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2,208 results for “coupling”
CESM2 data for "Ocean complexity shapes sea surface temperature variability in a CESM2 coupled model hierarchy" - submitted to JCLI
<p><strong>CESM2 Experiment names:</strong></p> <ul> <li>FC = fully coupled model, CESM2 (variables freely available on https://esgf-node.llnl.gov/search/cmip6/)</li> <li>MD = mechanically decoupled model, CESM2</li> <li>SOM = slab ocean model, CESM2</li> </ul> <p>All datasets are for pre-industrial forcing (e.g., piControl), nominal 1-degree horizontal resolution </p> <p>---</p> <p>Decoding the files names:</p> <ul> <li><strong>climatology_monthly </strong>= 12 month climatology </li> <li><strong>climatology_annual</strong> = time mean climatology</li> <li><strong>variance</strong> = anomaly variance computed over time</li> </ul> <p>---</p> <p>Variables:</p> <ul> <li><strong>PRECL</strong> = large-scale convective precipitation</li> <li><strong>PRECC</strong> = convective precipitation</li> <li><strong>total precipitation (not provided but can be calculated)</strong> = PRECC + PRECL</li> <li><strong>HMXL</strong> = mixed layer depth</li> <li><strong>SST</strong> = sea surface temperature </li> </ul> <p><strong>Files for the CESM2 MD piControl run:</strong></p> <ol> <li>forcing_coupled.F90: POP2 (ocean) source code changes for cesm2.1.4-rc08 (search for "slarson" throughout code to find our changes</li> <li>cesm2.1.4-exp03-CTRL_B1850_f09_g17_hourlyclim_TAUX.nc: 6 hourly climatology for TAUX, from a FC run of CESM2. This file and the TAUY climatology are opened and read in the "rotate wind stress" subroutine in forcing_coupled.F90. This file is named "x2oavg_Foxx_taux_6hourly.nc" in forcing_coupled (we wanted a shorter file name in the code)</li> <li>cesm2.1.4-exp03-CTRL_B1850_f09_g17_hourlyclim_TAUY.nc: 6 hourly climatology for TAUY. This file is named "x2oavg_Foxx_tauy_6hourly.nc" in forcing_coupled (we wanted a shorter file name in the code) </li> </ol> <p> </p>
Electron-phonon coupling and vibrational properties of size-selected linear carbon chains by resonance Raman scattering
<p>Data repository for the paper "Electron-phonon coupling and vibrational properties of size-selected linear carbon chains by resonance Raman scattering"</p>
Research Data Supporting "Coupling Lipid Nanoparticle Structure and Automated Single Particle Composition Analysis to Design Phospholipase Responsive Nanocarriers"
<p>Raw research data supporting Barriga, Pence, et al. 2022, Advanced Materials. <a href="https://doi.org/10.1002/adma.202200839">https://doi.org/10.1002/adma.202200839</a></p>
National Data Files for Pre-built Sector-coupled Euro-Calliope Model
<p>National time series data derived from the <a href="https://zenodo.org/record/5774988#.YtUQ9-zP3Ph">Sector-coupled Euro-Calliope Pre-built Model</a></p>
Coupled dynamics of the North Equatorial Countercurrent and Intertropical Convergence Zone with relevance to the double-ITCZ problem
<p>The North Equatorial Counter Current (NECC) is an important component of the tropical Pacific upper-ocean circulation. This current flows against easterly trade winds and transports warm water from the Western Pacific warm pool eastward. Its core follows the latitudinal position of the Intertropical Convergence Zone (ITCZ) – a salient feature of tropical atmospheric circulation characterized by convective clouds and intense rainfall. The trade winds converge towards the ITCZ, creating a local minimum in zonal winds and generating a positive wind stress curl that maintains an eastward current despite the westward winds. That the wind curl associated with the ITCZ is a critical driver