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313 results for “destructions”
Seasonal non-destructive vegetation measurements at 15 net primary production (NPP) study sites at Jornada Basin LTER, 1989-ongoing
This data package contains non-destructive quadrat measurements collected for the long-term Net Primary Production (NPP) study at the Jornada Basin LTER. Data here include measurements of horizontal cover and vertical height of plants observed at permanent NPP quadrats at 15 study sites. Sites were selected to represent the 5 major ecosystem types in the Chihuahuan Desert (upland grasslands, playa grasslands, mesquite-dominated shrublands, creosotebush-dominated shrublands, tarbush-dominated shrublands). For each ecosystem type, three sites were selected to represent the range in variability in production and plant diversity; thus the locations are not replicates. All sites are excluded from domestic grazing. Eleven sites are in non-grazed pastures, and at the other four sites 1 hectare areas around the observational plots were fenced in 1988. At all sites a grid of 49 (48 at one playa location) 1m x 1m replicate quadrats was laid out when sampling began in 1989. Grids consist of 49 quadrats arranged in a square 7 x 7 pattern, with quadrats 10 m apart (P-COLL has 48 quadrats in a 3 x 16 pattern). Standing vegetation in quadrats is sampled three times a year: in winter (February - March), before shrubs begin spring growth; in spring (May), when shrubs and spring annuals have reached peak biomass; in fall (late summer; October), when summer annuals have reached peak biomass but before killing frosts. Additional observations of plant count and phenological stage are also made. This dataset is subsequently used to determine quadrat biomass and net primary production. Details and linked data packages are described in the methods element. This is an ongoing dataset with new quadrat measurements collected in the spring, fall and winter of each year. Attention: 1) For most species, these data are not appropriate for estimates of percentage cover because of the way the data are collected. See Note 1 in the methods element for further details.
Accompanying data for the paper "Non destructive qualification of bovine meat tenderness"
<p>This repository contains the raw data used in the article "Non destructive qualification of bovine meat tenderness",<br>authored by Sarah Iaquinta, Axel Conti, Mathis Coué, Étienne Roquefeuil, Jonathan Barés, Romain Léger, Arnaud Regazzi,<br>Stéphane Corn, Xavier Aleyrangues, Alain Peyron, and Anne-Sophie Caro.</p> <p>The repository is organized in three kind of files, where the raw data from the experiments are stored. <br>- "compression_test.xlsx" : Workbook containing the time-displacement-force measured during the compression tests;<br>- "indentation_relaxation.xlsx" : Workbook containing the time-displacement-force measured during the indentation-relaxation tests;<br>- .csv files containing the laser acquisition of the z_distance of the center of the indented surface during time.</p> <p>* The nomenclature of each sheetname and .csv file starts with the experiment date (April the 04th, 2023), then is the meatcut identifier <br>(FF = "faux filet", the french for "sirloin", and RDG = "rond-de-gîte", the french for "round". <br>Then are numbers to identify the number of the testing. </p>
Destruction of the Cultural-Archaeological Landscape in the Gaza Strip
This dataset contains 10 archaeological sites in Gaza strip destroyed during the war 2023-2024. The dataset was compiled by Ministry of Tourism & Antiquities of Palestine (MOTA) team, Dr. Sufyan Deis.
