cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.24760665.v1
title: rawdata.7z

description: Instruction: More and more attention has been paid to the role of synonymous substitution in evolution, in which codon usage preference can affect gene expression distribution and protein structure and function. Vesicular glutamate transporter (VGLUT) consists of three isoforms, among which VGLUT3 is significantly different from other VGLUTs in functional importance, expression level, and distribution range, whose reason is still unclear. This study sought to analyze the role of codon preference in VGLUT differentiation.Method: To conduct an evolutionary analysis of the three VGLUTs, this paper uses bioinformatics research methods to analyze the coding sequences of the three VGLUTs in different species and compare the codon usage patterns. Furthermore, the differences among the three VGLUTs were analyzed by combining functional importance, expression level, distribution range, gene structure, protein relationship network, expression at specific developmental stages, and evolutionary relationship tree, and the influence of codon usage pattern was explored.Result: The results showed that the greater the VGLUT codon usage preference was, the less important the gene function was, the lower the expression specificity was, and the longer the time needed for development to maturity was. VGLUT3 has the largest interspecies evolutionary distance, VGLUT2 has the smallest interspecies evolutionary distance, VGLUT3 has a large evolutionary distance with the rest of the VGLUT, and the degree of codon usage preference of VGLUT is positively correlated with the rate of protein evolution.Conclusion: Codon usage preference is a potential factor affecting VGLUT developmental expression and protein evolution.
DOI: 10.21947/1959697
title: WFIP3 - SHIP site - NREL Profiling Lidar (Windcube v2.1) / Reviewed data

description: This dataset contains reviewed data from the profiling lidar (Windcube v2.1) deployed at AWAKEN site B. The reviewed files herein are based on the lidar's RTD files (i.e., the real-time raw data files at near 1 Hz resolution).
DOI: 10.21947/1972653
title: LLNL Wind Cube V2 Lidar / Raw Data

description: This dataset contains vertical profiling lidar datafrom AWAKEN site A1. This is the WindCube v2 lidar. The Windcube v2 replaced the ZX300 at this location at the end of March 2023.
DOI: 10.25984/1988650
title: Dataset for Evaluating the Impact of Wildfire Smoke on Solar Photovoltaic Production

description: This dataset was used to support machine learning model development for wildfire impacts on utility-scale solar in the United States. The dataset includes information regarding energy generation, PM2.5, clearness index, temperature, wind speed, precipitation, and site size.
DOI: 10.7910/dvn/rsob70
title: Replication Data for:Is Larger Scale in Manufacturing Linked to Greater Carbon Efficiency?

description: 29 industries in the Chinese manufacturing sector from 2012 to 2021.
DOI: 10.5281/zenodo.10278913
title: Preeclampsia dataset
DOI: 10.5281/zenodo.10278914
title: Preeclampsia dataset
DOI: 10.25982/161632.357/2228677
title: KBase Narrative - Complete Genome Sequence of Acidovorax temperans strain LMJ

description: We have isolated a new strain of Acidovorax temperans strain LMJ (hereafter called strain LMJ) from a contaminated Tris-Acetate-Phosphate (TAP) medium plate of a green micro-alga Chlamydomonas reinhardtii strain LMJ.SG0182 (a Chlamydomonas Library project (CLiP) strain). We sequenced the whole genome of the strain LMJ using the PacBio Sequel II technology and have submitted it to NCBI along with the SRA and PacBio methylation motif data. We present the whole genome sequence of this strain that offer insights into its coding and non-coding genes and its nearest taxonomic neighbors.
DOI: 10.21947/2228704
title: AWAKEN Virtual Tower / Derived Data

