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CMIP6 scenarios' radiative forcing of non-CO2 greenhouse gases and aerosols for UVic ESCM simulations (1850-2500)
<h1>Overview</h1> <p>This repository contains the input files for the UVic Earth System Climate Model (ESCM) that are required to simulate the historical period (1850-2014) and the extended CMIP6 SSP-RCP scenarios SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP4-3.4, SSP4-6.0, SSP5-3.4, SSP5-8.5 (2015-2500).</p> <p>For simulations of these scenarios, the model is forced with aggregated non-CO2 greenhouse gas radiative forcing, land use cover, aerosol radiative forcing, and either CO2 concentration or CO2 emissions. The radiative forcing of CO2 is calculated internally by the UVic ESCM.</p> <p>The following files are included in this repository:</p> <p><strong>CO2 concentrations (for concentration-driven simulations)</strong></p> <p>A_co2_hist.nc</p> <p>A_co2_119.nc</p> <p>A_co2_126.nc</p> <p>A_co2_245.nc</p> <p>A_co2_370.nc</p> <p>A_co2_434.nc</p> <p>A_co2_460.nc</p> <p>A_co2_534.nc</p> <p>A_co2_585.nc</p> <p> </p> <p><strong>CO2 emissions (for emission-driven simulations)</strong></p> <p>F_co2emit_119.nc</p> <p>F_co2emit_126.nc</p> <p>F_co2emit_245.nc</p> <p>F_co2emit_370.nc</p> <p>F_co2emit_434.nc</p> <p>F_co2emit_460.nc</p> <p>F_co2emit_534.nc</p> <p>F_co2emit_585.nc</p> <p> </p> <p><strong>Land use cover fractions (pasture and crops)</strong></p> <p>L_agricfra_hist_and_ssp119.nc</p> <p>L_agricfra_hist_and_ssp126.nc</p> <p>L_agricfra_hist_and_ssp245.nc</p> <p>L_agricfra_hist_and_ssp370.nc</p> <p>L_agricfra_hist_and_ssp434.nc</p> <p>L_agricfra_hist_and_ssp460.nc</p> <p>L_agricfra_hist_and_ssp534.nc</p> <p>L_agricfra_hist_and_ssp585.nc</p> <p> </p> <p><strong>Aggregated non-CO2 greenhouse gas forcing</strong></p> <p>A_aggfor_hist.nc</p> <p>A_aggfor_119.nc</p> <p>A_aggfor_126.nc</p> <p>A_aggfor_245.nc</p> <p>A_aggfor_370.nc</p> <p>A_aggfor_434.nc</p> <p>A_aggfor_460.nc</p> <p>A_aggfor_534.nc</p> <p>A_aggfor_585.nc</p> <p> </p> <p><strong>Aerosol optical depth</strong></p> <p>A_sulphod_hist.nc</p> <p>A_sulphod_119.nc</p> <p>A_sulphod_126.nc</p> <p>A_sulphod_245.nc</p> <p>A_sulphod_370.nc</p> <p>A_sulphod_434.nc</p> <p>A_sulphod_460.nc</p> <p>A_sulphod_534.nc</p> <p>A_sulphod_585.nc</p> <p> </p> <h1>Detailed description</h1> <h2>1. CO2 concentrations</h2> <p>The CO2 concentrations are provided here as the annual global mean mole fraction of CO2 in ppm and identical with the CMIP6 input data available at <a href="https://esgf-node.llnl.gov/search/input4mips/">https://esgf-node.llnl.gov/search/input4mips/</a>.</p> <h2>2. CO2 emissions</h2> <p>The CO2 emissions are the same as provided by RCMIP (Meinshausen et al., 2020). Here the Agriculture, Forestry and Other Land Use (AFOLU) emissions are represented as “F_co2eland” emissions. Also, the sector based emissions from Aircraft, the Industrial Sector, International Shipping, Residential Commercial Other, Solvents Production and Application, the Transportation Sector, and Waste are aggregated into the Fossil and Industrial emissions and represented as “F_co2efuel” emissions. Both the F_co2eland and F_co2efuel emissions are finally aggregated into total CO2 emissions represented as “F_co2emit”. These aggregated CO2 emissions are likewise identical to globally averaged CMIP6 input data available at <a href="https://esgf-node.llnl.gov/search/input4mips/">https://esgf-node.llnl.gov/search/input4mips/</a>. All three CO2 emission variables are included in the “F_co2emit*.nc” files. In addition to the SSP-RCP-scenario CO2 emissions also the historical CO2 emissions are included in all files (starting in year 1750).