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104 results for “accretion”
Risk Factors for Occurrence of Placenta Accrete Spectrum Following Primary Cesarean Delivery
ClinicalTrials.gov study NCT04264169. IPD Sharing: NO. Countries: 1. Publications: 1.
The Effect of Vitamin D on Bone Accretion and Turn-Over in Young Girls
ClinicalTrials.gov study NCT00267540. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Neuromuscular Electrical Stimulation (NMES) and Muscle Protein Accretion (ES-PRO)
ClinicalTrials.gov study NCT01615276. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Fertility Post Placenta Accrete
ClinicalTrials.gov study NCT02821702. IPD Sharing: NO. Countries: 1. Publications: 9.
Data from: Indirect human impacts reverse centuries of carbon sequestration and salt marsh accretion
Open the record for dataset details and reuse information.
Cataloging Accreted Stars within Gaia DR2 Using Deep Learning
<p>In [<a href="http://arxiv.org/abs/1907.06652">1907.06652</a>], a neural network is used to determine whether a star was accreted onto the Milky Way or was born in situ using only 5D phase space information. The inputs to the network are [l, b, parallax, pmra, pmdec] from the Gaia DR2 catalog. Stars with a score greater than 0.75 are marked as accreted. The subset of stars with 6D phase space information was studied in more detail in [<a href="https://arxiv.org/abs/1907.07190">1907.07190</a>] and [<a href="http://arxiv.org/abs/1907.07681">1907.07681</a>].</p> <p>In this release of the catalog, the data is in the HDF5 file format. We recommended using the pandas package within python (with <a href="https://www.pytables.org/usersguide/installation.html">pytables</a> installed). Then the DataFrame can be loaded with </p> <pre><code class="language-python">Stars = pd.read_hdf('Public_AllStars_6D.h5')</code></pre> <p>We have included 4 different files. The scores for all of the stars with <span class="math-tex">\(\delta \varpi / \varpi < 0.1\)</span> are given in Public_AllStars_5D.h5. The subset of these stars which also have line-of-sight velocity measurements are given in Public_AllStars_6D.h5. The files denoted with SelectedByNetwork have only stars which pass the neural network selection as being accreted, with 5D and 6D the same as for AllStars. Each file include the columns</p> <ol> <li><strong>source_id, int64</strong></li> <li><strong>l, float64</strong></li> <li><strong>b, float64</strong></li> <li><strong>ra, float64</strong></li> <li><strong>dec, float64</strong></li> <li><strong>parallax, float64</strong></li> <li><strong>pmra, float64</strong></li> <li><strong>pmdec, float64</strong></li> <li><strong>phot_g_mean_mag, float32</strong></li> <li><strong>phot_bp_mean_mag, float32</strong></li> <li><strong>phot_rp_mean_mag, float32</strong></li> <li><strong>radial_velocity, float64</strong></li> <li><strong>Score, float32</strong></li> <li><strong>PhotometricScores, float32</strong></li> </ol> <p>The "source_id" should match with Gaia DR2, allowing for easy cross referencing. The "Score" column is the output of the network using only 5D kinematic information, to be considered as accreted, the score needs to be larger than 0.75. The "PhotometricScores" column is the network output for the neural network which uses the "phot" columns along with the 5D kinematics. The optimal cut for this is 0.9, but as shown in the paper, <em>these results are less robust than the first network</em>.</p> <p> </p> <p>If you use our catalog in any of your works, please cite <br> @article{Ostdiek:2019gnb,<br> author = "Ostdiek, Bryan and Necib, Lina and Cohen, Timothy and<br> Freytsis, Marat and Lisanti, Mariangela and<br> Garrison-Kimmel, Shea and Wetzel, Andrew and Sanderson,<br> Robyn E. and Hopkins, Philip F.",<br> title = "{Cataloging Accreted Stars within Gaia DR2 using Deep<br> Learning}",<br> year = "2019",<br> eprint = "1907.06652",<br> archivePrefix = "arXiv",<br> primaryClass = "astro-ph.GA",<br> SLACcitation = "%%CITATION = ARXIV:1907.06652;%%”<br> }<br> and <br> @article{Necib:2019zka,<br> author = "Necib, Lina and Ostdiek, Bryan and Lisanti, Mariangela<br> and Cohen, Timothy and Freytsis, Marat and<br> Garrison-Kimmel, Shea",<br> title = "{Chasing Accreted Structures within Gaia DR2 using Deep<br> Learning}",<br> year = "2019",<br> eprint = "1907.07681",<br> archivePrefix = "arXiv",<br> primaryClass = "astro-ph.GA",<br> SLACcitation = "%%CITATION = ARXIV:1907.07681;%%”<br> }.<br> Also, please cite <br> @article{Necib:2019zbk,<br> author = "Necib, Lina and Ostdiek, Bryan and Lisanti, Mariangela<br> and Cohen, Timothy and Freytsis, Marat and<br> Garrison-Kimmel, Shea and Hopkins, Philip F. and Wetzel,<br> Andrew and Sanderson, Robyn",<br> title = "{Evidence for a Vast Prograde Stellar Stream in the Solar<br> Vicinity}",<br> year = "2019",<br> eprint = "1907.07190",<br> archivePrefix = "arXiv",<br> primaryClass = "astro-ph.GA",<br> SLACcitation = "%%CITATION = ARXIV:1907.07190;%%"<br> }<br> for any follow up study of Nyx.</p>
Maldives Coral Reef Crest Accretion Data 2018-2020
<p>Annual monitoring data of coral reef accretion between 2018 and 2020 on the Keleihutta reef flat, Huvadhoo atoll, southern Maldives.</p> <p>Data generated using a coral reef accretion frame at four sites on the outer reef flat.</p>
Datasets of GRMHD Simulations of Accreting Neutron Stars with Non-Dipole Fields
<p>Datasets for GRMHD Simulations of Accreting Neutron Stars with Non-Dipole Fields</p>
Data used for the paper "Birth and decline of magma oceans in planetesimals. Part 2: Structure and thermal history of early accreted small planetary bodies". Submitted to JGR - Planets.
