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990 results for “quantification”
MR Spectra from rat hippocampus with LCModel quantification and the corresponding basis set
<p>This folder contains the LCModel quantifications of spectra acquired in hippocampus from 7 rats. The spectra were quntified using six different DKNTMN (spline stiffness) values (0.1, 0.25, 0.4, 0.5, 1, 5). In the folder Control_files_Basis_set you can find all the control files used in this quantification along with the corresponding basis set (metabolites/simulated using NMRScopeB from jMRUI and <em>in vivo </em>parameters + full MM spectrum).</p> <p>Please cite the following manuscript if you are using the data</p> <p><a href="https://pubmed.ncbi.nlm.nih.gov/34268821/">In vivo macromolecule signals in rat brain 1 H-MR spectra at 9.4T: Parametrization, spline baseline estimation, and T2 relaxation times - PubMed (nih.gov)</a><br> </p>
Datasets of ASONAM-2015 paper "Tweet sentiment: From classification to quantification"
<p>Datasets used for the following ASONAM 2015 paper:<br> ---------------------------------------------------------------------------------------------------<br> Title: Tweet Sentiment: From Classification to Quantification<br> Authors: Wei Gao and Fabrizio Sebastiani<br> Organization: Qatar Computing Research Institute, Hamad Bin Khalifa University, Doha, Qatar<br> ---------------------------------------------------------------------------------------------------</p> <p>[Content]</p> <p>* SemEval2013, SemEval2014, SemEval2015 datasets:<br> - semeval.train.feature.txt: Training set for learning sentiment models at development stage<br> - semeval.dev.feature.txt: Held-out set for tuning parameters<br> - semeval.train+dev.feature.txt: Training set for learning the final sentiment model<br> - semeval13.test.feature.txt: SemEval2013 test set<br> - semeval14.test.feature.txt: SemEval2014 test set<br> - semeval15.test.feature.txt: SemEval2015 test set<br> <br> * Other datasets: sanders, sst, omd, hcr, gasp<br> - X.train.feature.txt: Training set for learning sentiment models at development stage<br> - X.dev.feature.txt: Held-out set for tuning parameters<br> - X.train+dev.feature.txt: Traing set for learning the final sentiment model<br> - X.test.feature.txt: Test set<br> where X is one of sanders, sst, omd, hcr and gasp.</p> <p>For more details, please refer to the paper.</p> <p><br> [Citation]<br> You can cite the folowing paper when referring to the dataset:</p> <p>@inproceedings{gao2015tweet,<br> title={Tweet sentiment: From classification to quantification},<br> author={Gao, Wei and Sebastiani, Fabrizio},<br> booktitle={2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)},<br> pages={97--104},<br> year={2015},<br> organization={IEEE}<br> }</p>
Quantification of plant morphology and leaf thickness with optical coherence tomography
<p>The uploaded scripts and data are used to obtain the figures 2, 4, 5, 6 and 7 in the publication. </p> <p>The code has been run with Python 3.7 in Spyder (Anaconda).</p> <p>There are three scripts, each needing specific datasets to run the code.</p> <p>1. The core is the segmentation of the leaf surface and this is subsequently used to calculate leaf thickness and obtain en-face images.</p> <p><a href="https://zenodo.org/api/files/89412f06-4c84-4516-9e7d-113796b42834/3D_segmentation_thickness_enface.py">3D_segmentation_thickness_enface.py</a>: This file loads the 3D processed OCT data, does the leaf surface segmentation and calculates the en face images. It needs the files processed_3Ddata.npy and videoim.npy</p> <p><a href="https://zenodo.org/api/files/89412f06-4c84-4516-9e7d-113796b42834/processed_3Ddata.npy">processed_3Ddata.npy</a>: This file contains the processed 3D OCT dataset (linear amplitude data), with respectively dimensions z,x,y. The data is saved as uint16 to save memory, and should be converted to double before further processing, as done in the script.