</p> <h2>3. Land use cover</h2> <p>The land-use forcing is provided as the pasture and cropland grid cell fraction (variable names: “L_cropfra” and “L_pastfra”; in file: “L_agricfra.nc”). The UVic ESCM translates pasture and cropland fractions internally into C3 grass or C4 grass fractions, depending on the local conditions. The land-use cover is based on LUH2v2f “states.nc” data (available at <a href="https://luh.umd.edu/data.shtml">https://luh.umd.edu/data.shtml</a>) and has been regridded and reaggregated for the UVic ESCM. The cropland fraction of the UVic ESCM input (“L_cropfra”) is the sum of all crop types given by LUH2v2f (“c3ann”, “c3nfxc”, “c3per”, “c4ann”, “C4per”), whereas the pasture fraction (“L_pastfra”) is the sum of LUH2v2f’s pasture fraction and rangeland fraction (“pastr”, “range”). The land-use forcing covers the period 850-2100.</p> <h2>4. Non-CO2 greenhouse gas radiative forcing</h2> <p>The aggregated radiative forcing of 44 non-CO2 greenhouse gases (GHG) was calculated from the respective atmospheric GHG concentrations (provided by RCMIP for CMIP6, see References), following the approach of Meinshausen et al. 2020 and Etminan et al. 2016. Radiative forcing of tropospheric ozone, stratospheric ozone, and stratospheric water vapor from methane oxidation was calculated as described in Smith et al. 2018.</p> <p>The following non-CO2 GHG are accounted for in the aggregated forcing files (“A_aggfor.nc”):</p> <p>N2O; CH4; CFC11; CFC12; HFC134a; C2F6; C6F14; CF4; HFC23; HFC32; HFC43_10; HFC125; HFC143a; HFC227ea; HFC245fa; SF6; CFC113; CFC114; CFC115; HCFC22; HCFC142B; HCFC141B; HALON1211; HALON1301; HALON2402; CH3BR; CH3CL; CCL4; CH2CL2; CH3CCL3; NF3; HFC365mfc; C3F8; C4F10; HFC236fa; C5F12; CHCL3; cC4F8; HFC152a; SO2F2; C7F16; C8F18; stratospheric and tropospheric O3; water vapor from CH4 oxidation.</p> <h2>5. Aerosol radiative forcing</h2> <p>Aerosol optical depth (AOD) 2D input data for the UVic ESCM was created using a UVic grid with the scripts and data provided by Stevens et al. (2017). The data provided describes nine different plumes globally which are scaled with time to produce monthly aerosol optical depth forcing for the years 1850-2018 (Stevens et al., 2017). For the future projection of the years 2018-2100, the same scripts were run with input data from Fiedler et al. (2019). To extend aerosol optical depth data from 2100 to 2500, the last year of available data (i.e. 2100) was repeated. </p> <p>Since the AOD input caused too great a negative forcing in the historical period, a scaling factor was implemented into the UVic ESCM, which allows to scale aerosol forcing from AOD data. The scaling factor was set to 0.7, which gives a globally averaged forcing of -1.03 Wm<sup>-2</sup> in 2011.</p> <p>Note that the file "A_sulphod_hist.nc" contains not only the data of the historical period (1850-2014) but also the data of the scenario SSP5-8.5 (extended until 2500).</p> <p> </p> <h2>References</h2> <p>Fiedler, S., Stevens, B., Gidden, M., Smith, S. J., Riahi, K., & van Vuuren, D. (2019). First forcing estimates from the future CMIP6 scenarios of anthropogenic aerosol optical properties and an associated Twomey effect. <em>Geoscientific Model Development</em>, <em>12</em>(3), 989-1007.Etminan, M., Myhre, G., Highwood, E., and Shine, K.: Radiative forcing of carbon dioxide, methane, and nitrous oxide: A significant revision of the methane radiative forcing, Geophys. Res. Lett., 43, 12614–12623,<a href="https://doi.org/10.1002/2016GL071930"> </a><a href="https://doi.org/10.1002/2016GL071930">https://doi.org/10.1002/2016GL071930</a>, 2016.