cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7533374
title: Dataset for article "From X-rays to physical parameters: a comprehensive analysis of thermal tidal disruption event X-ray spectra"

description: This repository contains the data used in the modeling of TDE X-ray emission within the article: Mummery et al. 2023, " From X-rays to physical parameters: a comprehensive analysis of thermal<br> tidal disruption event X-ray spectra" published as Mummery et al. 2023, MNRAS, 519, 5828
DOI: 10.5281/zenodo.7774654
title: Hubble and Webb Space Telescope data for the cluster field RXJ-2129

description: This repository contains reduced image mosaics from the Hubble and Webb Space Telescopes of the galaxy cluster RXJ-2129. Data were processed with the grizli software pipeline, as described by Valentino et al. (2023, ApJ, in press), with the software and observatory calibrations available as of October 2022. For a given broad-band filter, the science data are given in the "sci.fits" files. Inverse variance weight images are provided in the "wht.fits" files. Aperture photometry following the methodology described by Valentino et al. is provided in "rxj2129-grizli-v4.0-fix_phot_apcorr.fits", and photometric redshift estimates derived with the eazy-py software are tabulated in "rxj2129-grizli-v4.0-fix.zout.fits".
DOI: 10.5281/zenodo.7774653
title: Hubble and Webb Space Telescope data for the cluster field RXJ-2129

description: This repository contains reduced image mosaics from the Hubble and Webb Space Telescopes of the galaxy cluster RXJ-2129. Data were processed with the grizli software pipeline, as described by Valentino et al. (2023, ApJ, in press), with the software and observatory calibrations available as of October 2022. For a given broad-band filter, the science data are given in the "sci.fits" files. Inverse variance weight images are provided in the "wht.fits" files. Aperture photometry following the methodology described by Valentino et al. is provided in "rxj2129-grizli-v4.0-fix_phot_apcorr.fits", and photometric redshift estimates derived with the eazy-py software are tabulated in "rxj2129-grizli-v4.0-fix.zout.fits".
DOI: 10.5281/zenodo.7260771
title: Daset from the paper "Compact object mergers: exploring uncertainties from stellar and binary evolution with SEVN"

description: This repository contains the dataset produced by the population-synthesis code SEVN for the paper:<br> "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<br> - InitialConditions: the folder contains all the intial conditions (and the code to generate them) that have been used for the Iorio+22 paper<br> - 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 sevnpeople@gmail.com
description: 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
DOI: 10.5281/zenodo.7794546
title: Daset from the paper "Compact object mergers: exploring uncertainties from stellar and binary evolution with SEVN"

description: This repository contains the dataset produced by the population-synthesis code SEVN for the paper:<br> "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<br> - InitialConditions: the folder contains all the intial conditions (and the code to generate them) that have been used for the Iorio+22 paper<br> - 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
description: 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
DOI: 10.5281/zenodo.7260770
title: Daset from the paper "Compact object mergers: exploring uncertainties from stellar and binary evolution with SEVN"

description: This repository contains the dataset produced by the population-synthesis code SEVN for the paper:<br> "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<br> - InitialConditions: the folder contains all the intial conditions (and the code to generate them) that have been used for the Iorio+22 paper<br> - 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
description: 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
DOI: 10.5281/zenodo.7578268
title: Catalog of Cool Host Stars with Established Rotation Periods

description: Catalog of 249 late K- and M-type exoplanet host stars with rotation periods obtained from the literature or new analysis of space- or ground-based time-series photometry as of August 2022. Model-based, metallicity-dependent corrections are provided but not included in gyrochronological age estimates. Please cite the reference paper if any information from this table is used. Table 1 in Gaidos et al. 2023, in press, in CDS format.
description: CDS table format
DOI: 10.5281/zenodo.7578269
title: Catalog of Cool Host Stars with Established Rotation Periods

description: Catalog of 249 late K- and M-type exoplanet host stars with rotation periods obtained from the literature or new analysis of space- or ground-based time-series photometry as of August 2022. Model-based, metallicity-dependent corrections are provided but not included in gyrochronological age estimates. Please cite the reference paper if any information from this table is used. Table 1 in Gaidos et al. 2023, in press, in CDS format.
description: CDS table format
DOI: 10.5281/zenodo.7473596
title: DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection

description: We present the data used in "DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection". It was also used in the conference paper presented in Machine Learning and the Physical Sciences workshop at NeurIPS 2022: "Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection". A plethora of AI methods, has already shown huge promise in increasing quality and speed of work with astronomical datasets, but high complexity of AI methods leads to extraction of dataset-specific non-robust features, which leads to models that cannot work on multiple datasets at the same time. We develop a Universal Domain Adaptation method <em><strong>DeepAstroUDA</strong></em>, capable of performing <strong>semi-supervised domain adaptation, that can be applied to datasets with different data distributions and class overlap</strong>. Extra classes can be present in any of the two datasets, and the method can even be used in the presence of unknown classes. We apply our model to three examples of galaxy morphology classification tasks of different complexities (3-class and 10-class problems), with anomaly detection i.e. in all our experiments we have one extra class in the unlabeled target dataset, which represents our anomaly class. <strong>DATA:</strong> <strong>1) DA across two different data releases of the same survey (LSST 1 and 10 years of observation):</strong> We use data from Ciprijanovic et al. 2022. which can also be found on Zenodoo: https://zenodo.org/record/5514180 . Data contains three classes: spiral (0), elliptical (1) and merging galaxies (3, anomaly class). <strong>2) DA across two surveys (SDSS and DeCALS): </strong>We create datasets using data and labels from the Galaxy Zoo project. Datasets contain 10 classes (9 known classes present in both SDSS and DeCALS data, and one unknown anomaly class present only in DeCALS data): disturbed (0), merging (1), round smooth (2), cigar shaped smooth (3), barred spiral (4), unbarred tight spiral (5), unbarred loose spiral (6), edge-on without bulge (7), edge-on with bulge (8), lenses (9, unknown anomaly class). SDSS (wide filed): datasets is split into two files - sdss_1.h5, sdss_2.h5 DeCALS: decals.zip <strong>3) DA between wide and deep observing fields of the same survey (SDSS):</strong> We create datasets using data and labels from the Galaxy Zoo project. Datasets contain same 10 classes as in 2), with the final lens anomaly class being only present in the SDSS deep field. SDSS (wide filed): the same data as in 2) SDSS (Strip 82 deep field): sdss_stripe82.zip All SDSS and DECaLS files contain full datasets (train, validation and test). Exact split that we performed (0.6 : 0.2 : 0.2) can be done using the code that accompanies this publication: https://github.com/deepskies/DeepAstroUDA .
DOI: 10.5281/zenodo.7473597
title: DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection

description: We present the data used in "DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection". It was also used in the conference paper presented in Machine Learning and the Physical Sciences workshop at NeurIPS 2022: "Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection". A plethora of AI methods, has already shown huge promise in increasing quality and speed of work with astronomical datasets, but high complexity of AI methods leads to extraction of dataset-specific non-robust features, which leads to models that cannot work on multiple datasets at the same time. We develop a Universal Domain Adaptation method <em><strong>DeepAstroUDA</strong></em>, capable of performing <strong>semi-supervised domain adaptation, that can be applied to datasets with different data distributions and class overlap</strong>. Extra classes can be present in any of the two datasets, and the method can even be used in the presence of unknown classes. We apply our model to three examples of galaxy morphology classification tasks of different complexities (3-class and 10-class problems), with anomaly detection i.e. in all our experiments we have one extra class in the unlabeled target dataset, which represents our anomaly class. <strong>DATA:</strong> <strong>1) DA across two different data releases of the same survey (LSST 1 and 10 years of observation):</strong> We use data from Ciprijanovic et al. 2022. which can also be found on Zenodoo: https://zenodo.org/record/5514180 . Data contains three classes: spiral (0), elliptical (1) and merging galaxies (3, anomaly class). <strong>2) DA across two surveys (SDSS and DeCALS): </strong>We create datasets using data and labels from the Galaxy Zoo project. Datasets contain 10 classes (9 known classes present in both SDSS and DeCALS data, and one unknown anomaly class present only in DeCALS data): disturbed (0), merging (1), round smooth (2), cigar shaped smooth (3), barred spiral (4), unbarred tight spiral (5), unbarred loose spiral (6), edge-on without bulge (7), edge-on with bulge (8), lenses (9, unknown anomaly class). SDSS (wide filed): datasets is split into two files - sdss_1.h5, sdss_2.h5 DeCALS: decals.zip <strong>3) DA between wide and deep observing fields of the same survey (SDSS):</strong> We create datasets using data and labels from the Galaxy Zoo project. Datasets contain same 10 classes as in 2), with the final lens anomaly class being only present in the SDSS deep field. SDSS (wide filed): the same data as in 2) SDSS (Strip 82 deep field): sdss_stripe82.zip All SDSS and DECaLS files contain full datasets (train, validation and test). Exact split that we performed (0.6 : 0.2 : 0.2) can be done using the code that accompanies this publication: https://github.com/deepskies/DeepAstroUDA .
DOI: 10.5281/zenodo.7595903
title: Swift BAT Mosaic Pattern Noise Maps

