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12,346 results for “derivatives”
Simulated structures of ansamer amanitin derivatives and precursors for atroposelective synthesis
<p>a-amanitin and its derivatives are eight-membered, cyclic peptides that are additionally bridged with a tryptathionine cross-link. Their synthesis sometimes yields isomers, which haven't been classified unambiguously so far. Until now, it was not clear whether these isomers differing in the positioning of the trpytathionine bridge indeed exist and if yes, under which conditions they are formed and how they shall be named. In our work, we therefore investigated the occurrence of these isomers. We have identified the structure and the dynamics of the amanitin derivative <strong>4a </strong>and its isomer <strong>4b</strong>. Investigating the impact of the macrocyclisation site on the occurrence of this isomer, we published an optimised strategy for atroposelective synthesis (including precursors <strong>3b</strong> and <strong>3c</strong>).</p> <p>Here, we present the simulated structures of the ansamers <strong>4a</strong> and <strong>4b</strong>, which both are in very good agreement to crystal data, together with the precursors <strong>3b</strong> and <strong>3c</strong>. <strong>4a</strong> and <strong>4b</strong> are the amanitin derivatives, for whose classification we propose the term <em>ansamers</em>.</p> <p>For further instructions, please refer to 'README'.</p> <p>For the files required to reproduce the MD simulations, please refer to the Zenodo repository '<em>MD setup data for ansamer amanitin derivatives and precursors for atroposelective synthesis</em>' available under DOI: 10.5281/zenodo.7125315. </p>
Supporting Dataset for Fault Friction Derived from Fault Bend Influence on Coseismic Slip During the 2019 Ridgecrest Mw 7.1 Mainshock
<p>Supporting Dataset for Fault Friction Derived from Fault Bend Influence on Coseismic Slip During the 2019 Ridgecrest M<sub>w</sub> 7.1 Mainshock, JGR</p> <p>See Readme text files for details. </p>
Supplementary data for: Comparison of optical flow derivation techniques for retrieving tropospheric winds from satellite image sequences
<p>This study introduces a validation technique for quantitative comparison of algorithms which retrieve winds from passive detection of cloud- and water vapor-drift motions, also known as Atmospheric Motion Vectors (AMVs). The technique leverages airborne wind-profiling lidar data collected in tandem with 1-min refresh rate geostationary satellite imagery. AMVs derived with different approaches are used with accompanying numerical weather prediction model data to estimate the full profiles of lidar-sampled winds which enables ranking of feature tracking, quality control, and height-assignment accuracy and encourages meso-scale, multi-layer, multi-band wind retrieval solutions. The technique is used to compare the performance of two brightness motion, or "optical flow," retrieval algorithms used within AMVs, 1) Patch Matching (PM; used within operational AMVs) and 2) an advanced Variational Optical Flow (VOF) method enabled for most atmospheric motions by new-generation imagers. The VOF AMVs produce more accurate wind retrievals than the PM method within the benchmark in all imager bands explored. It is further shown that image regions with low texture and multi-layer-cloud scenes in visible and infrared bands are tracked significantly better with the VOF approach, implying VOF produces representative AMVs where PM typically breaks down. It is also demonstrated that VOF AMVs have reduced accuracy where the brightness texture does not advect with the mean wind (e.g. gravity waves), where the image temporal noise exceeds the natural variability, and when the height-assignment is poor. Finally, it is found that VOF AMVs have improved performance when using fine-temporal refresh rate imagery, such as 1-min versus 10-min data.</p>
Derivation of plant functional type (PFT) maps from the ESA CCI Land Cover product
<p><em>This package supplements the following paper submitted to ESSD: <strong>Gross and net land cover changes of the main plant functional types derived from the annual ESA CCI land cover maps (1992-2015).</strong></em></p> <p><em>Li, W., MacBean, N., Ciais, P., Defourny, P., Lamarche, C., Bontemps, S., Houghton, R. A. and Peng, S.: Gross and net land cover changes based on plant functional types derived from the annual ESA CCI land cover maps, Earth Syst. Sci. Data Discuss., 1–23, doi:10.5194/essd-2017-74, 2017.</em></p> <p><em>This package contains the protocol of converting the original annual ESA CCI Land Cover product into plant functional types (PFTs) that can be used by land surface models and the corresponding cross-walking table.</em></p> <p><em>The original ESA LC class data and translated PFTs in 2000 as an example are attached in the .zip file. The annual ESA CCI PFT maps from 1992 to 2015 at half degree resolution are also added in a .zip file.</em></p>
