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Table S1-1 Quantification of GHG and Nr flow intensities [kg CO2eq (kg product)-1 yr-1] or [g N (kg product)-1 yr-1] with the CAPRI N-LCA model for six main livestock products (BEEF: beef, PORK: pork, EGGS: eggs, POUM: poultry meat; DAIR: milk and dairy products, SGMP: meat from sheep and goats) and six main vegetable food groups (POTA: potatoes, SUGB: sugar beet before processing, OILP: oil seeds before processing; CERR: cereals, LEGU: leguminous crops) as well as other crops (OCRP) and aggregated livestock (ANIMP) and vegetable (CROPP) food. Table S2-1 Quantification of the main N budget flows in the EU25 agriculture sector

This ORDA repository location contains estimated contemporary and future surface relief, depression volume and surface lake volume maps of the Greenland Ice Sheet. The maps are produced for 3 time slices which each use different greenhouse gas emission scenarios (i.e. historical or Representative Concentration Pathways (RCP)):Two separate versions are produced for future time-slices:1. incorporating the effects of the changing surface mass balance2. incorporating the effects of both the changing surface mass balance and the changing ice sheet thickness and flow velocityThese datasets are the key outputs of our manuscript entitled: "Enhanced formation of surface lakes in the interior of the Greenland Ice Sheet after 2100 due to growing surface relief".The files are presented in GeoTiff (.tif) format.For further information please see "Readme.txt"

Replication data and materials for "Changing Prices in a Changing Climate: Electoral Competition and Fossil Fuel Taxation".

Replication Package: Software Architecture Assessment for Sustainability: A Case Study This repository contains the supplementary material to support the paper published at the International Conference on Software Architecture (ECSA) 2024 titled, "Software Architecture Assessment for Sustainability: A Case Study". This repository can be used to replicate the study and carry out a Software Architecture Evaluation of other software systems.The online version can be browsed on the linked Github Repository

This dataset consists of hourly internal and daily external temperature data from 82 certified Passivhaus dwellings in the UK. The data can be used for calculating overheating risk and guaging how comfortable a home would be in the summer. This data come from 16 different sites and includes houses and flats. Some of the data is from the living room only, for other dwellings there were sensors in muitple rooms and these are indicated. As this data was compared to CIBSE TM59 "Design methodology for the assessment of overheating risk in homes", there is a calculation of the running mean temperature and maximum temperature. The variables are Timestamp = time and date SiteID = Site number (1-16) DWType = dwelling type (House or Flat) HouseID = unique reference number for each dwelling in dataset Room = room type LR = living room , BR= bedroom, KI= Kitchen, BT= bathroom T.int = internal temperature (mean hourly) T.ext.daily = external temperature (mean daily) T.rm = running mean temperature calculated using the method described in CIBSE TM59 T.max = maximum daily intenral temperature calculated using the method described in CIBSE TM59 This data was provided by the Technology Stratergy Board Building Performance Evaluation Program, and is available from the digital catapault. Other data was provided by WARM low energy Consultancy and indidiual home owners. All data has been anonymised

The dataset comprises full list of species of macrozoobenthos collected from the Pechora Sea (SE Barents Sea). Grab samples were collected from 10 stations in the Pechora Bay from aboard RV Kartesh in 2020-2021. Macrobenthic invertebrates were identified with the maximum level of certainty through optical microscopy using regional taxonomic keys. All taxonomic names were standardised using the World Register of Marine Species (WoRMS). All specimens have been counted and weighted (wet biomass) on Ohaus Adventurer scales with reported accuracy to 0.01 g. Bivalve molluscs and gastropods were weighed in shells. Biomass (g. m-2) and abundance (ind m-2) are used to characterise macrozoobenthos. The sampling and identification work was carried out in collaboration with specialists from Lomonosov Moscow State University Marine Research Center and P.P. Shirshov Institute of Oceanology.

Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.AerChemMIP.HAMMOZ-Consortium.MPI-ESM-1-2-HAM' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The MPI-ESM1.2-HAM climate model, released in 2017, includes the following components: aerosol: HAM2.3, atmos: ECHAM6.3 (spectral T63; 192 x 96 longitude/latitude; 47 levels; top level 0.01 hPa), atmosChem: sulfur chemistry (unnamed), land: JSBACH 3.20, ocean: MPIOM1.63 (bipolar GR1.5, approximately 1.5deg; 256 x 220 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run by the ETH Zurich, Switzerland; Max Planck Institut fur Meteorologie, Germany; Forschungszentrum Julich, Germany; University of Oxford, UK; Finnish Meteorological Institute, Finland; Leibniz Institute for Tropospheric Research, Germany; Center for Climate Systems Modeling (C2SM) at ETH Zurich, Switzerland (HAMMOZ-Consortium) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 250 km, ocnBgchem: 250 km, seaIce: 250 km.

Conventional analyses suggest the metabolism of heterotrophs is thermally more sensitive than that of autotrophs, implying that warming leads to pronounced trophodynamic imbalances. However, these analyses inappropriately combine within- and across-taxa trends. We present a novel mathematic framework to separate these, revealing that the higher temperature sensitivity of heterotrophs is mainly caused by within-taxa responses which account for 92% of the difference between autotrophic and heterotrophic protists. This dataset contains both the datasets and R codes of per capita growth rates of autotrophic and heterotrophic protists as well as heterotrophic bacteria and insects. The datasets of per capita growth rates against temperature were compiled from the literature. Experimental data were included if they met the following criteria: at least 3 data points with positive growth rate (µ) and at least 2 unique temperatures at which positive µ were measured. To calculate apparent activation energy, we also removed data points with nonpositive µ and those with temperatures above the optimal growth temperature (defined as the temperature corresponding to the maximal µ). We use the free software R (version 4.2.0) with R packages (foreach, nlme, plyr, dplyr) to analyse these datasets. R codes are also provided.

Survey used and data gathered for research into adoption of carbon management strategies amongst universities 2022, which forms part of a PhD thesis and will be submitted for publication in a journal.