cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.24026469.v1
title: Effects of referential structure on pronoun interpretation

description: Pronoun interpretation is guided by various factors. While most previously-investigated factors involve properties occurring before the pronoun, less attention has been paid to properties of the pronoun-containing clause. We investigate whether pronoun interpretation is influenced by the referential structure of the pronoun-containing clause (i.e. whether another referent from the preceding clause is mentioned), which contributes to discourse coherence. We report three experiments showing referential structure effects: whether subject-position pronouns are ultimately interpreted as referring to the preceding subject or object depends on whether the clause contains another pronoun (e.g. <i>she called Lisa</i> vs. <i>she called her</i>). More specifically, subject-position pronouns exhibit a stronger object preference when only one of the prior antecedents is mentioned, compared to when both are mentioned. We show that this effect is separate from effects of verb semantics and cannot be reduced to semantic or syntactic parallelism effects. Implications for models of pronoun resolution are discussed.
DOI: 10.26093/cds/aladin/2b0z-9h4
title: Mars MRO CTX HiPS

description: The Context Camera (CTX) is a camera on board the Mars Reconnaissance Orbiter (MRO) acquiring grayscale images at 6 meters per pixel scale over a swath 30 kilometers wide. CTX provides context images for the MRO HiRISE and CRISM observations, is used to monitor changes occurring on the planet, and acquires stereo pairs of selected, critical science targets.
DOI: 10.5281/zenodo.7761377
title: Új generációs intelligens központi épületenergetikai vezérlőegység integrált médiacenterrel

description: A projekt eredményeképpen egy olyan innovatív központi okos otthon vezérlő egység kerül legyártásra amely egy új szemlélet szerint egy egységes és integrált felületen fogja biztosítani az eszközt használó számára, hogy meghatározott távolságon belül elérhető okos kiegészítők vezérlését és irányítását elvégezhesse, az azok által sugárzott tartalmat (tipikusan kamerák esetén) megjelenítse egy hozzá csatlakoztatott megjelenítő segítségével, továbbá hozzáférést biztosítson a felhő tárhelyhez, és elérhetővé tegye digitális médiatartalmak fogadását, lejátszását és rögzítését. A központi egységhez legyártásra kerülnek még olyan végponti elemek amelyek valós időben képesek monitorozni a rájuk kötött elektromos tárgyak fogyasztását, ezt az információt képesek továbbítani a központi egység felé, továbbá beépített irányfény funkcióval és fényérzékelő szenzorral rendelkeznek. Az összegyűjtött adatokat a központi egység eltovábbítja egy biztonságos kapcsolaton keresztül a felhasználó egyedi saját felhő tárhelyére, amely teljes egészében izolálva van minden más fióktól, majd a felhasználó, igénye szerint ezeket az adatokat lekérdezheti, exportálhatja későbbi elemzés vagy kalkuláció céljából. Mindezen funkciókat és a vezérlőeszköz valamint a végponti elemek működését akár munkaállomáson található webböngésző segítségével akár okos eszközre (telefon, tablet) fejlesztett applikáció segítségével is irányíthatja.
DOI: 10.5281/zenodo.10286628
title: Inovis: Instant Novel-view Synthesis

description: Abstract: Novel-view synthesis is an ill-posed problem in that it requires inference of previously unseen information. Recently, reviving the traditional field of image-based rendering, neural methods proved particularly suitable for this interpolation/extrapolation task; however, they often require a-priori scene-completeness or costly preprocessing steps and generally suffer from long (scene-specific) training times. Our work draws from recent progress in neural spatio-temporal supersampling to enhance a state-of-the-art neural renderer's ability to infer novel-view information at inference time. We adapt a supersampling architecture~\cite{xiao2020neural}, which resamples previously rendered frames, to instead recombine nearby camera images in a multi-view dataset. These input frames are warped into a joint target frame, guided by the most recent (point-based) scene representation, followed by neural interpolation. The resulting architecture gains sufficient robustness to significantly improve transferability to previously unseen datasets. In particular, this enables novel applications for neural rendering where dynamically streamed content is directly incorporated in a (neural) image-based reconstruction of a scene. As we will show, our method reaches state-of-the-art performance when compared to previous works that rely on static and sufficiently densely sampled scenes; in addition, we demonstrate our system's particular suitability for dynamically streamed content, where our approach is able to produce high-fidelity novel-view synthesis even with significantly fewer available frames than competing neural methods.
description: Source Code: https://github.com/mharrer97/inovis
Website: https://reality.tf.fau.de/publications/2023/harrerfranke2023inovis/
DOI: 10.5281/zenodo.10286627
title: Inovis: Instant Novel-view Synthesis