Figure 1. Bipolar limb derivations-EKG Through Sound-Card
<p>Since the human body is a conductive mass, an electrode attached to the arm is the electric<br> equivalent of a connection to the shoulder and an electrode attached to the foot is the equivalent of a<br> connection to the abdomen. Using this principle, we obtain the following three standard bipolar<br> limb derivations (Figure1):<br> • Derivation I: negative electrode to the right hand and positive electrode to the left hand<br> • Derivation II: positive electrode to the left foot, negative to the right hand<br> • Derivation III: positive electrode to the left foot, negative to the left hand.</p>
Vallée de la Sionne Snow Avalanche n. 20213009: High-speed camera recording and derived variables
<p>This repository hosts data obtained from high-speed camera measurements conducted within a large powder snow avalanche (No. 20213009) that occurred naturally at the Vallée de la Sionne test site in Switzerland. Positioned 14 meters above the ground on a vertical pylon, the high-speed camera captures visualizations of snow particles within the aerial layers. These images reveal diverse particle clusters, identifiable as bright spots due to their higher light reflectance compared to the surrounding air-snow crystal mixture.</p> <p>Contained within this repository is an overview video recording along with corresponding data on the average brightness of each image captured by the high-speed camera. This dataset facilitates the reconstruction of the temporal evolution and frequency of particle clustering, with brightness intensity acting as a proxy for mass transport. The average brightness for each image is computed from the averaging of values from 2048 x 2048 pixels (greyscale 0 to 255). These datasets complement the findings presented in the following publication:</p> <p>B. Sovilla, E. Marchetti, M. Kyburz, A. Koehler, P. Huguenin, I. Calic, M.J. Kohler, E. Surinach, and C. Pérez-Guillén, under review. "The dominant source mechanism of infrasound generation in powder snow avalanches," submitted to Geophysical Research Letters.</p>
FIGURE 8. Molfetta dinosaur tracks, photogrammetry derived 3D in The use of aerial and close-range photogrammetry in the study of dinosaur tracksites: Lower Cretaceous (upper Aptian/lower Albian) Molfetta ichnosite (Apulia, southern Italy)
FIGURE 8. Molfetta dinosaur tracks, photogrammetry derived 3D models and interpretations; 1-2, DEM and contour line map of a theropod footprint (contour lines have an interval of 0.2 cm); 4-5, DEM and contour line map of an ornitischian footprint; 3 and 6, interpretative outline drawings of the studied tracks.
Supplement code and data for "Accounting for trade in derived products when estimating European Union's role in driving deforestation"
<p>This repository includes the code to reproduce the results and visualizations as well as the complete output underlying the publication:</p> <div> <div>Laroche, P.C.S.J., Gómez-Suárez, M., Persson, U.M., Pendrill, F., Schwarzmueller, F., Schulp, C.J.E., Kastner, T., 2024. Accounting for trade in derived products when estimating European Union’s role in driving deforestation. Ecological Economics 224, 108288. <a href="https://doi.org/10.1016/j.ecolecon.2024.108288">https://doi.org/10.1016/j.ecolecon.2024.108288</a></div> </div> <p> </p> <p>The file "01_calculations.R" calculates the files included in the "output" folder. These files can be used to visualize the results using the file "02_visualization.R".<br>The folder input_data contains some of the files necessary to run the code in "01_calculations.R". Note that additional data has to be downloaded to run the calculations, as specified in the R file. The folder "results" includes an xlsx file with the complete output of the calculation using the "mean" conversion factors.</p>
Derived-ECVs for Case Studies (monthly timeseries)