</p> <p><a href="https://zenodo.org/api/files/89412f06-4c84-4516-9e7d-113796b42834/videoim.npy">videoim.npy</a>: This file contains the RGB image of Fig. 6(a) as image matrix.</p> <p>2. The non-infiltrated and infiltrated image (Figure 4)</p> <p><a href="https://zenodo.org/api/files/89412f06-4c84-4516-9e7d-113796b42834/2D_fig4.py">2D_fig4.py</a>: This script produces Figure 4 of the paper and also shows the two RGB images that indicate the scan location on the leaf. It needs the files OCTdata_figure4.npy (containing OCT data) and videoimages_figure4.npy</p> <p><a href="https://zenodo.org/api/files/89412f06-4c84-4516-9e7d-113796b42834/OCTdata_figure4.npy">OCTdata_figure4.npy</a>: This file contains the processed 2D OCT dataset (linear amplitude data), with respectively dimensions (a/b),z,x. The data is saved as uint16 to save memory, and should be converted to double before further processing, as done in the script.</p> <p><a href="https://zenodo.org/api/files/89412f06-4c84-4516-9e7d-113796b42834/videoimages_figure4.npy">videoimages_figure4.npy</a> This file contains the two RGB images that show the scan area of the data in Figure 4.</p> <p>3. The calculation of the refractive index and making Figure 5</p> <p><a href="https://zenodo.org/api/files/89412f06-4c84-4516-9e7d-113796b42834/refractiveindex_fig5.py">refractiveindex_fig5.py</a>: this script segments the cuvette wall and leaf surface on 2D images and calculates the refractive index by evaluating equation 1 of the publication. It needs the file images_refractiveindex.npy</p> <p><a href="https://zenodo.org/api/files/89412f06-4c84-4516-9e7d-113796b42834/images_refractiveindex.npy">images_refractiveindex.npy</a>: This file contains the processed 2D OCT dataset (linear amplitude data), with respectively dimensions (leaf/empty),z,x. The data is saved as uint16 to save memory, and should be converted to double before further processing, as done in the script.</p>
Computational atomic coordinate files for Quantification of Ni-N-O bond angles and NO activation by X-ray emission spectroscopy
<p>Geometry optimized coordinates and other atomic coordinate files in xyz format used to calculate X-ray emission spectra of beta-diketiminate nickel nitrosyl complexes.</p>
SPIKEPIPE: A metagenomic pipeline for the accurate quantification of eukaryotic species occurrences and intraspecific abundance change using DNA barcodes or mitogenomes
<p>The accurate quantification of eukaryotic species abundances from bulk samples remains a key challenge for community ecology and environmental biomonitoring. We resolve this challenge by combining shotgun sequencing, mapping to reference DNA barcodes or to mitogenomes, and three correction factors: (a) a percent‐coverage threshold to filter out false positives, (b) an internal‐standard DNA spike‐in to correct for stochasticity during sequencing, and (c) technical replicates to correct for stochasticity across sequencing runs. The SPIKEPIPE pipeline achieves a strikingly high accuracy of intraspecific abundance estimates (in terms of DNA mass) from samples of known composition (mapping to barcodes R<sup>2</sup> = .93, mitogenomes R<sup>2</sup> = .95) and a high repeatability across environmental‐sample replicates (barcodes R<sup>2</sup> = .94, mitogenomes R<sup>2</sup> = .93). As proof of concept, we sequence arthropod samples from the High Arctic, systematically collected over 17 years, detecting changes in species richness, species‐specific abundances, and phenology. SPIKEPIPE provides cost‐efficient and reliable quantification of eukaryotic communities.</p>
A Tool for Uncertainty Quantification in Reconstructing Sparse Water Quality Time Series Data to Assess Risk Metrics for Watershed Health and TMDL Analysis
<p>The uploaded file contains the input and output data which can be used to reproduce the results in the research article 'Uncertainty Quantification in Reconstruction of Sparse Water Quality Time Series: Implications for Watershed Health and Risk-Based TMDL Assessment'. Please refer to the file '<a href="https://zenodo.org/api/files/31b59cce-8eb2-4ee7-93aa-61474c6f6359/dst_2019_SJRW_TP_TDS.zip?versionId=2af2b54d-d5fb-4720-919d-de2e827595e2">dst_2019_SJRW_TP_TDS.zip'</a> for updated files..</p>
