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177 results for “ICP”
ICP Displacement Fields from the 2016 Mw 7.8 Kaikōura Earthquake over the Papatea Fault, New Zealand
<p>Three-dimensional displacement fields produced over the Papatea Fault, South Island, New Zealand following the 2016 Mw 7.8 Kaikōura earthquake. The dataset was generated from pre- and post-event aerial image point clouds using a windowed implementation of the iterative closest point algorithm.</p> <p>Creation Date: 8/28/2021</p> <p>Authors: Colin Bloom (University of Canterbury, Christchurch, New Zealand), Tim Stahl (University of Canterbury), and Andy Howell (University of Canterbury/GNS Science, Lower Hutt, New Zealand)</p> <p>Projection: New Zealand Transverse Mercator</p> <p>Scale: 25 m/pixel</p> <p>Notes: There are three displacement directions, east, north, and vertical. Positive values represent east, north, and up in the vertical direction respectively in relation to the pre-event surface. Displacement values are in meters. Extremely high or low data values likely represent noise in the dataset.</p>
Data-driven plasma modelling: Fluorocarbon ICP data set
<p>This is an open source dataset of optical emission spectra and optical images in fluorocarbon plasmas, along with associated tool logs, captured from a Oxford Intruments Plasma Technology PP100 ICP etcher. The dataset consists of Ar, O<sub>2</sub>, Ar/O<sub>2</sub>, CF<sub>4</sub>/O<sub>2</sub> and SF<sub>6</sub>/O<sub>2</sub> gas mixtures etching Si wafers.</p> <p>The data has been split into chunks for uploading to Zenodo, to reconstruct them:</p> <p>$ cat generative_model-fluorocarbon_data_set.tar.xz* > generative_model-fluorocarbon_data_set.tar.xz </p> <p>$ tar -xvf generative_model-fluorocarbon_data_set.tar.xz </p> <p>You can find the code to train an autoencoder model using the optical emission spectra and optical images at our github repo, https://github.com/gregdaly/generative_modelling_for_optical_plasma_diagnostics </p>
Ionome analysis of Salmonella mutants by Inductively coupled plasma mass spectrometry (ICP-MS)
<p>In many Gram-negative bacteria, the stress sigma factor of RNA polymerase, σS/RpoS, remodels global gene expression to reshape the physiology of quiescent cells and ensure their survival under non-optimal growth conditions. In the foodborne pathogen <i>Salmonella enterica</i> serovar Typhimurium, σS is also required for biofilm formation and virulence.</p><p>We have previously shown that a Δ<i>rpoS</i> mutation affects the <i>Salmonella</i> ionome. Indeed, inductively coupled plasma mass spectrometry analyses have unraveled a significant effect of the Δ<i>rpoS </i>mutation on the cellular concentration of manganese, magnesium, cobalt and potassium, suggesting that σS controls fluxes of ions that might be important for the fitness of quiescent cells (Metaane et al. 2022, PLoS ONE 17(3): e0265511).</p><p>Study: These findings prompted us to evaluate the impact on the<i> Salmonella</i> ionome of deletions of genes encoding the <i>Salmonella</i> Mn2+ transporters (<i>sitABCD</i> and <i>mntH</i>), the Co2+ transporter (<i>cbiMNQO</i> operon) and small proteins of unkown function (<i>yqaE</i> and <i>yqjDEK</i>) that accumulate in quiescent <i>Salmonella</i> under the tight control of σS (Levi-Meyrueis et al. PloS one. 2014; 9(5):e96918, Lago et al. Scientific reports. 2017; 7(1):2127 and Metaane et al. 2022, PLoS ONE 17(3): e0265511).</p><p>Material and Methods: Cell-associated contents of several elements were measured by inductively coupled plasma mass spectrometry (ICP-MS) as previously described in Metaane <i>et al </i>2022 PLoS ONE 17(3): e0265511.Dried cell pellets were prepared by V. Monteil and F. Norel (Institut Pasteur, Université de Paris, CNRS UMR3528, Biochimie des Interactions Macromoléculaires, F-75015, Paris, France). Cell-associated contents of several elements were measured by S. Ayrault and L. Bordier (ICP-MS platform, Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRSUVSQ,Université Paris-Saclay, 91191, Gif-sur-Yvette, France)</p><p><strong>This work was supported by the French National Research Agency (ANR-19-CE44-0005-01, PERIOMET project).