<p>Monthly timeseries (.csv) of derived-ecvs spatially averaged over Case Studies for different climate scenarios (historical, SSP1-2.6, SSP2-4.5, SSP5-8.5) and time horizons (1985-2014, 2015-2100). Data are created by RethinkAction project using statistical downscaling method from CMIP6 simulations.</p> <p>We acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP6. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the data and providing access, and the multiple funding agencies who support CMIP6 and ESGF.</p> <p>Moreover, we acknowledge the Copernicus Climate Change Service (C3S) Climate Data Store (CDS) to provide access to CMIP6, CERRA, ERA5 and ERA5-Land data:</p> <ul> <li>Copernicus Climate Change Service, Climate Data Store, (2021): CMIP6 climate projections. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.c866074c.</li> <li>Schimanke S., Ridal M., Le Moigne P., Berggren L., Undén P., Randriamampianina R., Andrea U., Bazile E., Bertelsen A., Brousseau P., Dahlgren P., Edvinsson L., El Said A., Glinton M., Hopsch S., Isaksson L., Mladek R., Olsson E., Verrelle A., Wang Z.Q., (2021): CERRA sub-daily regional reanalysis data for Europe on single levels from 1984 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.622a565a</li> <li>Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D.,Thépaut, J-N. (2023): ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.adbb2d47</li> <li>Muñoz Sabater, J. (2019): ERA5-Land hourly data from 1950 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.e2161bac</li> </ul> <p>Acknowledgement also to:</p> <ul> <li>DRAAC, 2023, Regional climate data provided by the Regional Ditectorate for the Environment and Climate Change of the Regional Autonomous Government of Azores (<a href="https://urldefense.com/v3/__https:/portal.azores.gov.pt/en/web/draac__;!!D9dNQwwGXtA!TAB9_FXZEEA4K_6AkmoIqX-krFMSiGcKRY--rOpV9psI98vjxa-sLAZQYR1s0G1fFmBrENoHHpZCdvP0s67vzss$" target="_blank" rel="noopener">https://portal.azores.gov.pt/en/web/draac</a>)</li> <li>SRAA\CCIAM, 2017. Programa Regional de Alterações Climáticas (PRAC), Secretaria Regional do Ambiente e Ação Climática (SRAA) of the Governo dos Açores, Climate Change Impacts, Adaptation and Modelling (CCIAM) of the Faculdade de Ciências da Universidade de Lisboa (FCUL), <a href="https://urldefense.com/v3/__https://snig.dgterritorio.gov.pt/rndg/srv/por/catalog.search*/metadata/8804acd9-9d0f-40fb-bc2e-e4dff8c2b4b1__;Iw!!D9dNQwwGXtA!SmILhnS0zICNt1ZcxuQ0VPP7VxtFeSEdLt4chAw8y5tsdlWqsgkyt9kESGhRu-00ZQqakzH36tvV-_DcQI7jGo-lOg$" target="_blank" rel="noopener">https://snig.dgterritorio.gov.pt/rndg/srv/por/catalog.search#/metadata/8804acd9-9d0f-40fb-bc2e-e4dff8c2b4b1</a></li> </ul>
Data & Codes used in: Quantum Mechanical Derived (VdW-DFT) Transferable Lennard-Jones and Morse Potentials to Model Cysteine and Alkanethiol Adsorption on Au(111)
<p>Here we provide the data, codes, and outline the procedure to reproduce the results presented in the paper "Quantum Mechanical Derived (VdW-DFT) Transferable Lennard-Jones and Morse Potentials to Model Cysteine and Alkanethiol Adsorption on Au(111)" by E. Ventura-Macias, P. M. Martinez, Rubén Pérez, and J. G. Vilhena.</p> <p>The following sections contain a detailed description of the data and codes. At the end of this README, you will find instructions on how to generate the classical force-field parameters (Morse and Lennard-Jones) from the potential energy surfaces (PES) computed at the DFT level.</p> <p>The procedure is general and applies to any given pair of molecule and surface. The provided codes will allow you to swiftly generate the PES at the DFT level, fit the Lennard-Jones and Morse potentials, and test them in a LAMMPS MD simulation.</p> <p>For a thorough explanation of the procedure and the relevance of these results, please refer to the original publication (ARTICLE_DOI).</p> <p>If you find this helpful, please consider citing the article (ARTICLE_DOI).</p> <h2>Data structure</h2> <p>The data is organized in the following way:</p> <ol> <li> <p>DFT</p> <ul> <li>The equilibrium adsorption geometry of methanethiol (MTH), propanethiol (PTH), and cysteine (CYS) for the Au-mol configuration with PBE+DFT-D3.</li> <li>Potential Energy Surface (PES) computed at the DFT level (Figure 3 of the main manuscript).</li> <li>The scripts used to generate the PES.</li> </ul> </li> <li> <p>MD</p> <ul> <li>Fitting code and general instructions on how to use it.</li> <li>General input scripts used to generate MD data within LAMMPS.</li> </ul> </li> </ol> <h3>DFT Data</h3> <p>The DFT data in <code>DFT.zip</code> is organized in the following way:</p> <ul> <li> <p><code>DFT_PES/</code></p> <p>This folder contains the DFT potential energy surfaces (PES) for the interaction of the sulfur atom of methanethiolate (<code>mth</code>), propanethiolate (<code>pth</code>), and cysteine (<code>cys</code>) with the Au(111) surface and the necessary scripts to reproduce it.