cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25919/eat6-zb44
title: Parkes observations for project P1021 semester 2021APRS_14

description: We propose to continue our observations of PSR J1653-45, a 951-ms pulsar in a long, 1.5-yr orbit, which experiences apparent eclipses whose nature have yet to be fully understood. Binary pulsars are valuable objects of scientific study, allowing for multiple applications including tests of gravity, probes of the neutron star equation of state, and fossil records of stellar evolution. Long spin-period pulsars in binary systems are generally much rarer than faster-spinning `recycled’ pulsars, and represent an under-explored region of pulsar binary evolution. Based upon previous Parkes and ATCA proposals, our understanding of this pulsar has significantly increased. We have detected the pulsar in previously unexplored regions of its orbit, localised its position, and have constrained the start of the eclipse, such that a targeted campaign in the 2021APRS semester (during which the next eclipse is expected) will provide the breakthrough needed to solve and understand this pulsar. We therefore propose a high-cadence campaign with observations at least 1.4 times more sensitive than any previously recorded with Parkes during the eclipse. These observations will constrain the end of the eclipse and ensure that we detect the pulsar as early and often as possible, leading to a phase connected timing solution which will unlock the pulsar’s scientific potential. Through this, we hope to gain a greater understanding of this pulsar’s properties (as well as those of its companion) and how it fits into this under-explored class of unrecycled pulsar binaries.
DOI: 10.25919/csa1-fk56
title: Parkes observations for project P1080 semester 2021APRS_03

description: We propose continued timing observations of a new pulsar discovered with the MWA. Thanks to the interferometric advantages of the MWA and our recent GMRT follow-up, we have been able to determine the pulsar's position to ~5 arcseconds precision, which may help a faster convergence to its timing solution. This will help determine the pulsar's spin and astrometric parameters. The analysis of Parkes and GMRT data suggests that the pulsar is likely a steep-spectrum, low-luminosity object. Observations with the ultra-wide band low-frequency receiver will help further reinforce this, besides allowing us to explore the frequency dependence of its polarisation and a large variability that is seen at multiple frequency bands. Most importantly, these observations will ensure continued timing of the pulsar through the next semester when the MWA is unlikely to be available for regular observations.
DOI: 10.18739/a2vx0639d
title: Radiosonde temperature and humidity profiles taken at Summit Station, Greenland, 2020

description: This data set contains profiles of temperature and humidity observed by Vaisala radiosondes that are launched nominally twice daily at Summit Station, Greenland as part of the Integrated Characterization of Energy, Clouds, Atmospheric state, and Precipitation at Summit (ICECAPS) Project. These radiosonde measurements did not initially contain wind tracking information, but transitioned towards including wind tracking in one sonde per data starting in 7/2011, then moving to twice-daily wind sondes in 6/2014. The data are processed using the standard Vaisala processing software. Detailed information on these parameters and other aspects of the dataset are included in the netCDF header information for each file.
DOI: 10.25345/c5r536
title: MassIVE MSV000087838 - GNPS - Dataset Creation from GNPS Molcular Networking - 39ed9b19f2e348f1b45bb8c78e11c16d
title: human urine samples for untargeted HILIC-MS/MS DDA (top2) ESI positive mode
DOI: 10.25919/0b5d-bx25
title: Parkes observations for project P1032 semester 2021APRS_09

description: This project aims to continue to perform rapid measurements of relativistic parameters of southern binary pulsar systems using data from the Parkes Ultra Wideband Low (UWL) receiver and the MeerKAT telescope thereby enabling measurement of their component masses and in some cases, test theories of gravity. We will use the UWL data to obtain significant orbital coverage and timing baseline on these pulsars and obtain important constraints on orbital and temporal DM variations that will help in identifying the nature of the companion star. We will then use MeerKAT data at crucial orbital phases to improve our chances of measuring the relativistic parameters. Increase in the number of measured NS masses has profound implications for NS internal structure and for stellar and binary evolution physics. From this semester, we are converting this proposal into a ``Large project" proposal.
DOI: 10.25919/enzp-qj17
title: Parkes observations for project P1038 semester 2021APRS_12