Orthophotos, DSMs and interpretation files of the remote sensing assessment of archaeological damage and destruction at Nineveh, Iraq, during the ISIS occupation
<p>Archaeological heritage has long been threatened by damage or destruction during armed conflicts. Recently, however, deliberate destruction has increasingly become a major part of daily threats in some areas. In that context these datasets describe the results of a programme of remote sensing of damage at Nineveh, within a wider research initiative involving six years of monitoring in northern Iraq. Analysis of satellite imagery, low-and level airphotography observation were combined in a comprehensive assessment of the damage. These datasets present an updated topographic map of Nineveh and its city walls, with a summary of the damage encountered.</p>
Destructive potential of planetary meteotsunami waves: ATAL ocean model results
<p> Based on the well-documented Hunga Tonga–Hunga Ha'apai volcano explosive eruption on 15 January 2022, we developed the "Atmospheric Tsunami Associated with Lamb waves" or ATAL ocean model and performed 12 realistic and process oriented numerical simulations to assess the sea-level hazards posed by planetary meteotsunami waves. Here, we provide the ATAL model results of maximum sea-levels during day 1 and day 2 after the eruption for:</p> <p>- the baseline simulation: trying to reproduce the event as realistically as possible</p> <p>- the 10 resonance simulations (_r_): trying to derive the speed of the Lamb waves which will generate the maximum resonance (i.e., Proudman resonance) in the ocean basins by dividing the baseline speed by r = 1.25, 1.40, 1.50, 1.60, 1.65, 1.75, 2.00, 3.00, 4.00, 5.00. The full Proudman resonance was obtained for r = 1.50</p> <p>- the amplification simulation (_amp_): trying to match the Proudman resonance amplification by multiplying by 10 the Lamb waves amplitudes</p>
Non-destructive thickness mapping of wafer-scale hexagonal boron nitride down to a monolayer
<p>Raw data associated with the publication "Non-Destructive Thickness Mapping of Wafer-Scale Hexagonal Boron Nitride<br> Down to a Monolayer" published in ACS Applied Materials & Interfaces under the DOI: 10.1021/acsami.8b08609</p>
Supplementary data to "Destruction and regrowth of lithospheric mantle beneath large igneous provinces"
<p>Database files to accompany "<em>Destruction and regrowth of lithospheric mantle beneath large igneous provinces</em>", By <a href="http://www.science.org/doi/10.1126/sciadv.adf6216">Stephenson et al. (2023)</a>. The article can be accessed by following <a href="http://www.science.org/doi/10.1126/sciadv.adf6216">this permanent link</a>.</p> <p>The primary resources in this database are (i) estimates of melt equilibration pressure and temperature calculated using the scheme of <a href="https://github.com/fmcnab/meltPT">McNab & Ball (2023)</a>; (ii) a database of lithospheric thickness estimates beneath modern intraplate magmatic provinces using geochemical and seismological techniques; (iii) a database of the outlines and ages of large igneous provinces, substantially updated from <a href="http://https://doi.org/10.1029/93RG02508">Coffin & Eldholm (1994)</a>, and <a href="https://doi.org/10.5670/oceanog.2006.13">Coffin et al. (2006)</a>; (iv) a database of large igneous province eruption centres; and (v) a document of references used to build these databases. Files are numbered as in the Supplementary Information of <a href="http://www.science.org/doi/10.1126/sciadv.adf6216">the paper.</a> Please see below for more details.</p> <ol> <li><strong>Data S1</strong>. A database of global geochemical compositions of mafic intraplate magmatic rocks compiled by <a href="http://doi.org/10.1038/s41467-021-22323-9">Ball et al (2021)</a>, and corresponding estimates of melt equilibration pressure and temperature P<sub>eq</sub> and T<sub>eq</sub>, respectively; <a href="http://www.science.org/doi/10.1126/sciadv.adf6216">this study</a>). Note that authors should cite <a href="http://doi.org/10.1038/s41467-021-22323-9">Ball et al. (2021)</a> in reference to the global geochemical database. They should cite <a href="http://www.science.org/doi/10.1126/sciadv.adf6216">Stephenson et al (2023)</a> in reference to the global equilibration pressure and temperature estimates, in which case they should also cite <a href="http://github.com/fmcnab/meltPT">McNab & Ball (2023)</a>, whose software was used to calculate P<sub>eq</sub> and T<sub>eq</sub>.