description: Virtual tower data consist of profiles of wind speed and direction at a number of locations near the A1 sites. These profiles were computed from dual-Doppler analysis of two Halo XR+ scanning Doppler lidars located at sites A5 and A7. Both lidars performed shallow RHI scans in the general direction of the A1 site. Scan azimuths were periodically adjusted to sample different locations. This resulted in a total of 14 unique tower locations over 5 periods between 12 November 2022 and 17 October 2023.
DOI: 10.21947/2228705
title: AWAKEN Virtual Tower / Derived Data

description: Virtual tower data consist of profiles of wind speed and direction at a number of locations near the A1 sites. These profiles were computed from dual-Doppler analysis of two Halo XR+ scanning Doppler lidars located at sites A5 and A7. Both lidars performed shallow RHI scans in the general direction of the A1 site. Scan azimuths were periodically adjusted to sample different locations. This resulted in a total of 14 unique tower locations over 5 periods between 12 November 2022 and 17 October 2023.
DOI: 10.21947/2228706
title: AWAKEN Virtual Tower / Derived Data

description: Virtual tower data consist of profiles of wind speed and direction at a number of locations near the A1 sites. These profiles were computed from dual-Doppler analysis of two Halo XR+ scanning Doppler lidars located at sites A5 and A7. Both lidars performed shallow RHI scans in the general direction of the A1 site. Scan azimuths were periodically adjusted to sample different locations. This resulted in a total of 14 unique tower locations over 5 periods between 12 November 2022 and 17 October 2023.
DOI: 10.21947/2228707
title: AWAKEN Virtual Tower / Derived Data

description: Virtual tower data consist of profiles of wind speed and direction at a number of locations near the A1 sites. These profiles were computed from dual-Doppler analysis of two Halo XR+ scanning Doppler lidars located at sites A5 and A7. Both lidars performed shallow RHI scans in the general direction of the A1 site. Scan azimuths were periodically adjusted to sample different locations. This resulted in a total of 14 unique tower locations over 5 periods between 12 November 2022 and 17 October 2023.
DOI: 10.21947/2228708
title: AWAKEN Virtual Tower / Derived Data

description: Virtual tower data consist of profiles of wind speed and direction at a number of locations near the A1 sites. These profiles were computed from dual-Doppler analysis of two Halo XR+ scanning Doppler lidars located at sites A5 and A7. Both lidars performed shallow RHI scans in the general direction of the A1 site. Scan azimuths were periodically adjusted to sample different locations. This resulted in a total of 14 unique tower locations over 5 periods between 12 November 2022 and 17 October 2023.
DOI: 10.21947/2228709
title: AWAKEN Virtual Tower / Derived Data

description: Virtual tower data consist of profiles of wind speed and direction at a number of locations near the A1 sites. These profiles were computed from dual-Doppler analysis of two Halo XR+ scanning Doppler lidars located at sites A5 and A7. Both lidars performed shallow RHI scans in the general direction of the A1 site. Scan azimuths were periodically adjusted to sample different locations. This resulted in a total of 14 unique tower locations over 5 periods between 12 November 2022 and 17 October 2023.
DOI: 10.21947/2228710
title: AWAKEN Virtual Tower / Derived Data

description: Virtual tower data consist of profiles of wind speed and direction at a number of locations near the A1 sites. These profiles were computed from dual-Doppler analysis of two Halo XR+ scanning Doppler lidars located at sites A5 and A7. Both lidars performed shallow RHI scans in the general direction of the A1 site. Scan azimuths were periodically adjusted to sample different locations. This resulted in a total of 14 unique tower locations over 5 periods between 12 November 2022 and 17 October 2023.
DOI: 10.21947/2228751
title: WFIP3 - RHOD site - NREL Scanning lidar (upgraded Galion SN:60) / Processed Data

description: This dataset contains processed data from the rear-facing Halo XR lidar on G06 in the King Plains wind farm.
DOI: 10.21947/2228752
title: WFIP3 - RHOD site - NREL Scanning lidar (upgraded Galion SN:60) / Raw Data

description: Rear-facing upgraded Galion lidar on G06 in the King Plains wind farm
DOI: 10.6084/m9.figshare.21298884
title: figure3&S3