description: PSR~J1759-2402 is one of a small handful of pulsars which are in orbit about high mass stars. Its orbital period is 8.3 years and the orbit is highly eccentric with periastron expected in 2021 March. Although discovered at Parkes, the Lovell telescope has been monitoring the pulsar over many years. However, it’s restricted bandwidth, maintenance schedule and poorer polarization capability make it unsuitable to follow the pulsar as it enters and leaves the eclipse by the companion. In contrast, the UWL on Parkes with its wide- band and excellent polarization characteristics means that we can, and already have determined scattering, polarization, dispersion measure and rotation measure changes as the pulsar enters its companion wind. We request a continuation of our weekly observations of the pulsar to track its progress on the far side of periastron.
DOI: 10.25919/h66q-en36
title: Parkes observations for project P1119 semester 2021APRS_02

description: Recently, low-frequency periodic radio pulses have been detected from the prototypical M-dwarf flare star, UV Ceti. This shows that the low-frequency emission from this star is driven by auroral activity, in stark contrast to the solar-like coronal activity exhibited at microwave–X-ray wavelengths. However, the driver of this auroral activity is unclear. Possible drivers of this aurora include breakdown of co-rotation with a magnetospheric plasma disk, or the sub-Alfv\'enic interaction of the star with a companion planet. Expected signatures of the latter mechanism include small deviations of the radio pulse period from the stellar rotation period, or a modulation of the pulsed radio emission at the planetary orbital period. To detect these signatures, we require long-term, wide-band monitoring of the radio pulses. In this proposal, we seek to accurately measure the pulse rotation period of UV Ceti by measuring the pulse arrival times over four epochs separated by timescales of days--weeks. This will form an important part of the groundwork for detecting signatures of the auroral engine in future long-term monitoring. If successful, this project may constitute the first steps toward unveiling the signature of star-planet interaction in this benchmark M-dwarf.
DOI: 10.18739/a2cz3256b
title: SOnic Detection And Ranging (SODAR) measurements taken at Summit Station, Greenland, 2020

description: This data set contains measurements from a sodar that is deployed at Summit Station, Greenland as part of the Integrated Characterization of Energy, Clouds, Atmospheric state, and Precipitation at Summit (ICECAPS) Project. This sodar operates at 2100 Hz and points vertically. Reflectivity measurements from the system are used to characterize gradients in the lower atmosphere that provide information on the boundary layer depth. Data files including the relative backscatter. Detailed information on file parameters and other aspects of the dataset are included in the netCDF header information for each file. The sodar is owned by the National Oceanic and Atmospheric Administrations's Earth System Research Laboratory. Field operations are supported by the National Science Foundation's Arctic Observing Network (AON) Program.
DOI: 10.5281/zenodo.5110977
title: XXX

description: XXX
DOI: 10.5281/zenodo.5110976
title: XXX

description: XXX
DOI: 10.18739/a2hh6c68c
title: Surface sediment samples collected from the Canadian Coast Guard Ship (CCGS) Sir Wilfrid Laurier 2019, Northern Bering Sea to Chukchi Sea

description: The Pacific sector of the Arctic Ocean is experiencing major reductions in seasonal sea ice extent and increases in sea surface temperatures. One of the key uncertainties in this region is how the marine ecosystem will respond to seasonal shifts in the timing of spring sea ice retreat and/or delays in fall sea ice formation. Variations in upper ocean water hydrography, planktonic production, pelagic-benthic coupling and sediment carbon cycling are all influenced by sea ice and temperature change. To more systematically track the broad biological response to sea ice retreat and associated environmental change, an international consortium of scientists have developed a coordinated Distributed Biological Observatory(DBO) that includes selected biological measurements at multiple trophic levels, along with satellite and mooring measurements. The DBO currently focuses on five regional biological "hotspot" locations along a latitudinal gradient that allows for consistent sampling and monitoring at five biologically productive locations across a latitudinal gradient: DBO 1 (SLIP)-south of St. Lawrence Island (SLI), DBO2 (Chirikov)-north of SLI, DBO3 (southern Chukchi Sea), DBO4-NE (northeast) Chukchi Sea, and DBO5-Barrow Canyon.
DOI: 10.6084/m9.figshare.14997759
title: Assemblies from SPAdes

description: Assembled contigs from raw sequences using SPAdes
DOI: 10.5517/ccdc.csd.cc27sqh8
title: CCDC 2079340: Experimental Crystal Structure Determination

description: Related Article: Alexander Brinkmeier, Roland A. Schulz, Moritz Buchhorn, Can-Jerome Spyra, Sebastian Dechert, Serhiy Demeshko, Vera Krewald, Franc Meyer|2021|J.Am.Chem.Soc.|143|10361|doi:10.1021/jacs.1c04316
DOI: 10.5517/ccdc.csd.cc27sqj9
title: CCDC 2079341: Experimental Crystal Structure Determination