</li> <li><strong>Data S2</strong>. A spreadsheet containing modern-day lithospheric thickness estimates beneath modern intraplate provinces. For complete references to geochemical analyses contained in this database, please see <a href="https://doi.org/10.1038/s41467-021-22323-9">Ball et al (2021)</a>. The database includes lithospheric thickness estimates obtained <ul> <li>by exploiting melt equilibration pressure and temperature <a href="http://www.science.org/doi/10.1126/sciadv.adf6216">(this study)</a>;</li> <li>by inverse modelling of rare earth element compositions <a href="https://doi.org/10.1038/s41467-021-22323-9">(Ball et al., 2021)</a>; and</li> <li>from the lithospheric thickness model of<a href="https://doi.org/10.1038/s41561-020-0593-2"> Hoggard et al. (2020)</a>, which is based upon the tomographic model of <a href="https://doi.org/10.1093/gji/ggt095">Schaeffer & Lebedev (2013)</a>.</li> </ul> </li> <li><strong>Data S3</strong> & <strong>S4</strong>. A database containing outlines of magmatic provinces dating back to 750 Ma, including <ul> <li>a directory (Data_S3.zip) containing the unfiltered database shape files (lips.shp, lips.shx, lips.dbf, lips.cpg). This directory also contains the same data in a multisegment text file for plotting in the Generic Mapping Tools (polys_ID_age_unfiltered.dat) in which each polygon is separated by '>' where the header indicates polygon ID and time since eruption. And</li> <li>a database filtered for final magmatic event in a given location (Data_S4.dat), where each polygon header also contains '>' ID age polygon_area'. See <a href="http://www.science.org/doi/10.1126/sciadv.adf6216">the paper</a> for methodological details.</li> </ul> </li> <li><strong>Data S5</strong>. A database of located LIP eruption centres.</li> <li><strong>Data S6</strong>. A pdf document of references. The document includes <ul> <li>references used to update locations and ages of the large igneous province database of <a href="http://doi.org/10.5670/oceanog.2006.13">Coffin et al. (2006)</a>;</li> <li>references for existing lithospheric thickness models used test our observed LAB depth as a function of time relationship; and</li> <li>references used to locate the eruption centres of mantle plumes (i.e. Data S5; <a href="http://www.science.org/doi/10.1126/sciadv.adf6216">this study</a>).</li> </ul> </li> </ol>
Shapefiles with the outline of maximum water spread resulting from the catastrophic release of the Kakhovka Reservoir after the destruction of the Kakhovka Hydroelectric Power Plant by Russian occupying forces
<p>The map is based on remote sensing data from Sentinel-2A (Processing Level L2A), dated June 8, June 13, and June 18, 2023, and Landsat-9 (Collection 2 Level-1), dated June 9, 2023. </p> <p>The following Sentinel-2 remote sensing data granules were used:<br>S2A_MSIL2A_20230608T084601_N0509_R107_T36TUS_20230608T132103.SAFE S2A_MSIL2A_20230608T084601_N0509_R107_T36TVS_20230608T132103.SAFE<br>S2A_MSIL2A_20230608T084601_N0509_R107_T36TWS_20230608T132103.SAFE<br>S2A_MSIL2A_20230608T084601_N0509_R107_T36TVT_20230608T132103.SAFE<br>S2B_MSIL2A_20230613T084609_N0509_R107_T36TUS_20230613T102806.SAFE<br>S2B_MSIL2A_20230613T084609_N0509_R107_T36TVS_20230613T102806.SAFE<br>S2B_MSIL2A_20230613T084609_N0509_R107_T36TWS_20230613T102806.SAFE<br>S2A_MSIL2A_20230618T084601_N0509_R107_T36TUS_20230618T151602.SAFE<br>S2A_MSIL2A_20230618T084601_N0509_R107_T36TVS_20230618T151602.SAFE<br>S2A_MSIL2A_20230618T084601_N0509_R107_T36TWS_20230618T151602.SAFE</p> <p>The following remote sensing data scenes from Landsat-9 were used:<br>LC09_L1TP_179028_20230609_20230610_02_T1<br>LC09_L1TP_179027_20230609_20230610_02_T1</p> <p>The contour of the maximum water spread was constructed using a method of manual visual interpretation of remote sensing data, relying on knowledge of the local terrain. We consciously chose not to use automated methods with water indices such as the Normalized Difference Water Index (NDWI) or the Modified Normalized Difference Water Index (MNDWI), as these do not effectively distinguish water surfaces in areas covered with forest or dense reed thickets. Similarly, we did not use the SRTM digital elevation model due to significant artifacts in the study area, where the model shows the height of the forest canopy instead of the ground surface in forested areas.