cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7142693
title: Discovery of 24 radio-bright quasars at 4.9

description: Properties of newly discovered high-z quasars (4.9&lt;z&lt;6.6) derived from photometric and spectroscopic observations. The radio measurements have been obtained using the LOFAR Two Metre Sky Survey (LoTSS-DR2; 144 MHz), VLA FIRST (1.4 GHz), and Very Large Array Sky Survey (VLASS; 2-4 GHz). The spectroscopic observations have been conducted using the Faint Object Camera and Spectrograph on the Subaru telescope, LRS2 on the Hobby-Eberly Telescope, and LRIS on Keck. The rest-frame UV magnitudes have been derived by combining the optical to mid-infrared observations and performing spectral energy distribution fitting using the template fitting code EAZY (Brammer et al 2008).
DOI: 10.5281/zenodo.7061954
title: The three-dimensional structure of Galactic molecular cloud complexes out to 2.5 kpc

description: Data files for Dharmawardena et al., accepted MNRAS, 2022, <em>The three-dimensional structure of Galactic molecular cloud complexes out to 2.5 kpc.</em> In this works we map the Interstellar Dust Extinction and Density in 3D using <em>Dustribution</em> code: Dharmawardena et al., 2022, Astronomy & Astrophysics, Volume 658, id.A166, 30 pp. https://www.aanda.org/articles/aa/full_html/2022/02/aa41298-21/aa41298-21.html The <em>Dustribution</em> code can be accessed from https://github.com/Thavisha/Dustribution The latest results from Dustribution can be interactively viewed and downloaded from https://www2.mpia-hd.mpg.de/homes/tmueller/projects/ThavishaDustDensity/index.html Data available here: - Trained gpy functions for all sixteen mapped regions which can be used to predict on any selected spherical or cartesian grid. -predicted cubes of 3D dust extinction and corresponding lbd boundary grids - predicted cubes of dust densities on cartesian grids and corresponding xyz boundary grids. - <em>astrodendro</em> derived dendrograms for all sixteen molecular cloud regions. - Tables of <em>astrodendro</em> produced parameters and calculated error bars for each region's substructure as described in appendix C of paper. - Tables 1,2,3 in main body of paper in .csv format. - 3D rendering videos of the sixteen mapped regions.
DOI: 10.5281/zenodo.7061955
title: The three-dimensional structure of Galactic molecular cloud complexes out to 2.5 kpc

description: Data files for Dharmawardena et al., accepted MNRAS, 2022, <em>The three-dimensional structure of Galactic molecular cloud complexes out to 2.5 kpc.</em> In this works we map the Interstellar Dust Extinction and Density in 3D using <em>Dustribution</em> code: Dharmawardena et al., 2022, Astronomy & Astrophysics, Volume 658, id.A166, 30 pp. https://www.aanda.org/articles/aa/full_html/2022/02/aa41298-21/aa41298-21.html The <em>Dustribution</em> code can be accessed from https://github.com/Thavisha/Dustribution The latest results from Dustribution can be interactively viewed and downloaded from https://www2.mpia-hd.mpg.de/homes/tmueller/projects/ThavishaDustDensity/index.html Data available here: - Trained gpy functions for all sixteen mapped regions which can be used to predict on any selected spherical or cartesian grid. -predicted cubes of 3D dust extinction and corresponding lbd boundary grids - predicted cubes of dust densities on cartesian grids and corresponding xyz boundary grids. - <em>astrodendro</em> derived dendrograms for all sixteen molecular cloud regions. - Tables of <em>astrodendro</em> produced parameters and calculated error bars for each region's substructure as described in appendix C of paper. - Tables 1,2,3 in main body of paper in .csv format. - 3D rendering videos of the sixteen mapped regions.
DOI: 10.5281/zenodo.7115094
title: Catalogues of active galactic nuclei from Gaia and unWISE data (updated to Gaia DR3 astrometric solution)

description: This is a minor update to the Gaia-unWISE AGN catalogue published in Shu et al. 2019, MNRAS, 489, 4741. It contains the same 2.7 million AGN candidates selected from the Gaia DR2 and unWISE data using a random forest classifier. In addition to the original columns (which are explained in detail in Shu et al. 2019), a few new columns that correspond to Gaia DR3 astrometric solutions are appended, i.e. dr3_source_id, dr3_pmra, dr3_pmra_err, dr3_pmdec, dr3_pmdec_err, dr3_plx, dr3_plx_err, dr3_pmsig, and dr3_plxsig.
DOI: 10.5281/zenodo.7115095
title: Catalogues of active galactic nuclei from Gaia and unWISE data (updated to Gaia DR3 astrometric solution)

