Find research datasets worth reusing

Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.

10,561

datasets available to search

ShareScore release 0.9.0

Dataset results

10,561 results for “Mechanisms”

Learn how ShareScore rates datasets ↗

zenodo44/100

Data for: Machine learning identifies robust matrisome markers and regulatory mechanisms in cancer

The expression and regulation of matrisome genes - the ensemble of extracellular matrix, ECM, ECM-associated proteins and regulators as well as cytokines, chemokines and growth factors - is of paramount importance for the many biological processes and signals within the tumor microenvironment. The availability of large and diverse multi-omics data enables mapping and understanding the regulatory circuitry governing the tumor matrisome to an unprecedented level, though such a volume of information requires robust approaches to data analysis and integration. In this study, we show that combining Pan-Cancer expression data from The Cancer Genome Atlas (TCGA) with genomics, epigenomics and microenvironmental features from TCGA and other sources enables the identification of “landmark” matrisome genes and machine learning-based reconstruction of their regulatory networks in 74 clinical and molecular subtypes of human cancers and approx. 6700 patients. These results, enriched for prognostic genes and cross-validated markers at the protein level, unravel the role of genetic and epigenetic programs in governing the tumor matrisome and allow the prioritization of tumor-specific matrisome genes (and their regulators) for the development of novel therapeutic approaches.

opencc-by-4.0Oct 2020View details →

zenodo44/100

Model data for Sequential Dynamics of Stearoyl-CoA Desaturase /Ligand Binding and Unbinding Mechanism: A Computational Study by Petroff et al. (submitted).

Model data for Sequential Dynamics of Stearoyl-CoA Desaturase /Ligand Binding and Unbinding Mechanism: A Computational Study by Petroff et al. (submitted). This folder contains the files needed to start each of the models described in the paper. The files were created using MOE 2020 software made by Chemical Computing Group and run on NAMD2. The models identifiers in the paper correspond to the following terms in the code: Substrate: "13_5_coa" Product: "13_5_coa_desat_fe3" Apoprotein: "13_5_no_ligand" Saturated Lipid: "13_5_nocoa" Desaturated Lipid: "13_5_nocoa_desat_fe3" CoA model: "13_5_coa_nolipid" Substrate-waterbox model: "13_5_coa_waterbox" Saturated Lipid-waterbox: "13_5_nocoa_waterbox"

opencc-by-4.0Nov 2020View details →

zenodo44/100

Mechanical data of rotary shear experiments and temperature measurements for the manuscript: "Fast and localized temperature measurements during simulated earthquakes in carbonate rocks"

Mechanical data of rotary shear experiments and temperature measurements Each experiment is presented in a file with the experiment name (mechanical data of rotary shear experiment) and a file with the experiment name and _Temp (temperature measurement with the optical fiber). Mechanical data are presented in a tab-delimited file with calibrated measurements of: <ul> <li>Time (milliseconds)</li> <li>Normal stress: Normal (MPa) </li> <li>Fault displacement: Slip (mm)</li> <li>Fault velocity: Velocity (mm/s)</li> <li>Shear stress: Shearstress (MPa)</li> <li>Axial shortening: Shortening (mm).</li> </ul>  In a separate file, temperature data are presented as tab-delimited file with calibrated measurements of: <ul> <li>Time (milliseconds)</li> <li>Temperature from optical fiber in the channel at 1.5 µm : Temperature_1,5 (°C) </li> </ul>

opencc-by-4.0Nov 2020View details →

zenodo44/100

Supplementary data for "The subgenual organ complex in stick insects: Functional morphology and mechanical coupling of a complex mechanosensory organ"

µCT-scans of the upper tibial regions of the foreleg (T1) and the midleg (T2) of Ramulus artemis (Westwood, 1859), Carausius morosus (Sinéty, 1901), and Sipyloidea sipylus (Westwood, 1859). For use of scans, please cite the following publication: Strauß, J., Moritz, L. & Rühr, P.T. (2021): The subgenual organ complex in stick insects: Functional morphology and mechanical coupling of a complex mechanosensory organ. Frontiers in Ecology and Evolution (Research Topic “Evolutionary Biomechanics of Sound Production and Reception”). doi: <a href="https://doi.org/10.3389/fevo.2021.632493">10.3389/fevo.2021.632493</a>. All scans were performed with a commercial μCT desktop system (Skyscan 1272, Bruker microCT, Kontich, Belgium) at the Zoological Research Museum Alexander Koenig, Leibniz Institute for Animal Biodiversity, Bonn, Germany. µCT scan settings of all samples: Ramulus artemis: <ul> <li>tube voltage = 30 kV</li> <li>ube current = 200 μA</li> <li>target = tungsten</li> <li>no filter</li> <li>total sample rotation = 360°</li> <li>angular step size = 0.2°</li> <li>exposure time = 1980 ms</li> <li>binning = 1x1</li> <li>averaging = 8</li> <li>random movement = 15 px</li> <li>voxel size = 1.8 μm</li> <li>fixation: Bouin's solution (24 hours)</li> <li>staining: 0.3% PTA (21 days)</li> <li>storage: 70% EtOH</li> <li>surrounding medium in scan: 70% EtOH</li> <li>filenames: Ramulus_artemis_T1.tif; Ramulus_artemis_T2.tif</li> </ul> Carausius morosus: <ul> <li>tube voltage = 29 kV</li> <li>ube current = 200 μA</li> <li>target = tungsten</li> <li>no filter</li> <li>total sample rotation = 360°</li> <li>angular step size = 0.2°</li> <li>exposure time = 1900 ms</li> <li>binning = 1x1</li> <li>averaging = 5</li> <li>random movement = 15 px</li> <li>voxel size = 1.0 μm</li> <li>fixation: Bouin's solution (24 hours)</li> <li>staining: 0.3% PTA (21 days)</li> <li>storage: 70% EtOH</li> <li>surrounding medium in scan: 70% EtOH</li> <li>filenames: Carausius_morosus_T1.tif; Carausius_morosus_T2.tif</li> </ul> Sipyloidea sipylus: <ul> <li>tube voltage = 29 kV</li> <li>ube current = 200 μA</li> <li>target = tungsten</li> <li>no filter</li> <li>total sample rotation = 360°</li> <li>angular step size = 0.2°</li> <li>exposure time = 1900 ms</li> <li>binning = 1x1</li> <li>averaging = 7</li> <li>random movement = 15 px</li> <li>voxel size = 1.8 μm</li> <li>fixation: Bouin's solution (24 hours)</li> <li>staining: 0.3% PTA (21 days)</li> <li>storage: 70% EtOH</li> <li>surrounding medium in scan: 70% EtOH</li> <li>filenames: Sipyloidea_sipylus_T1.tif; Sipyloidea_sipylus_T2.tif</li> </ul>

opencc-by-4.0Jan 2021View details →

zenodo44/100

L'Aquila 2009 seismic sequence: integrated dataset of automatic first motion polarities focal mechanisms and RMT with HypoDD high quality relative earthquake locations