</p> <p>Meinshausen, M., Nicholls, Z. R., Lewis, J., Gidden, M. J., Vogel, E., Freund, M., ... & Wang, R. H. (2020). The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500. <em>Geoscientific Model Development</em>, <em>13</em>(8), 3571-3605.</p> <p>Smith, C. J., Forster, P. M., Allen, M., Leach, N., Millar, R. J., Passerello, G. A., & Regayre, L. A. (2018). FAIR v1. 3: a simple emissions-based impulse response and carbon cycle model. <em>Geoscientific Model Development</em>, <em>11</em>(6), 2273-2297.</p> <p>Stevens, B., Fiedler, S., Kinne, S., Peters, K., Rast, S., Müsse, J., Smith, S. J., and Mauritsen, T.: MACv2-SP: a parameterization of anthropogenic aerosol optical properties and an associated Twomey effect for use in CMIP6, Geosci. Model Dev., 10, 433-452, https://doi.org/10.5194/gmd-10-433-2017, 2017</p> <p>RCMIP GHG concentration data:<a href="../record/4589756/files/rcmip-concentrations-annual-means-v5-1-0.csv"> </a><a href="../record/4589756/files/rcmip-concentrations-annual-means-v5-1-0.csv">https://zenodo.org/record/4589756/files/rcmip-concentrations-annual-means-v5-1-0.csv</a></p> <p>RCMIP Emissions data:</p> <p><a href="https://rcmip-protocols-au.s3-ap-southeast-2.amazonaws.com/v5.1.0/rcmip-emissions-annual-means-v5-1-0.csv">https://rcmip-protocols-au.s3-ap-southeast-2.amazonaws.com/v5.1.0/rcmip-emissions-annual-means-v5-1-0.csv</a></p> <p>Input4mips CO2 concentration data: <a href="https://esgf-node.llnl.gov/search/input4mips/">https://esgf-node.llnl.gov/search/input4mips/</a></p> <p>LUH2 land-use cover data: <a href="https://luh.umd.edu/data.shtml">https://luh.umd.edu/data.shtml</a></p>
Supplementary data to research paper "Linking local climate scenarios to global warming levels: Applicability, prospects and uncertainties"
<p>This file contains supplementary data for the research paper "<span>Linking local climate scenarios to global warming levels: Applicability, prospects and uncertainties"", submitted to IOP Publishing Environmental Research: Climate. The DOI and link to the paper will be added once it is published.</span></p> <p><span>Each file lists the annual mean temperature anomalies relative to the period 1991-2020 for a model of the Austrian climate scenarios OEKS15 (Leuprecht, 2018). The full dataset is available here: https://data.hub.geosphere.at/dataset/oks15_bias_corrected<br></span></p> <p> </p> <div> <div>Leuprecht, A. (2018). <em>ÖKS15 Bias Corrected EURO-CORDEX Model Precipitaion, Radiation, Temperature</em> [dataset]. <a href="https://doi.org/10.60669/B37Q-JD39">https://doi.org/10.60669/B37Q-JD39</a></div> </div>
Figure 2 in Hyperparasitism among larval stages of Digenea in snail hosts: sophisticated life strategy or pure randomness? The scenario of Cotylurus sp.
Figure 2. The mean intensity of tetracotyle metacercariae in snail hosts infected or not infected with sporocysts/rediae.
Figure 3 in Hyperparasitism among larval stages of Digenea in snail hosts: sophisticated life strategy or pure randomness? The scenario of Cotylurus sp.
Figure 3. The mean intensity of tetracotyle metacercariae in relationship to presence or absence of hyperparasitism in the snail host.