Supplementary Information S1 - Detailed results of the CAPRI N-LCA and S2 - Quantification of the main N budget flows in the EU25 agriculture sector of Leip, A., Billen, G., Garnier, J., Grizzetti, B., Lassaletta, L., Reis, S., Simpson, D., Sutton, M. a, de Vries, W., Weiss, F., Westhoek, H. (2015). Impacts of European livestock production: nitrogen, sulphur, phosphorus and greenhouse gas emissions, land-use, water eutrophication and biodiversity. Environ. Res. Lett. 10, 115004. doi:10.1088/1748-9326/10/11/115004
<p>Table S1-1 Quantification of GHG and Nr flow intensities [kg CO2eq (kg product)<sup>-1</sup> yr<sup>-1</sup>] or [g N (kg product)<sup>-1</sup> yr<sup>-1</sup>] with the CAPRI N-LCA model for six main livestock products (BEEF: beef, PORK: pork, EGGS: eggs, POUM: poultry meat; DAIR: milk and dairy products, SGMP: meat from sheep and goats) and six main vegetable food groups (POTA: potatoes, SUGB: sugar beet before processing, OILP: oil seeds before processing; CERR: cereals, LEGU: leguminous crops) as well as other crops (OCRP) and aggregated livestock (ANIMP) and vegetable (CROPP) food. </p> <p>Table S2-1 Quantification of the main N budget flows in the EU25 agriculture sector</p>
Increased accuracy of starch granule type quantification using mixture distributions
<p>This release accompanies the paper "Increased accuracy of starch granule type quantification using mixture distributions".</p>
Code and Source Data for "Knowledge-Guided Machine Learning can improve C cycle quantification in agroecosystems"
<p>Datasets for code and Source Data for the study "Knowledge-Guided Machine Learning can improve C cycle quantification in agroecosystems" https://doi.org/10.1038/s41467-023-43860-5. All files belong to Licheng Liu and Zhenong Jin at University of Minnesota. deposit_code_v2.zip contains packaged codes and sample runs for KGML-ag-Carbon training, validation and implementations. Source Data.zip contains data for generating the figures inside the study. </p> <p>Note: We used Pytorch 1.6.0 (<a href="https://pytorch.org/get-started/previous-versions/">https://pytorch.org/get-started/previous-versions/</a>, last access: 21 Oct 2023) and Python 3.7.11 (<a href="https://www.python.org/downloads/release/python-3711/">https://www.python.org/downloads/release/python-3711/</a>, last access: 21 Oct 2023) as the programming environment for model development. Statistical analysis, such as linear regression, was conducted using Statsmodels 0.14.0 (<a href="https://github.com/statsmodels/statsmodels/">https://github.com/statsmodels/statsmodels/</a>, last access: 21 Oct 2023) In order to use a GPU to speed-up the training process, we installed the CUDA Toolkit 10.1.243 (<a href="https://developer.nvidia.com/cuda-toolkit">https://developer.nvidia.com/cuda-toolkit</a>, last access: 21 Oct 2023). </p> <p><strong>To use the full kgml_lib function, please create a new environment with the same python and libs above.</strong></p>
Quantification of soil organic carbon: the challenge of biochar-induced spatial heterogeneity
<p>R-script and output from model on spatially discrete biochar application and its influence on representative SOC sampling. An additional document to explain the data curation is also available ("Comment on Data curation").</p><p> </p>
Fecal-bbu-genes-quantification-predicts-L-carnitine-mediated-TMAO-production-and-serves-as-a-biomarker-for-precision-nutrition-code-20231201
<p>Custom code related to the original research article "Fecal bbu genes quantification predicts L-carnitine-mediated TMAO production and serves as a biomarker for precision nutrition"</p>
Image quantification data for: Activity-dependent mitochondrial ROS signaling regulates recruitment of glutamate receptors to synapses
