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CASCADE is a global dataset for 139 extant coccolithophore taxonomic units. CASCADE includes a trait database (size and cellular organic and inorganic carbon contents) and taxonomic-specific global spatiotemporal distributions (Lat/Lon/Depth/Month/Year) of coccolithophore abundance and organic and inorganic carbon stocks. CASCADE covers all ocean basins over the upper 275 meters, spans the years 1964-2019 and includes 33,119 taxonomic-specific abundance observations. Within CASCADE, we characterise the underlying uncertainties due to measurement errors by propagating error estimates between the different studies. Full details of the data set are provided in the associated Scientific Data manuscript. The repository contains five main folders: 1) "Classification", which contains YAML files with synonyms, family-level classifications, and life cycle phase associations and definitions; 2) "Concatenated literature", which contains the merged datasets of size, PIC and POC and which were corrected for taxonomic unit synonyms; 3) "Resampled cellular datasets", which contains the resampled datasets of size, PIC and POC in long format as well as a summary table; 4) "Gridded data sets", which contains gridded datasets of abundance, PIC and POC; 5) "Species lists", which contains spreadsheets of the "common" (>20 obs) and "rare" (<20 obs) species and their number of observations. The CASCADE data set can be easily reproduced using the scripts and data provided in the associated github repository: https://github.com/nanophyto/CASCADE/ (zenodo.12797197) Correspondence to: Joost de Vries, joost.devries@bristol.ac.uk v.0.1.2 has some fixes: 1. The wrongly specified S. neapolitana was removed from synonyms.yml (this species is now S. nana)2. Longitudes were corrected for Guerreiro et al., 20233. A double entry for Dimizia et al., 2015 was fixed4. Units in Sal et al., 2013 were correct to cells/L (previously cells/ml)5. Data from Sal et al., 2013 was re-done, as some species were missing6. Duplicate entries from Baumann et al., 2000 were dropped

This spreadsheet contains the results for the article, "Meeting the costs of decarbonising industry – the potential effects on prices and competitiveness (a case study of the UK)". These include projected impacts for industrial process decarbonisation (costs, fuel use, residual emissions), for key years (2030, 2040, 2050), distributed in the following ways: - Directly allocated to industrial sector in which they occur - Shared between sectors in proportion to the share of GVA of each supply chain - Embodied in final products - Embodied in final products, aggregated to consumption patterns The source of the projections and the method to perform the distribution are described in detail in the associated article. Further relevant documentation may be found in the following resources. Cooper, S. J.G., Allen, S. R., Gailani, A., Norman, J. B., Owen, A., Barrett, J., and Taylor, P., 2024. Meeting the costs of decarbonising industry – The potential effects on prices and competitiveness (a case study of the UK). Energy Policy, 184, 113904. Available from: https://doi.org/10.1016/j.enpol.2023.113904. For details of the methods used, please see the associated journal article.

Dataset including raw Photoluminescence (PL) spectral data, UV-vis Absorbance (ABS) spectral data, Photoluminescence quantum yield (PLQY) data and calculations, and the average Suprapartice (SP) size data.

