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Focal mechanism solutions of 162 earthquakes occurred between 2014 and 2023 in the Southeastern Alps
<p>Focal mechanism solutions of 162 earthquakes occurred between 2014 and 2023 in the Southeastern Alps</p> <p>Andrea Magrin, Monica Sugan, Adriano Snidarcig, Maria Adelaide Romano, Mariangela Guidarelli, Marco Santulin, Paolo Di Bartolomeo, Angela Saraò</p> <p><strong> </strong></p> <p>Description</p> <p>This dataset includes focal mechanisms (FPFIT_solution.pdf, FPFIT_solution.csv) obtained with the FPFIT algorithm (Reasenberger and Oppenheimer, 1985) for 162 earthquakes with magnitudes Md between 2.8 and 3.6 that occurred between 2014 and 2023 in the Southeastern Alps region (45°N-47.5°N and 10°E-15°E). This dataset updates the catalogues Saraò et al., 2021a, b, and Sugan et al., 2020.</p> <p>The first polarities used to calculate the focal mechanisms are manually picked from seismograms recorded by the National Institute of Oceanography and Applied Geophysics (OGS) northeastern Italy seismic and deformation network (Sistema di Monitoraggio terrestre dell’Italia Nord Orientale - SMINO) (Priolo et al., 2005; Bragato et al., 2011; Bragato et al., 2021) and various Italian and European seismological observatories using temporary and permanent seismic networks. The permanent and temporary networks are as follows: CH – Swiss Seismological Service, 1983; GU – University of Genoa, 1967; IV – INGV Seismological Data Centre, 2005; MN – MedNet Project Partner Institutions, 1990; NI – OGS and University of Trieste, 2002; OE – ZAMG, 1987, 1990; OX – OGS, 2016; RF – University of Trieste, 1993; SI - Sudtirol Network, Italy (https://www.fdsn.org/networks/detail/S.I/; SL – Slovenian Environment Agency, 1990; ST – Geological Survey – Provincia Autonoma Di Trento, 1981; XT – Zhao et al, 2018; Y5 – Swiss Seismological Service, 2022; Z3 – AASN, 2015; ZO - Massa et al, 2021; ZS - Heit et al., 2017.</p> <p>The locations of the events are taken from the seismological bulletins of the OGS and the associated catalogues, which were published continuously from 1977 to 2014 (annual files) and are accessible via the Internet (http://www.crs.ogs.it/bollettino/RSFVG/). Since 2015, annual catalogues have been published in text format and annual bulletins in Quakeml format (Snidarcig et al., 2015, 2016, 2017, 2018, 2019, 2020a, 2021a, 2022a).</p> <p>In addition, unpublished solutions calculated for the studied area are extracted, reviewed and added from the annual reports of the OGS (Snidarcig et al., 2020b, 2021b, 2022b, 2023).</p> <p><strong> </strong></p> <p>References:</p> <p>Bragato, P.L., Di Bartolomeo, P., Pesaresi, D., Plasencia Linares, M., Saraò, A.: Acquiring, archiving, analyzing and exchanging seismic data in real time at the Seismological Research Center of the OGS in Italy, Ann. Geophys. 54, 67–75, https://doi.org/10.4401/ag-4958, 2011.</p> <p>Bragato, P. L., Comelli, P., Saraò, A., Zuliani, D., Moratto, L., Poggi, V., Rossi, G., Scaini, C., Sugan, M., Barnaba, C., Bernardi, P., Bertoni, M., Bressan, G., Compagno, A., Del Negro, E., Di Bartolomeo, P., Fabris, P., Garbin, M., Grossi, M., Magrin, A., Magrin, E., Pesaresi, D., Petrovic, B., Linares, M. P. P., Romanelli, M., Snidarcig, A., Tunini, L., Urban, S., Venturini, E., Parolai, S.: The OGS–Northeastern Italy Seismic and Deformation Network: Current Status and Outlook, Seismol. Res. Lett., 92, 1704–1716, https://doi.org/10.1785/0220200372, 2021. </p> <p>Priolo, E., Barnaba, C., Bernardi, P., Bernardis, G., Bragato, P.L., Bressan, G., Candido, M., Cazzador, E., Di Bartolomeo, P., Durì, G., Gentili, S., Govoni, A., Klinc, P., Kravanja, S., Laurenzano, G., Lovisa, L., Marotta, P., Michelini, A., Ponton F., Restivo, A., Romanelli, A., Snidarcig, A., Urban, S., Vuan, A., Zuliani, D.: Seismic monitoring in northeastern Italy: A ten-year experience, Seismol. Res. Lett., 76, 446–454, https://doi.org/10.1785/gssrl.76.4.446, 2005.