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3,684 results for “extract”
Green synthesis of ZnO nanoparticles using Cocos nucifera leaf extract: Characterization, antimicrobial, antioxidant, and photocatalytic activity
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LC-MS raw dataset of Dendrobium extracts
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Sample extraction and SNP sequencing data for: Identification of sex-linked SNP markers in wild populations of monomorphic birds
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Relationship between Channel Island Melospiza melodia (Song Sparrow) bill size and vegetation, seed extraction time, bite force, and climate (2014-2016)
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Improving the recovery for dissolved eDNA State: A comparative analysis of isopropanol precipitation, magnetic bead extraction, and centrifugal dialysis
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Post-extraction restored peatland methane production and emission
Peatland degradation due to human activities is contributing to rising atmospheric CO2 levels. Restoring the carbon (C) sink function in degraded peatlands and preventing further stored C losses is a key climate mitigation strategy, given the global scale of peatland disturbance. Active restoration at a post-extraction peatland in Canada has been shown to successfully re-establish net CO2 uptake rates similar to undisturbed peatlands within a decade or two. However, lower than expected CH4 emissions suggest recovery of belowground C cycling processes may lag behind recovery of the surface net flux. Using closed chamber measurements over a warm season, we determined that restored Sphagnum, which covers two thirds of the site, was a net zero source of CH4. Emissions from the restored site were primarily attributed to vascular plant substrate inputs, measured as acetate, and plant-mediated transport. The carbon isotopic fractionation factor for CH4 and CO2 in the restored former peat field pore water exhibited α < 1.060 even deeper in the cutover peat profile (0.8 m depth), evidence of a dominance in acetoclastic methane production. In contrast, isotopic fractionation in the former drainage ditches showed a balance of acetoclastic and hydrogenotrophic methanogenesis deeper in the profile, indicative of some bulk peat C turnover. This study shows that the legacy of substrate quality in the cutover peat in reducing CH4 production and thus emission, can aid in reducing the climate warming impact of newly restored peatlands.
Repeated freeze-thaw cycles increase extractable, but not total, carbon and nitrogen in a Maine coniferous soil
This dataset contains processed data for the publication Patel et al. 2021. "Repeated freeze-thaw cycles increase extractable, but not total, carbon and nitrogen in a Maine coniferous soil". Geoderma. https://doi.org/10.1016/j.geoderma.2021.115353. Northeastern North America has been experiencing warmer winters with reduced snow accumulation, with more frequent winter freeze-thaw cycles. We conducted a laboratory experiment to investigate how increased frequency of freeze-thaw cycles (FTC) would alter soil C and N availability. Organic (O) and mineral (B) horizon soils were collected from a coniferous forest in Maine, processed to exclude roots, and then frozen in the laboratory (-10 °C) with one (FTC-1), two (FTC-2), or six (FTC-6) thaw periods (+5 °C). Soils were analyzed for extractable ammonium (NH4-N), water extractable organic carbon (WEOC), carbon dioxide flux (respiration), and total C and N. Extractable NH4-N increased following FTC (all levels), for both horizons. While WEOC concentrations did not change for FTC vs. control, the WEOC in O horizons had a lower SUVA254 in FTC soils compared to control, indicating a stronger microbial influence (i.e., microbial cell lysis) in these soils after FTC. Respiration in O horizon soils decreased post-incubation and did not differ between FTC and Control soils. In the B horizon, however, FTC soils showed greater respiration than Control soils, suggesting that the newly available nutrients may have stimulated microbial activity. In contrast to these results, total C and N remained unaltered by FTC, presumably because the FTC disturbances represented mostly a translocation of C and N from one pool into another, and losses due to respiration were too small to significantly influence the large TC and TN pools. The effect of FTC on NH4-N did not change with FTC frequency, suggesting that a single FTC is sufficient to alter both C and N availability and/or quality, and that additional FTC may not have a significant further