</p> <ul> <li> <p><code>results/</code></p> <p>The PES are given in one csv file per molecule named as <em><code>mol</code></em> + <code>_PES_S-Au111.csv</code>, where <em><code>mol</code></em> is the molecule name. Columns are as follows:</p> <table> <tbody> <tr> <td><strong>Label</strong></td> <td><strong>Definition</strong></td> </tr> <tr> <td><em>i</em></td> <td>calculation number</td> </tr> <tr> <td><em>site</em></td> <td>adsorption site</td> </tr> <tr> <td><em>z</em></td> <td>distance of the S atom to the surface</td> </tr> <tr> <td><em>deltaz</em></td> <td>distance difference from the minimum energy position of the S atom</td> </tr> <tr> <td><em>pbed3</em></td> <td>PBE+D3 binding energy</td> </tr> <tr> <td><em>pbe</em></td> <td>PBE component of the binding energy</td> </tr> <tr> <td><em>d3</em></td> <td>DFT-D3 component of the binding energy</td> </tr> </tbody> </table> </li> </ul> </li> </ul> <ul> <li> <ul> <li> <p><code>mth/</code> | <code>pth/</code> | <code>cys/</code></p> <p>Each folder contains the CONTCAR (VASP) file for the optimized geometry of the molecule adsorbed on the Au(111) surface with PBE+D3.</p> </li> <li> <p><code>setup_grid.py</code></p> <p>Python script to set up the POSCAR files for the PES calculations.</p> </li> <li> <p><code>read_results.py</code></p> <p>Python script to read the results of the PES calculations.</p> </li> <li> <p><code>sub_array.sh</code></p> <p>Bash script to submit the PES calculations to an SLURM-based cluster.</p> </li> <li> <p><code>INCAR</code> | <code>KPOINTS</code> | <code>surf.CONTCAR</code></p> <p>VASP input files for the PES calculations.</p> </li> </ul> </li> </ul> <h3>MD fitting</h3> <p>The MD data in MD.zip is are organized as:</p> <ul> <li> <p><code>Fitting/</code></p> <ul> <li><code>optimize_Morse.py</code></li> </ul> <p>Contains the Python script used for the fitting procedure of Morse potential.</p> <ul> <li><code>optimize_LJ.py</code></li> </ul> <p>Contains the Python script used for the fitting procedure of LJ potential.</p> </li> <li> <p><code>Histogram/</code></p> <ul> <li><code>in.test</code></li> </ul> <p>LAMMPS input script to extract an XY file for the position of the S atom in an NVT simulation.</p> <ul> <li><code>coord.data</code></li> </ul> <p>Au surface for LAMMPS simulations.</p> <ul> <li><code>SCH3.data</code></li> </ul> <p>SCH3 molecule for LAMMPS simulations.</p> <ul> <li><code>sheng.eam</code></li> </ul> <p>EAM potential file in case Au dynamics are wished to be included.</p> </li> <li> <p><code>Single_point_scan/</code></p> <ul> <li><code>in.scan</code></li> </ul> <p>LAMMPS input script to perform single-point energy scan of a given molecule.</p> <ul> <li><code>coord.data</code></li> </ul> <p>Au surface for LAMMPS simulations.</p> <ul> <li><code>SCH3.data</code></li> </ul> <p>SCH3 molecule for LAMMPS simulations.</p> <ul> <li><code>launch.sh</code></li> </ul> <p>Launches the single point scan.</p> <ul> <li><code>plot_scan.py</code></li> </ul> </li> <li> <p><code>Molecules</code></p> <p>LAMMPS sample geometries for the 3 molecules.</p> </li> </ul> <h2>Steps to reproduce the results</h2> <p>The PES calculations were performed using the VASP code and two Python scripts for setup and results parsing.</p> <h3>Requirements</h3> <ul> <li>VASP (tested with version 5.4.4) <ul> <li><code>PAW_PBE</code> pseudopotentials set version 5.4</li> </ul> </li> <li>Python3 with packages: <ul> <li>ASE (Atomic Simulation Environment)</li> <li>Numpy</li> <li>Pandas</li> <li>matplotlib (optional)</li> </ul> </li> </ul> <h3>Steps</h3> <p>Each molecule has its own directory with the necessary files to reproduce the results. The following steps are for the methanethiolate molecule (<code>mth</code>).</p> <ol> <li>Set up the grid of points for the PES calculations by running the <code>setup_grid.py</code> script. It will create a subfolder <code>run/</code> inside the molecule's directory with the POSCAR files for each point in the grid.</li> </ol> <blockquote> <p>python setup_grid.py mth</p> </blockquote> <ol> <li> <p>Create the corresponding <code>POTCAR</code> file and place it in the molecule's directory.</p> </li> <li> <p>Change the <code>sub_array.sh</code> script to match the number of calculations in the array numbers and the MOL variable.</p> </li> </ol> <pre><code>#SBATCH --array=1-number of calculations MOL=mth</code></pre> <ol> <li>Submit the calculations to a SLURM-based cluster by running the <code>sub_array.sh</code> script from the <code>DFT_PES</code> directory.</li> </ol> <blockquote> <p>sbatch sub_array.sh</p> </blockquote> <ol> <li>After the calculations are finished, run the <code>read_results.py</code> script to parse the results and generate the PES csv files. It takes the arguments <code>--surf</code> and <code>--mol</code> to specify the surface and molecule PBE+D3 and D3 total energies.