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3,206 results for “property (T)”
Ensemble of NEMO present-day (1989-2009) and future (2080-2100 under RCP8.5) ocean properties and ice shelf melt rates in the Amundsen Sea
<p>Model outputs used in <a href="https://www.essoar.org/doi/10.1002/essoar.10511482.3">Jourdain et al. (GRL, 2022)</a></p> <p>The output files consist of monthly climatologies over either 1989-2009 or 2080-2100. The file names have the form:</p> <p><strong>climato_monthly_AMUXL12-GNJ002_<simu>_<group>_1989_2009.nc</strong>, where :</p> <ul> <li><simu> is either : <ul> <li>"BM02MAR" (ensemble member A, present-day),</li> <li>"BM03MAR" (ensemble member B, present-day),</li> <li>"BM04MAR" (ensemble member C, present-day),</li> <li>"BM02MARrcp85" (ensemble member A, future for both surface and lateral boundaries),</li> <li>"BM03MARrcp85" (ensemble member B, future for surface BUT NOT for lateral boundaries),</li> <li>"BM03MARrcBDY" (ensemble member B, future for both surface and lateral boundaries),</li> <li>"BM04MARrcp85" (ensemble member C, future for both surface and lateral boundaries),</li> </ul> </li> <li><group> is either : <ul> <li>"SBC" (surface boundary conditions),</li> <li>"icemod" (sea ice variables),</li> <li>"gridT" (temperature, salinity),</li> <li>"gridU" (zonal velocities),</li> <li>"gridV" (meridional velocities).</li> </ul> </li> </ul> <p>Grid information in:</p> <ul> <li>mesh_mask_AMUXL12_BedMachineAntarctica-2019-05-24.nc (ensemble member A),</li> <li>mesh_mask_AMUXL12_BedMachineAntarctica-2020-07-15_v02_ICB380.nc (ensemble members B & C).</li> </ul> <p>where:</p> <ul> <li>glamt : longitude</li> <li>gphit: latitude</li> <li>e1t, e2t, e3t_0 : mesh size (in meters) along x, y, z</li> <li>tmask = 1 for ocean mesh, = 0 otherwise (land, continental ice).</li> </ul> <p> </p> <p><strong>Acknowledgments:</strong> This work was granted access to the HPC resources of CINES (occigen) under the allocation A0100106035 attributed by GENCI.</p>
Soil properties as point estimations over the Lithuanian pilot area (2021)
<p>In the context of the EU-funded project DIONE (No. 870378), VNIR topsoil reflectance was captured with DIONE’s Soil Scanning System and transformed through Machine Learning modelling to a set of soil properties that are meaningful for the assessment of soil health. The captured reflectance measurements correspond to locations distributed within the pilot areas of Lithuania and are indicated after the analysis of EO multispectral imagery, aiming to create a collection of point locations that well represent the soil characteristics of the area, and provide valuable information about soil condition through the estimations of the following soil properties:</p> <ul> <li>Sand %</li> <li>Clay %</li> <li>Silt %</li> <li>Electrical Conductivity (mS/m)</li> <li>pH</li> <li>Calcium carbonate %</li> <li>Soil Organic Carbon %</li> </ul> <p>The dataset is delivered in a shapefile format (DIONE_LT_point_estimations_2021.shp - EPSG:4326 - WGS 84) containing the following fields:</p> <table> <caption><br> <strong>Description of the information contained in the corresponding "LTH points estimations" dataset</strong></caption> <thead> <tr> <th scope="col">Field</th> <th scope="col">Type</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>Sample_ID</td> <td>String</td> <td>Unique ID</td> </tr> <tr> <td>lat</td> <td>Real</td> <td>Latitude </td> </tr> <tr> <td>lon</td> <td>Real</td> <td>Longitude</td> </tr> <tr> <td>Sand</td> <td>Real</td> <td>Sand fraction</td> </tr> <tr> <td>Clay</td> <td>Real</td> <td>Clay fraction</td> </tr> <tr> <td>Silt</td> <td>Real</td> <td>Silt fraction</td> </tr> <tr> <td>EC</td> <td>Real</td> <td>Electrical Conductivity</td> </tr> <tr> <td>pH_H20</td> <td>Real</td> <td>pH</td> </tr> <tr> <td>CaCO3</td> <td>Real</td> <td>Calcium Carbonate </td> </tr> <tr> <td>SOC</td> <td>Real</td> <td>Soil Organic Carbon</td> </tr> </tbody> </table> <p><br> </p>
Soil properties as point estimations over the Cypriot pilot area (2021)
<p>In the context of the EU-funded project DIONE (No. 870378), VNIR topsoil reflectance was captured with DIONE’s Soil Scanning System and transformed through Machine Learning modelling to a set of soil properties that are meaningful for the assessment of soil health. The captured reflectance measurements correspond to locations distributed within the pilot areas of Cyprus and are indicated after the analysis of EO multispectral imagery, aiming to create a collection of point locations that well represent the soil characteristics of the area, and provide valuable information about soil condition through the estimations of the following soil properties:</p> <ul> <li>Sand %</li> <li>Clay %</li> <li>Silt %</li> <li>Electrical Conductivity (mS/m)</li> <li>pH</li> <li>Calcium carbonate %</li> <li>Soil Organic Carbon %</li> </ul> <p>The dataset is delivered in a shapefile format (DIONE_CY_point_estimations_2021.shp - EPSG:4326 - WGS 84) containing the following fields:</p> <table> <caption><strong>Description of the information contained in the corresponding "CY points estimations" dataset</strong></caption> <thead> <tr> <th scope="col">Field</th> <th scope="col">Type</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>Sample_ID</td> <td>String</td> <td>Unique ID</td> </tr> <tr> <td>lat</td> <td>Real</td> <td>Latitude </td> </tr> <tr> <td>lon</td> <td>Real </td> <td>Longitude</td> </tr> <tr> <td>Sand</td> <td>Real</td> <td>Sand fraction</td> </tr> <tr> <td>Clay</td> <td>Real</td> <td>Clay fraction</td> </tr> <tr> <td>Silt</td> <td>Real</td> <td>Silt fraction</td> </tr> <tr> <td>EC</td> <td>Real</td> <td>Electrical Conductivity</td> </tr> <tr> <td>pH_H20</td> <td>Real</td> <td>pH</td> </tr> <tr> <td>CaCO3</td> <td>Real</td> <td>Calcium Carbonate </td> </tr> <tr> <td>SOC</td> <td>Real</td> <td>Soil Organic Carbon</td> </tr> </tbody> </table> <p> </p>