</p> <p>Reasenberg, P., Oppenheimer, D.: FPFIT, FPPLOT and FPPAGE: Fortran computer programs for calculating and displaying earthquake fault-plane solutions, Open-File Rep., 85-739, USGS, Menlo Park, 109 pp., 1985.</p> <p>Saraò A., Sugan M., Bressan G., Renner G., Restivo A.: Focal mechanisms of Southeastern Alps and surroundings, doi: 10.5281/zenodo.4284971, 2020, a.</p> <p>Saraò, A., Sugan, M., Bressan, G., Renner, G., 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, b.</p> <p>Snidarcig, A., Bernardi, P., Bragato, P.L., Di Bartolomeo, P., Garbin, M., Urban, S.: Bollettino della Rete Sismometrica dell’Italia Nord Orientale (RSINO), [Data set]. Istituto Nazionale di Oceanografia e di Geofisica Sperimentale - OGS, Trieste, Italy, doi: 10.6092/58ff169a-2f02-46ae-908a-bdfcacea069c, 2015.</p> <p>Snidarcig, A., Bernardi, P., Bragato, P.L., Di Bartolomeo, P., Garbin, M., Urban, S.: Bollettino della Rete Sismometrica dell’Italia Nord Orientale (RSINO), [Data set]. Istituto Nazionale di Oceanografia e di Geofisica Sperimentale - OGS, Trieste, Italy, doi: 10.6092/a608d853-755e-4177-aada-992857ccb44e, 2016.</p> <p>Snidarcig, A., Bernardi, P., Bragato, P.L., Di Bartolomeo, P., Garbin, M., Urban, S.: Bollettino della Rete Sismometrica dell’Italia Nord Orientale (RSINO), [Data set]. Istituto Nazionale di Oceanografia e di Geofisica Sperimentale - OGS, Trieste, Italy, doi: 10.6092/3ff3c323-d7a4-4183-bea0-1a53814ac8b9, 2017.</p> <p>Snidarcig, A., Bernardi, P., Bragato, P.L., Di Bartolomeo, P., Garbin, M., Urban, S.: Bollettino della Rete Sismometrica dell’Italia Nord Orientale (RSINO), [Data set]. Istituto Nazionale di Oceanografia e di Geofisica Sperimentale - OGS, Trieste, Italy, doi: 10.6092/c53f37ce-bcf3-453c-a2cf-1894d48cfbbb, 2018.</p> <p>Snidarcig, A., Bernardi, P., Bragato, P.L., Di Bartolomeo, P., Garbin, M., Urban, S.: Bollettino della Rete Sismometrica dell’Italia Nord Orientale (RSINO), [Data set]. Istituto Nazionale di Oceanografia e di Geofisica Sperimentale - OGS, Trieste, Italy, doi: 10.6092/58ff169a-2f02-46ae-908a-bdfcacea069c, 2019.</p> <p>Snidarcig, A., Bernardi, P., Bragato, P.L., Di Bartolomeo, P., Garbin, M., Urban, S.: Bollettino della Rete Sismometrica dell’Italia Nord Orientale (RSINO), [Data set]. Istituto Nazionale di Oceanografia e di Geofisica Sperimentale - OGS, Trieste, Italy, doi: 10.13120/108b8d94-361a-45f3-8195-fc4e8f73d264, 2020a</p> <p>Snidarcig, A., Bragato, P.L., Barnaba, C., Bertoni, M., Bressan, G., Comelli, P., Del Negro, E., Fabris, P., Gentili, S., Moratto, L., Peresan, A., Peruzza, L., Poggi, V., Priolo, E ,Rebez, A., Rossi, G., Sandron, D., Saraò, A., Scaini, C., Sugan, M., Tufaro, T., Urban, S., Zuliani, D., Bernardi, P., Di Bartolomeo, P., Grossi, M., Pesaresi, D., Pettenati, F., Plasencia Linares, M., Ponton, C., Restivo, A., Romanelli, M.: Accordo tra la Regione Autonoma Friuli Venezia Giulia e l’OGS per la collaborazione nell’ambito del monitoraggio sismico e meteomarino Relazione 2019 – Ambito Sismologico. Relazione Interna OGS 2020/22 CRS 7 SIRE. http://hdl.handle.net/20.500.14083/6700, 2020b.</p> <p>Snidarcig, A., Bernardi, P., Bragato, P.L., Di Bartolomeo, P., Garbin, M., Urban, S.: Bollettino della Rete Sismometrica dell’Italia Nord Orientale (RSINO), [Data set]. Istituto Nazionale di Oceanografia e di Geofisica Sperimentale - OGS, Trieste, Italy, doi: 10.13120/8b252b09-314f-456f-812a-b05268ecd001, 2021a.