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2,904 results for “Solute”
Dataset From: Oscillatory structural forces between charged interfaces in solutions of oppositely charged polyelectrolytes
<p>The dataset for the publication "Oscillatory structural forces between charged interfaces in solutions of oppositely charged polyelectrolytes". DOI: 10.1039/d0sm01257b.</p> <p>Files containing data have .dat extension and are in text format.</p>
Dataset for the paper: TaskTracker tool: a Toolkit for Tracking of Code Snapshots and Activity Data During Solution of Programming Tasks (SIGCSE Technical Symposium 2021))
<pre>It is a dataset for the <em>TaskTracker-tool: a Toolkit for Tracking of Code Snapshots and Activity Data During Solution of Programming Tasks</em> paper from <a href="https://sigcse2021.sigcse.org/">SIGCSE Technical Symposium 2021</a>. The dataset consists of code snapshots, IDE actions, and demographic information gathered by <a href="https://github.com/JetBrains-Research/codetracker">this</a> tool. We had 148 participants, aged 11 to 40 (mean age is 19 years), take part in the data gathering process. </pre> <pre>During data gathering, solutions were accepted in one of four languages: Python, Java, Kotlin, or C++. However, some of the students chose not to submit tasks or solved some tasks incorrectly. At the same time, some students solved some tasks many times in multiple languages. All submitted solutions are included in the final dataset.</pre> <pre>To get more information see the <em>README</em> file.</pre>
First motion data and focal mechanism solutions of 108 earthquakes occurred between 1928 and 2019 in the Southeastern Alps
<p>This dataset contains the P-wave polarities readings (FPS_polarities_input.zip) and the focal mechanisms (FPFIT_solution.pdf, FPFIT_solution.csv) obtained by the FPFIT algorithm (Reasenberger and Oppenheimer, 1985) of 108 earthquakes with 1.9 ≤ M<sub> </sub>≤ 4.8 occurring between 1928 and 2019 in the Southeastern Alps area (latitude 45°N-47.5°N and longitude 10°E-15°E). The preferred solution for each earthquake has been reported in the focal mechanism catalogue of Saraò et al. (2020).</p> <p>The first polarities used to compute the focal mechanisms were manually picked from seismograms of the National Institute of Oceanography and Applied Geophysics (OGS) northeastern Italy seismic and deformation network (Priolo et al., 2005; Bragato et al., 2011, Bragato et al., 2020)or extracted from the Bulletin of the International Seismological Centre the Seismological Bulletin of Slovenia. The polarities were also read from the seismograms archived in various Italian and European seismological observatories, many of which are no longer operating (Osservatorio meteorico-sismico nel Seminario - Chiavari; ENEL, Osservatorio Ximeniano - Florence, Osservatorio Astronomico "Brera" -Milan, &nbspDipartimento di Fisica dell’Università di Padova - Padua,;Osservatorio S. Domenico – Prato, Osservatorio meteoro-sismico nel Santuario di N.S. - Oropa, Osservatorio Bina - Perugia, Osservatorio "Valerio"- Pesaro, Osservatorio meteorico-sismico nel Collegio Alberoni - Piacenza, Osservatorio Meteorico Istituto Fisica - University of Siena,Sismografi Lungo Periodo di Mantovani (Bologna, Bolzano, Grosseto, Naples, Olbia, Palermo, Turin), Osservatorio meteorico-sismico nel Seminario Maggiore - Treviso, Osservatorio meteorico-sismico nel Seminario Patriarcale – Venice, Ljubljana, Munich, Stuttgart, Vienna).</p> <p>For more details</p> <p>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 Discuss. [preprint], https://doi.org/10.5194/essd-2020-369, in review, 2021.