Networks files of main scenarios analysed in "Distributed photovoltaics provides key benefits for a highly renewable European energy system"
<p>This repository contains the network files (.nc) of the main scenarios (A, B, C, and D) used for analysis in the paper. The code for reproducing these files plus other network files used for sensitivity analysis plus the Jupyter notebooks used for creating all the figures in the paper are available at: https://github.com/Parisra/Distributed-PV-paper </p>
The BAU Scenario Clic Sand , R.E Scenario Clic Sand and an integrated BAU and R.E Visualization Results for Energy Policy for Uganda Using OSeMOSYS
<p>Business As Usual (BAU) Scenario , Renewable Energy (R.E) Scenario Clic Sand are used as data sets to run the model. The integrated BAU and R.E. visualization result templates contain the findings of the models. For instance, power generation graphs, installed energy capacity graphs, annual capital cost graphs, annual carbon dioxide emissions graphs , capital cost and any other parameters of interest related to the model can be found in the result visualization template.</p>
Predicting Potential Distribution of Ageratina adenophora (Spreng.) King and H. Rob. in Shivapuri Nagarjun National Park, Nepal under Climate Change Scenarios
<p>Data used in the paper</p>
Outputs from fitted models across the cross-validation scenarios for 'Space-time species distribution modeling with opportunistic presence-only data: a case study of passerines in a protected area'
<p>Three Zenodo repositories are linked to the preprint <em>Space-time Species Distribution Modeling for Opportunistic Presence-Only Data: A Case Study of Passerines in a Protected Area </em>(Lasgorceux et al., unpublished, <a href="https://hal.science/hal-04616332">https://hal.science/hal-04616332</a>):</p> <ul> <li>Data, scripts and, code (Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12545052">https://doi.org/10.5281/zenodo.12545052</a>)</li> <li>Outputs from fitted models across the cross-validation scenarios (Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12544212">https://doi.org/10.5281/zenodo.12544212</a>)</li> <li>Supplementary information at (Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12541412">https://doi.org/10.5281/zenodo.12541412</a>)</li> </ul> <p>This repository contains the outputs from fitted models across the cross-validation scenarios.</p> <p>In the folder <em>Ouputs_cross_validation</em>, each species is represented by a .RData file, numbered from 1 to 77 (excluding 7, which corresponds to <em>Bombycilla garrulus</em>; see the preprint for details). This dataset is specifically used to generate Figure 1, which shows the AUC of various cross-validation scenarios. To reproduce this figure in R, place all the files in the <em>Results/Fitted_models</em> folder and run the <em>Models_Outputs.R</em> script located in the <em>Results</em> folder of Lasgorceux et al., Zenodo, <a href="https://doi.org/10.5281/zenodo.12545052">https://doi.org/10.5281/zenodo.12545052.</a></p> <p>Note: These data have been separated due to memory requirements (23.14GB).</p>
Supplementary material - Blind comparison of binaural auralisations to a real loudspeaker in an audiovisual virtual classroom scenario: Effect of room acoustic simulation, HRTF dataset and head worn devices on rated room-acoustical attributes
<p>Additional Material for the paper "Blind comparison of binaural auralisations to a real loudspeaker in an audiovisual virtual classroom scenario: Effect of room acoustic simulation, HRTF dataset and head worn devices on rated room-acoustical attributes".</p> <p> </p> <p>This work is funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) under the project ID 422686707, SPP2236 – AUDICTIVE – Auditory Cognition in Interactive Virtual Environments</p>
Observing scenarios simulations for HLVK-Configuration for O4 Runs, using 20 million injections. This simulation led to 17,009 BNS useful for training Parameter Estimations of EM counterparts of GW. (July 2024 edition).
