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38,569 results for “c”
CHARMM27 dynamics simulation trajectories of α-conotoxin LsIA and its C-terminal carboxylated analogue bound at α3β2 nAChR
<p>The whole simulation trajectories (28 individual trajectories with 27ns for each) contain the coordinates and parameters of atoms with time for α-conotoxin LsIA and its C-terminal carboxylated analogue anchored to rat α3β2 nAChR, respectively. The GROMACS 4.6.5 with the CHARMM27 force field is used for the simulation. The trajectory (.xtc) files are saved every 100ps time for each protein complex only. The portable binary run input (.tpr) files are also uploaded with the data. </p>
Data set supporting journal article: Markwitz, C., Knohl, A. and Siebicke, L.: "Evapotranspiration over agroforestry sites in Germany", Biogeosciences, 2020
<p>This data set contains all necessary data needed to replicate figures and analysis presented in the research article: Markwitz, C., Knohl, A. and Siebicke, L.: "Evapotranspiration over agroforestry sites in Germany", Biogeosciences, 2020.</p> <p>In detail, this data set contains 1) meteorological data and half-hourly evapotranspiration rates obtained by a conventional eddy covariance set-up, a low-cost eddy covariance set-up and an energy balance eddy covariance set-up for measurement campaigns of approximately four weeks duration (*_Fluxes_Campaigns_*); 2) raw data to recalculate flux footprints for the campaigns of approximately four weeks duration (*_Campaign_Footprints_*) and for the whole year (*_Annual_Footprints_*); 3) half-hourly evapotranspiration rates obtained by a low-cost eddy covariance set-up and an energy balance eddy covariance set-up gap-filled and corrected for energy balance closure (*_Fluxes_Annual_*). The data were collected at five agroforestry systems and five monoculture agriculture systems without trees across Northern Germany. </p>
Dataset de Segunda Ejecución YCrO3 Antiferromagnético Tipo C
<p>Scripts de ejecución de Quantum ESPRESSO para la perovskita YCrO3 con arreglo antiferromagnético tipo C. Segunda simulación.</p>
Dataset de Primera Ejecución YCrO3 Antiferromagnético Tipo C
<p>Scripts de ejecución de Quantum ESPRESSO para la perovskita YCrO3 con arreglo antiferromagnético tipo C. Primera simulación.</p>
MD simulation data: An Entropic Safety Catch Controls Hepatitis C Virus Entry and Antibody Resistance
<p><strong>Background</strong></p> <p>Equilibration, relaxation and production runs were performed on GPUs using the CUDA version of PMEMD in AMBER 16 and AMBER ff14SB force field. Minimisation steps were performed on a CPU using PMEMD in AMBER 16 and the AMBER ff14SB force field. All software is available from http://ambermd.org/. </p> <p><strong>Contents</strong></p> <p>There are three tarball (<strong>.tar.gz</strong>) files containing the <strong>core simulation data</strong>: one for wild type (WT), the second for the I438V A524T mutant and the third for the S449P mutant. Each contains:</p> <p>1. a source PDB (<strong>.pdb</strong>) file</p> <p>2. Five AMBER trajectory (<strong>.nc</strong>) files for five independent MD simulations, numbered 1 to 5. <strong>Note: </strong>each of these files is over 2GB.</p> <p>There is an additional tarball containing the <strong>control files</strong> <strong>and scripts</strong> used for running the MD simulations:</p> <p>1. Multiple control (<strong>.ctl</strong>) files numbered 1 to 10 that are used to minimize (<strong>min</strong> prefix), relax (<strong>rel</strong> prefix) and equilibrate (<strong>equ</strong> prefix) the model</p> <p>2. Executable <strong>do_md</strong> that performed all the minimisation, relaxation and equilibration steps</p> <p>3. control file <strong>prod.ctl</strong> used for the production run </p> <p>4. Executable <strong>run_prod</strong> that was used to perform the production run</p> <p>5. Two control files (<strong>prod_short.ctl </strong>and <strong>prod_short_2.ctl</strong>) for the short runs used to de-correlate the simulation for the independent runs</p> <p>6. Executable <strong>run_short</strong> and <strong>run_short_2</strong> used to carry out the de-correlated production runs.</p>
Experimental measurements of creep deformation of Tournemire shale loaded at specified pressure (10 MPa) and room temperature (26°C)
<p>Following the experimental protocol used in (Geng<em> et al.</em>, 2018), we performed the stepping creep experiments at a confining pressure of 10 MPa. We first loaded the samples under hydrostatic conditions up to 10 MPa at a pressure rate of 0.3 MPa/min. Hydrostatic conditions were maintained for ~18 h at 26 °C. Next, differential stress (axial stress minus confining pressure) was increased to a fixed initial stress (30 MPa) and maintained (creep status) for 24 h. The differential stress was repeatedly increased by 5 MPa and maintained for 24 h, until brittle failure. All the experiments were conducted using the triaxial apparatus installed at the Laboratoire de Géologie of ENS-Paris (France). There were few constraints on the natural saturation state of the samples because of their low permeability (10<sup>-19</sup> 10<sup>-21</sup> m<sup>2</sup>). To avoid exposition redundancy, an additional description of the technical performance of the triaxial apparatus can be referred to (Brantut<em> et al.