This dataset is related to the L'Aquila 2009 seismic sequence that happened in Central Apennines (Italy). It contains: <ul> <li>2782 quality selected focal mechanisms produced with the standard software FPFIT based on automatically determined first motion polarities of automatically detected and analyzed foreshocks and aftershocks recorded from January 2009 to December 2009 (flag fty in the header is MP)</li> <li>475 (out of 627) quality selected focal mechanisms produced with the standard software FPFIT also based on automatically determined first motion polarities but for only 3204 ML >= 1.9 earthquakes and by using take-off angles calculated within a local 3d tomographic velocity model (<a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2011GL047365">Di Stefano et al., 2011</a>) , published and released in <a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2011JB008352">Chiaraluce et al., 2011</a> (flag fty in the header is JG)</li> <li>165 (out of 181) Regional Moment Tensors determined for earthquakes ML >= 3.0 based on broadband waveform inversion of ground velocities and published by <a href="https://pubs.geoscienceworld.org/ssa/bssa/article-abstract/101/3/975/349796/Regional-Moment-Tensors-of-the-2009-L-Aquila">Hermann et al., 2011</a> (flag fty in the header is HM)</li> <li>The hypocenters of the total 3422 earthquakes reported in the present focal solutions dataset have been taken from the very high quality double difference locations of the about 64000 aftershocks reported in <a href="https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/jgrb.50130">Valoroso et al., 2013</a> and published, as part of the full dataset, <a href="https://doi.org/10.5281/zenodo.4036248">on Zenodo</a>. </li> </ul> The association between the focal solutions and the HypoDD hypocenters has been performed through the direct use of the HypoDD event identifier where possible (the whole MP dataset) and through spatial and temporal earthquakes coordinates matching in all the other case by using the capability of a MySQL database.  Two files are uploaded, one in plain text with blank separator, the second in plain text with ";" separator and .csv extension. Here below the header is explained. OT_Date: date of the origin time in the format YYYY-MM-DD OT_Time: time of the origin time in the format HH:mm:ss.dcm lat: hypocenter latitude expressed in degrees  lon: hypocenter longitude east of Greenwich, expressed in degrees dep: hypocenter depth expressed in km  ML: local magnitude (pure number) from <a href="https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/jgrb.50130">Valoroso et al., 2013</a> (see last column notes also)   id_dd: the hypoDD event identifier, allowing to directly connect to the <a href="https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/jgrb.50130">Valoroso et al., 2013</a> full dataset IMPORTANT NOTE about st1 and st2 (below): the focal solutions are presented here based on the convention they where produced or published, so there are two different (but compatible) conventions for the fault plains orientation in the 3d space st1: <ul> <li>for fty=HM or JG this is the strike of plane 1 (CMT convention)</li> <li>for fty=MP this is the strike of the dip direction of plain 1 (FPFIT convention)</li> </ul> dip1: dip of plane 1 rk1: rake of plane 1 st2: <ul> <li>for fty=HM or JG this is the strike of plane 2 (CMT convention)</li> <li>for fty=MP this is the strike of the dip direction of plain 2 (FPFIT convention)</li> </ul> dip2: dip of plane 2 rk2: rake of plane 2 fty: flag to distinguish the type of solution, CMT=HM or JG, FPFIT=MP MW: only for HM, this columns reports also MW from <a href="https://pubs.geoscienceworld.org/ssa/bssa/article-abstract/101/3/975/349796/Regional-Moment-Tensors-of-the-2009-L-Aquila">Hermann et al., 2011</a>