description: This is a minor update to the Gaia-unWISE AGN catalogue published in Shu et al. 2019, MNRAS, 489, 4741. It contains the same 2.7 million AGN candidates selected from the Gaia DR2 and unWISE data using a random forest classifier. In addition to the original columns (which are explained in detail in Shu et al. 2019), a few new columns that correspond to Gaia DR3 astrometric solutions are appended, i.e. dr3_source_id, dr3_pmra, dr3_pmra_err, dr3_pmdec, dr3_pmdec_err, dr3_plx, dr3_plx_err, dr3_pmsig, and dr3_plxsig.
DOI: 10.6084/m9.figshare.21514104
title: Efficiency of QTL mapping under genotype x environment interaction

description: SNP and phenotype data for 50 samples of 300 RILs in six environments.
DOI: 10.6084/m9.figshare.21514104.v1
title: Efficiency of QTL mapping under genotype x environment interaction

description: SNP and phenotype data for 50 samples of 300 RILs in six environments.
DOI: 10.5281/zenodo.6918082
title: The Large Magellanic Cloud Revealed in Gravitational Waves with LISA: Population Release

description: The Large Magellanic Cloud (LMC)’s binary populations for study by the <em>Laser Interferometer Space Antenna (LISA)</em> as generated by Keim et al. in a paper submitted to MNRAS (Keim, M. A., Korol, V., Rossi, E. M. The Large Magellanic Cloud Revealed in Gravitational Waves with LISA. <em>Monthly Notices of the Royal Astronomical Society</em>, 2022, submitted). The files include all current double white dwarfs in the LISA band (‘LISABand’), all which will be detectable with a S/N&gt;7 after 4 yrs (‘Detect’), and all which are detached/non-accreting, i.e. sure LISA sources (‘Detached’). This release represents a 2.7*10^9 stellar mass LMC, and includes distribution models based on observation (‘M1’) and simulation (‘M3’). For more information, please refer to Keim et al. (2022). We request that researchers utilising any of these populations cite Keim et al. (2022). The data columns are as follows: Column 1 = Right Ascension (Degrees) Column 2 = Declination (Degrees) Column 3 = Age (Myr, since formation of Main Sequence Pair) Column 4 = Mass of White Dwarf One (Msun) Column 5 = Mass of White Dwarf Two (Msun) Column 6 = Radius of White Dwarf One (Rsun) Column 7 = Radius of White Dwarf Two (Rsun) Column 8 = Orbital Radius (Rsun) Column 9 = Frequency (Hz) Column 10 = Chirp (Hz^2) Column 11 = Latitude (Radians) Column 12 = Longitude (Radians) Column 13 = Amplitude (Defined with a prefactor of 2) Column 14 = Inclination (Radians) Column 15 = Polarization (Radians) Column 16 = Orbital Phase (Radians) Column 17 = Distance (kpc) Column 18 = Mass Transfer (1= Yes, i.e. Roche Lobe Overfill, 0= No)
DOI: 10.5281/zenodo.6918083
title: The Large Magellanic Cloud Revealed in Gravitational Waves with LISA: Population Release

description: The Large Magellanic Cloud (LMC)’s binary populations for study by the <em>Laser Interferometer Space Antenna (LISA)</em> as generated by Keim et al. in a paper submitted to MNRAS (Keim, M. A., Korol, V., Rossi, E. M. The Large Magellanic Cloud Revealed in Gravitational Waves with LISA. <em>Monthly Notices of the Royal Astronomical Society</em>, 2022, submitted). The files include all current double white dwarfs in the LISA band (‘LISABand’), all which will be detectable with a S/N&gt;7 after 4 yrs (‘Detect’), and all which are detached/non-accreting, i.e. sure LISA sources (‘Detached’). This release represents a 2.7*10^9 stellar mass LMC, and includes distribution models based on observation (‘M1’) and simulation (‘M3’). For more information, please refer to Keim et al. (2022). We request that researchers utilising any of these populations cite Keim et al. (2022). The data columns are as follows: Column 1 = Right Ascension (Degrees) Column 2 = Declination (Degrees) Column 3 = Age (Myr, since formation of Main Sequence Pair) Column 4 = Mass of White Dwarf One (Msun) Column 5 = Mass of White Dwarf Two (Msun) Column 6 = Radius of White Dwarf One (Rsun) Column 7 = Radius of White Dwarf Two (Rsun) Column 8 = Orbital Radius (Rsun) Column 9 = Frequency (Hz) Column 10 = Chirp (Hz^2) Column 11 = Latitude (Radians) Column 12 = Longitude (Radians) Column 13 = Amplitude (Defined with a prefactor of 2) Column 14 = Inclination (Radians) Column 15 = Polarization (Radians) Column 16 = Orbital Phase (Radians) Column 17 = Distance (kpc) Column 18 = Mass Transfer (1= Yes, i.e. Roche Lobe Overfill, 0= No)
DOI: 10.5281/zenodo.6413232
title: dataset for "A Scintillation Arc Survey of 22 Pulsars with Low to Moderate Dispersion Measures"