</em>, 2011, Sarout & Guéguen, 2008).</p> <p>Compressive stresses and compactive strains are denoted as positive. Axial creep deformation was measured using three capacitive gap sensors that externally monitored the overall axial displacement of the piston during creep deformation. Volumetric strain during creep was estimated by adding the average of axial strains (axial displacement of the piston divided by the sample length) and two average radial strains measured by four radial strain gauges glued uniformly around the cylindrical rock surface. As the deformation rate generally stabilized during the last 8 h in most creep periods (Geng<em> et al.</em>, 2018), we estimated the average axial strain rate over the last 8 h of each step to characterize the creep strain rate under the corresponding axial loading stress. More technical details of the sample configuration and creep rates estimation can be found in (Geng<em> et al.</em>, 2018).</p>
Fibrinogen-like globe domain of human Tenascin-C (hFBG-C); A Target Enabling Package
<p>Chronic activation of the innate immune system by the damage-associated molecular pattern FBG-C (C-terminal fibrinogen-like globe domain of Tenascin-C) contributes to a variety of inflammatory diseases including arthritis, systemic sclerosis, and cancer. This TEP summarizes the first reported efforts to develop small-molecule FBG-C binders, with the aim to disrupt FBG-C-mediated pro-inflammatory protein-protein interactions (PPIs). We present the soluble expression of disulphide-containing human FBG-C (hFBG-C) in <em>E. coli</em>, the novel structure of hFBG-C, and preliminary chemical matter against hFBG-C derived from a crystallographic fragment screen. Finally, we introduce two robustly validated cellular assays, in either immortalized monocytes or primary human macrophages, which provide a route to development of small molecules which inhibit hFBG-C-activated inflammation.</p>
Localizing Spherical Fiducials in C‐arm Based Cone‐Beam CT
<p>This dataset was acquired as part of the work described in: "Localizing spherical fiducials in C-arm based cone-beam CT", Z. Yaniv, Med. Phys., Vol. 36(11), pp. 4957-4966, 2009, <a href="https://doi.org/10.1118/1.3233684">doi.org/10.1118/1.3233684</a>.</p> <p>The data includes two phantom imaging studies acquired with a Cone-Beam CT (CBCT) system. Each of the two datasets includes projection images (cine loops) and 3D reconstructions. Additionally, the data includes the CBCT system's projection matrices and MATLAB code for reading the projection images and overlaying epipolar lines onto them. All images are stored in the DICOM format.</p> <p>The full MATLAB code for fiducial localization described in the manuscript is available <a href="https://www.yanivresearch.info/software/cbctSphericalFiducialLocalization.zip">here</a>.</p> <p> </p>
Licenze Open Access: perché non c'è più bisogno di discuterne?
<p><strong>Perché non c’è più bisogno di discutere su quali siano le licenze più adeguate per fare Open Access?</strong><br> </p> <p>Per un semplice motivo: perché la Dichiarazione di Berlino del 2003 è così chiara su quell’aspetto che non ha più senso continuare a chiedersi quali siano le licenze adeguate per fare Open Access. La Dichiarazione di Berlino è il documento manifesto in cui fin dal 2003 sono cristallizzati i principi cardine dell'Open Access e che è stato riconosciuto e sottoscritto da quasi tutti gli atenei e le istituzioni di ricerca del pianeta.</p> <p>Chiunque sostiene che ci sia bisogno di ulteriore dibattito o non ha letto/compreso quel documento o ha interesse a diffondere incertezza.</p> <p>Potremmo fermarci qui e rimandare alla lettura della Dichiarazione di Berlino. Ma, per chiarire la questione in modo definitivo, vediamo puntualmente che cosa dice questo documento in merito alla gestione del diritto d'autore.</p> <p>La Dichiarazione di Berlino pone due semplici requisiti per rientrare nella definizione di Open Access. Il primo dei due requisiti è proprio dedicato alla gestione dei diritti d'autore e letteralmente recita:</p> <p>L’autore e il detentore dei diritti del contenuto devono garantire a tutti gli utilizzatori il diritto d’accesso gratuito, irrevocabile ed universale e l’autorizzazione a riprodurlo, utilizzarlo, distribuirlo, trasmetterlo e mostrarlo pubblicamente e a produrre e distribuire lavori da esso derivati, mantenendo comunque l’attribuzione della paternità intellettuale originaria.</p> <p> </p> <p><strong>Le licenze per fare Open Access</strong><br> </p> <p>Di conseguenza, usando come riferimento il set di licenze offerto da Creative Commons, le licenze coerenti con la definizione di Open Access sono:</p> <ul> <li><em>CC Zero</em> [che tecnicamente non è una licenza, ma un atto di rinuncia ai diritti]</li> <li><em>Attribution</em></li> <li><em>Attribution – Share Alike</em></li> </ul> <p>Le altre licenze (<em>Attribution – No Derivatives</em>; <em>Attribution – Non commercial</em>; <em>Attribution – Non commercial – Share Alike</em>; <em>Attribution – Non commercial – No Derivatives</em>) escono dal solco dell'Open Access perché impongono restrizioni eccessive. </p> <p> </p> <p><strong>Le principali obiezioni a questo approccio (e le relative smentite)</strong><br> </p> <p><em> 0) "Stai semplificando troppo. L'Open Access è un tema più complesso!"