of the NECC is well established. However, here we show that the relationship between the ITCZ and the NECC is more complex and involves positive feedback: a stronger NECC transports warmer water from the western Pacific to the east thus increasing SST along its path, intensifying convection within the ITCZ and hence strengthening wind stress curl, further strengthening the NECC. We refer to this large-scale feedback as the Wind curl - Advection - SST - Precipitation (WASP) feedback. To confirm the NECC-ITCZ coupling we conduct numerical simulations using a coupled model (CESM) in which we progressively strengthen the surface Ekman component of the NECC and observe the intensification of the ITCZ and the entire NECC. Consequently, a weak NECC leads to a weak ITCZ, which can contribute to the double ITCZ problem in climate models, since weak wind convergence north of the equator enables stronger convergence to the south.</p>
Source data for "Ca2+ channels couple spiking to mitochondrial metabolism in substantia nigra dopaminergic neurons"
<p><strong>Fig.1Aa-c.tif</strong></p> <p>2PLSM images (Fura-2 filled neuron) used for the reconstruction in Fig.1A</p> <p> </p> <p><strong>Fig1CDJ.xlsx</strong></p> <p>Numerical data for the charts in Fig. 1C, Fig.1D, Fig.1J</p> <p> </p> <p><strong>Fig.1F_GCEPIA1er.tif</strong></p> <p>Confocal image (green channel, G-CEPIA1er) for Fig. 1F</p> <p> </p> <p><strong>Fig.1F_TH.tif</strong></p> <p>Confocal image (blue channel, anti-TH immunostaining) for Fig. 1F</p> <p> </p> <p><strong>Fig.1G_GCEPIA1er.tif</strong></p> <p>Confocal image (green channel, G-CEPIA1er) for Fig. 1G</p> <p> </p> <p><strong>Fig.1G_CRT.tif</strong></p> <p>Confocal image (magenta channel, anti-CRT immunostaining) for Fig. 1G</p> <p> </p> <p><strong>Fig.1H_GCEPIA1er.tif</strong></p> <p>Confocal image (green channel, G-CEPIA1er) for Fig. 1H</p> <p> </p> <p><strong>Fig.1H_TH.tif</strong></p> <p>Confocal image (blue channel, anti-TH immunostaining) for Fig. 1H</p> <p> </p> <p><strong>Fig. 2B_mitoGCaMP6.tif</strong></p> <p>Confocal image (green channel, mito-GCaMP6) for Fig. 2B</p> <p> </p> <p><strong>Fig.2B_TH.tif</strong></p> <p>Confocal image (blue channel, anti-TH immunostaining) for Fig. 2B</p> <p> </p> <p><strong>Fig. 2C_mitoGCaMP6.tif</strong></p> <p>Confocal image (green channel, mito-GCaMP6) for Fig. 2C</p> <p> </p> <p><strong>Fig.2C_COXIV.tif</strong></p> <p>Confocal image (magenta channel, anti-COXIV immunostaining) for Fig. 2C</p> <p> </p> <p><strong>Fig. 2D_mitoGCaMP6.tif</strong></p> <p>Confocal image (green channel, mito-GCaMP6) for Fig. 2D</p> <p> </p> <p><strong>Fig.2D_TH.tif</strong></p> <p>Confocal image (blue channel, anti-TH immunostaining) for Fig. 2D</p> <p> </p> <p><strong>Fig.2FGJL.xlsx</strong></p> <p>Numerical data for the charts in Fig. 2F, Fig.2G, Fig.2J, Fig.2L</p> <p> </p> <p><strong>Fig.3BDE.xlsx</strong></p> <p>Numerical data for the charts in Fig. 3B, Fig.3D, Fig.3E</p> <p> </p> <p><strong>Fig.3C_Alexa.tif</strong></p> <p>MAX Projection of z-stack of 2PLSM images (magenta channel, Alexa 594 dye) used to generate Fig.3C top panel</p> <p> </p> <p><strong>Fig.3C_mitoGCaMP6.tif</strong></p> <p>MAX Projection of z-stack of 2PLSM images (green channel, mito-GCaMP6) used to generate Fig.3C top panel</p> <p> </p> <p><strong>Fig.3C_inset_dendritic_mitoGCaMP6.tif</strong></p> <p>2PLSM image (green channel, mito-GCaMP6) for Fig.3C bottom left panel</p> <p> </p> <p><strong>Fig.3C_inset_soma_mitoGCaMP6.tif</strong></p> <p>2PLSM image (green channel, mito-GCaMP6) for Fig.3C bottom right panel</p> <p> </p> <p><strong>Fig. 4A_PercevalHR.tif</strong></p> <p>Confocal image (green channel, PercevalHR) for