Weather station data at five on-glacier stations in Peru and the ecohydrological model Tethys-Chloris. Data includes: - Hourly weather station data from Shallap Glacier, Artesonraju Glacier, Cuchillacocha Glacier, Quisoquipina Glacier and Quelccaya Ice Cap. Given as .csv files and as model input files. - The model code used to input the data and set the correct parameters for these sites. - The model code for the point version of Tethys-Chloris, an ecohydrological model which is used in this case to calculate glacier melt and mass balance. Full details about this dataset can be found at https://doi.org/10.5285/b69b8849-6897-47eb-a820-f488f8bca437

Averaged outputs from the WRF (Weather Research and Forecasting) model for the Rio Santa and Vilcanota, Urubamba and Vilcabamba catchments in Peru. Averaging was applied over the entire model period from 1980 to 2018. Data includes: - Averaged precipitation and air temperature records and the related standard deviation at a 4km resolution (annually and for each season) for each catchment. Monthly averaged and monthly totals of air temperature and precipitation (averaged over each catchment). - WRF model input elevation for each catchment. - WRF total precipitation and maximum/minimum air temperature at the location of five on-glacier weather stations (Artesonraju Glacier, Shallap Glacier, Cuchillacocha Glacier, Quisoquipina Glacier and Quelccaya Ice Cap) at a daily resolution from 1980 to 2018. Full details about this dataset can be found at https://doi.org/10.5285/7dbb2d72-7032-4cfa-bc9b-aa02bebe8df5

Code to compare the mass and energy balance of five Peruvian glaciers, based on outputs from the energy balance model Tethys-Chloris. Also includes code to compare the results of climate sensitivity experiments (where the air temperature and precipitation were varied). The main outputs of the analysis at each of the sites are also stored. Full details about this dataset can be found at https://doi.org/10.5285/5f6661e4-1d34-4b01-8f3a-9fc86c546f73

This dataset consists of survival and heights of trees planted for forest restoration in South and Southeast Asia and the associated analytical code. The data consists of tree censuses collated from published studies, grey literature and data provided by co-authors, up to/including May 2021. Data are collated from 176 sites in areas where disturbance or clearance of the natural forest had occurred and where trees were then planted and monitored over time. The analyses included here model height growth, extract annual size-standardised growth rates and test the effects of biophysical and climatic conditions and planting regimes on survival and growth. This dataset was created to represent the current state of knowledge on forest restoration outcomes in South and Southeast Asia. This is the full dataset for the survival and height analysis. Full details about this dataset can be found at https://doi.org/10.5285/935781e1-9119-4673-bd09-3fc76ae627d5

This dataset consists of structure, biomass (carbon density) and biodiversity (plant species richness) from forest inventory plots at forest restoration sites in South and Southeast Asia and the code for the analyses of these data as conducted in Banin, Raine et al (2023). The recorded data consists of plot level censuses carried out up to May 2021 collated from published studies, grey literature and data provided by co-authors. This represents the collation of data from 11 sites in areas where disturbance had led to the clearance or degradation of natural forest. Plots where tree seedlings were planted (active restoration) and plots where no seedling planting took place (natural regeneration) were censused for structure, biomass and/or biodiversity. Some of the sites in the dataset also recorded data at old growth forest plots for reference, and/or provided repeat measures of forest metrics over time. The dataset also includes the code used for analysis of this plot level data, used to compare the outcome of different restoration approaches. Full details about this dataset can be found at https://doi.org/10.5285/3d3b1d09-9e7a-4144-b8a1-b09a3c573466