Data from: Effects of plastic fragments on plant performance are mediated by soil properties and drought
<p>In recent years, the effects of plastic contamination on soil and plants have received growing attention. Plastic can affect soil water content and thus may interact with the effects of drought on soil and plants. However, the effects of plastic on soil are highly context-dependent, and interactions with drought have been hardly tested. We conducted two greenhouse experiments to test the combined effects of plastic fragments (of varying size and concentration), water availability and soil texture, on soil water content and performance of the plant <em>Arabidopsis thaliana</em>. Plastic fragments had stronger negative effects on soil water content in low water availability, and the shape of this response (linear <em>vs.</em> unimodal) was mediated by soil texture. Conversely, increasing concentration of plastic had positive effects on plant growth. We suggest that plastic fragments introduce fracture points within soil aggregates. This increases number and size of soil pores favoring water loss but also facilitating root growth. Our results suggest complex interactive effects of plastic and drought, that may lead to a decoupling of plant and soil response. These processes should be taken into account in ecological studies and agricultural practices.</p>
Dataset for: "Dynamical properties of solid and hydrated collagen: Insight from nuclear magnetic resonance relaxometry"
<p>The dataset contains a full set of 1H magnetization curves (1H magnetization versus time) for solid and hydrated collagen and collagen-based artificial tissues.</p> <p>DOI of article: <a href="https://doi.org/10.1063/5.0191409" target="_blank" rel="noopener">https://doi.org/10.1063/5.0191409</a></p> <p>This research was funded by the National Science Centre, Poland, Grant No. 2021/43/B/NZ5/01602.</p>
Synthesis, Structure and Redox Properties of Single-atom Bridged Diuranium Complexes Supported by Aryloxides
<p>This upload contains raw data (NMR, X-Ray Diffraction, Electrochemistry, SQUID and Elemental Analysis) files for the article</p>
Dataset Effect of hypnotic suggestion on knee extensor neuromuscular properties in resting and fatigued states
<p>The .xlsx file contains individual data from all figures / tables of the associated manuscript and each .csv file contains information from one figure / table.</p> <p> </p> <p><strong>Dataset Fig 2</strong></p> <p>Table 1. Maximal voluntary contraction force (Newton) from the knee extensor muscles measured before (pre) and after (post) control / hypnosis suggestion</p> <p>Table 2. Maximal voluntary activation level (%) from the knee extensor muscles measured before (pre) and after (post) control / hypnosis suggestion</p> <p>Table 3. Peak doublet force (Newton) evoked from 100 Hz paired stimuli at the knee extensor level measured before (pre) and after (post) control / hypnosis suggestion</p> <p> </p> <p><strong>Dataset Fig 4</strong></p> <p>Table 1. Time to task failure (s) of a submaximal isometric contraction performed at 20% maximal voluntary contraction force with the knee extensors for the control session and the hypnosis session</p> <p> </p> <p><strong>Dataset Fig 5</strong></p> <p>Table 1. Maximal voluntary contraction force (Newton) from the knee extensor muscles measured before (pre exercise) and after (post exercise) exercise during the control session and the hypnosis session</p> <p>Table 2. Maximal voluntary activation level (%) from the knee extensor muscles measured before (pre exercise) and after (post exercise) exercise during the control session and the hypnosis session</p> <p>Table 3. Peak doublet force (Newton) evoked from 100 Hz paired stimuli at the knee extensor level measured before (pre exercise) and after (post exercise) exercise during the control session and the hypnosis session</p> <p> </p> <p><strong>Dataset Fig 6</strong></p> <p>Table 1. Electromyographic activity (in %, expressed as root mean square values normalized to maximal electromyographic activity measured during the maximal voluntary contraction performed before exercise) of the vastus lateralis muscle measured during the sustained isometric contraction at every 25% of time to task failure for the control session and the hypnosis session</p> <p>Table 2. Electromyographic activity (in %, expressed as root mean square values normalized to maximal electromyographic activity measured during the maximal voluntary contraction performed before exercise) of the vastus medialis muscle measured during the sustained isometric contraction at every 25% of time to task failure for the control session and the hypnosis session</p> <p>Table 3. Electromyographic activity (in %, expressed as root mean square values normalized to maximal electromyographic activity measured during the maximal voluntary contraction performed before exercise) of the rectus femoris muscle measured during the sustained isometric contraction at every 25% of time to task failure for the control session and the hypnosis session</p> <p> </p> <p><strong>Dataset Fig 7</strong></p> <p>Table 1. Motor evoked potential peak to peak amplitude from the vastus lateralis muscle measured during the sustained isometric contraction at every 50% of time to task failure for the control session and the hypnosis session. Values are expressed in %, i.e. expressed as a ratio between the motor evoked potential peak-to-peak amplitude expressed in mV (Table 2) and the peak-to-peak M-wave amplitude expressed in mV (Table 3).</p> <p>Table 4. Motor evoked potential peak to peak amplitude from the vastus medialis muscle measured during the sustained isometric contraction at every 50% of time to task failure for the control session and the hypnosis session. Values are expressed in %, i.e. expressed as a ratio between the motor evoked potential peak-to-peak amplitude expressed in mV (Table 5) and the peak-to-peak M-wave amplitude expressed in mV (Table 6).