</em><br> Indubbiamente sto semplificando [forse perché questo è un post divulgativo e non un articolo scintifico o un manuale. Per un testo più articolato rimando al mio capitolo all'interno del libro "Fare Open Access" <a href="https://aliprandi.org/books/fare-openaccess/">disponibile liberamente qui</a>]. Ad ogni modo, le cose stanno davvero così; il requisito 1 della dichiarazione di Berlino è molto chiaro e cristallino; gli aspetti dell'Open Access che meritano ulteriore dibattito sono altri (e sono per lo più legati all'interpretazione del requisito 2).</p> <p><em> 1) Ma il sito DOAJ.org indicizza anche le riviste con licenze diverse da quelle tre...</em><br> Certo, infatti i responsabili del progetto DOAJ.org sbagliano; l'ho detto in varie occasioni e lo ribadisco. Capisco l'intento di indicizzare tutte le riviste scientifiche rilasciate con licenze open; ma se si mette tutto in un unico calderone (nel quale, pericolosamente, ci sono poi anche alcune licenze scritte dalle case editrici e non riconosciute come open da organizzazioni indipendenti) si rischia di creare confusione negli utenti. Basterebbe indicare le riviste sotto licenza CC BY e CC BY-SA con un colore diverso rispetto alle altre, o anche distinguerle con un asterisco che rimanda a una nota in cui si precisa che solo quelle sono coerenti con la definizione di Open Access.</p> <p><em> 2) Ma l’editore XY ha una sezione “open access” sul suo sito e da lì lascia scaricare i PDF di libri e articoli senza alcuna licenza…</em><br> Certo; ma quello è marketing, non è vero Open Access. Purtroppo c'è un utilizzo strumentale del termine "open access"; spesso viene artatamente e impropriamente associato al concetto di "gratuito" per cercare di attirare traffico sul proprio sito web o, anche qui, per confondere le acque e diffondere incertezza.</p>
Dense inner C-S-H of 28 d hydrated alite, prepared using BIB at 20 °C
<p>These are images of 28 days hydtrated alite (M3 polymorph, Vustah, Czech Republic). The preparation of the specimens will be described in the corresponding Paper "<em>Argon Broad Ion beam sectioning and high resolution scanning electron microscopy imaging of hydrated alite</em>" published in Cement and Concrete Research.</p> <p>This dataset shows SEM images of the specimens prepared at <strong>20 °C</strong> in a tripple Argon Broad Ion Beam device.</p> <p>For all images described in the supplementary data of the paper, thee corresponding pore segmentation and ignored area is provided as well. This excludes the file "C3S 28d BIB 6KV 6h_019.tif".</p> <p>All files were acquired usng a Nova Nano SEM 230, FEI in SE mode at 2 KV and nearly the same magnification.</p>
Dense inner C-S-H of 28 d hydrated alite, prepared using BIB at -140 °C
<p>These are images of 28 days hydtrated alite (M3 polymorph, Vustah, Czech Republic). The preparation of the specimens will be described in the corresponding Paper "<em>Argon Broad Ion beam sectioning and high resolution scanning electron microscopy imaging of hydrated alite</em>" published in Cement and Concrete Research.</p> <p>This dataset shows SEM images of the specimens prepared at <strong>-140 °C</strong> in a tripple Argon Broad Ion Beam device.</p> <p>For all images described in the supplementary data of the paper, thee corresponding pore segmentation and ignored area is provided as well. This excludes the file "C3S 28d cryo BIB 6h_015.tif".</p> <p>All files were acquired usng a Nova Nano SEM 230, FEI in SE mode at 2 KV and nearly the same magnification.</p> <p> </p>
A Bayesian Approach to Detect Pedestrian Destination-Sequences from WiFi Signatures: Data (Transp. Res. Part C, 2014)
<p>This dataset contains and describes the data used in</p> <p>Danalet, A., Farooq, B., & Bierlaire, M. (2014). A Bayesian approach to detect pedestrian destination-sequences from WiFi signatures. <em>Transportation Research Part C: Emerging Technologies</em>, <strong>44</strong>, 146-170. doi:10.1016/j.trc.2014.03.015</p> <p>Specifically it contains WiFi traces, pedestrian Semantically-Enriched Routing Graph (SERG), and Potential Attractivity measure (PAM).</p>
Corpus der amtlichen Entscheidungssammlung des Bundesverfassungsgerichts (C-BVerfGE)
<p> </p> <p><strong>Überblick</strong></p> <p>Das <strong>Corpus der amtlichen Entscheidungssammlung des Bundesverfassungsgerichts (C-BVerfGE)</strong> ist eine digitale Zusammenstellung von möglichst vielen Entscheidungen, die in der amtlichen Entscheidungssammlung des Bundesverfassungsgerichts (BVerfGE) veröffentlicht sind. Der Datensatz enthält alle Entscheidungen, die auf der <a href="https://www.bundesverfassungsgericht.de">amtlichen Webseite des Bundesverfassungsgerichts</a> am jeweiligen Stichtag in der <a href="https://www.bundesverfassungsgericht.de/DE/Entscheidungen/Entscheidungen/Amtliche%20Sammlung%20BVerfGE.html">Auflistung der Entscheidungen der BVerfGE</a> verlinkt waren.</p> <p><em>Bitte beachten Sie das beiliegende Codebook!</em> Es enthält wichtige Informationen zur korrekten Nutzung des Datensatzes. Es hilft auch bei der Entscheidung, welche Variante für Sie am besten geeignet ist. In der Regel empfehle ich für quantitative Forschung die CSV-Dateien und für traditionelle juristische Arbeit die PDF-Sammlung.