Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
610
datasets available to search
ShareScore release 0.9.0
Dataset results
610 results for “armor”
Effects of Small-scale Armoring and Residential Development on the Salt Marsh/Upland Ecotone in Coastal Georgia, USA
Use of small-scale armoring placed near the marsh-upland interface to protect single-family homes from flooding is a widespread coastal development practice, but its effect on the environment is under studied. We compared the biota and environmental characteristics of 60 marshes on the coast of Georgia, USA, that were adjacent to either a bulkhead, a residential backyard with no armoring, or an intact forest during June-July 2013 using a nested, spatially blocked sampling design. For each plot in each sampling site, we used real-time kinematic (RTK) GPS to measure the location and elevation of the upper marsh. We collected cores to determine porewater salinity and nutrient concentrations as well as grain size distribution of marsh sediments. We quantified flora (vegetation composition) and fauna (snail, bivalve, and crab abundance) in the upper marsh ecotone. For sites with bulkheads, we recorded the height, thickness and condition of the bulkhead, as well as surveyed the animals living on and around the bulkheads.
ARMOR and NALMA data corresponding to "Observations of anomalous charge structures in supercell thunderstorms in the Southeastern United States"
<p>Dataset includes dual-polarization C-band University of Alabama in Huntsville (UAH) Advanced Radar for Meteorological and Operational Research (ARMOR) data in Raw and quality-controlled Universal Format (UF) from a selected period on 10 April 2009 as well as the National Aeronautics and Space Administration (NASA) Marshall Space Flight Center (MSFC) North Alabama Lightning Mapping Array (NALMA) data in American Standard Code for Information Interchange (ASCII) format from selected period on 10 April 2009. </p> <p>The ARMOR is located at the Huntsville International Airport in Huntsville, Alabama at 34.64597, -86.77131, 200 m MSL. A set of 15 radar sampling volumes between 1712 UTC and 1821 UTC on 10 April 2009 are included in the dataset. Each of the raw and corrected UF files contains horizontal reflectivity (dBZ), differential reflectivity (dB), Doppler velocity (m s<sup>-1</sup>), spectrum width (m s<sup>-1</sup>), differential phase (°), and total power (dBZ) data. The corrected UF files additionally contain horizontal reflectivity and differential reflectivity data corrected for attenuation and differential attenuation following the methods of Bringi et al. (2001). The corrected files also contain estimated differential propagation phase (°) and computed specific differential phase (° km<sup>-1</sup>) data (Hubbert and Bringi 1995). </p> <p> </p> <p>ARMOR file naming conventions are as follows: </p> <p> </p> <p>RAW_NA_000_125_20090410171216.gz</p> <p>RAW: file format</p> <p>125: can scan type, where 125 indicates a full or sector volume plan position indicator </p> <p>20090410171216: date and time in the order of year, month, day, hour, minute, and second</p> <p> </p> <p>ARMOR_20090410171216_qc1.uf.gz</p> <p>ARMOR: radar name</p> <p>20090410171216: date and time in the order of year (YYYY), month (MM), day (DD), hour (HH), minute (MM), and second (SS)</p> <p>qc1: denotes ARMOR processed data</p> <p>uf: denotes the file format </p> <p> </p> <p>NALMA data consist of undecimated VHF source-level lightning measurements in hourly files. The center of the network is located at 34.72461, -86.64533. The network consisted of 11 sensors distributed throughout north Alabama and south-central Tennessee. Information about contributing stations is available in the header of each hourly file, including the station location, status, and the number of sources detected by each station. Further network-specific information documented by Koshak et al. (2004) while Rison et al. (1999) discuss LMA characteristics.