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IODP Expedition 361 Magnetic susceptibility (section half)
<p>Magnetic susceptibility was measured on section halves on the Section Half Multisensor Logger (SHMSL) using a Bartington MS2 meter and either a MS2E or MS2K probe. Because all JRSO cores meet minimum size requirements for these two probes, MSPOINT data are corrected for volume and recorded in SI susceptibility units (x10<sup>-5</sup>).</p>
FIG. 6. — A1-A3 in Early Miocene Gastropods from the Felli Section (Proto-Mediterranean Sea NW Greece)
FIG. 6. — A1-A3, Costoanachis cf. terebralis (Grateloup, 1834), AMPG(IV) 2467; B1, B2, Pusia cf. pyramidella (Brocchi, 1814), AMPG(IV) 2469; C1-C4, Athleta rarispina (Lamarck, 1811), AMPG(IV) 2462; D1-D4, Mangelia (s.l.) sp.: D1, D2, AMPG(IV) 2474; D3, D4, AMPG(IV) 2475, detail of microsculpture (SEM images). Scale bars: A1, A2, B1, B2, D1, D2, 1 mm; C1-C4, 10 mm; D3, 300 μm; D4, 100 μm.
FIG. 9. — A1-A3 in Early Miocene Gastropods from the Felli Section (Proto-Mediterranean Sea NW Greece)
FIG. 9. — A1-A3, Turbonilla (s.l.) sp. 1, AMPG(IV) 2580; B1-B3, Turbonilla (s.l.) sp. 2, AMPG(IV) 2583; C1, C2, Turbonilla (s.l.) sp. 3, AMPG(IV) 2584; D1-D3, Turbonilla (s.l.) sp. 4: D1, AMPG(IV) 2586 (SEM image); D2, D3, AMPG(IV) 2687. Scale bars: 500 µm.
FIG. 8. — A1, A2 in Early Miocene Gastropods from the Felli Section (Proto-Mediterranean Sea NW Greece)
FIG. 8. — A1, A2, 'Odostomia' sp. 2, AMPG(IV) 2558; B1-B3, Megastomia sp. 1, AMPG(IV) 2560; C1, C2, Brachystomia sp., AMPG(IV) 2570; D1-D3, Megastomia sp. 2: D1, AMPG(IV) 2563; D2, D3, AMPG(IV) 2564; E1-E4, Pyramistomia aliakmoni n. sp.: E1, E2, holotype, AMPG(IV) 1500; E3, paratype 1, AMPG(IV) 1501 (SEM image); E4, AMPG(IV) paratype 1, AMPG(IV) 1501, protoconch (SEM image); F1-F4, Parthenina sp. 1: F1, F2, AMPG(IV) 1573; F3, AMPG(IV) 1574 (SEM image); F4, AMPG(IV) 1574, apex (SEM image); G1, G2, Parthenina sp. 2: G1, AMPG(IV) 2575; G2, AMPG(IV) 2575, apex (SEM image); H,?Syrnola sp., AMPG(IV) 2577 (SEM image). Scale bars: A1, A2, B1-B3, D1, G1, H, 500 µm; C1, C2, D2, D3, E1, E2, 1 mm; E3, F1, F2, 400 µm; E4, F3, 200 µm; F4, G2, 100 µm.
