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6,452 results for “Application”
Figure 1 from: Hasanuddin DNA, Garmana AN, Sasongko L (2024) HPLC method for the determination of nifedipine in rat plasma: development, validation, and application to pharmacokinetic drug-herb interaction study. Pharmacia 71: 1-6. https://doi.org/10.3897/pharmacia.71.e119198
Figure 1 HPLC chromatogram of nifedipine. A. Plasma sample spiked with 5 mL/mL of kaempferol; B. Nifedipine-free plasma; C. Plasma sample spiked with 1000 ng/mL of nifedipine.
Dataset for "Application of Digital Twins for Simulation Based Tailoring of Laser Induced Graphene"
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Simulation Data and postprocessing code for "Two-Dimensional Nonlocal Eshelby's Inclusion Theory: Eigenstress-Driven Formulation and Applications"
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Supplementary material 1 from: Matenaar D (2024) Application of extended depth of field 3D imagery to tackle the challenges of cryptic species: a use case in the genus Betiscoides Sjöstedt, 1924 (Orthoptera, Caelifera, Lentulidae) and its taxonomic implications. Evolutionary Systematics 8(1): 65-90. https://doi.org/10.3897/evolsyst.8.117735
Specimens collections sites and Genbank Acc. numbers
APPLICATION OF MARKETING CONCEPTS AT INDUSTRIAL ENTERPRISES: PROBLEMS AND SOLUTIONS
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Selection of supporting material for DCMS Application
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Investigation of an anomolous 3'-termini mismatch bypass for phosphorothioated primers and application as molecular switch
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The Matlab application code of these models used in this study.
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Wikidata: the potential of structured data to enable applications such as Histropedia
<p>Despite the vast number of uses for Wikipedia, you still can’t ask it even simple questions like “who is the Mayor of London?” – Computers simply do not understand the unstructured data. Wikidata is a ground-breaking new Wikimedia project that aims to tackle this problem and many others, by making the sum of all human knowledge machine-readable. It will provide a<br>back end to Wikipedia, potentially allowing a single change in Wikidata to correct and improve information represented in multiple places in Wikipedia, across all language versions.<br>In this presentation I will give a basic overview of how Wikidata works, including information about how the data is structured and connected to other resources on the internet, as well as the methods being used to collect and organize the data. I will also highlight the major similarities and differences between Wikidata and the already widely used DBpedia.Finally, I will go on to explain the implications of Wikidata’s development for the future of Wikipedia and the rest of the web, giving examples of applications that have already been built upon data from Wikidata such as Histropedia, which aims to visualize the sum of all knowledge using interactive timelines.</p>
MARKETING SYSTEM AND POSSIBILITIES OF ITS APPLICATION IN AGRICULTURAL ENTERPRISES.
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Semgrep*: Further Improving Static Application Security Testing (SAST) Tools: An Industry Case-Study
<p>Scripts to download and build data.</p> <p> </p> <p>Yaml files for additional SAST rules.</p>
A GUIDE TOWARDS FULL STACK DEVELOPMENT which was presented by Er.T.Nageswari, Technology Lead, Infosys Ltd, Trivandrum., organized by Department of Computer Applications in association with FX Alumni Association of Francis Xavier Engineering College, Tirunelveli, Tamil Nadu on 06-06-2020.
