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1,899 results for “Running”
Time to run: Late rather than early exercise training in mice promotes fat mass loss and reduces atherosclerosis development
<p>This dataset contains forward and reverse reads of 16S sequencing of the V3-V4 region of caecal bacteria. As indicated in the metadata file, mice are divided into three groups - SED (sedentary mice, E-SED for early sedentary and L-SED for late sedentary), E-RUN (early runners) and L-RUN (late runners). For more information about the groups, sequencing etc., consult the manuscript.</p>
BeerDEcoded Nanopore Sequencing Run of Chimay Now. 2019
<p>The BeerDEcoded workshops are a project of the Street Science Community. https://streetscience.community/projects/beerdecoded/<br> We are extracting DNA from different Beers and sequence them using the Nanopore sequencing technique. The data contains the reads extracted from the beer of the brand Chimay and was performed on 26.11.2019. <br> The Sequencing was performed on a MinION using a Flow Cell and the MinKNOW software. The Software collects sequencing data in real-time and has a base-calling integrated to convert the fast5 files into fastq files.</p>
Dataset from: Multi-messenger observations of binary neutron star mergers in the O4 run
<p>The binary neutron stars population data from the paper <strong><em>"Multi-messenger observations of binary neutron star mergers in the O4 run" </em>(<a href="https://arxiv.org/abs/2204.07592">https://arxiv.org/abs/2204.07592</a>).</strong></p> <p>Details of how to use the data, as well as the scripts to reproduce the figures of the main text of the paper are given in the accompanying Github repository <a href="https://github.com/acolombo140/O4NSNS">https://github.com/acolombo140/O4NSNS</a></p> <p>If you use this data, please cite the above manuscript.</p> <p> </p>
Hydraulic scale model experiments on the two-dimensional run-up and overtopping of solitary waves at a vertical wall and a dam-like structure
<p>This dataset includes the experimental data, which were generated during the study on the run-up and overtopping of solitary waves at the Laboratory of Hydraulics, Hydrology and Glaciology (VAW), ETH Zurich.</p>
BeerDEcoded Nanopore Sequencing Run of Chimay Now. 2019 Flongle
<p>The BeerDEcoded workshops are a project of the Street Science Community. https://streetscience.community/projects/beerdecoded/<br> We are extracting DNA from different Beers and sequence them using the Nanopore sequencing technique. The data contains the reads extracted from the beer of the brand Chimay and was performed on 26.11.2019. <br> The Sequencing was performed on a MinION using a flongle Flow Cell and the MinKNOW software. The Software collects sequencing data in real-time and has a base-calling integrated to convert the fast5 files into fastq files.</p>
Text-fig. 8. Carpolithes (a–t). a–e: Carpolithes sp. 1. USNM PAL 772366. Scale bar = 1 cm. a: Lateral view of endocarp, note two longitudinal ridges. b: Lateral view of endocarp rotated 90° from (a), note single lateral ridge in center, a, b reflected light, palladium coated. c: Lateral view, Micro-CT scan surface rendering. d: View of rounded end of the endocarp, reflected light, palladium coated. e: View of the opposite (pointed) end of the endocarp, note split; reflected light, palladium coated. f–j: Carpolithes sp. 2. USNM PAL 772367. Scale bar = 5 mm. f: Lateral view, base down; note raphe-like structure (arrow), reflected light, palladium coated. g: Lateral view, the raphe-like structure extending vertically from the base. h: Lateral view, rotated 90° from (g). i: Lateral view, the opposite face to that in (h). j: Basal view, raphe-like structure running from the center to the right of the image. g–j: CT scan surface renderings. k–o: Carpolithes sp. 3 USNM PAL 772368. Scale bar = 5 mm. k: Ventral view of the specimen, note flared apical extension, reflected light, uncoated. l: Dorsal view illustrating the flared apical extension, rotated 180o from (k). m: Lateral view rotated 90° from that in (l). n: Apical view, the apical extension with central pore (arrow) and a clear lineation running down the side to the top of the image. o: Basal view. l–o: Micro-CT scan surface renderings. p–t: Carpolithes sp. 4. USNM PAL 772369. Scale bar = 3 mm. p: Basal view illustrating the concentric rings of radiating possible cells surrounding a central depression. q: Lateral view, base down, note possible cellular pattern. r: Lateral view, rotated 180° from (q), base down; p–r: reflected light, palladium coated. s, t: Basal and lateral views, micro-CT scan surface renderings. in The Early Middle Eocene Wagon Bed Carpoflora Of Central Wyoming, U.S.A.