<p>We have conducted a simulation of the HLVK-configuration deployed during the ongoing O4 run. This project supports the training of kilonova regression with machine learning processes, requiring thousands of BNS to pass the threshold cutoff. Here we have 17,009 BNS passing the SNR threshold, along with 3,148 NSBH and 121,718 BBH, from 20 million CBCs injected. The upper-lower limit between NS and BH is 3 sun masses.</p> <p>Due to the large file sizes, we have split them into three parts and uploaded them to Zenodo with the following DOIs:<br><br></p> <ol> <li><strong>The first files is located :</strong> <code>runs_part_aa</code> and <code>runs_part_ab</code> : <a title="https://zenodo.org/doi/10.5281/zenodo.12693652" href="../doi/10.5281/zenodo.12693652">https://zenodo.org/doi/10.5281/zenodo.12693652</a></li> <li><strong>The second files is located : </strong><code>runs_part_ac</code> and <code>runs_part_ad</code> : <a href="../doi/10.5281/zenodo.12694779">https://zenodo.org/doi/10.5281/zenodo.12694779</a></li> <li><strong>The third files is located : </strong><code>runs_part_ae</code> and <code>runs_part_af</code> : <a href="../doi/10.5281/zenodo.12696695">https://zenodo.org/doi/10.5281/zenodo.12696695</a></li> </ol> <blockquote> <p>Download them or use this Python script from GitHub to download all of them by running the script: </p> <p> <a href="https://github.com/weizmannk/ObservingScenariosInsights/blob/main/chunk-xml/zenodo-process/download_split_chunk_data.py">hchunk-files-downloader</a>.</p> </blockquote> <p>After downloading them (6 files ), you will need to combine them in a single file using the following process:</p> <blockquote> <p>1.Combine the parts:<br><code>cat runs_part_* > runs.zip</code></p> </blockquote> <blockquote> <p>2.Verify the combined file:<br><code>ls -lh runs.zip</code><br><code>file runs.zip</code></p> </blockquote> <blockquote> <p>3.Unzip the combined file:<br><code>unzip runs.zip</code></p> </blockquote> <p> </p> <p>In the <code>runs</code> folder, we have three subfolders:</p> <ul> <li><code>O4</code>: This contains the <code>.fits</code> files for skymap localization and all GW parameters of the CBCs that passed the threshold cutoff of 8.</li> <li><code>statistics_results</code>: This contains the summary results of the statistical predictions of GW detections.</li> <li><code>subpopulations</code>: This folder is the split of BNS, NSBH, and BBH events. It is useful for those who need quick parameters of BNS and NSBH for their lightcurve simulations or EM counterpart statistical estimation.</li> </ul> <p> </p> <p>For more information, visit: <a href="https://github.com/lpsinger/observing-scenarios-simulations" target="_new" rel="noreferrer">Observing Scenarios Simulations</a></p> <p>Contact: <a rel="noreferrer">weizmann.kiendrebeogo@oca.eu</a> or <a rel="noreferrer">kiend.weizman7@gmail.com</a></p>
Planetary boundaries framework scenarios
<p>The planetary boundaries framework serves as a comprehensive method to define a safe operating space for humanity on Earth. This set of scenarios projects eight out of nine planetary boundaries processes under different scenarios to 2050, both with and without strong environmental policy response strategies.</p>
Priority areas for boreal songbird conservation in Canada: Results for 128 scenarios based on Zonation conservation planning software
<p>Priority areas for boreal songbird conservation in Canada: Results for 128 scenarios based on Zonation conservation planning software</p> <p>Published in:<br> Stralberg, D., A. Camfield, M. Carlson, C. Lauzon, N. K. S. Barker, A. Westwood, and F. K. A. Schmiegelow. in press. Strategies for identifying priority areas for songbird conservation in Canada's boreal forest. Avian Conservation and Ecology.</p> <p>Scenarios: <br> -----------------<br> # Name<br> 1 Representation<br> 2 Representation + Disturbance<br> 3 Representation + BCR Strata<br> 4 Representation + BCR Strata + Disturbance<br> 5 Representation + Forest Birds<br> 6 Representation + Disturbance + Forest Birds<br> 7 Representation + BCR Strata + Forest Birds <br> 8 Representation + BCR Strata + Disturbance + Forest Birds<br> 9 Representation + Conservation Status<br> 10 Representation + Disturbance + Conservation Status<br> 11 Representation + BCR Strata + Conservation Status<br> 12 Representation + BCR Strata + Disturbance + Conservation Status<br> 13 Representation + Forest Birds + Conservation Status<br> 14 Representation + Disturbance + Forest Birds + Conservation Status<br> 15 Representation + BCR Strata + Forest Birds + Conservation Status<br> 16 Representation + BCR Strata + Disturbance + Forest Birds + Conservation Status<br> 17 Representation + Current Uncertainty<br> 18 Representation + Current Uncertainty + Disturbance<br> 19 Representation + Current Uncertainty + BCR Strata<br> 20 Representation + Current Uncertainty + BCR Strata + Disturbance<br> 21 Representation + Current Uncertainty + Forest Birds<br> 22 Representation + Current Uncertainty + Disturbance + Forest