description: Abstract: Novel-view synthesis is an ill-posed problem in that it requires inference of previously unseen information. Recently, reviving the traditional field of image-based rendering, neural methods proved particularly suitable for this interpolation/extrapolation task; however, they often require a-priori scene-completeness or costly preprocessing steps and generally suffer from long (scene-specific) training times. Our work draws from recent progress in neural spatio-temporal supersampling to enhance a state-of-the-art neural renderer's ability to infer novel-view information at inference time. We adapt a supersampling architecture~\cite{xiao2020neural}, which resamples previously rendered frames, to instead recombine nearby camera images in a multi-view dataset. These input frames are warped into a joint target frame, guided by the most recent (point-based) scene representation, followed by neural interpolation. The resulting architecture gains sufficient robustness to significantly improve transferability to previously unseen datasets. In particular, this enables novel applications for neural rendering where dynamically streamed content is directly incorporated in a (neural) image-based reconstruction of a scene. As we will show, our method reaches state-of-the-art performance when compared to previous works that rely on static and sufficiently densely sampled scenes; in addition, we demonstrate our system's particular suitability for dynamically streamed content, where our approach is able to produce high-fidelity novel-view synthesis even with significantly fewer available frames than competing neural methods.
description: Source Code: https://github.com/mharrer97/inovis
Website: https://reality.tf.fau.de/publications/2023/harrerfranke2023inovis/
DOI: 10.26138/sxs:bbh:0256v1.5
title: Binary black-hole simulation SXS:BBH:0256
DOI: 10.26138/sxs:bbh:0257v1.1
title: Binary black-hole simulation SXS:BBH:0257
DOI: 10.26138/sxs:bbh:0257v1.2
title: Binary black-hole simulation SXS:BBH:0257
DOI: 10.26138/sxs:bbh:0257v1.3
title: Binary black-hole simulation SXS:BBH:0257
DOI: 10.26138/sxs:bbh:0257v1.4
title: Binary black-hole simulation SXS:BBH:0257
DOI: 10.26138/sxs:bbh:0257v1.5
title: Binary black-hole simulation SXS:BBH:0257
DOI: 10.26138/sxs:bbh:0258v1.1
title: Binary black-hole simulation SXS:BBH:0258
DOI: 10.26138/sxs:bbh:0258v1.2
title: Binary black-hole simulation SXS:BBH:0258
DOI: 10.26138/sxs:bbh:0258v1.3
title: Binary black-hole simulation SXS:BBH:0258
DOI: 10.26138/sxs:bbh:0258v1.4
title: Binary black-hole simulation SXS:BBH:0258
DOI: 10.26138/sxs:bbh:0258v1.5
title: Binary black-hole simulation SXS:BBH:0258
DOI: 10.26138/sxs:bbh:0259v1.1
title: Binary black-hole simulation SXS:BBH:0259
DOI: 10.26138/sxs:bbh:0259v1.2
title: Binary black-hole simulation SXS:BBH:0259
DOI: 10.26138/sxs:bbh:0259v1.3
title: Binary black-hole simulation SXS:BBH:0259