<p>A GUIDE TOWARDS FULL STACK DEVELOPMENT which was presented<br>by Er.T.Nageswari, Technology Lead, Infosys Ltd, Trivandrum., organized by Department<br>of Computer Applications in association with FX Alumni Association of Francis Xavier<br>Engineering College, Tirunelveli, Tamil Nadu on 06-06-2020.</p>
Supplementary Videos for dissertation: Tunable magnetic microparticles and hydrogel biomaterials for biomedical imaging and tissue engineering applications
<p>This work contains supplementary videos of cell migration in hydrogels for the chapter 4 of the dissertation.</p>
SAUUHUPP Networked Periodic Table Based Explorations and Applications
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Appendix_Data_Industrial application of Multi-Robot Partial Destructive Disassembly Line Balancing for Multi-Product Scenarios_v1.docx
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Benchmark Dataset of Cropland Parcel Boundaries from Multi-Source Remote Sensing Imagery for AI application
<p><span>We provide a standardized dataset of diversified parcel labels based on multi-source remote sensing imagery. The parcels included in this dataset are sourced from four countries: the Netherlands, Denmark, Spain, and China, encompassing over 1</span><span>4</span><span>0,000 </span><span>cropland parcel</span><span>. The average parcel size across different study areas ranges from 0.05 hectares to 10 hectares. The remote sensing imagery is derived from more than 10 data sources, including satellites, UAV (unmanned aerial vehicle), and public mapping platforms, with spatial resolutions varying from 0.05 meters to 10 meters.</span></p>
Dataset for Application of machine learning to choke performance
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A hybrid approach to the small unannotated corpus-based language comparison and its application to the Old East Slavic charters - Supplementary material 5 (Corpus-based language distance measurement results)
<h1>General description</h1> <p>These are the results of the experiments with the use of <a href="https://doi.org/10.5281/zenodo.11395683" target="_blank" rel="noopener">corpus-based language distance measurement package</a> on the material of <a href="https://doi.org/10.5281/zenodo.14057668" target="_blank" rel="noopener">Old East Slavic</a>, <a href="https://doi.org/10.5281/zenodo.14148179" target="_blank" rel="noopener">modern East Slavic</a>, and <a href="https://doi.org/10.5281/zenodo.14148561">modern standard Slavic</a> lects. There are 40 possible experiments for each data set, divided by the usage of:</p> <ul> <li>topic antimodelling heuristic,</li> <li>Soerensen-Dice coefficient-based normalisation,</li> <li>the presence of hybridisation of frequency-based metric for coinciding units and combined frequency-based metric and string similarity measure for non-coinciding units,</li> <li>hybridisation type,</li> <li>exact type of string similarity measure used for combination,</li> <li>alphabet entropy-based normalisation for vector-based string similarity measures.</li> </ul> <p>In addition, modern standard Slavic dataset undergoes experiments 4 times that differ by the share of its size, used for measurements (0.1, 0.3, 0.6 and 1).</p> <p>For further information on each of the experiment parameters, refer to the documentation of the package.</p> <h1>Data set structure</h1> <h2>Executive summary</h2> <p>Data set consists of 240 folders that represent information on the experiments and 1 <code>.csv</code>-file that aggregates the resulting values into a single table.</p> <p>Folders with indices 1-20 and 121-140 contain experiments with 0.1 share of the modern standard Slavic dataset; the first sequence applies topic antimodelling heuristic, the second sequence does not employ it.</p> <p>Folders with indices 21-40 and 141-160 contain experiments with 0.3 share of the modern standard Slavic dataset; the first sequence applies topic antimodelling heuristic, the second sequence does not employ it.</p> <p>Folders with indices 41-60 and 161-180 contain experiments with 0.6 share of the modern standard Slavic dataset; the first sequence applies topic antimodelling heuristic, the second sequence does not employ it.</p> <p>Folders with indices 61-80 and 181-200 contain experiments with the full share of the modern standard Slavic dataset; the first sequence applies topic antimodelling heuristic, the second sequence does not employ it.</p> <p>Folders with indices 81-100 and 201-220 contain experiments with the full share of the modern East Slavic dataset; the first sequence applies topic antimodelling heuristic, the second sequence does not employ it.