</p> <p>Alle die <em>Corona-Pandemie</em> betreffenden Entscheidungen des Bundesverfassungsgerichts finden Sie zusätzlich separat dokumentiert und analysiert im Datensatz <a href="https://doi.org/10.5281/zenodo.4459405"><strong>Corona-Rechtsprechung des Bundesverfassungsgerichts (BVerfG-Corona)</strong></a><strong>.</strong></p> <p>Das <em>C-BVerfGE</em> sollte nicht mit dem <strong><a href="http://doi.org/10.5281/zenodo.3902658">Corpus der Entscheidungen des Bundesverfassungsgerichts (CE-BVerfG)</a> </strong>verwechselt werden! Letzterer zielt auf eine Abbildung <em>aller</em> auf <a href="https://www.bundesverfassungsgericht.de">www.bundesverfassungsgericht.de</a> veröffentlichten Entscheidungen und ist mit über 8000 Entscheidungen mehr als zehnmal so groß.</p> <p> </p> <p><strong>Aktualisierung</strong></p> <p>Dieser Datensatz wird <em>1-2 mal im Jahr</em> aktualisiert. Benachrichtigungen über neue und aktualisierte Datensätze veröffentliche ich immer zeitnah auf Mastodon unter <a href="https://fediscience.org/@seanfobbe">@seanfobbe@fediscience.org</a></p> <p> </p> <p> </p> <p><strong>NEU in Version 2025-08-20</strong></p> <ul> <li>Vollständige Aktualisierung der Daten (bis einschließlich Band 169)</li> <li>LIZENZÄNDERUNG: Source Code jetzt unter GNU General Public License Version 3 (GPLv3) oder später lizenziert</li> <li>Neue Variablen: Tenor und Beschlussformel</li> <li>Variable entfernt: Kurzbeschreibung (wird vom BVerfG nicht mehr angeboten)</li> <li>R-Version auf 4.4.0 aktualisiert (wegen CVE-2024-27322)</li> <li>Expliziter R Package Version Lock für 2024-06-13 (CRAN Date)</li> <li>Paralellisierung von Quanteda repariert</li> <li>Überarbeitung des Dockerfiles</li> <li>Anpassung von Compose File an Debian 11</li> <li>Docker Zeitzone auf Berlin eingestellt</li> <li>Zusätzliches Lösch-Skript mit Docker-Integration</li> <li>Vereinfachung der Run-Skripte und stärkere Integration mit Docker Compose</li> <li>/tmp in Arbeitsspeicher ausgelagert</li> </ul> <p> </p> <p><strong>Features</strong></p> <ul> <li>Insgesamt bis zu 35 Variablen</li> <li>Fortlaufende Aktualisierung</li> <li>Urheberrechtsfreiheit</li> <li>Offene und plattformunabhängige Formate (PDF, TXT, CSV, HTML)</li> <li>Entscheidungsnamen und BVerfGE-Fundstelle</li> <li>Verknüpfung mit PräsidentIn/Vize-PräsidentIn</li> <li>Linguistische Kennzahlen</li> <li>Umfangreiches Codebook</li> <li>Compilation Report um den Erstellungs-Prozess zu erläutern</li> <li>Dutzende Diagramme und Tabellen für alle Zwecke (im ZIP-Archiv 'ANALYSE').</li> <li>Jedes Diagramm liegt in einem für den Druck (PDF) und das Web (PNG) optimierten Format vor. Tabellen sind im CSV-Format bereitgestellt und sind damit sowohl für Menschen als auch für Maschinen gut lesbar</li> <li>Kryptographisch signiert</li> <li><a href="../doi/10.5281/zenodo.4265933">Veröffentlichung des Source Codes</a></li> </ul> <p> </p> <p><strong>Eckdaten</strong></p> <p><em>Stichtag:</em> 20. August 2025</p> <p><em>Inhaltlicher Umfang</em>: 970 Entscheidungen</p> <p><em>Zeitlicher Umfang:</em> 1997 bis 2024, plus vereinzelte Entscheidungen aus anderen Jahren</p> <p><em>Formate:</em><strong> </strong>PDF, TXT, CSV und HTML</p> <p> </p> <p><strong>Source Code und Compilation Report</strong></p> <p>Der gesamte Erstellungs-Prozess ist ab Version 2021-01-03 vollautomatisiert und detailliert dokumentiert. Mit jeder Kompilierung des vollständigen Datensatzes wird auch ein umfangreicher Compilation Report in einem attraktiv designten PDF-Format erstellt (ähnlich dem Codebook).</p> <p>Der Compilation Report enthält den vollständigen Source Code, dokumentiert relevante Rechenergebnisse, gibt sekundengenaue Zeitstempel an und ist mit einem klickbaren Inhaltsverzeichnis versehen. Er ist zusammen mit dem Source Code hinterlegt. Wenn Sie sich für Details des Erstellungs-Prozesses interessieren, lesen Sie diesen bitte zuerst.</p> <p>Der <em>vollständige Source Code</em> — sowohl für die Erstellung des Datensatzes, als auch für das Codebook — ist <em>öffentlich einsehbar und dauerhaft erreichbar</em> im wissenschaftlichen Archiv des CERN unter diesem Link hinterlegt: <a href="../doi/10.5281/zenodo.4265933">https://zenodo.org/doi/10.5281/zenodo.4265933</a></p> <p> </p> <p><strong>Kryptographische Signaturen</strong></p> <p>Die Integrität und Echtheit der einzelnen Archive des Datensatzes sind durch eine <em>Zwei- Phasen-Signatur</em> sichergestellt.</p> <p>In <em>Phase I</em> werden während der Kompilierung für jedes ZIP-Archiv Hash-Werte in zwei verschiedenen Verfahren (SHA2-256 und SHA3-512) berechnet und in einer CSV-Datei dokumentiert.</p> <p>In <em>Phase II</em> wird diese CSV-Datei mit meinem persönlichen geheimen GPG-Schlüssel signiert. Dieses Verfahren stellt sicher, dass die Kompilierung von jedermann durchgeführt werden kann, insbesondere im Rahmen von Replikationen, die persönliche Gewähr für Ergebnisse aber dennoch vorhanden ist.