DOI: 10.26138/sxs:bbh:0259v1.4
title: Binary black-hole simulation SXS:BBH:0259
DOI: 10.26138/sxs:bbh:0259v1.5
title: Binary black-hole simulation SXS:BBH:0259
DOI: 10.26138/sxs:bbh:0260v1.1
title: Binary black-hole simulation SXS:BBH:0260
DOI: 10.26138/sxs:bbh:0260v1.2
title: Binary black-hole simulation SXS:BBH:0260
DOI: 10.26138/sxs:bbh:0260v1.3
title: Binary black-hole simulation SXS:BBH:0260
DOI: 10.26138/sxs:bbh:0260v1.4
title: Binary black-hole simulation SXS:BBH:0260
DOI: 10.26138/sxs:bbh:0260v1.5
title: Binary black-hole simulation SXS:BBH:0260
DOI: 10.26138/sxs:bbh:0261v1.1
title: Binary black-hole simulation SXS:BBH:0261
DOI: 10.26138/sxs:bbh:0261v1.2
title: Binary black-hole simulation SXS:BBH:0261
DOI: 10.26138/sxs:bbh:0261v1.3
title: Binary black-hole simulation SXS:BBH:0261
DOI: 10.26138/sxs:bbh:0261v1.4
title: Binary black-hole simulation SXS:BBH:0261
DOI: 10.26138/sxs:bbh:0261v1.5
title: Binary black-hole simulation SXS:BBH:0261
DOI: 10.26138/sxs:bbh:0262v1.1
title: Binary black-hole simulation SXS:BBH:0262
DOI: 10.26138/sxs:bbh:0262v1.2
title: Binary black-hole simulation SXS:BBH:0262
DOI: 10.26138/sxs:bbh:0262v1.3
title: Binary black-hole simulation SXS:BBH:0262
DOI: 10.26138/sxs:bbh:0262v1.4
title: Binary black-hole simulation SXS:BBH:0262
DOI: 10.26138/sxs:bbh:0262v1.5
title: Binary black-hole simulation SXS:BBH:0262
DOI: 10.26138/sxs:bbh:0263v1.1
title: Binary black-hole simulation SXS:BBH:0263
DOI: 10.26138/sxs:bbh:0263v1.2
title: Binary black-hole simulation SXS:BBH:0263
DOI: 10.26138/sxs:bbh:0263v1.3
title: Binary black-hole simulation SXS:BBH:0263
DOI: 10.26138/sxs:bbh:0263v1.4
title: Binary black-hole simulation SXS:BBH:0263
DOI: 10.26138/sxs:bbh:0263v1.5
title: Binary black-hole simulation SXS:BBH:0263
DOI: 10.26138/sxs:bbh:0264v1.1
title: Binary black-hole simulation SXS:BBH:0264
DOI: 10.26138/sxs:bbh:0264v1.2
title: Binary black-hole simulation SXS:BBH:0264
DOI: 10.26138/sxs:bbh:0264v1.3
title: Binary black-hole simulation SXS:BBH:0264
DOI: 10.26138/sxs:bbh:0264v1.4
title: Binary black-hole simulation SXS:BBH:0264
DOI: 10.26138/sxs:bbh:0264v1.5
title: Binary black-hole simulation SXS:BBH:0264
DOI: 10.26138/sxs:bbh:0265v1.1
title: Binary black-hole simulation SXS:BBH:0265
DOI: 10.26138/sxs:bbh:0265v1.2
title: Binary black-hole simulation SXS:BBH:0265
DOI: 10.26138/sxs:bbh:0265v1.3
title: Binary black-hole simulation SXS:BBH:0265
DOI: 10.26138/sxs:bbh:0265v1.4
title: Binary black-hole simulation SXS:BBH:0265
DOI: 10.26138/sxs:bbh:0265v1.5
title: Binary black-hole simulation SXS:BBH:0265
DOI: 10.26138/sxs:bbh:0266v1.1
title: Binary black-hole simulation SXS:BBH:0266
DOI: 10.26138/sxs:bbh:0266v1.2
title: Binary black-hole simulation SXS:BBH:0266
DOI: 10.26138/sxs:bbh:0266v1.3
title: Binary black-hole simulation SXS:BBH:0266
DOI: 10.5281/zenodo.10214706
title: Tuning spinaron and Kondo resonances via quantum confinement