Birds<br> 23 Representation + Current Uncertainty + BCR Strata + Forest Birds<br> 24 Representation + Current Uncertainty + BCR Strata + Disturbance + Forest Birds<br> 25 Representation + Current Uncertainty + Conservation Status<br> 26 Representation + Current Uncertainty + Disturbance + Conservation Status<br> 27 Representation + Current Uncertainty + BCR Strata + Conservation Status<br> 28 Representation + Current Uncertainty + BCR Strata + Disturbance + Conservation Status<br> 29 Representation + Current Uncertainty + Forest Birds + Conservation Status<br> 30 Representation + Current Uncertainty + Disturbance + Forest Birds + Conservation Status<br> 31 Representation + Current Uncertainty + BCR Strata + Forest Birds + Conservation Status<br> 32 Representation + Current Uncertainty + BCR Strata + Disturbance + Forest Birds + Conservation Status<br> 33 Representation + Current/Future Uncertainty<br> 34 Representation + Current/Future Uncertainty + Disturbance<br> 35 Representation + Current/Future Uncertainty + BCR Strata<br> 36 Representation + Current/Future Uncertainty + BCR Strata + Disturbance<br> 37 Representation + Current/Future Uncertainty + Forest Birds<br> 38 Representation + Current/Future Uncertainty + Disturbance + Forest Birds<br> 39 Representation + Current/Future Uncertainty + BCR Strata + Forest Birds<br> 40 Representation + Current/Future Uncertainty + BCR Strata + Disturbance + Forest Birds<br> 41 Representation + Current/Future Uncertainty + Conservation Status<br> 42 Representation + Current/Future Uncertainty + Disturbance + Conservation Status<br> 43 Representation + Current/Future Uncertainty + BCR Strata + Conservation Status<br> 44 Representation + Current/Future Uncertainty + BCR Strata + Disturbance + Conservation Status<br> 45 Representation + Current/Future Uncertainty + Forest Birds + Conservation Status<br> 46 Representation + Current/Future Uncertainty + Disturbance + Forest Birds + Conservation Status<br> 47 Representation + Current/Future Uncertainty + BCR Strata + Forest Birds + Conservation Status<br> 48 Representation + Current/Future Uncertainty + BCR Strata + Disturbance + Forest Birds + Conservation Status<br> 49 Representation + Future Uncertainty<br> 50 Representation + Future Uncertainty + Disturbance<br> 51 Representation + Future Uncertainty + BCR Strata<br> 52 Representation + Future Uncertainty + BCR Strata + Disturbance<br> 53 Representation + Future Uncertainty + Forest Birds<br> 54 Representation + Future Uncertainty + Disturbance + Forest Birds<br> 55 Representation + Future Uncertainty + BCR Strata + Forest Birds<br> 56 Representation + Future Uncertainty + BCR Strata + Disturbance + Forest Birds<br> 57 Representation + Future Uncertainty + Conservation Status<br> 58 Representation + Future Uncertainty + Disturbance + Conservation Status<br> 59 Representation + Future Uncertainty + BCR Strata + Conservation Status<br> 60 Representation + Future Uncertainty + BCR Strata + Disturbance + Conservation Status<br> 61 Representation + Future Uncertainty + Forest Birds + Conservation Status<br> 62 Representation + Future Uncertainty + Disturbance + Forest Birds + Conservation Status<br> 63 Representation + Future Uncertainty + BCR Strata + Forest Birds + Conservation Status<br> 64 Representation + Future Uncertainty + BCR Strata + Disturbance + Forest Birds + Conservation Status<br> 65 Diversity<br> 66 Diversity + Disturbance<br> 67 Diversity + BCR Strata<br> 68 Diversity + BCR Strata + Disturbance<br> 69 Diversity + Forest Birds<br> 70 Diversity + Disturbance + Forest Birds<br> 71 Diversity + BCR Strata + Forest Birds <br> 72 Diversity + BCR Strata + Disturbance + Forest Birds<br> 73 Diversity + Conservation Status<br> 74 Diversity + Disturbance + Conservation Status<br> 75 Diversity + BCR Strata + Conservation Status<br> 76 Diversity + BCR Strata + Disturbance + Conservation Status<br> 77 Forest Birds + Conservation Status<br> 78 Disturbance + Forest Birds + Conservation Status<br> 79 BCR Strata + Forest Birds + Conservation Status<br> 80 BCR Strata + Disturbance + Forest Birds + Conservation Status<br> 81 Diversity + Current Uncertainty<br> 82 Diversity + Current Uncertainty + Disturbance<br> 83 Diversity + Current Uncertainty + BCR Strata<br> 84 Diversity + Current Uncertainty + BCR Strata + Disturbance<br> 85 Diversity + Current Uncertainty + Forest Birds<br> 86 Diversity + Current Uncertainty + Disturbance + Forest Birds<br> 87 Diversity + Current Uncertainty + BCR Strata + Forest Birds<br> 88 Diversity + Current