description: figure3&S3
DOI: 10.6084/m9.figshare.21298884.v1
title: figure3&S3

description: figure3&S3
DOI: 10.6084/m9.figshare.21298884.v2
title: figure3&S3

description: figure3&S3
DOI: 10.34740/kaggle/dsv/5811178
title: Classification dataset
DOI: 10.25952/pw9r-ge81
title: The effects of different sources and levels of trace minerals on poultry performance and gut health

description: The dataset contains three raw data in excel files for three experiments outlined in the thesis. The data was collected during conducting the experiments and analysing chemicals in the lab.  The data used in experiments 1 and 2 were collected at Animal House, The Centre for Animal Research and Teaching at The Unviersity of New England, Armidale, NSW.  The data used in experiment 3 was collected at UNE SMART Farm at Teaching at The Unviersity of New England, Armidale, NSW.
description: The dataset contains three raw data in excel files for three experiments outlined in the thesis. The data was collected during conducting the experiments and analysing chemicals in the lab.  The data used in experiments 1 and 2 were collected at Animal House, The Centre for Animal Research and Teaching at The Unviersity of New England, Armidale, NSW.  The data used in experiment 3 was collected at UNE SMART Farm at Teaching at The Unviersity of New England, Armidale, NSW.
DOI: 10.5281/zenodo.10215639
title: The role of bottom friction in mediating the response of the Weddell Gyre circulation to changes in surface stress and buoyancy fluxes

description: This repository contains code to reproduce the figures in the paper titled "The role of bottom friction in mediating the response of the Weddell Gyre circulation to changes in surface stress and buoyancy fluxes" published in the Journal of Physical Oceanography (DOI: https://doi.org/10.1175/JPO-D-23-0165.1), as well as the barotropic vorticity budget terms for each of simulation in the paper.
description: The Jupyter notebooks contain the code used to produce the figures in the paper, and the file barotropic-vorticity-budget-calculation.py contains code to calculate the terms in the barotropic vorticity budget, following the methodology of Khatri et al. 2023 (DOI: 10.22541/essoar.168394747.71837050/v1). 
The netcdf files contain the budget terms for each simulation within the Weddell Gyre region, where:

BVB-ctrl.nc: control simulation
BVB-drag05.nc: simulation with halved bottom friction coeffiecient (DRAG0.5x)
BVB-drag2.nc: simulation with doubled bottom friction coeffiecient (DRAG2x)
BVB-mw-down.nc: simulation with halved meltwater input (MW-)
BVB-mw-up.nc: simulation with doubled meltwater input (MW+)
BVB-wind-down.nc: simulation with 10% reduction in surface wind velocities (WIND-)
BVB-wind-up.nc: simulation with 10% increase in surface wind velocities (WIND+)
DOI: 10.5281/zenodo.10215640
title: The role of bottom friction in mediating the response of the Weddell Gyre circulation to changes in surface stress and buoyancy fluxes

description: This repository contains code to reproduce the figures in the paper titled "The role of bottom friction in mediating the response of the Weddell Gyre circulation to changes in surface stress and buoyancy fluxes" published in the Journal of Physical Oceanography (DOI: https://doi.org/10.1175/JPO-D-23-0165.1), as well as the barotropic vorticity budget terms for each of simulation in the paper.
description: The Jupyter notebooks contain the code used to produce the figures in the paper, and the file barotropic-vorticity-budget-calculation.py contains code to calculate the terms in the barotropic vorticity budget, following the methodology of Khatri et al. 2023 (DOI: 10.22541/essoar.168394747.71837050/v1). 
The netcdf files contain the budget terms for each simulation within the Weddell Gyre region, where:

BVB-ctrl.nc: control simulation
BVB-drag05.nc: simulation with halved bottom friction coeffiecient (DRAG0.5x)
BVB-drag2.nc: simulation with doubled bottom friction coeffiecient (DRAG2x)
BVB-mw-down.nc: simulation with halved meltwater input (MW-)
BVB-mw-up.nc: simulation with doubled meltwater input (MW+)
BVB-wind-down.nc: simulation with 10% reduction in surface wind velocities (WIND-)
BVB-wind-up.nc: simulation with 10% increase in surface wind velocities (WIND+)
DOI: 10.6084/m9.figshare.24719019.v1
title: An EEG Dataset of Neural Signatures in a Competitive Two-Player Game Encouraging Deceptive Behavior

description: The dataset features 12 pairs of participants in a two-player deception game, with role switching. EEG data were recorded using 30 electrodes per player, complemented by behavioral logs and EEG trigger timestamps. It includes both raw and preprocessed EEG data, catering to various research needs. This comprehensive dataset, offering perspectives of both players and observers in a deceptive context, is a robust foundation for exploring cognitive processes during interactive deceptive tasks.
DOI: 10.6084/m9.figshare.24760371.v1
title: temp_summary.csv

description: This data is associated with the project 'Behavioural responses of Australian lizards towards feral cats'. We used a field based study to test two Australian lizard species using arena trials and model treatment objects to investigate their responses towards stationary visual cues of feral cats. The datasets include behaviours performed lizards during arena trials, proportions of trial time spent engaging in each behaviour, treatment types, trial dates, along with mean and maximum ambient temperatures during trials.
DOI: 10.6084/m9.figshare.24760371
title: temp_summary.csv

description: This data is associated with the project 'Behavioural responses of Australian lizards towards feral cats'. We used a field based study to test two Australian lizard species using arena trials and model treatment objects to investigate their responses towards stationary visual cues of feral cats. The datasets include behaviours performed lizards during arena trials, proportions of trial time spent engaging in each behaviour, treatment types, trial dates, along with mean and maximum ambient temperatures during trials.
DOI: 10.6084/m9.figshare.24760365
title: lizard_df_full.csv

description: This dataset is associated with the project 'Behavioural responses of Australian lizards towards feral cats'. We used a field based study to test two Australian lizard species using arena trials and model treatment objects to investigate their responses towards stationary visual cues of feral cats. The datasets include behaviours performed lizards during arena trials, proportions of trial time spent engaging in each behaviour, treatment types, trial dates, along with mean and maximum ambient temperatures during trials.
DOI: 10.6084/m9.figshare.24760365.v1
title: lizard_df_full.csv

description: This dataset is associated with the project 'Behavioural responses of Australian lizards towards feral cats'. We used a field based study to test two Australian lizard species using arena trials and model treatment objects to investigate their responses towards stationary visual cues of feral cats. The datasets include behaviours performed lizards during arena trials, proportions of trial time spent engaging in each behaviour, treatment types, trial dates, along with mean and maximum ambient temperatures during trials.
DOI: 10.5281/zenodo.10279057
title: Data for: Next-generation even-denominator fractional quantum Hall states of interacting composite fermions

description: This is the repository of the original data for our research article "Next-generation even-denominator fractional quantum Hall states of interacting composite fermions"
DOI: 10.5281/zenodo.10279058
title: Data for: Next-generation even-denominator fractional quantum Hall states of interacting composite fermions