This repository contains a series of .csv files developed for the study titled "Plant canopies promote climatic disequilibrium in Mediterranean recruit communities", authored by: Perez-Navarro MA, Lloret F, Molina-Venegas R, Alcántara JM and Verdú M. The author of these files is Perez-Navarro MA. These files are used to characterize species niches, estimate climatic disequilibrium for recruit communities growing under plant canopies and open spaces, and conduct statistical analyses. Variables description of each table is compiled in the METADATA.txt file. Please visit Github readme () to correctly place these files in the folder tree and check for the corresponding scripts where they are required. Please notice that although alternative approaches were calibrated to estimate species niche (accordingly producing multiple niche, distances and disequilibrium dataframes), only niche centroid calibrated discarding 95 percentile of lowest niche density was used for paper results and figures. Also, in case of univariate analyses only bio01, bio06 and bio12 were used in analyses, though species niche and further niche and community estimations were obtained for all 19 variables. This is version 2 (v2) and include extra intermediate .csv required to run all the R scripts included in the abovementioned Github repository. NAs or empty cells present in the .csv files of this repository means no data and do not contribute to the analyses. Visit METADATA.txt file for variables description. These data are under CC0 license. It is possible to share, copy and redistribute the material in any medium or format, and adapt, remix, transform, and build upon the material for any purpose. Studies using R scripts or any data files from these study should cite the abovementioned paper (Perez-Navarro MA, Lloret F, Molina-Venegas R, Alcantara JM, Verdu M. (2024). Plant canopies promote climatic disequilibrium in Mediterranean recruit communities). Please contact m.angeles582@gmail.com in case of having doubts or problems with the existing files and scripts. Current rates of climate change are exceeding the capacity of many plant species to track climate, thus leading communities to be in disequilibrium with climatic conditions. Plant canopies can contribute to this disequilibrium by buffering macro-climatic conditions and sheltering poorly adapted species to the oncoming climate, particularly in their recruitment stages. Here we analyze differences in climatic disequilibrium between understory and open ground woody plant recruits in 28 localities, covering more than 100,000 m2, across an elevation range embedding temperature and aridity gradients in the southern Iberian Peninsula. This study demonstrates higher climatic disequilibrium under canopies compared with open ground, supporting that plant canopies would affect future community climatic lags by allowing the recruitment of less arid-adapted species in warm and dry conditions, but also it endorse that canopies could favor warm-adapted species in extremely cold environments as mountain tops, thus pre-adapting communities living in these habitats to climate change.

This dataset contains the underlying data for the following publication: Doukas, H., Spiliotis, E., Jafari, M. A., Giarola, S. & Nikas, A. (2021). Low-cost emissions cuts in container shipping: Thinking inside the box. Transportation Research Part D: Transport and Environment, 94, 102815, https://doi.org/10.1016/j.trd.2021.102815.

This file collection contains the estimated spatial distribution of the above-ground biomass density (AGB) by the end of the 21st century across the Brazilian Atlantic Forest domain and the respective uncertanty. To develop the models, we used the maximum entropy method with projected climate data to 2100, based on the Intergovernmental Panel on Climate Change (IPCC) Representative Concentration Pathway (RCP) 4.5 from the fifth Assessment Report (AR5). The dataset is composed of four files in GeoTIFF format: calibrated-AGB-distribution.tif: raster file representing the present spatial distribution of the above-ground biomass density in the Atlantic Forest from the calibrated model. Unit: Mg/ha estimated-uncertanty-for-calibrated-agb-distribution.tif: raster file representing the estimated spatial uncertanty distribution of the calibrated above-ground biomass density. Unit: percentage. projected-AGB-distribution-under-rcp45.tif: raster file representing the projected spatial distribution of the above-ground biomass density in the Atlantic Forest by the end of 2100 under RCP 4.5 scenario. Unit: Mg/ha estimated-uncertanty-for-projected-agb-distribution.tif: raster file representing the estimated spatial uncertanty distribution of the projected above-ground biomass density. Unit: percentage. Spatial resolution: 0.0083 degree (ca. 1 km) Coordinate reference system: Geographic Coordinate System - Datum WGS84

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.CMIP.MOHC.HadGEM3-GC31-MM.historical' 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 HadGEM3-GC3.1-N216ORCA025 climate model, released in 2016, includes the following components: aerosol: UKCA-GLOMAP-mode, atmos: MetUM-HadGEM3-GA7.1 (N216; 432 x 324 longitude/latitude; 85 levels; top level 85 km), land: JULES-HadGEM3-GL7.1, ocean: NEMO-HadGEM3-GO6.0 (eORCA025 tripolar primarily 0.25 deg; 1440 x 1205 longitude/latitude; 75 levels; top grid cell 0-1 m), seaIce: CICE-HadGEM3-GSI8 (eORCA025 tripolar primarily 0.25 deg; 1440 x 1205 longitude/latitude). The model was run by the Met Office Hadley Centre, Fitzroy Road, Exeter, Devon, EX1 3PB, UK (MOHC) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, land: 100 km, ocean: 25 km, seaIce: 25 km.

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 Challenger cruise 135 in 1997. Billett, D.S.M. et al. (1998). RRS Challenger Cruise 135, 15 Oct-30 Oct 1997. 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. 19, 49pp.| https://www.bodc.ac.uk/resources/inventories/cruise_inventory/reports/ch135_97.pdf