description: This repository contains raw data files for STM topography and tunneling spectra on Co and H2Pc in quantum corrals on Ag(111). Analysis scripts used to extract Fano resonance parameters can be found at https://github.com/abekipnis/Kondo-Aalto.
Abstract of the paper: Controlling zero bias anomalies in magnetic atoms provides a promising strategy to engineer tunable quantum many-body excitations. Here we show how two different quantum impurities featuring spinaron and Kondo excitations can be controlled via quantum confinement engineering by using circular quantum corrals on a Ag(111) surface. In corrals built from both Ag and Co adatoms, the width of the zero bias anomaly in the central Co adatom oscillates as a function of corral radius with a period of half of the Ag(111) surface state wavelength. Parameters extracted for Co/Ag(111) show only small differences in extracted spinaron zero-bias anomaly between corral walls built from Ag or Co adatoms. In quantum corrals occupied with metal-free phthalocyanine, a paradigmatic S=1/2 Kondo system, we observe notable changes in the zero bias anomaly lineshape as a function of corral radius. Our results offer insight into many-body Kondo and spinaron resonances where the electronic density is controlled by confinement engineering.
DOI: 10.5281/zenodo.10214707
title: Tuning spinaron and Kondo resonances via quantum confinement

description: This repository contains raw data files for STM topography and tunneling spectra on Co and H2Pc in quantum corrals on Ag(111). Analysis scripts used to extract Fano resonance parameters can be found at https://github.com/abekipnis/Kondo-Aalto.
Abstract of the paper: Controlling zero bias anomalies in magnetic atoms provides a promising strategy to engineer tunable quantum many-body excitations. Here we show how two different quantum impurities featuring spinaron and Kondo excitations can be controlled via quantum confinement engineering by using circular quantum corrals on a Ag(111) surface. In corrals built from both Ag and Co adatoms, the width of the zero bias anomaly in the central Co adatom oscillates as a function of corral radius with a period of half of the Ag(111) surface state wavelength. Parameters extracted for Co/Ag(111) show only small differences in extracted spinaron zero-bias anomaly between corral walls built from Ag or Co adatoms. In quantum corrals occupied with metal-free phthalocyanine, a paradigmatic S=1/2 Kondo system, we observe notable changes in the zero bias anomaly lineshape as a function of corral radius. Our results offer insight into many-body Kondo and spinaron resonances where the electronic density is controlled by confinement engineering.
DOI: 10.26138/sxs:bbh:0266v1.4
title: Binary black-hole simulation SXS:BBH:0266
DOI: 10.26138/sxs:bbh:0266v1.5
title: Binary black-hole simulation SXS:BBH:0266
DOI: 10.26138/sxs:bbh:0267v1.1
title: Binary black-hole simulation SXS:BBH:0267
DOI: 10.26138/sxs:bbh:0267v1.2
title: Binary black-hole simulation SXS:BBH:0267
DOI: 10.26138/sxs:bbh:0267v1.3
title: Binary black-hole simulation SXS:BBH:0267
DOI: 10.26138/sxs:bbh:0267v1.4
title: Binary black-hole simulation SXS:BBH:0267
DOI: 10.26138/sxs:bbh:0267v1.5
title: Binary black-hole simulation SXS:BBH:0267
DOI: 10.21985/n2-r2je-fv08
title: Neo4j mtDNA grass file
DOI: 10.26138/sxs:bbh:0268v1.1
title: Binary black-hole simulation SXS:BBH:0268
DOI: 10.26138/sxs:bbh:0268v1.2
title: Binary black-hole simulation SXS:BBH:0268
DOI: 10.26138/sxs:bbh:0268v1.3
title: Binary black-hole simulation SXS:BBH:0268