description: These are the 54 Dynamic Spectrum files (.FITS format) behind the paper "A Scintillation Arc Survey of 22 Pulsars with Low to Moderate Dispersion Measures," submitted to the AAS Journals on 2022 April 06. We also include the Tables, Figures, and Text from the work, as well as some explanatory README files.
DOI: 10.5281/zenodo.6413233
title: dataset for "A Scintillation Arc Survey of 22 Pulsars with Low to Moderate Dispersion Measures"

description: These are the 54 Dynamic Spectrum files (.FITS format) behind the paper "A Scintillation Arc Survey of 22 Pulsars with Low to Moderate Dispersion Measures," submitted to the AAS Journals on 2022 April 06. We also include the Tables, Figures, and Text from the work, as well as some explanatory README files.
DOI: 10.6073/pasta/05c470f08b44e1cb2e89f35b118cce8e
title: Lake ice clearance and formation data for Green Lakes Valley, 1968 - ongoing.

description: Records were based on intermittent observation of the extent of ice cover on Silver Lake, Lake Albion, and Green Lakes 1-5, dependent on observers present for other reasons (or in some cases from photos or satellite data). Dates of freeze-up (formation), breakup (first melt or open water), and lake-ice clearance were recorded, along with visual estimates of percent ice cover at various dates prior to complete meltout in spring. The latter estimates are only included starting in 2019.
DOI: 10.5281/zenodo.5809644
title: Raw geochemical data and additional images of: New insights on micro–scale variations of geochemical and oxygen isotope compositions in conodont and shark tooth bioapatite

description: The files contain the Research Data for the article entitled <strong>'New insights on micro–scale variations of geochemical and oxygen isotope compositions in conodont and shark tooth bioapatite'</strong>. It includes raw geochemical data (stable isotope analysis and elemental analysis) with its respective normalizations, images (scanning electron microscopy) and results from additional samples. Statistical tests are also encompassed. Also, profilometer figures that show samples’ topography are available (please consult the README file). Any doubt or question about these are welcomed by me (Zoneibe Luz) and Torsten Vennemann for discussion or further considerations.
DOI: 10.5281/zenodo.5809643
title: Raw geochemical data and additional images of: New insights on micro–scale variations of geochemical and oxygen isotope compositions in conodont and shark tooth bioapatite

description: The files contain the Research Data for the article entitled <strong>'New insights on micro–scale variations of geochemical and oxygen isotope compositions in conodont and shark tooth bioapatite'</strong>. It includes raw geochemical data (stable isotope analysis and elemental analysis) with its respective normalizations, images (scanning electron microscopy) and results from additional samples. Statistical tests are also encompassed. Also, profilometer figures that show samples’ topography are available (please consult the README file). Any doubt or question about these are welcomed by me (Zoneibe Luz) and Torsten Vennemann for discussion or further considerations.
DOI: 10.5281/zenodo.7299423
title: Paper2Fig100k dataset

description: <strong>Paper2Fig100k dataset</strong> A dataset with over 100k images of figures and text captions from research papers. Images of figures display diagrams, methodologies, and architectures of research papers in arXiv.org. We provide also text captions for each figure, and OCR detections and recognitions on the figures (bounding boxes and texts). The dataset structure consists of a directory called "figures" and two JSON files (train and test), that contain data from each figure. Each JSON object contains the following information about a figure: <strong>figure_id</strong>: Figure identification based on the arXiv identifier: &lt;yymm&gt;.&lt;xxxxxx&gt;-Figure&lt;I&gt;-&lt;k&gt;.png. <strong>captions</strong>: Text pairs extracted from the paper that relates to the figure. For instance, the actual caption of the figure or references to the figure in the manuscript. <strong>ocr_result</strong>: Result of performing OCR text recognition over the image. We provide a list of triplets (bounding box, confidence, text) present in the image. <strong>aspect</strong>: Aspect ratio of the image (H/W). Take a look at the <strong>OCR-VQGAN</strong> GitHub repository, which uses the <strong>Paper2Fig100k </strong>dataset to train an image encoder for figures and diagrams, that uses OCR perceptual loss to render clear and readable texts inside images. The dataset is explained in more detail in the paper <strong>OCR-VQGAN: Taming Text-within-Image Generation @WACV 2023</strong> <strong>Paper abstract</strong> <em>Synthetic image generation has recently experienced significant improvements in domains such as natural image or art generation. However, the problem of figure and diagram generation remains unexplored. A challenging aspect of generating figures and diagrams is effectively rendering readable texts within the images. To alleviate this problem, we present OCR-VQGAN, an image encoder, and decoder that leverages OCR pre-trained features to optimize a text perceptual loss, encouraging the architecture to preserve high-fidelity text and diagram structure. To explore our approach, we introduce the Paper2Fig100k dataset, with over 100k images of figures and texts from research papers. The figures show architecture diagrams and methodologies of articles available at arXiv.org from fields like artificial intelligence and computer vision. Figures usually include text and discrete objects, e.g., boxes in a diagram, with lines and arrows that connect them. We demonstrate the superiority of our method by conducting several experiments on the task of figure reconstruction. Additionally, we explore the qualitative and quantitative impact of weighting different perceptual metrics in the overall loss function.</em>
DOI: 10.5281/zenodo.7299422
title: Paper2Fig100k dataset