IO Islamic 1981. Maâthir-i-'Âlamgirî, History of the Full Reign of Âlamgir, Extracts
<p>IO Islamic 1981. Maâthir-i-‘Âlamgirî, History of the Full Reign of Âlamgir, Extracts</p>
VAMDC extraction with identifier = 7974a04d-3adc-4d26-a1df-10c0e3be44ba
<div>This is a dataset extracted from http://vald.astro.uu.se/atoms-12.07/tap/ VAMDC node.</div><div>Query originating this dataset: query=select * where ( atomsymbol in ['h'] and radtranswavelength <= 11634.49275862069 and radtranswavelength >= 8827.596206896555 ); </div><div> Data source version: 2020-01-24</div><div>Data format: XSAMS 12.07</div><div>Query uuid in VAMDC query store: 7974a04d-3adc-4d26-a1df-10c0e3be44ba</div>
VAMDC extraction with identifier = 7974a04d-3adc-4d26-a1df-10c0e3be44ba
<div>This is a dataset extracted from http://vald.astro.uu.se/atoms-12.07/tap/ VAMDC node.</div><div>Query originating this dataset: query=select * where ( atomsymbol in ['h'] and radtranswavelength <= 11634.49275862069 and radtranswavelength >= 8827.596206896555 ); </div><div> Data source version: 2020-01-24</div><div>Data format: XSAMS 12.07</div><div>Query uuid in VAMDC query store: 7974a04d-3adc-4d26-a1df-10c0e3be44ba</div>
Comparison of different gas extraction techniques to analyze CH4 and N2O compositions in gas trapped in permafrost ice wedges
<p>Methane (CH4) and nitrous oxide (N2O) compositions in ground ice may provide information on their production mechanisms in permafrost. However, existing gas extraction methods have not been well tested. We tested conventional wet and dry gas extraction methods using ice wedges from Alaska and Siberia, finding that both methods can extract gas from the easily extractable parts of the ice (e.g., gas bubbles), and yield similar results for CH4 and N2O mixing ratios. We also found insignificant effects of microbial activity during wet extraction. However, both techniques were unable to fully extract gas from the ground ice, presumably because gas molecules adsorbed onto or enclosed in soil aggregates are not easily extractable. Estimation of gas production in subfreezing environment of permafrost should consider such incomplete gas extraction.</p>
MitoFinder: efficient automated large-scale extraction of mitogenomic data in target enrichment phylogenomics
<p><strong>MitoFinder: efficient automated large-scale extraction of mitogenomic data in target enrichment phylogenomics</strong></p> <p>Rémi Allio<sup>1</sup>, Alex Schomaker-Bastos<sup>2,†</sup>, Jonathan Romiguier<sup>1</sup>, Francisco Prosdocimi<sup>2</sup>, Benoit Nabholz<sup>1</sup>, and Frédéric Delsuc<sup>1</sup></p> <p><sup>1</sup><em>Institut des Sciences de l’Evolution de Montpellier (ISEM), CNRS, EPHE, IRD, Université de Montpellier, Montpellier, France.</em></p> <p><sup>2</sup><em>Laboratório Multidisciplinar para Análise de Dados (LAMPADA), Instituto de Bioquímica Médica Leopoldo de Meis, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brasil.</em></p> <p><sup>†</sup><em> In Memoriam (08/01/2015) </em></p> <p> </p> <p><em><strong>Correspondence</strong></em></p> <p>Rémi Allio</p> <p>Email: <a href="mailto:remi.allio@umontpelier.fr">remi.allio@umontpellier.fr</a></p> <p>Frédéric Delsuc</p> <p>Email: <a href="mailto:frederic.delsuc@umontpellier.fr">frederic.delsuc@umontpellier.fr</a></p> <p> </p> <p><strong><em>Running head</em></strong></p> <p>Mitochondrial signal from UCE capture data</p> <p> </p> <p><strong>Abstract</strong><strong> </strong></p> <p>Thanks to the development of high-throughput sequencing technologies, target enrichment sequencing of nuclear ultraconserved DNA elements (UCEs) now allows routinely inferring phylogenetic relationships from thousands of genomic markers. Recently, it has been shown that mitochondrial DNA (mtDNA) is frequently sequenced alongside the targeted loci in such capture experiments. Despite its broad evolutionary interest, mtDNA is rarely assembled and used in conjunction with nuclear markers in capture-based studies. Here, we