description: This is the repository of the original data for our research article "Next-generation even-denominator fractional quantum Hall states of interacting composite fermions"
DOI: 10.26138/sxs:bbh:0001v1.1
title: Binary black-hole simulation SXS:BBH:0001
DOI: 10.26138/sxs:bbh:0001v1.2
title: Binary black-hole simulation SXS:BBH:0001
DOI: 10.26138/sxs:bbh:0001v1.3
title: Binary black-hole simulation SXS:BBH:0001
DOI: 10.26138/sxs:bbh:0001v1.4
title: Binary black-hole simulation SXS:BBH:0001
DOI: 10.26138/sxs:bbh:0001v1.5
title: Binary black-hole simulation SXS:BBH:0001
DOI: 10.26138/sxs:bbh:0001v1.6
title: Binary black-hole simulation SXS:BBH:0001
DOI: 10.26138/sxs:bbh:0002v1.1
title: Binary black-hole simulation SXS:BBH:0002
DOI: 10.26138/sxs:bbh:0002v1.2
title: Binary black-hole simulation SXS:BBH:0002
DOI: 10.26138/sxs:bbh:0002v1.4
title: Binary black-hole simulation SXS:BBH:0002
DOI: 10.26138/sxs:bbh:0002v1.5
title: Binary black-hole simulation SXS:BBH:0002
DOI: 10.26138/sxs:bbh:0002v1.6
title: Binary black-hole simulation SXS:BBH:0002
DOI: 10.26138/sxs:bbh:0002v1.7
title: Binary black-hole simulation SXS:BBH:0002
DOI: 10.26138/sxs:bbh:0003v1.1
title: Binary black-hole simulation SXS:BBH:0003
DOI: 10.26138/sxs:bbh:0003v1.2
title: Binary black-hole simulation SXS:BBH:0003
DOI: 10.26138/sxs:bbh:0003v1.3
title: Binary black-hole simulation SXS:BBH:0003
DOI: 10.26138/sxs:bbh:0003v1.4
title: Binary black-hole simulation SXS:BBH:0003
DOI: 10.26138/sxs:bbh:0003v1.5
title: Binary black-hole simulation SXS:BBH:0003
DOI: 10.26138/sxs:bbh:0004v1.1
title: Binary black-hole simulation SXS:BBH:0004
DOI: 10.26138/sxs:bbh:0004v1.2
title: Binary black-hole simulation SXS:BBH:0004
DOI: 10.26138/sxs:bbh:0004v1.3
title: Binary black-hole simulation SXS:BBH:0004
DOI: 10.26138/sxs:bbh:0004v1.4
title: Binary black-hole simulation SXS:BBH:0004
DOI: 10.26138/sxs:bbh:0004v1.5
title: Binary black-hole simulation SXS:BBH:0004
DOI: 10.26138/sxs:bbh:0005v1.1
title: Binary black-hole simulation SXS:BBH:0005
DOI: 10.26138/sxs:bbh:0005v1.2
title: Binary black-hole simulation SXS:BBH:0005
DOI: 10.26138/sxs:bbh:0005v1.3
title: Binary black-hole simulation SXS:BBH:0005
DOI: 10.26138/sxs:bbh:0005v1.4
title: Binary black-hole simulation SXS:BBH:0005
DOI: 10.26138/sxs:bbh:0005v1.5
title: Binary black-hole simulation SXS:BBH:0005
DOI: 10.26138/sxs:bbh:0006v1.1
title: Binary black-hole simulation SXS:BBH:0006
DOI: 10.26138/sxs:bbh:0006v1.2
title: Binary black-hole simulation SXS:BBH:0006
DOI: 10.26138/sxs:bbh:0006v1.3
title: Binary black-hole simulation SXS:BBH:0006
DOI: 10.26138/sxs:bbh:0006v1.4
title: Binary black-hole simulation SXS:BBH:0006
DOI: 10.26138/sxs:bbh:0006v1.5
title: Binary black-hole simulation SXS:BBH:0006