description: Related Article: Alexander Brinkmeier, Roland A. Schulz, Moritz Buchhorn, Can-Jerome Spyra, Sebastian Dechert, Serhiy Demeshko, Vera Krewald, Franc Meyer|2021|J.Am.Chem.Soc.|143|10361|doi:10.1021/jacs.1c04316
DOI: 10.5517/ccdc.csd.cc27w016
title: CCDC 2081527: Experimental Crystal Structure Determination

description: Related Article: Subhi A. Al-Jibori, Ghassan H. H. Al-Jibori, Syrran S. Saleh, Ahmed S. Al-Janabi, Mariano Laguna, Christoph Wagner|2021|Polyhedron|206|115353|doi:10.1016/j.poly.2021.115353
DOI: 10.5517/ccdc.csd.cc27w027
title: CCDC 2081528: Experimental Crystal Structure Determination

description: Related Article: Subhi A. Al-Jibori, Ghassan H. H. Al-Jibori, Syrran S. Saleh, Ahmed S. Al-Janabi, Mariano Laguna, Christoph Wagner|2021|Polyhedron|206|115353|doi:10.1016/j.poly.2021.115353
DOI: 10.6084/m9.figshare.14998278
title: Particulate sulfate and oxalate sampled during two aircraft campaigns over continental North America

description: Airborne data of particulate sulfate and oxalate collected by a particle-into-liquid sampler (PILS) and analyzed by ion chromatography (IC) for two aircraft campaigns: (1) the International Consortium for Atmospheric Research on Transport and Transformation (ICARTT; Ohio River Valley, USA; August 2004), and (2) the Gulf of Mexico Atmospheric Composition and Climate Study (GoMACCS; Texas, USA; August – September 2006).
DOI: 10.6084/m9.figshare.14998278.v1
title: Particulate sulfate and oxalate sampled during two aircraft campaigns over continental North America

description: Airborne data of particulate sulfate and oxalate collected by a particle-into-liquid sampler (PILS) and analyzed by ion chromatography (IC) for two aircraft campaigns: (1) the International Consortium for Atmospheric Research on Transport and Transformation (ICARTT; Ohio River Valley, USA; August 2004), and (2) the Gulf of Mexico Atmospheric Composition and Climate Study (GoMACCS; Texas, USA; August – September 2006).
DOI: 10.6084/m9.figshare.14997759.v1
title: Assemblies from SPAdes

description: Assembled contigs from raw sequences using SPAdes
DOI: 10.5281/zenodo.5082189
title: Derived data for: "Magnetic reconnection near the planet as a possible driver of Jupiter's mysterious polar auroras"

description: Derived data for "Magnetic reconnection near the planet as a possible driver of Jupiter’s mysterious polar auroras", accepted for publication in the Journal of Geophysical Research: Space Physics.
DOI: 10.5281/zenodo.5082188
title: Derived data for: "Magnetic reconnection near the planet as a possible driver of Jupiter's mysterious polar auroras"

description: Derived data for "Magnetic reconnection near the planet as a possible driver of Jupiter’s mysterious polar auroras", accepted for publication in the Journal of Geophysical Research: Space Physics.
DOI: 10.5281/zenodo.5047408
title: Comparison of CIMI simulations and TWINS observations on 28th--29th June 2013

description: The file contains results of CIMI simulations for 28-29 June 2013. Variation of ion pressure, anisotropy, intensity and ion spectra with locations given by radial distance rho and magnetic local time is included.
DOI: 10.5281/zenodo.5047407
title: Comparison of CIMI simulations and TWINS observations on 28th--29th June 2013

description: The file contains results of CIMI simulations for 28-29 June 2013. Variation of ion pressure, anisotropy, intensity and ion spectra with locations given by radial distance rho and magnetic local time is included.
DOI: 10.5281/zenodo.5069945
title: Dataset of Instagram images related to Korean museums

description: This dataset contains images crawled from Instagram using two hashtags related to two exhibitions held Seoul (South Korea) in 2020 and 2021. One is a generic hashtag for the Museum of Modern and Contemporary Art, MMCA, (#국립현대미술관), the other is for the "instagrammable" exhibition "Yumi's Cell Special Exhibition" (#유미의세포들특별전). The dataset contains more than 20,000 images (Image_all_*.zip). It also includes a selection of images in which there are humans interacting with objects and art installations: 9,409 (Yumi) and 810 (MMCA) (Image_Human_*.zip). The two archives Skeleton_*.zip contain the results of the skeleton analysis of such images, done with OpenPose.
DOI: 10.5281/zenodo.5069944
title: Dataset of Instagram images related to Korean museums