developed MitoFinder, a user-friendly bioinformatic pipeline, to efficiently assemble and annotate mitogenomic data from hundreds of UCE libraries. As a case study, we used ants (Formicidae) for which 501 UCE libraries have been sequenced whereas only 29 mitogenomes are available. We compared the efficiency of four different assemblers (IDBA-UD, MEGAHIT, MetaSPAdes, and Trinity) for assembling both UCE and mtDNA loci. Using MitoFinder, we show that metagenomic assemblers, in particular MetaSPAdes, are well suited to assemble both UCEs and mtDNA. Mitogenomic signal was successfully extracted from all 501 UCE libraries allowing confirming species identification using COI barcoding. Moreover, our automated procedure retrieved 296 cases in which the mitochondrial genome was assembled in a single contig, thus increasing the number of available ant mitogenomes by an order of magnitude. By leveraging the power of metagenomic assemblers, MitoFinder provides an efficient tool to extract complementary mitogenomic data from UCE libraries, allowing testing for potential mito-nuclear discordance. Our approach is potentially applicable to other sequence capture methods, transcriptomic data, and whole genome shotgun sequencing in diverse taxa.</p> <p> </p> <p><strong><em>Figures & Tables</em></strong></p> <p><strong>Figure 1.</strong> Conceptualization of the pipeline used to assemble and extract UCE and mitochondrial signal from ultraconserved element sequencing data.</p> <p><strong>Figure 2</strong>. Comparison of the efficiency of the assemblers in terms of: A) computational time, B) number of potentially mitochondrial contigs identified, and C) number of mitochondrial genes annotated. Violin plots reflect the data distribution with a horizontal line indicating the median. Note that for the three metagenomic assemblers, 5 CPUs were used compared to 35 CPUs for Trinity. Plots were obtained using PlotsOfData (Postma & Goedhart 2019).</p> <p><strong>Figure 3.</strong> Phylogenomic relationships of ants (Formicidae). AA) Mito-nuclear phylogenetic differences among subfamily relationships based on the UCE and mtDNA supermatrices obtained with the assembler MetaSPAdes assembler. Clades corresponding to subfamilies were collapsed. Inter-subfamily relationships with UFBS < 95% were collapsed. Non-maximal node support values are reported. B) The topology obtained reflects the results of phylogenetic analyses based on the amino acid mitochondrial supermatrix (using MetaSPAdes as assembler). Histograms reflect the percent of UCEs (light grey) and mitochondrial genes (dark grey) recovered for each species. Illustrative pictures (*): <em>Diacamma sp</em>. (Ponerinae; top left), <em>Formica sp</em>. (Formicinae; top right), and <em>Messor barbarus </em>(Myrmicinae; bottom right).</p> <p><strong>Table 1. </strong>Summary statistics on assembly results according to the assembler used. The values are averages over the 501 assemblies, except for the assembly time, which is a median value. The two tables report specific statistics for A) ultraconserved elements data, and B) mitochondrial data. Note that 35 CPUs were used for Trinity whereas 5 CPUs were used for other assemblers.</p> <p><strong>Table 2.</strong> Statistical comparison between the performances of the different assemblers. Statistical significance was estimated with a paired non parametric test (paired wilcoxon test). *** = <em>p</em><0.001; ** = <em>p</em><0.01; * = <em>p</em><0.05; NS = <em>p</em>>0.05; and (+)/(-) is the result of the comparison between the row and the column.</p> <p> </p> <p><strong><em>Appendices</em></strong></p> <p><strong>Appendix S1.</strong> List of the 501 UCE libraries (SRA accessions) and associated metadata.</p> <p><strong>Appendix S2.</strong> Summary statistics on mitochondrial signal recovered per species and depending on the assembler used. The table provides the number of contigs and genes recovered with MitoFinder and the size of each annotated gene.</p> <p><strong>Appendix S3.</strong> Summary statistics of barcoding analyses. Detailed results for both BOLDsystem and Megablast analyses are provided for each CO1 recovered with MitoFinder using MetaSPAdes.</p> <p><strong>Appendix S4.