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Consensus-seeking and conflict-resolving: an fMRI study on college couples’ shopping interaction
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Dataset of "Electronic structure and defect states in bismuth and antimony sulphides identified by energy-resolved electrochemical impedance spectroscopy"
Understanding the nature of the defects in the absorber materials, namely point defects, their formation mechanism and the contribution to the properties is essential for the photovoltaic device performance improvement. They are one the reasons why chalcogenide-based solar cells do not yet meet expected high power conversion efficiencies. Here we identify and present energy distribution of defects in Bi2S3 and Sb2S3, and their (SbxBi(100-x))2S3 alloys (with x = 0, 10, 33, 50, 67, 90, 100 at% Sb content) chalcogenides, being explored for emerging photovoltaic applications as they are earth-abundant and highly absorbing in the visible light range. We show that their density of states (DOS) and related parameters can be obtained experimentally by energy-resolved electrochemical impedance spectroscopy (ER-EIS) in a technically simple and quick way, where ER-EIS data are well correlated with theoretical DFT calculations. ER-EIS reveals that in Bi2S3 there are only shallow defects at CBM. In Sb2S3, ER-EIS reveals also midgap states which can be the cause of low electrical conductivity of Sb2S3. We also explain the discrepancy in the reported values of ionisation potentials and the bandgaps of the Bi- and Sb-chalcogenides. Dominant sulphur vacancy defect was identified in Bi- and Sb-chalcogenides whereas in ternary (SbxBi(100-x))2S3 system, merely 10 at.% of Bi transforms the midgap sulphur defects to shallow ones. This provides novel strategy for healing the midgap defects in Sb2S3, which is crucial for boosting the PV performance and tuning the electrical conductivity in Sb2S3.
Dynamic Reconstructions of Sagittarius A* with Resolve from 2017 EHT data
<p>This repository contains the dynamic reconstructions of Sagittarius A* (SgrA*) from (EHT) data using the Resolve framework, as presented in "Resolving Horizon-Scale Dynamics of Sagittarius A*".</p>
Dataset - Decrypting lysine deacetylase inhibitor action and protein modifications by dose-resolved proteomics
<h4><strong>Dataset Summary</strong></h4> <p>Lysine deacetylase inhibitors (KDACis) are approved for cutaneous T-cell lymphoma (CTCL), peripheral T-cell lymphoma (PTCL), and multiple myeloma. Despite the mechanism of action(s) (MoA) remains elusive, these inhibitors lead to increasing acetylation levels of histones and other proteins, altered gene expression and cell death. To characterize the MoA of these drugs in more detail, we systematically measured dose-dependent changes in protein expression, acetylation, and phosphorylation in response to 21 clinical and pre-clinical KDACis. MV4-11 cells were treated for 6 h with 1 vehicle control and 10 increasing doses of the respective drug (from 100 pM to 30 mM). Proteins were digested with trypsin, and the resulting 11 peptide preparations corresponding to one drug dose each were encoded by stable isotopes (tandem mass tags, TMT-11plex) and combined. Acetylated peptides were subsequently enriched by immunoprecipitation and phosphopeptides by immobilized metal affinity chromatography (IMAC). PTM-carrying and unmodified peptides were analyzed separately by liquid chromatography tandem mass spectrometry (LC-MS/MS) for peptide and protein identification and quantification. Additionally, Vorinostat and Panobinostat were also recorded as time-dependent experiments at their pEC50 concentration, respectively. </p> <h4><strong>Dataset structure</strong></h4> <p>Here, we provide all curve data processed with CurveCurator v0.4.0 (<a href="https://github.com/kusterlab/curve_curator">https://github.com/kusterlab/curve_curator</a>). Each drug is a zip folder containing acetylome, phosphoproteome, and fullproteome data. Next to each data set is the toml parameter file used to generate the curves.txt and dashboard.html files. Time-dependent data is indicated by "td" and dose-dependent data is indicated by "dd".</p> <p> </p>
A vertically-resolved atmospheric dust reanalysis for Mars Years 28-29 using Analysis Correction
<p>This is a dataset of meteorological variables for the atmosphere of Mars, obtained by assimilating measurements (retrievals) of atmospheric temperature and dust opacity into a 3-dimensional, time-dependent numerical model of the Martian atmospheric circulation (known as a “reanalysis”).</p> <p>The observations come from two spacecraft - the Mars Climate Sounder (MCS) instrument on board NASA’s Mars Reconnaissance Orbiter (e.g. Kleinboehl et al. 2009) and the Thermal Emission Imaging Spectrometer (THEMIS) on board NASA’s Mars Odyssey spacecraft, and cover the period from 21 September 2006 until 5 November 2009 (Mars Years 28:Ls=109.98 - 30:Ls=4.78). MCS observations include profiles of temperature and dust opacity from near the surface up to altitudes of around 80 km obtained from infrared limb-sounding (MCS version 3 retrievals, based on opacities at around 21.6 micron wavelengths), while THEMIS measurements are of column dust opacity in the infrared (centred around 9.3 micron wavelength). Further details can be found on the websites</p> <p>https://pds-geosciences.wustl.edu/missions/odyssey/themis.html,<br> https://atmos.nmsu.edu/data and services/atmospheres data/MARS/aerosols.html</p> <p>The model into which the observations are assimilated is the UK version of Laboratoire de Météorologie Dynamique Mars Global Circulation Model (LMDMGCM), a 3-dimensional, time-dependent numerical circulation model of the Martian atmosphere and near-surface environment, simulating the changing winds, temperature, pressure and dust content of the atmosphere across the whole planet. The model solves the equations of motion, mass and energy conservation using a spherical harmonic representation in the horizontal and finite difference formulation in the vertical direction, but outputs the data here on a regular longitude-latitude grid with 72 points in longitude, 36 points in latitude and 25 terrain-following sigma levels in the vertical direction (where sigma = pressure/surface pressure) on a stretched vertical grid that extends from the surface to an altitude of approximately 100 km. More details can be found in publications by Forget et al. (1999), Newman et al. (2001), Mulholland et al. (2013).