</p> <p> </p> <p>References:</p> <p>Bragato, P.L., Di Bartolomeo, P., Pesaresi, D., Plasencia Linares, M., and 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., P. Comelli, A. Saraò, D. Zuliani, L. Moratto, V. Poggi, G. Rossi, C. Scaini, M. Sugan, C. Barnaba, P. Bernardi, M. Bertoni, G. Bressan, A. Compagno, P. Di Bartolomeo, E. Del Negro, P. Fabris, M. Garbin, M. Grossi, A. Magrin, E. Magrin, D. Pesaresi, B. Petrovic, M.P. Plasencia Linares, M. Romanelli, A. Snidarcig, L. Tunini, S. Urban, E. Venturini and S. Parolai (2020). The OGS- North-Eastern Italy Seismic and Deformation Network: current status and outlook. Submitted to Seism. Res. Lett. </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., 2020: Focal mechanisms of Southeastern Alps and surroundings, doi: 10.5281/zenodo.4284971 .</p>
Supplementary Material: Microfluidic Fabrication Solutions for Tailor-Designed Fiber Suspensions
<p>Supplementary material for Berthet, H.; du Roure, O.; Lindner, A. Microfluidic Fabrication Solutions for Tailor-Designed Fiber Suspensions. <em>Appl. Sci.</em> <strong>2016</strong>, <em>6</em>, 385.</p> <p><strong>Video S1:</strong> Microfluidic fabrication technique of fibers by in situ photopolymerization</p> <p><strong>Video S2: </strong>In situ microfluidic measurement of the fiber’s Young’s modulus</p> <p><strong>Video S3: </strong>Microfluidic fabrication technique of fibers by super-paramagnetic particles self-assembly</p> <p><strong>Video S4: </strong>Fiber oscillating between the two lateral walls of a microfluidic channel</p> <p><strong>Video S5: </strong>Flow through a constriction of a suspension of parallel fibers fabricated by photo-polymerization</p> <p><strong>Video S6: </strong>Flow through a constriction of a suspension of rigid perpendicular fibers fabricated by photo-polymerization</p> <p><strong>Video S7: </strong>Flow through a constriction of a suspension of flexible perpendicular fibers fabricated by photo-polymerization</p> <p><strong>Video S8: </strong>Concentrated suspension of fibers flowing through a microfluidic constriction</p> <p><strong>Video S9: </strong>Fibers made by colloids self-assembly flowing through a constriction and forming non-permanent clusters.</p>
Land use-based Adaptation and Mitigation Solution (LAMS) suitability maps final version
<div>These maps provide the suitable area (maximum available area) for the potential implementation of the specific proposed LAMS. The suitability map includes 4 classes: not suitable (0), least suitable (1), moderately suitable (2) and most suitable (3). Maps for 13 different LAMS (from the LAMS catalogue V1-1) have been developed for the six RethinkAction case studies, when possible. This v2 includes the metadata.</div> <div>The codes and the names of the LAMS are the following:</div> <div>LAMS03-EstGra: Establishment (conversion to) of permanent grassland</div> <div>LAMS14-SpaPla: Spatial planning for the sustainable deployment of energy on land</div> <div>LAMS15-PhoPla: Photovoltaic plants</div> <div>LAMS21-AgrPla: Agrovoltaic farms</div> <div>LAMS22-IncFor: Increased portion of forests included under protected areas</div> <div>LAMS23-RefAff: Reforestation/afforestation</div> <div>LAMS31-UrbSpr: Limiting urban sprawl</div> <div>LAMS32-GreUrb: Establishment and maintenance of green urban ecosystems</div> <div>LAMS44-IncCul: Increase in cultivated area</div> <div>LAMS49-FloSol: Floating solar photovoltaic panels in water bodies</div> <div>LAMS50-SolPan: Solar panels in rooftops/buildings</div> <div>LAMS55-WatHar: Water harvesting: collect and store rain water in reservoirs</div> <div>LAMS59-LanMan: Land management of solar photovoltaic systems land</div>
Dataset of corrosion parameters for rebar in simulated pore solution and mortar