description: This dataset is the noise pattern maps that are needed to analyze Swift BAT data and produce "time integrated" mosaic images using the BatAnalysis python package. The file can be untarred and placed into a directory accessible by python such that these files can be used in the analysis of BAT data. These pattern maps are from 2004 day 350 (2004-12-15) to 2019 day 212 (2019-07-31).
DOI: 10.5281/zenodo.7595904
title: Swift BAT Mosaic Pattern Noise Maps

description: This dataset is the noise pattern maps that are needed to analyze Swift BAT data and produce "time integrated" mosaic images using the BatAnalysis python package. The file can be untarred and placed into a directory accessible by python such that these files can be used in the analysis of BAT data. These pattern maps are from 2004 day 350 (2004-12-15) to 2019 day 212 (2019-07-31).
DOI: 10.5281/zenodo.7629239
title: Catalogue of Wide Binaries from GAIA EDR3

description: This dataset contains candidate wide binary systems from Gaia EDR3, used to perform tests of Modified Gravity theories in the low acceleration regime; as shown in the paper "Wide Binaries from GAIA EDR3: preference for GR over MOND ?" by Charalambos Pittordis & Will Sutherland. Accepted by Open Journal of Astrophysics, 31 Jan 2023. There are two files: <strong>CleanedWB_EDR3_Prlx300pc_Gmag20_20230111_Size73087_ZenodoSample.csv</strong>: .CSV table with 73087 rows and 230 columns. <br> <strong>00README_WideBinaries_EDR3_PS2023.txt : </strong>README file, describing the columns within the above dataset.
DOI: 10.5281/zenodo.7629240
title: Catalogue of Wide Binaries from GAIA EDR3

description: This dataset contains candidate wide binary systems from Gaia EDR3, used to perform tests of Modified Gravity theories in the low acceleration regime; as shown in the paper "Wide Binaries from GAIA EDR3: preference for GR over MOND ?" by Charalambos Pittordis & Will Sutherland. Accepted by Open Journal of Astrophysics, 31 Jan 2023. There are two files: <strong>CleanedWB_EDR3_Prlx300pc_Gmag20_20230111_Size73087_ZenodoSample.csv</strong>: .CSV table with 73087 rows and 230 columns. <br> <strong>00README_WideBinaries_EDR3_PS2023.txt : </strong>README file, describing the columns within the above dataset.
DOI: 10.5281/zenodo.7648653
title: GalaxiaData associated with GaiaMock

description: All the necessary files to run a Galaxia simulation with an updated Besancon model, isochrones, and 3D extinction and the Nbody simulation data (clusters, SMC, LMC, Halo). The current content corresponds to Gaia Early DR3 Mock Stellar Catalog, Rybizki et al. 2020, 2020PASP..132g4501R The tarball contains the directory used by the Galaxia code. During configuration, a user should provide a path for it. This directory should be somewhere in the home to avoid root access while running setup later on (some extra files are created during setup). Warning: The data directory will grow up to 2.5GB in size (after precomputed BH trees).
description: {"references": ["Gaia Early DR3 Mock Stellar Catalog, Rybizki et al. 2020, 2020PASP..132g4501R", "Galaxia: A Code to Generate a Synthetic Survey of the Milky Way, Sharma, S., et al. 2011, ApJ...730....3S"]}
DOI: 10.5281/zenodo.7648652
title: GalaxiaData associated with GaiaMock

