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Data for Ecological Niche Models: Global-scale Environmental parameters at 0.1° and 0.5° resolutions, Presence and Absence Records of 1508 European-seas Species.

This project systematically reviewed the literature for measurements of aboveground carbon stocks in monoculture plantation forests. The data compiled here are for monoculture (single-species) plantation forests, which are a subset of a broader review to identify empirical measurements of carbon stocks across all forest types. The database is structured similarly to that of the ForC (https://forc-db.github.io/) and GROA databases (https://github.com/forc-db/GROA). When using these data, please cite: Bukoski, J.J., Cook-Patton, S.C., Melikov, C., Ban, H., Liu, J.C., Harris, N., Goldman, E., and Potts, M.D. 2022. Rates and drivers of aboveground carbon accumulation in global monoculture plantation forests. Nature Communications 13(4206). doi: 10.1038/s41467-022-31380-7 The code for all analyses in Bukoski et al., 2022 (paper associated with this dataset) is available at https://github.com/jbukoski/GPFC (doi: 10.5281/zenodo.6588710).

10-year return levels of precipitation for hourly, 3-hourly, 6-hourly, 12-hourly and 24-hourly durations are calculated based on the 50-member regional climate model (RCM) large ensemble CRCM5-LE at a spatial resolution of 0.11° (~ 12.5km). The return levels thereby provide estimations for areal extreme precipitation for the time periods of near future (NF: 2010-2039), mid future (MF: 2040-2069), and far future (FF: 2070-2099) under the emission scenario RCP 8.5. If compared to in-situ observations, areal reduction factors have to be applied. The return levels are derived for each of the 50 RCM members separately, which induces a range of 50 return level values at each grid cell. The data are stored as txt-files featuring comma separated values in order to ensure an easy access, also for non-scientific applications. The data set includes the median value as well as the 5%- and 95%-quantiles. Additionally, the geographic coordinates (WGS84) are given. Hence, data can be processed via spreadsheet software, GIS applications and scientific program code. More details in: https://doi.org/10.1088/1748-9326/ac0849 The 10-year return levels for historical climate conditions are available at https://doi.org/10.5281/zenodo.3878888 and described in https://doi.org/10.5194/essd-2020-145

We have gathered data on the power generation of seven different PV modules from three demonstration sites in Oslo, Touzer, and Sevilla for a comprehensive analysis. This data was sourced from TIGO cloud for the PV modules and Solcast, an open-source platform, for historical weather information. The data set is spanning from May 2021 to November 2023. These datasets are characterized by high-resolution recordings taken every 5 minutes.

{"references": ["UNSD Demographic Statistics, available at http://data.un.org", "The World Bank GDP data, available at https://data.worldbank.org/", "UNFCCC: Greenhouse Gas Inventory Data, available at https://unfccc.int/process/transparency-and-reporting/greenhouse-gas-data/what-is-greenhouse-gas-data"]} Dataset containing all greenhouse gas emissions data submitted by countries under climate change convention (including CRF data) as published by the UNFCCC secretariat at 2021-12-03. The dataset is also available via datalad. To obtain the dataset with datalad, see the instructions at https://github.com/mikapfl/unfccc_di_data .

This repository contains the post-processed model outputs underlying the main figures in the paper "Climate change and terrigenous inputs decrease the efficiency of the future Arctic Ocean’s biological carbon pump" by Oziel et al. in Nature Climate Change. The repository also contains the jupyter notebooks (python) scripts used to produce the figures, the custom model code as well as the mesh informations to reproduce the model run.

The HANZE dataset covers riverine, pluvial, coastal and compound floods that have occurred in 42 European countries. It contains: 2521 historical floods with impact data (1870-2020); 237 further historical floods with significant impacts, but without precise impact data (1950-2020) Nearly 15,000 modelled floods with a potential to cause significant impacts, classified by actual historical occurrence or non-occurrence impacts (1950-2020). Historical floods and the classification of modelled floods was completed by extensive data-collection from more than 900 sources ranging from news reports through government databases to scientific papers. Impact data collected or modelled include area inundated, fatalities, persons affected or economic loss. Economic losses were inflation- and exchange-rate adjusted to 2020 value of the euro. The historical catalogue (lsit A) also includes losses in the original currencies and price levels. The spatial footprint of affected areas is consistently recorded using more than 1400 subnational units corresponding, with minor exceptions, to the European Union’s Nomenclature of Territorial Units for Statistics (NUTS), level 3. Apart from the possibility to download the data, the database can be viewed, filtered and visualized online: https://naturalhazards.eu. The dataset contains the following files (CSV comma-delimited, UTF8, and ESRI shapefiles in zipped folders): HANZE_historical_floods_catalogue_listA.csv - historical floods with impact data (1870-2020) HANZE_historical_floods_catalogue_listB.csv - historical floods without impact data (1950-2020) HANZE_potential_flood_catalogue_all.csv - modelled potential floods (1950-2020) HANZE_list_of_references.csv - List of all references used in the catalogues HANZE_model_completness_analysis.csv - Comparison between modelled and reported footprints of historical floods Regions_v2010_simplified.zip - Map of subnational regions (v2010) Regions_v2021_simplified.zip - Map of subnational regions (regions v2021) v1.1: errors in two records in "HANZE_historical_floods_catalogue_listB.csv" (wrong country code in event ID 8227 and wrong start date in event ID 8237) were corrected. This work was supported by the German Research Foundation (DFG) through project "Decomposition of flood losses by environmental and economic drivers" (FloodDrivers), project no. 449175973 

For more detailed information, one can visit 10.1039/D4TC00466C Sanjay Sahare thanks project No. 2021/43/P/ST3/02599 co-funded by the National Science Centre and the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska–Curie grant agreement no. 945339.