</p> <p>For visual interpretation of Sentinel-2A remote sensing data, we used combinations of spectral bands NIR-Red-Green (8-4-3) and SWIR2-NIR-Green (12-8-3). For the visual interpretation of Landsat-9 remote sensing data, we used combinations of bands SWIR1-NIR-Red (6-5-4) and NIR-Red-Green (5-4-3). To better align the resolution of Sentinel-2A remote sensing data (10 m/pixel) with that of Landsat-9 (30 m/pixel), the latter's data was enhanced using the panchromatic channel (Band 8) through IHS-based pansharpening to 15 m/pixel. The pansharpening was performed using a custom bash script, utilizing command-line tools and utilities such as ImageMagick (<a href="https://imagemagick.org" rel="nofollow">https://imagemagick.org</a>), listgeo, and geotifcp (<a href="https://github.com/OSGeo/libgeotiff">https://github.com/OSGeo/libgeotiff</a>). To expedite the pansharpening process, both Landsat scenes were cropped to the study region and merged by bands using the gdal_translate and gdal_merge.py utilities from the GDAL library (<a href="https://gdal.org/" rel="nofollow">https://gdal.org/</a>). For convenience, the Sentinel-2A data tiles T36TUS, T36TVS, and T36TWS were also cropped and merged by bands using custom scripts available at <a href="https://doi.org/10.5281/zenodo.13205058" rel="nofollow">https://doi.org/10.5281/zenodo.13205058</a>.</p> <p>During visual interpretation, the above-mentioned remote sensing data were compared with satellite images acquired before the destruction of the Kakhovka Hydroelectric Power Plant. In particular, Landsat-9 remote sensing data were compared with Landsat-8 data from June 1, 2023, and Sentinel-2A data were compared with Sentinel-2B data from June 3, 2023.</p> <p>Repository files:<br>floodMax_UTM36N.zip — contains the shapefile in UTM36N projection (EPSG:32636);<br>floodMax_WGS84.zip — contains the shapefile in geographic coordinates in WGS84 (EPSG:4326);<br>floodMax_WGS84.geojson.zip — contains a GeoJSON file in WGS84 coordinates (EPSG:4326).</p> <div> <h1>Web version of the map</h1> </div> <p>The web version of the maximum water spread map is available at:<br><a href="https://yumoskalenko.github.io/floodmap_Kakhovka2023/" rel="nofollow">https://yumoskalenko.github.io/floodmap_Kakhovka2023/</a></p> <p> </p> <p>Embed code for the map on a webpage:</p> <div> <pre><code><iframe style="border: 1px solid black" src="https://yumoskalenko.github.io/floodmap_Kakhovka2023/index.html" marginwidth="0" marginheight="0" scrolling="no" width="100%" height="360" frameborder="0"></iframe> </code></pre> <div> </div> </div> <p><em><strong>This scientific and technical product was created by the scientists of the Black Sea Biosphere Reserve of the National Academy of Sciences of Ukraine during the implementation of research on the topic "Monitoring the condition of natural complexes of the Black Sea Biosphere Reserve (‘Chronicle of Nature’)" (state registration number 0121U109174).</strong></em></p>
Рис. 7. Удельный вес ЭкЗемплЯров глицимериса (%, от обЩего числа раковин и створок) со следами периостракума на иЗученном участке. Fig. 7. Proportions of the Glycymeris yessoensis specimens with traces of periostracum (of the total number of shells and valves) on the beach studied. in Shell destruction of the bivalve mollusk Glycymeris yessoensis on a beach in Possjet Bay (the Sea of Japan)
Рис. 7. Удельный вес ЭкЗемплЯров глицимериса (%, от обЩего числа раковин и створок) со следами периостракума на иЗученном участке. Fig. 7. Proportions of the Glycymeris yessoensis specimens with traces of periostracum (of the total number of shells and valves) on the beach studied.