</strong></p><p><strong>Linked studies:</strong></p><ul><li>NOREL Francoise, MONTEIL Veronique, DOUCHE Thibaut, & MATONDO Mariette. (2023). Global effects of deletions of the sitABCD, mntH, cbiMNQO and corA genes, encoding transporters for manganese, cobalt and magnesium on protein abundance in Salmonella enterica serovar Typhimurium grown to stationary phase in LB. [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8279780</li><li>Metaane S, Monteil V, Douché T, Giai Gianetto Q, Matondo M, Maufrais C, Norel F. Loss of CorA, the primary magnesium transporter of <i>Salmonella, </i>is alleviated by MgtA and PhoP-dependent compensatory mechanisms. PloS one. 2023;18(9):e0291736.</li></ul>
IODP Expedition 361 ICP-AES elemental analysis (interstitial water)
<p>Elemental concentration in interstitial water samples was measured by inductively coupled plasma - atomic emission spectroscopy (ICP-AES). Data are presented by element-wavelength pair (e.g., more than one calcium line may be reported). Elemental lines for which data do not exist for a particular expedition will not appear.</p>
IODP Expedition 366 IW elemental analysis (ICP-solids)
<p>Elemental contents in hard rock and sediment samples was measured by inductively coupled plasma - atomic emission spectrocopy (ICP-AES). Data are presented by element-wavelength pair (e.g., more than one calcium line may be reported). Elemental lines for which data do not exist for a particular expedition will not appear.</p>
IODP Expedition 366 ICP-AES elemental analysis (interstitial water)
<p>Elemental concentration in interstitial water samples was measured by inductively coupled plasma - atomic emission spectroscopy (ICP-AES). Data are presented by element-wavelength pair (e.g., more than one calcium line may be reported). Elemental lines for which data do not exist for a particular expedition will not appear.</p>
Supplement to "The virtual spot approach: a simple method for image U-Pb carbonate geochronology by high-repetition rate LA-ICP-MS" by Hoareau et al
<p>This repository contains supplementary data, excel datasets and python / R codes as supplement to the publication by Hoareau et al.</p> <p>Files are:</p> <ul> <li>pdf with Supplementary material (S1 to S6)</li> <li>Excel spreadsheet with all image ratio and CPS data (Table S7)</li> <li>Iolite4 python plugin for virtual spot calculation (Iolite4_plugin_virtual_spot.py)</li> <li>Jupyter .ipynb file for U-Pb age calculation (Multiage.ipynb, R language)</li> <li>Jupyter .ipynb file for cp-sat minimum rectangle problem calculation (Rects.ipynb, Python language)</li> </ul>
Dataset for publication "Multi-phase quantitative compositional mapping by LA-ICP-MS: analytical approach and data reduction protocol implemented in XMapTools"
<p>Datasets for the publication "Multi-phase quantitative compositional mapping by LA-ICP-MS: analytical approach and data reduction in XMapTools"</p>
An Approach Based on an Increased Bandpass for Enabling the Use of Internal Standards in Single Particle ICP-MS: Application to AuNPs Characterization
<p>This dataset contains the raw data corresponding to the figures of the publication https://doi.org/10.3390/nano13121838</p>
Data of microbiological decomposition monitoring of pine coniferous litter in the soils at the Moscow region by the ICP IM method
<p>Данные мониторинга микробиологического разложения хвойного опада сосны в почвах Московской области по методу ICP IM</p> <p>Data of microbiological decomposition monitoring of pine coniferous litter in the soils at the Moscow region by the ICP IM method</p> <p> </p> <p>Исследования проведены на базе двух особо охраняемых природных территориях (далее ООПТ) в Московской области и г. Москве, расположенных на расстоянии 90 км друг от друга. На обеих ООПТ работы проводились на постоянных пробных площадках (ППП) площадью 1 га.