</p> <p>Source data include information about the time the source was detected (UTC seconds of the day), latitude and longitude (decimal degrees), altitude (m), reduced chi<sup>2</sup> value associated with post-processing (unitless), power (dBW), and a network mask indicating the detecting stations (unitless). The format is (f15.9 f10.6 f11.6f 7.1 f5.2 f5.1 4x). </p> <p> </p> <p>Hourly file naming conventions are as follows:</p> <p> </p> <p>LYLOUT_090410_160000_3600.dat.gz</p> <p>LYLOUT: LMA file designator</p> <p>090410: date in order of last two digits of year (YY), month (MM), and day (DD)</p> <p>160000: time in order of hour (HH), minute (MM), and second (SS)</p> <p>3600: length of period covered in file in seconds (3600 s = 1 hour)</p> <p> </p> <p>Acknowledgments: </p> <p>Data were collected with support from NASA MSFC Award NNM05AA22A.</p> <p> </p> <p>References:</p> <p>Bringi, V. N., Keenan, T. D., & Chandrasekar, V. (2001). Correcting C-band radar reflectivity and differential reflectivity data for rain attenuation: A self-consistent method with constraints. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, <em>39</em>(9), 1906–1915. https://doi.org/10.1109/36.951081</p> <p>Hubbert, J., and V. N. Bringi, 1995: An iterative filtering technique for the analysis of copolar differential phase and dual-frequency radar measurements. <em>Journal of Atmospheric and Oceanic Technology</em>, <strong>12</strong>, 643–648. </p> <p>Koshak, W. J., Solakiewicz, R. J., Blakeslee, R. J., Goodman, S. J., Christian, H. J., Hall, J. M., … Cecil, D. J. (2004). North Alabama Lightning Mapping Array (LMA): VHF source retrieval algorithm and error analyses. <em>Journal of Atmospheric and Oceanic Technology</em>, <em>21</em>(4), 543–558. https://doi.org/10.1175/1520-0426(2004)021<0543:NALMAL>2.0.CO;2</p> <p>Rison, W., Thomas, R. J., Krehbiel, P. R., Hamlin, T., & Harlin, J. (1999). A GPS-based three-dimensional lightning mapping system: Initial observations in Central New Mexico. <em>Geophysical Research Letters</em>, <em>26</em>(23), 3573–3576.</p>
Supplementary data for "Armored with skin and bone: A combined histological and µCT study of the exceptional integument of the Antsingy leaf chameleon Brookesia perarmata (Angel, 1933)"
<p>This project contains the supplementary µCT-scans of the whole body and a lateral flank integumentary armor of <em>Brookesia perarmata</em> (Angel, 1933) (Squamata: Iguania: Chamaeleonidae) belonging to the following publication:</p> <p>Schucht P, Rühr PT, Geier B, Glaw F & M LAmbertz (<strong>2020</strong>): Armored with skin and bone: A combined histological and µCT study of the exceptional integument of the Antsingy leaf chameleon <em>Brookesia perarmata</em> (Angel, 1933). <em>Journal of Morphology</em>. doi: <a href="https://onlinelibrary.wiley.com/doi/full/10.1002/jmor.21135">10.1002/jmor.21135</a>.</p> <p> </p> <p><strong>Whole body scan:</strong></p> <ul> <li>specimen: ZSM 17/2006, Zoologische Staatssammlung München</li> <li>machine: phoenix nanotom m (GE Measurement & Control)</li> <li>scan settings: <ul> <li>tube voltage = 110 kV</li> <li>ube current = 70 μA</li> <li>target = tungsten</li> <li>no filter</li> <li>total sample rotation = 360°</li> <li>angular step size = 0.24°</li> <li>exposure time = 750 ms</li> <li>binning = 1</li> <li>averaging = 4</li> <li>voxel size = 37.8 μm</li> </ul> </li> <li>filename: Schucht_B_perarmata_whole.tif</li> </ul> <p> </p> <p><strong>Lateral flank integumentary armor scan:</strong></p> <ul> <li>specimen: ZSM 862/2000, Zoologische Staatssammlung München</li> <li>machine: Skyscan 1272 device (Bruker microCT)</li> <li>scan settings: <ul> <li>tube voltage = 70 kV</li> <li>ube current = 142 μA</li> <li>target = tungsten</li> <li>filter = Al 0.5 mm</li> <li>total sample rotation = 180°</li> <li>angular step size = 0.19°</li> <li>exposure time = 1925 ms</li> <li>binning = 2x2</li> <li>averaging = 8</li> <li>random movement = 15</li> <li>voxel size = 4.4 μm</li> </ul> </li> <li>filename: Schucht_B_perarmata_osteoderm.tif</li> </ul>
