is Lat: 20.595274 degrees; Long: -89.711301 degrees), Abala Well (Lat: 20.649044 degrees; Long: -89.679814 degrees) and Xanil ha Cave (Lat: 20.650809 degrees; Long: -89.697426 degrees) Rainwater sampler was located adjacent to the lake. Refer to accompanying map for the precise location of the sampling sites.

  • The dataset contains the day of year for the onset and end of thermal stratification and its total length in Blelham Tarn, Esthwaite Water, Windermere, north basin and Windermere, south basin between 1951 and 2017. 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 dataset can be found at https://doi.org/10.5285/d029bb43-5722-4932-9c14-be316a2e7144

  • This dataset contains genotypes (in three digit-format) for unique clones of the freshwater bryozoan species Cristatella mucedo and Fredericella sultana at microsatellite loci and representing sampling sites across the UK. Cristatella mucedo data additionally covers Northern Ireland. Full details about this nonGeographicDataset can be found at https://doi.org/10.5285/9d63e68b-2499-40fb-b05b-4fa2a55399fc

  • This dataset contains weather conditions, water quality, water chemistry and crustacean zooplankton counts sampled at Loch Leven throughout the year 2019. Loch Leven is a lowland lake in Scotland, United Kingdom. The data were collected as part of a long-term monitoring programme, which began in 1968 and is still underway. Sampling occurs roughly every 2 weeks with laboratory analysis and data processing being performed at the UK Centre for Ecology & Hydrology Edinburgh site. The sampling and processing 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 dataset can be found at https://doi.org/10.5285/e404f64c-ddbc-4e3e-8dca-9bea3d68959a

  • This dataset contains various global lake products (1992-2019) produced by the European Space Agency (ESA) Lakes Climate Change Initiative (Lakes_cci) project. Lakes are of significant interest to the scientific community, local to national governments, industries and the wider public. A range of scientific disciplines including hydrology, limnology, climatology, biogeochemistry and geodesy are interested in distribution and functioning of the millions of lakes (from small ponds to inland seas), from the local to the global scale. Remote sensing provides an opportunity to extend the spatio-temporal scale of lake observation. The five thematic climate variables included in this dataset are: • Lake Water Level (LWL): a proxy fundamental to understand the balance between water inputs and water loss and their connection with regional and global climate changes. • Lake Water Extent (LWE): a proxy for change in glacial regions (lake expansion) and drought in many arid environments, water extent relates to local climate for the cooling effect that water bodies provide. • Lake Surface Water temperature (LSWT): correlated with regional air temperatures and a proxy for mixing regimes, driving biogeochemical cycling and seasonality. • Lake Ice Cover (LIC): freeze-up in autumn and advancing break-up in spring are proxies for gradually changing climate patterns and seasonality. • Lake Water-Leaving Reflectance (LWLR): a direct indicator of biogeochemical processes and habitats in the visible part of the water column (e.g. seasonal phytoplankton biomass fluctuations), and an indicator of the frequency of extreme events (peak terrestrial run-off, changing mixing conditions). Data generated in the Lakes_cci project are derived from data from multiple instruments and multiple satellites including; TOPEX/Poseidon, Jason, ENVISAT, SARAL, Sentinel, Landsat, ERS, Terra/Aqua, Suomi NPP, Metop and Orbview. For more information please see the product user guide in the documents.