</p> <p>Snidarcig, A., Bragato, P.L., Barnaba, C., Bertoni, M., Bressan, G., Comelli, P., Del Negro, E., Gentili, S., Magrin, A., Moratto, L., Peresan, A., Rebez, A., Rossi, G., Sandron, D., Saraò, A., Sugan, M., Tamaro, A., Urban, S., Zuliani, D., Bernardi, P., Compagno, A., Di Bartolomeo, P., Bottaro, G., Grossi, M., Magrin, E., Pesaresi, D., Pettenati, F., Plasencia Linares, M., Poggi, V., Ponton, C., Restivo, A., Romanelli, M.: Accordo tra la Regione Autonoma Friuli Venezia Giulia e l’OGS per la collaborazione nell’ambito del monitoraggio sismico e meteomarino. Relazione 2020 – Ambito Sismologico. Relazione Interna OGS 2021/29 CRS 7 RETI. https://hdl.handle.net/20.500.14083/18770, 2021b.</p> <p>Snidarcig, A., Bernardi, P., Bragato, P.L., Di Bartolomeo, P., Garbin, M., Urban, S.: Bollettino della Rete Sismometrica dell’Italia Nord Orientale (RSINO), [Data set]. Istituto Nazionale di Oceanografia e di Geofisica Sperimentale - OGS, Trieste, Italy, http://www.crs.ogs.it/bollettino_new/, last access 16/02/2024, 2022a.</p> <p>Snidarcig, A., Bragato, P.L., Barnaba, C., Bertoni, M., Bressan, G., Comelli, P., Del Negro, E., Gentili, S., Klin, P., Laurenzano, G., Magrin, A., Moratto, L., Parolai, S., Peresan, A., Petrovic, B., Poggi, V., Priolo, E., Rebez, A., Rossi, G., Sandron, D., Saraò, A., Scaini, C., Sugan, M., Tamaro, A., Urban, S., Vuan, A., Zuliani, D., Bernardi, P., Compagno, A., Di Bartolomeo, P., Bottaro, G., Grossi, M., Magrin, E., Pesaresi, D., Pettenati, F., Plasencia Linares, M., Ponton, C., Romanelli, M.: Accordo tra la Regione Autonoma Friuli Venezia Giulia e l’OGS per la collaborazione nell’ambito del monitoraggio sismico e meteomarino. Relazione 2021 – Ambito Sismologico. Relazione 2022/74 Sez. CRS 7 RETI. https://hdl.handle.net/20.500.14083/17863, 2022b.</p> <p>Snidarcig, A., Bragato, P.L., Barnaba, C., Bertoni, M., Brondi, P., Comelli, P., Del Negro, E., Gentili, S., Magrin, A., Magrin, E., Moratto, L., Peresan, A., Petrovic, B., Poggi, V., Rebez, A., Rossi, G., Sandron, D., Saraò, A., Scaini, C., Sugan, M., Tunini, L., Zuliani, D., Bernardi, P., Compagno, A., Di Bartolomeo, P., Bottaro, G., Grossi, M., Pesaresi, D., Pettenati, F., Plasencia Linares, M., Ponton, C., Romanelli, M.: Accordo tra la Regione Autonoma Friuli Venezia Giulia e l’OGS per la collaborazione nell’ambito del monitoraggio sismico e meteomarino Relazione 2022 – Ambito Sismologico. Relazione Interna OGS 2023/72. https://hdl.handle.net/20.500.14083/24083, 2023.</p> <p>Sugan, M., Renner, G., Bressan, G., Restivo, A., Saraò, A.: First motion data and focal mechanism solutions of 108 earthquakes occurred between 1928 and 2019 in the Southeastern Alps [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4284929, 2020</p> <p>CH - Swiss Seismological Service (SED) At ETH Zurich: National Seismic Networks of Switzerland [Data set], ETH Zürich, Zurich, <a href="https://doi.org/10.12686/sed/networks/ch">https://doi.org/10.12686/sed/networks/ch</a>, 1983.</p> <p>GU - University of Genoa: Regional Seismic Network of North Western Italy [Data set], International Federation of Digital Seismograph Networks, <a href="https://doi.org/10.7914/SN/GU">https://doi.org/10.7914/SN/GU</a>, 1967.</p> <p>IV - INGV Seismological Data Centre: Rete Sismica Nazionale (RSN) [Data set], Istituto Nazionale di Geofisica e Vulcanologia (INGV), Italy, <a href="https://doi.org/10.13127/SD/X0FXnH7QfY">https://doi.org/10.13127/SD/X0FXnH7QfY</a>, 2005.</p> <p>MN - MedNet Project Partner Institutions: Mediterranean Very Broadband Seismographic Network (MedNet) [Data set], Istituto Nazionale di Geofisica e Vulcanologia (INGV), <a href="https://doi.org/10.13127/SD/fBBBtDtd6q">https://doi.org/10.13127/SD/fBBBtDtd6q</a>, 1990.</p> <p>NI - OGS – Istituto Nazionale Di Oceanografia E Di Geofisica Sperimentale – and University Of Trieste: North-East Italy Broadband Network [Data set], International Federation of Digital Seismograph Networks, <a href="https://doi.org/10.7914/SN/NI">https://doi.org/10.7914/SN/NI</a>, 2002.