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ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2023
License: CC BY
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2022
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2022
License: CC BY
Data sources: ZENODO
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Daset from the paper "Compact object mergers: exploring uncertainties from stellar and binary evolution with SEVN"

Authors: Iorio; Costa; Mapelli; Spera; Trani; Escobar; Sgalletta; +5 Authors

Daset from the paper "Compact object mergers: exploring uncertainties from stellar and binary evolution with SEVN"

Abstract

This repository contains the dataset produced by the population-synthesis code SEVN for the paper: "Compact object mergers: exploring uncertainties from stellar and binary evolution with SEVN" In this paper, we exploit the SEVN code (publicly available at https://gitlab.com/sevncodes/sevn) to analyse the formation and properties of binary compact objects. The repository also includes the initial condistions used as input and the SEVN version used to run the simulations. #Content The repository contains the following folders: - data_from_simulations: the folder contains all the data produced by the simulations and used in the Iorio+22 paper - InitialConditions: the folder contains all the intial conditions (and the code to generate them) that have been used for the Iorio+22 paper - SEVN_iorio22: this folder contains the version of the SEVN code that has been used to run the simulations in the Iorio+22 paper Each folder contains a specific README with additional information #Contatcts giuliano.iorio.astro@gmail.com

We acknowledge financial support from the European Research Council for the ERC Consolidator grant DEMOBLACK under contract no. 770017 We also acknowledge financial support upport from PRIN-MIUR 2020 METE, under contract no. 2020KB33TP. T

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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OpenAIRE UsageCountsViews provided by UsageCounts
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