</strong> Detailed results of tree distance analyses realized with Dquad (Ranwez, Criscuolo, & Douzery 2010). Trees obtained with each assembler with mitochondrial amino acid supermatrix, mitochondrial nucleotide supermatrix, and UCE nucleotide supermatrix were compared with each others.</p> <p><strong>Appendix S5</strong>. List of Genbank accession numbers for newly generated mitchondrial contigs.</p> <p> </p> <p><strong><em>Zenodo supplementary files</em></strong></p> <p><strong>Assembly_results.tar.gz</strong> Contains all contigs obtained for each species with the different assemblers implemented in MitoFinder.</p> <p><strong>MitoFinder_annotations.tar.gz</strong> Contains MitoFinder annotations for each species. (based on the contigs obtained with MetaSPAdes)</p> <p><strong>UCE_results.tar.gz</strong> Contains all annotated UCE obtained for each species after UCE identification with PHYLUCE. (MetaSPAdes)</p> <p><strong>Final_mtDNA_alignments.tar.gz</strong> Contains the final mitochondrial gene alignments. (MetaSPAdes)</p> <p><strong>Final_UCE_alignments.tar.gz</strong> Contains the final UCE alignments. (MetaSPAdes)</p> <p><strong>Final_mtDNA_matrices.tar.gz</strong> Contains the final mi tochondrial supermatrices (AA and NT) used for the phylogenetic analyses. (MetaSPAdes)</p> <p><strong>Metaspades_final_UCE_matrix.phy</strong> The final UCE supermatrix used for the phylogenetic analyses. (MetaSPAdes)</p>
Replication Package: UI Screens Identification and Extraction from Mobile Programming Screencasts
<p>The replication package of the paper "UI Screens Identification and Extraction from Mobile Programming Screencasts" including the dataset, results, and code.</p>
Fine-grained automated visual analysis of herbarium specimens for phenological data extraction: an annotated dataset of reproductive organs in Strepanthus herbarium specimens
<p>This dataset contains annotations of 31 herbarium specimens of <em>Streptanhus tortuosus Kellogg</em> for which we have we carefully and manually drew and annotated the contours of four reproductive organs: “bud”, “flower”, “immature fruit” and “mature fruit”.</p> <p>The dataset can be used to assess the ability of automated methods to count and detect precisely the shapes of these reproductive organs, with a view to conducting phenological studies.</p> <p>The annotations are formatted in accordance with the COCO data format, a usual format for object detection tasks in the field of Computer Vision. The annotations are divided into two files:</p> <ul> <li>train_21_full_masks.json contains the mask coordinates and labels of 21 herbarium sheets that can be used for training models</li> <li>test_10_full_masks.json contains the mask coordinates and labels of 10 other herbarium that can be used as a groundtruth file for evaluating the predictions, typically with the COCO evaluation scripts (<a href="https://github.com/cocodataset/cocoapi">https://github.com/cocodataset/cocoapi</a>)</li> </ul> <p>Please refer to the following publication for a first assessment of this dataset with a Mask-RCNN approach:</p> <p><em>H. Goëau, A. Mora-Fallas, J. Champ, N. Love, S. Mazer, E. Mata-Montero, A. Joly, P. Bonnet. </em>2020. New fine-grained method for automated visual analysis of herbarium specimens: a case study for phenological data extraction. <em>Applications in Plant Sciences </em></p> <p> </p> <p> </p> <p> </p> <p> </p>
FucoSan: Extraction of fucoidans from different brown algae species using different methods and their chemical and biological characterization
<p>This is a dataset extracted from the database of the Interreg project “FucoSan – Health from the sea” which includes information on the used algae species, the extraction of fucoidans as well as their chemical and pharmacological characterization.