</p> <p>OE - ZAMG – Zentralanstalt Für Meterologie Und Geodynamik: Austrian Seismic Network [Data set], International Federation of Digital Seismograph Networks, <a href="https://doi.org/10.7914/SN/OE">https://doi.org/10.7914/SN/OE</a>, 1987.</p> <p>OX - OGS – Istituto Nazionale Di Oceanografia E Di Geofisica Sperimentale: North-East Italy Seismic Network [Data set], International Federation of Digital Seismograph Networks, <a href="https://doi.org/10.7914/SN/OX">https://doi.org/10.7914/SN/OX</a>, 2016.</p> <p>RF - University of Trieste: Friuli Venezia Giulia Accelerometric Network [Data set], International Federation of Digital Seismograph Networks, Trieste, <a href="https://doi.org/10.7914/SN/RF">https://doi.org/10.7914/SN/RF</a>, 1993.</p> <p>SL - Slovenian Environment Agency, Seismic Network of the Republic of Slovenia [Data set]. International Federation of Digital Seismograph Networks. <a href="https://doi.org/10.7914/SN/SL">https://doi.org/10.7914/SN/SL</a>, 1990</p> <p>ST - Geological Survey – Provincia Autonoma Di Trento: Trentino Seismic Network, International Federation of Digital Seismograph Networks [Data set], Trento, <a href="https://doi.org/10.7914/SN/ST">https://doi.org/10.7914/SN/ST</a>, 1981.</p> <p>XT - Zhao, L., Paul, A., Solarino, S., & RESIF., Seismic network XT: CIFALPS-2 temporary experiment (China-Italy-France Alps seismic transect #2 [Data set]. RESIF - Réseau Sismologique et géodésique Français. <a href="https://doi.org/10.15778/RESIF.XT2018">https://doi.org/10.15778/RESIF.XT2018</a>, 2018</p> <p>Y5 - Swiss Seismological Service (SED) at ETH Zurich, Swiss Contribution to AdriaArray Temporary Network; ETH Zurich. Other/Seismic network. <a href="https://doi.org/10.12686/SED/NETWORKS/Y5">https://doi.org/10.12686/SED/NETWORKS/Y5</a>, 2022</p> <p>Z3 - AlpArray Seismic Network: AlpArray Seismic Network (AASN) temporary component [Data set], AlpArray Working Group, <a href="https://doi.org/10.12686/alparray/z3_2015">https://doi.org/10.12686/alparray/z3_2015</a>, 2015.</p> <p>ZO - Massa, M., Rizzo, A. L., Lorenzetti, A., Lovati, S., D’Alema, E., Puglia, R., … Luzi, L.: Rete di monitoraggio multiparametrico del Garda (Nord Italia) - PDnet (Version 1.0) [Data set]. Istituto Nazionale di Geofisica e Vulcanologia (INGV). <a href="https://doi.org/10.13127/SD/YHCFOMCBO_">https://doi.org/10.13127/SD/YHCFOMCBO_</a>, 2021.</p> <p>ZS - Heit, B.; Weber, M.; Tilmann, F.; Haberland, C.; Jia, Y.; Pesaresi, D.: The Swath-D Seismic Network in Italy and Austria. GFZ Data Services. Other/Seismic Network. <a href="http://doi.org/10.14470/MF7562601148">doi:10.14470/MF7562601148</a>, 2017.</p>
Spatiotemporally distinct responses to mechanical forces shape the developing seed of Arabidopsis
<p><span>Source Data File related to the manuscript:</span></p> <p><a name="_Hlk141697088"></a><span> </span></p> <p><span>Spatiotemporally distinct responses to mechanical forces</span></p> <p><span>shape the developing seed of Arabidopsis</span></p> <p> </p> <p><span>See Description Dataset file for full description</span></p> <p><span> </span></p>
Data from: Tool use increases mechanical foraging success and tooth health in southern sea otters (Enhydra lutris nereis)