</p> <p>Эталон – Приокско-Террасный государственный природных биосферный заповедник. В заповеднике заложены 4 ППП, расположенные в бассейне малой реки Тоденка, большая часть бассейна которой находится в границах Заповедника. ППП расположены не далее 2 км от русла реки, в преобладающих по площади в ООПТ типах лесах: сосняке сложном (две ППП), березняке сложном широкотравном и дубраве широкотравной. В сосняках Заповедника были заложены 2 ППП, различающиеся по увлажнению и месторасположению, на террасе и на коренном берегу.</p> <p>Модельная находится под более высокой антропогенной нагрузкой в г. Москве с лесопарковая часть природно-исторического парка «Кузьминки-Люблино» (далее – Лесопарк), имеющая также схожий с Заповедником рельеф и породный состав лесов. Территории Заповедника и Лесопарка имеют сходство физико-географических условий формирования: обе ООПТ расположены на надпойменных террасах крупных рек Волжского бассейна (рек Оки и Москвы соответственно), на обеих территориях формируются слабо дифференцированные дерново-подзолистые почвы ржавоземы на флювиогляциальных песках (Brunic Arenosols) под сосновыми и березовыми лесами. Основные физико-химические свойства почв Лесопарка соответствуют естественным аналогам.</p> <p><strong>Метод изучения скорости разложения опада.</strong> Методической основой проводимых измерений скорости биоразложения опада является метод подпрограммы «MB Microbial decomposition» программы ICP IM [https://www.syke.fi/en-US/Research__development/Nature/Monitoring/Integrated_Monitoring/Manual_for_Integrated_Monitoring] с некоторыми изменениями.</p> <p>Изменения методики касались срока экспозиции. Мы использовали стандартный срок экспозиции в 1 год и отказались от экспозиции иголок более 1 года, как рекомендовано в методике ICP IM. Такая модификация метода позволила существенно сократить трудозатраты при получении сравнимых результатов.</p> <p>Измерение разложения опада проводились методом закладки конвертов из нейлоновой сетки 8 на 8 см с ячеей 1 мм с упакованными в них пробами на срок 1 год. Конверты запечатывались металлическими скобами. Пробы закладывались и снимались в последней декаде октября – начале ноября. В таблицах и тексте год указывается по году снятия образца, соответственно, пробы, заложенные в 2013 г. и собранные в 2014 г. относятся к 2014 г.</p> <p>Согласно методике, в экспериментах по изучению разложения опада использовались иголки сосны обыкновенной (<em>Pinus</em> <em>sylvestris</em> L.), которые собирались с ветвей невысоких деревьев (10–20 лет) в одном и том же квартале Заповедника и только пожелтевшие, перед их массовым опадом (обычно в начале октября). Предварительно все пробы высушивались до абсолютно сухого веса при 85 <sup>0</sup>С и взвешивались перед упаковкой в конверты. Взвешивание осуществлялось с точностью до 0.001 г, вес проб соснового опада 1 г.</p> <p>Конверты каждый год раскладывались на каждой ППП размерами 100×100 м возле углов и в центре пробных площадей в пределах квадрата 3×3 м в фиксированных точках. Конверты располагались в верхних 5 см почвы, под углом в 15<sup>0</sup>, под моховым покровом (при его наличии) или подстилкой и привязывались леской для удобства поиска. Не допускалось размещение конверта в приствольном круге деревьев. После экспозиции в течение 12 месяцев (350–380 суток) конверты снимались, а содержимое проб аккуратно с использованием пинцета промывалось дистиллированной водой от твердых частиц субстрата и мицелия грибов до остатков иголок и высушивалось до абсолютно сухого веса. Измерение потери массы каждой пробы проводилось методом взвешивания с точностью 0.001 г. Ежегодно закладывалось по 5 проб на каждой ППП в сосняке и березняке, но через год не всегда удавалось найти все заложенные конверты, так как происходили ветровалы и иные нарушения. На ППП в дубняке закладывалось по 10 конвертов.</p>
IODP Expedition 372A ICP-AES elemental analysis (interstitial water)
<p>Elemental concentration in interstitial water samples was measured by inductively coupled plasma - atomic emission spectroscopy (ICP-AES). Data are presented by element-wavelength pair (e.g., more than one calcium line may be reported). Elemental lines for which data do not exist for a particular expedition will not appear.</p>