</p> <p>Table 7. Motor evoked potential peak to peak amplitude from the rectus femoris muscle measured during the sustained isometric contraction at every 50% of time to task failure for the control session and the hypnosis session. Values are expressed in %, i.e. expressed as a ratio between the motor evoked potential peak-to-peak amplitude expressed in mV (Table 8) and the peak-to-peak M-wave amplitude expressed in mV (Table 9).</p> <p>Table 10. Short intracortical inhibition peak to peak amplitude from the vastus lateralis muscle measured during the sustained isometric contraction at every 50% of time to task failure for the control session and the hypnosis session. Values are expressed in %, i.e. expressed as a ratio between the short intracortical inhibition peak-to-peak amplitude expressed in mV (Table 11) and the motor evoked potential peak-to-peak amplitude expressed in mV (Table 12).</p> <p>Table 13. Short intracortical inhibition peak to peak amplitude from the vastus medialis muscle measured during the sustained isometric contraction at every 50% of time to task failure for the control session and the hypnosis session. Values are expressed in %, i.e. expressed as a ratio between the short intracortical inhibition peak-to-peak amplitude expressed in mV (Table 14) and the motor evoked potential peak-to-peak amplitude expressed in mV (Table 15).</p> <p>Table 16. Short intracortical inhibition peak to peak amplitude from the rectus femoris muscle measured during the sustained isometric contraction at every 50% of time to task failure for the control session and the hypnosis session. Values are expressed in %, i.e. expressed as a ratio between the short intracortical inhibition peak-to-peak amplitude expressed in mV (Table 17) and the motor evoked potential peak-to-peak amplitude expressed in mV (Table 18).</p> <p><br> <strong>Dataset Fig 8</strong></p> <p>Table 1. Rate of perceived exertion (6-20 Borg scale) measured during the sustained isometric contraction at every 25% of time to task failure for the control session and the hypnosis session</p> <p> </p> <p><strong>Dataset Table 1</strong></p> <p>Table 1. Motor evoked potential peak to peak amplitude from the vastus lateralis muscle measured before (pre) and after (post) control / hypnosis suggestion. Values are expressed in %, i.e. expressed as a ratio between the motor evoked potential peak-to-peak amplitude expressed in mV (Table 2) and the peak-to-peak M-wave amplitude expressed in mV (Table 3).</p> <p>Table 4. Motor evoked potential peak to peak amplitude from the vastus medialis muscle measured before (pre) and after (post) control / hypnosis suggestion. Values are expressed in %, i.e. expressed as a ratio between the motor evoked potential peak-to-peak amplitude expressed in mV (Table 5) and the peak-to-peak M-wave amplitude expressed in mV (Table 6).</p> <p>Table 7. Motor evoked potential peak to peak amplitude from the rectus femoris muscle measured before (pre) and after (post) control / hypnosis suggestion. Values are expressed in %, i.e. expressed as a ratio between the motor evoked potential peak-to-peak amplitude expressed in mV (Table 8) and the peak-to-peak M-wave amplitude expressed in mV (Table 9).</p> <p>Table 10. Short intracortical inhibition peak to peak amplitude from the vastus lateralis muscle measured before (pre) and after (post) control / hypnosis suggestion. Values are expressed in %, i.e. expressed as a ratio between the short intracortical inhibition peak-to-peak amplitude expressed in mV (Table 11) and the motor evoked potential peak-to-peak amplitude expressed in mV (Table 12).</p> <p>Table 13. Short intracortical inhibition peak to peak amplitude from the vastus medialis muscle measured before (pre) and after (post) control / hypnosis suggestion. Values are expressed in %, i.e. expressed as a ratio between the short intracortical inhibition peak-to-peak amplitude expressed in mV (Table 14) and the motor evoked potential peak-to-peak amplitude expressed in mV (Table 15).</p> <p>Table 16. Short intracortical inhibition peak to peak amplitude from the rectus femoris muscle measured before (pre) and after (post) control / hypnosis suggestion. Values are expressed in %, i.e. expressed as a ratio between the short intracortical inhibition peak-to-peak amplitude expressed in mV (Table 17) and the motor evoked potential peak-to-peak amplitude expressed in mV (Table 18).</p> <p> </p> <p><strong>Dataset table 2</strong></p> <p>Table 1. M-wave peak to peak amplitude (mV) from the vastus lateralis muscle measured before (pre) and after (post) control / hypnosis suggestion</p> <p>Table 2. M-wave peak to peak amplitude (mV) from the vastus medialis muscle measured before (pre) and after (post) control / hypnosis suggestion</p> <p>Table 3. M-wave peak to peak amplitude (mV) from the rectus femoris muscle measured before (pre) and after (post) control / hypnosis suggestion</p>
Dataset for: Statistical properties of meso-scale plasma flows in the nightside high-latitude ionosphere