opencc-by-4.0Feb 2021View details →

zenodo44/100

DMS multiphase chemistry mechanism and model results

Open Access DMS multiphase chemistry mechanism This repository contains the complete DMS multiphase chemistry mechanism developed and applied in Wollesen de Jonge et al. (2021). The DMS multiphase mechanism is located in the folder named DMS_chemistry. The executable mechanism consist of a number of Fortran f90 files, which were generated with the kinetic pre-processor (KPP) (Damian et al., 2002) using the KPP input file DMSchem.def and DMSchem.kpp file. Both the executable Fortran code and the KPP input files are stored in the subfolder DMS_multiphase_chem. The DMSchem.def file list all reactions and reaction rates in the DMS multiphase chemistry mechanism similar to the supplementary Tables S1 in Wollesen de Jonge et al. (2021).  The executable DMS multiphase chemistry mechanism consist of the following Fortran f90 files: DMSchem_Main.f90 DMSchem_Function.f90 DMSchem_Initialize.f90 DMSchem_Integrator.f90 DMSchem_Jacobian.f90 DMSchem_JacobianSP.f90 DMSchem_LinearAlgebra.f90 DMSchem_mex_Fun.f90 DMSchem_mex_Jac_SP.f90 DMSchem_Model.f90 DMSchem_Monitor.f90 DMSchem_Parameters.f90 DMSchem_Precision.f90 DMSchem_Rates.f90 DMSchem_Util.f90 DMSchem_Global.f90 These f90-files can be linked and compiled with gfortran using the provided Makefile.   The subfolder photolysis contain vectors with absorption cross sections (cs), quantum yields (qy) and the spectral actinic flux of the UV-lamps in the AURA chamber. A simplified model setup is provided to illustrate how the DMS-multiphase chemistry routines can be run. The DMS multiphase chemistry mechanism is called and run from a program named main.f90. In the main.f90 program the temperature, humidity, pressure and initial concentrations of all gas and aqueous phase species in the DMS multiphase chemistry are declared. After this the main program call the subroutines getKVALUES and getJVALUES from the Fortran module reaction_rates.f90. <ul> <li>getKVALUES calculates a number of complex reaction rates (mainly pressure dependent three-body reactions).</li> <li>getJVALUES calculates all gas phase photolysis rates in the DMS multiphase chemistry mechanism using the absorption cross sections, quantum yields and the spectral actinic flux files stored in the photolysis subfolder</li> </ul> The main.f90 program saves the concentration of all species in a file called conc.dat, the time step vector (time.dat) and all species names in SPC_NAMES.dat. A short Matlab script called plot_concentrations.m is provided to illustrate how the concentrations of all species listed in SPC_NAMES.dat can be plotted along the saved time vector. The simplified model (only used for demonstration purpose) can be compiled and executed with GFortran using the provided Makefile by typing the following commands in the command line (terminal): make ./main.exe   Stored model results presented in Wollesen de Jonge et al. (2021) We have saved all model data from each simulated smog chamber experiment and atmospheric relevant base case and sensitivity run presented in Wollesen de Jonge et al. (2021) in the form of 'OutputTable' files. The columns in the tables related to the DMS smog chamber experiments are classified as follows: <ul> <li>1 time [h] (simulation time starting from -1 h hour and ending at 15 h, time = 0 h is defined as the time when the UV-lights were turned on in the AURA smog chamber).</li> <li>2 PN_1.7nm [#/cm^3] (Total particle number concentration of particles 1.7 nm in diameter).</li> <li>3 PN_2.5nm [#/cm^3] (Total particle number concentration of particles 2.5 nm in diameter)</li> <li>4 PN_10nm [#/cm^3] (Total particle number concentration of particles 10 nm in diameter)</li> <li>5 PM_SO4 [μg/m^3] (Sulfate particle mass)</li> <li>6 PM_CH3SO3 [μg/m^3] (Methane sulfonic acid (MSA) particle mass)</li> <li>7 PM_NH4 [μg/m^3] (Ammonium particle mass)</li> <li>8 DMS [ppbv] (Dimethyl sulfide gas phase concentration)</li> <li>9 O3 [ppbv] (Ozone gas phase concentration)</li> <li>10 PV [μm^3/cm^3] (Total particle volume concentration)</li> <li>11 PM [μg/m^3] (Total particle mass concentration)</li> <li>12 NH3 [ppbv] (Ammonia gas phase concentration)</li> <li>13 MSIA [#/cm^3] (Methane sulphinic acid gas phase concentration)</li> <li>14 SO2 [ppbv] (Sulfur dioxide gas phase concentration)</li> <li>15 DMSO [#/cm^3] (Dimethyl sulfoxide gas phase concentration)</li> <li>16 HPMTF [#/cm^3] (Hydroperoxymethyl thioformate  gas phase concentration)</li> <li>17 H2O2 [ppbv] (Hydrogen peroxide gas phase concentration)</li> <li>18 HO2 [#/cm^3] (Hydroperoxyl radical gas phase concentration)</li> <li>19 OH [#/cm^3] (Hydroxyl radical gas phase concentration)</li> <li>20-219 dN/dlogDp [#/m^3] (Particle number size distributions)</li> </ul>   The header line, rows 20-219, give the corresponding aerosol particle geometric mean diameters (Dp) in unit m, which were used to represent the modelled particle number size distributions (dN/dlogDp). The gas-phase concentrations given in unit ppb are given at the standard temperature and pressure of 273.15 K and 1E5 Pa.   The columns in the tables related to the atmospheric relevant runs are classified as follows: <ul> <li>1 time [h] ] (simulation time).</li> <li>2 DMS [#/cm^3]  (Dimethyl sulfide gas phase concentration)</li> <li>3 H2SO4 [#/cm^3] (Sulfuric acid gas phase concentration)</li> <li>4 MSA [#/cm^3] (Methane sulfonic acid gas phase concentration)</li> <li>5 HPMTF [#/cm^3] (Hydroperoxymethyl thioformate  gas phase concentration)</li> <li>6 MSIA [#/cm^3] (Methane sulphinic acid gas phase concentration)</li> <li>7 DMSO [#/cm^3] (Dimethyl sulfoxide gas phase concentration)</li> <li>8 UVflux [] (Relative UV light intensity, i.e. UVflux = 1 maximum sunlight, UVflux = 0 no sunlight)</li> <li>9 sinkCL [#/cm^3/s] (DMS loss rate by reactions with Cl radicals)</li> <li>10 sinkOHabs [#/cm^3/s] (DMS loss rate by reactions with OH via the abstraction pathway)</li> <li>11 sinkBrO [#/cm^3/s] (DMS loss rate by reactions with BrO radicals)</li> <li>12 sinkOHadd [#/cm^3/s] (DMS loss rate by reactions with OH via the addition pathway)</li> <li>13 sinkNO3 [#/cm^3/s] (DMS loss rate by reactions with NO3 radicals)</li> <li>14 sinkO3aq [#/cm^3/s] (DMS loss rate by reactions with O3 in the aqueous phase)</li> <li>15 PM_CH3SO3 [μg/m^3] (Methane sulfonic acid (MSA) particle mass)</li> <li>16 PM_SO4 [μg/m^3] (Sulfate particle mass)</li> <li>17 PM_NH4 [μg/m^3] (Ammonium particle mass)</li> <li>18 PM_NO3 [μg/m^3] (Nitrate particle mass)</li> <li>19-218 dN/dlogDp [#/m^3]. (Particle number size distributions)</li> </ul>   In this case, the header line, rows 19-218, give the corresponding geometric mean diameters in unit m. The modelled smog chamber experiments result files were named according to the date when the DMS experiments were performed: Exp. DMS1 - 20180405 Exp. DMS2 - 20180519 Exp. DMS3 - 20180521 Exp. DMS4 - 20180523 Exp. DMS5 - 20180526 Exp. DMS6 - 20190226 Exp. DMS7 - 20190301   Details for each output table are given here: OutputTable_AtmMain: Base case atmospheric model run OutputTable_lowWindAtm: Atmospheric model run with 2 m/s wind speed OutputTable_PolAtm: Atmospheric model run with higher O3 and NOx concentrations OutputTable_woAqAtm: Atmospheric model simulation without aqueous phase chemistry reactions OutputTable_woCloudAtm: Atmospheric model simulation without clouds   OutputTable20180405_1: Base case smog chamber simulation experiment DMS1 OutputTable20180519_1: Base case smog chamber simulation experiment DMS2 OutputTable20180521_1: Base case smog chamber simulation experiment DMS3 OutputTable20180523_1: Base case smog chamber simulation experiment DMS4 OutputTable20180526_1: Base case smog chamber simulation experiment DMS5 OutputTable20190226_1: Base case smog chamber simulation experiment DMS6 OutputTable20190301_1: Base case smog chamber simulation experiment DMS7    OutputTable20180519_2: MSIA+OH rate analogues to Yin et al, exp. DMS2  OutputTable20180519_3: MSIA+OH rate analogues to Lucas & Prinn et al. , exp. DMS2  OutputTable20180519_4: MSIA+OH rate set to 0, exp. DMS2  OutputTable20180519_5: No gas partitioning to the liquid water film on the chamber walls, exp. DMS2  OutputTable20180519_6: MCM gas-phase chem. setup, exp. DMS2  OutputTable20180519_9: CH3SOO isomerization set to 0, exp. DMS2  OutputTable20180519_10: HPMTF pathway set to 0, exp. DMS2    OutputTable20190226_2: HPMTF pathway analogues to Veres et al. , exp. DMS6  OutputTable20190226_3: HPMTF pathway analogues to Yin et al. , exp. DMS6  OutputTable20190226_4: HPMTF patway set to 0 , exp. DMS6  OutputTable20190226_5: No gas partitioning to the liquid water film on the chamber walls , exp. DMS6  OutputTable20190226_6: CH3SOO isomerization set to 0 , exp. DMS6  OutputTable20190226_7: MSIA+OH rate set to 0 , exp. DMS6    OutputTable20180405_12: O3 wall accommodation coefficient = 1E-8, exp. DMS1  OutputTable20180519_12: O3 wall accommodation coefficient = 1E-8, exp. DMS2  OutputTable20180521_12: O3 wall accommodation coefficient = 1E-8, exp. DMS3  OutputTable20180523_12: O3 wall accommodation coefficient = 1E-8, exp. DMS4  OutputTable20180526_12: O3 wall accommodation coefficient = 1E-8, exp. DMS5  OutputTable20190226_12: O3 wall accommodation coefficient = 1E-8, exp. DMS6  OutputTable20190301_12: O3 wall accommodation coefficient = 1E-8, exp. DMS7    OutputTable20180405_13: O3 wall accommodation coefficient = 1E-6, exp. DMS1  OutputTable20180519_13: O3 wall accommodation coefficient = 1E-6, exp. DMS2  OutputTable20180521_13: O3 wall accommodation coefficient = 1E-6, exp. DMS3  OutputTable20180523_13: O3 wall accommodation coefficient = 1E-6, exp. DMS4  OutputTable20180526_13: O3 wall accommodation coefficient = 1E-6, exp. DMS5  OutputTable20190226_13: O3 wall accommodation coefficient = 1E-6, exp. DMS6  OutputTable20190301_13: O3 wall accommodation coefficient = 1E-6, exp. DMS7    OutputTable20180405_14: SO2 wall accommodation coefficient = 1E-8, exp. DMS1  OutputTable20180519_14: SO2 wall accommodation coefficient = 1E-8, exp. DMS2  OutputTable20180521_14: SO2 wall accommodation coefficient = 1E-8, exp. DMS3  OutputTable20180523_14: SO2 wall accommodation coefficient = 1E-8, exp. DMS4  OutputTable20180526_14: SO2 wall accommodation coefficient = 1E-8, exp. DMS5  OutputTable20190226_14: SO2 wall accommodation coefficient = 1E-8, exp. DMS6  OutputTable20190301_14: SO2 wall accommodation coefficient = 1E-8, exp. DMS7    OutputTable20180405_15: SO2 wall accommodation coefficient = 1E-6, exp. DMS1  OutputTable20180519_15: SO2 wall accommodation coefficient = 1E-6, exp. DMS2  OutputTable20180521_15: SO2 wall accommodation coefficient = 1E-6, exp. DMS3  OutputTable20180523_15: SO2 wall accommodation coefficient = 1E-6, exp. DMS4  OutputTable20180526_15: SO2 wall accommodation coefficient = 1E-6, exp. DMS5  OutputTable20190226_15: SO2 wall accommodation coefficient = 1E-6, exp. DMS6  OutputTable20190301_15: SO2 wall accommodation coefficient = 1E-6, exp. DMS7    OutputTable20180405_16: DMS wall accommodation coefficient = 1E-8, exp. DMS1  OutputTable20180519_16: DMS wall accommodation coefficient = 1E-8, exp. DMS2  OutputTable20180521_16: DMS wall accommodation coefficient = 1E-8, exp. DMS3  OutputTable20180523_16: DMS wall accommodation coefficient = 1E-8, exp. DMS4  OutputTable20180526_16: DMS wall accommodation coefficient = 1E-8, exp. DMS5  OutputTable20190226_16: DMS wall accommodation coefficient = 1E-8, exp. DMS6  OutputTable20190301_16: DMS wall accommodation coefficient = 1E-8, exp. DMS7    OutputTable20180405_17: DMS wall accommodation coefficient = 1E-6, exp. DMS1  OutputTable20180519_17: DMS wall accommodation coefficient = 1E-6, exp. DMS2  OutputTable20180521_17: DMS wall accommodation coefficient = 1E-6, exp. DMS3  OutputTable20180523_17: DMS wall accommodation coefficient = 1E-6, exp. DMS4  OutputTable20180526_17: DMS wall accommodation coefficient = 1E-6, exp. DMS5  OutputTable20190226_17: DMS wall accommodation coefficient = 1E-6, exp. DMS6  OutputTable20190301_17: DMS wall accommodation coefficient = 1E-6, exp. DMS7    OutputTable20180405_18: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-6, exp. DMS1  OutputTable20180519_18: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-6, exp. DMS2  OutputTable20180521_18: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-6, exp. DMS3  OutputTable20180523_18: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-6, exp. DMS4  OutputTable20180526_18: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-6, exp. DMS5  OutputTable20190226_18: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-6, exp. DMS6  OutputTable20190301_18: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-6, exp. DMS7    OutputTable20180405_19: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-4, exp. DMS1  OutputTable20180519_19: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-4, exp. DMS2  OutputTable20180521_19: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-4, exp. DMS3  OutputTable20180523_19: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-4, exp. DMS4  OutputTable20180526_19: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-4, exp. DMS5  OutputTable20190226_19: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-4, exp. DMS6  OutputTable20190301_19: DMSO, DMSO2, MSIA and HPMTF wall accommodation coefficient = 1E-4, exp. DMS7    OutputTable20180519_20: Liquid water content on walls (LWC wall) = 0.3 mg/m^3, exp. DMS2  OutputTable20190226_20: Liquid water content on walls (LWC wall) = 1.5 g/m^3, exp. DMS6  OutputTable20190301_20: Liquid water content on walls (LWC wall) = 15 g/m^3, exp. DMS7    OutputTable20180519_21: Liquid water content on walls (LWC wall) = 30 mg/m^3, exp. DMS2  OutputTable20190226_21: Liquid water content on walls (LWC wall) = 150 g/m^3, exp. DMS6  OutputTable20190301_21: Liquid water content on walls (LWC wall) = 1000 g/m^3, exp. DMS7   References Wollesen de Jonge, R., Elm, J., Rosati, B., Christiansen, S., Hyttinen, N., Lüdemann, D., Bilde, M., and Roldin, P.: Secondary aerosol formation from dimethyl sulfide – improved mechanistic understanding based on smog chamber experiments and modelling, Atmos. Chem. Phys. <a href="https://doi.org/10.5194/acp-2020-1324">https://doi.org/10.5194/acp-2020-1324</a> (2021)    Damian, V., Sandu, A., Damian, M., Potra, F., and Carmichael, G. R.: The kinetic preprocessor KPP-a software environment for solving chemical kinetics, Comput. Chem. Eng., 26, 1567–1579, https://doi.org/10.1016/S0098-1354(02)00128-X, 2002.