Uncertainty + BCR Strata + Disturbance + Forest Birds<br> 89 Diversity + Current Uncertainty + Conservation Status<br> 90 Diversity + Current Uncertainty + Disturbance + Conservation Status<br> 91 Diversity + Current Uncertainty + BCR Strata + Conservation Status<br> 92 Diversity + Current Uncertainty + BCR Strata + Disturbance + Conservation Status<br> 93 Diversity + Current Uncertainty + Forest Birds + Conservation Status<br> 94 Diversity + Current Uncertainty + Disturbance + Forest Birds + Conservation Status<br> 95 Diversity + Current Uncertainty + BCR Strata + Forest Birds + Conservation Status<br> 96 Diversity + Current Uncertainty + BCR Strata + Disturbance + Forest Birds + Conservation Status<br> 97 Diversity + Current/Future Uncertainty<br> 98 Diversity + Current/Future Uncertainty + Disturbance<br> 99 Diversity + Current/Future Uncertainty + BCR Strata<br> 100 Diversity + Current/Future Uncertainty + BCR Strata + Disturbance<br> 101 Diversity + Current/Future Uncertainty + Forest Birds<br> 102 Diversity + Current/Future Uncertainty + Disturbance + Forest Birds<br> 103 Diversity + Current/Future Uncertainty + BCR Strata + Forest Birds<br> 104 Diversity + Current/Future Uncertainty + BCR Strata + Disturbance + Forest Birds<br> 105 Diversity + Current/Future Uncertainty + Conservation Status<br> 106 Diversity + Current/Future Uncertainty + Disturbance + Conservation Status<br> 107 Diversity + Current/Future Uncertainty + BCR Strata + Conservation Status<br> 108 Diversity + Current/Future Uncertainty + BCR Strata + Disturbance + Conservation Status<br> 109 Diversity + Current/Future Uncertainty + Forest Birds + Conservation Status<br> 110 Diversity + Current/Future Uncertainty + Disturbance + Forest Birds + Conservation Status<br> 111 Diversity + Current/Future Uncertainty + BCR Strata + Forest Birds + Conservation Status<br> 112 Diversity + Current/Future Uncertainty + BCR Strata + Disturbance + Forest Birds + Conservation Status<br> 113 Diversity + Future Uncertainty<br> 114 Diversity + Future Uncertainty + Disturbance<br> 115 Diversity + Future Uncertainty + BCR Strata<br> 116 Diversity + Future Uncertainty + BCR Strata + Disturbance<br> 117 Diversity + Future Uncertainty + Forest Birds<br> 118 Diversity + Future Uncertainty + Disturbance + Forest Birds<br> 119 Diversity + Future Uncertainty + BCR Strata + Forest Birds<br> 120 Diversity + Future Uncertainty + BCR Strata + Disturbance + Forest Birds<br> 121 Diversity + Future Uncertainty + Conservation Status<br> 122 Diversity + Future Uncertainty + Disturbance + Conservation Status<br> 123 Diversity + Future Uncertainty + BCR Strata + Conservation Status<br> 124 Diversity + Future Uncertainty + BCR Strata + Disturbance + Conservation Status<br> 125 Diversity + Future Uncertainty + Forest Birds + Conservation Status<br> 126 Diversity + Future Uncertainty + Disturbance + Forest Birds + Conservation Status<br> 127 Diversity + Future Uncertainty + BCR Strata + Forest Birds + Conservation Status<br> 128 Diversity + Future Uncertainty + BCR Strata + Disturbance + Forest Birds + Conservation Status</p> <p><br> Projection information<br> -------------------<br> "+proj=lcc +lat_1=49 +lat_2=77 +lat_0=0 +lon_0=-95 +x_0=0 +y_0=0 +ellps=GRS80 +units=m +no_defs"<br> -------------------<br> Projection LAMBERT<br> Spheroid GRS80<br> Units METERS<br> Zunits NO<br> Xshift 0.0<br> Yshift 0.0<br> Parameters <br> 49 0 0.0 /* 1st standard parallel<br> 77 0 0.0 /* 2nd standard parallel<br> -95 0 0.0 /* central meridian<br> 0 0 0.0 /* latitude of projection's origin<br> 0.0 /* false easting (meters)<br> 0.0 /* false northing (meters)</p>
Plane-wave propagation path data from wideband MIMO channel sounding in an urban microcellular scenario
<p>We provide plane-wave propagation path data from a wideband MIMO radio channel sounding measurement in an urban microcell scenario. The binary Matlab file includes: direction of departure (DOD: variables "par.PhiTx" and "par.ThetaTx" in [rad]), direction of arrival (DOA: "par.PhiRx" and "par.ThetaRx" in [rad]), delay ("par.Tau" to be multiplied with 8.3ns, the tab length), and complex polarimetric path gain ("par.Alpha" is a 2x2 matrix, where element [1,1]=TXtheta ->RXtheta, [1,2]=TXphi ->RXtheta, [2,1]=TXtheta->RXphi, and [2,2]=TXphi->RXphi), for the 30 strongest signal paths (from TX to RX) at each of the 4574 RX locations along the route described below. In the element names above, the term "theta" refers to the vertically polarised component, and accordingly the term "phi" referes to the horizontally polarised component.