<p>Although it is well documented that tool use can enable the utilization of novel resources, the fitness benefits associated with this innovative behavior are difficult to test. Using longitudinal data from 196 radio-tagged southern sea otters, we found that individuals, particularly females, with frequent tool use gained access to harder, larger prey items. In turn, the mechanical advantages of tool use during food processing translated to reduced tooth damage in tool users. We also found that tool use diminishes trade-offs between access to different prey types, tooth health, and caloric intake that are highly dependent on the relative availability of prey in the environment. Overall, tool use allows individuals to maintain caloric requirements through the processing of alternative prey that are otherwise inaccessible without the use of tools, indicating that this innovative behavior is a necessity for the survival of southern sea otters in environments with depleted preferred prey.</p>
Plate interface geometry complexity and persistent heterogenous coupling revealed by a high-resolution earthquake focal mechanism catalog in Mentawai, Sumatra
<p>This website contains all the outputs from the study entitled “Plate interface geometry complexity and persistent heterogenous coupling revealed by a high-resolution earthquake focal mechanism catalog in Mentawai, Sumatra”. The contents include the seismic stations used in this study, obtained focal mechanism solutions, corresponding waveform fits, relocation results, and depth-phase modeling results. Each figure (started with ${ID}) is corresponding to the Earthquake ID as shown in Table S1.txt.</p>
A systematic review and meta-analysis of eyespot anti-predator mechanisms
<p>This is the raw data, analysis script, and supplementary materials for "<em>A systematic review and meta-analysis of eyespot anti-predator mechanisms</em>." (<a href="https://doi.org/10.7554/eLife.96338.2">https://doi.org/10.7554/eLife.96338</a>)</p>
Dataset for: Mechanisms of uropathogenic E. coli mucosal association in the gastrointestinal tract (Science Advances)
<p>This dataset contains the plotted data for Figures 1B, 1B, 1F, Figure 4C, Figures 5C, 5E, 5G, 5H, 5J, 5K, Figure S5B, Figure S6A, S6B, S6C, Figure S7 A, S7C, S7D, and Figure S8C.</p>
Dataset for "From CO2 to Solid Carbon: Reaction Mechanism, Active Species, and Conditioning the Ce-Alloyed GaInSn Catalyst"
<p>Experimental raw data for the article "<em>From CO<sub>2</sub> to Solid Carbon: Reaction Mechanism, Active Species, and Conditioning the Ce-alloyed GaInSn Catalyst</em>", published in <em>Journal of Physical Chemistry C </em>(2024). DOI:10.1021/acs.jpcc.4c05482.</p> <p>The data set is organized according to the publication's figures. XPS data given here is the raw data without binding energy calibration. For the manuscript, binding energies in a series of samples were calibrated by taking the strongest peak, where no chemical shift was to be expected, as a reference.</p>
Data Repository submission for "The influence of density driven mixing mechanisms on ureolysis induced carbonate precipitation"
<p>This folder includes the data and files that support the manuscript titled "The Influence of Density-Driven Mixing Mechanisms on Ureolysis-Induced Carbonate Precipitation". Please see included Readme for more information.</p>
The spreading of magnetic reconnection X-line in particle-in-cell simulations– mechanism and the effect of drift-kink instability
<p>This dataset contains data and Python scripts in "The spreading of magnetic reconnection X-line in particle-in-cell simulations– mechanism and the effect of drift-kink instability" prepared to submit to the Journal of Geophysical Research. </p>
Thickness dependence of the mechanical properties of piezoelectric high-Q_m nanomechanical resonators made from aluminium nitride
<p>Data used for figures in "Thickness dependence of the mechanical properties of piezoelectric high-Q_m nanomechanical resonators made from aluminium nitride" Anastasiia Ciers et.al <a href="https://doi.org/10.1088/2633-4356/ad9b64">Materials for Quantum Technology <strong>4</strong>, 046301 (2024)</a>; <a href="https://doi.org/10.48550/arXiv.2410.03944">arXiv:2410.03944 [cond-mat.mes-hall] (2024)</a></p>