Data_text section 3.3_Enzymatic interconversion of the oxysterols 7β,25 dihydroxycholesterol and 7-keto,25-hydroxycholesterol by 11β-hydroxysteroid dehydrogenase type 1 and 2
<p>Data from kinetic characterization (Km and vmaxapp) described in text section 3.3 of Enzymatic interconversion of the oxysterols 7β,25 dihydroxycholesterol and 7-keto,25-hydroxycholesterol by 11β-hydroxysteroid dehydrogenase type 1 and 2</p> <p>Dataset (doi:<a href="https://doi.org/10.1016/j.jsbmb.2019.03.011">10.1016/j.jsbmb.2019.03.011</a>) contains values from kinetic characterization (10.1016_j.jsbmb.2019.03.011_text3.3_Km_vmax) described in text section 3.3 corresponding to raw data obtained from LC-MS/MS analysis provided as three files in CSV format (31003A-179400_date_name_25Oxysterol_4_10_1-3). All further experiment related information and subsequent data analysis provided as two meta-data-files: (31003A-179400_date_name_25Oxysterol_4_9_M_1-2) as TXT and pdf format.</p>
IODP Expedition 362 P-wave velocity bayonet (section)
<p>P-wave velocity data were measured on undisturbed section halves using pairs of piezoelectric transducers mounted in bayonets that are inserted into soft sediment along the JRSO-defined y-axis and/or z-axis. Report includes P-wave velocity in y and/or z direction, bayonet separation, traveltime between transducers, and first arrival picks.</p>
IODP Expedition 362 Magnetic susceptibility (section half)
<p>Magnetic susceptibility was measured on section halves on the Section Half Multisensor Logger (SHMSL) using a Bartington MS2 meter and either a MS2E or MS2K probe. Because all JRSO cores meet minimum size requirements for these two probes, MSPOINT data are corrected for volume and recorded in SI susceptibility units (x10<sup>-5</sup>).</p>
IODP Expedition 362 Thin section images
<p>Hard rock and sediment thin section images were acquired using either the JRSO-developed Petrographic Image Capture and Archival Tool (PICAT) imager or (rarely) an upright microscope and a digital camera. Sample images are acquired in unpolarized, polarized, and/or cross-polarized light. Image files are presented compressed by hole.</p>
IODP Expedition 362 Section-half images
<p>Digital section images were taken of the flat face of split cores on the Section Half Imaging Logger (SHIL) using a linescan camera at a resolution of 20 lines/mm (50 micron pixels). Cores were imaged as soon as possible after splitting to minimize color changes that occur through oxidation and drying. The SHIL produces TIF files as well as reduced-size JPG files. The TIF files are not kept online but users may request them from the <a href="mailto:database@iodp.tamu.edu">IODP-JRSO Data Librarian</a>.</p>
Geophysical data set for San Ramon Fault master section
<p>This data set is a multivariable analysis carried out in the San Ramon Fault (SRF) along a master section perpendicular to the main fault scarp. These data include: (1) a ~ 1 km long Electrical resistivity tomography (ERT) with a dipolo-dipolo configuration, 48 channels every 20 meters (named as the master section). (2) Differential GPS data with the location of the ERT electrodes and gravity stations. (3) Data of the gravity stations, with a Garmin GPS data. (4) Seismic data of an active experiment of 24 channels every 5 m, with several hammer strikes, which are explained in a text-document inside each seismic file (TRV_seismic_data and SRF_seismic_data). (5) Time-series of the Nakamura stations located along the ERT master profile. (6) The voltage decay curve of a TEM-station in the western edge of the master section, which is ready for modeling.</p> <p> </p>
Symptoms in health care workers during the COVID-19 epidemic. A cross-sectional survey.
<p>data collected during the COVID-19 epidemics on workers of the Health Care Unit Roma4, Civitavecchia. Paper submitted.</p>
FIG. 5. — Dendrobium veillonii M in Une espèce nouvelle du genre Dendrobium Sw. (Orchidaceae) de Nouvelle-Calédonie et une clé pour la section Kinetochilus Schltr.
FIG. 5. — Dendrobium veillonii M. Pignal, sp. nov.: A, B, fleur; A, face, 29.VII.2018; B, profil, 25.VIII.2018; C, gaine fendue laissant passer l'inflorescence, tige, 25.VIII.2018; D, fruit; E, plantule adventive (keiki), 25.VIII.2018; F, tige et feuilles faces adaxiales, 25.VIII.2018; G, inflorescence, 21.VIII.2018; H, tige et feuilles faces abaxiales, 25.VIII.2018; I, feuille face abaxiale, 10.XI.2018; J, fruit détail, 25.VIII.2018. Toutes les photos sont issues d'une culture à Nouméa, provenant de la concession Cornwall à Tiébaghi. Échelles: A, B, longueur du pétale: 5 mm; C, E, F, largeur de la tige: 5 mm; G, H, I, longueur de la feuille: 22 mm; D, J, longueur du fruit: 13 mm.
Figure 8. Spheniopsis brasiliensis. A transverse section through a in The organs of prey capture and digestion in the miniature predatory bivalve Spheniopsis brasiliensis (Anomalodesmata: Cuspidarioidea: Spheniopsidae) expose a novel life-history trait
Figure 8. Spheniopsis brasiliensis. A transverse section through a single digestive tubule. AM, Amoebocyte; CRC, crypt cell; DC, digestive cell.