</p> <p>The observations and model are linked by an assimilation scheme, based on the Analysis Correction (AC) algorithm developed by Lorenc et al. (1991) and adapted for Mars by Lewis et al. (2007). Previous reanalyses of Mars observations using this scheme include the MACDA dataset (Montabone et al. 2014) and OPENMars (Holmes et al. 2020). This new dataset, however, makes use of an extension of the AC scheme to enable assimilation of both column integrated dust opacity measurements and dust opacity profiles in the vertical direction (see Ruan et al. 2021). This new dataset therefore provides a more realistic representation of the distribution of dust loading in the Martian atmosphere than previous work, which may also result in improved representation of other meteorological variables, notably temperature.</p> <p>Data are provided as 2D and 3D fields of variables in netCDF format as generated by the numerical model on the (longitude, latitude, sigma) grid at 2-hourly intervals. Each file contains 360 time steps covering 30 Martian days or sols. The variables contained in each file are as follows:</p> <p> Variables and attributes<br> 0 lon: FLOAT(72) = FLOAT(lon)<br> 0 long_name: longitude<br> 1 units: degrees_east<br> 1 lat: FLOAT(36) = FLOAT(lat)<br> 0 long_name: latitude<br> 1 units: degrees_north<br> 2 sigma: FLOAT(25) = FLOAT(sigma)<br> 0 long_name: sigma<br> 1 units: sigma_level = p/ps<br> 3 soil: FLOAT(18) = FLOAT(soil)<br> 0 long_name: soil levels (i.e. levels below the surface to represent thermal variations)<br> 1 units: none<br> 4 time: FLOAT(360) = FLOAT(time)<br> 0 long_name: model time<br> 1 units: days since 00:00:00 (the beginning of the file)<br> 5 controle: FLOAT(100) = FLOAT(lentable)<br> 0 long_name: Table of run parameters<br> 1 description: MGCM run 5.000<br> 6 Ls: FLOAT(360) = FLOAT(time)<br> 0 Physics_diagnostic: Solar longitude (such that Ls=0 is northern Spring equinox)<br> 1 units: deg<br> 7 tsurf: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> 0 Physics_diagnostic: Surface temperature<br> 1 units: K<br> 8 ps: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> 0 Physics_diagnostic: surface pressure<br> 1 units: Pa<br> 9 co2ice: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> 0 Physics_diagnostic: co2 ice thickness (column mass density)<br> 1 units: kg.m-2<br> 10 fluxsurf_lw: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> 0 Physics_diagnostic: fluxsurf_lw (surface infrared radiative flux)<br> 1 units: W.m-2<br> 11 fluxsurf_sw: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> 0 Physics_diagnostic: fluxsurf_sw (surface visible radiative flux)<br> 1 units: W.m-2<br> 12 temp: FLOAT(72,36,25,360) = FLOAT(lon,lat,sigma,time)<br> 0 Physics_diagnostic: temperature<br> 1 units: K<br> 13 u: FLOAT(72,36,25,360) = FLOAT(lon,lat,sigma,time)<br> 0 Physics_diagnostic: Zonal (east-west) wind<br> 1 units: m.s-1<br> 14 v: FLOAT(72,36,25,360) = FLOAT(lon,lat,sigma,time)<br> 0 Physics_diagnostic: Meridional (north-south) wind<br> 1 units: m.s-1<br> 15 rho: FLOAT(72,36,25,360) = FLOAT(lon,lat,sigma,time)<br> 0 Physics_diagnostic: density<br> 1 units: kg.m-3<br> 16 udrag: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> 0 Physics_diagnostic: Drag velocity<br> 1 units: m/s<br> 17 udragt: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> 0 Physics_diagnostic: Threshold velocity for dust lifting<br> 1 units: m/s<br> 18 aerosol: FLOAT(72,36,25,360) = FLOAT(lon,lat,sigma,time)<br> 0 Physics_diagnostic: dust opacity considering layer thickness<br> 1 units: SI (opacity/m)<br> 19 taudustvis: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> 0 Physics_diagnostic: Dust optical depth<br> 1 units: SI<br> 20 q01: FLOAT(72,36,25,360) = FLOAT(lon,lat,sigma,time)<br> 0 Physics_diagnostic: mix. ratio<br> 1 units: kg/kg<br> 21 dqsdevtot: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> 0 Physics_diagnostic: dust devil lift rate<br> 1 units: kg.m-2.s-1<br> 22 dqsstrtot: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> 0 Physics_diagnostic: near surface wind stress dust lifting rate<br> 1 units: kg.m-2.s-1<br> 23 dqssedtot: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> 0 Physics_diagnostic: dust sedimentation rate<br> 1 units: kg.m-2.s-1</p> <p> </p>
Time-resolved core-level photoemission data of tungsten diselenide
<p>Pump-probe core-level photoemission spectroscopy data of tungsten diselenide (WSe2) measured using an electron momentum microscope at the FLASH Free-electron laser. </p>
Data used in "BIOPERIANT12: a mesoscale resolving coupled physics-biogeochemical model for the Southern Ocean"
<div> <p>This repository contains the data used to generate the figures for the submitted manuscript "BIOPERIANT12: a mesoscale resolving coupled physics-biogeochemical model for the Southern Ocean".</p> </div> <h3>Contents</h3> <div> <ul> <li> <p>Model input:</p> <ul> <li> <p>INPUTS: ocean model input/grid files</p> </li> <li> <p>PISCES_INPUTS: BGC input files</p> </li> <li> <p>OBC: open boundary forcing </p> </li> <li> <p>WEIGHTS: weight files for ERA interim forcing</p> </li> </ul> </li> </ul> </div> <div> <ul> <li> <p>Manuscript files:</p> <ul> <li> <p>data: files used to generate manuscript images</p> </li> <li> <p>config, src, notebooks: Python code and Jupyter notebooks used to generate images</p> </li> <li> <p>figures, supplementary: manuscript figures and supplementary figures</p> </li> </ul> </li> </ul> </div> <div> </div> <div><strong>Abstract: </strong>"We present BIOPERIANT12, a regional model configuration of the Southern Ocean (SO) at a mesoscale-resolving 1/12 degree. This is a stable, ocean–ice–biogeochemical configuration derived from the Nucleus for European Modelling of the Ocean (NEMO) modelling platform. It is specifically designed to investigate questions related to the mean state, seasonal cycle variability and mesoscale processes in the mixed layer and within the upper ocean (<1000 m). In particular, the focus is on understanding processes behind carbon and heat exchange, systematic errors in biogeochemistry and assumptions underlying the parameters chosen to represent these SO processes. The dynamics of the ocean model play a large role in driving ocean biogeochemistry and we show that over the chosen period of analysis 2000–2009 that the simulated dynamics in the upper ocean provide a stable mean state, as compared to observation-based datasets (themselves subject to biases such as sparsity of data, cloud cover, etc.), and through which the characteristics of variability can be described. Using ocean biomes to delineate the major regions of the SO, the model demonstrates a useful representation of ocean biogeochemistry and partial pressure of carbon dioxide (pCO2). In addition to a reasonable model mean state performance, through model–data metrics BIOPERIANT12 highlights several pathways for improving Southern Ocean model simulations such as the representation of temporal variability and the overestimation of biological biomass."</div>