Text-fig. 8. Carpolithes (a–t). a–e: Carpolithes sp. 1. USNM PAL 772366. Scale bar = 1 cm. a: Lateral view of endocarp, note two longitudinal ridges. b: Lateral view of endocarp rotated 90° from (a), note single lateral ridge in center, a, b reflected light, palladium coated. c: Lateral view, Micro-CT scan surface rendering. d: View of rounded end of the endocarp, reflected light, palladium coated. e: View of the opposite (pointed) end of the endocarp, note split; reflected light, palladium coated. f–j: Carpolithes sp. 2. USNM PAL 772367. Scale bar = 5 mm. f: Lateral view, base down; note raphe-like structure (arrow), reflected light, palladium coated. g: Lateral view, the raphe-like structure extending vertically from the base. h: Lateral view, rotated 90° from (g). i: Lateral view, the opposite face to that in (h). j: Basal view, raphe-like structure running from the center to the right of the image. g–j: CT scan surface renderings. k–o: Carpolithes sp. 3 USNM PAL 772368. Scale bar = 5 mm. k: Ventral view of the specimen, note flared apical extension, reflected light, uncoated. l: Dorsal view illustrating the flared apical extension, rotated 180o from (k). m: Lateral view rotated 90° from that in (l). n: Apical view, the apical extension with central pore (arrow) and a clear lineation running down the side to the top of the image. o: Basal view. l–o: Micro-CT scan surface renderings. p–t: Carpolithes sp. 4. USNM PAL 772369. Scale bar = 3 mm. p: Basal view illustrating the concentric rings of radiating possible cells surrounding a central depression. q: Lateral view, base down, note possible cellular pattern. r: Lateral view, rotated 180° from (q), base down; p–r: reflected light, palladium coated. s, t: Basal and lateral views, micro-CT scan surface renderings.
Text-fig. 9. Carpolithes (a–r). a–d: Carpolithes sp. 5. USNM PAL 772370. Scale bar = 5 mm, reflected light, palladium coated. a: Lateral view of seed, apex up, possible raphe descending from apex toward viewer. b: Lateral view of seed, apex up, possible raphe on right. c: Lateral view, opposite side, apex up, possible raphe on left. d: Apical view, note central pit with raphe descending towards bottom margin. e–h: Carpolithes sp. 6. USNM PAL 772371. Scale bar = 5 mm. e: Basal view illustrating depression and keel in plane of bisymmetry, reflected light, palladium coated. f–h: Micro-CT scan surface rendering. f: Lateral view showing relatively smooth rounded surface. g: Specimen rotated 180° from (f), surface partially eroded. h: Longitudinal view, showing median keel. i–m: Carpolithes sp. 7 USNM PAL 772372. Scale bar = 5 mm. i: View of intact face of globose fruit, possible apical constriction at top. j: Lateral view, intact surface to right, possible apical constriction at top, both micro-CT scan surface renderings. k: Apical view. l: Face view illustrating the mineral filling and the fine, radiating structure of the fruit wall on the left and right margins, both reflected light, palladium coated. m: Closeup of the cellular layer on the left of (l), micro-CT scan surface rendering. n–p: Carpolithes sp. 8. USNM PAL 772373. Scale bar = 3 mm, reflected light, palladium coated. n: Lateral view of pyrene-like structure, one ridge running vertically in the center of view, the other two forming the left and right margins. o: Lateral view of pyrene-like structure, ridge in (n) on the left. p: End-on view illustrating one convex, one concave, and one relatively flat to very slightly concave face. q, r: Carpolithes sp. 9 USNM PAL 772374. Scale bar = 5 mm, reflected light, palladium coated. q: Exterior of the smooth broken half-sphere. r: Interior of the broken half-sphere. in The Early Middle Eocene Wagon Bed Carpoflora Of Central Wyoming, U.S.A.
Text-fig. 9. Carpolithes (a–r). a–d: Carpolithes sp. 5. USNM PAL 772370. Scale bar = 5 mm, reflected light, palladium coated. a: Lateral view of seed, apex up, possible raphe descending from apex toward viewer. b: Lateral view of seed, apex up, possible raphe on right. c: Lateral view, opposite side, apex up, possible raphe on left. d: Apical view, note central pit with raphe descending towards bottom margin. e–h: Carpolithes sp. 6. USNM PAL 772371. Scale bar = 5 mm. e: Basal view illustrating depression and keel in plane of bisymmetry, reflected light, palladium coated. f–h: Micro-CT scan surface rendering. f: Lateral view showing relatively smooth rounded surface. g: Specimen rotated 180° from (f), surface partially eroded. h: Longitudinal view, showing median keel. i–m: Carpolithes sp. 7 USNM PAL 772372. Scale bar = 5 mm. i: View of intact face of globose fruit, possible apical constriction at top. j: Lateral view, intact surface to right, possible apical constriction at top, both micro-CT scan surface renderings. k: Apical view. l: Face view illustrating the mineral filling and the fine, radiating structure of the fruit wall on the left and right margins, both reflected light, palladium coated. m: Closeup of the cellular layer on the left of (l), micro-CT scan surface rendering. n–p: Carpolithes sp. 8. USNM PAL 772373. Scale bar = 3 mm, reflected light, palladium coated. n: Lateral view of pyrene-like structure, one ridge running vertically in the center of view, the other two forming the left and right margins. o: Lateral view of pyrene-like structure, ridge in (n) on the left. p: End-on view illustrating one convex, one concave, and one relatively flat to very slightly concave face. q, r: Carpolithes sp. 9 USNM PAL 772374. Scale bar = 5 mm, reflected light, palladium coated. q: Exterior of the smooth broken half-sphere. r: Interior of the broken half-sphere.