DOI: 10.26138/sxs:bbh:0268v1.4
title: Binary black-hole simulation SXS:BBH:0268
DOI: 10.26138/sxs:bbh:0268v1.5
title: Binary black-hole simulation SXS:BBH:0268
DOI: 10.26138/sxs:bbh:0269v1.1
title: Binary black-hole simulation SXS:BBH:0269
DOI: 10.26138/sxs:bbh:0269v1.2
title: Binary black-hole simulation SXS:BBH:0269
DOI: 10.26138/sxs:bbh:0269v1.3
title: Binary black-hole simulation SXS:BBH:0269
DOI: 10.26138/sxs:bbh:0269v1.4
title: Binary black-hole simulation SXS:BBH:0269
DOI: 10.26138/sxs:bbh:0269v1.5
title: Binary black-hole simulation SXS:BBH:0269
DOI: 10.26138/sxs:bbh:0270v1.1
title: Binary black-hole simulation SXS:BBH:0270
DOI: 10.26138/sxs:bbh:0270v1.2
title: Binary black-hole simulation SXS:BBH:0270
DOI: 10.26138/sxs:bbh:0270v1.3
title: Binary black-hole simulation SXS:BBH:0270
DOI: 10.26138/sxs:bbh:0270v1.4
title: Binary black-hole simulation SXS:BBH:0270
DOI: 10.26138/sxs:bbh:0270v1.5
title: Binary black-hole simulation SXS:BBH:0270
DOI: 10.26138/sxs:bbh:0271v1.1
title: Binary black-hole simulation SXS:BBH:0271
DOI: 10.26138/sxs:bbh:0271v1.2
title: Binary black-hole simulation SXS:BBH:0271
DOI: 10.26138/sxs:bbh:0271v1.3
title: Binary black-hole simulation SXS:BBH:0271
DOI: 10.26138/sxs:bbh:0271v1.4
title: Binary black-hole simulation SXS:BBH:0271
DOI: 10.26138/sxs:bbh:0271v1.5
title: Binary black-hole simulation SXS:BBH:0271
DOI: 10.26138/sxs:bbh:0272v1.1
title: Binary black-hole simulation SXS:BBH:0272
DOI: 10.26138/sxs:bbh:0272v1.2
title: Binary black-hole simulation SXS:BBH:0272
DOI: 10.26138/sxs:bbh:0272v1.3
title: Binary black-hole simulation SXS:BBH:0272
DOI: 10.26138/sxs:bbh:0272v1.4
title: Binary black-hole simulation SXS:BBH:0272
DOI: 10.26138/sxs:bbh:0272v1.5
title: Binary black-hole simulation SXS:BBH:0272
DOI: 10.26138/sxs:bbh:0273v1.1
title: Binary black-hole simulation SXS:BBH:0273
DOI: 10.26138/sxs:bbh:0273v1.2
title: Binary black-hole simulation SXS:BBH:0273
DOI: 10.26138/sxs:bbh:0273v1.3
title: Binary black-hole simulation SXS:BBH:0273
DOI: 10.26138/sxs:bbh:0273v1.4
title: Binary black-hole simulation SXS:BBH:0273
DOI: 10.26138/sxs:bbh:0273v1.5
title: Binary black-hole simulation SXS:BBH:0273
DOI: 10.26138/sxs:bbh:0274v1.1
title: Binary black-hole simulation SXS:BBH:0274
DOI: 10.26138/sxs:bbh:0274v1.2
title: Binary black-hole simulation SXS:BBH:0274
DOI: 10.26138/sxs:bbh:0274v1.3
title: Binary black-hole simulation SXS:BBH:0274
DOI: 10.26138/sxs:bbh:0274v1.4
title: Binary black-hole simulation SXS:BBH:0274
DOI: 10.26138/sxs:bbh:0274v1.5
title: Binary black-hole simulation SXS:BBH:0274
DOI: 10.26138/sxs:bbh:0275v1.1
title: Binary black-hole simulation SXS:BBH:0275
DOI: 10.26138/sxs:bbh:0275v1.2
title: Binary black-hole simulation SXS:BBH:0275
DOI: 10.26138/sxs:bbh:0275v1.3
title: Binary black-hole simulation SXS:BBH:0275
DOI: 10.26138/sxs:bbh:0275v1.4
title: Binary black-hole simulation SXS:BBH:0275
DOI: 10.26138/sxs:bbh:0275v1.5
title: Binary black-hole simulation SXS:BBH:0275
DOI: 10.26138/sxs:bbh:0276v1.1
title: Binary black-hole simulation SXS:BBH:0276
DOI: 10.26138/sxs:bbh:0276v1.2
title: Binary black-hole simulation SXS:BBH:0276
DOI: 10.26138/sxs:bbh:0276v1.3
title: Binary black-hole simulation SXS:BBH:0276
DOI: 10.26138/sxs:bbh:0276v1.4
title: Binary black-hole simulation SXS:BBH:0276
DOI: 10.26138/sxs:bbh:0276v1.5
title: Binary black-hole simulation SXS:BBH:0276
DOI: 10.26138/sxs:bbh:0277v1.1
title: Binary black-hole simulation SXS:BBH:0277
DOI: 10.26138/sxs:bbh:0277v1.2
title: Binary black-hole simulation SXS:BBH:0277
DOI: 10.26138/sxs:bbh:0277v1.3
title: Binary black-hole simulation SXS:BBH:0277