Fig.4A</p> <p> </p> <p><strong>Fig.4A_TH.tif</strong></p> <p>Confocal image (blue channel, anti-TH immunostaining) for Fig.4A</p> <p> </p> <p><strong>Fig. 4B_PercevalHR.tif</strong></p> <p>Confocal image (green channel, PercevalHR) for Fig.4B</p> <p> </p> <p><strong>Fig.4B_TH.tif</strong></p> <p>Confocal image (blue channel, anti-TH immunostaining) for Fig.4B</p> <p> </p> <p><strong>Fig.4G.xlsx</strong></p> <p>Numerical data for the charts in Fig.4G</p> <p> </p> <p><strong>Fig.5BDFHI.xlsx</strong></p> <p>Numerical data for the charts in Fig.5B, Fig.5D, Fig.5F, Fig.5H, Fig.5I</p> <p> </p> <p><strong>Fig.6ADEFJKLN.xlsx</strong></p> <p>Numerical data for the charts in Fig.6A, Fig.6D, Fig.6E, Fig.6F, Fig.6J, Fig.6K, Fig.6L, Fig.6N</p> <p> </p> <p><strong>Fig.6H_mitoroGFP.tif</strong></p> <p>2PLSM image (green channel, mito-roGFP) for Fig.6H</p> <p> </p> <p><strong>Fig.6M_MCU-KO.tif</strong></p> <p>Combined EM micrographs used to generate Fig. 6M right side</p> <p> </p> <p><strong>Fig.6M_wildtype.tif</strong></p> <p>Combined EM micrographs used to generate Fig. 6M left side</p> <p> </p> <p><strong>Fig.7CEFG.xlsx</strong></p> <p>Numerical data for the charts in Fig.7C, Fig.7E, Fig.7F, Fig.7G</p> <p> </p> <p><strong>Fig.8CEHJK.xlsx</strong></p> <p>Numerical data for the charts in Fig.8C, Fig.8E, Fig.8H, Fig.8J, Fig.8K</p> <p> </p> <p><strong>Fig.S1A_bottom.tif</strong></p> <p>2PLSM image (green channel, G-CEPIA1er) for Fig.S1A bottom panel (low Ca2+)</p> <p> </p> <p><strong>Fig.S1A_top.tif</strong></p> <p>2PLSM image (green channel, G-CEPIA1er) for Fig.S1A top panel (high Ca2+)</p> <p> </p> <p><strong>Fig.S1B.xlsx</strong></p> <p>Numerical data for the chart in Fig.S1B</p> <p> </p> <p><strong>Fig.S2A_baseline.tif</strong></p> <p>2PLSM image (green channel, mito-GCaMP6) for Fig.S2A middle panel (baseline)</p> <p> </p> <p><strong>Fig.S2A_MAX.tif</strong></p> <p>2PLSM image (green channel, mito-GCaMP6) for Fig.S2A top panel (high Ca2+)</p> <p> </p> <p><strong>Fig.S2A_min.tif</strong></p> <p>2PLSM image (green channel, mito-GCaMP6) for Fig.S2A bottom panel (low Ca2+)</p> <p> </p> <p><strong>Fig.S2C_baseline.tif</strong></p> <p>2PLSM image (green channel, mito-GCaMP6) for Fig.S2C middle panel (baseline)</p> <p> </p> <p><strong>Fig.S2C_MAX.tif</strong></p> <p>2PLSM image (green channel, mito-GCaMP6) for Fig.S2C top panel (high Ca2+)</p> <p> </p> <p><strong>Fig.S2C_min.tif</strong></p> <p>2PLSM image (green channel, mito-GCaMP6) for Fig.S2C bottom panel (low Ca2+)</p> <p> </p> <p><strong>Fig.S2E_baseline.tif</strong></p> <p>2PLSM image (green channel, mito-GCaMP6) for Fig.S2E bottom left panel (baseline)</p> <p> </p> <p><strong>Fig.S2E_end.tif</strong></p> <p>2PLSM image (green channel, mito-GCaMP6) for Fig.S2E bottom right panel</p> <p> </p> <p><strong>Fig.S2E_peak.tif</strong></p> <p>2PLSM image (green channel, mito-GCaMP6) for Fig.S2E bottom center panel</p> <p> </p> <p><strong>Fig.S2F.xlsx</strong></p> <p>Numerical data for the chart in Fig.S2F</p> <p> </p> <p><strong>Fig.S3ABC.xlsx</strong></p> <p>Numerical data for the charts in Fig.S3A, Fig.S3B, Fig.S3C</p> <p> </p> <p><strong>Fig.S4CD.xlsx</strong></p> <p>Numerical data for the charts in Fig.S4C, Fig.S4D</p> <p> </p> <p><strong>Fig.S5ABC.xlsx</strong></p> <p>Numerical data for the charts in Fig.S5A, Fig.S5B, Fig.S5C</p> <p> </p> <p><strong>Fig.S7A.tif</strong></p> <p>2PLSM image (green channel, GCaMP6) for Fig.S7A</p> <p> </p> <p><strong>Fig.S7CEFGH.xlsx</strong></p> <p>Numerical data for the charts in Fig.S7C, Fig.S7E, Fig.S7F, Fig.S7G, Fig.S7H</p> <p> </p> <p><strong>Fig.S8ABCDEF.xlsx</strong></p> <p>Numerical data for the charts in