ARMOR and NALMA data corresponding to 2008 storms analyzed in "Examining conditions supporting the development of anomalous charge structures in supercell thunderstorms in the Southeastern United States"
<p>Total lightning and dual-polarization Doppler velocity data are available from the National Aeronautics and Space Administration (NASA) Marshall Space Flight Center (MSFC) North Alabama Lightning Mapping Array (NALMA) and the C-band University of Alabama in Huntsville (UAH) Advanced Radar for Meteorological and Operational Research (ARMOR), respectively, over selected periods on 6 February 2008 and 11 April 2008. NALMA data are provided in American Standard Code for Information Interchange (ASCII) format and ARMOR data are provided in Raw and quality-controlled Universal Format (UF), where quality control methods are described below. </p> <p> </p> <p>The NALMA data are provided in hourly files which include undecimated point location (source-level) data corresponding to the detection of very high frequency (VHF) radiation emitted during the breakdown of lightning (Rison et al., 1999; Thomas et al., 2001). Source locations were reported from active sensors configured in an 11-sensor array distributed throughout North Alabama and South Central Tennessee, the center of which is located at 34.72641, -86.64533 (Koshak et al. 2004). Data files include information on the time that each source was detected (UTC seconds of the day), the latitude, longitude, and altitude of each source’s location (decimal degrees and m, respectively), the reduced chi<sup>2</sup> value associated with data processing (unitless), a station mask indicating which sensors contributed to the resolved location of each source (unitless). These data are provided in a line-by-line format of (f15.9 f10.6 f11.6f 7.1 f5.2 f5.1 4x). The 2008 data files additionally include a header section that provides further information about each sensor in the network and its relative contribution to the dataset. </p> <p> </p> <p>The hourly fine naming conventions are as follows for the February 2008 data:</p> <p>LMA_NA_6.2_125_2008-02-06_10-00-00.dat.gz</p> <p>LMA_NA: LMA file designator corresponding to the NALMA</p> <p>2008-02-06: year (YYYY)-month (MM)-day (DD)</p> <p>10-00-00: UTC time, (HH)-minute (MM)-second (SS)</p> <p> </p> <p>And for the April 2008 data:</p> <p>LYLOUT_080411_180000_3600.dat.gz</p> <p>LYLOUT: LMA file designator</p> <p>080411: date in order of last two digits of year (YY), month (MM), and day (DD)</p> <p>180000: UTC time in order of hour (HH), minute (MM), and second (SS)</p> <p>3600: length of period covered in file in seconds (3600 s = 1 hour)</p> <p> </p> <p>ARMOR data are provided as sets of 14 (14) sampling volumes corresponding to the 6 February 2008 (11 April 2008) periods between 1002 UTC and 1119 UTC (1844 UTC and 1952 UTC). Each RAW and processed UF file contains horizontal reflectivity (dBZ), differential reflectivity (dB), Doppler velocity (m s<sup>-1</sup>), spectrum width (m s<sup>-1</sup>), differential phase (º), and total power (dBZ) data. Horizontal reflectivity and differential reflectivity data were corrected for attenuation and differential attenuation, differential propagation phase (º) was estimated, and specific differential phase (º km<sup>-1</sup>) was calculated during post-processing (Hubbert and Bringi 1995, Bringi et al. 2001).</p> <p> </p> <p>Acknowledgments: </p> <p>NALMA data were collected with support from NASA MSFC Award NNM05AA22A.</p> <p> </p> <p>References:</p> <p>Bringi, V. N., Keenan, T. D., & Chandrasekar, V. (2001). Correcting C-band radar reflectivity and differential reflectivity data for rain attenuation: A self-consistent method with constraints. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, <em>39</em>(9), 1906–1915. https://doi.org/10.1109/36.951081</p> <p>Hubbert, J., and V. N. Bringi, 1995: An iterative filtering technique for the analysis of copolar differential phase and dual-frequency radar measurements. <em>Journal of Atmospheric and Oceanic Technology</em>, <strong>12</strong>, 643–648. </p> <p>Koshak, W. J., Solakiewicz, R. J., Blakeslee, R. J., Goodman, S. J., Christian, H. J., Hall, J. M., … Cecil, D. J. (2004). North Alabama Lightning Mapping Array (LMA): VHF source retrieval algorithm and error analyses. <em>Journal of Atmospheric and Oceanic Technology</em>, <em>21</em>(4), 543–558. https://doi.org/10.1175/1520-0426(2004)021<0543:NALMAL>2.0.CO;2</p> <p>Rison, W., Thomas, R. J., Krehbiel, P. R., Hamlin, T., & Harlin, J. (1999). A GPS-based three-dimensional lightning mapping system: Initial observations in Central New Mexico. <em>Geophysical Research Letters</em>, <em>26</em>(23), 3573–3576.</p> <p>Thomas, R. J., Krehbiel, P. R., Hamlin, T., Harlin, J., & Shown, D. (2001). Observations of VHF source powers radiated by lightning. <em>Geophysical Research Letters</em>, <em>28</em>(1), 143–146. https://doi.org/10.1029/2000GL011464</p> <p> </p>
Fig. 5 in A new species of whiptail armored catfish, genus Pseudohemiodon (Siluriformes: Loricariidae) from the Orinoco River basin, Llanos region of Colombia and Venezuela
Fig. 5. Map of northern South America (Colombia and Venezuela) showing capture localities of Pseudohemiodon unillano, red star is type locality, some symbols may represents more than one lot.
Fig. 3 in A new species of whiptail armored catfish, genus Pseudohemiodon (Siluriformes: Loricariidae) from the Orinoco River basin, Llanos region of Colombia and Venezuela
Fig. 3. Pseudohemiodon unillano, paratype, IAvH-P 19088, 183.2 mm SL. Detail of buccal ornamentation and teeth. Photograph by L. M. Mesa.
Fig. 1 in A new species of whiptail armored catfish, genus Pseudohemiodon (Siluriformes: Loricariidae) from the Orinoco River basin, Llanos region of Colombia and Venezuela
Fig. 1. Pseudohemiodon unillano, new species, holotype, IAvH-P 19034, 162.0 mm SL. Photograph by J. Lopez-Castaño.
Fig. 2 in A new species of whiptail armored catfish, genus Pseudohemiodon (Siluriformes: Loricariidae) from the Orinoco River basin, Llanos region of Colombia and Venezuela
Fig. 2. Pseudohemiodon unillano, paratype, IAvH-P 19088, 183.2 mm SL. Detail of mouth in live specimen. Photograph by A. Ortega-Lara.
Fig. 4 in Photo-identification as a technique for recognition of individual fish: a test with the freshwater armored catfish Rineloricaria aequalicuspis Reis & Cardoso, 2001 (Siluriformes: Loricariidae)
Fig. 4. Percentage of correct matches (a) and expended minutes (b) between naked-eye and computer-assisted field test photo-identification for individual recognition of Rineloricaria aequalicuspis (n = 9). Boxplots show median (central thicker line), first and third quartile (box limits), 95% confidence interval of median (whiskers), and outliers.
Fig. 3 in Photo-identification as a technique for recognition of individual fish: a test with the freshwater armored catfish Rineloricaria aequalicuspis Reis & Cardoso, 2001 (Siluriformes: Loricariidae)
Fig. 3. Variation in number, shape, size and organization of the bony plates covering the abdominal surface of six different Rineloricaria aequalicuspis individuals with more than 10 cm total length. These are examples of photographs taken during the field test. (a) 175 mm TL; (b) 138 mm TL; (c) 156 mm TL; (d) 145 mm TL; (e) 141 mm TL; (f) 151 mm TL.
Fig. 2 in Photo-identification as a technique for recognition of individual fish: a test with the freshwater armored catfish Rineloricaria aequalicuspis Reis & Cardoso, 2001 (Siluriformes: Loricariidae)
Fig. 2. Diagram showing the steps employed to assess the performance of photo-identification technique in laboratory (a) and field (b) conditions for Rineloricaria aequalicuspis.