CAMELS-LUX: Highly Resolved Hydro-Meteorological and Atmospheric Data for Physiographically Characterized Catchments around Luxembourg
<p>The CAMELS-LUX dataset encompasses hydro-meteorological time series and catchment attributes for 56 partly nested stream gauges feeding into the Luxembourgish stream network. The data is available at three temporal resolutions: daily, hourly and at a 15-minute resolution and spans the hydrological years from 2004-11-01 to 2021-10-31. The static catchment attributes cover parameters classifying the topography, geology and land use as well as climatic and hydrologic annual statistics of the 17-year time period.</p> <p>While an in depth description of the dataset as well as background information on catchments, the environment and exact calculation methods is provided in the accompanying publication in ESSD, the dataset description below isolates information on the available parameters and data structure contained in the provided files.</p> <p>Please note that the dataset might not include data corrections or validations that are subject to a date later than the date of the retrieval of the data for the processing of this dataset. This dates back to 2022 for most hydrologic time series, and to 2023 for the reanalysis data or the precipitation data. We are aware of duplicate rows in the time series file with a resolution of 15 minutes for catchment 16 as well as time stamp shifts in the precipitation data. We are working on correcting these data to update this dataset.</p> <p><strong>Data structure</strong></p> <p><strong>Time series data</strong></p> <ol> <li>Hydrologic parameters</li> <li>Precipitation parameters</li> <li>Air temperature and potential evapotranspiration parameters</li> <li>Thunderstorm relevant atmospheric parameters</li> <li> Soil Moisture parameters</li> </ol> <p><strong>Static catchment attributes</strong></p> <ol> <li>Basin IDs</li> <li>Meta catchment attributes</li> <li>Climatic catchment attributes</li> <li>Geologic catchment attributes</li> <li>Land use catchment attributes</li> <li>Topographic catchment attributes</li> </ol> <p><strong>Spatial data - shapefiles</strong></p>
BNNOz - Infilled vertically resolved ozone dataset
<p>This vertical ozone dataset is a fusion of an existing ozone dataset (<a href="http://www.bodekerscientific.com/data/monthly-mean-global-vertically-resolved-ozone">Bodeker Scientific</a>) with chemistry-climate model output from the Chemistry-Climate modelling initiative.</p> <p>The vertically and latitudinally resolved ozone dataset (zmo3_BNNOz.nc) has been produced by fusing the above data within a <a href="https://proceedings.neurips.cc/paper/2020/file/0d5501edb21a59a43435efa67f200828-Paper.pdf">Bayesian neural network</a>.</p> <p>More information about this processing and the data can be found <a href="https://github.com/mattramos/VertOzone-BNN">here</a>.</p> <p>In addition to the output product we include the training dataset of observed and modelled ozone as a python pickled dataframe. The code to use this training dataset can be found <a href="https://github.com/mattramos/VertOzone-BNN">here</a>.</p> <p>This data submission supports a manuscript submission to ESSD.</p>
Super-resolving ocean dynamics from space with computer vision algorithms: training datasets
<p>We provide here the datasets used for the development of the dilated Adaptive Residual Network for the super-resolution of ocean Absolute Dynamic Topography described in <em>Buongiorno Nardelli et al.</em> (2022). The model is designed to combine satellite altimetry and thermal observations and provides super-resolved dynamic topography. The training/test datasets have been built starting from the data originally prepared for an Observing System Simulation Experiment carried out in the framework of the European Space Agency CIRCOL project [<em>Ciani et al.</em>, 2021]. They consist of one year of synthetic daily Absolute Dynamic Topography (ADT), surface geostrophic currents and sea surface temperature data obtained from Copernicus Marine Service Mediterranean Forecasting System (MFS) (Product ID: MEDSEA-ANALYSIS- FORECAST-PHY-006-013) [<em>Clementi et al. 2021</em>]. Synthetic Altimeter-derived ADT maps were obtained by first sampling the model output along the actual tracks of a synthetic constellation composed of 4 Radar Altimeters: Jason-3, Sentinel-3A, SARAL/Altika, and Cryosat-2 missions (this step is achieved by running the SWOT simulator software [<em>Gaultier et al.</em>, 2016]) and successively applying the DUACS (<em>Data Unification and Altimeter Combination System)</em> mapping method. The original input images cover the entire Mediterranean domain at 1/24° spatial resolution, leading to an individual image size of 380x1000 pixels. Here, we have randomly chosen 40 dates (~11% of the total) to be kept aside as fully independent test data, and successively re-sampled the original images extracting much smaller tiles (76x100), which are used as input to the network training. The tiles are extracted by going through a double loop on latitude and longitude, imposing a spatial overlap of 50%. Full details on data pre-processing (e.g.normalization strategies) are given in the paper:</p> <ul> <li>Buongiorno Nardelli, B.; Cavaliere, D.; Charles, E.; Ciani, D. Super-Resolving Ocean Dynamics from Space with Computer Vision Algorithms. <em>Remote Sens.</em>, <strong>2022</strong>, 14, 1159. https://doi.org/10.3390/rs14051159</li> </ul>
Urban form data for climate modelling: Sydney at 300 m resolution derived from building-resolving and 2 m land cover datasets
