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Code and data for results and figures of the manuscript "Multi-million year cycles in modelled δ13C as a response to astronomical forcing of organic matter fluxes." submitted to Earth System Dynamics
<p>This dataset contains the code of the model used in the manuscript submitted to Earth System Dynamics "Multi-million year cycles in modelled δ13C as a response to astronomical forcing of organic matter fluxes.". It also contains some model outputs and code to draw the figures.</p>
Data set for "The effect of pulling and twisting forces on chameleon sequence peptides"
<p>Data set for the publication "The effect of pulling and twisting forces on chameleon sequence peptides"<br> <br> The data contains all energy landscapes explored, structures extracted and analysed, and the necessary utility scripts, written by James Meadows. The README details which data is in each directory. The image directory contains the disconnectivity graphs associated with this data.</p>
Tectonic forcing of the extreme aridification of the East Asian interior at around 900 ka-insights from the spatially inconsistent magnetization of Chinese loess
<p>Paleomagnetic and rock magnetic data of loess unit L9 from the Chinese Loess Plateau</p>
Model simulation data used in "An inconsistency in aviation emissions between CMIP5 and CMIP6 and the implications for short-lived species and their radiative forcing" (Thor et al., GMD, 2022)
<p>This archive contains files that were used to produce the results published in the article "An inconsistency in aviation emissions between CMIP5 and CMIP6 and the implications for short-lived species and their radiative forcing" by Thor et al.</p> <p>The directory nml contains namelist setups (configuration files) used for each of the simulations that were performed for this study.<br> The used MESSy version is d2.54.0.3-pre2.55-02-2077-g6eca90858-dirty_6eca90858ecb4ee8fb8900681a94a79ac3d612af_2021-01-26T11:10:08+01:00_2021-02-18T09:14:01+0100 for the QCTM simulations and d2.54.0.3-pre2.55-02-1466-g1fb086944_1fb0869442bf4f1bb10a7dd5f36e4bde5c0cf6d7_2020-10-27T18:17:50+01:00_2020-10-27T18:24:16+0100 for the aerosol simulations (http://www.messy-interface.org).</p> <p>The directory figures contains ipython scripts that were used to produce the figures in the paper.</p>
POPG lipid bilayer simulation at T298K ran with MODEL_CHARMM_GUI force field and Gromacs
<p>POPG lipid bilayer simulation at T298K ran 100ns with the force field given by CHARMM gui using Gromacs.</p> <p>118 POPG, 4110 TIP3P and 118 potassium molecules.</p> <p> </p>
Activation of biceps femoris long head reduces tibiofemoral anterior shear force and tibial internal rotation torque in healthy subjects
<p>Experimental data & Matlab code</p>
Simulations of POPC lipid bilayer in water solution with various molar fractions of cationic surfactant dihexadecylammonium using ECC-POPC force field
<p>Classical molecular dynamics simulations of a POPC lipid bilayer in water solution with various molar fractions of cationic surfactant dihexadecylammonium using ECC-POPC force field parameters, SPC/E water model and ECC-ions.</p> <p>Simulation at pure water is in a separate Zenodo deposit<br> https://doi.org/10.5281/zenodo.1118266</p> <p>file names report molar fraction of cations (i.e. not bulk concentrations)</p> <p>simulations performed with Gromacs 5.1.4 (*.xtc files) and openMM 7 (*.dcd files)</p> <p>simulation length 200 ns</p> <p>temperature 313 K (otherwise noted)</p>
Simulations of POPC lipid bilayer in water solution at various NaCl and CaCl2 concentrations using ECC-POPC force field and various water models
<p>Classical molecular dynamics simulations of a POPC lipid bilayer in water solution at various NaCl and CaCl2 concentrations using ECC-POPC force field parameters, various water models and ECC-ions.</p> <p>Simulations with SPC/E water model are in a separate Zenodo deposit<br> https://doi.org/10.5281/zenodo.1118266</p> <p>file names report molar fraction of cations (i.e. not bulk concentrations)</p> <p>simulations performed with Gromacs 5.1.4 (*.xtc files) and openMM 7 (*.dcd files)</p> <p>simulation length 300 ns</p> <p>temperature 313 K (otherwise noted)</p>
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>
A stylized study of the climate response to longwave and shortwave forcing at the altitude of aviation-induced cirrus
<p>Data and figures including MATLAB code used for plotting figures and creating tables of this study.<br>Case Deatils.xlsx: Details of all cases analysed in this study, which can be used to reproduce the results of this study. <br><br></p>
Changes Monitoring in Hongjiannao Lake from 1987-2023 using Google Earth Engine and Analysis of Climatic and Anthropogenic Forces
Open the record for dataset details and reuse information.
