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3,684 results for “extract”
Manual Dependency Annotation of Three German Text Extracts from the Project hermA (Gold Standard Data)
<p>This dataset was created in the digital humanities project hermA (www.herma.uni-hamburg.de) and comprises annotated extracts of the following three texts:</p> <ul> <li>Modern literature (Lit2009): novel <em>Corpus Delicti: Ein Prozess </em>by German author Juli Zeh, published in Frankfurt/Main in 2009.</li> <li>Non-contemporary literature (Lit1850):<em> Eine Frauenfahrt um die Welt </em>('A woman’s journey around the world') by Austrian author Ida Pfeiffer (1850). Full text available at Deutsches Textarchiv: http://www.deutschestextarchiv.de/pfeiffer_frauenfahrt01_1850/6.</li> <li>Modern academic writing (Aca2009): <em>Stand, Möglichkeiten und Grenzen der Telemedizin in Deutschland</em> ('Telemedicine in Germany: status, chances and limits') by Rüdiger Klar and Ernst Pelikan, published in Bundesgesundheitsblatt ('Federal Health Gazette') in 2009. DOI 10.1007/s00103-009-0787-7.</li> </ul> <p>The texts are annotated for part-of-speech and dependency syntax and are made available in CoNLL file format. We describe the annotation process and report inter-annotator agreements in:</p> <p>Adelmann, Benedikt, Melanie Andresen, Wolfgang Menzel & Heike Zinsmeister. 2018. Evaluation of Out-Of Domain Dependency Parsing for its Application in a Digital Humanities Project. <em>Proceedings of the 14th Conference on Natural Language Processing (KONVENS 2018)</em>. Vienna, Austria.</p>
Bibliographic references extracted from theses and dissertations in the Electronic Theses and Dissertations Library of the Federal University of Paraná, Brazil
<p>Bibliographic references obtained from a <a href="https://doi.org/10.5281/zenodo.1169586">subset of PDF files in the Electronic Theses and Dissertations Library of the Federal University of Paraná, Brazil</a>, extracted through ParsCit.</p>
An unsupervised deep learning framework with variational autoencoders for genome-wide DNA methylation analysis and biologic feature extraction applied to breast cancer
<p>Supplemental data for the paper titled "An unsupervised deep learning framework with variational autoencoders for genome-wide DNA methylation analysis and biologic feature extraction applied to breast cancer"</p>
Expression data of the flowering time genes in chickpea, extracted from Ridge et al. (2017). Plant Physiology 175, 802-815.
<p>This data is supplementary to the following paper: Gursky, V.V., Kozlov, K.N., Nuzhdin, S.V., and Samsonova, M.G. (2018) Dynamical Modeling of the Core Gene Network Controlling Flowering Suggests Cumulative Activation from the <em>FLOWERING LOCUS T </em>Gene Homologs in Chickpea. <em>Frontiers in Genetics</em>. 9:547. doi: 10.3389/fgene.2018.00547</p> <p>The data was obtained by digitizing Figure 5 of the following paper: Ridge, S., Deokar, A., Lee, R., Daba, K., Macknight, R. C., Weller, J. L., and Tar'an, B. (2017). The chickpea Early flowering 1 (Efl1) locus is an ortholog of arabidopsis ELF3. <em>Plant Physiology </em>175, 802-815. doi:10.1104/pp.17.00082</p> <p>The archive contains files (in csv format) with the expression data of each of the following ten genes: <em>FTa1</em>, <em>FTa2</em>, <em>FTa3</em>, <em>FTb</em>, <em>FTc</em>, <em>AP1</em>, <em>FD</em>, <em>TFL1a</em>, <em>TFL1c</em>, and <em>LFY</em>, for the cultivars CDC Frontier and ICCV 96029 and for two growth conditions (long day, LD, and short day, SD). Each file is named according to the following scheme: <Gene name>_<Cultivar name>_<Growth conditions>.csv. Each file contains values in the following three columns (separated by commas): time (in days after sowing), relative transcription level (%ACTIN), and standard error. In the case of the genes <em>AP1</em>, <em>FD</em>, <em>TFL1a</em>, <em>TFL1c</em>, and <em>LFY</em>, the standard error was assumed equal to the size of the points in the figure when the actual error range was smaller than that size (and, thus, not visible in the figure). In the case of the genes <em>FTa1</em>, <em>FTa2</em>, <em>FTa3</em>, <em>FTb</em>, and <em>FTc</em>, the standard error was recorded as 0 for such points (the error for these genes was not used in the study).</p> <p>The data was extracted with the help of the web-based tool <em>WebPlotDigitizer</em> (https://automeris.io/WebPlotDigitizer).</p>