<p>We are pleased to announce the initial release of our dataset, "Corrosion Parameters for Rebar in Simulated Pore Solution and Mortar." This comprehensive dataset provides in-depth data on the corrosion potential, corrosion current density, and Tafel slopes of rebar subjected to various levels of chloride contamination and carbonation treatment in simulated environments.</p> <p>This release is aimed at researchers, academicians, and practitioners in civil engineering and materials science, providing valuable data for further research and practical application in understanding and mitigating rebar corrosion.</p> <p>Your feedback and contributions to this dataset are welcomed and appreciated!</p>
Dataset and analysis file for 3-factor solution for heat pump perception study using Q-methodology in Groningen, the Netherlands
<p>Dataset and analysis using KEN-Q method for a 3-factor solution for heat pump perception study using Q-methodology in Groningen, the Netherlands</p>
Lysozyme, BSA, Thyroglobulin: SAXS measurement in solution in two capillary thicknesses (1 mm and 1.5 mm) with in situ UV-Vis
<p>SAXS measurement of three different proteins in solution in two capillary thicknesses (1 mm and 1.5 mm). Data include in situ UV-Vis absorption spectroscopy and UV-Vis absoroption spectroscopy using microvolume spectrometer (DeNovix DS-11) for comparison.</p> <p>Proteins used: Lysozyme, bovine serum albumine, thyroglobuline</p> <table> <tbody> <tr> <td><strong>File Name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>sample_description.csv</td> <td>detailed description of individual samples</td> </tr> <tr> <td>DS11_UV_Tyr_Lys_BSA.zip</td> <td>data from microvolume spectrometer </td> </tr> <tr> <td>SAXS_1mm_raw.zip</td> <td>Raw unreduced SAXS data (2D images in H5 format), collected in 1mm capillary. Detailed description is in included HTML file.</td> </tr> <tr> <td>SAXS_1mm_reduced.zip</td> <td>SAXS data collected in 1mm capillary, reduced to 1D curves. Q-values are in 1/nm</td> </tr> <tr> <td>SAXS_1mm_subtracted.zip</td> <td>SAXS data collected in 1mm capillary, buffer subtracted. Q-values are in 1/nm</td> </tr> <tr> <td>SAXS_1p5mm_raw.zip</td> <td>Raw unreduced SAXS data (2D images in H5 format), collected in 1.5mm capillary. Detailed description is in included HTML file.</td> </tr> <tr> <td>SAXS_1p5mm_reduced.zip</td> <td>SAXS data collected in 1.5mm capillary, reduced to 1D curves. Q-values are in 1/nm</td> </tr> <tr> <td>SAXS_1p5mm_subtracted.zip</td> <td>SAXS data collected in 1.5mm capillary, buffer subtracted. Q-values are in 1/nm</td> </tr> <tr> <td>SAXS_UV_1mm_absorbance_sample_nonaveraged.zip</td> <td>UV-Vis absorption data collected using in-situ spectrometer. Mesurement in 1mm capillary. Only spectra collected when sample was present in capillary</td> </tr> <tr> <td>SAXS_UV_1mm_raw.zip</td> <td>Raw UV-Vis absorption data collected using in-situ spectrometer. Mesurement in 1mm capillary.</td> </tr> <tr> <td>SAXS_UV_1p5mm_absorbance_sample_nonaveraged.zip</td> <td>UV-Vis absorption data collected using in-situ spectrometer. Mesurement in 1.5mm capillary. Only spectra collected when sample was present in capillary</td> </tr> <tr> <td>SAXS_UV_1p5mm_raw.zip</td> <td>Raw UV-Vis absorption data collected using in-situ spectrometer. Mesurement in 1.5mm capillary.</td> </tr> </tbody> </table> <p> </p>
Dataset for "Exploring Mechanism of Corrosion Inhibition of WE43 and AZ31 Alloys by Aqueous Molybdate in Hank's Solution by Multisine Impedimetric Monitoring"
<p>This dataset contains raw data of the publication “Exploring Mechanism of Corrosion Inhibition of WE43 and AZ31 Alloys by Aqueous Molybdate in Hank’s Solution by Multisine Impedimetric Monitoring”. Corrosion Science, Volume 231, 2024, 111979, ISSN 0010-938X, <a href="https://doi.org/10.1016/j.corsci.2024.111979">https://doi.org/10.1016/j.corsci.2024.111979</a>. The data was collected at Jerzy Haber Institute of Catalysis and Surface Chemistry, Polish Academy of Sciences (Poland) and Gdansk University of Technology (Poland). All data are provided under CC0 license. The dataset includes Scanning electron and optical microscopy images (.tiff and ,jpg format); X-ray photoelectron spectroscopy spectra (.vms format); Electrochemical impedance spectra (.txt format). Dzmitry Kharytonau gratefully acknowledges the financial support of this study by the National Science Centre (Poland) under research Grant Sonatina no. 2021/40/C/ST5/00266. This work was also supported by Gdansk University of Technology under the Aurum Supporting International Research Team Building – ‘Excellence Initiative – Research University’ [grant number: 2/2021/IDUB/II.1.3].