Figure 7. World model construction and artificial intelligent structure running on the PDA
<p>In this section the AI part of the software is briefly introduced. There are three distinct<br> layers: AI Core, Role Engine and Behavior Engine. AI Core receives the computed field data from<br> World Map Modeling unit and determines the play state according to the ball, opponents and our<br> robots positions.<br> Considering the current game strategy, determination of the play state is done by fuzzy<br> decision-making to avoid undesirable and sudden changes of roles or behaviors.[3-4]</p>
The Cloud that Runs the Mobile Internet: A Measurement Study of Mobile Cloud Services
<p>In order to assess the performance of domains hosted in Cloud Service Providers (CSP), we run a dedicated measurements campaign on the MONROE measurements platform.</p> <p>For this study we use nodes in 4 countries and the Mobile Network Operators (MNO) over which we measure are:</p> <ul> <li>Norway: Telenor, Telia, <strong>Telia (SE)</strong></li> <li>Italy: Vodafone, Wind, TIM</li> <li>Spain: Yoigo, Orange, <strong>Vodafone (IT)</strong></li> <li>Sweden: Telia, Telenor, 3</li> </ul> <p>* MNOs written in bold letters are SIM cards that are roaming internationally (home country code in brackets).</p> <p>We benefit from MONROE’s openness and capabilities to run long-lived active measurements in realistic but controlled scenarios in commercial MNOs.</p> <p>Namely:</p> <ul> <li><strong>DNS test:</strong> We run DNS lookups for each target mobile domain towards: 1) the default MNO DNS resolver, 2) Google's public resolver and 3) OpenDNS's public resolver. This will allow us to compare the quality of the responses and identify possible DNS-level inefficiencies during the replica selection. All our DNS queries include the EDNS edns-client-subnet flag as it may be used by CSPs like EdgeCast and Amazon services to locate the end user and improve the quality of replica selection. If we do not receive a reply, as it occurs for Vodafone (IT) possibly due to the presence of an in-path DNS proxy filtering those requests, we repeat the request without the flag.</li> <li><strong>Traffic performance test:</strong> We measure the TCP connection and TLS session establishment time, if applicable, towards the resolved IP addresses. We repeat the same experiment over TCP ports 80 and 443, in case the MNO performs traffic shaping or deploys TCP-splitting proxies. The TCP connection, as well as the TLS session establishment are triggered by an HTTP GET request for the favicon.ico object. The presence of the object in the server is not relevant as the handshakes are triggered regardless of its existence.</li> <li><strong>Network topology test: </strong>For each resolved IP address, we run UDP traceroutes to study CSP-MNO peering and topological relationships. We also run pings towards the same IP address.</li> </ul> <p>We also log all the related metadata that are provided by the platform.</p> <p>The schema of the database can be found at:</p> <p>https://github.com/FoivosMichelinakis/cloudmap/blob/master/supportingFiles/parsers/cloudmap.sql</p> <p>The container code used on the MONROE nodes to generate this dataset can be found at:</p> <p>https://github.com/FoivosMichelinakis/cloudmap/</p> <p> </p> <p>We run the aforementioned experiments in isolation from other experiments continuously over 4 weeks, from April 5, 2017 until May 6, 2017.</p> <p>The combined results of the three tests for a given FQDN produce a "sample". The measurements are continuously run and the periodicity of the same sample varies between 4 and 24 hours. Thus, each sample has several instances across time of day and various radio conditions.</p>
Daily climatology of 3D ocean currents, SST and SSH based on a 22 year run of the SEA-COFS model
<p>This dataset is a daily climatology created based on a 22 year free run of the SEA-COFS model full domain (EAC 25.25 - 45.55 °S) forced with BARRA-R winds and tides, spanning from January 1994 to September 2016. The model has 30 sigma-stretch vertical levels and an across-shore resolution of 2.5 km (on shelf) – 6 km (off shelf) and 5 km along-shore. For a full description of this version of the model and its validation refer to the following papers:</p> <ul> <li><strong>Li, J, Roughan, M. & Kerry, C.</strong> (2021). <a href="https://doi.org/10.1029/2021GL094115%20">Dynamics of interannual eddy kinetic energy modulations in a Western Boundary Current</a>. <em>Geophysical Research Letters,</em>Vol 48, October 2021.DOI: <a href="https://doi.org/10.1029/2021GL094115">https://doi.org/10.1029/2021GL094115</a> <a href="http://www.oceanography.unsw.edu.au/private/publications/2021/2021GL094115.pdf">[PDF file]</a></li> <li> </li> <li><strong>Li, J, Roughan, M. & Kerry, C.