<br> Note #1: The exact RX location for each individual measured radio channel was NOT recorded (see route description below). <br> Note #2: The complex path gain (par.Alpha) is NOT calibrated, but depends on the initially fixed AGC level in the receiver, which was chosen to provide the best dynamic range for the given mobile (RX) route.<br> Both these limitations are seen reasonable since this dataset is meant for the realistic *statistical comparison* of the performance of different RX antennas in a microcell environment (and not to determine the actual received power at each exact location of the measured route).<br> Background information: The provided dataset is processed and is based on a radio channel sounder measurement at 5.3 GHz, carried out in downtown Helsinki, Finland, in April 2004. The uniform rectangular transmit (TX) array was placed at 10 m height in Aleksanterinkatu-street (an approx. 15-m wide street canyon), in front of the Nordea building, broadside pointing westwards (towards Stockmann building). The semishperical receive (RX) array was moved at 1.6-m height and for about 50 m along Aleksanterinkatu-street in line-of-sight (LOS), i.e. from in front of Kluuvi shopping centre westwards just across the crossing of Kluuvikatu-street. The TX and RX arrays cover the relevant azimuth and elevation ranges, so that this plane wave propagation path data can directly be combined with the polarimetric directional radiation pattern(s) of an antenna (array).</p>
ANGUS II Scenarios Raw Data
<p>Raw data for ANGUS II project.</p>
Carp yield projections based on climatic scenarios
<p>Carp yield projections based on managerial and climatic scenarios. "Debrecen" and "Szeged" are Hungarian cities, representing Northern and the Southern regions of the Great Plain, Hungary.</p>
Fig. 3 in Cryptic diversity, sympatry, and other integrative taxonomy scenarios in the Mexican Ceratozamia miqueliana complex (Zamiaceae)
Fig. 3 Leaflet variation at the population level. a Ceratozamia becerrae, La Pila. b Cerro Madrigal. c C. zoquorum, Cerro Blanco. d Agustín Rubio 1. e Agustín Rubio 2. f C. santillanii. g C. subroseophylla (variation in all populations). h C. miqueliana (variation in all populations). i C. euryphyllidia (variation in all populations)
Fig. 1 in Cryptic diversity, sympatry, and other integrative taxonomy scenarios in the Mexican Ceratozamia miqueliana complex (Zamiaceae)
Fig. 1 Distribution map of the species; inset: points 1 The Los Tuxtlas mountain range, 2 Uxpanapa-Chimalapas region, 3 Northern mountain range of Chiapas. The type localities for the species are represented by large figures
FIG. 6 in Deciphering Geographical Affinity and Reconstructing Invasion Scenarios of Boa imperator on the Caribbean Island of Cozumel
FIG. 6. Bayesian clustering for two main continental clades of Boa imperator (Gulf of Mexico and Yucatán Peninsula) and the Cozumel population using STRUCTURE (Pritchard et al., 2000), based on the resulting K ¼ 3 for (A) microsatellite loci and (B) cytochrome b haplotypes. Maps show spatial interpolations of individual assignment into the three genetic clusters obtained with (C) microsatellites and (D) cyt b. Circles indicate individuals from the Gulf of Mexico clade (n ¼ 30), triangles from the Yucatán Peninsula clade (n ¼ 54), and diamonds from Cozumel (n ¼ 16).
FIG. 2 in Deciphering Geographical Affinity and Reconstructing Invasion Scenarios of Boa imperator on the Caribbean Island of Cozumel
FIG. 2. Graphical representation of the demographical reconstruction based on ABC analyses implemented in DIYABC (Cournet et al., 2014) considering different molecular datasets and priors schemes (see text for a detailed explanation).
FIG. 3 in Deciphering Geographical Affinity and Reconstructing Invasion Scenarios of Boa imperator on the Caribbean Island of Cozumel
FIG. 3. (A) Bayesian maximum clade credibility tree based on 220 unique cytochrome b haplotypes for the genus Boa inferred with BEAST (Drummond et al., 2012). Black dots represent the main clades obtained. Posterior probabilities and bootstrap support values are shown above and below the diagonal, respectively. (B) Rooted network based on the same 220 unique cyt b haplotypes, inferred with SPLITSTREE (Huson and Bryant, 2006). Arrows indicate the position of the Cozumel unique haplotypes within Boa imperator. See Data Accessibility for tree file.
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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