</li> </ol> <blockquote> <p>python read_results.py mth --surf -128.5228 -17.7147 --mol -22.6026 -0.0083</p> </blockquote> <h3>Reference values for the PBE+D3 and D3 surface and molecule energies</h3> <table> <tbody> <tr> <td> </td> <td><strong>Surface</strong></td> <td> </td> <td><strong>Molecule</strong></td> <td> </td> </tr> <tr> <td><strong>Molecule</strong></td> <td><strong>PBE+D3<br></strong></td> <td><strong>D3</strong></td> <td><strong>PBE+D3</strong></td> <td><strong>D3</strong></td> </tr> <tr> <td>mth</td> <td>-128.5228</td> <td>-17.7147</td> <td>-22.6026</td> <td>-0.0083</td> </tr> <tr> <td>pth</td> <td>-128.5228</td> <td>-17.7147</td> <td>-55.8615</td> <td>-0.0086</td> </tr> <tr> <td>cys</td> <td>-128.5228</td> <td>-17.7147</td> <td>-74.0338</td> <td>-0.1611</td> </tr> </tbody> </table> <h3>Fitting procedure</h3> <p>Both the .xyz and .csv files should be located at the same folder as the script. Then, simply run the code (<code>mth</code> is used as an example):</p> <blockquote> <p>python optimize_Morse.py mth</p> </blockquote> <p>or</p> <blockquote> <p>python optimize_LJ.py mth</p> </blockquote> <p>The script will print the optimized parameters: [De, re, α] or [ϵ, σ] for Morse or LJ respectively. It will also plot a fitting plot and a birdview of the resulting PES.</p> <h3>Using the potential. Histogram example</h3> <p>The code will run for the optimized <code>mth</code> Morse parameters and extract a <code>occ.lammpstrj</code> containing the (x,y) positions of the S atom throughout the NVT simulation. Note that only Au-S interaction is included. Start as:</p> <blockquote> <p>lmp -in in.test</p> </blockquote> <p>This is easily adaptable to other routines or molecules and is thought to be a generic LAMMPS starting input.</p> <h3>Single-point energy scan</h3> <p>Go to the 2C) folder and launch the scan with:</p> <blockquote> <p>./launch.sh</p> </blockquote> <p>This will create a folder named <code>fine_scan</code> containing 128 folders. Each folder is assigned to an (x,y) position. Then, inside each folder, a single-point energy evaluation is performed at various Z heights around the absolute minima.</p> <p>The <code>in.scan</code> file should be modified accordingly with the appropriate potentials. It is set to perform the scan with the optimized Morse potential by default.</p> <p>The output is gathered in the <code>E_readout.dat</code> folder with the following structure (all energies in eV):</p> <table> <tbody> <tr> <th>Total Energy</th> <th>Intramolecular energy</th> <th>Au-mol vdW interaction energy</th> <th>Au-S interaction energy</th> </tr> </tbody> <tbody> <tr> <td>-133.62</td> <td>0.123112</td> <td>-0.2403</td> <td>-1.31765</td> </tr> <tr> <td>-133.721</td> <td>0.123112</td> <td>-0.28-401</td> <td>-1.37853</td> </tr> </tbody> </table> <p>Therefore, the total adsorption energy will be the sum of the last two columns.</p> <p>Energies are ordered in increasing Z for the same (x,y) point. That is, the first 12 lines correspond to 12 heights of the starting (x,y) coordinate, the next 12 lines to heights at the second (x,y) configuration and so on.</p> <p>The python script <code>plot_scan.py</code> may be used to plot the results. The <code>E_readout.dat</code> file and <code>.csv</code> must be in the same folder.</p> <blockquote> <p>python plot_scan.py mth</p> </blockquote>
UAV-derived cluster greenness and pathlength of individual trees collected at Marden Park, UK
<p>Inidivual tree cluster greenness (gcc) and pathlength used in the study "UAV-derived greenness and within-crown spatial patterning can detect ash dieback in individual trees".</p>
A fungal-derived adjuvant amplifies the antitumoral potency of BCG
<p>A repository of data for the mouse single-cell multiome (RNA+ATAC) dataset of lineage negative bone marrow samples pre- and post- treatment BCG or b-glucan treated mice.</p>
Identifying Episodes of Hypovigilance in Intensive Care Units Using Routine Physiological Parameters and Artificial Intelligence: a Derivation Study. Open Code and Dataset
<p>The purpose of this project is to detect hypogilance using the EVEILS database.</p> <p>Database is ICU data from Hôtel-Dieu De Lévis , Québec, Canada. Please cite us if you use either the data or code. </p> <p>This code was written during Raphaëlle Giguère Msc in Computer Science. The goal of her project is to detect hypovigilance using machine learning in the ICU. In this repository, you have the data set before preprocessing:</p> <ul> <li>df_hypovigilance : Contains the hours, date and value of the vigilance level, using either the RASS or Ramsay and already converted using the thresholds shown in the paper.</li> <li>raw_df : Contains the raw values from the gateway for each participant. All of the identifying values have been removed.</li> </ul> <p>At the end of the preprocessing_anonymous script, you should generate a new dataset called "df_final". This dataset is used for the training_model script.</p> <p>The cross validation employs groups of random size meaning the results might differ from time to time but should stay consistent.</p> <p> </p>
Industrial Two-phase Olive Pomace Slurry-Derived Hydrochar Fuel for Energy Applications