Рис. 1. Раковина хоккайдского глицимериса Glycymeris yessoensis (Sowerby III, 1889) снаруЖи и иЗнутри. Японское море, Зал. Посьета, береговые выбросы, длина раковины 38.5 мм, ЗоомуЗей ДВФУ № 10034/Bv-493. in Shell destruction of the bivalve mollusk Glycymeris yessoensis on a beach in Possjet Bay (the Sea of Japan)
Рис. 1. Раковина хоккайдского глицимериса Glycymeris yessoensis (Sowerby III, 1889) снаруЖи и иЗнутри. Японское море, Зал. Посьета, береговые выбросы, длина раковины 38.5 мм, ЗоомуЗей ДВФУ № 10034/Bv-493.
Рис. 6. Удельный вес ЭкЗемплЯров глицимериса (%, от обЩего числа раковин и створок) с механическими повреЖдениЯми на иЗученном участке. Fig. 6. Proportions of the Glycymeris yessoensis specimens with mechanical damage (of the total number of shells and valves) on the beach studied. in Shell destruction of the bivalve mollusk Glycymeris yessoensis on a beach in Possjet Bay (the Sea of Japan)
Рис. 6. Удельный вес ЭкЗемплЯров глицимериса (%, от обЩего числа раковин и створок) с механическими повреЖдениЯми на иЗученном участке. Fig. 6. Proportions of the Glycymeris yessoensis specimens with mechanical damage (of the total number of shells and valves) on the beach studied.
Fig. 2. A in Shell destruction of the bivalve mollusk Glycymeris yessoensis on a beach in Possjet Bay (the Sea of Japan)
Fig. 2. A map of Possjet Bay with indication of Cape Shelekha and the area studied with sample's numbers (A).
Fig. 5. A in Non-destructive analysis of pathological belemnite rostra by micro-CT techniques
Fig. 5. A. Rostrum of belemnite Gonioteuthis sp., RUB-Pal 11301, Campanian, Höver (NW-Germany) with forma aegra clavata (coll. L. Kaecke). Surface images showing no irregularities except for silification rings (A1–A3). Longitudinal section showing a homogenous internal rostrum with a dark layer along its outer margin (silica) (A4). Volume rendering image shows a darker centre due to the maximum thickness of the structure but no additional features (A5). Detail of the rostrum surface showing silification rings (A6). Cross section with a homogeneous centre and a dark margin (A7). B. Rostrum of belemnite Hibolithes jaculoides Swinnerton, 1937, RUB-Pal 11303, Hauterivian, Resse (NW-Germany) with forma aegra clavata (coll. U. Frerichs). Surface images showing the overall irregular rostrum morphology and the blunt and hollow apex in lateral (B1, B2), ventral (B3), and dorsal (B4), apical (B7) views. Longitudinal sections (central, subcentral) showing the broken juvenile rostrum, parts of the preserved phragmocone, and notable the lack of the apical line after the injury took place, note the irregular outline of the hollow central canal (B5, B6). Cross section with the juvenile rostrum, and subsequently deposited homogeneous material, white areas indicate the presence of pyrite (B8).
Fig. 3. A in Non-destructive analysis of pathological belemnite rostra by micro-CT techniques
Fig. 3. A. Rostrum of belemnite Neoclavibelus subclavatus (Voltz, 1830), SNSB-BSPG-83264, Toarcian, Mistelgau (SW-Germany) with forma aegra bullata (coll. H. Keupp). Surface images showing the bump-shaped irregular rostrum growth (A1–A4); longitudinal sections showing presence of sediment (diffuse grey) and pyrite (white) within the rostrum, and increasing irregular growth increments (A5, A6). B. Rostrum of belemnite Belemnitella sp., RE 551.763.333 A 5238, Late Cretaceous, NW-Germany with forma aegra bullata (coll. Baschin). Surface images showing the bump-shaped irregular rostrum growth and imprints of an organic network on the rostrum surface in dorsolateral (B1, B2) and ventrolateral (B3, B4) views; longitudinal sections (B5, B6), and cross section (B7). B5–B7 showing silification (black) along the rostrum surface but also along the malformed area.