description: <strong>Paper2Fig100k dataset</strong> A dataset with over 100k images of figures and text captions from research papers. Images of figures display diagrams, methodologies, and architectures of research papers in arXiv.org. We provide also text captions for each figure, and OCR detections and recognitions on the figures (bounding boxes and texts). The dataset structure consists of a directory called "figures" and two JSON files (train and test), that contain data from each figure. Each JSON object contains the following information about a figure: <strong>figure_id</strong>: Figure identification based on the arXiv identifier: &lt;yymm&gt;.&lt;xxxxxx&gt;-Figure&lt;I&gt;-&lt;k&gt;.png. <strong>captions</strong>: Text pairs extracted from the paper that relates to the figure. For instance, the actual caption of the figure or references to the figure in the manuscript. <strong>ocr_result</strong>: Result of performing OCR text recognition over the image. We provide a list of triplets (bounding box, confidence, text) present in the image. <strong>aspect</strong>: Aspect ratio of the image (H/W). Take a look at the <strong>OCR-VQGAN</strong> GitHub repository, which uses the <strong>Paper2Fig100k </strong>dataset to train an image encoder for figures and diagrams, that uses OCR perceptual loss to render clear and readable texts inside images. The dataset is explained in more detail in the paper <strong>OCR-VQGAN: Taming Text-within-Image Generation @WACV 2023</strong> <strong>Paper abstract</strong> <em>Synthetic image generation has recently experienced significant improvements in domains such as natural image or art generation. However, the problem of figure and diagram generation remains unexplored. A challenging aspect of generating figures and diagrams is effectively rendering readable texts within the images. To alleviate this problem, we present OCR-VQGAN, an image encoder, and decoder that leverages OCR pre-trained features to optimize a text perceptual loss, encouraging the architecture to preserve high-fidelity text and diagram structure. To explore our approach, we introduce the Paper2Fig100k dataset, with over 100k images of figures and texts from research papers. The figures show architecture diagrams and methodologies of articles available at arXiv.org from fields like artificial intelligence and computer vision. Figures usually include text and discrete objects, e.g., boxes in a diagram, with lines and arrows that connect them. We demonstrate the superiority of our method by conducting several experiments on the task of figure reconstruction. Additionally, we explore the qualitative and quantitative impact of weighting different perceptual metrics in the overall loss function.</em>
DOI: 10.5281/zenodo.5889252
title: Empirical laws of plasmapause and plasmasphere outer edge location from the Van Allen Probes

