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Data from: 30 Years Brings Changes to the Arthropod Community of Kibale National Park, Uganda by Opito, E.A., T. Alanko, U. Kalbitzer, M. Nummelin, P. Omeja, A. Valtonen, and Colin A. Chapman. 2023, Biotropica, Article DOI: 10.1111/btp.13206

Datasets on the electron- elastic and electron-impact ionization cross sections of N2O Elastic Cross Sections Elastic differential (DCS) as well as integral (ICS) and momentum transfer (MTCS) cross sections are given in the energy range 30-800eV and angular range 20°-150° (experimental), 0°-20° and 155°-180° (extrapolated experimental data using the IAM-SCAR+I model) Ionization Cross Sections Doubly differential electron-impact ionization cross sections (DDCS) are given for primary energies T=30-800 eV and secondary energies up to (T-I)/2, where I is the ionization threshold. The angular range of the measurements is 30°-150°. The singly differential cross sections (SDCS) and total ionization cross sections (TICS) were numerically integrated from the DDCS. Additional Information can be found in the README.

Aim: Climate change threatens freshwater faunal diversity. To prioritize areas for conservation, patterns in the distribution of species must be understood. We apply genetic analysis and species distribution models to identify patterns in the distribution of freshwater amphipods around Xinjiang, China, and project the impact of climate change on endemic species. Location: Xinjiang, China. Methods: A time-calibrated tree containing 37 freshwater amphipod molecular samples from Xinjiang is built to calculate phylogenetic diversity, the standardized effect sizes of phylogenetic diversity, weighted endemism, and phylogenetic endemism, in 100 × 100 km grid cells. Niche differentiation among species in an area of high phylogenetic endemism is explored using n-dimensional hypervolumes and principal components analyses. Present-day and projected future suitability of habitat of endemic freshwater amphipod species is described using species distribution models. Results: Areas of high freshwater amphipod diversity occur along the western boundary of Xinjiang; Areas north of Irtysh River, Tian Shan mountains, and the eastern margin of Pamir, have high phylogenetic endemism. Seasonal temperature and average annual water temperature contribute most to niche differentiation between geographically related freshwater species, negatively affect the projected distributions of endemic amphipods, and with continued warming, reduce future range distributions or latitudinal shifts of species. Main Conclusions: High freshwater amphipod phylogenetic endemism occurs in Xinjiang. Environmental factors are responsible for niche differentiation of endemic species. Future climate change will substantially affect the geographic distributions of endemic amphipods. Conservation efforts should be prioritized in areas with highly concentrated phylogenetic endemism. # Diversity of endemic cold-water amphipods threatened by climate warming in northwestern China [https://doi.org/10.5061/dryad.h44j0zpsg](https://doi.org/10.5061/dryad.h44j0zpsg) Datasets for phylogenetic analysis. ## Description of the data and file structure 1.gene\_partition.txt: Used to explain the position of each gene in a tandem sequence. 2.xinjiang\_28S\_COI.fasta: A file of tandem sequence. 3.RAxML\_xinjiang\_tree.tre: A phylogenetic tree from the 52-tip data set. 4.MCMC\_tree.tre: A time-calibrated tree using three calibration points. ##

This dataset includes input data to estimate learning-by-doing (LbD) and learning-by-researching (LbR) rates for onshore wind and solar PV in the United States. Using different learning curve approaches the simulated technology cost developments are also presented. Coefficient of determination (R square) and Root Mean Square Error (RMSE) were applied for quantification of the agreement between simulated and observed technology costs.

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.ScenarioMIP.DKRZ.MPI-ESM1-2-HR.ssp126' 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-HR climate model, released in 2017, includes the following components: aerosol: none, prescribed MACv2-SP, atmos: ECHAM6.3 (spectral T127; 384 x 192 longitude/latitude; 95 levels; top level 0.01 hPa), land: JSBACH3.20, landIce: none/prescribed, ocean: MPIOM1.63 (tripolar TP04, approximately 0.4deg; 802 x 404 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 Deutsches Klimarechenzentrum, Hamburg 20146, Germany (DKRZ) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, land: 100 km, landIce: none, ocean: 50 km, ocnBgchem: 50 km, seaIce: 50 km.

Climate trends during maize growing period and their impacts on spring maize yield in North China was investigated. This dataset contains: 1) information of stations in cultivation region for spring maize in North China; 2) Trend in temperature and its effect on yield in cultivation region for spring maize in North China; 3) Trend in radiation and its effect on yield in cultivation region for spring maize in North China; 4) Trend in precipitation and its effect on yield in cultivation region for spring maize in North China. Climate trends during maize growing period and their impacts on spring maize yield in North China was investigated. This dataset contains: 1) information of stations in cultivation region for spring maize in North China; 2) Trend in temperature and its effect on yield in cultivation region for spring maize in North China; 3) Trend in radiation and its effect on yield in cultivation region for spring maize in North China; 4) Trend in precipitation and its effect on yield in cultivation region for spring maize in North China.

The zipped file of this repository contains code and data to reproduce the results of the publication: Kotz et al, Nature, The effect of rainfall changes on economic production. (2021). https://doi.org/10.1038/s41586-021-04283-8 Economic data are derived from the DoSE database: https://doi.org/10.5281/zenodo.4681305 Climate data are derived from the ERA-5 reanalysis: https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5 Please see the README document for detailed: - Descriptions of the code and data provided - Lists of the required dependencies - Naming conventions for variables

Comprehensive and reliable information on anthropogenic sources of greenhouse gas emissions is required to track progress towards keeping warming well below 2°C as agreed upon in the Paris Agreement. Here we provide a dataset on anthropogenic GHG emissions 1970-2019 with a broad country and sector coverage. We build the dataset from recent releases from the “Emissions Database for Global Atmospheric Research” (EDGAR) for CO2 emissions from fossil fuel combustion and industry (FFI), CH4 emissions, N2O emissions, and fluorinated gases and use a well-established fast-track method to extend this dataset from 2018 to 2019. We complement this with information on net CO2 emissions from land use, land-use change and forestry (LULUCF) from three available bookkeeping models.