Dataset records and references extracted from GBIF used for wild boar distribution modelling
<p>These are the datasets containing wild boar records downloaded from Global Biodiversity Information Facility (GBIF) used to fit presence-only and presence-background models. It indicates the data key, publishing organization key, licenses and references.</p>
Scoping review of the term 'genetic identity' - data extraction table
<p>This dataset comes from a systmatic scoping reivew of the term ‘genetic identity’ within different academic discourses. This review systematically identifies all uses of the term ‘genetic identity’ within the academic literature. Content analysis is used to code and categorize its use, and the emerging discourses are related to disciplinary perspective, year of publication, and geographical setting.</p>
Hypernyms extracted from a large text corpus using Hearst lexical-syntactic patterns
<p><br> The list of hyponym-hypernym pairs was obtained by applying lexical-syntactic patterns described in Hearst (1992) on the corpus prepared by Panchenko et al. (2016). This corpus is a concatenation of the English Wikipedia (2016 dump), Gigaword, ukWaC and English news corpora from the Leipzig Corpora Collection. The lexical-syntactic patterns proposed by Marti Hearst (1992) and further extended and implemented in the form of FSTs by Panchenko et al. (2012) for extracting (noisy) hyponym-hypernym pairs are as follows -- (i) such NP as NP, NP[,] and/or NP; (ii) NP such as NP, NP[,] and/or NP; (iii) NP, NP [,] or other NP; (iv) NP, NP [,] and other NP; (v) NP, including NP, NP [,] and/or NP; (vi) NP, especially NP, NP [,] and/or NP. Pattern extraction on the corpus yields a list of 27.6 million hyponym-hypernym pairs along with the frequency of their occurrence in the corpus. </p>
Data for scenario extraction ESR 12
<p>Data for scenario extraction by Mobileye camera.</p>
Fig. 1 in Extraction and elemental composition of meconium in Polistes dominulus (Hymenoptera: Vespidae)
Fig. 1. Meconia in the bottom of the cells of a Polistes dominulus nest.
Fig. 2 in Extraction and elemental composition of meconium in Polistes dominulus (Hymenoptera: Vespidae)
Fig. 2. Meconia of Polistes dominulus.
Catalogue of Life: Catalog of Life Extract for DH (TRAM-797)
CoL with the following branches removed: Viruses (using ICTV), Bacteria, Archaea, Chromista, Protozoa (using NCBI + WoRMS + CoL Protist Extract), Aves (using IOC), Amphibia (using ASW), Odonata (using WOL), Bombycoidea (using Kitching et al. 2018), Anthelidae (now Bombycoidea), Onychophora (using Oliveira et al. 2012), Phthiraptera (duplicates Psocodea Species File data). Data sets for the assembly of the EOL dynamic hierarchy.
Data and Scripts for the Article "Structural Descriptors and Information Extraction from X-ray Emission Spectra: Aqueous Sulfuric Acid"
<p>Data and scripts for the article titled "Structural Descriptors and Information Extraction from X-ray Emission Spectra: Aqueous Sulfuric Acid".</p> <p>For further details on the contents, see the "readme.md"-file.</p> <p>Article available at <a href="https://doi.org/10.1039/D4CP02454K">10.1039/D4CP02454K</a>.</p>
FIGURE 2 in DNA extracted from museum specimens of the 19 century provides a taxonomic resolution on the identity of the characid fish Psalidodon jequitinhonhae (Ostariophysi: Characiformes)
FIGURE 2 | Lectotype of Tetragonopterus rutilus jequitinhonhae, NMW 57760:2, 67.52 mm SL.
Figure 2 in Explore the antiproliferative phytocompounds from ethanolic extracts of Citrus paradisi against liver cancer cell line by chemical analysis using TLC and FT-IR spectroscopy
Figure 2. Anti-proliferative activity of ethanolic Citrus paradisi leaves extract.
Figure 3 in Explore the antiproliferative phytocompounds from ethanolic extracts of Citrus paradisi against liver cancer cell line by chemical analysis using TLC and FT-IR spectroscopy
Figure 3. FTIR analysis of ethanolic Citrus paradisi fruits extract.
Figure 1 in Explore the antiproliferative phytocompounds from ethanolic extracts of Citrus paradisi against liver cancer cell line by chemical analysis using TLC and FT-IR spectroscopy
Figure 1. Anti-proliferative activity of ethanolic Citrus paradisi fruit extract.
