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228 results for “burials”
Burial of a nomad warrior AIK2B2.
Russia, Republic of Bashkortostan, Abzelilovsky district. Avlasovsky necropolis. Kurgan №2. Burial №2. Fragment of the collective burial of soldiers. Early Iron Age. Chronology: V-VI centuries BC. Early Sarmatian culture. Россия, Республика Башкортостан, Абзелиловский район. Некрополь Авласовский. Курган №2. Захоронение №2. Фрагмент коллективного захоронения воинов. Ранний железный век. Хронология: V-VI века до н.э. Ранняя сарматская культура. Source: Objaverse 1.0 / Sketchfab
Zalezlice_bronze age burial_feature 5
Source: Objaverse 1.0 / Sketchfab
Supplementary Data for "Sedimentary conditions drive modern pyrite burial flux to exceed oxidation"
<h2>Supplementary Data for "Sedimentary conditions drive modern pyrite burial flux to exceed oxidation"</h2> <p><strong><br>The Supplementary Data</strong> is divided into the following folders:</p> <ol> <li> <p><strong>Model Validation</strong><br>This folder contains a collection of downcore profiles used for model validation. The profiles were compiled from various datasets to assess the accuracy and performance of the model. This Folder also contains a Validation subfolder, where model-data fits for all profiles are shown. </p> </li> <li> <p><strong>Raw Data</strong><br>This folder includes all the original global datasets utilized in the study. These data were compiled from multiple sources and serve as the foundational input for the analysis presented in the publication.</p> </li> <li> <p><strong>Processed Data</strong><br>This folder contains the processed datasets, which include global products derived from the raw data. Additionally, it contains validation data that has been extracted and compiled for use in the study. Subfolders provide gridded global data used as model inputs and the corresponding gridded global outputs generated by the model. All gridded data is provided as comma-delimited <code>.txt</code> files.</p> </li> </ol> <p><strong>Data Structure and Files</strong></p> <p>Within the processed data, you will find the following key files:</p> <ul> <li> <p><strong><code>latitude.txt</code></strong><br>Contains gridded latitude values.</p> </li> <li> <p><strong><code>longitude.txt</code></strong><br>Contains gridded longitude values.</p> </li> <li> <p><strong>Variable Files (e.g., <code>pyrite_burial_rate.txt</code>):</strong><br>Each variable you wish to work with or display is stored in its own file.</p> </li> <li> <p><strong>Output Grids: <br></strong>pyrite burial rate is <strong><code>pyrite_burial_rate.txt</code> </strong>in g cm⁻² y⁻¹<br>pyrite isotopic composition is <strong><code>delta_pyrite.txt</code> </strong>in ‰ (permil)<br>pyrite formation depth is <strong><code>z_max.txt</code> </strong>in cm <br>total depth-integrated pyrite content is <strong><code>total_mols_pyrite.txt</code></strong> in mol cm⁻².</p> </li> </ul> <p>When working with the data, you need to combine the <code>lat</code> and <code>long</code> grids with the variable file of interest to visualize or analyze the dataset.</p> <h2>Working with the Data in MATLAB</h2> <p>If you are using MATLAB, you can display the data using the <code>geoshow</code> function. For example, to display the pyrite burial rate:</p> <ol> <li> <p><strong>Read the Data:</strong></p> <div> <div><code>latitude = read('latitude.txt'); </code></div> <div><code>longitude = read('longitude.txt'); </code></div> <div><code>data = read('pyrite_burial_rate.txt'); </code></div> </div> </li> <li> <p><strong>Display the Data on a World Map:</strong></p> <div> <div> </div> <div><code>worldmap world </code></div> <div><code>geoshow(latitude, longitude, data, 'DisplayType', 'texturemap')</code></div> <div><code>set(gca,'ColorScale','log')</code></div> <div> </div> <div>Alternatively, you can use the provided <code>plot_global_data.m</code> function located in the <code>/plotting_functions</code> <a href="https://zenodo.org/uploads/14808279">Model</a> folder. </div> <div> </div> </div> </li> </ol> <h2>Working with the Data in Python</h2> <p>If you are using Python, you can work with the gridded data as follows:<code> </code></p> <ol> <li> <p><strong>Read the Data:</strong></p> <div> <div><code>import pandas as pd </code></div> <div><code>import numpy as np </code></div> <div><code>import matplotlib.pyplot as plt </code></div> <div><code>import cartopy.crs as ccrs</code></div> <div><code>from matplotlib.colors import LogNorm</code></div> <div> </div> <div><code>latitude = pd.read_csv('GlobalGridsOutput/latitude.txt', delimiter=',', header=None).values </code></div> <div><code>longitude = pd.read_csv('GlobalGridsOutput/longitude.txt', delimiter=',', header=None).values </code></div> <div><code>data = pd.read_csv('GlobalGridsOutput/pyrite_burial_rate.txt', delimiter=',', header=None).values </code></div> </div> <p> </p> </li> <li> <p><strong>Plotting the Data:</strong><br>You can plot the data on a world map using <code>cartopy</code>:</p> <div> <div> </div> <div><code>fig = plt.figure(figsize=(10, 5)) </code></div> <div><code>ax = plt.axes(projection=ccrs.PlateCarree()) </code></div> <div><code>ax.coastlines() </code></div> <div><code>mesh = ax.pcolormesh(longitude, latitude, data, transform=ccrs.PlateCarree(), cmap='viridis') </code></div> <div><code>plt.colorbar(mesh, ax=ax, orientation='vertical', label='Pyrite Burial Rate') </code></div> <div><code> plt.show() </code></div> </div> </li> </ol> <div> <div> </div> </div> <p><br><br></p>
Changes in the Carbon Dioxide Content in the Body During a Simulated Avalanche Burial With and Without the Use of a Breathing Tube System.