<p>This dataset is a compilation of statistical results from Gabrielse et al. [2018] (<a href="https://doi.org/10.1029/2018JA025440">https://doi.org/10.1029/2018JA025440</a>). If you would like to use the dataset, please contact Christine Gabrielse (cgabrielse@ucla.edu, cgabrielse@gmail.com). Depending on how the results are used, the main authors request co-authorship on publications. </p> <p>The following list describes the columns in each data file labeled, ***_FLOW-DATA-PCvsAO_YYYY.txt <br> Files named ***_FLOW-DATA-PCvsAO_YYYY_poleward.txt are for poleward-directed flows. <br> Each text file is for a different year (YYYY). <br> AO=auroral oval<br> PC=polar cap</p> <p> time [YYYYMMDDhhmmss]<br> flagAO [-1=flow could not be observed. 0=flow could be observed, but was not. 1=flow was observed]<br> flagPC [-1=flow could not be observed. 0=flow could be observed, but was not. 1=flow was observed]<br> FWHMavg_AO [degrees]<br> FWHMkmavg_AO=[km]<br> longtestranges=[ignore]<br> Velmaxavg_AO=[m/s, actual average of max V in each range gate used]<br> VelmaxFITavg_AO=[m/s, determined from the Gaussian fits]<br> FWHMavg_PC=[degrees]<br> FWHMkmavg_PC=[km]<br> Velmaxavg_PC=[m/s, actual average of max V in each range gate used]<br> VelmaxFITavg_PC=[m/s, determined from the Gaussian fits]<br> ;;For the bearings/orientation, see the orientation text files. The following four variables were calculated in a first step but are not<br> ;;those used in the paper. They were not found with the strict selection criteria. Please do not use.<br> mbearingAO=[degrees in magnetic coordinates, a negative value is South of East (clockwise from East), a positive value is North of East (CC)]<br> mbearingPC=[degrees in magnetic coordinates, a negative value is South of East (clockwise from East), a positive value is North of East (CC)] <br> gbearingAO=[degrees in geographic coordinates, a negative value is South of East (clockwise from East), a positive value is North of East (CC)]<br> gbearingPC=[degrees in geographic coordinates, a negative value is South of East (clockwise from East), a positive value is North of East (CC)]<br> ;;;;;;;;;;;;;;;<br> minlatAO=[degrees, min geographic latitude of the flow]<br> maxlatAO=[degrees, max geographic latitude of the flow]<br> minlatPC=[degrees, min geographic latitude of the flow]<br> maxlatPC=[degrees, max geographic latitude of the flow]<br> mltAO=[degrees (MLT)]<br> mltPC=[degrees (MLT)]<br> AE=[nT]<br> AL=[nT]<br> SYMH=[nT]<br> IMFBz=[nT]<br> IMFBy=[nT]<br> F107=[sfu]</p> <p>The following list describes the columns in each data file labeled, ***_orientation_YYYY.txt <br> Files named ***_orientation_YYYY_poleward.txt are for poleward-directed flows. <br> Each text file is for a different year (YYYY). <br> The orientation was determined when enough bearings between RGs were available. See Gabrielse et al. [2018] for description. <br> https://doi.org/10.1029/2018JA025440 <br> AO=auroral oval<br> PC=polar cap</p> <p> time [YYYYMMDDhhmmss]<br> mbearingAO [degrees clockwise from magnetic North]<br> gbearingAO [degrees clockwise from geographic North]<br> mbearingPC [degrees clockwise from magnetic North]<br> gbearingPC [degrees clockwise from geographic North]</p> <p>The following list describes the columns in each data file labeled, ***_SPEC_TEST_***_noRG1-2.txt</p> <p> time [YYYYMMDDhhmmss]<br> RG [the range gate number at which the polar cap boundary was determined at RNK, or the auroral oval's equatorial boundary at SAS]</p>
Multilingual Wikidata Property Translation Flow Dataset
<p>Multilingual Wikidata property translation Flow dataset contains the translation flow of Wikidata properties as collected on July 7, 2019. It contains four columns: timestamp, property, language, type. Every line in the dataset corresponds to the <strong>first-time action</strong> related to a language, i.e., the time at which the first translation of a label, description or an alias was made in a given language for a given property.</p> <p>Taking an example line from this dataset,</p> <p><em>2013-09-10T22:43:54Z,P856,en,label </em></p> <p>corresponds to the action that an <em>English</em> <em>label</em> of Property <em>P856</em> was added for the first time at <em>2013-09-10T22:43:54Z</em>.</p> <p>Following is a description of each column.</p> <p>1. timestamp: the time at which an action was made. For example, <em>2013-09-10T22:43:54Z</em></p> <p>2. property: Wikidata property identifier. It uses the P-number, For example, <em>P856</em></p> <p>3. language: the language in which a label/description/alias was first translated</p> <p>4. type: It could be one of the following values: label, description and alias</p>
RSOI: Sea ice properties collected during the detection of oil on-in-and-under ice experiment
<p>Data collected during the detection of oil on-in-and-under ice oil experiment lead at CRREL in 2014/2015.<br> - Sea ice core properties (salinity and temperature)</p> <p>- Sea ice porosity and permeability field, derived from salinity and temperature</p> <p>- Oil volumes, in the lens derived from underwater acoustic measurement, are included in the RSOI-data-*.xlsx spreadsheet.</p>
A European aerosol phenomenology – 9: LIGHT ABSORPTION PROPERTIES OF CARBONACEOUS AEROSOL PARTICLES ACROSS SURFACE EUROPE
<p>Carbonaceous aerosols (CA), composed of black carbon (BC) and organic aerosols (OA), exert an important role on the climate system through their interaction with solar radiation. Light absorption properties of CA particles are of special interest due to their important contribution to global and regional warming. Among atmospheric particulate matter (PM), BC and the absorbing components of OA (or brown carbon, BrC) are characterized by the highest absorption efficiency but their role in the current climate change, especially that of BrC, is still uncertain. Here we present the absorption properties of BC and BrC PM at 44 sites across Europe using aethalometer data collected at different types of environment (6 traffic (TR), 16 urban (UB), 7 suburban (SUB), 10 regional background (RB) and 5 mountain (M) sites). The absorption Ångström exponent (AAE) method was used to assign total measured absorption to the contributions of BC (bAbs,BC) and BrC (bAbs,BrC) to total absorption (bAbs). The results showed a clear dependence of the absorption coefficients bAbs, bAbs,BC and bAbs,BrC on station settings as follows: TR > UB > SUB > RB > M, even if significant exceptions were observed. The relative contribution of bAbs,BrC to bAbs (%AbsBrC) at 370 nm was on average lower at traffic sites (11-20%) reaching at some SUB and RB sites median annual values that accounted for more than 30% and 10% of the absorption at 370 and 660 nm, respectively. The median AAE of CA