<p>This dataset contains extended data files for the publication entitled "Industrial Two-phase Olive Pomace Slurry-Derived Hydrochar Fuel for Energy Applications."</p> <p>Data files includes: <br>- Dataset description.txt (provides abbreviations or codes of samples and their properties)<br>- Extended data.xlsx (data files for the biochemical, proximate, ultimate, HHV, and mineral characterisitics of raw material (two-phase olive pomace slurry) and hydrochars<br>- 13C-NMR data.zip (raw data files for 13C-solid nuclear magnetic resonance analysis of raw material and hydrochars)<br>- TGA-DTA data.zip (raw data for thermal gravimetric analysis of raw material and hydrochars)<br>- FTIR data.zip (raw data for fourier transform infrared analysis of raw material and hydrochars) </p> <p>Checksum numbers for enclosed data files: <br>- MD5 Checksum number for file named Extended dataset v1.0 = 6c542debe0a8b4297b8730830e7d5b58<br>- MD5 Checksum number for file named 13C-NMR data = bb25d31771d281ce1ca6518c4dc1cb41 <br>- MD5 Checksum number for file named FTIR data = 0df81ec3a25b4d130be09cef59736d30<br>- MD5 Checksum number for file named TGA-DTA data = 7205cc5a24daa94e60c82a43ae748fb2</p>
"Monthly velocity and seasonal variations of the Mont Blanc glaciers derived from Sentinel-2 between 2016-2024" - supplementary materials
<p>The repository contains the supplementary materials to be downloaded relative to the research article:</p> <p>“Monthly velocity and seasonal variations of the Mont Blanc glaciers derived from Sentinel-2 between 2016-2024” </p> <p>https://doi.org/10.5194/egusphere-2023-2771</p> <p>The available files are:</p> <p>-92 raster maps of monthly velocity of the study area.</p> <p>-Shapefiles whith the glacier outlines of the 30 studied glaciers.</p> <p>-Shapefiles of the velocity time series extraction areas.</p> <p>-Velocity time series 2016-2024 of the 30 glaciers from the study. </p>
Mean age of air anomaly derived from ACE-FTS N2O
<p>The deseasonalized mean age of air (AoA) anomaly is derived using N2O observations from ACE-FTS and simulations of N2O and AoA from CLaMS. Results are provided as a monthly zonal mean in 10 degree latitude bins and on 19 pressure levels ranging from 100 hPa to 3.162 hPa. Four different versions of the AoA are provided, each using CLaMS simulations that were forced with a different reanalysis (ERA5, MERRA-2, JRA-55, ERA-Interim). AoA based on ERA5 is available from 2004/2 to 2021/12. AoA based on the other reanalyses is available from 2004/2 to 2017/12.</p>
Physics-based Simulations of 3D Wave Propagation - Case study deriving from the Le Teil earthquake
<p>This dataset contains 4,000 simulation results of the 3D elastic wave equation in a setting deriving from the Le Teil earthquake (France, 2019). The elastic wave equation governs the propagation of waves in a 3D propagation medium. Two types of data are given in this dataset: a materials dataset and a velocity dataset.</p> <h2>Materials dataset</h2> <p>Each material describes the propagation domain used for one numerical simulation. It is built from non-stationary random fields added to the reference 1D velocity profile and corresponds to the velocity of shear waves. The minimum value is 1500m/s and the maximum is 4500m/s. All materials contain a 1800m-thick bottom layer with a constant velocity of 4500m/s. </p> <p>All materials are 3D arrays of shape 32 x 32 x 32.They correspond to a physical size of 9.6 x 9.6 x 9.6km³. </p> <h3>Practical use</h3> <p>Materials are provided as `.npy` arrays, readable with python: `a = np.load(‘materials0-1999.npy’)`<br>Each file contains 2000 materials. Therefore, `a` is of shape (2000, 32, 32, 32). Indices correspond to the material index, the x coordinate (from West to East), the y coordinate (from South to North), and the z coordinate (from bottom to top). </p> <h2>Velocity dataset</h2> <p>The velocity dataset contains the velocity wavefields simulated at the surface of each propagation domain. They have been generated by solving the 3D elastic wave equation with the high-performance computing code SEM3D based on the Spectral Element Method (https://github.com/sem3d/SEM). To each material described above corresponds one velocity field, obtained by the propagation of waves through this material.</p> <p>Velocity fields were recorded by a grid of 16 x 16 virtual sensors located at the surface of the propagation domain between 150m and 450m (600m between consecutive sensors). Each sensor records the 3-component velocity with a 100Hz sampling between 0s and 20s. </p> <p>Computational details: The computational mesh was designed with elements of size 300m and 7 Gauss-Lobato-Legendre quadrature points. It can accurately represent the propagation of waves up to 5Hz frequency. Waves were generated by a point-wise source placed at the bottom of the domain, inside the constant layer (the position of the source is 4800, 4800, -8400m). The seismic source derives from the Le Teil earthquake [Delouis et al., 2021, doi:10.5802/crgeos.78]. The seismic source is described by a moment tensor with fixed orientation (strike = 48°, dip = 45°, and rake = 88°) and amplitude (moment magnitude M0=2.47 · 10^16 N.m).