</p> <p>Die während der Kompilierung des Datensatzes erstellte CSV-Datei mit den Hash-Prüfsummen ist mit meiner <em>persönlichen GPG-Signatur</em> versehen. Der mit dieser Version korrespondierende Public Key ist sowohl mit dem Datensatz als auch mit dem Source Code hinterlegt. Er hat folgende Kenndaten:</p> <p><em>Name:</em> Sean Fobbe (fobbe-data@posteo.de)</p> <p><em>Fingerabdruck:</em> FE6F B888 F0E5 656C 1D25 3B9A 50C4 1384 F44A 4E42</p> <p> </p> <p><strong>Kein Urheberrecht: Public Domain</strong></p> <p>An den Entscheidungstexten und amtlichen Leitsätzen besteht gem. § 5 Abs. 1 UrhG <em>kein </em>Urheberrecht, da sie amtliche Werke sind. § 5 UrhG ist auf amtliche Datenbanken analog anzuwenden (BGH, Beschluss vom 28.09.2006 - I ZR 261/03, "Sächsischer Ausschreibungsdienst"). Alle eigenen Beiträge (z.B. durch Zusammenstellung und Anpassung der Metadaten) und damit den gesamten Datensatz stelle ich gemäß einer <a href="https://creativecommons.org/publicdomain/zero/1.0/legalcode">CC0 1.0 Universal Public Domain License</a> vollständig urheberrechtsfrei.</p> <p> </p> <p><strong>Disclaimer</strong></p> <p>Dieser Datensatz ist eine private wissenschaftliche Initiative und steht weder mit dem Bundesverfassungsgericht noch mit den Herausgebern der BVerfGE in Verbindung.</p> <p> </p> <p><strong>Alternative Online-Quellen zur BVerfGE</strong></p> <p>Die Urteile im C-BVerfGE sind mit Randnummern versehen, enthalten aber keine der amtlichen BVerfGE entsprechende Paginierung, hier empfehle ich die DFR-Sammlung von Tschentscher. Für statistische Analysen von älteren Entscheidungen sollten Sie auf die Datensätze von Coupette oder Möllers/Shadrova/Wendel zurückgreifen. Diese Datensätze stehen unter teils sehr restriktiven Lizenzen.</p> <p> </p> <p><em>[BVerfGE 1951-2019]</em> Möllers, Christoph, Shadrova, Anna, & Wendel, Luisa. (2021). "BVerfGE-Korpus". <a href="https://doi.org/10.5281/zenodo.4551408">https://doi.org/10.5281/zenodo.4551408</a></p> <p>[Senatsentscheidungen 1972-2010] Engst, Benjamin G., Thomas Gschwend, Christoph Hönnige & Caroline E. Wittig. (2020).<em> “The Constitutional Court Database." </em><a href="https://ccdb.eu/">https://ccdb.eu/</a><em><br></em></p> <p><em>[BVerfGE 1951-2015 als TXT-Datensatz]</em> Coupette, Corinna: "Juristische Netzwerkforschung: Modellierung, Quantifizierung und Visualisierung relationaler Daten im Recht (Online-Appendix)". <a href="https://doi.org/10.1628/978-3-16-157012-4-appendix">https://doi.org/10.1628/978-3-16-157012-4-appendix</a></p> <p><em>[BVerfGE von 1951 bis heute, mit Paginierung nach gedruckter BVerfGE!]</em> Tschentscher, Axel (Hrsg): "Deutschsprachiges Fallrecht (DFR)". <a href="https://www.servat.unibe.ch/dfr/dfr_bverfg.html">https://www.servat.unibe.ch/dfr/dfr_bverfg.html</a></p> <p> </p> <p><strong>Weitere Open Access Veröffentlichungen (Fobbe)</strong></p> <p>Website<em> </em>—<em> </em><a href="https://www.seanfobbe.de">www.seanfobbe.de</a></p> <p>Open Data — <a href="../communities/sean-fobbe-data/">zenodo.org/communities/sean-fobbe-data/</a></p> <p>Source Code — <a href="../communities/sean-fobbe-code/">zenodo.org/communities/sean-fobbe-code/</a></p> <p>Volltexte regulärer Publikationen — <a href="../communities/sean-fobbe-publications/">zenodo.org/communities/sean-fobbe-publications/</a></p> <p> </p> <p><strong>Kontakt</strong></p> <p>Fehler gefunden? Anregungen? Kommentieren Sie gerne im Issue Tracker oder kontaktieren Sie mich über www.seanfobbe.de</p> <p> </p>
Robustness assessment of a C++ implementation of a quantized (int8) version of the LeNet-5 convolutional neural network
<p>The architecture of the LeNet-5 convolutional neural network (CNN) was defined by LeCun in its paper "Gradient-based learning applied to document recognition" (<a href="https://ieeexplore.ieee.org/document/726791">https://ieeexplore.ieee.org/document/726791</a>) to classify images of hand written digits (MNIST dataset).</p><p>This architecture has been customized to use Rectified Linear Unit (ReLU) as activation functions instead of Sigmoid, and 8-bit integers for weights and activations instead of floating-point.</p><p>It consists of the following layers:</p><ul><li><strong>conv1</strong>: Convolution 2D, 1 input channel (28x28), 3 output channels (28x28), kernel size 5, stride 1, padding 2.</li><li><strong>relu1</strong>: Rectified Linear Unit (3@28x28).</li><li><strong>max1</strong>: Subsampling buy max pooling (3@14x14).</li><li><strong>conv2</strong>: Convolution 2D, 3 input channels (14x14), 6 output channels (14x14), kernel size 5, stride 1, padding 2.</li><li><i><strong>relu2</strong></i>: Rectified Linear Unit (6@14x14).</li><li>max2: Subsampling buy max pooling (6@7x7).</li><li><i><strong>fc1</strong></i>: Fully connected (294, 147)</li><li><i><strong>fc2</strong></i>: Fully connected (147, 10)</li></ul><p>The fault hypotheses for this work include the occurrence of:</p><ul><li><strong>BF</strong>: single, double-adjacent and triple-adjacent bit-flip faults</li><li><strong>S0</strong>: single, double-adjacent and triple-adjacent stuck-at-0 faults</li><li><strong>S1</strong>: single, double-adjacent and triple-adjacent stuck-at-1 faults</li></ul><p>In the memory cells containing all the parameters of the CNN: </p><ul><li><strong>w</strong>: weights (int8)</li><li><strong>zw</strong>: zero point of the weights (int8)</li><li><strong>b</strong>: biases (int32)</li><li><strong>z</strong>: zero point (int8)</li><li><strong>m</strong>: m (int32)</li></ul><p>Images 200 to 249 from the MNIST dataset have been used as workload.