particles was correspondingly low at TR sites (1.1-1.2) where internal combustion engines dominated the CA mass concentration. Low AAE were also observed at some remote RB and M sites, likely due to the lack of proximity from BrC sources or lack of sufficiently strong secondary processes resulting in BrC. On average, AAE was lower in Western Europe (<1.3) compared to Eastern Europe (>1.3), likely due to a more extensive use of coal and biomass burning in eastern countries. The median AAE of BrC PM (AAEBrC) showed a wide range of values, from 2.5 to 6, with no clear relationship with station background or region. Assessing the seasonal variability revealed, overall, an increase of bAbs, bAbs,BC, bAbs,BrC in winter, which was attributed to meteorological conditions and more heating related emissions. Accordingly, bAbs,BrC exhibited a stronger increase than bAbs,BC, resulting in higher AAE and %AbsBrC during the winter season. The diel cycles differed between bAbs,BC and bAbs,BrC, with bAbs,BC showing the bimodal peaks during the morning and evening rush hours, whereas bAbs,BrC, together with %AbsBrC, AAE and AAEBrC, peaked at night. Decade-long trend analysis performed for a subset of stations across Europe revealed a decrease of bAbs, driven by declining bAbs,BC, whereas, overall, bAbs,BrC, %AbsBrC and AAE increased with time. This strongly implies an efficient reduction of BC mass concentrations from traffic sources in Europe and a less effective reduction of emissions from BrC sources. The observed increasing trends of AAE reflected a progressive change in the chemical composition of CA particles driven by a relative increase/decrease of BrC/BC content in CA with time.</p>
The impact of beech deadwood on soil properties and microbial diversity
<p><span><span>Our research is an attempt to determine the role of decaying wood in shaping the properties of forest soils in mountain ecosystems.</span></span><span><span> </span></span></p>
Data for the publication: Recombinant silk protein condensates show widely different properties depending on the sample background
<p>This entry includes raw data for the publication "Recombinant silk protein condensates show widely different properties depending on the sample background". The original publication was published in: Journal of Materials Chemistry B, DOI: 10.1039/d4tb01422g</p> <p>The folder "Videos_Micropipette_Aspiration_Zenodo.zip" contains 9 TIF files, labeled Number1 - Number9. The numbering corresponds to the numbering of IMAC condensates studied with micropipette aspiration in the publication. Each TIF file is an image stack from a time series.</p> <p>The folders "Videos_IMAC_silk_with_BG_lysate_coalescence.zip", "Videos_HT_silk_coalescence.zip", and "Videos_IMAC_silk_coalescence.zip" all contain subfolders labeled with the purification method, the framerate of the videos and then consecutive numbering. Each of these folders contains the frames of the video as single TIF files.</p> <p>Please find more information in the read_me file uploaded.</p>
Ensemble of ice shelf basal melt rates and ocean properties for tipped-over continental shelves
<p><strong>Summary</strong><strong>:</strong></p> <p>This dataset contains the reference and tipped states from several model configurations developed at the <a href="https://www.awi.de/en/">Alfred Wegener Institute (AWI)</a> and the <a href="https://www.ige-grenoble.fr/?lang=en">Institut des Géosciences de l’Environnement (IGE)</a>. They were gathered here in the context of the <a href="https://www.tipaccs.eu">TiPACCs European project</a> and constitute a useful ensemble of reference and tipped ocean–ice-shelf simulations that <strong>can be used to feed ice-sheet simulations or to train melt parameterizations</strong>.</p> <p>The simulations produced by AWI are based on the <a href="https://fesom.de">FESOM</a> global ocean–sea-ice model using either Z- or Sigma- coordinates and all show a cold-to-warm tipping point for Filchner-Ronne Ice Shelf. The two sets of simulations produced by IGE are based on the <a href="https://www.nemo-ocean.eu">NEMO</a> ocean–sea-ice model. They include a global configuration showing a cold-to-warm tipping point for Ross Ice Shelf, and regional Amundsen Sea configuration showing a warm-to-warmer transition (likely not a proper tipping point). </p> <p>The files include 3-dimensional and sea-floor ocean temperatures and salinities, ice-shelf melt rates, as well as topographic and grid data. All variables are interpolated onto the common 8km stereographic grid that was used to provide ocean forcing in ISMIP6 (<a href="https://doi.org/10.5194/tc-14-2331-2020">Nowicki et al. 2020</a>).</p> <p>We provide the reference state and the anomaly, so that the tipped state is:</p> <ul> <li><em>Tipped = Reference + Anomaly</em></li> </ul> <p>To have an overview of the reference and tipped states, have a look at these figures:</p> <ul> <li><em>figure_ref_and_anomalies_1.pdf</em></li> <li> <p><em>figure_ref_and_anomalies_2.pdf</em></p> </li> <li> <p><em>figure_seafloor_temp_zooms.pdf</em></p> </li> </ul> <p> </p> <p>_______________________________________________</p> <p><strong>Detailed Data Description</strong><strong>:</strong></p> <p> </p> <ul> <li><strong>reference_high_FESOM_sigma_AWI_TiPACCs.nc</strong> <ul> <li>contact: Ralph Timmermann <a href="mailto:ralph.timmermann@awi.de">ralph.timmermann@awi.de</a>, Verena Haid <a href="mailto:verena.haid@awi.de">verena.haid@awi.de</a></li> <li>model: FESOM1.4, sigma-coordinates (global with refined grid around Antarctica)</li> <li>atmospheric forcing: HadCM3 20C</li> <li>provided average: 1990-1999 (10-year mean)</li> <li>more: <a href="https://doi.org/10.1007/s10236-013-0642-0">Timmermann and Hellmer (2013)</a></li> </ul> </li> </ul> <p> </p> <ul> <li><strong>reference_low_FESOM_sigma_AWI_TiPACCs.nc</strong> <ul> <li>contact: Ralph Timmermann <a