description: This webpage provides access to empirical laws of the plasmapause position and the dense outer edge of the plasasphere position (i.e. location of the 100 #/cc electron density) established from spacecraft charging of the Van Allen Probes (RBSP), using data from Probe B from 26 September 2012 to 16 July 2019. These empirical laws are published of the following article: Ripoll, J.-F., Thaller, S. A., Hartley, D. P., Cunningham, G. S., Pierrard, V., Kurth, W. S., et al. (2022). Statistics and empirical models of the plasmasphere boundaries from the Van Allen Probes for radiation belt physics. Geophysical Research Letters, 49, e2022GL101402. https://doi.org/10.1029/2022GL101402. Please refer to the article and this link if you make any use of the data. We first deliver 3 files which contain the plasmapause position versus a given geomagnetic index, either Kp, AE, or Dst. These data files are MLT-averaged. The filename is Lpp_v_XX_stats_1.txt with XX the index name. We then deliver 3 files which contains the plasmapause position versus a geomagnetic starred index (i.e. a max taken during the last 24 hours for Kp and AE and a min for Dst), either Kp*, AE*, or Dst* for 4 MLT sectors written successively in each file and then for all sectors averaged together. These data files are MLT-dependent for the 4 first blocks and then MLT-averaged in the fifth block. The filename is Lpp_v_XX_star_MLT_stats_2-1.txt with XX the starred index name. The MLT range is given in the last two columns. When the plasmapause location is undetermined (i.e. Lpp = 0 or no value), there is no MLT, so that in the file we just have the total number of points (all real) and not the three values of total, number of real, number of Nans. Similarly, we deliver 3 files which contains the plasmapause position versus a geomagnetic non starred index, either Kp, AE, or Dst for 4 MLT sectors written successively in each file. Filenames have the form "Lpp_v_XX_MLT_stats_1.txt" with XX the name of the index. Figures associated to this data were not given in the article and have been added here with a filename of the form Lpp_by_mlt_XX.pdf with XX the name of the index. Finally, the zip file "RBSP-A Figures and Laws" contains Figures (same format as each figure of Figure 4 in the article) and empirical laws (same format as above) for RBSP-A data (10/2012-04/2016). Being more limited in time, we rather recommend to use RBSP B data. They are provided to confirm both RBSP A and B data agree when statistics are converged.
description: {"references": ["Ripoll, J.-F., Thaller, S. A., Hartley, D. P., Cunningham, G. S., Pierrard, V., Kurth, W. S., et al. (2022). Statistics and empirical models of the plasmasphere boundaries from the Van Allen Probes for radiation belt physics. Geophysical Research Letters, 49, e2022GL101402. https://doi.org/10.1029/2022GL101402"]}
DOI: 10.5281/zenodo.5889253
title: Empirical laws of plasmapause and plasmasphere outer edge location from the Van Allen Probes

description: This webpage provides access to empirical laws of the plasmapause position and the dense outer edge of the plasasphere position (i.e. location of the 100 #/cc electron density) established from spacecraft charging of the Van Allen Probes (RBSP), using data from Probe B from 26 September 2012 to 16 July 2019. These empirical laws are published of the following article: Ripoll, J.-F., Thaller, S. A., Hartley, D. P., Cunningham, G. S., Pierrard, V., Kurth, W. S., et al. (2022). Statistics and empirical models of the plasmasphere boundaries from the Van Allen Probes for radiation belt physics. Geophysical Research Letters, 49, e2022GL101402. https://doi.org/10.1029/2022GL101402. Please refer to the article and this link if you make any use of the data. We first deliver 3 files which contain the plasmapause position versus a given geomagnetic index, either Kp, AE, or Dst. These data files are MLT-averaged. The filename is Lpp_v_XX_stats_1.txt with XX the index name. We then deliver 3 files which contains the plasmapause position versus a geomagnetic starred index (i.e. a max taken during the last 24 hours for Kp and AE and a min for Dst), either Kp*, AE*, or Dst* for 4 MLT sectors written successively in each file and then for all sectors averaged together. These data files are MLT-dependent for the 4 first blocks and then MLT-averaged in the fifth block. The filename is Lpp_v_XX_star_MLT_stats_2-1.txt with XX the starred index name. The MLT range is given in the last two columns. When the plasmapause location is undetermined (i.e. Lpp = 0 or no value), there is no MLT, so that in the file we just have the total number of points (all real) and not the three values of total, number of real, number of Nans. Similarly, we deliver 3 files which contains the plasmapause position versus a geomagnetic non starred index, either Kp, AE, or Dst for 4 MLT sectors written successively in each file. Filenames have the form "Lpp_v_XX_MLT_stats_1.txt" with XX the name of the index. Figures associated to this data were not given in the article and have been added here with a filename of the form Lpp_by_mlt_XX.pdf with XX the name of the index. Finally, the zip file "RBSP-A Figures and Laws" contains Figures (same format as each figure of Figure 4 in the article) and empirical laws (same format as above) for RBSP-A data (10/2012-04/2016). Being more limited in time, we rather recommend to use RBSP B data. They are provided to confirm both RBSP A and B data agree when statistics are converged.
description: {"references": ["Ripoll, J.-F., Thaller, S. A., Hartley, D. P., Cunningham, G. S., Pierrard, V., Kurth, W. S., et al. (2022). Statistics and empirical models of the plasmasphere boundaries from the Van Allen Probes for radiation belt physics. Geophysical Research Letters, 49, e2022GL101402. https://doi.org/10.1029/2022GL101402"]}
DOI: 10.6084/m9.figshare.3761562.v2395
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.