</p> <h3>Practical use</h3> <p>Results are given in .feather dataframes, readable with pandas library in Python: v = pd.read_feather(‘velocity0-99.feather’). Each dataframe contains 100 simulation results. Each row of the dataframe has the following format: </p> <table> <tbody> <tr> <td>run</td> <td>field</td> <td>x</td> <td>y</td> <td>z</td> <td>0.0</td> <td>0.01</td> <td>0.02</td> <td>...</td> <td>19.98</td> <td>19.99</td> </tr> <tr> <td>12</td> <td>Veloc E</td> <td> <p>150.0</p> </td> <td>770.0</td> <td>-1.0</td> <td>0</td> <td>0</td> <td>0</td> <td>...</td> <td>1.1e-5</td> <td>1.0e-5</td> </tr> <tr> <td>12</td> <td>Veloc N</td> <td> <p>150.0</p> </td> <td>770.0</td> <td>-1.0</td> <td>0</td> <td>0</td> <td>0</td> <td>...</td> <td>3e-6</td> <td>3e-6</td> </tr> <tr> <td>12</td> <td>Veloc Z</td> <td> <p>150.0</p> </td> <td>770.0</td> <td>-1.0</td> <td>0</td> <td>0</td> <td>0</td> <td>...</td> <td>-2.6e-5</td> <td>-2.7e-5</td> </tr> <tr> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> <td>...</td> </tr> </tbody> </table> <p>where `run` indicates the index of the material used in this simulation, `field` indicates the component of the velocity field (`Veloc E` for East-West, `Veloc N` for North-South, `Veloc Z` for Vertical). `x`, `y`, `z` are the coordinates of the sensor (in meters). The next 2000 columns contain the velocity field for times 0, 0.01, …, 19.99.</p> <h1>Related work</h1> <p>This dataset was used to fine-tune a Factorized Fourier Neural Operator (F-FNO, Lehmann et al. 2024, doi:10.1016/j.cma.2023.116718) to predict ground motion wavefields from 3D geologies. The code to train the F-FNO is available at https://github.com/lehmannfa/HEMEW3D</p>
Satellite-derived crop field boundaries in heterogeneous smallholder-dominated regions in the North of Mozambique
<p>Satellite-based field delineation has rapidly evolved due to recent advances in machine learning for computer vision. However, the scarcity of labeled data for complex and dynamic smallholder landscapes remains a major bottleneck for operational field delineation and downstream applications, particularly in Sub-Saharan Africa. We here provide reference field boundaries collected in the scope of a research project funded by the F.R.S.-FNRS. A detailed description of the data can be found in our corresponding <a href="https://doi.org/10.48550/arXiv.2312.08384">pre-print</a>.</p> <p>The dataset contains multiple files:</p> <ul> <li>sites.gpkg: Vector dataset containing the sampling sites covered by the dataset. We covered 513 sites of 600x600m, or 36 ha each. </li> <li>human_fields_train.gpkg: 1,518 field delineations distributed across 200 sites. Individual fields were manually digitized based on very high resolution satellite imagery in Google Earth Pro. Each polygon contains the corresponding image acquisition date. These fields were used for model training in the paper. </li> <li>human_fields_test.gpkg: 2,199 field delineations distributed across 313 sites. Individual fields were manually digitized based on very high resolution satellite imagery in Google Earth Pro. Each polygon contains the corresponding image acquisition date. These fields were used for evaluation of all experiments described in the paper. </li> <li>pseudo_fields_train.gpkg: 766 pseudo labels obtained from predictions using a <a href="../doi/10.5281/zenodo.7315089">pre-trained FracTAL ResUNet model</a>. The pseudo-labels correspond to the selection using P99(SemCN). For other sets of pseudo-labels please contact us.</li> </ul> <p><strong>Brief description of methods</strong></p> <p>For <strong>site selection</strong>, we developed a stratified random sampling scheme to sample from regions with actively used cropland. To identify these regions, we used an existing map of active and fallow cropland for the growing season of September 2020 through August 2021 (<a href="https://doi.org/10.1016/j.jag.2022.102937">Rufin et al., 2022</a>). We aggregated the map to a 1 ha grid and calculated the proportions of active cropland. We sampled 1,000 sites from regions mapped as containing at least 50% of active cropland within a one-hectare grid cell. We defined a site extent of 600 by 600 meters, or 36 ha, in order to assure that a sufficient number of fields can be delineated, even in regions with comparatively large field size. The selected sites were screened for VHR image quality and acceptable visibility of at least five