opencc-by-4.0Jun 2021View details →

zenodo44/100

Raw data corresponding to the scientific paper: "A modular telerehabilitation architecture for upper limb robotic therapy" (Advances in Mechanical Engineering 2017, Vol. 9(1) 1-13)

Acquired raw data necessary to implement the adaptive control strategy grounded on multimodal information. In addition, raw data for the computation of the communication parameters needed for the assessment of the implemented telerehabilitation architecture are provided. a) End-effector positions and velocities (x, y, vx, vy) in three conditions: healthy (Fig 9) and constraint simulated stroke behaviour (Fig 10) without robotic assistance and simulated stroke behavior with robotic assistance (Fig 11) b) Performance indicators and control parameters for all the recruited subjects in both conditions healthy behaviour and simulated stroke behaviour (Fig 12a and Fig 12b) c) Computational values for evaluating telerehabilitation performance (Table 1)

opencc-by-4.0Dec 2016View details →

zenodo44/100

Acquired data necessary to perform the control algorithm introduced in the scientific paper: "Multilevel control of an anthropomorphic prosthetic hand for grasp and slip prevention" (Advances in Mechanical Engineering, 2016, vol. 8, pp. 1-13)

Acquired data necessary to perform the control algorithm introduced in this paper. a) Figure 6: Calibration data for the three FSRs placed on the prosthetic hand and covered with silicon caps. b) Figure 9: Data for the cost during the learning of two grasping tasks of an egg: bi-digital grasp and tri-digital grasp. c) Figure 10 and Figure 11: Data for the experimental results with the plastic cup and with the highlighter shown in the paper.

opencc-by-4.0Sep 2016View details →

zenodo44/100

Dataset of the scientific paper " Multimodal robotic system for upper-limb rehabilitation in physical environment" (Advances in Mechanical Engineering)

There are eight files with the following information: - pos_stateXX.bin, binary file with information of the end effector position of the robot device in meters along the three axis (X, Y, Z) during state XX of the experiment - target_stateXX.bin, binary file with information of the target position for the robot device in meters along the three axis (X, Y, Z) during state XX of the experiment - emg_channelXX.bin, binary file with information of channel 1 of the EMG sensor in mV during during the whole time of the experiment - color_stateXX.bin, binary file with information of color filter information during state XX of the experiment. This information is the percentage of pixels with the correct color (yellow, cyan or magenta) inside the region of interest

opencc-by-4.0Aug 2016View details →

zenodo44/100

Repository for: Patterned invagination prevents mechanical instability during gastrulation

This is the repository for the paper: Vellutini, B. C., Cuenca, M. B., Krishna, A., Szałapak, A., Modes, C. D. & Tomancak, P. Patterned invagination prevents mechanical instability during gastrulation. Nature (2025). doi:<a href="https://10.1038/s41586-025-09480-3">10.1038/s41586-025-09480-3</a> Here are all the associated repositories: <ul> <li>Main repository (code and data): <a href="https://doi.org/10.5281/zenodo.7781947">https://doi.org/10.5281/zenodo.7781947</a></li> <li>Model and simulations (code and data): <a href="https://doi.org/10.5281/zenodo.7784906">https://doi.org/10.5281/zenodo.7784906</a></li> <li>Lightsheet and in situ experiments (imaging data): <a href="https://doi.org/10.5281/zenodo.15876638">https://doi.org/10.5281/zenodo.15876638</a></li> <li>Laser perturbation experiments (imaging data): <a href="https://doi.org/10.5281/zenodo.15876646">https://doi.org/10.5281/zenodo.15876646</a></li> <li>Figures and videos (media files): <a href="https://doi.org/10.5281/zenodo.7781916">https://doi.org/10.5281/zenodo.7781916</a></li> </ul> The main repository is maintained at <a href="https://github.com/bruvellu/cephalic-furrow">https://github.com/bruvellu/cephalic-furrow</a>.

opencc-by-4.0Oct 2023View details →

zenodo44/100

The coupling mechanism of ligands with SERT distinguishes substrates from inhibitors (raw data)

Raw data of the manuscript: Ligand coupling mechanism of the human serotonin transporter differentiates substrates from inhibitors Abstract: The presynaptic serotonin transporter (SERT) reuptakes the serotonin (5HT) released into the synaptic cleft, thus ensuring temporal and spatial regulation of serotonergic signalling. Clinically approved drugs used for the treatment of neurological disorders, including depression and anxiety modulate SERT by trapping the transporter in the outward-open conformation. Illicit drugs of abuse as amphetamines act as substrates but reverse the transport direction, thereby releasing intracellular accumulated 5HT. Both mechanisms increase extracellular 5HT levels. Stoichiometry of the transport cycle has been described by kinetic schemes, the structures of the main conformations within the transport cycle revealed static coordinates. By combining in-silico approaches with in-vitro experiments and making use of a homologous series of 5HT analogues, we decoded the essential coupling mechanism between the substrate and the transporter which triggers uptake. The free energy calculations showed that only scaffold-bound substrates can correctly close the extracellular gate by pulling on the bundle domain through long-range electrostatic interactions. The associated spatial and physico-chemical requirements define substrate and inhibitor properties, opening new possibilities for rational drug design approaches.