<p>Our understanding of mitochondrial signaling in the nervous system has been limited by the technical challenge of analyzing mitochondrial function <em>in vivo</em>. In the transparent genetic model <em>Caenorhabditis elegans, </em>we were able to manipulate and measure mitochondrial ROS (reactive oxygen species) signaling of individual mitochondria as well as neuronal activity of single neurons <em>in vivo</em>. Using this approach, we provide evidence supporting a novel role for mitochondrial ROS signaling in dendrites of excitatory glutamatergic <em>C. elegans</em> interneurons. Specifically, we show that following neuronal activity, dendritic mitochondria take up calcium (Ca<sup>2+</sup>) via the mitochondrial Ca<sup>2+</sup> uniporter MCU-1 which results in an upregulation of mitochondrial ROS production. We also observed that mitochondria are positioned in close proximity to synaptic clusters of GLR-1, the <em>C. elegans</em> ortholog of the AMPA subtype of glutamate receptors that mediate neuronal excitation. We show that synaptic recruitment of GLR-1 is upregulated when MCU-1 function is pharmacologically or genetically impaired but is downregulated by mitoROS signaling. Thus, signaling from postsynaptic mitochondria may regulate excitatory synapse function to maintain neuronal homeostasis by preventing excitotoxicity and energy depletion.</p>
Calibration-free reaction yield quantification by HPLC with a machine-learning model of extinction coefficients
<p>This repository contains all the data and code associated with the manuscript "Calibration-free reaction yield quantification by HPLC with a machine-learning model of extinction coefficients"</p> <p>Mass spec and absorption chromatogram data are in reaction_set_1.zip, reaction_set_2.zip, and simulated_reaction_set.zip. The chemprop model trained on the Deep4Chem dataset is in Deep4Chem_chemprop.zip.</p>
Data from: Direct quantification of ion composition and mobility in organic mixed ionic-electronic conductors
<p>Ion transport in organic mixed ionic-electronic conductors (OMIECs) is crucial due to its direct impact on device response time and fundamental operating mechanisms but are often assessed indirectly or rely on extra assumptions. Operando X-ray fluorescence (XRF) is a powerful, direct probe useful for elemental characterization of bulk OMIECs, and was employed to directly quantify ion composition and mobility in a model OMIEC, PEDOT:PSS, during device operation. The first cycle revealed slow electrowetting and cation-proton exchange. Subsequent cycles showed rapid response with minor cation fluctuation (~5%). Comparison with optical-tracked electrochromic fronts revealed a mesoscale structure dependent proton transport. The calculated effective ion mobility demonstrated thickness-dependent behavior, emphasizing an interfacial ion transport pathway with a higher mobile ion density. The decoupling of bulk and interfacial effects on ion mobility, and the decoupling of cation and proton transport contributes to our understanding of ion transport in conventional and emerging OMIEC-based devices, and has broader implications for ion transport in other ionic conductors writ large.</p>
Calibration curves for Folin-Ciocalteu assay for reliable quantification of total polyphenols.
<p><span>Five different protocols (P1-P5) for determining total polyphenol content in samples were investigated and compared. Detailed information on the protocols are provided in: <br></span></p> <p><span>P1 </span><span>Singleton, V.L.; Rossi, J.A. Colorimetry of Total Phenolics with Phosphomolybdic-Phosphotungstic Acid Reagents. <em>Am J Enol Vitic.</em> <strong>1965</strong>, <em>16</em>, 144–158, doi:10.5344/ajev.1965.16.3.144.</span></p> <p><span>P2<span> </span>Singleton, V.L.; Orthofer, R.; Lamuela-Raventós, R.M. [14] Analysis of Total Phenols and Other Oxidation Substrates and Antioxidants by Means of Folin-Ciocalteu Reagent. In <em>Methods in Enzymology</em>; Oxidants and Antioxidants Part A; Academic Press, 1999; Vol. 299, pp. 152–178.