DOI: 10.26138/sxs:bbh:0007v1.1
title: Binary black-hole simulation SXS:BBH:0007
DOI: 10.26138/sxs:bbh:0007v1.2
title: Binary black-hole simulation SXS:BBH:0007
DOI: 10.26138/sxs:bbh:0007v1.3
title: Binary black-hole simulation SXS:BBH:0007
DOI: 10.26138/sxs:bbh:0007v1.4
title: Binary black-hole simulation SXS:BBH:0007
DOI: 10.26138/sxs:bbh:0007v1.5
title: Binary black-hole simulation SXS:BBH:0007
DOI: 10.26138/sxs:bbh:0008v1.1
title: Binary black-hole simulation SXS:BBH:0008
DOI: 10.26138/sxs:bbh:0008v1.2
title: Binary black-hole simulation SXS:BBH:0008
DOI: 10.26138/sxs:bbh:0008v1.3
title: Binary black-hole simulation SXS:BBH:0008
DOI: 10.26138/sxs:bbh:0008v1.4
title: Binary black-hole simulation SXS:BBH:0008
DOI: 10.26138/sxs:bbh:0008v1.5
title: Binary black-hole simulation SXS:BBH:0008
DOI: 10.26138/sxs:bbh:0009v1.1
title: Binary black-hole simulation SXS:BBH:0009
DOI: 10.26138/sxs:bbh:0009v1.2
title: Binary black-hole simulation SXS:BBH:0009
DOI: 10.26138/sxs:bbh:0009v1.3
title: Binary black-hole simulation SXS:BBH:0009
DOI: 10.26138/sxs:bbh:0009v1.4
title: Binary black-hole simulation SXS:BBH:0009
DOI: 10.26138/sxs:bbh:0009v1.5
title: Binary black-hole simulation SXS:BBH:0009
DOI: 10.26138/sxs:bbh:0010v1.1
title: Binary black-hole simulation SXS:BBH:0010
DOI: 10.26138/sxs:bbh:0010v1.2
title: Binary black-hole simulation SXS:BBH:0010
DOI: 10.26138/sxs:bbh:0010v1.3
title: Binary black-hole simulation SXS:BBH:0010
DOI: 10.26138/sxs:bbh:0010v1.4
title: Binary black-hole simulation SXS:BBH:0010
DOI: 10.26138/sxs:bbh:0010v1.5
title: Binary black-hole simulation SXS:BBH:0010
DOI: 10.26138/sxs:bbh:0011v1.1
title: Binary black-hole simulation SXS:BBH:0011
DOI: 10.26138/sxs:bbh:0011v1.2
title: Binary black-hole simulation SXS:BBH:0011
DOI: 10.26138/sxs:bbh:0011v1.3
title: Binary black-hole simulation SXS:BBH:0011
DOI: 10.26138/sxs:bbh:0011v1.4
title: Binary black-hole simulation SXS:BBH:0011
DOI: 10.26138/sxs:bbh:0011v1.5
title: Binary black-hole simulation SXS:BBH:0011
DOI: 10.26138/sxs:bbh:0012v1.1
title: Binary black-hole simulation SXS:BBH:0012
DOI: 10.26138/sxs:bbh:0012v1.2
title: Binary black-hole simulation SXS:BBH:0012
DOI: 10.26138/sxs:bbh:0012v1.3
title: Binary black-hole simulation SXS:BBH:0012
DOI: 10.26138/sxs:bbh:0012v1.4
title: Binary black-hole simulation SXS:BBH:0012
DOI: 10.26138/sxs:bbh:0012v1.5
title: Binary black-hole simulation SXS:BBH:0012
DOI: 10.26138/sxs:bbh:0013v1.1
title: Binary black-hole simulation SXS:BBH:0013
DOI: 10.26138/sxs:bbh:0013v1.2
title: Binary black-hole simulation SXS:BBH:0013
DOI: 10.26138/sxs:bbh:0013v1.3
title: Binary black-hole simulation SXS:BBH:0013
DOI: 10.26138/sxs:bbh:0013v1.4
title: Binary black-hole simulation SXS:BBH:0013