description: This dataset contains images crawled from Instagram using two hashtags related to two exhibitions held Seoul (South Korea) in 2020 and 2021. One is a generic hashtag for the Museum of Modern and Contemporary Art, MMCA, (#국립현대미술관), the other is for the "instagrammable" exhibition "Yumi's Cell Special Exhibition" (#유미의세포들특별전). The dataset contains more than 20,000 images (Image_all_*.zip). It also includes a selection of images in which there are humans interacting with objects and art installations: 9,409 (Yumi) and 810 (MMCA) (Image_Human_*.zip). The two archives Skeleton_*.zip contain the results of the skeleton analysis of such images, done with OpenPose.
DOI: 10.5281/zenodo.5061352
title: LeafSnap30

description: LeafSnap30 is a (modified) subset of the images from the 30 species with the highest number of images from the LeafSnap dataset. LeafSnap is an electronic field guide for identifying tree species from photos of their leaves. The original dataset consists of images taken from 2 different sources as well as their segmented versions using the LeafSnap segmentation algorithm. The two sources are: high quality "lab" images of pressed leaves from the Smithsonian collection and "field" images taken by mobile devices in outdoor environments. The original "lab" images contain size and color calibration rulers, which interfere with the training of end-to-end Deep Learning (DL) models for automatic tree species classification. Therefore, we have semi-manually cropped the "lab" images of the 30 species with most number of images in order to keep only the leaves and we do not include the segmentation masks. The original "lab" leaf images are also included in the dataset, but the file paths point only to the cropped ones. The original dataset has been released in 2012 (before the DL revolution in Computer Vision) in order to promote further research in leaf recognition. The authors ask their paper to be sited (see original link above) if the dataset is used. We are releasing the cropped subset as the LeafSnap30 dataset in order to demonstrate the performance of eXplainable AI (XAI) methods applied on DL models trained to solve simple, yet realistic scientific problem.
description: This dataset will be used for demonstration purposes in the open-source Deep Insight and Neural Network Analysis (DIANNA) project, whose goal is to provide a library for explainable AI methods for scientists. DIANNA is work in progress at the time of publishing this dataset version (July 2021): https://github.com/dianna-ai/
DOI: 10.5281/zenodo.5014700
title: FeM dataset – An iron ore labeled images dataset for segmentation training and testing

description: This dataset is composed of 81 pairs of correlated images. Each pair contains one image of an iron ore sample acquired through reflected light microscopy (RGB, 24-bit), and the corresponding binary reference image (8-bit), in which the pixels are labeled as belonging to one of two classes: ore (0) or embedding resin (255). The sample came from an itabiritic iron ore concentrate from Quadrilátero Ferrífero (Brazil) mainly composed of hematite and quartz, with little magnetite and goethite. It was classified by size and concentrated with a dense liquid. Then, the fraction -149+105 μm with density greater than 3.2 was cold mounted with epoxy resin and subsequently ground and polished. Correlative microscopy was employed for image acquisition. Thus, 81 fields were imaged on a reflected light microscope with a 10× (NA 0.20) objective lens and on a scanning electron microscope (SEM). In sequence, they were registered, resulting in images of 999×756 pixels with a resolution of 1.05 µm/pixel. Finally, the images from SEM were thresholded to generate the reference images. Further description of this sample and its imaging procedure can be found in the work by Gomes and Paciornik (2012). This dataset was created for developing and testing deep learning models on semantic segmentation tasks. The paper of Filippo et al. (2021) presented a variant of the DeepLabv3+ model that reached mean values of 91.43% and 93.13% for overall accuracy and F1 score, respectively, for 5 rounds of experiments (training and testing), each with a different, random initialization of network weights. For further questions and suggestions, please do not hesitate to contact us. <strong>Contact email</strong>: ogomes@gmail.com If you use this dataset in your own work, please cite this DOI: 10.5281/zenodo.5014700 Please also cite this paper, which provides additional details about the dataset: Michel Pedro Filippo, Otávio da Fonseca Martins Gomes, Gilson Alexandre Ostwald Pedro da Costa, Guilherme Lucio Abelha Mota. <em>Deep learning semantic segmentation of opaque and non-opaque minerals from epoxy resin in reflected light microscopy images</em>. <strong>Minerals Engineering</strong>, Volume 170, 2021, 107007, https://doi.org/10.1016/j.mineng.2021.107007.
description: The support of Brazilian funding agencies CAPES, CNPq, FAPERJ, and Finep is gratefully acknowledged.
description: {"references": ["Gomes, O.D.M., Paciornik. S., 2012. Multimodal microscopy for ore characterization. In Kazmiruk, V., (Ed.) Scanning Electron Microscopy. IntechOpen, London. p. 313\u201334. https://doi.org/10.5772/34180.", "Filippo, M.P.; Gomes, O.D.M.; da Costa, G.A.O.P.; Mota, G.L.A. Deep learning semantic segmentation of opaque and non-opaque minerals from epoxy resin in reflected light microscopy images. Minerals Engineering, Volume 170, 2021, 107007, https://doi.org/10.1016/j.mineng.2021.107007."]}
DOI: 10.5281/zenodo.5014699
title: FeM dataset – An iron ore labeled images dataset for segmentation training and testing