fields, resulting in 513 sample sites. </p> <p>For <strong>human labels</strong>, we tasked human annotators to collect sparse labels (i.e. at least five fields) per site. We tasked the interpreters to collect only fields containing non-tree crops by systematically excluding tree crop plantations from our data. While individual trees in the field interior were included in our labels, trees overlapping with the field boundaries were avoided and the tree canopy was considered as the field boundary for completing the labels. All field delineations underwent an iterative quality assessment, where 18% of the initial field delineations and 7% of the field delineations in a second iteration were discarded. </p> <p>The <strong>pseudo labels </strong>provided here were selected using the 1% most confident predictions from the pre-trained model. The confidence scores were computed as the median of all pixel-level field extent probabilities for each field instance. </p> <p>For more details please read the corresponding paper:</p> <p>Rufin, Wang, Lisboa, Hemmerling, Tulbure & Meyfroidt, P. (2023). <em>Taking it further: Leveraging pseudo labels for field delineation across label-scarce smallholder regions.</em> <a href="https://doi.org/10.48550/ARXIV.2312.08384">https://doi.org/10.48550/ARXIV.2312.08384</a></p>
Gene expression QTL mapping in stimulated iPSC-derived macrophages provides insights into common complex diseases.
<p>Many disease-associated variants are thought to be regulatory but are not present in existing catalogues of expression quantitative trait loci (eQTL). We hypothesise that these variants may regulate expression in specific biological contexts, such as stimulated immune cells. Here, we used human iPSC-derived macrophages to map eQTLs across 24 cellular conditions. We found that 76% of eQTLs detected in at least one stimulated condition were also found in naive cells. The percentage of response eQTLs (reQTLs) varied widely across conditions (3.7% - 28.4%), with reQTLs specific to a single condition being rare (1.11%). Despite their relative rarity, reQTLs were overrepresented (p=0.05, Fisher's exact test) among disease-colocalizing eQTLs. We nominated an additional 21.7% of disease effector genes at GWAS loci via colocalization of reQTLs, with 38.6% of these not found in the Genotype–Tissue Expression (GTEx) catalogue. Our study highlights the diversity of genetic effects on expression and demonstrates how condition-specific regulatory variation can enhance our understanding of common disease risk alleles.</p>
Data from: In vitro to in vivo extrapolation from three-dimensional hiPSC-derived cardiac microtissues and physiologically based pharmacokinetic modeling to inform next-generation arrythmia risk assessment
<p>Proarrhythmic cardiotoxicity remains a substantial barrier to drug development as well as a major global health challenge. <em>In vitro</em> human pluripotent stem cell-based new approach methodologies have been increasingly proposed and employed as alternatives to existing <em>in vitro</em> and <em>in vivo</em> models that do not accurately recapitulate human cardiac electrophysiology or cardiotoxicity risk. In this study, we expanded the capacity of our previously established three-dimensional human cardiac microtissue model to perform quantitative risk assessment by combining it with a physiologically based pharmacokinetic model, allowing a direct comparison of potentially harmful concentrations predicted <em>in vitro</em> to <em>in vivo</em> therapeutic levels. This approach enabled the measurement of concentration responses and margins of exposure for two physiologically relevant metrics of proarrhythmic risk (<em>i.e.</em>, action potential duration and triangulation assessed by optical mapping) across concentrations spanning three orders of magnitude. The combination of both metrics enabled accurate proarrhythmic risk assessment of four compounds with a range of known proarrhythmic risk profiles (<em>i.e., </em>quinidine, cisapride, ranolazine, and verapamil) and demonstrated close agreement with their known clinical effects. Action potential triangulation was found to be a more sensitive metric for predicting proarrhythmic risk associated with the primary mechanism of concern for pharmaceutical-induced fatal ventricular arrhythmias, delayed cardiac repolarization due to inhibition of the rapid delayed rectifier potassium channel, or hERG channel. This study advances human induced pluripotent stem cell-based three-dimensional cardiac tissue models as new approach methodologies that enable <em>in vitro</em> proarrhythmic risk assessment with high precision of quantitative metrics for understanding clinically relevant cardiotoxicity.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.