Supplementary data for "Mechanism of surface solar irradiance variability under broken cloud cover"
<p>Open Data for manuscript to be submitted in ACP: "Mechanisms of surface solar irradiance variability under broken clouds". Refer to the README for details. Most (larger) files within the .zip archives are gzipped. Use `gunzip` to decompress.</p>
Tensile2d: 2D quasistatic non-linear structural mechanics solutions, under geometrical variations
<p>This dataset contains 2D quasistatic non-linear structural mechanics solutions, under geometrical variations. </p> <p>A Description is provided in <a href="https://arxiv.org/pdf/2305.12871.pdf">the MMGP paper</a> Sections 4.1 and A.2.</p> <p>The file format is PLAID, see <a href="https://plaid-lib.readthedocs.io/ ">the plaid documentation</a>.</p> <p>The variablity in the samples are 6 input scalars and the geometry (mesh). Outputs of interest are 4 scalars and 6 fields.</p> <p>Seven nested training sets of sizes 8 to 500 are provided, with complete input-output data. A testing set of size 200, as well as two out-of-distribution sample, are provided, for which outputs are not provided. </p> <p> </p> <p>Tips to access the data:</p> <p>After decompressing the downloaded file:</p> <p>from plaid.containers.dataset import Dataset<br>from plaid.problem_definition import ProblemDefinition</p> <p>dataset = Dataset()<br>problem = ProblemDefinition()</p> <p>problem._load_from_dir_(os.path.join(/path/to/data,'problem_definition'))<br>dataset._load_from_dir_(os.path.join(/path/to/data,'dataset'), verbose = True)</p> <p>print("problem =", problem)<br>print("dataset =", dataset)</p> <p>sample = dataset[0]<br>print("sample =", sample)</p> <p>for fn in sample.get_field_names():<br> print(f"{fn} =", sample.get_field(fn))<br>for sn in sample.get_scalar_names():<br> print(f"{sn} =", sample.get_scalar(sn))</p> <p>print("nodes =", sample.get_nodes())<br>print("elements =", sample.get_elements())<br>print("nodal_tags =", sample.get_nodal_tags())</p> <p> </p>
Growth-related formation mechanism of I3-type basal stacking fault in epitaxially grown hexagonal Ge-2H
<p>The hexagonal-2H crystal phase of Ge recently emerged as a promising direct bandgap semiconductor in the mid-infrared range providing new prospects of additional opto-electronic functionalities of group-IV semiconductors (Ge and SiGe). The controlled synthesis of such hexagonal (2H) Ge phase is a challenge that can be overcome by using wurtzite GaAs nanowires as a template. However, depending on growth conditions, unusual basal stacking faults (BSFs) of I<sub>3</sub>-type are formed in the metastable 2H structure. The growth of such core/shell heterostructures is observed <em>in situ</em> and in real-time by means of environmental transmission electron microscopy using chemical vapour deposition. The observations provide direct evidence of a step-flow growth of Ge-2H epilayers and reveal the growth-related formation of I<sub>3</sub>-BSF during unstable growth. Their formation conditions are dynamically investigated. Through these <em>in situ</em> observations, we can propose a scenario for the nucleation of I<sub>3</sub>-type BSFs that is likely valid for any metastable hexagonal 2H or wurtzite structures grown on m-plane substrates. Conditions are identified to avoid their formation for perfect crystalline synthesis of SiGe-2H.</p> <p>This data set contains all the processed supporting videos of in-situ TEM observations .</p> <p> </p>
Data of "Recurrent Neural Networks (RNNs) with dimensionality reduction and break down in computational mechanics; application to multi-scale localization step."
