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The Excel spreadsheets contain the input data defining the optimisations of the hybrid electricity systems: Astypalaia_input_file.xlsx - configuration, resource and demand data for Astypalaia Westray_input_file.xlsx - configuration, resource and demand data for Westray The csv files contain the data from individual sheets of the Excel spreadsheets: Astypalaia_demands.csv - Demand data for Astypalaia Astypalaia_resources.csv - Resource data for Astypalaia Westray_demands.csv - Demand data for Westray Westray_resources.csv - Resource data for Westray

These data record the results of processing otter trawl catches (OTSB14; Merrett & Marshall, 1980) from the National Oceanography Centre (NOC, UK) long-term study of the Porcupine Abyssal Plain (PAP), including the PAP-Sustained Observatory time-series. The data concern catches recovered during the RRS Discovery III Cruise 250 in 2000. Collins, M. et al. (2000). RRS Discovery Cruise 250, 15 Sep- 10 Oct 2000. Metabolism, activity and distribution patterns in demersal deep-sea fish. Aberdeen University. | https://www.bodc.ac.uk/resources/inventories/cruise_inventory/report/5358/.

See "EGY_SupplementaryInformation_RRenaldi.pdf".

The paper "Blue Food policy objectives: an analysis of opportunities and trade-offs" integrates the findings of an initiative to assess the multiple roles of blue foods in food systems worldwide (https://www.bluefood.earth/) and translates them into four policy objectives aimed at realizing the contributions of aquatic foods to more nutritious, just, resilient and environmentally sustainable food systems. This dataset contains the variables used to assess conditions (at the level of nations) when blue food policy objectives are likely to be relevant. The R code used for Boolean analysis is available here: https://github.com/emmywas/BFA_Policy_analysis The paper "Blue Food policy objectives: an analysis of opportunities and trade-offs" is part of the Blue Food Assessment ( https://www.bluefood.earth/ ) a comprehensive examination of the role of aquatic foods in building healthy, sustainable, and equitable food systems. The assessment was supported by the Builders Initiative, the MAVA Foundation, the Oak Foundation, and the Walton Family Foundation. BC also thanks the Erling Persson Family Foundation. The data comes from publicly available (or published) datasets

Positive biodiversity-ecosystem function relationships (BEFRs) have been widely documented, but it is unclear if BEFRs should be expected in disturbance-driven systems. Disturbance may limit competition and niche differentiation, which are frequently posited to underlie BEFRs. We provide the first exploration of the relationship between tree species diversity and biomass, one measure of ecosystem function, across southern African woodlands and savannas, an ecological system rife with disturbance from fire, herbivores and humans. We used >1000 vegetation plots distributed across 10 southern African countries, and structural equation modelling, to determine the relationship between tree species diversity and aboveground woody biomass, accounting for interacting effects of resource availability, disturbance by fire, tree stem density and vegetation type. We found positive effects of tree species diversity on aboveground biomass, operating via increased structural diversity. The observed BEFR was highly dependent on organismal density, with a minimum threshold of c. 180 mature stems ha-1. We found that water availability mainly affects biomass indirectly, via increasing species diversity. The study underlines the close association between tree diversity, ecosystem structure, environment and function in highly disturbed savannas and woodlands. We suggest that tree diversity is an under-appreciated determinant of wooded ecosystem structure and function.

These data record the results of processing otter trawl catches (OTSB14; Merrett & Marshall, 1980) from the National Oceanography Centre (NOC, UK) long-term study of the Porcupine Abyssal Plain (PAP), including the PAP-Sustained Observatory time-series. The data concern catches recovered during the RRS Discovery III Cruise 237 in 1998. Sibuet, M. et al. (1999). RRS Discovery Cruise 237, 25 Sep-08 Oct 1998. BENGAL: High resolution temporal and spatial study of the BENthic biology and Geochemistry of a north-eastern Atlantic abyssal Locality. Southampton Oceanography Centre Cruise Report, No. 23, 38pp.| https://www.bodc.ac.uk/resources/inventories/cruise_inventory/reports/d237.pdf

The data provided here is for the electromagnetic heating of magnetite under radio-frequencies. The data includes that from material characterisation, in-situ magnetometry, calorimetry, in-situ power absorption measurement and arc-tangent modelling results. The data is aligned to the figures in the research publication and is intended that future researchers can make their own analysis based upon our work. Further relevant documentation may be found in the following resources. Noble, J. P. P., Bending, S. J., Sartbaeva, A., Muxworthy, A. R., and Hill, A. K., 2021. A Novel In Situ High‐Temperature Magnetometry Method for Radiofrequency Heating Applications. Advanced Energy Materials, 12(1), 2102515. Available from: https://doi.org/10.1002/aenm.202102515. The data is collected using techniques described fully in the accompanying research article. The data follows the figure numbers from the paper which should make it easy to navigate.

Monthly CO2 emissions projections 2015-2025, modified by country-specific impacts of COVID-19 lockdown in 2020-2023, with 4 different projections for the period 2024-2025. This repository holds the netcdf files for CO2 emissions from ground-level and aviation sources from the MESSAGE_GLOBIOM scenario SSP2-4.5, from the Scenario4MIPs database (https://esgf-node.llnl.gov/search/input4mips/), modified by the country and sector activity levels associated with lockdown for 2020. Sector activity level in 2020 is based on data up until June, and a fixed estimate is used thereafter. This is the monthly equivalent of https://zenodo.org/record/3951601#.XxYBsihKhPY for this time period. Funding was provided by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement nos. 820829 (CONSTRAIN) http://constrain-eu.org/ see https://github.com/Priestley-Centre/COVID19_emissions for more details. Note that the data and normalisation process used in the aviation data is subtly updated since the version 4 found in related databases, hence that is referred to as v4.5. It still does not cover the whole month of July, due to publication date. The whole dataset is now considered version 4.5.1 as it features more variables and differently cropped timeframes, however is still consistent with version 4.5 in terms of the input data.