description: All the necessary files to run a Galaxia simulation with an updated Besancon model, isochrones, and 3D extinction and the Nbody simulation data (clusters, SMC, LMC, Halo). The current content corresponds to Gaia Early DR3 Mock Stellar Catalog, Rybizki et al. 2020, 2020PASP..132g4501R The tarball contains the directory used by the Galaxia code. During configuration, a user should provide a path for it. This directory should be somewhere in the home to avoid root access while running setup later on (some extra files are created during setup). Warning: The data directory will grow up to 2.5GB in size (after precomputed BH trees).
description: {"references": ["Gaia Early DR3 Mock Stellar Catalog, Rybizki et al. 2020, 2020PASP..132g4501R", "Galaxia: A Code to Generate a Synthetic Survey of the Milky Way, Sharma, S., et al. 2011, ApJ...730....3S"]}
DOI: 10.34740/kaggle/dsv/6092010
title: Youtube Trending Video - Taiwan & Hong Kong
DOI: 10.25345/c5sx64m2x
title: MassIVE MSV000092365 - A proteomics analysis of fnr regulation of anaerobic metabolism of food dye and drug metabolism by Escherichia coli
title: Proteomic data from E. coli grown with or without the azo dye, Red Dye number 40. WT and delta fnr E. coli were cultured in Luria Broth media and cultures were inoculated with 250 uM Red No. 40 at either mid-exponential or stationary growth phases. Vehicle control samples were also analyzed at mid-exponential and stationary growth phases.
DOI: 10.6084/m9.figshare.19633404.v20
title: Code snippet suggestions for exploratory programming - User study

description: This dataset contains participant notebooks, scripts for analysis, study instruments and pilot study results.
DOI: 10.5281/zenodo.8122489
title: Nonresonant scattering of energetic electrons by electromagnetic ion cyclotron waves: spacecraft observations and theoretical framework

description: This repository contains the simulation dataset, plotting scripts, and optimization package for the manuscript ``Nonresonant scattering of energetic electrons by electromagnetic ion cyclotron waves: spacecraft observations and theoretical framework''. *.h5 are snapshots of EMIC wave field (By and Bz) from deltaf-PIC simulations. test_particle.zip contains the test particle data in the wave field of deltaf-PIC simulations. *.py are plotting scripts to visualize the wave field, test particle simulation results, and theoretical predictions. optimization.zip contains the main subroutines for optimization of wave power spectrum to produce the measured electron precipitation. plt_style.txt is an auxillary style file for matplotlib plot.
DOI: 10.5281/zenodo.7058383
title: Vibration and IMU Sensing Human Activity Dataset

description: This dataset contains fine-grained human daily activity data collected by infrastructure vibration sensors and one on-wrist IMU sensor. This dataset is collected from six persons from two domestic homes, in total, there are 12 sub-datasets. For the naming, "p" means person and "l" means location. Each dataset has 11 columns, 1o of them stands for sensors' reading. * Due to the uploading platform, please<strong> <em>ignore</em> </strong>all files in the folder '__MACOSX', and files whose names start with '._'. These are computer system files, not parts of the shared dataset. ** If you are going to use this dataset for any publications, we will appreciate you to cite this dataset properly. ************************************************************ The following content is copied from README.txt in the compressed folder: -----------------------<br> Labels: Keyboard typing 1<br> Using mouse 2<br> Handwriting 3<br> Cutting vegetables 4<br> Stir-frying vegetables 5<br> Wiping the table 6<br> Sweeping floor 7<br> Using vacuum to vacuum floor: 8<br> Open and close drawer: 9 None Activity: 10 -----------------------<br> 11 Columns:<br> 1: Activity label<br> 2: Vibration sensor put on the Living Area floor<br> 3: Vibration sensor put on the Living Area table<br> 4: Vibration sensor put on the Studying Area floor<br> 5: Vibration sensor put on the Studying Area desk<br> 6, 7, 8: Accelerometer X,Y,Z<br> 9, 10, 11: Gyroscope X,Y,Z -----------------------<br> All signals are zero-meaned.<br> The vibration sensors' sampling rate is roughly around 6500Hz, and the IMU sensors' original sampling rate is roughly around 235Hz. ************************************************************ New in Version 2: - Added extracted features from IMU data and vibration data for reference. - IMU signal is applied with a sliding window of 1.5 seconds with 0.75 seconds overlapping, then the feature is extracted in each window. The feature's description can be found here: https://dl.acm.org/doi/abs/10.1145/3410530.3414320 - The vibration signal is applied with event detection to extract events in the vibration signal. For each event, we normalize it by its energy, then extract 10~490 Hz frequency amplitude as the feature. Disclaimer: Both event detection and feature extraction are empirical, we don't guarantee it is an optimal one.