<br><br></p>
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>
Absorption and Emission Spectral Data of Room-temperature Rhodamine 6G Dye Solution and some typical Dye Microcavity parameters
<p>The repository contains spectral absorption and emission data [absorption cross section and Einstein coefficients] of rhodamine 6G dye solved in ethylene glycol at room temperature over the visible spectral range from 400.25nm to 619.85nm. In addition, typical values for the cavity loss rate are given for the same wavelength range. The data can be used e.g. for studies of two-dimensional thermalized photon gases and Bose-Einstein condensates of photons inside dye-filled optical microcavities.</p> <p><strong>Methodology</strong></p> <p>The absorption data has been obtained by white-light absorption spectroscopy of dye solutions with increasing concentration {0.01,0.1,1} mMol/Litre. The combined spectra have been calibrated with the rhodamine absorption cross section at 532nm wavelength, which we have independently determined in transmission measurements with a 532nm laser. The absorption cross section in this data repository constitutes a universal material property that is generally valid for rhodamine 6G solved in ethylene glycol at room temperature.</p> <p>The Einstein coefficient for absorption B_12 has been obtained specifically for the volume of the transverse ground mode in an optical microcavity formed by two curved mirrors with radius of curvature R = 1 and cavity length D = 1.5µm; see e.g. Klaers et al., <em>Nature</em> <strong>468</strong>, 545–548 (2010), Schmitt, <em>Phys. B: At. Mol. Opt. Phys.</em> <strong>51</strong>, 173001 (2018) and related work by the authors. For typical dye concentrations near 1mMol/Litre, approximately 10^8 molecules are contained in the ground mode volume. The Einstein coefficient for emission B_21 is deduced from B_12 assuming the Kennard Stepanov relation: B_21/B_12 = Exp[-h*c*(1/lambda - 1/lambda_zpl)/(k_B T)], where lambda_zpl = 545nm denotes the zero-phonon line of rhodamine 6G dye (h: Planck's constant, c: speed of light, lambda: wavelength, k_B: Boltzmann's constant, T: temperature). We have verified that the resulting B_21 spectrum agrees well with reference fluorescence spectra of rhodamine 6G. </p> <p>The spectral cavity loss rate c/(n0*D)*(1-R-A) with refractive index n0 = 1.43 and mirror absorption loss A = 1ppm is deduced from the wavelength-dependent mirror reflectivity R, which we have measured in cavity ring-down measurements at more than 10 wavelengths in the interval between 530nm to 605nm. For this, a tuneable dye laser was resonantly coupled into a 3.3cm-long cavity formed by the corresponding highly-reflecting dielectric mirrors. Note that the reciprocal values of the loss rates give the 1/e lifetime of the photons in the cavity.</p> <p><strong>Data format</strong></p> <p>The file 'data.dat' contains all data sorted by columns: wavelength (in units of nm), absorption cross section (in units of m^2), Einstein coefficients for absorption and emission (both in units of Hz), cavity loss rate (in units of Hz).</p>
Dataset for Main Text and SI - Squeezing the threshold of metal-halide perovskite micro-crystal lasers grown by solution epitaxy by Shuyu Zhou et al.