<p><strong>Sydney morphology and land surface dataset</strong></p> <p>This dataset for Sydney, Australia, represents land cover, building morphology, vegetation morphology and other parameters appropriate for input into local or mesoscale urban climate models.</p> <p>The dataset is provided in netCDF4 and GeoTiff formats.</p> <p>Associated manuscript:</p> <blockquote> <p><a href="https://doi.org/10.3389/fenvs.2022.866398">A transformation in city-descriptive input data for urban climate models</a></p> </blockquote> <p>Citation for the open dataset:<br> - Lipson, M., Nazarian, N., Hart, M. A., Nice, K. A., and Conroy, B.: Urban form data for climate modelling: Sydney at 300 m resolution derived from building-resolving and 2 m land cover datasets (v1.01), <a href="https://doi.org/10.5281/zenodo.6579061">https://doi.org/10.5281/zenodo.6579061</a>, 2022.</p> <p>Citation for the associated manuscript:<br> - Lipson, M. J., Nazarian, N., Hart, M. A., Nice, K. A., and Conroy, B.: A Transformation in City-Descriptive Input Data for Urban Climate Models, Frontiers in Environmental Science, 10, <a href="https://doi.org/10.3389/fenvs.2022.866398">https://doi.org/10.3389/fenvs.2022.866398</a>, 2022.</p> <p>Location of associated processing code:<br> - <a href="https://github.com/matlipson/geoscape_processing_public.git">https://github.com/matlipson/geoscape_processing_public.git</a></p> <p><strong>Acknowledgments</strong></p> <p>We gratefully acknowledge the Australian Urban Research Infrastructure Network (AURIN) and Geoscape Australia for <br> providing the datasets necessary for this study, drawing on Geoscape Buildings, Surface Cover and Trees datasets, <br> © Geoscape Australia, 2020: https://geoscape.com.au/legal/data-copyright-and-disclaimer/. <br> This research was supported by the Australian Research Council (ARC) Centre of Excellence for Climate System Science <br> (grant CE110001028), the ARC Centre of Excellence for Climate Extremes (grant CE170100023). </p> <p> </p>
Photoelectron circular dichroism in angle-resolved photoemission from liquid fenchone - data
<p>Data set pertaining to the article "Photoelectron circular dichroism in angle-resolved photoemission from liquid fenchone" | Physical Chemistry Chemical Physics, rsc.org, doi: <a href="https://doi.org/10.1039/D1CP05748K">10.1039/D1CP05748K</a></p> <p>Files with extension .h5 are hdf5-files structured according to the NeXus standard v2022.06, see<br> https://www.nexusformat.org/<br> https://fairmat-experimental.github.io/nexus-fairmat-proposal/50433d9039b3f33299bab338998acb5335cd8951/mpes-structure.html<br> NeXus data files can be opened with any software capable of opening hdf5-structured files. The following viewers are adapted to the specifics of the NeXus data format:<br> * nexpy (distributed with python)<br> * https://h5web.panosc.eu/h5wasm (web-based NeXus viewer maintained by the European Photon and Neutron Open Science Cloud-consortium)</p> <p>Files with extension .txt are ascii-files. Files with extension .csv are comma-separated ascii-files, designed to be opened with a spreadsheet programme.</p> <p><br> The following files are provided:</p> <p>Photoemission data from liquid (1R,4S)-Fenchone<br> 1R-fenchone_dset1.h5<br> 1R-fenchone_dset2.h5<br> 1R-fenchone_dset3.h5</p> <p>Photoemission data from liquid (1S,4R)-Fenchone<br> 1S-fenchone.h5</p> <p><br> Numeric representations of the traces shown in the following figures:<br> fig1.txt<br> fig2a_asymmetry.txt : Asymmetry data shown in Fig. 2a<br> fig2a_traces.txt : Photoemission curves shown in Fig. 2a<br> fig2b_expo_m.txt : Photoemission curve shown in Fig. 2b, (l-CPL)<br> fig2b_expo_p.txt : Photoemission curve shown in Fig. 2b, (r-CPL)<br> fig2c_sum_m.txt<br> fig2c_sum_p.txt</p> <p> </p> <p>Data points shown in Fig. 3 and Table 1.:<br> fig3.csv<br> This file uses the following conventions:<br> Values are given for <em>b</em><sub>1</sub>*100.<br> Rows are labelled:<br> b_ds_1_L_exp : (1R,4S)-Fenchone, dataset 1, exp-model<br> ...<br> b_ds_3_R_exp : (1S,4R)-Fenchone, exp-model<br> ...<br> b_average_L : Averaged data, for (1R,4S)-Fenchone<br> b,liq_average_L : Averaged data corrected for presence of gas phase and dependence on angular distribution parameter, for (1R,4S)-Fenchone<br> b_average_R : Averaged data, for (1S,4R)-Fenchone<br> ...</p> <p><br> Numeric representations of the traces shown in the following figures from the supplementary material:<br> sfig1_traces-p.txt<br> sfig2_totalcounts.txt<br> sfig3_ttrace.txt<br> sfig4_roi_301.txt<br> sfig5_asymm_301.txt</p> <p> </p> <p>Contact: Uwe Hergenhahn, uhe@fhi.mpg.de .</p>
Selected data(s) from : Femtosecond direct laser writing of silver clusters in phosphate glasses for x-ray spatially-resolved dosimetry
<p>The data selected is based on the figures below, published in the linked article (see the doi).</p> <p><strong>- Figure 1.</strong> Microscopy fluorescence image of ARGOi glass sample (excitation at 365 nm) of laser-inscribed structures for the different writing irradiances at two different depths: (<strong>a</strong>) structures at 150 µm below the glass front surface, (<strong>b</strong>) structures at 550 µm below the glass front surface, and at 150 µm from the glass rear surface. <strong>(Only picture)</strong></p> <p>- <strong>Figure 2.</strong> (<strong>a</strong>) Transparent color before irradiation (ARGO glass sample), (<strong>b</strong>) yellow color after X-ray irradiation with 222 Gy (ARGO* glass sample). <strong>(Only picture)</strong></p> <p><strong>- </strong> <strong>Figure 3.</strong> (<strong>a</strong>) Absorption spectra of the ARGO and ARGO* glass sample after various X-ray doses and the difference absorption coefficient spectrum for 222 Gy vs. pristine. (<strong>b</strong>) Fit of the radiation-induced spectrum (difference between 222 Gy and pristine) considering Gaussian energy contributions for ARGO and ARGO*. (<strong>c</strong>) Absorption spectra for the GPN and GPN* glasses for X-ray doses from 5 mGy to 3 kGy [<a href="https://www.mdpi.com/2227-9040/10/3/110/htm#B39-chemosensors-10-00110">39</a>]. (<strong>d</strong>) The difference absorption coefficient spectra between different doses conditions for GPN and GPN* [<a href="https://www.mdpi.com/2227-9040/10/3/110/htm#B39-chemosensors-10-00110">39</a>]. (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure3_2022-03-03_V01. <strong>Figure 3</strong></li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure3_Datas_2022-03-03_V01. Datas : <strong>wavelength, effective absorption coefficient (cm-1)</strong></li> </ol> <p>- <strong>Figure 4.</strong> Micro-luminescence of GPN* glass performed on the optically polished glass side: (<strong>a</strong>) integrated fluorescence intensity at different depths, (<strong>b</strong>) normalized spectrum evolution with depth for the 500 Gy dose [<a href="https://www.mdpi.com/2227-9040/10/3/110/htm#B39-chemosensors-10-00110">39</a>]. (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure4_2022-03-03_V01. Figure 4</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure4_Datas_2022-03-03_V01. Datas</li> </ol> <p>- <strong>Figure 5.</strong> Estimated depth-dependent profiles in absolute values of the linear absorption coefficient at 405 nm. (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure5_2022-03-03_V01. Figure 5</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure5_Datas_2022-03-03_V01. Datas : <strong>sample depth (mm) ; scaled linear absorption coefficient profile at 405 nm (mm-1)</strong></li> </ol> <p>- <strong>Figure 6.