</p><p>This dataset contains the raw data obtained from running exhaustive fault injection campaigns for all considered fault models, targeting all considered locations and for all the images in the workload.</p><p>In addition, the raw data have been lightly processed to obtain global data related to the particular bits and parameters affected by the faults, and the obtained failure modes.</p><h3>Files information</h3><ul><li><i>golden_run.csv</i>: Prediction obtained for all the images considered in the workload in the absence of faults (Golden Run). This is intended to act as oracle to determine the impact of injected faults. </li><li><i>single_faults/bit_flip</i> folder: Prediction obtained for all the images considered in the workload in presence of single bit-flip faults. There is one file for each parameter of each layer.</li><li><i>single_faults/stuck_at_0</i> folder: Prediction obtained for all the images considered in the workload in presence of single stuck-at-0 faults. There is one file for each parameter of each layer.</li><li><i>single_faults/stuck_at_1</i> folder: Prediction obtained for all the images considered in the workload in presence of single stuck-at-1 faults. There is one file for each parameter of each layer.</li><li><i>double_adjacent_faults/bit_flip</i> folder: Prediction obtained for all the images considered in the workload in presence of double adjacent bit-flip faults. There is one file for each parameter of each layer.</li><li><i>double_adjacent_faults/stuck_at_0</i> folder: Prediction obtained for all the images considered in the workload in presence of double adjacent stuck-at-0 faults. There is one file for each parameter of each layer.</li><li><i>double_adjacent_faults/stuck_at_1</i> folder: Prediction obtained for all the images considered in the workload in presence of double adjacent stuck-at-1 faults. There is one file for each parameter of each layer.</li><li><i>triple_adjacent_faults/bit_flip</i> folder: Prediction obtained for all the images considered in the workload in presence of triple adjacent bit-flip faults. There is one file for each parameter of each layer.</li><li><i>triple_adjacent_faults/stuck_at_0</i> folder: Prediction obtained for all the images considered in the workload in presence of triple adjacent stuck-at-0 faults. There is one file for each parameter of each layer.</li><li><i>triple_adjacent_faults/stuck_at_1</i> folder: Prediction obtained for all the images considered in the workload in presence of triple adjacent stuck-at-1 faults. There is one file for each parameter of each layer.</li></ul><h3>Methodology information</h3><p>First, the CNN was used to classify all the images of the workload in the absence of faults to get a reference to determine the impact of faults. This is golden_run.csv file.</p><p>After that, one fault injection experiment was executed for each bit of each element of each parameter of the CNN.</p><p>Each experiment consisted in:</p><ul><li>Affecting the bits (inverting it in case of bit-flip faults, setting it to 0 or 1 in case of stuck-at-0 or atuck-at-1 faults) identified by the mask.</li><li>Classifying all the images of the workload in the presence of this fault. The obtained output was stored in a given .csv file.</li><li>Removing the fault from the CNN by restoring the affected bits to its previous value.</li></ul><h3>List of variables (Name : Description (Possible values))</h3><ul><li><strong>IMGID</strong>: Integer number identifying the considered image (200-249).</li><li><strong>TENSORID</strong>: Integer number identiying the parameter affected by the fault (0 - No fault, 1 - conv1.w, 2 - conv1.zw, 3 - conv1.m, 4 - conv1.b, 5 - conv1.z, 6 - conv2.w, 7 - conv2.zw, 8 - conv2.m, 9 - conv2.b, 10 - conv2.z, 11 - fc1.w, 12 - fc1.zw, 13 - fc1.m, 14 - fc.b, 15 - fc1.z, 16 - fc2.w, 17 - fc2.zw, 18 - fc2.m, 19 - fc2.b, 20 - fc2.z)</li><li><strong>ELEMID</strong>: Integer number identiying the element of the parameter affected by the fault (-1 - No fault, [0-2] - {conv1.b, conv1.m, conv1.zw}, [0-74] - conv1.w, 0 - conv1.z, [0-5] - {conv2.b, conv2.m, conv2.zw}, [0-149] - conv2.w, 0 - {conv1.z, conv2.z, fc1.z, fc2.z}, [0-146] - {fc1.b, fc1.m, fc1.zw}, [0-43217] - fc1.w, [0-9] - {fc2.b, fc2.m, fc2.zw}, [0-1469] - fc2.w)</li><li><strong>MASK</strong>: 8-digit hexadecimal number identifying those bits affected by the fault ([00000000 - No fault, FFFFFFFF - all 32 bits faulty])</li><li><strong>FAULT</strong>: String identiying the type of fault (NF - No fault, BF - bit-flip, S0 - Stuck-at-0, S1 - Stuck-at-1)</li><li><strong>OUTPUT</strong>: 10 integer numbers provided by the CNN as output after processing the image. The highest value identifies the selected category for classification.</li><li><strong>SOFTMAX</strong>: 10 decimal numbers obtained after applying the softmax function to the provided output. They represent the probability of the image of belonging to the corresponding category for classification.</li><li><strong>PRED</strong>: Integer number representing the category predicted for the processed image.</li><li><strong>LABEL</strong>: integer number representing the actual category for the processed image.</li></ul>
NewsEye / READ AS training dataset from French Newspapers (19th, early 20th C.)