opencc-by-4.0Dec 2022View details →

zenodo44/100

Focal mechanisms of the Southeastern Alps and surroundings

We report the focal mechanisms (FPS) of earthquakes that occurred in the southeastern Alps and surrounding areas (latitude ~ 45°N-47.5°N and longitude ~ 10°E-15°E) from 1928 to 2023. The FPS have been collected and revised from literature or, depending on data availability, newly computed both by first polarities inversion or by means of seismic moment tensor. For more details about the catalogue (V 1.0, V 1.1) refer to the paper: Saraò, A., Sugan, M., Bressan, G., Renner, G., and Restivo, A.: A focal mechanism catalogue of earthquakes that occurred in the southeastern Alps and surrounding areas from 1928–2019, Earth Syst. Sci. Data, 13, 2245–2258, https://doi.org/10.5194/essd-13-2245-2021, 2021 Cite as: Sugan, M., Saraò, A., Magrin, A., Snidarcig, A., Bressan, G., Renner, G., Romano, M. A., Guidarelli, M., Santulin, M., Di Bartolomeo, P., & Restivo, A. (2024). Focal mechanisms of the Southeastern Alps and surroundings (2.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10853582   V 2.0, March - doi: 10.5281/zenodo.10853582 <ul> <li>corrected some typos;</li> <li>added new FPS solutions for the period 2014-2023</li> </ul> V 1.1, April 2021 - doi: 10.5281/zenodo.4660412 <ul> <li>corrected some typos;  </li> <li>added priority criteria code</li> <li>added code to describe the changes applied with respect to the original solutions </li> </ul> V 1.0, November 2020 - doi: 10.5281/zenodo.4284971  <pre> </pre> <pre> </pre> <pre>                      </pre>

opencc-by-4.0Mar 2024View details →

zenodo44/100

Droplet-based Microfluidics Reveals Insights into Cross-Coupling Mechanisms over Single-Atom Heterogeneous Catalysts

Data set supporting the publication of : "Droplet-based Microfluidics Reveals Insights into Cross-Coupling Mechanisms over Single-Atom Heterogeneous Catalysts" (<a href="https://doi.org/10.1002/anie.202401056">https://doi.org/10.1002/anie.202401056</a>) by T. Moragues, G. Giannakakis, A. Ruiz-Ferrando, C. N. Borca, T. Huthwelker, A. Bugaev, A. J. deMello, J. Pérez-Ramírez and S. Mitchell.

opencc-by-4.0Mar 2024View details →

zenodo44/100

Supplemental data for "Intramolecular feedback regulation of the LRRK2 Roc G domain by a LRRK2 kinase dependent mechanism" (Gilsbach et al., eLife 2024, doi:10.7554/eLife.91083)

Supportive data for the eLife version of record. (1) Data used for the Michaelis Menten Kinetics. HPLC-based assay. Steady-state kinetic measurements of LRRK2-mediated GTP hydrolysis were performed as previously described (Ahmadian et al., 1997). Briefly, 0.1 µM of full-length LRRK2 was incubated with different amounts of GTP (0, 25, 75, 150, 250, 500, 1000, 2000, 3000 and 5000 µM) and production of GDP was monitored by reversed phase C18 HPLC. To this end, the samples (10 µl) were directly injected on a reversed-phase C18 column (pre-column: Hypersil Gold, 3µm particle size, 4.6x10mm; main column: Hypersil Gold, 5µm particle size, 4.6x250mm, Thermo Scientific) using an Ultimate 3000 HPLC system (Thermo Scientific, Waltham, MA, USA) in HPLC-buffer containing 50 mM KH2PO4/K2HPO4 pH 6.0, 10 mM tetrabutylammonium bromide and 10-15% acetonitrile. Subsequently, samples were analyzed using the HPLC integrator (Chromeleon 7.2, Thermo Scientific, Waltham, MA, USA). Initial rates of GDP production were plotted against the GTP concentration using GraFit5 (v.5.0.13, Erithacus Software). The number of experiments is indicated in the graph and data point is the average (±s.e.m.) of indicated repetitions. The Michaelis-Menten equation was fitted to determine KM (±s.e.) and kcat (±s.e.). Excel sheets used for the calculation of means are provided. No values are reported if the HPLC separation failed (e.g. unstable baseline). Charcoal GTP hydrolysis assay. The [γ-32P]GTP charcoal assay was performed as previously described (Bollag and McCormick, 1995). Briefly, 0.1 µM full-length LRRK2 or 0.5 µM 6xHIS-MBP-RocCOR was incubated with different GTP concentrations, ranging from 75 µM to 8 mM, in the presence of [γ-32P] GTP in GTPase assay buffer (30 mM Tris pH 8, 150 mM NaCl, 10 mM MgCl2, 5% (v/v) Glycerol and 3 mM DTT). Samples were taken at different time-points and immediately quenched with 5% activated charcoal in 20 mM phosphoric acid. All non-hydrolyzed GTP and proteins were stripped by the activated charcoal and sedimented by centrifugation. The radioactivity of the isolated inorganic phosphates was then measured by scintillation counting. The initial rates of γ-phosphate release and the Michaelis-Menten kinetics were calculated as described above. (2) Profile plots (Raw data) obtained for the Mass photometry analysis for T1343A vs WT LRRK2. MP was performed as described in (Guaitoli et al., 2023). Briefly, the dimer ratio of LRRK2 was determined on a Refeyn Two MP instrument (Refeyn). Prior to the experiment, a standard curve relating particle contrasts to molecular weight was established using a Native molecular weight standard (Invitrogen, 1:200 dilution in HEPES-based elution buffer: 50 mM HEPES [pH 8.0], 150 mM NaCl supplemented with 200 µM desthiobiotin). Prior to mass photometry, the proteins, either WT or T1343A LRRK2, were incubated with 0.5 mM ATP or buffer (control) for 30 min at 30 ℃. The LRRK2 protein was diluted to 2x of the final concentration (end concentrations: 75 nM and 100 nM) in elution buffer. The optical setup was focused in 10 μl elution buffer before adding 10 µl of the adjusted protein sample. Depending on the obtained count numbers, acquisition times were chosen between 20 s to 1 min. The dimer ratio in each measurement was normalize according to the equation. The measurement was perfomed in triplicates. (3) AlphaFold3 model of LRRK2-pT1343 either bound to GDP/Mg or GTP/Mg. Using AlphaFold3 (Abramson et al., 2024), we modeled and compared the GDP vs the GTP-state of phospho-T1343 LRRK2. Interestingly, the AlphaFold3 model suggests, that the phosphate group of the pT1343 residue is orientated inwards thereby substituting the gamma phosphate of the GTP in the GDP-bound state of LRRK2. This finding is in well agreement with MD simulations published recently (Stormer et al., 2023). (4) Western blot RAW files for the cell-based phospho Rab asssay (RAW data for Figure 6 supplement 2/ Supplemental Figure 4 in the preprint version, Gilsbach et al, 2024) Cell-based LRRK2 activity assays were performed as previously described (Singh et al., 2022). Briefly, HEK293T cells were cultured in DMEM (supplemented with 10% Fetal Bovine Serum and 0.5% Pen/Strep). For the assay, the cells were seeded onto six-well plates and transfected at a confluency of 50-70% with SF-tagged LRRK2 variants using PEI-based lipofection. After 48 hours cells were lysed in lysis buffer [30 mM Tris-HCl (pH7.4), 150 mM NaCl, 1% NonidentP-40 substitute, complete protease inhibitor cocktail, PhosStop phosphatase inhibitors (Roche)]. Lysates were cleared by centrifugation at 10,000 x g and adjusted to a protein concentration of 1 µg/µl in 1x Laemmli Buffer. Samples were subsequently subjected to SDS PAGE and Western Blot analysis to determine LRRK2 pS935 and Rab10 T73 phosphorylation levels, as described below. Total LRRK2 and Rab10 levels were determined as a reference for normalization. For Western blot analysis, protein samples were separated by SDS–PAGE using NuPAGE 10% Bis-Tris gels (Invitrogen) and transferred onto PVDF membranes (Thermo Fisher). To allow simultaneous probing for LRRK2 on the one hand and Rab10 on the other hand, membranes were cut horizontally at the 140 kDa MW marker band. After blocking non-specific binding sites with 5% non-fat dry milk in TBST (1 h, RT) (25 mM Tris, pH 7.4, 150 mM NaCl, 0.1% Tween-20), membranes were incubated overnight at 4°C with primary antibodies at dilutions specified below. Phospho-specific antibodies were diluted in TBST/ 5% BSA (Roth GmbH). Non-phospho-specific antibodies were diluted in TBST/ 5% non-fat dry milk powder (BioRad). Phospho-Rab10 levels were determined by the site-specific rabbit monoclonal antibody anti-pRAB10(pT73) (Abcam, ab230261) and LRRK2 pS935 was determined by the site-specific rabbit monoclonal antibody UDD2 (Abcam, ab133450), both at a dilution of 1:2,000. Total LRRK2 levels were determined by the in-house rat monoclonal antibody anti-pan-LRRK2 (clone 24D8; 1:10,000) (Carrion et al., 2017). Total Rab10 levels were determined by the rabbit monoclonal antibody anti-RAB10/ERP13424 (Abcam, ab181367) at a dilution of 1:5,000. For detection, goat anti-rat IgG or anti-rabbit IgG HRP-coupled secondary antibodies (Jackson ImmunoResearch) were used at a dilution of 1:15,000 in TBST/ 5% non-fat dry milk powder. Antibody–antigen complexes were visualized using the ECL plus chemiluminescence detection system (GE Healthcare) using the Stella imaging system (Raytest) for detection and quantification. Figure 6 Source Data 1: Images generated by the Stella system are shown which were used for quantification. The annotation file equals Figure6-figure supplement 2 (Gilsbach et al., eLife 2024, doi:10.7554/eLife.91083). The lines corresponding to LRRK2 pS935, total LRRK2, Rab10 pT73 and total Rab10 were used for the quantification shown in Figure 6.

