• Energy Research
• 2016-2025close
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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

Log and boot files of game 77

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 .

The data contains three supporting datasets: 1. Mid-intertidal data 2. Vertical transect data 3. GPS coordinates for all sites

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.