<p>Data related to<br> ===========<br> title = "Recurrent Neural Networks (RNNs) with dimensionality reduction and break down in computational mechanics; application to multi-scale localization step.",<br> journal = "Computer Methods in Applied Mechanics and Engineering",<br> volume ="390",<br> year = "2022",<br> doi = "https://doi.org/<a href="http://dx.doi.org/10.1016/j.cma.2021.114476">10.1016/j.cma.2021.114476</a> ",<br> pages = "114476 ",<br> author = "Wu, Ling and Noels, Ludovic"</p> <p>We would be grateful if you could cite the paper in the case in which you are using the data</p> <p> </p> <p>The files replace version 1 whose zip was corrupted.</p> <p> </p>
Raw and analysed data for contribution paper "Investigation of deoxidation mechanisms on Cu surfaces in a dielectric barrier discharge plasma"
<p><strong>Abstract</strong><br> In this study, we present a laboratory setup for the deoxidation of 10 × 10 mm2 samples via dielectric barrier discharge (DBD) plasma and we show results for the deoxidation of copper in an Ar/H2 atmosphere at 1000 hPa. The effect of combined heat and DBD plasma treatment was investigated by heating surfaces up to 250 °C during plasma operation. All results were compared for surfaces before and after plasma treatment. The chemical structure of surfaces was studied using X-ray Photoelectron Spectroscopy (XPS) and the morphology was measured via Confocal Laser Scanning Microscopy (CLSM). Additionally, the properties of the plasma during the reduction of copper oxides were investigated via Optical Emission Spectroscopy (OES).</p>
Source data for the publication "Tracking excited state decay mechanisms of pyrimidine nucleosides in real time", Nature Communications, 2021
<p>The archives contain the raw data used to generate the transient absorption spectra for uridine (Figure 1) and 5-methyluridine (Figure 2) presented in the main paper, as well as the trajectory plots and auxiliary spectra presented in the Supplementary Information of the paper "Tracking excited state decay mechanisms of pyrimidine nucleosides in real time" authored by R. Borrego-Varillas et al. published in Nature Communications, 2021. Specifically:</p> <p><strong>URD</strong>: folder with raw data from the uridine trajectories (56 trajectories) performed at the SS-CASPT2/SA-2-CASSCF(10,8) and SS-CASPT2/SA-2-CASSCF(10,10) level of theory</p> <p><strong>5mURD</strong>: folder with raw data from the 5-methyluridine trajectories (57 trajectories) performed at the SS-CASPT2/SA-2-CASSCF(10,8) and SS-CASPT2/SA-2-CASSCF(10,10) level of theory</p> <p>The raw data of each trajectory is inside a folder named <em>geom_XXX</em> where <em>XXX</em> stands for a 3-digit label of the trajectory. The trajectories have been selected out of a pool of 500 trajectories according to the S0-S1 vertical gap so that only trajectories whose energy gap falls under the envelope of the pulse are selected</p> <p><strong>URD</strong>: 003 005 006 011 015 023 039 040 054 056 060 083 098 104 112 114 116 121 122 147 152 158 161 171 173 175 177 186 189 200 204 211 219 223 225 232 234 235 236 246 251 252 257 259 265 268 271 272 279 286 287 289 305 313 318 336</p> <p><strong>5mURD</strong>: 010 044 045 048 052 057 065 074 085 094 097 099 100 105 110 112 113 121 131 137 138 140 144 145 159 164 170 179 182 183 184 186 189 199 203 205 209 214 219 220 221 239 243 250 251 273 284 290 295 301 302 320 325 327 328 333 334</p> <p>In each geom_XXX folder there are following files:</p> <p><strong>S1-S<em>Y</em>.dat</strong>: ASCII files () in which the individual columns correspond to </p> <p>col1: time [fs] </p> <p>col2: transition energy of state S<em>Y</em> with respect to S1 [cm-1] where S0 is the ground state</p> <p>col3-5: X, Y and Z components of the transition dipole moment between S1 and S<em>Y</em> [a.u.]</p> <p>col6: magnitude of the transition dipole moment between S1 and S<em>Y</em> [a.u.] </p> <p>col7: angle between transition dipole moment at time t and t=0 [deg]</p> <p>Note that in URD S1-S0.dat contains in most cases about 500 data points (0-500 fs), in 5mURD S1-S0.dat contains 1000 data points (0-1000 fs) except for a few cases in which the trajectories were interrupted earlier. This data has been used to simulate the stimulated emission before the hopping event and the hot ground state photoinduced absorption after hopping. S1-S<em>Y</em>.dat () contain only data points until the hopping event which have been used to simulate the excited state photoinduced absorption.</p> <p>The spectra reported in the main article (Figs 1 & 2) as well as in the SI can be reproduced following eq. 13-18 in the Supplementary Information.</p> <p> </p> <p><strong>HighMediumLayer_traj.xyz.zip</strong>: archived Cartesian coordinates of the High Layer (nucleobase) and Medium Layer (sugar and waters within 5 Å distance from nucleobase) along the dynamics</p> <p>Note that due to the different number of waters in each trajectory the size of the Medium layer (and thus the size of the system) may vary from trajectory to trajectory.</p> <p>Note that due to the different duration of each trajectory the number of geometries may vary from trajectory to trajectory.</p> <p><strong>LowLayer.xyz:</strong> Cartesian coordinates of the Low Layer (waters > 5 Å from the nucleobase); the coordinates of these waters are kept fixed along the trajectory.</p> <p>The coordinates of High, Medium and Low layers can be used to reproduce the QMMM calculations (energies, gradients and transition dipole moments along each trajectory) with the official COBRAMM release (<a href="https://gitlab.com/cobrammgroup/cobramm.git">https://gitlab.com/cobrammgroup/cobramm.git</a>) following the parameters provided in Supplementary Note 2 of the Supplementary Information.</p>