</p> <p>Folders with indices 101-120 and 221-240 contain experiments with the full share of the Old East Slavic dataset; the first sequence applies topic antimodelling heuristic, the second sequence does not employ it.</p> <h2>.csv-file</h2> <p>Named <code>aggregated_results.csv</code>, lies in the root of the dataset. Separator is <strong>comma</strong> (<strong>,</strong>). Contains <strong>13 columns</strong> and <strong>241 row</strong>. The first row is <strong>header</strong>, the other 240 rows contain description for each conducted experiment and its resulting values, according to the columns. The columns are the following (in <strong>rtl</strong> order):</p> <ul> <li><strong>X.</strong> (<em>int</em>) - experiment ID; column is used as index.</li> <li><strong>Material </strong>(<em>string</em>) - the data set used for language distance measurement. The possible values are: <ul> <li>Slavic standard - Croatian, Slovenian, Slovak standard lects.</li> <li>Modern East Slavic - Northern Russian lect Megra, Central Russian lect Belogornoje, and Northern Belarusian lect Zialionka.</li> <li>Old East Slavic - Novgorod, Polack and Smolensk parts of the Old East Slavic continuum.</li> </ul> </li> <li><strong>Gensim </strong>(<em>int</em>) - the binary numeric indicator (0 or 1) of using the heuristic of <em>topic antimodelling</em>, namely, cleaning the words that were defined as a topic words by <em>gensim</em> Latent Dirichlet Association implementation (Rehurek & Sojka, 2010). The intention of using this heuristic is to remove the tokens that are characteristic for the genre of the texts presented in the corpus for the sake of increasing the presence of the tokens that are characteristic of the lects themselves.</li> <li><strong>Split</strong> (<em>float</em>) - the used share of the data set (from 0 to 1); required to check the influence of the data set size on the metric efficiency.</li> <li><strong>Hybridisation</strong> (<em>string</em>) - the indicator of implementation of the hybridisation between the frequency-based metric between the 3-shingles (character 3-grams) that coincide for the compared lect pair, and the combination of frequency-based metric and string similarity measure between the 3-shingles that do not coincide for the compared lect pair. The possible values are: <ul> <li>TRUE: the experiment utilises hybridisation</li> <li>FALSE: the experiment does not utilise hybridisation.</li> </ul> </li> <li><strong>Hybridisation_type</strong> (<em>string</em>) - the indicator of how the frequency metric between coinciding 3-shingles and the combined metric between non-coinciding 3-shingles undergo the hybridisation process. The values are:<br> <ul> <li>JOINED - the approach is to multiply the means of the two.</li> <li>ARRAY - the approach is to join all the values into a single list, and then to score the mean.</li> <li>NOT_USED - experiment does not employ hybridisation. (<strong>Hybridisation </strong>is FALSE).</li> </ul> </li> <li><strong>Soerensen_normalisation</strong> (<em>string</em>) - the indicator of whether the frequency-based metric value undergoes normalisation with the division by Soerensen-Dice coefficient (a measure of number of coincidences between two lists) (Soerensen, 1948), in order to compensate the skewing between the coinciding and non-coinciding 3-shingles of the lects. The values are: <ul> <li>NOT_USED - <strong>Hybridisation_type </strong>is ARRAY, so there are no values to use Soerensen-Dice coefficient on.</li> <li>TRUE - the frequency-based metric undergoes division by the Soerensen-Dice coefficient.</li> <li>FALSE - the algorithm does not apply the normalisation by the Soerensen-Dice coefficient.</li> </ul> </li> <li><strong>Alphabet_normalisation</strong> (<em>string</em>) - indicator of whether the algorithm applies normalisation with the alphabet entropy (Shannon, 1948), the measure of differences in the skewings of symbols distribution in the texts, between the given lects. The values are:<br> <ul> <li>NOT_USED - a heuristic may not be implemented; present either in the cases, when <strong>Hybridisation </strong>is FALSE, or when the next parameter, <strong>Auxiliary_metrics</strong> is not VDND or VWJDND.</li> <li>TRUE<strong> </strong>- the experiment employs the heuristic.</li> <li>FALSE - the experiment does not employ the heuristic.</li> </ul> </li> <li><strong>Auxiliary_metrics </strong>(<em>string</em>) - the string similarity measure, used for the combination with the frequency-based metric for non-coinciding 3-shingles between analysed lects. There are five possible values: <ul> <li>LDND (Levenshtein distance normalised between analysed 3-shingles) (Holman et al., 2008).