ClinicalTrials.gov study NCT06802744. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Data from: Effects of organism and substrate size on burial mechanics of English sole, Parophrys vetulus
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Data from: Buried alive: the behavioural response of the mussels, Modiolus modiolus and Mytilus edulis to sudden burial by sediment
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Data from: The value of trophic interactions for ecosystem function: dung beetle communities influence seed burial and seedling recruitment in tropical forests
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Data from: Warming enhances sedimentation and decomposition of organic carbon in shallow macrophyte-dominated systems with zero net effect on carbon burial
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Data from: Understory succession after burial by tephra from Mount St. Helens
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Data from: Sediment burial intolerance of marine macroinvertebrates
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Associations among cotyledon developmental stability, canalization and phenotypic plasticity in response to shading and burial depth in five herbaceous species at early seedling stage
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Species interactions and plant traits of dune grasses in a burial experiment
A multi-factorial greenhouse experiment of three common dune grasses (Ammophila breviligulata, Uniola paniculata, and Spartina patens) in different interspecific combinations was performed, using sand burial and salinity sea spray as abiotic stressors. Sand burial (25 cm) was applied once at the beginning of the study. Sea spray was applied three times per week. Morphological measurements (leaf elongation, maximum root length, biomass, leaf C, N and isotopes) were collected after 12 weeks.
FIG. 4 in The worked bone industry and intrusive fauna associated with the prehistoric cave burials of Abri des Autours (Belgium)
FIG. 4. — Some of the animal bones from the Neolithic collective burial of Abri des Autours: A, inferior left canine of a male suid; B, awl made of a sheep or goat metatarsal; C, E, rods made of red deer antler; D, unmodified fragment of roe deer metatarsal; F, rib of a large bovid fashioned into a point (from left to right: internal, lateral and external views). Scale bar: 5 cm.
Data from: Interactive effects between physical forces and ecosystem engineers on seed burial: a case study using Spartina anglica
Seed burial (i.e. vertical seed dispersal) has become increasingly valued for its relevance for seed fate and plant recruitment. While ecosystem engineers have been generally considered as the most important drivers of seed burial, the role of physical forces, such as wind or water flow, has been largely overlooked. Using tidal habitats as a model system, and a combination of flume and mesocosm experiments, we investigated the effects of 1) currents, 2) benthic animals with different engineering activities and 3) their interplay on seed burial of a common salt marsh pioneer plant, Spartina anglica. Our results reveal that in such systems, water flow can be of equal or higher importance than ecosystem engineers for seed burial. For passive seed-burying engineers (PSE), coupling their actions with currents produced synergistic seed burial effects, whereas the interactive effects were only additive for active seed-burying engineers (ASE). This paper extends current understanding of seed burial and seed bank formation by revealing the need to incorporate physical forces into seed burial mechanisms. We provide the first empirical evidence that physical forces influence seed burial by synergistically interacting with ecosystem engineers, thus highlighting the role of biophysical interactions as important drivers for vertical seed movement.
Data from: Ecology and evolution of the diaspore 'burial syndrome'
Hygroscopically active awns or "bristles" have long intrigued scientists. Experimental evidence shows that they are important for diaspore burial in the correct orientation, thereby increasing successful seed germination and seedling survival. Despite these ecological advantages, 38 of the 280 species of grasses in Danthonioideae lack awns. We provide the first study of awns in a phylogenetic context and show that whilst the awnless state has arisen ca. 25 times independently, the ecological disadvantage of not having an awn also applies in an evolutionary context. Only in Tribolium and Schismus have awnless ancestors diversified to form a clade of primarily awnless descendents. Several of the awnless species in these genera are annual and we find a significant correlation between the evolution of awns and the evolution of life history. A suite of other diaspore traits accompany the awned or awnless states. We interpret the awn as being the visible constituent of a compound "burial syndrome", the two ecological extremes of which may explain the correlation between awns and life history and provide an explanation why awnless species in Tribolium and Schismus persist.