<p>The dataset comprises French newspaper pages from 19th and early 20th century with annotated text. The page images were provided by the <a href="https://www.bnf.fr/en">French National Library</a> and comprise 183 pages (training set). The data are formed according to the PAGE format (cf. Cf. <a href="https://github.com/PRImA-Research-Lab/PAGE-XML/">https://github.com/PRImA-Research-Lab/PAGE-XML/</a>) and were produced with the <a href="http://read.transkribus.eu/">Transkribus </a>platform with support of the <a href="http://newseye.eu/">NewsEye</a> and the <a href="http://read.transkribus.eu/">READ </a>project. The guidelines with which the AS GT was created are uploaded here as well.</p>
Kinetic Study on the Reactivity of Azanone (HNO) toward Cyclic C-Nucleophiles
<p>1. Equipment</p> <p>UV-Vis absorption spectra were collected using an Agilent 8453 spectrophotometer equipped with a photodiode array detector and thermostated cell holder.</p> <p>2. Chemicals</p> <p>The Angeli’s salt stock solution was prepared in 1 mM NaOH. Its concentration was determined by measuring the absorbance at 248 nm (ε = 8.3 · 10<sup>3</sup> M<sup>−1</sup>cm<sup>−1</sup>). The solution was kept on ice. 1-(4-Methoxybenzyl)-2,4-piperidinedione, 2-acetyl-1,3-cyclohexanedione were purchased from Angene Chemical. 1,3-Cyclopentanedione, 2-methyl-1,3-cyclopentanedione, 1,3-cyclohexanedione, 2-methyl-1,3-cyclohexanedione, 1,3-cycloheptanedione, and 2,4-piperidinedione were purchased from Fluorochem, United Kingdom. All other chemicals (of the highest purity available) were sourced from Sigma-Aldrich Corp. All solutions were prepared using deionized water (Millipore Milli-Q system).</p> <p>3. Kinetic Experiments</p> <p>The HNO flux was determined from the rate of FlBA oxidation in aerated aqueous solution of Angeli’s salt, monitored at 490 nm. The initial concentration of Angeli’s salt was equal to 20 µM. Due to the scavenging of HNO by O<sub>2</sub> and other scavengers, the steady-state concentration of azanone is very low. The HNO dimerization was therefore negligible and was not taken into consideration. HNO released from Angeli’s salt reacts either with the HNO scavenger or with the molecular oxygen to form peroxynitrite, which was detected with the use of the FlBA probe (25 µM). Its reaction with ONOO<sup>–</sup> results in the formation of fluorescein. The formation of fluorescein was monitored spectrophotometrically by following the increase in its characteristic absorbance at 490 nm. The reaction mixtures contained Angeli’s salt (20 µM), the fluorescein-based monoborate probe FlBA (25 µM), phosphate buffer (50 mM, pH 7.4), dtpa (100 µM), and the HNO scavenger (at an appropriate concentration). In addition, each solution contained 5% (vol.) CH<sub>3</sub>CN. The rate constants were determined with the assumption that the concentration of molecular oxygen was equal to 225 µM. Each rate constant was determined in at least three independent experiments.</p> <p>The dataset contains ASCII files with UV-Vis spectra of the reaction mixtures recorded for an incubation time of 10 min. (see Artelska, A.; Rola, M.; Rostkowski, M.; Pięta, M.; Pięta, J.; Michalski, R.; Sikora, A.B. Kinetic Study on the Reactivity of Azanone (HNO) toward Cyclic C-Nucleophiles. Int. J. Mol. Sci. (2021), in press.).</p> <p>4. Computational Details</p> <p>Quantum mechanical calculations were performed in the Gaussian G09 suite of programs, Revision E01. Stationary points were found by geometry optimization algorithms with tight convergence criteria except for transition state structure in the reaction of HNO with 1-(4-methoxybenzyl)-2,4-piperidinedione, where default criteria had to be used due to lack of computation convergence. To verify the nature of stationary points, as well as to compute Gibbs free energies, respective frequencies were computed. In calculations, the presence of the water environment was described by the Gaussian default continuum solvation model (IEFPCM). Density functional theory (DFT) functional B2PLYP with Grimme’s D3 dispersion correction (B2PLYP-D3) combined with 6-311+(2df,2p) split valence basis set, was used. The theory level was selected based on the fact that double-hybrid DFT functionals perform well in describing chemical system properties as well as reaction energy barriers, especially when London dispersion corrections are employed.</p> <p>The file AtomicCoordinatesOfStationaryPoints.txt contains geometries of stationary points obtained at the B2PLYP-D3/6-311+(2df,2p) theory level used for Gibbs free energey calculations in computational studies (see Artelska, A.; Rola, M.; Rostkowski, M.; Pięta, M.; Pięta, J.; Michalski, R.; Sikora, A.B. Kinetic Study on the Reactivity of Azanone (HNO) toward Cyclic C-Nucleophiles. Int. J. Mol. Sci. (2021), in press.).</p>
Dataset for the manuscript "ExSTED microscopy reveals contrasting functions of dopamine and somatostatin CSF-c neurons along the central canal"
<p>Description: Dataset that supports the expansion-STED and light sheet microscopy methods in spinal cord and support the findings in the manuscript: ExSTED microscopy reveals contrasting functions of dopamine and somatostatin CSF-c neurons along the central canal (Elham Jalalvand, Jonatan Alvelid, Giovanna Coceano, Steven Edwards, Brita Robertson, Sten Grillner, Ilaria Testa).</p> <p>The software used to open the files and perform the analysis: Imspector v0.10_rev8575 and ImageJ 1.52i.</p> <p>The preprint of the manuscript can be found here: https://doi.org/10.1101/2021.08.17.456595</p>
Data for study "Realisation of Paris Agreement pledges may limit warming just below 2°C"
<p>This is the data repository with country-level data and individual scenario data for the study “Realisation of Paris Agreement pledges may limit warming just below 2°C”. Please find below a description of the different data sources available. </p> <p><strong>Emissions Scenario Data (scenarios_12Nov2021a_CR.csv). </strong>The complete set of infilled and extended emissions for each scenario presented in the study. The emissions timeseries span from 2015 to 2100 for 52 emissions species required to run MAGICC7.</p> <p><strong>Scenario Statistics Table (stats_12Nov2021a_CR_short.csv).</strong> Summary statistics for the scenarios used in the sensitivity study. Statistics available include Exceedance probabilities, peak warming, year of peak warming and warming in 2100.</p> <p><strong>Supplementary Table S1 (supplementary_table-s1.csv) - </strong>Estimate of total GHG emissions (excl. LULUCF) in 1990, 2010, 2019 and for 2025 and 2030 under the NDCs. The ‘lower’ and ‘higher’ end of the range span the four cases of full implementation and ‘unconditional-only’ NDC quantification, with their respective ranges (e.g. Australia’s 26%-28% target). Historical data based on country-level data in NDCs and PRIMAP-hist CR. Aggregation of CO2, CH4, N2O, HFCs, PFCs, and SF6 emissions performed in GWP-100 AR6 metrics. Note that the common reporting guidelines under the Paris Agreement’s transparency framework currently chose GWP-100 AR5 metric as default (Decision 5/CMA.3, UNFCCC, 2021).