</strong> (<strong>a</strong>) X-ray energy spectra simulated by SpekPy for each irradiation facility, normalized by integral. (<strong>b</strong>) Geant4-simulated dose inside each sample, normalized by the surface dose; filled areas show uncertainties at 95% confidence. (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure6_2022-03-03_V01. Figure 6</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure6_Datas_2022-03-03_V01. Datas : <strong>ARGO 100KV_dose ; GPN-20KV_dose ; GPN-32KV_dose</strong></li> </ol> <p>- <strong>Figure 7.</strong> Radio-photoluminescence measurement of the GPNi* glass for the inscribed structure [<a href="https://www.mdpi.com/2227-9040/10/3/110/htm#B39-chemosensors-10-00110">39</a>]. (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure7_2022-03-03_V01. Figure 7</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure7_Datas_2022-03-03_V01. Datas : <strong>wavelength ; relative intensity a.u.</strong></li> </ol> <p>- <strong>Figure 8.</strong> Normalized RPL spectra excited at 325 nm: (<strong>a</strong>) for the ARGO (pristine—right axis) and ARGO* (X-ray irradiation at 222 Gy—left axis) glasses collected around 150 µm below the surface, (<strong>b</strong>,<strong>c</strong>) for the highest DLW irradiance structure for ARGOi and ARGOi* in the front- and the rear-inscribed surfaces, respectively. (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure8_2022-03-03_V01. Figure 8</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure8_Datas_2022-03-03_V01. Datas : <strong>inscribed glass...</strong></li> </ol> <p>- <strong>Figure 9.</strong> (<strong>a</strong>) Differential linear absorption coefficient of the laser-inscribed structures (11 TW/cm<sup>2</sup>) for the two planes after irradiation at 222 Gy X-ray dose in the ARGOi* glass sample. (<strong>b</strong>) Average differential absorption of the inscribed structures for all DLW irradiance (as from <a href="https://www.mdpi.com/2227-9040/10/3/110/htm#fig_body_display_chemosensors-10-00110-f009">Figure 9</a>a). (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure9_2022-03-03_V01. Figure 9</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure9_Datas_2022-03-03_V01. Datas : <strong>integrated differential linear absoprtion percentage ; irradiance (TW/cm2)</strong></li> </ol> <p>- <strong>Figure 10.</strong> (<strong>a</strong>) Phase image under white light illumination of the laser inscribed structure (11 TW/cm<sup>2</sup>) before irradiation. (<strong>b</strong>) Optical path difference determined from the phase image. (<strong>c</strong>) The refractive index modification Δ<em>n</em> as a function of laser irradiance before/after 222 Gy-dose for the two planes in ARGOi, ARGOi* glass sample. (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure10_2022-03-03_V01. Figure 10</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure10_Datas_2022-03-03_V01. Datas : <strong>refractive index modification ; irradiance (TW/cm2), Error bar</strong></li> </ol> <p><strong>- Figure 11.</strong> Comparison between calculated and measured Δ<em>n</em>ˆ after irradiation for a decrease in the initial value of <em>N</em><em>α</em>3 by 0.48%: (<strong>a</strong>,<strong>c</strong>) the real part Δ<em>n</em> for the front and rear surfaces, respectively; (<strong>b</strong>,<strong>d</strong>) their imaginary counterparts Δ<em>κ</em>, respectively. (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure11_2022-03-03_V01. Figure 11</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure11_Datas_2022-03-03_V01. Datas : <strong>rear surface...</strong></li> </ol> <p><strong>- Figure 12.</strong> Integrated measure of the amplitude of fluorescence intensity for the different laser irradiance before and after 222 Gy-dose for the two planes in ARGOi and ARGOi* glass sample. (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure12_2022-03-03_V01. Figure 12</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure12_Datas_2022-03-03_V01. Datas : <strong>integrated measure of the amplitude of fluorescence intensity ; Irradiance (TW/cm2) ; Error bar </strong></li> </ol> <p><strong>- Figure 13.</strong> (<strong>a</strong>) Composite FLIM and fluorescence intensity microscopy images of the laser-induced structure (11 TW/cm<sup>2</sup>) before and after irradiation for an emission at 425 nm from the front surface; the color-code represents the mean lifetime obtained by FAST-FLIM algorithm (color scale from 0 to 31 ns); inset: luminescence intensity only (grey-scale from 0 to 45 counts). (<strong>b</strong>) Same composite FLIM and luminescence intensity images for an emission at 510 nm. (<strong>c</strong>) Luminescence decays in arbitrary units for the emission at 425 nm of the same structure before and after irradiation for the two surfaces, and fitting curves thereof using three exponential decay functions. (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure13_2022-03-03_V01. Figure 13</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure13_Datas_2022-03-03_V01. Datas : <strong>fluorescence intensity (arbitrary units) ; time (ms)</strong></li> </ol> <p>- <strong>Figure 14.</strong> Dose-dependent evolution of the amplitude ratio of extracted spectral bands for (<strong>a</strong>) the GPNi* glass sample for DLW irradiance of 13.4 TW/cm<sup>2</sup> at 160 µm below the glass surface, (<strong>b</strong>) the ARGOi and ARGOi* glass sample for DLW irradiance of 11 TW/cm<sup>2</sup> at 550 µm below the glass surface (rear surface). (<strong>Picture, xls datas</strong>)</p> <ol> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure14_2022-03-03_V01. Figure 14</li> <li>ANR_ ARCHIFLUO_ICMCB_Article_Chemosensors _Figure14_Datas_2022-03-03_V01. Datas : <strong>ratio of amplitudes of spectral bands ; doses (gy)</strong>.</li> </ol>
Datasets for "Single-molecule and super-resolved imaging deciphers membrane behaviour of onco-immunogenic CCR5"