</li> <li>WJWDND (weighted Jaro-Winkler distance normalised between analysed 3-shingles) (Gueddah et al., 2015).</li> <li>VDND (Euclidean distance between the sums of symbol vector values between 3-shingles).</li> <li>VWJDND (VDND multiplied by scoring Jaro (Jaro, 1989) distance between analysed 3-shingles).</li> </ul> </li> <li><strong>Outgroup.identification</strong> (<em>string</em>) - the indicator of whether the outgroup detected in the given experiment coincides with the lect that preliminary manual classification supposes to be the outgroup. There are two possible values:<br> <ul> <li>CORRECT - the detected outgroup coincides with the supposed one.</li> <li>INCORRECT - the detected outgroup does not coincide with the supposed one.</li> </ul> </li> <li><strong>Outer.distance.split </strong>(<em>float</em>) - the length of the outgroup branch.</li> <li><strong>Inner.distance.split</strong> (<em>float</em>) - the distance between the split between the outgroup and the ingroup, and the split between the two ingroup lects.</li> <li><strong>Split.difference</strong> (<em>float</em>) - the division of <strong>Outer.distance.split</strong> by <strong>Inner.distance.split</strong>.</li> </ul> <h2>Folders</h2> <p>Each folder contains 6 files, each named according to the used experiment setup:</p> <ul> <li>3 <code>.csv</code>-files that contain unit-by-unit comparison between each pair of the analysed lects. Each <code>.csv</code>-file is <strong>semi-colon</strong>-separated, and has <strong>4 </strong>columns, <strong>header row</strong>, and rows that describes each unit-to-unit comparison. The columns contain the following information (in <strong>rtl</strong> order):<br> <ul> <li><code>[Name of the first compared lect]</code> : unit (character 3-shingle, or just 3-shingle) of the [name of the first compared lect] that undergoes comparison with units of the [name of the second compared lect]; datatype: string.</li> <li><code>[Name of the second compared lect] </code>: unit (character 3-shingle, or just 3-shingle) of the [name of the second compared lect] that undergoes comparison with units of the [name of the first compared lect]; if units coincide, contains value <code>id.</code>; datatype: string.</li> <li><code>[Experiment setup]</code>: name of the metric, a combination of the [experiment setup](concatenated through <code><em>-</em></code> parameters) and its exact part, which compares the two units; datatype: string. The possible values are: <ul> <li><code>[experiment setup] - DistRank</code> - the frequency-based metric that compares identical units</li> <li><code>[experiment setup] - hybrid</code> - the string similarity measure for non-identical units, combined with the frequency-based metric </li> </ul> </li> <li><code>Distance</code>: value of the metric; datatype: float</li> </ul> </li> <li><code>.info</code>-file that contains data on branch lengths along with coincidence/non-coincidence of the detected outgroup with the manually defined one. The file is a <strong>tabular-separated plain text</strong> that always contains three values: coincidence (CORRECT)/non-coincidence (INCORRECT) of the yielded classification with the supposed one; outer distance split (the length of the outgroup branch; datatype: float) and inner distance split (the length of the ingroup branch before split of its lects; datatype: float).</li> <li><code>.newick</code> -file that contains the result of an experiment, the phylogenetic tree built by UPGMA classifier. One can read it with <a href="https://cran.r-project.org/web/packages/TreeTools/vignettes/load-trees.html">ape::read.tree</a> (R), or <a href="https://biopython.org/wiki/Phylo">Phylo.read</a> (Python).</li> <li><code>.png</code> -file that contains the phylogenetic tree visualisation.</li> </ul> <p> </p> <h1>How-to</h1> <p>For the analysis of the results, download and unpack the archive, and further refer to the <a href="https://doi.org/10.5281/zenodo.14169792" target="_blank" rel="noopener">companion R notebook</a>. </p>
Koopman-based Control for Stochastic Systems: Application to Enhanced Sampling
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Black Gold Layers Preparation via Thermal Evaporation, Material and Optical Properties, Application Potential for Gas Sensors
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ScienceDex guides
Understand access before you commit
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.