Fig.S8A, Fig.S8B, Fig.S8C, Fig.S8D, Fig.S8E, Fig.S8F</p> <p> </p> <p><strong>Fig.S9AHIJK.xlsx</strong></p> <p>Numerical data for the charts in Fig.S9A, Fig.S9H, Fig.S9I, Fig.S9J, Fig.S9K</p> <p> </p> <p><strong>Fig.S9B_wildtype_DLStr.tif</strong></p> <p>Confocal image of dorso-laterateral striatum in wildtype mouse (red channel, anti-TH immunostaining) for Fig.S9B</p> <p> </p> <p><strong>Fig.S9C_MCU-KO_DLStr.tif</strong></p> <p>Confocal image of dorso-laterateral striatum in MCU-KO mouse (red channel, anti-TH immunostaining) for Fig.S9C</p> <p> </p> <p><strong>Fig.S9D_wildtype_SN.tif</strong></p> <p>Confocal image of midbrain in wildtype mouse (red channel, anti-TH immunostaining) for Fig.S9D</p> <p> </p> <p><strong>Fig.S9E_MCU-KO_SN.tif</strong></p> <p>Confocal image of midbrain in MCU-KO mouse (red channel, anti-TH immunostaining) for Fig.S9E</p> <p> </p> <p><strong>Fig.S9F_wildtype_openfield.png</strong></p> <p>Open field path tracked for wildtype mouse for Fig.S9F</p> <p> </p> <p><strong>Fig.S9G_MCU-KO_openfield.png</strong></p> <p>Open field path tracked for MCU-KO mouse for Fig.S9G</p> <p> </p> <p><strong>Fig.S10A.xlsx</strong></p> <p>Numerical data for the charts in Fig.S10A</p>
Encapsulation Enhances the Catalytic Activity of C-N Coupling: Reaction Mechanism of a Cu(I)/Calix[8]arene Supramolecular Catalyst - XYZ Structure files
<p>XYZ Structures corresponding to DOI: 10.1002/cctc.202200662</p>
Data for Magnetosphere-Ionosphere-Thermosphere Coupling Study at Jupiter Based on Juno's First 30 Orbits and Modeling Tools
<p>Data used in the code associated to the manuscript "Magnetosphere-Ionosphere-Thermosphere Coupling Study at Jupiter Based on Juno’s First 30 Orbits and Modeling Tools", by Al Saati et al. (2022, Journal of Geophysical Research - Space Physics, https://doi.org/10.1029/2022JA030586). Please read the documentation associated with the corresponding code.</p>
Theta dominates cross-frequency coupling in hippocampal-medial entorhinal circuit during awake-behavior in rats
<p>Hippocampal theta and gamma rhythms are hypothesized to play a role in the physiology of higher cognition. Prior research has reported that an offset in theta cycles between the entorhinal cortex, CA3, and CA1 regions promotes independence of population activity across the hippocampus. In line with this idea, it has recently been observed that CA1 pyramidal cells can establish and maintain coordinated place cell activity intrinsically, with minimal reliance on afferent input. Counter to these observations is the contemporary hypothesis that CA1 neuron activity is driven by a gamma oscillation arising from the medial entorhinal cortex (MEC) that relays information by providing precisely timed synchrony between MEC and CA1. Reinvestigating this in rats during appetitive track running, we found that theta is the dominant frequency of cross-frequency coupling between the MEC and hippocampus, with hippocampal gamma largely independent of entorhinal gamma.</p>
Urine-enriched biochar: coupling sustainability in sanitation and agriculture
<p>Nitrogen adsorption and plant uptake data and growth data from an urine-enriched biochar experiment. </p>
The coupling of the hydrated proton to its first solvation shell
<p>This repository contains tabulated raw data for figures 1, 2, and 4 for the manuscript entitled "The coupling of the hydrated proton to its first solvation shell". Also, all necessary inputs and instructions to reproduce the data are provided.</p>
Data for: Two is better than one: Coupling DNA metabarcoding and stable isotope analysis improves dietary characterizations for a riparian-obligate, migratory songbird