DOI: 10.26138/sxs:bbh:0277v1.4
title: Binary black-hole simulation SXS:BBH:0277
DOI: 10.26138/sxs:bbh:0277v1.5
title: Binary black-hole simulation SXS:BBH:0277
DOI: 10.26138/sxs:bbh:0278v1.1
title: Binary black-hole simulation SXS:BBH:0278
DOI: 10.26138/sxs:bbh:0278v1.2
title: Binary black-hole simulation SXS:BBH:0278
DOI: 10.26138/sxs:bbh:0278v1.3
title: Binary black-hole simulation SXS:BBH:0278
DOI: 10.26138/sxs:bbh:0278v1.4
title: Binary black-hole simulation SXS:BBH:0278
DOI: 10.26138/sxs:bbh:0278v1.5
title: Binary black-hole simulation SXS:BBH:0278
DOI: 10.26138/sxs:bbh:0279v1.1
title: Binary black-hole simulation SXS:BBH:0279
DOI: 10.26138/sxs:bbh:0279v1.2
title: Binary black-hole simulation SXS:BBH:0279
DOI: 10.26138/sxs:bbh:0279v1.3
title: Binary black-hole simulation SXS:BBH:0279
DOI: 10.26138/sxs:bbh:0279v1.4
title: Binary black-hole simulation SXS:BBH:0279
DOI: 10.26138/sxs:bbh:0279v1.5
title: Binary black-hole simulation SXS:BBH:0279
DOI: 10.26138/sxs:bbh:0280v1.1
title: Binary black-hole simulation SXS:BBH:0280
DOI: 10.26138/sxs:bbh:0280v1.2
title: Binary black-hole simulation SXS:BBH:0280
DOI: 10.26138/sxs:bbh:0280v1.3
title: Binary black-hole simulation SXS:BBH:0280
DOI: 10.26138/sxs:bbh:0280v1.4
title: Binary black-hole simulation SXS:BBH:0280
DOI: 10.26138/sxs:bbh:0280v1.5
title: Binary black-hole simulation SXS:BBH:0280
DOI: 10.26138/sxs:bbh:0281v1.1
title: Binary black-hole simulation SXS:BBH:0281
DOI: 10.26138/sxs:bbh:0281v1.2
title: Binary black-hole simulation SXS:BBH:0281
DOI: 10.26138/sxs:bbh:0281v1.3
title: Binary black-hole simulation SXS:BBH:0281
DOI: 10.26138/sxs:bbh:0281v1.4
title: Binary black-hole simulation SXS:BBH:0281
DOI: 10.26138/sxs:bbh:0281v1.5
title: Binary black-hole simulation SXS:BBH:0281
DOI: 10.26138/sxs:bbh:0282v1.1
title: Binary black-hole simulation SXS:BBH:0282
DOI: 10.26138/sxs:bbh:0282v1.2
title: Binary black-hole simulation SXS:BBH:0282
DOI: 10.26138/sxs:bbh:0282v1.3
title: Binary black-hole simulation SXS:BBH:0282
DOI: 10.26138/sxs:bbh:0282v1.4
title: Binary black-hole simulation SXS:BBH:0282
DOI: 10.26138/sxs:bbh:0282v1.5
title: Binary black-hole simulation SXS:BBH:0282
DOI: 10.26138/sxs:bbh:0283v1.1
title: Binary black-hole simulation SXS:BBH:0283
DOI: 10.26138/sxs:bbh:0283v1.2
title: Binary black-hole simulation SXS:BBH:0283
DOI: 10.26138/sxs:bbh:0283v1.3
title: Binary black-hole simulation SXS:BBH:0283
DOI: 10.26138/sxs:bbh:0283v1.4
title: Binary black-hole simulation SXS:BBH:0283
DOI: 10.26138/sxs:bbh:0283v1.5
title: Binary black-hole simulation SXS:BBH:0283
DOI: 10.26138/sxs:bbh:0284v1.1
title: Binary black-hole simulation SXS:BBH:0284
DOI: 10.26138/sxs:bbh:0284v1.2
title: Binary black-hole simulation SXS:BBH:0284
DOI: 10.26138/sxs:bbh:0284v1.3
title: Binary black-hole simulation SXS:BBH:0284
DOI: 10.26138/sxs:bbh:0284v1.4
title: Binary black-hole simulation SXS:BBH:0284
DOI: 10.26138/sxs:bbh:0284v1.5
title: Binary black-hole simulation SXS:BBH:0284
DOI: 10.26138/sxs:bbh:0285v1.1
title: Binary black-hole simulation SXS:BBH:0285
DOI: 10.26138/sxs:bbh:0285v1.2
title: Binary black-hole simulation SXS:BBH:0285
DOI: 10.26138/sxs:bbh:0285v1.3
title: Binary black-hole simulation SXS:BBH:0285
DOI: 10.26138/sxs:bbh:0285v1.4
title: Binary black-hole simulation SXS:BBH:0285
DOI: 10.26138/sxs:bbh:0285v1.5
title: Binary black-hole simulation SXS:BBH:0285
DOI: 10.26138/sxs:bbh:0286v1.1
title: Binary black-hole simulation SXS:BBH:0286
DOI: 10.26138/sxs:bbh:0286v1.2
title: Binary black-hole simulation SXS:BBH:0286
DOI: 10.57979/l2erid/eo9daa
title: Mg_rates_auger.txt