description: This dataset is composed of 81 pairs of correlated images. Each pair contains one image of an iron ore sample acquired through reflected light microscopy (RGB, 24-bit), and the corresponding binary reference image (8-bit), in which the pixels are labeled as belonging to one of two classes: ore (0) or embedding resin (255). The sample came from an itabiritic iron ore concentrate from Quadrilátero Ferrífero (Brazil) mainly composed of hematite and quartz, with little magnetite and goethite. It was classified by size and concentrated with a dense liquid. Then, the fraction -149+105 μm with density greater than 3.2 was cold mounted with epoxy resin and subsequently ground and polished. Correlative microscopy was employed for image acquisition. Thus, 81 fields were imaged on a reflected light microscope with a 10× (NA 0.20) objective lens and on a scanning electron microscope (SEM). In sequence, they were registered, resulting in images of 999×756 pixels with a resolution of 1.05 µm/pixel. Finally, the images from SEM were thresholded to generate the reference images. Further description of this sample and its imaging procedure can be found in the work by Gomes and Paciornik (2012). This dataset was created for developing and testing deep learning models on semantic segmentation tasks. The paper of Filippo et al. (2021) presented a variant of the DeepLabv3+ model that reached mean values of 91.43% and 93.13% for overall accuracy and F1 score, respectively, for 5 rounds of experiments (training and testing), each with a different, random initialization of network weights. For further questions and suggestions, please do not hesitate to contact us. <strong>Contact email</strong>: ogomes@gmail.com If you use this dataset in your own work, please cite this DOI: 10.5281/zenodo.5014700 Please also cite this paper, which provides additional details about the dataset: Michel Pedro Filippo, Otávio da Fonseca Martins Gomes, Gilson Alexandre Ostwald Pedro da Costa, Guilherme Lucio Abelha Mota. <em>Deep learning semantic segmentation of opaque and non-opaque minerals from epoxy resin in reflected light microscopy images</em>. <strong>Minerals Engineering</strong>, Volume 170, 2021, 107007, https://doi.org/10.1016/j.mineng.2021.107007.
description: The support of Brazilian funding agencies CAPES, CNPq, FAPERJ, and Finep is gratefully acknowledged.
description: {"references": ["Gomes, O.D.M., Paciornik. S., 2012. Multimodal microscopy for ore characterization. In Kazmiruk, V., (Ed.) Scanning Electron Microscopy. IntechOpen, London. p. 313\u201334. https://doi.org/10.5772/34180.", "Filippo, M.P.; Gomes, O.D.M.; da Costa, G.A.O.P.; Mota, G.L.A. Deep learning semantic segmentation of opaque and non-opaque minerals from epoxy resin in reflected light microscopy images. Minerals Engineering, Volume 170, 2021, 107007, https://doi.org/10.1016/j.mineng.2021.107007."]}
DOI: 10.25345/c5mc2n
title: MassIVE MSV000087839 - GC-MS Aristolochia gigantea and Aristolochia warmingii
title: GC-MS analysis of the fractions of the extracts from Aristolochia gigantea and Aristolochia warmingii
DOI: 10.6084/m9.figshare.14919726.v1
title: KSTAR Supplementary Data

description: This contains all raw data and predictions of the studies covered in the KSTAR publication. Data folders are broken up based on figures and match KSTAR data folder structures for results.