<p>While an increasing number of studies are adopting molecular and chemical methods for dietary characterization, these studies often employ only one of these laboratory-based techniques; an approach which may yield an incomplete, or even biased, understanding of diet due to each method's inherent limitations. To explore the utility of coupling molecular and chemical techniques for dietary characterizations, we applied DNA metabarcoding alongside stable isotope analysis to characterize the dietary niche of breeding Louisiana waterthrush (<em>Parkesia motacilla</em>), a migratory songbird hypothesized to preferentially provision their offspring with pollution-intolerant, aquatic arthropod prey. While DNA metabarcoding was unable to determine if waterthrush provision aquatic and terrestrial prey in different abundances, we found that specific aquatic taxa were more likely to be detected in successive seasons than their terrestrial counterparts, thus supporting the aquatic specialization hypothesis. Our isotopic analysis added greater context to this hypothesis by concluding that breeding waterthrush provisioned Ephemeroptera and Plecoptera, two pollution-intolerant, aquatic orders, in higher quantities than other prey groups, and expanded their functional trophic niche when such prey were not abundantly provisioned. Finally, we found that the dietary characterizations from each approach were often uncorrelated, indicating that the results gleaned from a diet study can be particularly sensitive to the applied methodologies. Our findings contribute to a growing body of work indicating the importance of high-quality, aquatic habitats for both consumers and their pollution-intolerant prey, while also demonstrating how the application of multiple, laboratory-based techniques can provide insights not offered by either technique alone.</p>
Cytosolic peptides encoding CaV1 C-termini downregulate the calcium channel activity-neuritogenesis coupling
<p><span>L-type Ca<sup>2+</sup> (Ca<sub>V</sub>1) channels transduce channel activities into nuclear signals critical to neuritogenesis. Also, standalone peptides encoded by </span><span><span>Ca<sub>V</sub>1</span> DCT (distal carboxyl-terminus) act as nuclear transcription factors reportedly promoting neuritogenesis. Here, by focusing on exemplary </span><span><span>Ca<sub>V</sub>1</span>.3 and cortical neurons under basal conditions, we discover that cytosolic DCT peptides downregulate neurite outgrowth by the interactions with </span><span><span>Ca<sub>V</sub>1</span>'s apo-calmodulin binding motif. Distinct from nuclear DCT, various cytosolic peptides exert a gradient of inhibitory effects on </span><span>Ca<sup>2+</sup></span><span> influx via CaV1 channels and neurite extension and arborization, and also the intermediate events including CREB activation and c-Fos expression. The inhibition efficacies of DCT are quantitatively correlated with its binding affinities. Meanwhile, c</span><span>ytosolic inhibition tends to facilitate neuritogenesis indirectly by favoring </span><span>Ca<sup>2+</sup>-sensitive nuclear retention of DCT. In summary, DCT peptides as a class of </span><span><span>Ca<sub>V</sub>1</span> inhibitors specifically regulate the channel activity-neuritogenesis coupling in a variant-, affinity-, and localization-dependent manner.</span></p>
Proteomic Profiling for Identification of Animal Skin Species in Ancient Egyptian Archaeological Leather using Liquid Chromatography Coupled with Tandem Mass Spectrometry (Nano LC-MS/MS)
<p><strong>Proteomic Profiling Dataset for Identification of Animal Skin Species in Ancient Egyptian Archaeological Leather using Liquid Chromatography Coupled with Tandem Mass Spectrometry (Nano LC-MS/MS)</strong></p>