<p><strong>Flow cytometry</strong></p> <p>Modality / instrument: <em>Flow cytometer</em> <em>(CytoFLEX LX, Beckman Coulter)</em></p> <p>File format:<em> FCS + XIT (CytExpert, Beckman Coulter).</em></p> <p>Samples and acquisitions:</p> <p>Fluorescent fusions in live Chinese Hamster ovary (CHO) cells.</p> <table> <tbody> <tr> <td> <p><em>File</em></p> </td> <td> <p><em>Cell line</em></p> </td> <td> <p><em>Runs</em></p> </td> <td> <p><em>Cells counted</em></p> </td> </tr> <tr> <td> <p>CONTROL.fcs</p> </td> <td> <p>CHO wild-type</p> </td> <td> <p>1</p> </td> <td> <p>7000</p> </td> </tr> <tr> <td> <p>GFP-CCR5.fcs</p> </td> <td> <p>CHO-GFP-CCR5</p> </td> <td> <p>1</p> </td> <td> <p>7000</p> </td> </tr> <tr> <td> <p>Exp_20220916_1_GFP.xit</p> </td> <td> <p>N/A - metadata</p> </td> </tr> </tbody> </table> <p>Approx. size 6 MB</p> <p> </p> <p><strong>PaTCH microscopy images</strong></p> <p>Imaging modality / instrument: <em>Brightfield</em> + <em>PaTCH fluorescence microscopy</em></p> <p>Image format:<em> OME TIFF (16 bit) + MicroManager metadata files</em></p> <p>Microscope settings:</p> <p><em>488 nm triggered excitation; split red/green detection, cropped to green (GFP) channel only; 10 ms/frame laser exposure; 13.5 ms/frame-to-frame; 53 nm/px. Photometrics Prime95b CMOS.</em></p> <p>Samples and acquisitions:</p> <p>Fluorescent fusions of GFP-CCR5 receptor in live CHO cells imaged with and without 100 nM CCL5 ligand. Each subfolder corresponds to a field of view and contains one brightfield and one PaTCH acquisition of the same cell.</p> <table> <tbody> <tr> <td> <p>Folder</p> </td> <td> <p>Condition</p> </td> <td> <p>Fields of view</p> </td> </tr> <tr> <td> <p>AC6 CONTROL sc</p> </td> <td> <p>CCL5-</p> </td> <td> <p>11</p> </td> </tr> <tr> <td> <p>AC6 CCL5 sc</p> </td> <td> <p>CCL5+ (100 nM)</p> </td> <td> <p>10</p> </td> </tr> </tbody> </table> <p>Approx. size before compression: 14 GB</p> <p> </p> <p><strong>Structured illumination microscopy - volumetric stacks</strong></p> <p>Imaging modality / instrument: <em>SIM fluorescence microscopy (custom setup at NPL based on Olympus IX71)</em></p> <p>Image format:<em> OME TIFF (16 bit) with intrinsic metadata (voxel size)</em></p> <p>Microscope settings: <em>638 nm excitation; 60x/1.3 NA; Flash 4.0, Hamamatsu Photonics. For additional details see the reference below (Hunter et al, bioRxiv).</em></p> <p>Samples and acquisitions:</p> <p>Dylight 650-MC-5 labeled CCR5 receptor in fixed CHO-CCR5 cells, imaged with and without 100 nM CCL5 ligand. Each acquisition is of a unique field of view and contains one SIM reconstruction as an XYZ volumetric stack. ‘Basal membrane’ acquisitions consist of 5 slices at 200 nm z-intervals across the range of the basal membrane. ‘Whole cell' acquisitions are made up of 7 slices with 500 nm z-interval ranging from just below the basal membrane to just above the apical membrane. </p> <table> <tbody> <tr> <td>Folder</td> <td>Subfolder/condition</td> <td>Fields of view</td> </tr> <tr> <td>Basal membrane</td> <td>CCL5-</td> <td>5</td> </tr> <tr> <td>Basal membrane</td> <td>CCL5+ (100 nM)</td> <td>6</td> </tr> <tr> <td>Whole cells</td> <td>CCL5-</td> <td>5</td> </tr> <tr> <td>Whole cells</td> <td>CCL5+ (100 nM)</td> <td>8</td> </tr> </tbody> </table> <p>Approx. size before compression: 300 MB</p>
Data for: Autonomous Micro-Focus Angle-Resolved Photoemission Spectroscopy
<p>This repository contains the data related to the publication</p> <p>Steinn Ýmir Ágústsson, Alfred J. H. Jones, Davide Curcio, Søren Ulstrup, Jill Miwa, Davide Mottin, Panagiotis Karras, Philip Hofmann; <strong>Autonomous micro-focus angle-resolved photoemission spectroscopy</strong>. <em>Rev. Sci. Instrum.</em> 1 May 2024; <strong>95</strong> (<em>5</em>): 055106.<em> DOI: <a href="https://doi.org/10.1063/5.0204663" target="_blank" rel="noopener">10.1063/5.0204663</a></em></p> <p>Please cite the paper above in case of re-use of these data in a scientific publication.</p> <p>The data were acquired at the SGM4 beamline of the ASTRID2 synchrotron in Arhus, DK as part of the development of an autonomous data acquisition software "SmartScan". Such software, together with all scripts necessary to load the present data, is available on GitHub at <a href="https://github.com/ARPES-ASTRID/smartscan">github.com/ARPES-ASTRID/smartscan</a></p>
Subcellular behavior model enables highly precise temporal super-resolved live-cell imaging
<div> <div>This repository contains the preprocessed dataset for [SuB-VFI](https://github.com/sduzzx857/SuB-VFI), including the real datasets we collected and the simulated testing and training datasets. You can refer to the Github repository for details.</div> <div> </div> <div>The simulated testing datasets can be downloaded from [the 2014 ISBI Particle Tracking Challenge](http://bioimageanalysis.org/track/).</div> <div>The EB1 datasets can be downloaded from the paper [The dynamic behavior of the APC-binding protein EB1 on the distal ends of microtubules](https://www.cell.com/current-biology/fulltext/S0960-9822(00)00600-X?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS096098220000600X%3Fshowall%3Dtrue). We used *Movie2* from the Supplementary data.</div> <br> <div>The CCR5 datasets can be downloaded from the paper [Tracking receptor motions at the plasma membrane reveals distinct effects of ligands on CCR5 dynamics depending on its dimerization status](https://elifesciences.org/articles/76281). We used *Video4* in the Results section. </div> <div> </div> <div>The Lysosome datasets can be downloaded from [Content-Aware Frame Interpolation Microscopy Datasets](https://zenodo.org/records/10076346). We used data from the `Zproject` folder within the compressed file `Source_Data_Lysosomes_z-proj_Fig_5.zip`</div> </div>
Data supporting the study "An organic crystalline state in ageing atmospheric aerosol proxies: spatially resolved structural changes in levitated fatty acid particles" by Milsom et al. (2021))
<p>Data supporting the figures and findings presented in the study <strong>"An organic crystalline state in ageing atmospheric aerosol proxies: spatially resolved structural changes in levitated fatty acid particles" by Milsom et al. (2021), <em>Atmos. Chem. Phys..</em></strong></p>
Top quark pair production at the LHC, with all hadronic resolved decays for solving event combinatorics
<p><strong>R&D Datasets for solving event combinatorics in all hadronic top quark pair events at the LHC.</strong></p> <p>Used in the development of Topographs: Topological Reconstruction of Particle Physics Processes using Graph Neural Networks</p> <p> </p> <p>The datasets contain 5.8M ttbar events in the all hadronic decay channel, with jets matched to the truth partons in the top quark decays.</p> <p> </p> <p><strong>Event generation</strong></p> <ul> <li>Centre of Mass energy: 13 TeV</li> <li>MC Generator: MadGraph5_aMC@NLO v3.1.0, with MadSpin modelling the decays of the top quarks and W bosons.</li> <li>Parton Shower: Pythia v .243</li> <li>Detector response: Delphes v3.4.2 using ATLAS-like geometry</li> <li>Jets reconstructed with anti-kt algorithm, R=0.4, using FastJet</li> <li>b-Tagging corresponds to inclusive 70% b-jet efficiency</li> </ul> <p><strong>Event selection and truth matching</strong></p> <ul> <li>All events are required to have at least six reconstructed jets and exactly zero leptons (electrons or muons)</li> <li>Partons are matched to jets using <span class="math-tex">\(\Delta R\)</span> matching, with <span class="math-tex">\(\Delta R < 0.4\)</span></li> <li>Events with partons matched to multiple jets or jets to multiple partons are discarded</li> <li>Up to 16 jets are stored per event</li> </ul> <p>In the training dataset 1,340,000 events have all partons from the ttbar decays matched to jets.