Figure 12. Spheniopsis brasiliensis. A section through a in The organs of prey capture and digestion in the miniature predatory bivalve Spheniopsis brasiliensis (Anomalodesmata: Cuspidarioidea: Spheniopsidae) expose a novel life-history trait
Figure 12. Spheniopsis brasiliensis. A section through a portion of a gonadial follicle. C, Cuticle; DN, dividing nucleus; DO, developing oocyte; EO, encapsulated oocyte; GE, germinal epithelium; N, nucleus; RT, regressing testes; STA, stalk; SPZ, spermatozoan; Y, yolk.
The Psychological Burden of the COVID-19 Pandemic and Its Associated Factors among the Frontline Doctors of Bangladesh: A Cross-sectional Study-Extended Data
<p>Using this document, we tried to assess the mental health status of the frontline doctors of Bangladesh during Coronavirus 2019 pandemic.</p>
Data of fossils and sections of the Geobiodiversity Database (GBDB)
<p>Geobiodiversity database (GBDB, www.geobiodiversity.com) is a comprehensive stratigraphic and palaeontological database and was started in 2006. It is geological section-based and include data of over 26 500 geological sections and over 114 000 fossil taxa.</p> <p>The stratigraphic data in the GBDB are based on those published in Chinese literature since 1920s. By November 2020, all stratigraphic horizons and nearly all published geological sections can be searched and browsed in the GBDB. The GBDB fossil occurrence data are included in the stratigraphic records and can be queried and outputted. The palaeontological data are linked to the fossil collections from individual geological sections and borehole cores. The data include taxonomy (species, genus, family, order, class and division), major group, synonym (opinion data with different authors) and description (key features). Most fossil collections and occurrences of all sections from China are included in the GBDB. Subsequent authors in further study amended a portion of fossil taxa from these sections. In this way, there are also plenty of opinion data in the GBDB.</p> <p>The GBDB was designed to facilitate regional and global scientific collaborations focusing on palaeobiodiversity, systematics, palaeogeography, palaeoecology, regional correlation, and quantitative stratigraphy.</p> <p> </p>
Evaluating registrations of serial sections with distortions of the ground truths. Supplemental data
<p><strong>Evaluating Registrations of Serial Sections With Distortions of the Ground Truths</strong></p> <p>This is the supplemental data for our paper on how to benchmark registrations of serial sections with ground truths. The files are named as follows:</p> <ul> <li>*_challenge.7z: local distortions and global rigid transformations applied, the input for the benchmark we used. Use this to test your rigid and non-rigid methods.</li> <li>*_local-only.7z: only local distortions applied.</li> <li>*_local-DIST.7z: the distortion maps for local distortions.</li> <li>*_SURF-rigid.7z: local distortions and global rigid transformations applied, rigid transformations undone with SURF-based rigid-only method. Local distortions remain. Use this if your method does not cope well with large rigid transformations.</li> <li>_*vis.7z: visualizations of distortions.</li> <li>_rigid_ground.7z: the real rigid transformations used in the global phase.</li> <li>*_ground.7z: the ground truth. All data fit each other, no distortions. Use this to compare your registration result to it.</li> </ul> <p>There are three main modalities and one further, as a reference:</p> <ul> <li>CT_*: µCT data, a rabbit lung, 600 images. (In ground truth, and local distortions, and global transformations we supply more images that went into the benchmark, 50 more from both beginning and end.)</li> <li>EM_*: an EM serial block-face (SBF-SEM) data set of adult mouse lung, 1000 images. (EM ground truth is individually normalized, see paper.)</li> <li>LS_*: a lung from the light sheet microscopy from a male 24 week-old rat, 300 images. (LS ground truth is individually normalized, too.)</li> <li>REAL_*: a region from real serial sections from a rabbit lung, 2 images.</li> </ul> <p>We also supply elastix parameter files.</p> <p>A preprint has been uploaded to <a href="https://arxiv.org/abs/2011.11060">arXiv</a>. The definite version is available from <a href="https://ieeexplore.ieee.org/abstract/document/9594850/media#media">IEEE</a>. The source code of the distorter is available from <a href="https://github.com/olegl/distort">GitHub</a>.</p>
Real and simulated cross-sectional and longitudinal images of hair