href="mailto:ralph.timmermann@awi.de">ralph.timmermann@awi.de</a>, Verena Haid <a href="mailto:verena.haid@awi.de">verena.haid@awi.de</a></li> <li>model: FESOM1.4, sigma-coordinates (global with refined grid around Antarctica)</li> <li>atmospheric forcing: HadCM3 20C</li> <li>provided average: 1990-1999 (10-year mean)</li> <li>more: <a href="https://doi.org/10.5194/os-13-765-2017">Timmermann and Goeller (2017)</a></li> </ul> </li> </ul> <p> </p> <ul> <li><strong>reference_FESOM_z_AWI_TiPACCs.nc</strong> <ul> <li>contact: Verena Haid <a href="mailto:verena.haid@awi.de">verena.haid@awi.de</a></li> <li>model: FESOM1.4, Z-coordinates (global with refined grid around Antarctica)</li> <li>atmospheric forcing: ERA Interim</li> <li>provided average: 2008-2017 (10-year mean), i.e. model year 30-39</li> <li>more: same mesh as <a href="https://doi.org/10.5194/tc-13-2317-2019">Gürses et al. (2019)</a></li> </ul> </li> </ul> <p> </p> <ul> <li><strong>reference_NEMO4_eORCA025.L121_IGE_TiPACCs.nc</strong> <ul> <li>contact: Pierre Mathiot <a href="mailto:pierre.mathiot@univ-grenoble-alpes.fr">pierre.mathiot@univ-grenoble-alpes.fr</a></li> <li>model: NEMO-4.0, eORCA025.L121 (Global, 1/4°, 121 vertical levels)</li> <li>atmospheric forcing: JRA55do</li> <li>provided average: 2<sup>nd</sup> cycle of 1989-1998 (10-year mean); we first run 1979-2018, and we redo 1979-1998 starting from the 2018 state.</li> <li>more: <a href="https://pmathiot.github.io/NEMOCFG/docs/build/html/simu_eORCA025_OPM021.html">https://pmathiot.github.io/NEMOCFG/docs/build/html/simu_eORCA025_OPM021.html</a></li> </ul> </li> </ul> <p> </p> <ul> <li><strong>reference_NEMO3_AMUXL12.L75_IGE_TiPACCs.nc</strong> <ul> <li>contact: Nicolas Jourdain <a href="mailto:nicolas.jourdain@univ-grenoble-alpes.fr">nicolas.jourdain@univ-grenoble-alpes.fr</a></li> <li>model: NEMO-3.6, AMUXL12.L75 (Amundsen, 1/12°, 75 vertical levels)</li> <li>atmospheric forcing: MAR (<a href="https://doi.org/10.5194/tc-14-229-2020">Donat-Magnin et al. 2020</a>)</li> <li>provided average: 1989-2009 (21-year mean)</li> <li>more: similar model set-up as <a href="https://doi.org/10.1016/j.ocemod.2018.11.001">Jourdain et al. (2019)</a>.</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>anomaly_high_FESOM_sigma_AWI_TiPACCs.nc</strong> <ul> <li>continuation of reference_high_FESOM_sigma_AWI_TiPACCs.nc</li> <li>forced with HadCM3 A1B</li> <li>provided average: 2190-2199 (10-year mean)</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>anomaly_low_FESOM_sigma_AWI_TiPACCs.nc</strong> <ul> <li>continuation of reference_low_FESOM_sigma_AWI_TiPACCs.nc</li> <li>forced with HadCM3 A1B</li> <li>provided average: 2190-2199 (10-year mean)</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>anomaly_high_FESOM_z_AWI_TiPACCs.nc</strong> <ul> <li>same model set-up as reference_FESOM_z_AWI_TiPACCs.nc</li> <li>atmospheric forcing south of 60°S HadCM3 A1B starting 2050, otherwise ERA Interim starting 1979</li> <li>provided average: model year 69-78 (10-year mean), i.e. 2008-2017 of 2<sup>nd</sup> 39yr-cycle</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>anomaly_medium_FESOM_z_AWI_TiPACCs.nc</strong> <ul> <li>same model set-up as reference_FESOM_z_AWI_TiPACCs.nc</li> <li>atmospheric forcing: ERA Interim modified with a strong imprint of the seasonal cycle of HadCM3 A1B 2070-2089</li> <li>provided average: model year 69-78 (10-year mean), i.e. 2008-2017 of 2<sup>nd</sup> 39yr-cycle</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>anomaly_low_FESOM_z_AWI_TiPACCs.nc</strong> <ul> <li>same model set-up as reference_FESOM_z_AWI_TiPACCs.nc</li> <li>atmospheric forcing: manipulated ERA Interim with prolongued summer and shorter, milder winter south of 50°S, additional modification of winds in Weddell Sea region</li> <li>provided average: model year 108-117 (10-year mean), i.e. 2008-2017 of 3<sup>rd</sup> 39yr-cycle</li> </ul> </li> </ul> <p> </p> <ul> <li><strong>anomaly_NEMO4_eORCA025.L121_IGE_TiPACCs.nc</strong> <ul> <li>similar to reference_NEMO4_eORCA025.L121_IGE_TiPACCs.nc</li> <li>perturbation of the model parameters: Different iceberg distribution and different sea-ice–ocean drag and snow conductivity on sea-ice, leading to less sea-ice production in the eastern Ross Sea.</li> <li>More: <a href="https://pmathiot.github.io/NEMOCFG/docs/build/html/simu_eORCA025_OPM020.html">https://pmathiot.github.io/NEMOCFG/docs/build/html/simu_eORCA025_OPM020.html</a></li> </ul> </li> </ul> <p> </p> <ul> <li><strong>anomaly_NEMO3_AMUXL12.L75_IGE_TiPACCs.nc</strong> <ul> <li>similar to reference_NEMO3_AMUXL12.L75_IGE_TiPACCs.nc</li> <li>perturbation of atmospheric forcing: MAR forced by the CMIP5 multi-model anomaly under the RCP8.5 scenario (<a href="https://doi.org/10.5194/tc-15-571-2021">Donat-Magnin et al. 2021</a>).</li> <li>provided average: 2080-2100 (21-year average)</li> </ul> </li> </ul> <p> </p>
Effect of Textural Properties and Presence of Co-cation on NH3-SCR Activity of Cu-Exchanged ZSM-5
<p><strong>Description of the dataset: </strong></p> <ul> <li><strong>Data type</strong>: Experimental spectroscopic measurements, computer simulation and analysis</li> <li>Files are with filename extensions: <strong>DSC</strong>, <strong>DAT</strong>, <strong>m</strong>, <strong>txt</strong></li> <li>Information on <strong>origin of the data</strong>:</li> </ul> <ul> <li>EPR spectroscopic measurements with filename extensions <strong>DSC</strong>, <strong>DTA.</strong></li> <li>EPR spectroscopic simulation and analyses with filename extension <strong>m</strong>.</li> <li>EPR spectra are exported as <strong>txt</strong> files in ASCII format.</li> </ul> <ul> <li>X-band CW-EPR spectroscopic measurements were generated by EMX spectrometer equipped with SHQ cavity produced by Bruker.</li> <li><strong>If t</strong> <ul> <li>Files in <strong>PARACAT_WP3_20210721_01_CW_Experimental</strong> folder includes X-band CW-EPR spectroscopic measurements; original data are in DTA/DSC and txt. formats.