<p>All data published in </p> <p><strong><span>Squeezing the threshold of metal-halide perovskite micro-crystal lasers grown by solution epitaxy</span></strong></p> <p><strong><span> by </span></strong><em><span>Shuyu Zhou, Viktor Rehm, Hany A. Afify, Yufei Han, Jędrzej Korczak, Andrzej Szczerbakow, Tomasz Story, Zijian Peng, Albert These, Anastasia Barabash, Andres Osvet, Christoph J. Brabec, Klaus Götz, Tobias Unruh, Felix Hilpert, Olaf Brummel, Jörg Libuda, Wolfgang Heiss</span></em></p> <p><em><span>are summarized in this rar file. </span></em></p>
Solution-processed PbS quantum dot infrared laser with room-temperature tuneable emission in the optical telecommunications window - Open Data
<p>This is a supplementary upload attached to the paper titled "Solution-processed PbS quantum dot infrared laser with room-temperature tuneable emission in the optical telecommunications window" 10.1038/s41566-021-00878-9.</p> <p><strong>Figures</strong></p> <p>All figure data from the publication can be obtained from the original MATLAB .fig files. If one does not have access to MATLAB the figures can be opened using the open source software GNU Octave.</p> <p><strong>FDFD Simulation</strong></p> <p>Also in the upload is the original matlab code used to perform the simulations presented in the paper.</p> <p>"FDFD_2D_Ez_Hz_DFB_laser_UPLOAD" - Variable gain FDFD solver is uploaded as .mat and .pdf files.</p> <p>To run the code the functions "Dgen" and "gen2xDFB" are required and the .mat files containing the refractive indices "PbS1520" and "Al2O3".</p> <p>Parameters to vary can be found in the "DASHBOARD" section of the code. The uploaded code solves for the out-of-plane electric field (Ez Mode).</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>
Model weights for a Weather4cast 2021 Challenge IEEE Big Data Cup Stage solution
<p>This repository contains the pre-trained model weights for the TensorFlow/Keras models used in the <a href="https://www.iarai.ac.at/weather4cast/2021-competition/challenge/">Weather4cast 2021 Challenge IEEE Big Data Cup Stage</a> by the team "antfugue". The model code can be found in <a href="https://github.com/jleinonen/weather4cast-bigdata">https://github.com/jleinonen/weather4cast-bigdata</a> along with instructions on where to extract the weights.</p>
Data from: Improving the Effectiveness of the Solid-Solution-Strengthening Elements Mo, Re, Ru and W in Single-Crystalline Nickel-Based Superalloys
<p>This dataset is the basis for the journal article "Improving the Effectiveness of the Solid-Solution-Strengthening Elements Mo, Re, Ru and W in Single-Crystalline Nickel-Based Superalloys" (<a href="https://doi.org/10.3390/met11111707">doi.org/10.3390/met11111707</a>). </p> <p>Differential Scanning Calorimetry (DSC), Electron-Probe Mirco-Analysis (EPMA equipped with WDS detectors), Compression Creep and CalPhaD calculation data are included in this dataset. In the resulting article the partitioning and solid solution strengthening behavior of the elements Mo, Re, Ru, and W are investigated in three different alloy collections:</p> <p>"Reference" alloys: ERBO/1 (based on the commercial alloy CMSX-4), ERBO/13 (optimized alloy: <a href="http://doi.org/10.1088/0965-0393/23/3/035004">doi.org/10.1088/0965-0393/23/3/035004</a>) and ERBO/15 (optimized alloy: <a href="http://doi.org/10.1002/9781119075646.ch4">doi.org/10.1002/9781119075646.ch4</a>)</p> <p>"Model" alloys: ERBO/17, ERBO/18, ERBO/19 and ERBO/32 - experimental SX Nickel-Based superalloys to investigate the influence of Ti and Ta on the partitioning behavior of W</p> <p>"Experimental" alloys: EXP10, EXP11, EXP12, EXP13, EXP14, EXP15, EXP16, EXP17, EXP18 - general investigation of the behavior of Mo, Re, Ru and W, in terms of partitioning and thermophysical properties.