Dataset related to article "Mechanisms of responsiveness to and resistance against trabectedin in murine models of human myxoid liposarcoma"
<p>This record contains data related to article "Mechanisms of responsiveness to and resistance against trabectedin in murine models of human myxoid liposarcoma".</p> <p>Myxoid liposarcoma (MLPS) is a rare soft-tissue sarcoma characterised by the expression of FUS-DDIT3 chimera. Trabectedin has shown significant clinical anti-tumour activity against MLPS. To characterise the molecular mechanism of trabectedin sensitivity and of resistance against it, we integrated genomic and transcriptomic data from treated mice bearing ML017 or ML017/ET, two patient-derived MLPS xenograft models, sensitive to and resistant against trabectedin, respectively. Longitudinal RNA-Seq analysis of ML017 showed that trabectedin acts mainly as a transcriptional regulator: 15 days after the third dose trabectedin modulates the transcription of 4883 genes involved in processes that sustain adipocyte differentiation. No such differences were observed in ML017/ET. Genomic analysis showed that prolonged treatment causes losses in 4p15.2, 4p16.3 and 17q21.3 cytobands leading to acquired-resistance against the drug. The results dissect the complex mechanism of action of trabectedin and provide the basis for novel combinatorial approaches for the treatment of MLPS that could overcome drug-resistance.</p>
Supplemental image data for "Mechanical annealing and memories in a disordered solid"
<p>This archive contains raw and subtracted experimental images of a 2D colloidal disordered solid, sufficient to verify the major qualitative results of the paper "Mechanical annealing and memories in a disordered solid" by Nathan C. Keim and Dani Medina. Descriptions of the images may be found in the main text and Supplementary Information for that paper, a preprint of which is available at arXiv.org.</p>
Supplementary Data: A peer-to-peer market mechanism incorporating multi-energy coupling and cooperative behaviors
<p>This dataset is the supplementary dataset for the case study used in the journal article (<a href="https://doi-org.tudelft.idm.oclc.org/10.1016/j.apenergy.2022.118572">https://doi.org/10.1016/j.apenergy.2022.118572</a>):</p> <p>A peer-to-peer market mechanism incorporating multi-energy coupling and cooperative behaviors</p> <p> </p> <p>Before using the dataset, please</p> <p>1. refer to the article for the details of the data used in the case study,</p> <p>2. read README.txt for the structure of the dataset.</p> <p> </p> <p>Please also kindly cite the journal article when using the dataset.</p>
Molecular mechanisms underlying plasticity in a thermally varying environment
<p><span>Adaptation to environmental variability is a prerequisite for species' persistence in their natural environments. With climate change predicted to increase the frequency and severity of temperature fluctuations, ectothermic organisms may increasingly depend on acclimation capacity to accommodate thermal variability. To elucidate the molecular basis of fluctuating temperature induced phenotypic plasticity, we investigated heat tolerance and the mechanisms induced by acclimation to thermal variability as compared to those seen at constant temperature. We ran genome-wide transcriptomic analysis on <em>Drosophila melanogaster</em> subjected to acclimation at constant (19 </span><span>±</span><span> 0</span><span>°</span><span>C) and fluctuating (19 </span><span>±</span><span> 8</span><span>°</span><span>C) temperatures and contrasted the induction of molecular mechanisms in adult males, adult females, and larvae. We found life stage and sex specific dynamics of the acclimation responses to fluctuating temperatures. Adult flies exposed to temperature fluctuations showed a constitutive improvement in heat tolerance while heat tolerance of larvae tracked thermal fluctuations. A constitutive down-regulation of gene expression was observed for several genes in the case of larvae exposed to fluctuations. Our results for adult females showed that, for several genes, fluctuating temperature acclimation resulted in canalization of gene expression. Both transcriptional and post-transcriptional machinery were greatly affected by fluctuations in the case of adult males. Gene ontology analysis showed enrichment of heat stress response involving several major heat shock proteins in both larvae and adults exposed to fluctuating temperatures, even though fluctuations were in a benign range of temperatures. Finally, molecular mechanisms related to environmental sensing seem to be an important component of insect response to thermal variability. </span></p>
ScienceDex guides
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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.