Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
7
datasets available to search
ShareScore release 0.9.0
Dataset results
7 results for “Galaxy Mergers”
Merger simulation (elliptical & spiral galaxy)
<p><strong>Gadget-2 (Springel 2005) simulation of 3.56 Gyr evolution of a shell-creating merger with 1:10 stellar-mass ratio, elliptical primary galaxy and secondary with inclined disk and 6 kpc impact parameter. One second of the video corresponds to 60 Myr. For more details, see <a href="https://ui.adsabs.harvard.edu/abs/2020A%26A...634A..73E/abstract">Ebrová et al. (2020, A&A 634, 73)</a> </strong></p>
Galaxy Merger Candidates
<p>A Galaxy Merger Candidates catalogue comprised of a subset of the DECaLS DR8 catalogue.</p>
Multi-wavelength AGN Properties of Post-Merger Galaxies
<p>It’s well studied that galaxy mergers can have a profound impact on triggering both star formation and an Active Galactic Nucleus (AGN). The AGN is expected to quench star formation through “feedback” processes, by heating or expelling gas out of the galaxy, leaving behind a merger remnant. Unfortunately, confirming that mergers trigger AGN is observationally challenging, because AGN in different merger stages can have discrepant luminosities and lifetimes. In addition, a multi-wavelength approach is required to provide a complete census of AGN. Here I present results based on a volume limited (0.02<z<0.06, 10.5<logM<12.0), visually identified sample of post-merger galaxies from SDSS-DR14. I will describe the multi-wavelength AGN demographics of this sample using Chandra, XMM X-ray imaging, optical BPT diagnostics based on SDSS single-fiber spectra, WISE infrared color and VLA FIRST radio images. I will discuss plans to expand our analysis of the merger-AGN connection to post merger galaxies at higher redshift using the Nancy Grace Roman Space Telescope and other multi-wavelength data.</p>
Demographics of Galaxies in the Nearby Universe: A case study with AGN and post-mergers
<p>Here I present a volume limited catalog of ~113,600 visually classified galaxies from SDSS and the NOAO legacy survey. The classifications include identifications of bars, rings, spiral arms, tidal tails, etc. I compare my classifications to those from other crowd sourced surveys and machine learning based catalogs to verify classification accuracy. Using this catalog, I will show results on the systematics which affect quantitative/ML classifications and implications for the Nancy Grace Roman space telescope galaxy surveys. Time permitting I will discuss (a) bar fractions in the local Universe and role of close pair interactions in triggering bars (b) the role of mergers in triggering AGN and the frequency of dual AGN in the local Universe.</p>
Molecular gas and star formation in nearby starburst galaxy mergers
<p>This compressed file contains a python script, fits images and numpy table produce the animations in the paper. To make animations, run the script `make_final_animation.py` in `scripts/` directory. The script is run in python 3.7. After running the script, the generated html animations will be stored in `figures/` directory.</p>
Initial conditions for galaxy merger setup in the Phantom smoothed particle hydrodynamics and magnetohydrodynamics code
<p>** these files are downloaded automatically by Phantom when running the code **</p> <p>The two files here contain initial conditions (particle positions, velocities etc) for the sample galaxy merger simulation shown in Figure 55 of the Phantom code paper (<a href="http://adsabs.harvard.edu/abs/2018PASA...35...31P">Price et al. 2018</a>). They were created by James Wurster as part of a code comparison with the Hydra code described in section 6.4 of the paper.</p> <p>The files were originally created for use in <a href="http://adsabs.harvard.edu/abs/2013MNRAS.431..539W">Wurster & Thacker (2013)</a></p> <p>For details of how to read these files, see the Phantom source code (<a href="https://github.com/danieljprice/phantom/blob/master/src/setup/setup_galaxies.f90">setup_galaxies.f90</a>)</p>
DATA MINING THE GALAXY ZOO MERGERS
DATA MINING THE GALAXY ZOO MERGERS STEVEN BAEHR*, ARUN VEDACHALAM*, KIRK BORNE*, AND DANIEL SPONSELLER* Abstract. Collisions between pairs of galaxies usually end in the coalescence (merger) of the two galaxies. Collisions and mergers are rare phenomena, yet they may signal the ultimate fate of most galaxies, including our own Milky Way. With the onset of massive collection of astronomical data, a computerized and automated method will be necessary for identifying those colliding galaxies worthy of more detailed study. This project researches methods to accomplish that goal. Astronomical data from the Sloan Digital Sky Survey (SDSS) and human-provided classifications on merger status from the Galaxy Zoo project are combined and processed with machine learning algorithms. The goal is to determine indicators of merger status based solely on discovering those automated pipeline-generated attributes in the astronomical database that correlate most strongly with the patterns identified through visual inspection by the Galaxy Zoo volunteers. In the end, we aim to provide a new and improved automated procedure for classification of collisions and mergers in future petascale astronomical sky surveys. Both information gain analysis (via the C4.5 decision tree algorithm) and cluster analysis (via the Davies-Bouldin Index) are explored as techniques for finding the strongest correlations between human-identified patterns and existing database attributes. Galaxy attributes measured in the SDSS green waveband images are found to represent the most influential of the attributes for correct classification of collisions and mergers. Only a nominal information gain is noted in this research, however, there is a clear indication of which attributes contribute so that a direction for further study is apparent.
ScienceDex guides
Understand access before you commit
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.