Fig. 1 in Photo-identification as a technique for recognition of individual fish: a test with the freshwater armored catfish Rineloricaria aequalicuspis Reis & Cardoso, 2001 (Siluriformes: Loricariidae)
Fig. 1. Lateral, dorsal and ventral views of a Rineloricaria aequalicuspis individual (110 mm TL). Ventral view shows the arrangement of the abdominal plates. Photograph courtesy of L. R. Malabarba.
Figures 10-12 in A new genus and species of armored scale insect (Hemiptera: Diaspididae) from Australia found in the historic Koebele Collection of the California Academy of Sciences John W. Dooley III
Figures 10-12. Diaspididae spp., habitus, detail of venter of L1 lobes, and pygidium ventral (left) and dorsal (right). 10) Dichosoma convexa (after Brimblecombe 1957). 11) Duplaspidiotus claviger (after Ferris 1937). 12) Eulaingia stenophyllae (after Borchsenius and Williams 1963).
Figure 3 in A new genus and species of armored scale insect (Hemiptera: Diaspididae) from Australia found in the historic Koebele Collection of the California Academy of Sciences John W. Dooley III
Figure 3. Protomorgania koebelei adult female (pygidium). A) L1 lobes fused ventrally, appressed dorsally; B) single simple plate between L1 and position of L2 seta; C) anal pore; D) sclerotized arch; E) chitinized and finely stippled cuticle around vulva.
Figure 2 in A new genus and species of armored scale insect (Hemiptera: Diaspididae) from Australia found in the historic Koebele Collection of the California Academy of Sciences John W. Dooley III
Figure 2. Protomorgania koebelei adult female (thorax and Abdomen). A) anterior perispiracular pores; B) dorsal microducts; C) dorsal microducts, magnified; D) roughened cuticle.
Figure 19 in A new genus and species of armored scale insect (Hemiptera: Diaspididae) from Australia found in the historic Koebele Collection of the California Academy of Sciences John W. Dooley III
Figure 19. Pseudotargionia glandulosa, habitus, detail of venter of L1 lobes, and pygidium ventral (left) and dorsal (right) (after Ferris 1937).
Figure 1. Protomorgania koebelei adult female. A in A new genus and species of armored scale insect (Hemiptera: Diaspididae) from Australia found in the historic Koebele Collection of the California Academy of Sciences John W. Dooley III
Figure 1. Protomorgania koebelei adult female. A) habitus; B) tubercle; C) anterior spiracle; D) posterior spiracle; E) pygidial lobes; F) slide mounted female habitus; G) habitus on host; H) close-up of habitus on host
Figures 7-9 in A new genus and species of armored scale insect (Hemiptera: Diaspididae) from Australia found in the historic Koebele Collection of the California Academy of Sciences John W. Dooley III
Figures 7-9. Diaspididae spp., habitus, detail of venter of L1 lobes, and pygidium ventral (left) and dorsal (right) (after Brimblecombe 1957). 7) Diaphoraspis orbata. 8) Diaspidopus distinctus. 9) Diastolaspis novata.
Figures 4-6 in A new genus and species of armored scale insect (Hemiptera: Diaspididae) from Australia found in the historic Koebele Collection of the California Academy of Sciences John W. Dooley III
Figures 4-6. Diaspididae spp., habitus, detail of venter of L1 lobes, and pygidium ventral (left) and dorsal (right) (after Brimblecombe 1957). 4) Achorphora obliqua. 5) Acontonidia triangulari. 6) Aspidonymus woodwardi.
Figures 16-18 in A new genus and species of armored scale insect (Hemiptera: Diaspididae) from Australia found in the historic Koebele Collection of the California Academy of Sciences John W. Dooley III
Figures 16-18. Diaspididae spp., habitus, detail of venter of L1 lobes, and pygidium ventral (left) and dorsal (right). 16) Neoleonardia extensa (after Ferris 1938). 17) Neomorgania eucalypti (after Ferris 1937). 18) Pseudaonidia duplex (after Ferris 1937).
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.