</p> <p>In the validation dataset, 71,000 events have all partons from the ttbar decays matched to jets.</p> <p>In the testing dataset 76,000 events have all partons from the ttbar decays matched to jets.</p> <p><strong>Dataset format</strong></p> <p>The dataset is in h5 format and the key 'delphes' has the following numpy arrays:</p> <pre><code>jets (16), jets_indices (16), matchability, nbjets, njets, partons (10) </code></pre> <p>Jets structured numpy array per event:</p> <ul> <li> <pre><code>(pt, eta, phi, energy, is_tagged)</code></pre> </li> </ul> <p>Jets_indices:</p> <ul> <li>Integer corresponding to the parton the jet is matched to</li> <li>From 0 to 5: b1 W1j1 W1j2 b2 W2j1 W2j2 (1= from top, 2=from antitop)</li> <li>-1 indicates not matched to a parton</li> <li>Properties of matched partons can be obtained from the partons array</li> </ul> <p>matchability:</p> <ul> <li>Which partons are matched to jets in event</li> <li>Binary representation with bits corresponding to each parton (length 6) 0b111111</li> <li>From left to right: b1 W1j1 W1j2 b2 W2j1 W2j2</li> <li>0b111000 (56) is one top fully matched, 0b000111 (7) is the other top fully matched, 0b111111 (63) is both tops fully matched</li> </ul> <p>njets, nbjets:</p> <ul> <li>How many jets/bjets in event</li> </ul> <p>partons:</p> <ul> <li>List of truth particles from ttbar decay: tops, Ws, quarks, ordered by top quark and its decays followed by anti-top and its decays</li> <li> <pre><code>PDGID, pt, eta, phi, mass</code></pre> </li> </ul> <p> </p> <p> </p> <p> </p>
Super-Resolved FRET Imaging by Confocal Fluorescence-Lifetime Single-Molecule Localization Microscopy
<p>FRET-based methods are a special tool for detecting interactions between (bio)molecules and their immediate environment. The spatial distribution of molecular interactions and functional states can be seen using FLIM (Fluorescence Lifetime IMaging) and FRET imaging. The spatial information, accuracy, and dynamic range of the observed signals are, however, constrained by the fact that conventional FLIM and FRET imaging only provides average information over an ensemble of molecules within a diffraction-limited volume. On the other hand, conventional Single Molecule Localization Microscopy (SMLM) relies on highly sensitive multi-pixel detectors (e.g. sCMOS or EM-CCD) whose time resolution is not suitable for fluorescence lifetime measurements.</p> <p>Here, we demonstrate a method for obtaining super-resolved FRET imaging using confocal fluorescence-lifetime single-molecule localization microscopy. The proof of concept was carried out using a DNA origami sample for performing DNA-PAINT measurements in combination with fluorogenic probes for reducing background signal. With this method, We show that FRET events separated by sub-diffraction distances can be distinguished based on lifetime modifications.</p>
Country resolved combined emission and socio-economic pathways based on the RCP and SSP scenarios
<p><strong>Recommended citation</strong></p> <p>Article citation will be added once the article is available.</p> <p><strong>Content</strong></p> <ul> <li><a href="#use-of-the-dataset-and-full-description">Use of the dataset and full description</a></li> <li><a href="#abstract">Abstract</a></li> <li><a href="#support">Support</a></li> <li><a href="#files-included-in-the-dataset">Files included in the dataset</a></li> <li><a href="#notes">Notes</a></li> <li><a href="#data-format-description-columns">Data format description (columns)</a></li> <li><a href="#data-sources">Data sources</a></li> <li><a href="#changelog">Changelog</a></li> <li><a href="#references">References</a></li> </ul> <p><strong>Use of the dataset and full description</strong></p> <p>Before using the dataset, please read this document and the article describing the methodology, especially the "Discussion and limitations" section.</p> <p>The article will be referenced here as soon as it is published.</p> <p>Please notify us (johannes.guetschow@pik-potsdam.de) if you use the dataset so that we can keep track of how it is used and take that into consideration when updating and improving the dataset.</p> <p>When using this dataset or one of its updates, please cite the DOI of the precise version of the dataset used and also the data description article which this dataset is supplement to (see above). Please consider also citing the relevant original sources when using the RCP-SSP-dwn dataset. See the full citations in the References section further below.</p> <p><strong>Support</strong></p> <p>If you encounter possible errors or other things that should be noted or need support in using the dataset or have any other questions regarding the dataset, please contact johannes.guetschow@pik-potsdam.de.</p> <p><strong>Abstract</strong></p> <p>This dataset provides country scenarios, downscaled from the RCP (Representative Concentration Pathways) and SSP (Shared Socio-Economic Pathways) scenario databases, using results from the SSP GDP (Gross Domestic Product) country model results as drivers for the downscaling process harmonized to and combined with up to date historical data.</p> <p><strong>Files included in the dataset</strong></p> <p>The repository comprises several datasets. Each dataset comes in a csv file. The file name is constructed from dataset properties as follows: <Source><Bunkers><Downscaling>.csv</p> <p><em><Source></em></p> <p>The "Source" flag indicates which input scenarios were used.</p> <ul> <li><strong>PMRCP:</strong> RCP scenarios downscaled using the SSPs: emissions and socio-economic data; scenarios are available both harmonized to historical data and non-harmonized.</li> <li><strong>PMSSP:</strong> Downscaled SSP IAM scenarios: emissions and socio-economic data; scenarios are available both harmonized to historical data and non-harmonized.</li> </ul> <p><em><Bunkers></em></p> <p>the "Bunkers" flag indicates if the input emissions scenarios have been corrected for emissions from international shipping and aviation (bunkers) before downscaling to country level or not. The flag is "B" for scenarios where emissions from bunkers have been removed before downscaling and "" (no flag) where they have not been removed.</p> <p><em><Downscaling></em></p> <p>The "Downscaling" flag indicates the downscaling technique used.</p> <ul> <li><strong>IE:</strong> Convergence downscaling with exponential convergence of emissions intensities and convergence before transition to negative emissions.</li> <li><strong>IC:</strong> Regional emission intensity growth rates for all countries.