</strong> (2022). <a href="https://doi.org/10.1175/JCLI-D-21-0622.1">Variability and Drivers of Ocean Temperature Extremes in a Warming Western Boundary Current</a>. <em>Journal Of Climate,</em> February 2022. DOI: <a href="https://doi.org/10.1175/JCLI-D-21-0622.1">https://doi.org/10.1175/JCLI-D-21-0622.1</a> <a href="http://www.oceanography.unsw.edu.au/private/publications/2022/Li_2022.pdf">[PDF file]</a></li> </ul> <p>This daily climatology of 365 days was created with NCO tools by taking the mean of the 22 daily average ROMS output files (one for each year) to generate each climatological day. Specific commands used are recorded in the NetCDF file history. Leap year days were excluded since its climatology was computed with only 5 instances.</p> <p>A sample file with the climatological data for January 1st is provided here as an example of the NetCDF format of the dataset. The entire dataset is one file of approximately 62GB and can be provided upon request. </p> <p><strong>NOTE: </strong>The ocean_time variable reflects the dates of the year 1994, but the values of the variables correspond to climatological values computed as described. </p> <p>The ROMS variables below are present in this daily climatology, as well as the S-coordinate stretching curves, grid defining variables and other time independent parameters:</p> <p>AKs = "time-averaged salinity vertical diffusion coefficient" [meter2 seconds-1]</p> <p>AKt = "time-averaged temperature vertical diffusion coefficient" [meter2 seconds-1]</p> <p>AKv = "time-averaged vertical viscosity coefficient" [meter2 seconds-1]</p> <p>bustr = "time-averaged bottom u-momentum stress" [newton meter-2]</p> <p>bvstr = "time-averaged bottom v-momentum stress" [newton meter-2]</p> <p>omega = "time-averaged S-coordinate vertical momentum component" [meter3 second-1]</p> <p>pvorticity = "time-averaged potential vorticity" [meter-1 second-1]</p> <p>pvorticity = "time-averaged 2D potential vorticity" [meter-1 second-1]</p> <p>rho = "time-averaged density anomaly" [kilogram meter-3]</p> <p>rvorticity = "time-averaged relative vorticity, vertical component" [second-1]</p> <p>rvorticity_bar = "time-averaged 2D relative vorticity" [second-1]</p> <p>salt: = "time-averaged salinity" [PSU]</p> <p>shflux = "time-averaged surface net heat flux" [watt meter-2]</p> <p>ssflux = "time-averaged surface net salt flux, (E-P)*SALT" [meter second-1]</p> <p>sustr = "time-averaged surface u-momentum stress" [newton meter-2]</p> <p>svstr = "time-averaged surface v-momentum stress" [newton meter-2]</p> <p>temp = "time-averaged potential temperature" [Celsius]</p> <p>u = "time-averaged u-momentum component" [meter second-1]</p> <p>u_eastward = "time-averaged eastward momentum component at RHO-points" [meter second-1]</p> <p>ubar = "time-averaged vertically integrated u-momentum component" [meter second-1]</p> <p>ubar_eastward = "time-averaged eastward vertically integrated momentum component at RHO-points" [meter second-1]</p> <p>uu = "time-averaged u-momentum times u-momentum" [meter2 second-2]</p> <p>uv = "time-averaged u-momentum times v-momentum" [meter2 second-2]</p> <p>v = "time-averaged v-momentum component" [meter second-1]</p> <p>v_northward = "time-averaged northward momentum component at RHO-points" [meter second-1]</p> <p>vbar = "time-averaged vertically integrated v-momentum component" [meter second-1]</p> <p>vbar_northward = "time-averaged northward vertically integrated momentum component at RHO-points" [meter second-1]</p> <p>vv = "time-averaged v-momentum times v-momentum" [meter2 second-2]</p> <p>w = "time-averaged vertical momentum component" [meter second-1]</p> <p>zeta = "time-averaged free-surface" [meter]</p>
Dense vegetation hinders sediment transport towards saltmarsh interiors - Supporting data and source code (Part III: Extra runs)
<p>This is Part III of the supporting data and source code for the paper entitled "Dense vegetation hinders sediment transport towards saltmarsh interiors", submitted to <em>Limnology and Oceanography Letters.</em> It contains all input and output files for the extra simulations used in the paper (Figures S3, S8-S10).</p> <p>Each zip file corresponds to a model run. </p> <p>TIGER_XX.zip: Scenario XX, hydro-morphodynamics and vegetation dynamics, years 0-100.<br>TIGER_XX_100.zip: Scenario XX, hydro-morphodynamics and vegetation dynamics, years 100-200.<br>TIGER_XX_HYYY.zip: Scenario XX, hydro-morphodynamics only, year YYY.