DOI: 10.26138/sxs:bbh:0013v1.5
title: Binary black-hole simulation SXS:BBH:0013
DOI: 10.26138/sxs:bbh:0014v1.1
title: Binary black-hole simulation SXS:BBH:0014
DOI: 10.26138/sxs:bbh:0014v1.2
title: Binary black-hole simulation SXS:BBH:0014
DOI: 10.26138/sxs:bbh:0014v1.3
title: Binary black-hole simulation SXS:BBH:0014
DOI: 10.26138/sxs:bbh:0014v1.4
title: Binary black-hole simulation SXS:BBH:0014
DOI: 10.26138/sxs:bbh:0014v1.5
title: Binary black-hole simulation SXS:BBH:0014
DOI: 10.26138/sxs:bbh:0015v1.1
title: Binary black-hole simulation SXS:BBH:0015
DOI: 10.26138/sxs:bbh:0015v1.2
title: Binary black-hole simulation SXS:BBH:0015
DOI: 10.26138/sxs:bbh:0015v1.3
title: Binary black-hole simulation SXS:BBH:0015
DOI: 10.26138/sxs:bbh:0015v1.4
title: Binary black-hole simulation SXS:BBH:0015
DOI: 10.26138/sxs:bbh:0015v1.5
title: Binary black-hole simulation SXS:BBH:0015
DOI: 10.26138/sxs:bbh:0016v1.1
title: Binary black-hole simulation SXS:BBH:0016
DOI: 10.26138/sxs:bbh:0016v1.2
title: Binary black-hole simulation SXS:BBH:0016
DOI: 10.26138/sxs:bbh:0016v1.3
title: Binary black-hole simulation SXS:BBH:0016
DOI: 10.26138/sxs:bbh:0016v1.4
title: Binary black-hole simulation SXS:BBH:0016
DOI: 10.26138/sxs:bbh:0016v1.5
title: Binary black-hole simulation SXS:BBH:0016
DOI: 10.26138/sxs:bbh:0017v1.1
title: Binary black-hole simulation SXS:BBH:0017
DOI: 10.26138/sxs:bbh:0017v1.2
title: Binary black-hole simulation SXS:BBH:0017
DOI: 10.26138/sxs:bbh:0017v1.3
title: Binary black-hole simulation SXS:BBH:0017
DOI: 10.26138/sxs:bbh:0017v1.4
title: Binary black-hole simulation SXS:BBH:0017
DOI: 10.26138/sxs:bbh:0017v1.5
title: Binary black-hole simulation SXS:BBH:0017
DOI: 10.26138/sxs:bbh:0018v1.1
title: Binary black-hole simulation SXS:BBH:0018
DOI: 10.26138/sxs:bbh:0018v1.2
title: Binary black-hole simulation SXS:BBH:0018
DOI: 10.26138/sxs:bbh:0018v1.3
title: Binary black-hole simulation SXS:BBH:0018
DOI: 10.26138/sxs:bbh:0018v1.4
title: Binary black-hole simulation SXS:BBH:0018
DOI: 10.26138/sxs:bbh:0018v1.5
title: Binary black-hole simulation SXS:BBH:0018
DOI: 10.26138/sxs:bbh:0019v1.1
title: Binary black-hole simulation SXS:BBH:0019
DOI: 10.26138/sxs:bbh:0019v1.2
title: Binary black-hole simulation SXS:BBH:0019
DOI: 10.26138/sxs:bbh:0019v1.3
title: Binary black-hole simulation SXS:BBH:0019
DOI: 10.26138/sxs:bbh:0019v1.4
title: Binary black-hole simulation SXS:BBH:0019
DOI: 10.26138/sxs:bbh:0019v1.5
title: Binary black-hole simulation SXS:BBH:0019
DOI: 10.26138/sxs:bbh:0020v1.1
title: Binary black-hole simulation SXS:BBH:0020
DOI: 10.26138/sxs:bbh:0020v1.2
title: Binary black-hole simulation SXS:BBH:0020
DOI: 10.26138/sxs:bbh:0020v1.3
title: Binary black-hole simulation SXS:BBH:0020