</p> <p>Macroalgae represent a vast source of renewable raw materials for research, development and application. An example of current high interest are fucoidans from brown algae. These fucose-containing sulfated polysaccharides exhibit a multitude of bioactivities offering attractive options for applications in medicine and cosmetics. The composition and chemical structure of fucoidans and thus their bioactivities may, however, largely vary depending on the algae species, extraction procedure and many other factors. Therefore, much research is still needed in the range from the algal source up to the targeted development of fucoidans optimized for respective applications. Since 2017, this is the topic of the Danish-German project Interreg project “FucoSan – health from the Sea” involving 11 partners with different expertise. Here, the current state of the fucoidan series produced and explored within the project is presented including information on the used algae species and batches, the extraction, purification and fractionation of the fucoidans as well as their chemical and pharmacological characterization. Based on their basic pharmacological activities, fucoidans are then selected and further tested in advanced experiments for specific applications in medicine and cosmetics.</p>
Guidelines for Software Product Line Experiments: SMS extracted data and guidelines evaluation
<p>Guidelines for Softwrae Product Line Experiments: SMS extracted data and guidelines evaluation</p>
Using ontologies to extract disease--phenotype associations from literature
<p>This dataset contains disease-phenotype associations. We developed and used a text-mining system which utilizes semantic relations in the phenotype ontologies and statistical methods to extract disease-phenotype associations from the literature.</p>
Extracted sinograms for VQC Phantom: GE SIGNA PET/MR
<p>This dataset includes:</p> <p>1. Extracted Emission Sinogram: sino_f2g1d0b0.hs; sino_f2g1d0b0.s</p> <p>2. Extracted Randoms Sinogram: randoms.hs; randoms.s</p> <p>3. Extracted Normalisation Sinogram: normsino.hs; normsino.s</p> <p>4. Extracted Geometric Sinogram: geo.hs; geo.s</p> <p>5. Normalisation Complete Sinogram (Normalisation+ Geometric correction effects accounted for in this sinogram): normgeo.hs; normgeo.s </p> <p> </p>
Wikipedia Citations: A comprehensive dataset of citations with identifiers extracted from English Wikipedia
<p>The dataset is composed of <strong>3 parts</strong>:</p> <p>1. The dataset of 29.276 million citations from 35 different citation templates, out of which 3.92 million citations already contained identifiers, and approximately 260,752 citations were equipped with identifiers from Crossref. This is under the filename: <strong>citations_from_wikipedia.zip</strong></p> <p>2. A minimal dataset containing a few of the columns from the citations from Wikipedia dataset. These columns are as follows: 'type_of_citation', 'page_title', 'Title', 'ID_list', metadata_file', 'updated_identifier'. This is under the filename: <strong>minimal_dataset.zip. </strong>The 'metadata_file' column can be used to refer to the metadata collected from CrossRef and page title, the title of the citation can be used to refer to the 'citations_from_wikipedia.zip' dataset and get more information for a particular citation (such as author, periodical, chapter).</p> <p>3. Citations classified as a journal and their corresponding metadata/identifier extracted from Crossref to make the dataset more complete. This is under the filename: <strong>lookup_data.zip</strong>. This zip file contains a CSV file: <strong>lookup_table.gzip</strong> (a parquet file containing all citations classified as a journal) and a folder:<strong> metadata_extracted </strong>(a folder containing the metadata from CrossRef for all the citations mentioned in the table)</p> <p><br> The data was parsed from the Wikipedia XML content dumps published in May 2020.</p> <p>The source code to extract and getting used to the pipeline can be found here: <strong>https://github.com/Harshdeep1996/cite-classifications-wiki</strong></p> <p>The taxonomy of the dataset in (1) can be found here: <strong>https://github.com/Harshdeep1996/cite-classifications-wiki/wiki/Taxonomy-of-the-parent-dataset</strong></p>
Col d'Olen, soil chemistry (extractable N-NH4) 2008-2019
<p>Dataset provides information about soil temperature, soil C and N dynamics at different sites in the alpine tundra, along an elevational gradient from 2500 to 2900 m asl during the period 2005-2018. The soil was sampled in triplicate in each the sites and the N-NH4 concentration is measured in soil K2SO4 extracts. The ongoing studies aimed at investigating how the meteorological variables affect the biogeochemistry of seasonally snow covered areas, with a special focus on the effect of the snow cover duration on soil and water C and N dynamics during the growing season.</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.