description: :unav
DOI: 10.26138/sxs:bbh:0286v1.3
title: Binary black-hole simulation SXS:BBH:0286
DOI: 10.57979/l2erid/wbmprq
title: Mg_rates_satellites.txt

description: :unav
DOI: 10.26138/sxs:bbh:0286v1.4
title: Binary black-hole simulation SXS:BBH:0286
DOI: 10.57979/l2erid/porkuc
title: Mg_rates_radiative.txt

description: :unav
DOI: 10.26138/sxs:bbh:0286v1.5
title: Binary black-hole simulation SXS:BBH:0286
DOI: 10.57979/l2erid/70pa7i
title: Mg_spectrum_auger.txt

description: :unav
DOI: 10.57979/l2erid/ema3wq
title: Mg_spectrum_diagram.txt

description: :unav
DOI: 10.26138/sxs:bbh:0287v1.1
title: Binary black-hole simulation SXS:BBH:0287
DOI: 10.57979/l2erid/8vtonz
title: Mg_rates_sums_sat.txt

description: :unav
DOI: 10.26138/sxs:bbh:0287v1.2
title: Binary black-hole simulation SXS:BBH:0287
DOI: 10.57979/l2erid/l7umv0
title: Mg_rates_sums.txt

description: :unav
DOI: 10.26138/sxs:bbh:0287v1.3
title: Binary black-hole simulation SXS:BBH:0287
DOI: 10.57979/l2erid/e4uvlu
title: Mg_spectrum_sat.txt

description: :unav
DOI: 10.26138/sxs:bbh:0287v1.4
title: Binary black-hole simulation SXS:BBH:0287
DOI: 10.57979/l2erid/tnb1zy
title: readme.pdf

description: Readme file fot the files on this dataset.
DOI: 10.26138/sxs:bbh:0287v1.5
title: Binary black-hole simulation SXS:BBH:0287
DOI: 10.57979/l2erid
title: Magnesium

description: Magnesium
DOI: 10.26138/sxs:bbh:0288v1.1
title: Binary black-hole simulation SXS:BBH:0288
DOI: 10.26138/sxs:bbh:0288v1.2
title: Binary black-hole simulation SXS:BBH:0288
DOI: 10.26138/sxs:bbh:0288v1.3
title: Binary black-hole simulation SXS:BBH:0288
DOI: 10.26138/sxs:bbh:0288v1.4
title: Binary black-hole simulation SXS:BBH:0288
DOI: 10.26138/sxs:bbh:0288v1.5
title: Binary black-hole simulation SXS:BBH:0288
DOI: 10.26138/sxs:bbh:0289v1.1
title: Binary black-hole simulation SXS:BBH:0289
DOI: 10.26138/sxs:bbh:0289v1.2
title: Binary black-hole simulation SXS:BBH:0289
DOI: 10.26138/sxs:bbh:0289v1.3
title: Binary black-hole simulation SXS:BBH:0289
DOI: 10.26138/sxs:bbh:0289v1.4
title: Binary black-hole simulation SXS:BBH:0289
DOI: 10.26138/sxs:bbh:0289v1.5
title: Binary black-hole simulation SXS:BBH:0289
DOI: 10.26138/sxs:bbh:0290v1.1
title: Binary black-hole simulation SXS:BBH:0290
DOI: 10.26138/sxs:bbh:0290v1.2
title: Binary black-hole simulation SXS:BBH:0290
DOI: 10.26138/sxs:bbh:0290v1.3
title: Binary black-hole simulation SXS:BBH:0290