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16 results for “TOC”
Table S1 The grain size, TOC and elemental composition of sediment samples in the northern South China Sea
<p><span>Grain size measurements of the sediments were carried out on a Mastersizer 3000 laser particle size analyzer</span><span>. Prior to analysis organic matter was removed by leaching the sediment with 15% H<sub>2</sub>O<sub>2</sub>. Total organic carbon (TOC) of the sediments was measured in </span><span>Thermo EA-IsoLink elemental analyzer</span><span>. Prior to analysis carbon bound to carbonate minerals was removed by leaching the sediment with 10% HCl. Total element concentrations (Al, Ca, Ti, Fe, Mn) of the bulk sediments were measured after acid digestion (HF, HNO<sub>3</sub> and HCl) by </span><span>Thermo iCAP 7400 ICP-OES</span><span>. Solid phase iron speciation data were measured following sequential Fe extraction (Fe<sub>carb</sub>, Fe<sub>ox</sub>, Fe<sub>mag</sub>, Fe<sub>py</sub>) by T</span><span>hermo iCAP 7400 ICP-OES</span><span>.</span></p>
Dataset for the Global Prediction Of Total Organic Carbon In Marine Sediments Using Deep Neural Networks (nn-toc)
<p>The data folder contains the raw features and labels used for training machine learning models to predict total organic carbon in marine sediments. </p> <p>The data folder has three subfolders:</p> <ol> <li>raw : contains the labels, features and other data used to train the machine learnign models</li> <li>interim : transformed data, which has to be reproduced</li> <li>output : output from the models, used for analysis and visualisation</li> </ol> <p>The data folder has to be integrated in the Git repository nn-toc, to execute the code.</p> <p> </p> <p> </p>
Data and code used in the article "Driving Factors of TOC Concentrations in Four Different Types of Estuaries"
<p>Data and code used in the article "Driving Factors of TOC Concentrations in Four Different Types of Estuaries", specifically included water quality, meteorological, and nutrient data from 4 in situ observations for the years 2002-2008, and example code for implementing BRT using R. Data (Figures 2, 3, 4, 10) can be uploaded for review purposes. Figures 5, 6, 7, 8, and 9 represent the output results of the machine learning model. These data can help the reader to better understand and replicate our research.</p>
PubChem Compound TOC: Drug and Medication Information
<p><strong>ABSTRACT: </strong>PubChem, a widely used chemical information resource, has undergone notable transformations in the last two years. Over 120 data sources have been incorporated into PubChem, enriching its data repository. Key highlights of the updates include the integration of Google Patents data, which significantly expanded the PubChem Patent collection's coverage. Additionally, new data collections for Cell Line and Taxonomy were introduced, offering convenient access to chemical information based on specific cell lines and taxa. The bioassay data model was updated to enhance its functionality. Moreover, PubChem's programmatic access protocols, PUG-REST and PUG-View, received enhancements, including support for target-centric data download and the 'standardize' option for returning standardized chemical structures. Furthermore, PubChemRDF underwent a substantial update. This paper presents a comprehensive overview of these transformative changes.</p> <p><strong>Instruction: </strong>Data underwent a cleaning process involving the removal of duplicates. Collaboratively with my colleague, we identified and eliminated redundant columns from the dataset. Additionally, we successfully modified the names of certain columns to enhance clarity and eliminate redundancy.</p> <p><strong>Inspiration: </strong>The dataset was uploaded to UBRITE for "DGR_DEPOT" summer 2023 team project</p> <p><strong>Acknowledgements: </strong>Sunghwan Kim, Jie Chen, Tiejun Cheng, Asta Gindulyte, Jia He, Siqian He, Qingliang Li, Benjamin A Shoemaker, Paul A Thiessen, Bo Yu, Leonid Zaslavsky, Jian Zhang, Evan E Bolton</p> <p>Kim, Sunghwan et al. “PubChem 2023 update.” <em>Nucleic Acids Res.</em> vol. 51,D1 (2023): D1373-D1380. <a href="https://doi.org/10.1093/nar/gkac956">doi:10.1093/nar/gkac956</a></p> <p><strong>UBRITE LAST UPDATED July 1, 2023</strong></p>
Isotope and faunal abundance and TOC data from eastern Arabian sea during last 25 kiloyear by Majumder et al., 2023
<p>We present multiproxy data spanning between ~25000 and 3500 calibrated years before the present (cal yr BP) from the eastern Arabian sea (EAS). This is possibly the first record of stable isotope record in pteropods from the EAS. Based on this dataset, we found that the Indian summer monsoon (ISM) was weak during the Last Glacial Maximum (LGM). Moreover, upwelling intensity was high during the early Holocene, which was followed by a significant weakening of ISM during the 4.2 ka event.</p>
Validation of 99mTc- EDDA - HYNIC -TOC Kits for Diagnosis of Neuroendocrine Tumors
ClinicalTrials.gov study NCT02691078. IPD Sharing: Not stated. Countries: 1. Publications: 1.
DOTA-TOC in Metastasized Neuroendocrine Tumors
ClinicalTrials.gov study NCT00978211. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Global Prediction Of Total Organic Carbon In Marine Sediments Using Deep Neural Networks (nn-toc) v2
<p>This is the second version that was uploaded to make the code available for the paper submission <strong><span>NN-TOC v1: global prediction of total organic carbon in marine sediments using deep neural networks</span></strong> to the Geoscientific Model Development journal. Here we create a deep neural network based approach for the geospatial predicition of total organic carbon percentages in marine sediments.</p> <p><span>The data folder contains "raw" features and labels, "interim" data for preprocessed features and labels and "output"s produced from the model. While the preprocessed folder contain all the other files that can be produced by running the code. The features are in .nc or .grd file format. The other files are in .xyz or .csv file format.</span></p> <p> </p>
GA-68 DOTA-TOC of Somatostatin Positive Malignancies
ClinicalTrials.gov study NCT02177773. IPD Sharing: NO. Countries: 1. Publications: 0.
SSTR2 Imaging With [68Ga]Ga-DOTA-TOC PET/CT in NPC
ClinicalTrials.gov study NCT06982300. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Feasibility of Using the CGM During TOC From in Persons With T2D Using Insulin
ClinicalTrials.gov study NCT04533945. IPD Sharing: Not stated. Countries: 1. Publications: 0.
68Ga-DOTA-TOC PET/CT in Imaging Participants With Neuroendocrine Tumors
ClinicalTrials.gov study NCT03001349. IPD Sharing: Not stated. Countries: 1. Publications: 0.
A Pilot Study Of Ga-68-DOTA-TOC Imaging In Participants With Small Bowel Carcinoid Tumors
ClinicalTrials.gov study NCT03057509. IPD Sharing: NO. Countries: 1. Publications: 0.
Yttrium-90 DOTA-TOC Intra-arterial (IA) Peptide Receptor Radionuclide Therapy (PRRT) for Neuroendocrine Tumor
ClinicalTrials.gov study NCT03197012. IPD Sharing: NO. Countries: 1. Publications: 0.
Pharmacist-Led Transition of Care Program in the Emergency Department (Pharm TOC-ED): A Pilot Trial
ClinicalTrials.gov study NCT07310199. IPD Sharing: NO. Countries: 1. Publications: 0.
PharmD Transitions of Care Program (PHARMD-TOC): A Community Pharmacy Transitions of Care Program
ClinicalTrials.gov study NCT03617380. IPD Sharing: Not stated. Countries: 0. Publications: 0.
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International Brain Laboratory public data
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OpenNeuro
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