</li> <li><strong>CS:</strong> Constant emission shares as a reference case independent of the socio-economic scenario.</li> </ul> <p>All files contain data for all countries and variables. For detailed methodology descriptions we refer to the paper this dataset is a supplement to. A reference to the paper will be added as soon as it is published.</p> <p>Finally the data description including detailed references is included: RCP-SSP-dwn_v1.0_data_description.pdf.</p> <p><strong>Notes</strong></p> <p>If you encounter problems with the size of the csv files please let us know, so we can find solutions for future releases of the data.</p> <p><strong>Data format description (columns)</strong></p> <p><em>"source"</em></p> <p>For <em>PMRCP</em> files source values are</p> <ul> <li>RCPSSP<Bunkers><Downscaling>: unharmonized downscaled RCP SSP scenarios</li> <li>PMRCP<Bunkers><Downscaling>: downscaled RCP SSP scenarios harmonized to and combined with historical data</li> <li>PMRCPMISC<Bunkers><Downscaling>: GDP and population data harmonized to and combined with historical data</li> </ul> <p>For <em>PMSSP</em> files source values are</p> <ul> <li>SSPIAM<Bunkers><Downscaling>: unharmonized downscaled SSP IAM scenarios</li> <li>PMSSP<Bunkers><Downscaling>: downscaled SSP IAM scenarios harmonized to and combined with historical data</li> <li>PMSSPMISC<Bunkers><Downscaling>: GDP and population data harmonized to and combined with historical data</li> </ul> <p>For possible values of <Bunkers> and <Downscaling> please see section <a href="#files-included-in-the-dataset">Files included in the dataset</a> above.</p> <p><em>"scenario"</em></p> <p>For <em>PMRCP</em> files the scenarios have the format <RCP><SSP><group>, where</p> <ul> <li><RCP> denotes the RCP scenario. Values are RCP3PD, RCP45, RCP6, and RCP85.</li> <li><SSP> denotes the SSP scenario. Values are SSP1, SSP2, SSP3, SSP4, and SSP5.</li> <li><groups> denotes the SSP basic elements GDP modeling group. Values are IIASA, OECD, and PIK. Not all RCP SSP combinations exist as some SSP storylines are not compatible with all RCP emissions scenarios. For details we refer to the paper this dataset is a supplement to. A reference to the paper will be added as soon as it is published.</li> </ul> <p>For <em>PMSSP</em> files the scenarios have the format <SSP><forcing><model> where</p> <ul> <li><SSP> denotes the SSP scenario. Values are SSP1, SSP2, SSP3, SSP4, and SSP5.</li> <li><forcing> denotes the radiative forcing level of the scenario. Values are 19, 26, 34, 45, 60, 85, and BL where 19 stands for 1.9W/m<sup>2</sup> etc. and BL stands for baseline.</li> <li><model> denotes the Integrated Assessment Model (IAM) used to generate the scenario. Values can be found below</li> </ul> <p>Model codes in scenario names</p> <ul> <li>AIMCGE: AIM-CGE</li> <li>IMAGE: IMAGE</li> <li>GCAM4: GCAM</li> <li>MESGB: MESSAGE-GLOBIOM</li> <li>REMMP: REMIND-MAGPIE</li> <li>WITGB: WITCH-GLOBIOM</li> </ul> <p><em>"country"</em></p> <p>ISO 3166 three-letter country codes or custom codes for groups:</p> <p>Additional "country" codes for country groups.</p> <ul> <li>EARTH: Aggregated emissions for all countries</li> <li>ANNEXI: Annex I Parties to the UNFCCC</li> <li>NONANNEXI: Non-Annex I Parties to the UNFCCC</li> <li>AOSIS: Alliance of Small Island States</li> <li>BASIC: BASIC countries (Brazil, South Africa, India and China)</li> <li>EU28: European Union (still including the UK)</li> <li>LDC: Least Developed Countries</li> <li>UMBRELLA: Umbrella Group</li> </ul> <p><em>"category"</em></p> <p>Category descriptions.</p> <ul> <li>IPCM0EL: Emissions: National Total excluding LULUCF</li> <li>ECO: Economical data</li> <li>DEMOGR: Demographical data</li> </ul> <p><em>"entity"</em></p> <p>Gases and gas baskets using global warming potentials (GWP) from either Second Assessment Report (SAR) or Fourth Assessment Report (AR4).</p> <p>Gases / gas baskets and underlying global warming potentials</p> <ul> <li>CH4: Methane (CH<sub>4</sub>)</li> <li>CO2: Carbon Dioxide (CO<sub>2</sub>)</li> <li>N2O: Nitrous Oxide (N<sub>2</sub>O)</li> <li>FGASES: Fluorinated Gases (SAR): HFCs, PFCs, SF<sub>6</sub>, NF<sub>3</sub></li> <li>FGASESAR4: Fluorinated Gases (AR4): HFCs, PFCs, SF<sub>6</sub>, NF<sub>3</sub></li> <li>KYOTOGHG: Kyoto greenhouse gases (SAR)</li> <li>KYOTOGHGAR4: Kyoto greenhouse gases (AR4)</li> </ul> <p><em>"unit"</em></p> <p>The following units are used:</p> <ul> <li>Million2011GKD: Million 2011 international dollars</li> <li>ThousandPers: Thousand persons</li> <li>kt: kilotonnes</li> <li>Mt: Megatonnes</li> <li>Gg: Gigagrams</li> <li>MtCO2eq: Megatonnes of CO<sub>2</sub> equivalents using the GWPs defined by "entity"</li> <li>GgCO2eq: Gigagrams of CO<sub>2</sub> equivalents using the GWPs defined by "entity"</li> </ul> <p><em>Remaining columns</em></p> <p>Years from 1850-2100.</p> <p><strong>Data Sources</strong></p> <p>The following data sources were used during the generation of this dataset:</p> <p><em>Scenario data</em></p> <ul> <li><strong>RCP scenarios</strong> <a href="https://tntcat.iiasa.ac.at/RcpDb/">website/data</a></li> <li><strong>SSP basic elements</strong> <a href="https://tntcat.iiasa.ac.at/SspDb">website/data</a></li> <li><strong>SSP IAM scenarios</strong> <a href="https://tntcat.iiasa.ac.at/SspDb">website/data</a></li> <li><strong>SSP CMIP6 scenarios</strong> <a href="https://tntcat.iiasa.ac.at/SspDb">website/data</a></li> </ul> <p><em>Historical data</em></p> <ul> <li><strong>CDIAC</strong> <a href="http://doi.org/10.3334/CDIAC/00001_V2017">data</a></li> <li><strong>CEDS CMIP6 data</strong> <a href="https://www.geosci-model-dev.net/11/369/2018/">paper/data</a></li> <li><strong>EDGAR version 4.3.2:</strong> <a href="http://doi.org/10.2904/JRC_DATASET_EDGAR">data</a>, <a href="https://doi.org/10.5194/essd-2017-79">paper</a></li> <li><strong>IMO GHG report</strong> <a href="http://www.imo.org/en/OurWork/Environment/PollutionPrevention/AirPollution/Documents/Third%20Greenhouse%20Gas%20Study/GHG3%20Executive%20Summary%20and%20Report.pdf">report</a></li> <li><strong>PRIMAP-hist v2.1</strong> <a href="http://www.earth-syst-sci-data.net/8/571/2016/">paper</a>, <a href="https://www.pik-potsdam.de/primap-live/primap-hist/">website</a>, <a href="https://doi.org/10.5880/PIK.2019.018">data</a></li> <li><strong>PRIMAP-hist SocioEco v2.1</strong> <a href="https://doi.org/10.5880/PIK.2019.019">data</a></li> </ul> <p><strong>Changelog</strong></p> <p>For future versions</p> <p><strong>References</strong></p> <p>For full references we refer to the pdf version of the data description available in this repository and the list of related identifiers.</p>
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