The effect of intensive oyster aquaculture on the regional organic carbon burial: insights from a half-century historical record
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Supplementary information_Limited organic carbon burial by the rusty carbon sink in Swedish fjord sediments.
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A quantitative analysis of primary dormancy and dormancy changes during burial in seeds of Brassica napus
<p>For plants inhabiting unpredictable environments, scheduling germination can be challenging. Various responses to environmental conditions have been evolved by plants; these responses combine with variation in local climate to construct germination niche. Germination process may be regulated by a number of factors, among them, the type of seed dormancy and dormancy cycling play an important role in promoting survival after dispersal. In the present study, seeds of <i>Brassica napus</i> were tested for primary conditional dormancy (CD). Dormancy changes were quantified through seed population thermal germination parameters to test whether different genotypes of <i>B. napus</i> seeds (<span>944, 966, Alestrom, Danube, Okanto and Rohan</span>) are non-dormant (ND) at the maturity or if they present primary dormancy (D or CD). In a burial experiment, <i>B. napus</i> seeds dormancy cycling in the natural soil seedbank was investigated. Germination of all genotypes decreased at 5 and >20 <sup>o</sup>C, showing narrower breadth of thermal niche for germination. Dormancy-breaking Treatments lead to the widening of thermal range permissive for germination. The lower limit (<i>T<sub>l(50)</sub></i>) and higher limit (<i>T<sub>h(50)</sub></i>) temperatures for germination decreased and increased, respectively, for non-dormant (after-ripened seeds treated with GA<sub>3</sub>) seeds compared with fresh seeds in all genotypes. In fresh seeds, the <i>T<sub>l(50) </sub></i>and <i>T<sub>h(50)</sub></i> for various genotypes ranged from 3.61 to 6.5<sup>o</sup>C and 25.0 to 29.0<sup>o</sup>C, respectively and ranged from 0.2 to 1.8<sup>o</sup>C and 35 to 41.0<sup>o</sup>C in non-dormant seeds. Thus, fresh seeds of <i>B. napus</i> are dormant at dispersal and adopt delayed germination strategies to avoid summer drought. In the burial experiment, the results indicated that<i> B. napus</i> must have D/ND cycle in which fresh seeds first become dormant and then the cycle begins (CD → D ↔ CD ↔ ND), thus adopting both risk-prone and risk- adverse strategies to spread the likelihood of survival over time.</p>
FIG. 3 in Animal Management, preparation and sacrifice: reconstructing burial 6 at the Moon Pyramid, Teotihuacan, México
FIG. 3. — Eagle tarsometatarsus bones with pathological deformation: A, Element 2069 left side; B, Element 2246 right side; C, Element 1961 right side.
FIG. 2. — A in Animals in Mesolithic Burials in Europe
FIG. 2. — A, Teeth of herbivores; B, Teeth of carnivores, omnivores and bone pendants in Mesolithic burials. (Denmark: 1 Dragsholm, 2 Henriksholm-Bøgebakken,3 Nederst,4 Nivå 10,5 Strøby Egede,6 Vedbaek,Gøngehusvej 7; France: 7 Aven des Iboussières,8 La Chaussée- Tirancourt,9 Le Cheix, 10 Concevreux "les Jombras",11 Hoëdic, 12 Sous Balme, 13 Téviec,14 La Vergne;Germany: 15 Abri Fuchskirche, 16 Bad Dürrenberg,17 Gross Fredenwalde,18 Grosse Ofnet,19 Plau,20 Rathsdorf, 21 Steinhagen;Great Britain: 22 Aveline's Hole;Italy: 23 Arene Candide,24 Grotta dell'Uzzo,25 Mondeval de Sora; Latvia: 26 Zvejnieki;Lithuania: 27 D(u)onkalnis,28 Spiginas;Poland: 29 Brajniki,30 Dudka, 31 Janisławice, 32 Kamieński, site 1, (33 Kasparus), (34 Konne), 35 Łojewo, 36 Mszano, 37 Pierkunowo, 38 Smoląg, (39 Żórawno); Russia: 40 Minino I, 41 Olenij ostrov, 42 Popovo; Spain: 43 La Braña-Arintero, 44 Los Canes; Sweden: 45 Skateholm I, 46 Skateholm II, 47 Tågerup).
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