Code and data for N Le et. al "Scalable and robust quantum computing on qubit arrays with fixed coupling"
<p>Simulation code and data used in N Le et. al "Scalable and robust quantum computing on qubit arrays with fixed coupling."</p>
Beyond the exponential horn: a bush-cricket with ear-canals which function as coupled resonators
<p>Bush-crickets have dual-input, tympanal ears located in the tibia of their forelegs. The sound will, first of all, reach the external sides of the tympana, before arriving to the internal sides through the bush-cricket ear-canal, the acoustic trachea (AT), with a phase lapse and pressure gain. It has been shown that for many bush-crickets, the AT has an exponential horn-shaped morphology and function, producing a significant pressure gain above a certain cut-off frequency. However, the underlying mechanism of different AT designs remains elusive. In this study, we demonstrate that the AT of the duetting bush-cricket <em>Pterodichopetala</em> <em>cieloi</em> function as coupled resonators, producing sound pressure gains at the sex-specific conspecific calling song frequency, and attenuating the remainder – a functioning mechanism significantly different than an exponential horn. Furthermore, it is demonstrated that despite the sexual dimorphism between the <em>P</em>. <em>cieloi</em> AT, both male and female AT have a similar biophysical mechanism. The analysis was carried out using an interdisciplinary approach, where micro-computed tomography was used for the morphological properties of the <em>P</em>. <em>cieloi</em> AT, and a finite-element analysis was applied on the precise tracheal geometry to further justify the experimental results and to go beyond experimental limitations.</p>
Material for manuscript submitted to Earth and Space Science "Evaluation of a mesoscale coupled ocean-atmosphere configuration for tropical cyclone forecasting in the South West Indian Ocean basin"
<p>Configuration files for AROME Indian Ocean, NEMO and OASIS which are necessary to reproduce the results in the publication :</p> <p>Corale, L; Malardel S. , Bielli S. and M-N Bouin (2022) Evaluation of a mesoscale coupled ocean-atmosphere configuration for tropical cyclone forecasting in the South West Indian Ocean basin. <em>Earth and Space Science.</em></p>
Supplementary material: General variational approach to nuclear-quadrupole coupling in rovibrational spectra of polyatomic molecules
<p>Supplementary material to the manuscript: General variational approach to nuclear-quadrupole coupling in rovibrational spectra of polyatomic molecules</p> <p>Composed of three files:</p> <p>1. nh3_efg.zip - compressed folder containing Fortran 90 program, together with the input/output examples and README file, for computing the electric field gradient tensor of NH3.</p> <p>2. nh3_linelist.tar - contains compressed file with the rovibrational line list of NH3 with quadrupole coupling components, together with the Python scripts for extracting desired transitions and README file.</p> <p>nh3_qmom_suppinfo.pdf - PDF file with equations skipped in the main manuscript.</p>
Data to reproduce "CIM-CitySim coupling" on the EPFL campus