opencc-by-4.0Nov 2024View details →

zenodo44/100

Dataset for manuscript Tracing Quartz Provenance: A Multi-Disciplinary Investigation of Luminescence Sensitisation Mechanisms of Quartz from Granite Source Rocks and Derived Sediments

Quartz optically stimulated luminescence (OSL) sensitivity as well as some electron spin resonance (ESR) and cathodoluminescence (CL) signals have been empirically proposed as provenance indicators. Sensitivity is defined as luminescence emitted in response to a given dose per unit mass. While it is largely believed to be acquired by earth surface processes, recent studies bring evidence that sensitisation processes depend on source geology. Here we combine OSL and thermoluminescence (TL), ESR and CL analyses to understand the mechanisms of quartz OSL sensitisation. We investigate granites and their derived sediments from catchments draining simple lithologies of known age that display contrasting OSL sensitisation behaviour both in nature and during irradiation and light exposure laboratory experiments. The sample displaying increased OSL sensitisation is characterised by TL emission at intermediate temperatures (150-250 °C), Ti-related signals in CL, and Ti and Ge lithium compensated signals in ESR. The insensitive samples either lack or exhibit very weak such characteristics and contain several times less amount of trace titanium measured by laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). We demonstrate that the OSL sensitisation results as an effect of the existence of certain defects and impurities in the quartz crystal in the parent rock, such as titanium and germanium. However, the degree of sensitisation reached in nature is significantly higher than in the laboratory.  As such, the existence of this precursor represents the potential for sensitisation, which can later be amplified by environmental factors during sedimentary history.

opencc-by-4.0Oct 2024View details →

zenodo44/100

Genome-wide association analyses identify novel Brugada syndrome risk loci and highlight a new mechanism of sodium channel regulation in disease susceptibility

The Brugada syndrome GWAS summary statistics Brugada syndrome is a cardiac arrhythmia disorder associated with sudden death in young adults. With the exception of SCN5A, encoding the cardiac sodium channel NaV1.5, susceptibility genes remain largely unknown. We performed a genome-wide association meta-analysis comprising 2,820 unrelated cases with Brugada syndrome and 10,001 controls.

opencc-by-4.0Dec 2021View details →

zenodo44/100

Raw and analyzed data to manuscript "Influence of air plasma pretreatments on mechanical properties in metal-reinforced laminated wood"

Abstract The use of wood-based materials in building and construction is constantly increasing as environmental aspects and sustainability gain importance. For structural applications, however, there are many examples where hybrid material systems are needed to fulfil the specific mechanical requirements of the individual application. In particular, metal reinforcements are a common solution to enhance the mechanical properties of a wooden structural element. Metal-reinforced wood components further help to reduce cross-sectional sizes of load-bearing structures, improve the attachment of masonry or other materials, enhance the seismic safety and tremor dissipation capacity, as well as the durability of the structural elements in highly humid environments and under high permanent mechanical load. A critical factor to achieve these benefits, however, is the mechanical joint between the different material classes, namely the wood and metal parts. Currently, this joint is formed using epoxy or polyurethane (PU) adhesives, the former yielding highest mechanical strengths, whereas the latter presents a compromise between mechanical and economical constraints. Regarding sustainability and economic viability, the utilization of different adhesive systems would be preferable, whereas mechanical stabilities yielded for metal-wood joints do not permit for the use of other common adhesive systems in such structural applications. This study extends previous research on the use of non-thermal air plasma pretreatments for the formation of wood-metal joints. The plasma treatments of Norway spruce (Picea abies (L.) Karst.) wood and anodized (E6/EV1) aluminum AlMgSi0.5 (6060) F22 were optimized, using water contact angle measurements to determine the effect and homogeneity of plasma treatments. The adhesive bond strengths of plasma-pretreated and untreated specimens were tested with commercial 2-component epoxy, PU, melamine-urea formaldehyde (MUF), polyvinyl acetate (PVAc), and construction adhesive glue systems. The influence of plasma treatments on the mechanical performance of the compounds was evaluated for one selected glue system via bending strength tests. The impact of the hybrid interface between metal and wood was isolated for the tests by using five-layer laminates from three wood lamellae enclosing two aluminum plates, thereby excluding the influence of congeneric wood-wood bonds. The effect of the plasma treatments is discussed based on the chemical and physical modifications of the substrates and the respective interaction mechanisms with the glue systems.