Liposome Mechanics Probed by AFM - HA and PBS filled liposomes with nonlinear force characteristics
<p>JPK files from AFM microscopy</p> <p> </p>
MD simulation of POPC bilayer using a force field calibrated to NMR order parameters
<p>Force field parameters and simulation trajectory for 34-lipid bilayer simulation where the force field has been calibrated to reproduce the NMR C-H 13C order parameters. Temperature is 300K, and simulation was conducted in NPT ensemble with asymmetric pressure coupling. The trajectory is 1 microsecond long. The simulation was started from an equilibrated conformation and few tens of nanoseconds of pre-equilibration was run before obtaining the 1 microsecond production run. The trajectory has been centered to the simulation box. The simulations where conducted on GROMACS 2020 (GPU). </p>
Global mean sea-level projections from MimiBRICK forced by uncertain emissions trajectories
<p>Model output for Darnell et al (2025), . Output contains csv files for components of GMSLR from 1850--2300, as well as parameter values for the simulations. There are two files: emissions_parameters.zip, which contains the parameter values used to generate the emissions trajectories, and ensemble_output.zip, which contains the model outputs. Rows correspond to ensemble members and columns correspond to years.</p>
Disentangling forced trends in the North Atlantic jet from natural variability using deep learning
<p>Code used to train the LLAE to obtain forced trends in the North Atlantic jet stream and to produce plots.</p>
Dataset and analyses for: Force-field perturbations and muscle vibration strengthen stability-related foot placement responses during steady-state gait in healthy adults
<p>We have collected kinematic data (heel and pelvis markers) from healthy adults during treadmill walking, whilst force-field perturbations and timed muscle vibrations were applied. We assessed the (after-)effects of these two interventions by evaluating foot placement control through outcome measures derived from a regression model which predicts foot placement based on the center-of-mass kinematic state. The data and analyses provided belong to the scientific publication: "Force-field perturbations and muscle vibration strengthen stability-related foot placement responses during steady-state gait in healthy adults". In the manuscript we place these analyses in the context of stability control and speculate on how these trainining interventions may improve stability control in patient populations.</p>
NEMO/SAS-SI3 setup, forcing and output for SI3-BBM and SI3-default idealized "cyclone" simulations (Brodeau et al., 2024, final version)
<p>Model data and setup/forcing/configuration files for the "Cyclone" test-case experiments (Mehlmann et al.,2021) discussed and analyzed in the following paper:</p> <p>Implementation of a brittle sea-ice rheology in an Eulerian, finite-difference, C-grid modeling framework: <br>Impact on the simulated deformation of sea-ice in the Arctic</p> <p>by Laurent Brodeau, Pierre Rampal, Einar Ólason and Véronique Dansereau, in Geoscientific Model Development (GMD), 2024.</p>
Data for insertion of needles into soft solids for determination of force components and material parameters such as toughness
<p>This dataset contains all the raw and processed data (including MATLAB code files) for the publication "Force decomposition and toughness estimation from puncture experiments in soft solids"</p> <p>The high-speed images synchronized with force measurements (raw data as .cine files that can be opened in PCC software) are included in folder "HighSpeedImaging_ForceMeasurementsRAW". <br>The processed data, including Tracker files, MATLAB codes, and all figures are included in "DataProcessing" folder.</p>
HOTSSea v1 Forcings - HRDPS
<p>High Resolution Deterministic Prediction System (HRDPS) data for the Salish Sea area, as NC files (daily). Downloaded from Environment and Climate Change Canada. Provided here to support publication of HOTSSea v1.0 NEMO oceanographic model of the Salish Sea. </p> <p>No DOI's or publications found for this at time of writing. See:</p> <p>https://eccc-msc.github.io/open-data/licence/readme_en/</p> <p>https://eccc-msc.github.io/open-data/msc-data/nwp_hrdps/readme_hrdps-datamart_en/</p>
HOTSSea v1 Forcings - CIOPSW, ERA5, ORAS5, Obs, Runoff etc
<p>Forcings and analysis files used in Oldford et al., 2024, HOTSSea v1 (GMD-2024-58; https://doi.org/10.5194/gmd-2024-58), resources here to accompany manuscript. </p> <p>All forcings provided here have been pre-processed for use in NEMO v3.6 HOTSSea v1 model. Original repositories are referenced below.</p> <p><em>Boundary</em>:</p> <p>ORAS5</p> <ul> <li>see https://doi.org/10.5194/os-15-779-2019</li> </ul> <p><em>Atmospheric</em>:</p> <p>ERA5</p> <ul> <li>see <a href="https://doi.org/10.1002/qj.3803">https://doi.org/10.1002/qj.3803</a></li> </ul> <p>CIOPS-West</p> <ul> <li>no DOI or publication currently available</li> <li>(Corresponding author email address: jean-philippe.paquin@ec.gc.ca)</li> <li>technical note: https://collaboration.cmc.ec.gc.ca/cmc/cmoi/product_guide/docs/tech_notes/technote_ciops-west-230_e.pdf</li> <li>technical specs: https://collaboration.cmc.ec.gc.ca/cmc/CMOI/product_guide/docs/tech_specifications/tech_specifications_CIOPS-WEST_e.pdf</li> <li>open data: https://eccc-msc.github.io/open-data/msc-data/nwp_ciops/readme_ciops_en/</li> <li>open data: https://open.canada.ca/data/dataset/390abee6-4ba0-4d6e-ae79-25753d1c43f3</li> </ul> <p><em>ANALYSIS files</em> output from running 'pypkg' scripts (see github repo)</p> <ul> <li>Model run codes were renamed in manuscript, with mapping below:</li> <li>OLD-CODE | NEW-CODE</li> <li>RUN100 | 0.1</li> <li>RUN120 | 0.12</li> <li>RUN141 | 0.14</li> <li>RUN160 | 0.16</li> <li>RUN181 | 0.18</li> <li>RUN202 | 1.01</li> <li>RUN203 | 1.02</li> </ul> <p><em>Observations </em>(OBS-West) :</p> <ul> <li>see manuscript (GMD-2024-58)</li> </ul>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.