<p>This is the dataset containing simulated and real data used in the analyses for the paper "<em><a href="https://www.biorxiv.org/content/10.1101/2020.11.24.392191v1">High-throughput phenotyping methods for quantifying hair fiber morphology</a>" </em>and is part of the<em> <a href="https://tinalasisi.github.io/2020_HairPheno_manuscript/index.html">Hair Phenotyping Methods Project</a> </em>run by <a href="https://www.tinalasisi.com/">Tina Lasisi</a>. </p> <p>The data can be analyzed with the <em>fibermorph</em> Python package available on <a href="https://pypi.org/project/fibermorph/">PyPi</a> and <a href="https://github.com/tinalasisi/fibermorph">Github</a>. </p> <p>This repository has 2 datasets with 2 different types of data:</p> <ol> <li>Simulated hair data <ol> <li>Cross-sectional data (simulated ellipses)</li> <li>Curvature data (simulated arcs)</li> </ol> </li> <li>Real hair data <ol> <li>Cross-sectional data (micrographs of hair fiber cross-sections)</li> <li>Curvature data (longitudinal images of hair fiber fragments)</li> </ol> </li> </ol> <p>Visit the <a href="https://tinalasisi.github.io/2020_HairPheno_manuscript/index.html"><em>Hair Phenotyping Methods Project</em></a> website for the most up to date information about this project and any updates relevant to this dataset.</p> <p>-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>Details</strong></p> <p><strong>Simulated data</strong></p> <ol> <li>Cross-sectional data <ul> <li>These ellipses were simulated with a python script developed as part of <a href="https://github.com/tinalasisi/fibermorph/"><em>fibemorph</em></a>. A version of that code that doesn't require the original Python package has been made available with the dataset (sim_ellipse.py).</li> <li>The script simulates a single cross-section per image.</li> <li>Each image has a width of 5200px and a height of 3900 with a resolution set to 4.25 px/micron.</li> </ul> </li> <li>Curvature data <ul> <li>An R script used for curvature simulation, written by <a href="https://www.arslanzaidi.com/">Arslan Zaidi,</a> has also been made available with this dataset (sim_curvature.R). </li> <li>The script generates 25 arcs per image. We used a set length of 1.57.</li> <li>Each image has a resolution of 132 px/mm. </li> </ul> </li> </ol> <p>Please note that due to the use of random generations, it is not possible to recreate the exact same datasets that are saved here. </p> <p><strong>Real data</strong></p> <p>The real data images are very large files and have been split into multiple zip files. Please check the specific instructions for unzipping split zip files for your OS. </p> <p>The images are from hair samples collected by the <a href="https://shriverlab.psu.edu/">Shriver Lab</a> at Penn State. There were a total of 192 samples, although not all images made it past quality control so certain IDs may have cross-section images but not curvature images or vice versa. </p> <p>The images have been de-identified and the hair samples for these individuals were collected with informed consent and ethical approval by The Pennsylvania State University Institutional Review Board (#44929 and #45727).</p> <ol> <li>Cross-sectional data <ol> <li>We developed and used <a href="https://www.protocols.io/view/sample-preparation-protocol-for-cross-sectional-mi-bbwcipaw">this protocol</a> to embed, section, and image the hairs.</li> <li>We embedded 6 samples per person and took images of both sides of the sectioned sample (A and B). These should be mirror images of each other.</li> </ol> </li> <li>Curvature data <ol> <li>We developed and used <a href="https://www.protocols.io/view/sample-preparation-protocol-for-hair-fiber-curvatu-bbweipbe">this protocol</a> to cut, wash, and image the hairs.</li> <li>We used 3-5 hairs per person where available. A number of samples did not have enough hair for this, so the images contain fewer fragments. We have made these available for full transparency although we filtered them from our analyses downstream.</li> </ol> </li> </ol> <p>Please see the <a href="https://github.com/tinalasisi/2020_HairPheno_manuscript">GitHub repository</a> for additional related participant data we used in our analyses.</p> <p> </p> <p> </p>
Data archive for the peer-reviewed journal article "Variability in the mass absorption cross-section of black carbon (BC) aerosols is driven by BC internal mixing state at a central European background site (Melpitz, Germany) in winter""
<p>Data archive for figures accompanying the peer-reviewed journal article "Variability in the mass absorption cross-section of black carbon (BC) aerosols is driven by BC internal mixing state at a central European background site (Melpitz, Germany) in winter". In 2020 this article was accepted for publication in the journal <em>Atmospheric Chemistry and Physics</em>. Data are uploaded in the form of Igor Pro experiment files (.pxp).</p>
FIG. 15 in Ostracods from the 'Raibl Beds' (Carnian, Late Triassic) of Belca section in Karavanke Mountains, northwestern Slovenia
FIG. 15. — Circular diagrams of faunal composition of ostracod assemblages from examined samples of the Belca section, Karavanke Mountains, northwestern Slovenia, Carnian, Late Triassic.
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.