</p> <p>Main scenarios:<br>- 01: Spartina (Figures 1-5, S3-S10)<br>- 02: Salicornia (Figures 1-5, S3-S10)<br>- 83: No vegetation (Figures 1-5, S3, S8-S10)</p> <p>Additional scenarios:<br>- 146: Spartina, low bulk drag coefficient (Figure S3)<br>- 147: Spartina, very low bulk drag coefficient (Figure S3)<br>- 148: Salicornia, low bulk drag coefficient (Figure S3)<br>- 149: Salicornia, very low bulk drag coefficient (Figure S3)<br>- 122: Spartina, low settling velocity (Figure S8)<br>- 123: Spartina, high settling velocity (Figure S8)<br>- 124: Salicornia, low settling velocity (Figure S8)<br>- 125: Salicornia, high settling velocity (Figure S8)<br>- 126: No vegetation, low settling velocity (Figure S8)<br>- 127: No vegetation, high settling velocity (Figure S8)<br>- 128: Spartina, low critical bed erosion shear stress (Figure S8)<br>- 129: Spartina, high critical bed erosion shear stress (Figure S8)<br>- 130: Salicornia, low critical bed erosion shear stress (Figure S8)<br>- 131: Salicornia, high critical bed erosion shear stress (Figure S8)<br>- 132: No vegetation, low critical bed erosion shear stress (Figure S8)<br>- 133: No vegetation, high critical bed erosion shear stress (Figure S8)<br>- 134: Spartina, low Partheniades constant (Figure S8)<br>- 143: Spartina, high Partheniades constant (Figure S8)<br>- 136: Salicornia, low Partheniades constant (Figure S8)<br>- 144: Salicornia, high Partheniades constant (Figure S8)<br>- 138: No vegetation, low Partheniades constant (Figure S8)<br>- 145: No vegetation, high Partheniades constant (Figure S8)<br>- 150: Spartina, low sediment dry bulk density (Figure S8)<br>- 151: Spartina, high sediment dry bulk density (Figure S8)<br>- 152: Salicornia, low sediment dry bulk density (Figure S8)<br>- 153: Salicornia, high sediment dry bulk density (Figure S8)<br>- 154: No vegetation, low sediment dry bulk density (Figure S8)<br>- 155: No vegetation, high sediment dry bulk density (Figure S8)<br>- 76: Spartina, replicate #1 (Figures S9-S10)<br>- 77: Spartina, replicate #2 (Figures S9-S10)<br>- 78: Spartina, replicate #3 (Figures S9-S10)<br>- 88: Spartina, replicate #4 (Figures S9-S10)<br>- 80: Salicornia, replicate #1 (Figures S9-S10)<br>- 81: Salicornia, replicate #2 (Figures S9-S10)<br>- 82: Salicornia, replicate #3 (Figures S9-S10)<br>- 89: Salicornia, replicate #4 (Figures S9-S10)<br>- 85: No vegetation, replicate #1 (Figures S9-S10)<br>- 86: No vegetation, replicate #2 (Figures S9-S10)<br>- 87: No vegetation, replicate #3 (Figures S9-S10)<br>- 90: No vegetation, replicate #4 (Figures S9-S10)</p> <p> </p>
Provenance Run Crate specification
<p><strong>Web version:</strong> <a href="https://w3id.org/ro/wfrun/provenance/0.5">https://w3id.org/ro/wfrun/provenance/0.5</a></p> <p>This specification is part of a collection of <a href="https://w3id.org/ro/crate/">RO-Crate</a> profiles for capturing the provenance of an execution of a computational workflow. The Provenance Run Crate profile can be used to describe the execution of a workflow including internal details such as step executions and intermediate outputs.</p>
Process Run Crate specification
<p><strong>Web version:</strong> <a href="https://w3id.org/ro/wfrun/process/0.5">https://w3id.org/ro/wfrun/process/0.5</a></p> <p>This specification is part of a collection of <a href="https://w3id.org/ro/crate/">RO-Crate</a> profiles for capturing the provenance of an execution of a computational workflow. Process Run Crate can be used to describe the execution of one or more tools that contribute to the same computation.</p>
Workflow Run Crate specification
<p><strong>Web version:</strong> <a href="https://w3id.org/ro/wfrun/workflow/0.5">https://w3id.org/ro/wfrun/workflow/0.5</a></p> <p>This specification is part of a collection of <a href="https://w3id.org/ro/crate/">RO-Crate</a> profiles for capturing the provenance of an execution of a computational workflow. The Workflow Run Crate profile can be used to describe the execution of a <em>workflow</em>, i.e., a computational tool that has orchestrated the execution of other tools.</p>
Future-ready Nordic homes: Responding to the Changing Climate with Free-running Buildings; Digital Repository of Obtained Results
<p>The figures provided here show complete results of the parametric building performance simulation for each studied location, which was a part of the Master thesis in Energy-efficient and Environmental Building Design (Faculty of Engineering, Lund University, Sweden) by Marko Ljubas.</p> <p>The simulations were performed using software IDA ICE 4.8 by EQUA.</p> <p>The weather files were generated using software Meteonorm 8.2 by Meteotest.</p>
Dataset: Sunrun Inc. (RUN) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Ozone 3D field from IRS assimilation run - June 2019
<p>In the frame of Vittorioso's PhD, an Observing System Simulation Experiment has bee set up to evaluate the future benefit of the geostationary IRS sounder on ozone field over Europe. The full set-up is composed of a Nature Run (reality), a Control Run (no assimilation) and an Assimilation Run (assimilation or IRS) over the months June to August 2019.<br>This dataset is Ozone 3D field from the assimilation run for June 2019. One file per day including hourly fields.</p>
ERA5 data to run example tutorials in RASCAL
<p>This is data from 2000 to 2023 to run some examples of reconstructions with the RASCAL model. It contains the geopotential height at 925hPa, and the temperature at 2m</p>
Input data for running forward simulations of CO2 atmospheric concentrations over Europe for the year 2019.