</span></p> <p><span>P3<span> </span>Cicco, N.; Lanorte, M.T.; Paraggio, M.; Viggiano, M.; Lattanzio, V. A Reproducible, Rapid and Inexpensive Folin–Ciocalteu Micro-Method in Determining Phenolics of Plant Methanol Extracts. <em>Microchemical Journal</em> <strong>2009</strong>, <em>91</em>, 107–110, doi:10.1016/j.microc.2008.08.011.</span></p> <p><span>P4. Sotenko, M.; Coles, S.; Barker, G.; Song, L.; Jiang, Y.; Longhurst, P.; Romanova, T.; Shuvaeva, O.; Kirwan, K. Phytoremediation-Biorefinery Tandem for Effective Clean-up of Metal Contaminated Soil and Biomass Valorisation. <em>International Journal of Phytoremediation</em> <strong>2017</strong>, <em>19</em>, 965–975, doi:10.1080/15226514.2016.1267705.</span></p> <p><span>P5<span> </span>Tsibranska, I.; Tylkowski, B.; Kochanov, R.; Alipieva, K. Extraction of Biologically Active Compounds from <em>Sideritis</em> Ssp. L. <em>Food and Bioproducts Processing</em> <strong>2011</strong>, <em>89</em>, 273–280, doi:10.1016/j.fbp.2010.10.004.</span></p> <p> </p> <p><span><span>Total polyphenol content was determined spectrophotometrically using different variation of Folin–Ciocalteu’s method by means of UV-VIS UV-1800 Shimadzu spectrophotometer (Tokio, Japan). </span><span>The Folin-Ciocalteu reagent, a</span><span> mixture of phosphomolybdic and phosphotungstic acids reacts with phenolic compounds and is reduced, forming a blue chromophore detectable spectrophotometrically. Seven concentration levels of gallic acid standard were used to construct the calibration curves: L1 - 0.6 g/L, L2 - 0.4 g/L, L3 - 0.2 g/L, L4 - 0.1 g/L, L5 - 0.05 g/L, L6 - 0.025 g/L and L7 - 0.0125 g/L. A 1 g/L gallic acid stock solution was prepared in Milli-Q water. Each concentration level was measured in triplicate using all the investigated polyphenol determination methods. Once the calibration curves for each method were prepared, </span><span>two independent standard solutions were used for methods validation. The first one was gallic acid at a concentration of 0.2 g/L prepared in Milli-Q water. The second standard was a commercial polyphenol extract Vitaflavan at a concentration of 0.2 g/L which contains 80% of total polyphenols. Each standard solution was measured in triplicate using all the methods investigated. </span></span></p>
Dataset for paper entitled 'Quantification of strain fields and grain refinement in Ti-6Al-4V inter-pass rolled wire-arc AM by EBSD misorientation analysis'.
<p>Dataset for paper entitled 'Quantification of strain fields and grain refinement in Ti-6Al-4V inter-pass rolled wire-arc AM by EBSD misorientation analysis'. doi: https://doi.org/10.1016/j.matchar.2020.110673</p>
Quantification of salt stress in wheat leaves by Raman spectroscopy and machine learning
<p>Train and test datasets used in the manusicript "Quantification of salt stress in wheat leaves by Raman spectroscopy and machine learning". Trained models are included.</p>
Diffraction-Limited Molecular Cluster Quantification with Bayesian Nonparametrics: Calibration Data
<p>Calibration data accompanying our work, "Diffraction-Limited Molecular Cluster Quantification with Bayesian Nonparametrics" by J Bryan IV, I Sgouralis, and S Presse.</p>
Diffraction-Limited Molecular Cluster Quantification with Bayesian Nonparametrics: 20 Binding Site Data A
<p>This is the original data for the manuscript "Diffraction-Limited Molecular Cluster Quantification with Bayesian Nonparametrics" by J Bryan IV, I Sgouralis, and S Presse. This repository contains movies of DNA origami with 20 binding sites. Because this data set is too large to fit in one single repository we have split it up into parts. This is part A</p>
Diffraction-Limited Molecular Cluster Quantification with Bayesian Nonparametrics: 20 Binding Site Data C
<p>This is the original data for the manuscript "Diffraction-Limited Molecular Cluster Quantification with Bayesian Nonparametrics" by J Bryan IV, I Sgouralis, and S Presse. This repository contains movies of DNA origami with 20 binding sites. Because this data set is too large to fit in one single repository we have split it up into parts. This is part C.</p>
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.