<p>Script and data to generates the figures displayed in the pre-print.</p>
The importance of Urca-process cooling in accreting ONe white dwarfs
<p>MESA inlists associated with <a href="http://adsabs.harvard.edu/abs/2017MNRAS.472.3390S">Schwab et al. (2017)</a>. MESA version r9793.</p>
Thermal runaway during the evolution of ONeMg cores towards accretion-induced collapse
<p>MESA inlists associated with <a href="http://adsabs.harvard.edu/abs/2015MNRAS.453.1910S">Schwab et al. (2015)</a>. MESA version r5118.</p>
Residual Carbon in Oxygen–Neon White Dwarfs and Its Implications for Accretion-induced Collapse
<p>MESA inlists associated with <a href="http://adsabs.harvard.edu/abs/2019ApJ...872..131S">Schwab & Rocha (2019)</a>. MESA version r9793 and r10108.</p>
Coastal erosion and accretion areas along the Beaufort Sea and Laptev Sea Coasts based on Landsat 1999 - 2014
<p>The dataset covers the Laptev Sea coast from 120 to 168 E and Alaska and Canadian Beaufort Sea Coast from 130 to 168 W. </p> <p>Probabilities of erosion and accretion (change of land to water and visa versa) have been derived from Landsat for the time period 1999–2014. A probability threshold of 50% was applied to separate erosion and accretion areas which are provided as polygons (shape files).</p> <p>Further information regarding the algorithm is available in Bartsch et al. (2020). </p>
Figure 8 from: Bertolino M, Cerrano C, Bavestrello G, Carella M, Pansini M, Calcinai B (2013) Diversity of Porifera in the Mediterranean coralligenous accretions, with description of a new species. ZooKeys 336: 1-37. https://doi.org/10.3897/zookeys.336.5139
Figure 8 - Eurypon denisae. A Tylostyles with variable head B Large acanthostyles C Small acanthostyles D Anisoxeas E Magnifications of the extremities of an anisoxea.
Figure 7 from: Bertolino M, Cerrano C, Bavestrello G, Carella M, Pansini M, Calcinai B (2013) Diversity of Porifera in the Mediterranean coralligenous accretions, with description of a new species. ZooKeys 336: 1-37. https://doi.org/10.3897/zookeys.336.5139
Figure 7 - Clathria (Microciona) haplotoxa. A Specimen on the surface of a coralligenous block B Strongyle C Large acanthostyle D Small acanthostyle E Isochela F Toxa.
Figure 6 from: Bertolino M, Cerrano C, Bavestrello G, Carella M, Pansini M, Calcinai B (2013) Diversity of Porifera in the Mediterranean coralligenous accretions, with description of a new species. ZooKeys 336: 1-37. https://doi.org/10.3897/zookeys.336.5139
Figure 6 - Clathria (Microciona) armata. A Specimen on the surface of the coralligenous block B Large acanthostyle heads C Small acanthostyle D Subtylostyle with spined head E Palmate isochelae F Toxas of variable size, with smooth extremities.
Figure 5 from: Bertolino M, Cerrano C, Bavestrello G, Carella M, Pansini M, Calcinai B (2013) Diversity of Porifera in the Mediterranean coralligenous accretions, with description of a new species. ZooKeys 336: 1-37. https://doi.org/10.3897/zookeys.336.5139
Figure 5 - Paratimea oxeata. A Specimen in the coralligenous accretions (arrows) B Large oxeas C Small oxeas D Oxyasters.
Figure 9 from: Bertolino M, Cerrano C, Bavestrello G, Carella M, Pansini M, Calcinai B (2013) Diversity of Porifera in the Mediterranean coralligenous accretions, with description of a new species. ZooKeys 336: 1-37. https://doi.org/10.3897/zookeys.336.5139
Figure 9 - Eurypon gracilis sp. n. A Holotype B Skeleton C Portion of the skeleton with large and small echinating acanthostyles D Long style E Oxea F Large acanthostyle with scattered small spines G Small acanthostyle.
Figure 2 from: Bertolino M, Cerrano C, Bavestrello G, Carella M, Pansini M, Calcinai B (2013) Diversity of Porifera in the Mediterranean coralligenous accretions, with description of a new species. ZooKeys 336: 1-37. https://doi.org/10.3897/zookeys.336.5139
Figure 2 - Porosity of the coralligenous concretion. A Holes and cavities of the coralligenous concretion B Magnification of the holes C Magnification of a natural hole occupied by spicules of Pachastrella monilifera D Spicules of Jaspis johnstoni in a natural cavity in the coralligenous concretion E Cavity excavated by a boring sponge with excavation marks (pits) on the wall F Border between the area excavated by a boring sponge (right) and the not excavated area (left).
Figure 14 from: Bertolino M, Cerrano C, Bavestrello G, Carella M, Pansini M, Calcinai B (2013) Diversity of Porifera in the Mediterranean coralligenous accretions, with description of a new species. ZooKeys 336: 1-37. https://doi.org/10.3897/zookeys.336.5139
Figure 14 - Haliclona (Gellius) marismedi. A Specimen on the surface of the coralligenous block and insinuating into it B Oxeas C Large toxas D Small toxas E Large sigma F Small sigma.
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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.