</p> <p><strong>Supplementary Table S2 (supplementary_table-s2.csv) </strong>An estimate of potentially additional methane reductions if the Global Methane Pledge were implemented on a country-by-country level with a 30% reduction. Of the 104 countries signed up by 2 Nov 2018, 101 are here considered (with no sufficient methane data availability for Niue, Palau and Federal State of Micronesia).<strong> </strong></p> <p><strong>Scenario Timeseries (timeseries_12Nov2021a_CR.csv). </strong>Surface Temperature and Kyoto Greenhouse Gas emissions excluding CO2 AFOLU for NDC scenarios presented in the study.<strong> </strong></p> <p><strong>IEA Scenario Timeseries (timeseries_IEA.csv). </strong>Surface Temperature and Kyoto Greenhouse Gas emissions excluding CO2 AFOLU for the IEA scenarios presented in the study.</p> <p>Contact Jared Lewis <jared.lewis@climate-resource.com> for additional data about the target scenarios.</p>
Preliminary Coastal Grain Size Portal (C-GRASP) dataset. Version 1, January 2022
<p>Provisional database: The data you have secured from the U.S. Geological Survey (USGS) database identified as <em>Preliminary Coastal Grain Size Portal (C-GRASP) dataset. Version 1, January 2022</em> have not received USGS approval and as such are provisional and subject to revision. The data are released on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from its authorized or unauthorized use.</p> <p>Version 1 (January 2022) of the the Coastal Grain Size Portal (C-GRASP) database. This is a preliminary internal deliverable for the National Oceanography Partnership Program (NOPP) Task 1 / USGS Gesch team and project partners only.</p> <p>The primary purpose of this Provisional data release is to provide National Oceanography Partnership Program (NOPP) project partners with programmatic access to this preliminary version of the Coastal Grain Size Portal (C-GRASP) database for internal project use. These data are preliminary or provisional and are subject to revision. They are being provided to meet the need for timely best science. The data have not received final approval by the U.S. Geological Survey (USGS) and are provided on the condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from the authorized or unauthorized use of the data.</p> <p>This preliminary data release contains various files that list grain size information collated from secondary data already in the public domain, in the form of public datasets, or in published literature.</p> <p>Where possible, we have indicated the source, location, and sampling methods used to obtain these data. Where not possible to establish these facts, those fields have been left empty.</p> <p>More information on our methods, data sources, and data processing and analysis codes are found on our <a href="https://github.com/C-GRASP">github page </a></p> <p>The dataset consists of one zipped file, Source_Files.zip, and 4 comma separated value (csv) files</p> <ol> <li>dataset_10kmcoast.csv- This is all data that is found to be within 10km of the Natural Earth coastline polyline</li> <li>Data_EstimatedOnshore.csv- This is all the data from dataset_10kmcoast.csv that lies within the Natural Earth United States Polygon</li> <li>Data_VerifiedOnshore.csv- This is all data that was able to be verified onshore from either sampling method, note, or location type data</li> <li>Data_Post2012_VerifiedOnshore.csv- This is all the data from Data_VerifiedOnshore.csv that is after 2012</li> </ol> <p>The files each have the following fields (no data is blank):</p> <p>'ID': row ID integer</p> <p>'Sample_ID': identifier to raw data source</p> <p>'Sample_Type_Code': code of sample id</p> <p>'Project': raw datasource project identifier</p> <p>'dataset': raw dataset major identifier</p> <p>'Date': date, where specified, and to whatever precision that is specified</p> <p>'Location_Type': where specified, code indicating type of location information</p> <p>'latitude': latitude in decimal degrees</p> <p>'longitude': longitude in decimal degrees</p> <p>'Contact': where specified, raw data originator</p> <p>'num_orig_dists': number of unique grain size distributions</p> <p>'Measured_Distributions': number iof measured grain size distributions</p> <p>'Grainsize': grain size is sometimes reported without specification</p> <p>'Mean', mean grain size in mm</p> <p>'Median', median grain size in mm</p> <p>'Wentworth', wentworth name (one of ['Clay', 'CoarseSand', 'CoarseSilt', 'Cobble', 'FineSand', 'FineSilt', 'Granule', 'MediumSand', 'MediumSilt', 'Pebble', 'VeryCoarseSand', 'VeryFineSand', 'VeryFineSilt'])</p> <p>'Kurtosis', kurtosis value (non-dim)</p> <p>'Kurtosis_Class', kurtosis category</p> <p>'Skewness', skewness value (non-dim)</p> <p>'Skewness_Class', skewness category</p> <p>'Std', standard deviation of grain sizes </p> <p>'Sorting', sorting category</p> <p>'d5', grain size distribution 5th percentile</p> <p>'d10', grain size distribution 10th percentile</p> <p>'d16', grain size distribution 16th percentile</p> <p>'d25', grain size distribution 25th percentile</p> <p>'d30', grain size distribution 30th percentile</p> <p>'d50', grain size distribution 50th percentile</p> <p>'d65', grain size distribution 65th percentile</p> <p>'d75', grain size distribution 75th percentile</p> <p>'d84',grain size distribution 84th percentile</p> <p>'d90', grain size distribution 90th percentile</p> <p>'d95', grain size distribution 95th percentile</p> <p>'Notes': notes - these can be informative and substantial, do not disregard</p> <p> </p> <p>Source_Files.zip contains 11 comma separated value files, namely bicms.csv boem.csv clark.csv dbseabed.csv ecstdb.csv mass.csv mcfall.csv rossi.csv sandsnap.csv sbell.csv ussb.csv, which contain raw datasets that have been collated and extracted from their native formats into csv format</p>
Documentation and digital files in support of "Aftershock regions of Aleutian–Alaska megathrust earthquakes, 1938–2021" by Carl Tape and Anthony Lomax: Parts B, C, and D
<p>These files support a manuscript to be submitted entitled "Aftershock regions of Aleutian–Alaska megathrust earthquakes, 1938-2021," by Carl Tape and Anthony Lomax. This collection contains Parts B, C, and D. A separate collection contains Part A. This research was supported by the U.S. Geological Survey (USGS), Department of the Interior, under USGS award number G19AP00050.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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.