<p>This dataset provides input data (fluxes, background concentrations, and observations) for running forward simulations of CO2 atmospheric concentrations over Europe for the year 2019 using chemical transport models (CTMs). While some components of the dataset are available in other repositories, this compilation serves to 1) streamline the data collection process for other users and 2) bypass the need to perform data aggregation.</p> <p>Here is a description of each dataset:</p> <p><strong>cams73_latest_co2_conc_surface_inst_2019*.nc</strong></p> <p>CO2 mole fractions from the CAMS global inversion-optimised product v20r2 (Chevallier et al., 2010).</p> <p>The data are provided at a resolution of 3.75° in longitude and 1.9° in latitude, with a 3-hourly temporal resolution. </p> <p><strong>monitor_CO2_CIF_2019.nc</strong></p> <div> <div> <div> <div> <div> <div> <p>Observed CO2 atmospheric mixing ratios in Europe, compiled in version V8 of the ICOS GlobalView Obspack (ICOS RI et al., 2023), include continuous measurements from 58 stations across Europe, incorporating both ICOS and non-ICOS facilities.</p> <p>The original dataset has been aggregated and adapted to match the format of the monitor files used in the Community Inversion Framework (CIF; Berchet et al., 2021).</p> </div> </div> </div> </div> </div> </div> <p><strong>EDGARv4.3_BP2021_CO2_EU2_2019.nc</strong></p> <p>Anthropogenic CO2 fluxes (European, hourly) obtained from EDGAR-v4.2 and BP.</p> <p>The anthropogenic CO2 emissions are based on the spatial distribution from the EDGAR-v4.2 inventory, national and annual budgets from British Petroleum (BP) statistics, and hourly temporal profiles derived using the COFFEE approach (Steinbach et al., 2011, available on the ICOS Carbon Portal). This data is provided at a 0.1° × 0.1° horizontal resolution and hourly temporal resolution.</p> <p><strong>FG2.TRENDY11.ORC3.S3.3H_NBP_resp_2019.nc</strong></p> <p>NBP CO2 fluxes (global, 3-hourly) obtained from ORCHIDEE simulations. </p> <p>The ORCHIDEE-TRENDY simulation is conducted as part of the TRENDY model intercomparison project (e.g., Sitch et al., 2015; Friedlingstein et al., 2022). This simulation uses inputs provided by the project, including the CRUERA atmospheric climate forcing (global, 6-hourly, 0.5-degree resolution), LUH2 land-use change dataset, global atmospheric CO2 concentration data, and nitrogen fertilizer input datasets. All TRENDY simulations adhere to a standardized protocol: a model spin-up phase using recycled forcing data from 1901-1920, with other inputs from 1700, continues until the model's carbon pools reach equilibrium (340 years of spin-up for ORCHIDEE). This is followed by a transient simulation from 1700-1900, varying CO2 and land-use data while recycling climate forcing, and a historical simulation from 1901-2020 with all data inputs varied.</p> <p><strong>FR2.ORC3v7267.CRUERA3.NBP_3H.2019.nc</strong></p> <p>NBP CO2 fluxes (Europe, 3-hourly) obtained from ORCHIDEE simulations. </p> <p>The ORCHIDEE-VERIFY simulation is performed as part of the VERIFY project over the European region. This simulation is driven by the CRUERA dataset, which is derived from the ERA5-Land dataset (originally global, 1-hourly, at 0.1-degree resolution), transformed to the VERIFY region of interest (35°N to 73°N, 25°W to 45°E, 3-hourly, at 0.125-degree resolution), and re-aligned with the CRU observation dataset (for air temperature, shortwave radiation, humidity, and precipitation). The Hilda+ dataset is used for land use, and the EMEP model outputs are used for nitrogen inputs. The VERIFY simulation follows the general protocol used in the TRENDY project.</p> <p><strong>FR2.ORC3v7267.CRUERA3.hetero_resp_3H.2019.nc</strong></p> <p>Heterotrophic respiration CO2 fluxes (Europe, 3-hourly) obtained from ORCHIDEE simulations as described in the previous section.</p> <p><strong>Becker_coastal_fluxes_RF_v2021_2_2019.nc</strong></p> <p>Ocean CO2 fluxes (Europe, daily). </p> <p>The ocean fluxes come from a hybrid product combining the University of Bergen coastal ocean flux estimate and the Rödenbeck global ocean estimate (Rödenbeck et al., 2014). This data is provided at a 0.125° × 0.125° horizontal resolution and at a daily temporal resolution.