</li> <li>Files in <strong>PARACAT_WP3_20210721_02_CW_Simulations</strong> folder includes computer simulations/analyses of the EPR measurements; data are in m and txt formats.</li> </ul> </li> <li><strong>Information on</strong>: <ul> <li>specialized abbreviations: <strong>EPR</strong> – Electron Paramagnetic Resonance, <strong>CW</strong> – Continuous Wave EPR, <strong>exp </strong>– experimental data, <strong>hyd </strong>– cw-EPR spectra related to hydrated state, <strong>dehyd </strong>– cw-EPR spectra related to the dehydrated state, <strong>sim </strong>– simulation data. <strong>Sys </strong>– copper species used for constructing the spin-Hamiltonian in EPR simulations.</li> <li>definitions of variables: <strong>Magnetic field, Temperature.</strong></li> <li>units of measurement: <strong>Gauss (G), K, degree (°), milliTesla (mT)</strong>.</li> </ul> </li> </ul>
Spectral dataset of daylights and surface properties of natural objects measured in Japan
<p>This is a spectral dataset of natural objects and daylights collected in Japan. </p> <p>We collected 359 natural objects and measured the reflectance of all objects and the transmittance of 75 leaves. We also measured daylights from dawn till dusk on four different days using a white plate placed (i) under the direct sun and (ii) under the casted shadow (in total 359 measurements). We also separately measured daylights at five different locations (including a sports ground, a space between tall buildings and a forest) with minimum time intervals to reveal the influence of surrounding environments on the spectral composition of daylights reaching the ground (in total 118 measurements).</p> <div> <div> <div> <p>If you use this dataset in your research, please cite the following publication.</p> </div> </div> </div> <div> <div> <div> </div> </div> </div> <div>Morimoto, T., Zhang, C., Fukuda, K., & Uchikawa, K. (2022). Spectral measurement of daylights and surface properties of natural objects in Japan. <em>Optics express</em>, <em>30</em>(3), 3183. https://doi.org/10.1364/OE.441063</div> <p> </p> <p>Dataset contains following Excel spread sheets and csv files:</p> <p><strong>(A) Surface properties of natural objects</strong></p> <p><strong> (A-1) Reflectance_ver1-2.xlsx and .csv</strong></p> <p><strong> (A-2) Transmittance_FrontSideUp_ver1-2.xlsx and .csv</strong></p> <p><strong> (A-2) Transmittance_BackSideUp_ver1-2.xlsx and .csv</strong></p> <p><strong>(B) Daylight measurements</strong></p> <p> <strong>(B-1) Daylight_TimeLapse_v1-2.xlsx and .csv</strong></p> <p> <strong>(B-2) Daylight_DifferentLocations_v1-2.xlsx and .csv</strong></p> <p> </p> <p>Data description</p> <p><strong>(A) Surface properties</strong></p> <p><strong>(A-1) Reflectance_ver1-2.xlsx and .csv</strong></p> <p>This file contains surface spectral reflectance data (380 - 780 nm, 5 nm step) of 359 natural objects, including 200 flowers, 113 leaves, 23 fruits, 6 vegetables, 8 barks, and 9 stones measured by a spectrophotometer (SR-2A, Topcon, Tokyo, Japan). Photos of all samples are included in the .xlsx file.</p> <p>For the analysis presented in the paper, we identified reflectance pairs that have a Pearson’s correlation coefficient across 401 spectral channels of more than 0.999 and removed one of reflectances from each pair. The column 'Used in analysis' indicates whether or not each sample is used for the analysis (TRUE indicates used and FALSE indicate not used).</p> <p>At the time of collection, we noted the scientific names of flowers, leaves and barks from a name board provided by the Tokyo Institute of Technology in which samples are collected. If not available, we used a smartphone software which automatically identifies the scientific name from an input image (<em>PictureThis - Plant Identifier</em> developed by Glority Global Group Ltd.). The names of 2 flowers and 9 stones whose name could not be identified through either method were left blank.</p> <p><strong>(A-2) Transmittance_FrontSideUp_v1-2.xlsx and .csv</strong></p> <p>This file contains surface spectral transmittance data (380 - 780 nm, 5 nm step) for 75 leaves measured by a spectrophotometer (SR-2A, Topcon, Tokyo, Japan). Photos of all samples are included in the .xlsx file.</p> <p>For this data, the transmittance was measured with the front-side of leaves up (the light was transmitted from the back side of the leaves). This is the data presented in the associated article.</p> <p><strong>(A-3) Transmittance_BackSideUp_v1-2.xlsx and .csv</strong></p> <p>Spectral transmittance data of the same leaves presented in (A-2).</p> <p>For this data, the transmittance was measured with the back-side of leaves up (the light was transmitted from the front side of the leaves).</p> <p> </p> <p><strong>(B) Daylight measurements</strong></p> <p><strong>(B-1) Daylight_TimeLapse_ver1-2.xlsx and .csv</strong></p> <p>This file contains daylight spectra from sunrise to sunset on four different days (2013/11/20, 2013/12/24, 2014/07/03 and 2014/10/27) measured by a spectrophotometer (SR-LEDW, Topcon, Tokyo, Japan) with a wavelength range from 380 nm to 780 nm with 1 nm step. We measured the reflected light from the white calibration plate placed either under a direct sunlight or under a casted shadow.</p> <p>The column 'Cloud cover' provides visual estimate of percentage of cloud cover across the sky at the time of each measurement. The column 'Red lamp' indicates whether an aircraft warning lamp at the measurement site was on (circle) or off (blank).</p> <p><strong>(B-2) Daylight_DifferentLocations_ver1-2.xlsx and .csv</strong></p> <p>This file includes daylight spectra measured at five different sites within the Suzukakedai Campus of Tokyo Institute of Technology with minimum time gap on 2014/07/08, using a spectroradiometer (IM-1000, Topcon) from 380 nm to 780 nm with 1 nm step. The instrument was oriented either towards the sun or towards the zenith sky. When the instrument was oriented to the sun, we measured spectra in two ways: (i) one using a black cylinder covering the photodetector and (ii) the other without using a cylinder.</p> <p>The column 'Cylinder' indicates whether the black cylinder was used (circle) or not (cross). The column 'Cloud cover' shows the visual estimate of percentage of cloud cover at the time of each measurement. The column 'Sun hidden in clouds' denotes whether the measurement was taken when the sun was covered by clouds (circle) or not (blank).</p>