</p> <p>_________</p> <p> </p> <p>CalPhaD data: short meta data file included in the respective subdirectory</p> <p>DSC data: Meta data as header in each file</p> <p>EPMA data: For each alloy a directory includes a general meta data file "0.cnd", an element specific meta data file "*.cnd" and the actual mapping data of each element "*.txt" (element name is listed in the corresponding specific meta data file). All element compositions are given in wt.-% in the mapping data .txt files. Exceptions: "COMPO" maps represent the total amount of detector counts; The values correspond to counts if the mapping .txt files contain non-float (e.g. integer) values (usually this means that there is very little or none of that element present and should therefore not be quantified to a wt.-% value).</p> <p>Creep: short meta data file included in the respective subdirectory</p>
Accurate Vertical Ionization Energy and Work Function Determinations of Liquid Water and Aqueous Solutions
<p>Dataset underlying report about a protocol to determine absolute binding energies from photoionization of liquid microjet samples, published as <a href="https://doi.org/10.1039/D1SC01908B">Accurate vertical ionization energy and work function determinations of liquid water and aqueous solutions</a>.</p>
Supplementary materials for the paper "Hyperstyle : A Tool for Assessing the Code Quality of Solutions to Programming Assignments"
<p>The set of the artefacts for a SIGCSE-2022 paper. The SIGCSE_atrifacts.pdf document contains detailed information about each folder, also this document has supplementary materials.</p> <p>Folders:<br> - comparison. This folder contains a public dataset in Java for comparison of our code assessment tool with the Tutor tool.<br> - dynamics. This folder contains two datasets of Python and Java public submissions from Stepik and Hyperskill platforms to check the influence of our tool on students code style.<br> - plots. This folder contains examples of charts that were plotted to analyze the tool thresholds.</p> <p>Supplementary materials:<br> - Examples of code quality issues categories. This section in the document contains code snippets with examples of issues for each code quality issue category for Python and Java.<br> - List of available subcategories in the tool.<br> - Tables with penalty coefficients for detection of recurring errors algorithm<br> - Examples of charts that were plotted to analyze the tool thresholds. This section is the same with the plots folder.</p>
Timeseries of lake margin, lake area, water level, mascon solutions of Lago Greve
<p>These are the dataset of timeseries of lake extent, lake area, water level, and mass around the lake, of Lago Greve presented in the study.</p> <p>1. Lake extent<br> Filename: LagoGreve.shp<br> Note: LagoGreve.cpg, LagoGreve.dbf, LagoGreve.prj, LagoGreve.shx are also needed.</p> <p>2. Timeseries of lake area<br> Filename: LakeArea_LagoGreve.csv</p> <p>3. Timeseries of water level<br> Filename: Lakelevel_LagoGreve.csv</p> <p>4. Timeseries of grace mascon solution<br> Filename: MeanMasconLagoGreve_CSRv06.csv</p> <p>5. Fitting model for mascon solution<br> Filename: MeanMasconFitting.csv</p>
Near-surface rheology and hydrodynamic boundary condition of semi-dilute polymer solutions
<p>Data appearing in the figures of the article DOI:10.1039/D0SM02116D.</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.