opencc-by-4.0Oct 2021View details →

zenodo44/100

Dataset of "Unveiling the gating mechanism of CRAC channel: a computational study"

Molecular Dynamics simulation trajectories of CRAC ion channel. All of the trajectories can be visualized using the topology file CRAC_topol.prmtop. Three trajectories refer to equilibrium simulations of the closed state of the channel PDB: 4HKR (4HKR_equil_100ns.dcd) and of the two putative open states PDB: 6BBF (6BBF_equil_100ns.dcd) and PDB ID: 6AKI (6AKI_equil_100ns.dcd). The remaining two trajectories refer to Targeted Molecular Dynamics simulations steering the molecular system from the closed to the open state (TMD_C_to_O_100ns.dcd) and from the open to the closed state (TMD_O_to_C_500ns.dcd). All simulations have been performed with the NAMD 2.11b2 suite of programs using the Amber ff15ipq force field for the protein, the Lipid17 force field for the phospholipids and the SPC/E water model.

opencc-by-4.0Sep 2021View details →

zenodo44/100

A coupled MD-FE methodology to characterize mechanical interphases in polymeric nanocomposites: pseudo-experimental data

readme.txt Abstract: (from [1]) <blockquote> This contribution introduces an unconventional procedure to characterize spatial profiles of elastic and inelastic properties inside polymer interphases around nanoparticles. Interphases denote those regions in the polymer matrix whose mechanical properties are influenced by the filler surfaces and thus deviate from the bulk properties. They are of particular relevance in case of nano-sized filler particles with a comparatively large surface-to-volume ratio and hence can explain the frequent observation that the overall properties of polymer nanocomposites cannot be determined by classical mixing rules, which only consider the behavior of the individual constituents. Interphase characterization for nanocomposites poses hardly solvable challengesto the experimenter and is still an unsolved problem in many cases. Instead of real experiments, we perform pseudo experiments using our recently developed Capriccio method, which is an MD-FE domain-decomposition tool specifically designed for amorphous polymers. These pseudo-experimental data then serve as input for a typical inverse parameter identification. With this procedure, spatially varying mechanical properties inside the polymer are, for the first time, translated into intuitively understandable profiles of continuum mechanical parameters. As a model material, we employ silica-enforced polystyrene, for which our procedure reveals exponential saturation profiles for Young’s modulus and the yield stress inside the interphase, where the former takes about seven times the bulk value at the particle surface and the latter roughly triples. Interestingly, hardening coefficient and Poisson’s ratio of the polymer remain nearly constant inside the interphase. Besides gaining insight into the constitutive influence of filler particles, these unexpected and intriguing results also offer interesting explanatory options for the failure behavior of polymer nanocomposites. </blockquote>   Contact: Maximilian Ries Institute of Applied Mechanics Friedrich-Alexander-Universiät Erlangen-Nürnberg Egerlandstr. 5 91058 Erlangen   License: Creative Commons Attribution 4.0 International   Context: Data set supplementing  journal paper: [1] Ries, M.; Possart, G.; Steinmann, P. & Pfaller, S., "A coupled MD-FE methodology to characterize mechanical interphases in polymeric nanocomposites," International Journal of Mechanical Sciences, Elsevier, 2021, 106564. This dataset contains the results of a multiscale study on polystyrene-silica nanocomposites using an atomistic-continuum coupling approach. 120 polystyrene samples, each containing 2 nano-sized silica particles are subjected to uniaxial tension. Here we use coarse-grained molecular dynamics (MD) domain embedded into a larger finite element (FE) region. These two resolutions are coupled in a concurrent multiscale fashion using the so-called Capriccio method. We observe the deformation state of the MD and FE domain, as well as the relative displacement of the two nanoparticles with respect to each other. Based on this pseudo-experimental data, we derive the material properties (Young's modulus, Poisson's ratio, yield stress, hardening) of the interphase forming in the proximity of the nanoparticles in [1]. A more detailed description of the used methods can be found in Ries et al.  [1].   Content: The attached text file contains the following quantities (columns) for all samples (rows): <ul> <li>sample: [initial nanoparticle distance]-ID</li> <li>d0_NP: initial distance of nanoparticles in nm</li> <li>rot_x: rotation of nanoparticles with respect to x-axis in degree</li> <li>d_NP: distance of nanoparticles in nm (after equilibration)</li> <li>Elements: number of finite elements</li> <li>Element_warnings: number of element warnings by Abaqus</li> <li>LS: loadstep 1-6</li> <li>eps_NP(LS): tensile strain of nanoparticles in loadstep LS in %</li> <li>eps_MD(LS): tensile strain of MD domain in loadstep LS in %</li> <li>eps_NP_MD(LS): tensile strain of nanoparticles normalized to eps_MD(LS) in loadstep LS</li> <li>eps_FE(LS): tensile strain of FE domain in loadstep LS in %</li> <li>eps_NP_FE(LS): tensile strain of nanoparticles normalized to eps_FE(LS) in loadstep LS</li> <li>eps_NP_FE(LS): tensile strain of nanoparticles normalized to eps_FE(LS) in loadstep LS</li> <li>u_max(LS): maximum displacement of FE nodes in load step LS in nm</li> <li>F_ext(LS):  external force in load step LS in E-11 N</li> </ul>

opencc-by-4.0Dec 2021View details →

zenodo44/100

Data supplement for "Gradient flows for coupling order parameters and mechanics"

In this data repository we provide additional information necessary for the generation of the images in the paper "Gradient flows for coupling order parameters and mechanics". The simulation results are generated by a FEniCS code, run on Google Colab and the code is available at <a href="https://github.com/schmellerl/gradient_flows_order_parameters_mechanics">https://github.com/schmellerl/gradient_flows_order_parameters_mechanics</a>. The preprint of the article can be found  under the DOI 10.20347/WIAS.PREPRINT.2909.

opencc-by-4.0Jan 2022View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record