pop-cosmos: Galaxy property and redshift catalog for COSMOS2020
<p>This record (v≥2.0.0) contains data products associated with the paper "<em>pop-cosmos: Insights from generative modeling of a deep, infrared-selected galaxy population</em>" by Thorp et al. (2025). Earlier versions of this record (v<2.0.0) contain data products associated with the paper "<em>pop-cosmos: Scaleable inference of galaxy properties and redshifts with a data-driven population model</em>" by Thorp et al. (2024), which are superseded by the contents of v2.0.0. In v≥2.0.0, we include results for all COSMOS2020 galaxies with $\textit{Ch.1}<26$ or $r<25$.</p> <p>The included products are derived from spectral energy distribution (SED) fits to 26-band COSMOS2020 photometry, using the 16-parameter SPS model described in Thorp et al. (2024, 2025), and the <code>pop-cosmos</code> prior from Thorp et al. (2025). All results are based on Markov Chain Monte Carlo (MCMC) runs using the configuration described in Thorp et al. (2024, 2025). Results correspond to v2.1 of the COSMOS2020 catalog.</p> <p>The current release includes the following files:</p> <ul> <li><strong>README_v2_2_0.txt</strong>: Detailed information about how to read the other files in the record.</li> <li><strong>mcmc_summaries.h5.gz</strong>: Zipped HDF5 file with summaries (percentiles) of the posteriors.</li> <li><strong>mcmc_samples_pop_cosmos.h5.gz</strong>: Zipped HDF5 file with posterior samples (using <code>pop-cosmos</code> prior).</li> <li><strong>mcmc_samples_Prospector.h5.gz</strong>: Zipped HDF5 file with posterior samples (using <code>Prospector</code>-$\alpha$ prior).</li> </ul> <p>If you make use of any of these products, please cite this repository and Thorp et al. (2024, 2025). Please also cite the <code>pop-cosmos</code> overview paper by Alsing et al. (2024), and the paper by Deger et al. (2025). If you make use of any COSMOS data products, please cite Weaver et al. (2022) and any other relevant publications. If you make use of COSMOS spectroscopic data, please cite Khostovan et al. (2025) and references therein.</p> <p>If you spot any issues or have any requests, please contact the corresponding author (Stephen Thorp) using the details in the README.</p> <p>If you want to access <code>pop-cosmos</code> mock galaxy catalogs, these can be found on <a href="https://doi.org/10.5281/zenodo.15622324">Zenodo</a>.</p> <p>Related software, including a demo notebook for working with the data in this record, can be found on <a href="https://github.com/Cosmo-Pop/pop-cosmos">GitHub</a>. </p> <p>References:</p> <ol> <li>Alsing et al. (2024). ApJS 274, 12. [<a href="https://arxiv.org/abs/2402.00935">arXiv:2402.00935</a>][<a href="https://doi.org/10.3847/1538-4365/ad5c69">doi</a>]</li> <li>Deger et al. (2025). MNRAS, submitted. [<a href="https://arxiv.org/abs/2509.20430">arXiv:2509.20430</a>]</li> <li>Khostovan et al. (2025). ApJ, submitted. [<a href="https://arxiv.org/abs/2503.00120">arXiv:2503.00120</a>]</li> <li>Thorp et al. (2024). ApJ 975, 145. [<a href="https://arxiv.org/abs/2406.19437">arXiv:2406.19437</a>][<a href="https://doi.org/10.3847/1538-4357/ad7736">doi</a>]</li> <li>Thorp et al. (2025). ApJ, accepted. [<a href="https://arxiv.org/abs/2506.12122">arXiv:2506.12122</a>]</li> <li>Weaver et al. (2022). ApJS 258, 11. [<a href="https://arxiv.org/abs/2110.13923">arXiv:2110.13923</a>][<a href="https://doi.org/10.3847/1538-4365/ac3078">doi</a>]</li> </ol>
Dataset on UAV RGB videos acquired over a vineyard property of Bodegas Terras Gauda at an early stage of Botrytis cinerea infection in 2021
<p>The videos were collected in a vineyard owned by Bodegas Terras Gauda, in June 2021. The videos were collected with a DJI Matrice 210 RTK UAV, which had a DJI Zenmuse X5S sensor onboard. A total of 4 rows were recorded with side videos. The flights were carried out on a sunny day with wind velocity lower than 0.5 m/s. Annotations of the grape clusters in the MOTS style are provided. </p>
The Thousand-Pulsar-Array program on MeerKAT -- IX. The time-averaged properties of the observed pulsar population: data set
<p>This archive contains pulsar data presented as part of the MNRAS paper: <em>"The Thousand-Pulsar-Array program on MeerKAT -- IX. The time-averaged properties of the observed pulsar population"</em>.</p> <p>Folded, time-averaged pulse profiles (4 Stokes parameters, 8 frequency channels, 1024 time bins across the period) of the 1271 pulsars listed in Table 1 of the MNRAS paper are included in the ar_files.zip. Ephemerides of these pulsars (as used in the MNRAS paper) are included in the eph_files.zip. The pulsar data are readable by the PSRCHIVE package, see e.g. van Straten et al., Astronomical Research and Technology 9, 237 (2012).</p> <p>Tables 1, 5, and 6 from the MNRAS paper are included in tables_files.zip as .csv files. The file column_descriptions.txt describes the quantities in columns of these tables.<br> </p>
QST - open data of the article Vanwindekens & Hardy (2022) - table 1 soil properties
<p>Soil properties of the long term fields trials linked to the paper "The QuantiSlakeTest, dynamic weighting of soil under water to measure soil structural stability" submitted to the SOIL journal by Vanwindekens & Hardy (2022).</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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