</p> <p> </p> <p><em><strong>References</strong></em> </p> <p> </p> <p>Berchet, A., Sollum, E., Pison, I., Thompson, R. L., Thanwerdas, J., Fortems-Cheiney, A., Peet, J. C. A. v., Potier, E., Chevallier, F., Broquet, G., and Berchet, A.: The Community Inversion Framework: codes and documentation, https://doi.org/10.5281/zenodo.6304912, 2022</p> <p>Chevallier, F., Ciais, P., Conway, T. J., Aalto, T., Anderson, B. E., Bousquet, P., Brunke, E. G., Ciattaglia, L., Esaki, Y., Fröhlich, M., Gomez, A., Gomez-Pelaez, A. J., Haszpra, L., Krummel, P. B., Langenfelds, R. L., Leuenberger, M., Machida, T., Maignan, F., Matsueda, H., Morguí, J. A., Mukai, H., Nakazawa, T., Peylin, P., Ramonet, M., Rivier, L., Sawa, Y., Schmidt, M., Steele, L. P., Vay, S. A., Vermeulen, A. T., Wofsy, S., and Worthy, D.: CO2 surface fluxes at grid point scale estimated from a global 21 year reanalysis of atmospheric measurements, Journal of Geophysical Research: Atmospheres, 115, https://doi.org/10.1029/2010JD013887, 2010</p> <p>Friedlingstein, P., O’Sullivan, M., Jones, M. W., Andrew, R. M., Gregor, L., Hauck, J., Le Quéré, C., Luijkx, I. T., Olsen, A., Peters, G. P.,Peters, W., Pongratz, J., Schwingshackl, C., Sitch, S., Canadell, J. G., Ciais, P., Jackson, R. B., Alin, S. R., Alkama, R., Arneth, A., Arora,V. K., Bates, N. R., Becker, M., Bellouin, N., Bittig, H. C., Bopp, L., Chevallier, F., Chini, L. P., Cronin, M., Evans, W., Falk, S., Feely, R. A., Gasser, T., Gehlen, M., Gkritzalis, T., Gloege, L., Grassi, G., Gruber, N., Gürses, O., Harris, I., Hefner, M., Houghton, R. A.,Hurtt, G. C., Iida, Y., Ilyina, T., Jain, A. K., Jersild, A., Kadono, K., Kato, E., Kennedy, D., Klein Goldewijk, K., Knauer, J., Korsbakken,J. I., Landschützer, P., Lefèvre, N., Lindsay, K., Liu, J., Liu, Z., Marland, G., Mayot, N., McGrath, M. J., Metzl, N., Monacci, N. M.,Munro, D. R., Nakaoka, S.-I., Niwa, Y., O’Brien, K., Ono, T., Palmer, P. I., Pan, N., Pierrot, D., Pocock, K., Poulter, B., Resplandy, L.,Robertson, E., Rödenbeck, C., Rodriguez, C., Rosan, T. M., Schwinger, J., Séférian, R., Shutler, J. D., Skjelvan, I., Steinhoff, T., Sun, Q., Sutton, A. J., Sweeney, C., Takao, S., Tanhua, T., Tans, P. P., Tian, X., Tian, H., Tilbrook, B., Tsujino, H., Tubiello, F., van der Werf,G. R., Walker, A. P., Wanninkhof, R., Whitehead, C., Willstrand Wranne, A., Wright, R., Yuan, W., Yue, C., Yue, X., Zaehle, S., Zeng, J., and Zheng, B.: Global Carbon Budget 2022, Earth System Science Data, 14, 4811–4900, https://doi.org/10.5194/essd-14-4811-2022,https://essd.copernicus.org/articles/14/4811/2022/, publisher: Copernicus GmbH, 2022</p> <p>ICOS RI, Bergamaschi, P., Colomb, A., De Mazière, M., Emmenegger, L., Kubistin, D., Lehner, I., Lehtinen, K., Lund Myhre, C., Marek, M., Platt, S. M., Plaß-Dülmer, C., Schmidt, M., Apadula, F., Arnold, S., Blanc, P.-E., Brunner, D., Chen, H., Chmura, L., Conil, S., Couret, C., Cristofanelli, P., Delmotte, M., Forster, G., Frumau, A., Gheusi, F., Hammer, S., Haszpra, L., Heliasz, M., Henne, S., Hoheisel, A., Kneuer, T., Laurila, T., Leskinen, A., Leuenberger, M., Levin, I., Lindauer, M., Lopez, M., Lunder, C., Mammarella, I., Manca, G., Manning, A., Marklund, P., Martin, D., Meinhardt, F., Müller-Williams, J., Necki, J., O’Doherty, S., Ottosson-Löfvenius, M., Philippon, C., Piacentino, S., Pitt, J., Ramonet, M., Rivas-Soriano, P., Scheeren, B., Schumacher, M., Sha, M. K., Spain, G., Steinbacher, M., Sørensen, L. L., Vermeulen, A., Vítková, G., Xueref-Remy, I., di Sarra, A., Conen, F., Kazan, V., Roulet, Y.-A., Biermann, T., Heltai, D., Hensen, A., Hermansen, O., Komínková, K., Laurent, O., Levula, J., Pichon, J.-M., Smith, P., Stanley, K., Trisolino, P., ICOS Carbon Portal, ICOS Atmosphere Thematic Centre, ICOS Flask And Calibration Laboratory, and ICOS Central Radiocarbon Laboratory: European Obspack compilation of atmospheric carbon dioxide data from ICOS and non-ICOS European stations for the period 1972-2023;<br>obspack_co2_466_GLOBALVIEWplus_v8.0_2023-04-26, https://doi.org/10.18160/CEC4-CAGK, 2023</p> <p>Rödenbeck, C., Bakker, D. C. E., Metzl, N., Olsen, A., Sabine, C., Cassar, N., Reum, F., Keeling, R. F., and Heimann, M.: Interannual sea–air CO2 flux variability from an observation-driven ocean mixed-layer scheme, Biogeosciences, 11, 