IODP Expedition 374 ICP-AES elemental analysis (interstitial water)
<p>Elemental concentration in interstitial water samples was measured by inductively coupled plasma - atomic emission spectroscopy (ICP-AES). Data are presented by element-wavelength pair (e.g., more than one calcium line may be reported). Elemental lines for which data do not exist for a particular expedition will not appear.</p>
IODP Expedition 352 ICP-AES elemental analysis (solids)
<p>Elemental contents in hard rock and sediment samples was measured by inductively coupled plasma - atomic emission spectrocopy (ICP-AES). Data are presented by element-wavelength pair (e.g., more than one calcium line may be reported). Elemental lines for which data do not exist for a particular expedition will not appear.</p>
IODP Expedition 351 ICP-AES elemental analysis (solids)
<p>Elemental contents in hard rock and sediment samples was measured by inductively coupled plasma - atomic emission spectrocopy (ICP-AES). Data are presented by element-wavelength pair (e.g., more than one calcium line may be reported). Elemental lines for which data do not exist for a particular expedition will not appear.</p>
IODP Expedition 351 ICP-AES elemental analysis (interstitial water)
<p>Elemental concentration in interstitial water samples was measured by inductively coupled plasma - atomic emission spectroscopy (ICP-AES). Data are presented by element-wavelength pair (e.g., more than one calcium line may be reported). Elemental lines for which data do not exist for a particular expedition will not appear.</p>
BRAIN Journal-Automatic Anthropometric System Development Using Machine Learning-Figure 4. Flowchart and results of ICP algorithm
<p>The key concept of the standard ICP algorithm can be summarized in two steps: - Compute correspondences between the two scans. - Compute a transformation which minimizes the distance between corresponding points. It is forced to add a maximum matching threshold dmax. In most implementations of ICP, the choice of dmax represents a tradeoff between convergence and accuracy. A low-value result in bad convergence, a large value causes incorrect correspondences to pull the final alignment away from the correct value. Figure 4 describes the steps of the algorithm which determines the point features closest to object boundary. The result of the algorithm is described by images cut from the program (Нгуен, 2016)</p>
IODP Expedition 350 ICP-AES elemental analysis (interstitial water)
<p>Elemental concentration in interstitial water samples was measured by inductively coupled plasma - atomic emission spectroscopy (ICP-AES). Data are presented by element-wavelength pair (e.g., more than one calcium line may be reported). Elemental lines for which data do not exist for a particular expedition will not appear.</p>
IODP Expedition 350 ICP-AES elemental analysis (solids)
<p>Elemental contents in hard rock and sediment samples was measured by inductively coupled plasma - atomic emission spectrocopy (ICP-AES). Data are presented by element-wavelength pair (e.g., more than one calcium line may be reported). Elemental lines for which data do not exist for a particular expedition will not appear.</p>
IODP Expedition 385 ICP-AES elemental analysis (interstitial water)
<p>Elemental concentration in interstitial water samples was measured by inductively coupled plasma - atomic emission spectroscopy (ICP-AES). Data are presented by element-wavelength pair (e.g., more than one calcium line may be reported). Elemental lines for which data do not exist for a particular expedition will not appear.</p>
IODP Expedition 385 ICP-AES elemental analysis (solids)
<p>Elemental contents in hard rock and sediment samples was measured by inductively coupled plasma - atomic emission spectrocopy (ICP-AES). Data are presented by element-wavelength pair (e.g., more than one calcium line may be reported). Elemental lines for which data do not exist for a particular expedition will not appear.</p>
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.