Table 3 in Phytochemical characterization, and antioxidant and antibacterial activities of the hydroethanolic extract of Anadenanthera peregrina stem bark
<p><b>Table 3.</b> Diameter of the inhibitory zone of the hydroethanolic extract of <i>Anadenanthera peregrina</i> stem bark against <i>Staphylococcus aureus</i> (ATCC 25923) and <i>Escherichia coli</i> (ATCC 25922).</p><table><tbody><tr><th></th><th><b>A. peregrina extract concentration</b></th><th></th><th></th></tr></tbody><tbody><tr><th><b>Strain</b></th><td></td><td></td><td></td><td><b>C +</b></td><td><b>C -</b></td></tr><tr><th></th><td><b>50 µL</b></td><td><b>100 µL</b></td><td><b>200 µL</b></td><td></td><td></td></tr><tr><th><i>E. coli</i></th><td>-</td><td>-</td><td>-</td><td>29 mm</td><td>-</td></tr><tr><th><i>S. aureus</i></th><td>10 mm</td><td>16 mm</td><td>20 mm</td><td>35 mm</td><td>-</td></tr></tbody></table>
Table 2 in Phytochemical characterization, and antioxidant and antibacterial activities of the hydroethanolic extract of Anadenanthera peregrina stem bark
<p><b>Table 2.</b> Physicochemical properties,antioxidant activity,and total phenolic content of the hydroethanolic extract of <i>A.peregrina</i> stem bark.</p><table><tbody><tr><th>Sample</th><th>pH</th><th>Density (g cm 3)</th><th>DPPH (IC 50)</th><th><b>Total Phenolics (g GAE 100 g-</b> 1)</th></tr></tbody><tbody><tr><th><b>A. peregrina extract</b></th><td>5.21 ± 0.01</td><td>0.956</td><td>44.13 mg mL-1</td><td>6.40 ± 0.08</td></tr></tbody></table>
Table 1 in Phytochemical characterization, and antioxidant and antibacterial activities of the hydroethanolic extract of Anadenanthera peregrina stem bark
<p><b>Table 1.</b> Phytochemical prospecting of the main secondary metabolite groups of the hydroethanolic extract of <i>A. peregrina</i> stem bark.</p><table><tbody><tr><th><b>Secondary metabolite</b></th><th><b>Hydroethanolic extract of A. peregrina</b></th></tr><tr><th><b>Cardiac glycosides</b></th></tr></tbody><tbody><tr><th>Kedd reagent test</th><td>++</td></tr><tr><th>Keller–Kiliani reagent test</th><td>++</td></tr><tr><th>Baljet reagent test</th><td>-</td></tr><tr><th>Raymond–Marthoud reagent test</th><td>+++</td></tr><tr><th><b>Alkaloids</b></th></tr><tr><th>Libermann–Bouchardat reagent test</th><td>-</td></tr><tr><th>Wagner reagent test</th><td>-</td></tr><tr><th>Mayer’s reagent test</th><td>-</td></tr><tr><th><b>Organic acids</b></th></tr><tr><th>Pascová reagent test</th><td>++</td></tr><tr><th><b>Reducing sugars</b></th></tr><tr><th>Fehling reagent test</th><td>++</td></tr><tr><th><b>Non-reducing sugars</b></th></tr><tr><th>Fehling + HCl test</th><td>-</td></tr><tr><th><b>Coumarins</b></th></tr><tr><th>UV light 254 and 365 nm</th><td>+</td></tr><tr><th><b>Saponins</b></th></tr><tr><th>Foamy</th><td>-</td></tr><tr><th>Haemolytic</th><td>+++</td></tr><tr><th><b>Polysaccharides</b></th></tr><tr><th>Reactive lugol</th><td>-</td></tr><tr><th><b>Phenols</b></th></tr><tr><th>FeCl 3 <b>Tannins</b></th><td>+++</td></tr><tr><th>FeCl3 <b>Flavonoids</b></th><td>Gr</td></tr><tr><th>Pb(C2 H 3O2)2</th><td>++</td></tr><tr><th><b>Purines</b></th><td><b>-</b></td></tr><tr><th><b>Catechins</b></th><td>+++</td></tr><tr><th><b>Benzoquinone derivatives</b></th><td>+++</td></tr><tr><th><b>Depsids and depsidones</b></th><td>+++</td></tr><tr><th><b>Steroids and triterpenoids</b></th><td><b>-</b></td></tr><tr><th><b>Sesquiterpenolactones</b></th><td>-</td></tr></tbody></table>
Yields, Cannabinoids Quantification, and Predictive Programming Codes Using Machine Learning for Non-Psychoactive Cannabis Flowers and Extracts (Cannabis sativa L.) Cultivated in Ecuador.
<p>This publication presents data from various extraction methods, including maceration, Soxhlet, and supercritical fluids, performed on different cannabis flower varieties (Cannabis sativa L.) under varying operating conditions. We quantified the amounts of CBD, THC, CBG, and CBN in the extracts produced by each method using High-Performance Liquid Chromatography (HPLC). Using this data, we developed a machine learning algorithm in RStudio to make predictions and determine the best conditions and yields for each extraction method. The analysis focuses on different varieties of non-psychoactive cannabis cultivated in Ecuador at altitudes over 2,450 m.a.s.l.</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.