4599–4613, https://doi.org/10.5194/bg-11-4599-2014, 2014</p> <p>Sitch, S., Friedlingstein, P., Gruber, N., Jones, S. D., Murray-Tortarolo, G., Ahlström, A., Doney, S. C., Graven, H., Heinze, C., Huntingford,C., Levis, S., Levy, P. E., Lomas, M., Poulter, B., Viovy, N., Zaehle, S., Zeng, N., Arneth, A., Bonan, G., Bopp, L., Canadell, J. G.,Chevallier, F., Ciais, P., Ellis, R., Gloor, M., Peylin, P., Piao, S. L., Le Quéré, C., Smith, B., Zhu, Z., and Myneni, R.: Recent trends and drivers of regional sources and sinks of carbon dioxide, Biogeosciences, 12, 653–679, https://doi.org/10.5194/bg-12-653-2015, https://bg.copernicus.org/articles/12/653/2015/, publisher: Copernicus GmbH, 2015.</p> <p>Steinbach, J., Gerbig, C., Rödenbeck, C., Karstens, U., Minejima, C., and Mukai, H.: The CO2 release and Oxygen uptake from Fossil Fuel Emission Estimate (COFFEE) dataset: effects from varying oxidative ratios, Atmospheric Chemistry and Physics, 11, 6855–6870,1160 https://doi.org/10.5194/acp-11-6855-2011, 2011</p> <p> </p> <p> </p> <p> </p>
Warming run (years 0001-0279) from Eisenman & Armour (2024)
<p>The following information is also included in the README.txt file accompanying the data.</p> <p><br>Citation for data:</p> <p>I. Eisenman and K. Armour (2024). The radiative feedback continuum from Snowball Earth to an ice-free hothouse. Nature Communications, https://doi.org/10.1038/s41467-024-50406-w</p> <p><br>Description of data:</p> <p>The Warming and Cooling simulations are both branched from the end of year 500 of a preexisting CESM2 pre-industrial (PI) simulation ("b.e21.B1850.f09_g17.CMIP6-piControl.001" by Danabasoglu et al., 2020). The kernel analysis uses the mean annual cycle averaged over years 480-499 of this PI simulation as the reference climate. Output from this PI simulation can be downloaded from<br>https://www.earthsystemgrid.org/dataset/ucar.cgd.cesm2.b.e21.B1850.f09_g17.CMIP6-piControl.001.atm.proc.monthly_ave.html</p> <p>For the first 150 years of the Warming run, we use a preexisting CESM2 simulation with CO2 increasing at 1%/year ("b.e21.B1PCT.f09_g17.CMIP6-1pctCO2.001" by Danabasoglu, 2019); data from this simulation is shared elsewhere and is mirrored here for convenience. This is followed by our simulation "WARMING1ppyCESM2cheyenne", which we ran until it failed with an error after year 0279. Hence the data for the Warming run comes from the following simulations:<br>- Year 0001-0150: b.e21.B1PCT.f09_g17.CMIP6-1pctCO2.001<br>- Year 0151-0279: WARMING1ppyCESM2cheyenne</p> <p>For the Cooling run, we decreased CO2 at 1%/year in the simulation "COOLING1ppyCESM2cheyenne". After year 140, we adjusted the processor (PE) layout for a more economical (but slower) load balancing, which required us to branch to a new case, "COOLING1ppyCESM2cheyenne2". This simulation failed with an error after year 278, and we commented out a line in the model code and re-ran starting from 12 years earlier using the case name "COOLING1ppyCESM2cheyenne3". This simulation failed with an error after year 332, and we commented out another line in the model code and re-ran starting in year 333 using the case name "COOLING1ppyCESM2cheyenne5" (there is no simulation "COOLING1ppyCESM2cheyenne4" in this collection). This simulation failed with an error after year 514. Hence the data for the Cooling run comes from the following simulations:<br>- Year 0001-0140: COOLING1ppyCESM2cheyenne<br>- Year 0141-0266: COOLING1ppyCESM2cheyenne2 [ignore the additional years 0267-0278 in this simulation)<br>- Year 0267-0332: COOLING1ppyCESM2cheyenne3<br>- Year 0333-0514: COOLING1ppyCESM2cheyenne5</p> <p>Further details regarding these simulations are given in the "Methods" section of the paper (Eisenman & Armour, 2024).</p> <p><br>Downloading the data:</p> <p>Model output from the Warming and Cooling runs, including all fields used in the kernel analysis, is split into three downloadable records on Zenodo:<br>- Warming run (years 0001-0279)<br>- Cooling run part 1 (years 0001-0266)<br>- Cooling run part 2 (years 0267-0514)<br>Links to the three records can be found at https://eisenman-group.github.io</p> <p><br>Please contact Ian Eisenman (eisenman@ucsd.edu) for model output fields not included in this collection.</p>
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