<p>The data to reproduce results from the paper " Multi-scale modelling to evaluate building energy consumption at the neighbourhood scale " with DOI : 10.1371/journal.pone.0183437 can be found in this repository.</p> <p>We provide the useful data to rerun the CitySim simulation and provide results also from the experiments conducted as well as data collected on the EPFL campus.</p> <ol> <li>Climate file for Ecublens issued from Meteonorm (Ecublens.cli)</li> <li>Climate file for Ecublens used as input for CIM (remove the header from the Ecublens.cli file)</li> <li>Ground surface temperature for the EPFL campus (epfl_surf_temp.dat)</li> <li>Geometrical characteristics for EPFL campus (epfl_geo_char.dat)</li> <li>Executable file for CIM (canopy.dbx)</li> <li>Climate file for Ecublens issued from CIM (Ecublens_cim.cli)</li> <li>Measured data at 2m above ground and from the LESO-rooftop for year 2015 (u_2m.txt, u_12m.txt, temp_2m.txt, temp_12m.txt, winddir_12m.txt).</li> <li>Simulated data from CIM for the year 2015 at 2m and 12m (u_2m-cim.txt, u_12m-cim.txt, temp_2m-cim.txt, temp_12m-cim.txt, winddir_12m-cim.txt).</li> </ol> <p>If you require anything else please contact the corresponding author.</p>
Supplementary Data: Code, Input Data and Result Summaries: Synergies of sector coupling and transmission extension in a cost-optimised, highly renewable European energy system
<p>Supplementary Data</p> <p><a href="https://arxiv.org/abs/1801.05290"><strong>Synergies of sector coupling and transmission extension in a cost-optimised, highly renewable European energy system</strong></a></p> <p>Authors: T. Brown, D. Schlachtberger, A. Kies, S. Schramm, M. Greiner</p> <p><a href="https://arxiv.org/abs/1801.05290">arXiv:1801.05290</a></p> <p>The files in this record contain the scripts to build the model, input data and result summaries for the model PyPSA-Eur-Sec-30 described in the above publication.</p> <p>The full results files (which include the post-processed input data) can be found in a <a href="https://zenodo.org/record/1146649">companion Zenodo repository</a>. (The supplementary data was split because of the size of the full results.)</p> <p><strong>WARNING:</strong> A newer, improved version of this model, <a href="https://github.com/PyPSA/pypsa-eur-sec">PyPSA-Eur-Sec</a>, is under construction on GitHub.</p> <p><strong>Scripts</strong></p> <p>To use the scripts, you need the following free software Python libraries:</p> <ul> <li><a href="https://github.com/PyPSA/PyPSA">PyPSA</a> for the modelling framework</li> <li><a href="https://github.com/FRESNA/vresutils">vresutils</a> for various helper functions to build the model instance</li> <li><a href="https://github.com/FRESNA/atlite">atlite</a> to process weather data into power system data</li> <li><a href="https://snakemake.readthedocs.io/en/latest/">snakemake</a> to organise the execution of the software</li> </ul> <p>and other standard libraries from the <a href="https://pypi.python.org/pypi">Python Package Index</a> (PyPI), such as pandas, pyomo, countrycode, etc.</p> <p>snakemake requires that all code runs with Python version 3. The code setup is known to work with the following versions: PyPSA 0.12.0, pandas 0.21.1, numpy 0.14.0, scipy 0.19.1, pyomo 5.2. You may need to downgrade your libraries to these versions for the scripts to work. If you insist on using the latest versions, please be aware that you'll need to make at least the following changes:</p> <p>i) To accommodate changes in pandas versions 0.22 and higher, in scripts/prepare_network.py change "costs = costs.loc[idx[:,cost_year,:],"value"].unstack(level=2).groupby("technology").sum()" to "costs = costs.loc[idx[:,cost_year,:],"value"].unstack(level=2).groupby(level="technology").sum(min_count=1)".</p> <p>ii) In later versions of PyPSA the component groups like "pypsa.components.one_port_components" have become network-specific and are stored instead at "network.one_port_components".