Fig. 7. A in Non-destructive analysis of pathological belemnite rostra by micro-CT techniques
Fig. 7. A. Rostrum of belemnite Gonioteuthis sp., SNSB-BSPG-83370, Campanian, Höver (NW-Germany) with forma aegra hamata (coll. H. Keupp, leg. C. Spaeth). Surface images showing the knee-like strongly bent and irregular morphology of the rostrum (A1–A4). Median section showing the irregular internal silification of the rostrum indicating poorly mineralized areas, growth and apical line partially visible (A5). Volume rendering image with the darkest areas represented by the thickest or densest areas (A6). B. Rostrum of belemnite Pseudobelus sp., RUB-Pal 3196, Valanginian, Barret-Meouge (France) with forma aegra hamata (coll. M.-C. Picollier). Surface images showing the strongly bent and irregular morphology of the rostrum with the apex growth in anterior direction (B3–B6). Median section overview and close up showing growth increments and the presence of pyrite along the apical line (white) (B1, B7, B8). Cross section showing four growth center representing a temporal sequence (B2).
Fig. 6 in Non-destructive analysis of pathological belemnite rostra by micro-CT techniques
Fig. 6. Rostra of belemnite Gonioteuthis spp. A. RUB-Pal 11302, Campanian, Höver (NW-Germany) with forma aegra angulata (coll. U Frerichs). Surface images showing the knee-like morphology of the rostrum, and the attachment-base of an oyster, in lateral (A1, A4), dorsal (A2), and ventral (A4) views; note the weak furrows in A3. Median sections perpendicular to each other showing silicified areas (darker) and the broken phragmocone now filled with sediment, no additional internal feature visible (A5, A6). B. SNSB-BSPG-83246, Campanian, Höver (NW-Germany) with forma aegra angulata (coll. H. Keupp, leg. C. Spaeth). Surface images showing the knee-like morphology of the rostrum, in ventral (B1), lateral (B2, B4), and dorsal (B5) views. Cross section (B3). Median sections showing silicified areas specifically at places heavily bent (darker) (B6, B7); see also A5, A6 for the same phenomenon. Black box indicates close up in B7, showing the broken juvenile rostrum with growth increment (forma aegra clavata), and the phragmocone partially filled with pyrite (white).
Fig. 1 in Non-destructive analysis of pathological belemnite rostra by micro-CT techniques
Fig. 1. Rostrum of belemnite?Acrocoelites sp., PIMUZ 37346, Toarcian, Altdorf (SW-Germany) with forma aegra saepia (leg. M. Weissmüller). A. Overview A1, A2). B. Close-up of the two apices, showing the radial furrows covering the apex that represent the "normal" tip of the rostrum (B1–B5).
Figure 1 in Needlepoint non-destructive internal tissue sampling for precious fish specimens
Figure 1. – Non-destructive tissue sampling. A: (from top to bottom) dental probe, 1 mL insulin syringe MYJECTOR ® U-100 and specimen 19058 (standard length: 141.1 mm), male Oostethus manadensis; B: View of the brooding pouch and eggs of specimen 14964 (standard length: 113.23 mm), Coelonotus argulus, male; missing eggs correspond to the sample for DNA extraction; C: Scratching the inside via the urogenital papilla with the dental probe; D: Internal tissue sampling using the syringe via the urogenital papilla; E: State of urogenital papilla before sampling; F: State of urogenital papilla after sampling. Photos B to F are ventral views of the specimens; the arrow in B gives the orientation of the specimen in photos A, B; the arrow is C gives the orientation of the specimen in photos C-F.
Fig. 2 in Post-harvest crop destruction effects on picture-winged fly (Diptera: Ulidiidae) emergence
Fig. 2. Spring trial mean (± SE) adult silk fly emergence from soil within cages erected over the treatment plots where plants were plowed, disked twice (2x), or lef standing.
Fig. 1 in Post-harvest crop destruction effects on picture-winged fly (Diptera: Ulidiidae) emergence
Fig. 1. Fall trial mean (± SE) adult silk fly emergence from soil within cages erected over the treatment plots where plants were disked once (1x), disked twice (2x), mowed, or lef standing.
ScienceDex guides
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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.