</p> <p>To solve the optimisation problem the scripts are coded to use the commercial solver <a href="http://www.gurobi.com/">Gurobi</a>. To solve the problems in a reasonable time, you will need <a href="http://www.gurobi.com/">Gurobi</a> or an equivalently fast solver such as <a href="https://www.ibm.com/analytics/data-science/prescriptive-analytics/cplex-optimizer">CPLEX</a>. <a href="http://www.gurobi.com/">Gurobi</a> and <a href="https://www.ibm.com/analytics/data-science/prescriptive-analytics/cplex-optimizer">CPLEX</a> both have cost-free licences for academic users.</p> <p>You will also need a computer with at least 64 GB of RAM, since pyomo and the solver are memory intensive.</p> <p>The Python scripts in this repository (in the directory scripts/) are released under the <a href="https://www.gnu.org/licenses/gpl-3.0.en.html">GNU General Public Licence Version 3.0</a> (GPL 3.0).</p> <p>The scripts build_*.py process all raw input data into a form where it can be used in the model.</p> <p>make_options.py prepares the options.yml file for each model run.</p> <p>prepare_network.py populates the PyPSA network for each model run with the input data.</p> <p>solve_network.py solves the optimisation problem with <a href="http://www.gurobi.com/">Gurobi</a> or the solver of your choice (this step takes several hours).</p> <p>make_summary.py aggregates the results into CSV files in the directory results/ (also provided in this repository).</p> <p>The scripts plot_*.py and paper_graphics*.py prepare graphical output.</p> <p>All scripts are managed with the <a href="http://snakemake.readthedocs.io/en/latest/">snakemake</a> workflow management tool.</p> <p>To run the scripts, adjust the parameters in config.yaml and cluster.yaml to your local configuration. Then simply execute</p> <pre><code>snakemake</code></pre> <p>for the rule you want to run.</p> <p>Since the jobs are computationally intensive you may want to run them on a cluster. To run the jobs on a cluster with <a href="https://slurm.schedmd.com/">Slurm</a>, then execute e.g.</p> <pre><code>./snakemake_cluster --jobs 6</code></pre> <p>The cluster is configured in cluster.yaml. You will need to create the directory for the logs, i.e. logs/cluster/, before running the script.</p> <p><strong>Data</strong></p> <p>All input data (in the directory scripts/) and results summaries (in the directory results/) are released under the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International Licence</a> (CC BY 4.0), except those where explicit sources and licences are mentioned in the data folders.</p> <p>The input data include:</p> <ul> <li>Electricity sector data, which largely follows the <a href="https://doi.org/10.5281/zenodo.804337">Zenodo repository</a> for <strong><a href="https://doi.org/10.1016/j.energy.2017.06.004">The Benefits of Cooperation in a Highly Renewable European Electricity Network</a></strong>, except the current repository uses the <a href="https://data.open-power-system-data.org/time_series/2017-07-09/">Open Power System Data Time Series Data Package</a> for load data and <a href="http://renewables.ninja/">Renewables.ninja</a> for solar time series.</li> <li>Heating time series based on the degree-day approximation, constructed with the library <a href="https://github.com/FRESNA/atlite">atlite</a>.</li> <li>Hourly traffic statistics for a week from the German Federal Highway Research Institute (BASt).</li> <li>Yearly energy per country per sector from the <a href="http://www.indicators.odyssee-mure.eu/energy-efficiency-database.html">Odyssee database</a> and <a href="http://ec.europa.eu/eurostat/web/energy/data/energy-balances">Eurostat</a>.</li> <li>A cost database with literature sources.</li> </ul>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
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.