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3,684 results for “extract”
Databases of "Economic criteria of Policosanol extraction" article: standardization, merge and removing duplication of results found in the Scopus Elsevier and Web of Science bases
<p>These eight databases derives from the processes of standardization, merge and removing duplication of results found in the Scopus Elsevier and Web of Science bases, in CSV format to be used with software suitable for replicating the results, new statistical, bibliometric, semantic and other treatments.</p> <p>DatabaseE1 - Extraction - BUSINESS ATTRACTIVENESS. </p> <p>DatabaseE2 - Extraction - COMMERCIAL AND EXPORT FEASIBILITY </p> <p>DatabaseE3 - Extraction - TECHNICAL AND INDUSTRIAL VIABILITY: </p> <p>DatabaseE4 - Extraction - ECOEFICIENCY</p> <p>DatabaseEG1 - General - BUSINESS ATTRACTIVENESS </p> <p>DatabaseEG2 - General - COMMERCIAL AND EXPORT FEASIBILITY </p> <p>DatabaseEG3 - General - TECHNICAL AND INDUSTRIAL VIABILITY: </p> <p>DatabaseEG4 - General - ECOEFICIENCY</p> <p>It is constitutive of the article entitled " Economic criteria of Policosanol extraction: a systematic literature review and patent prospection", by professors Rilton Gonçalo Bonfim Primo, Jesus Martín-Gil, Fernando Cardoso Pedrão, Carlos Narciso Bouza and Ricardo de Araújo Kalid.</p>
Databases of Patent Prospection of "Economic criteria on Policosanol extraction" study: CSV and PDF documents
<p>These databases derive from the processes of patent prospection on Economic Criteria in Patents on Policosanol by Advanced Search and IP Business Intelligence of Orbit seekers (version 1.9.8), in CSV format to be used with software suitable for replicating the results, new statistical, semantic and other treatments.</p> <p>They are doubted in two groups: 1) economic criteria referred to in patents on Policosanol, in a broad way, involving processes before or after obtaining them (mode FamPat); 2) economic criteria mentioned in patents on Policosanol, specifically involving its extraction processes (mode FullPat).</p> <p>No results were found, for extraction modality, in searches 03 for Ibero-American countries and 04, so the corresponding databases were not generated.</p> <p>Three PDF achieves with the first page of the patents/family and its technical links are available for easy consults of main information. </p> <p>These elements are constitutive of the article entitled "Economic criteria on Policosanol extraction: a systematic literature review and patents prospection", by professors Rilton Gonçalo Bonfim Primo, Jesus Martín-Gil, Fernando Cardoso Pedrão, Carlos Narciso Bouza and Ricardo de Araújo Kalid.</p>
Technical report with partial results of the article "Technological routes of Policosanols extraction"
<p>It is constitutive of the article entitled "Technological routes of Policosanols extraction: a systematic literature review and patent prospection", by professors Rilton Gonçalo Bonfim Primo, Jesus Martín-Gil, Fernando Cardoso Pedrão, Carlos Narciso Bouza and Ricardo de Araújo Kalid.</p> <p>In the Table 1, Table 2 and Table 3 one care taken was to carry out the “Extended Search” (i.e., in all fields of the articles, not just in the title, abstract and keywords) in cases where the results were less than ten. This inclusiveness naturally implies refiltering articles outside the scope of the research. Policosanol spelling variants, with "y" or "i", and suffix variations “ultrasonic” or “ultrasound” were considered in these searches.</p> <p>Methodological, computational and bibliometric aspects, as well as their historical, research, discussions and inferences contexts can be found in the referred article, of which this document is only a data repository of leading partial results, through quantitative and qualitative analyzes, to the final results achieved there.</p>
FIGURE 1 in Accelerating taxonomic discovery through automated character extraction
FIGURE 1. Schematicdiagramofaproposedautomatedcharacterextractionsystem.
Hourly NEXRAD regridded and reduced domain precip accums extracted for SGP April 2017-March 2022
<p>Data regridded using METplus RegridDataPlane to the HRRR grid. Small domain extracted to coincide with ARM Southern Great Plains (SGP) rain gauge location I10. Presented as netcdf.</p><p>Data created and provided in annual tar bundles for ongoing post-graduate projects under the University of Bath Maths4DL programme.</p><p>Missing data: 2019-04-04 all 24h are missing</p><p> </p><p>Data provided by NCAR/EOL under the sponsorship of the National Science Foundation. https://data.eol.ucar.edu/</p>
Dataset for the paper "TestDossier: A Dataset of Tested Values Automatically Extracted from Test Execution"
<p><strong>Dataset for the paper "TestDossier: A Dataset of Tested Values Automatically Extracted from Test Execution".</strong></p><p> </p><p>We store values of the basic build-in Python types: numeric (int, float, and complex), boolean (bool), and text sequence (str). We also store values of the types: sequence (list, tuple, and range), set (frozenset and set), and mapping (dict) that include only basic types. To create the dataset, we monitored the test suites of 15 Python Standard Libraries. TestDossier contains 1,234 distinct argument/variable names and 133,169 dis- tinct values, leading to a total of 12,9M individual values.</p>
Dataset for the paper "TestDossier: A Dataset of Tested Values Automatically Extracted from Test Execution"
<p><strong>Dataset for the paper "TestDossier: A Dataset of Tested Values Automatically Extracted from Test Execution".</strong></p><p>We store values of the basic build-in Python types: numeric (int, float, and complex), boolean (bool), and text sequence (str). We also store values of the types: sequence (list, tuple, and range), set (frozenset and set), and mapping (dict) that include only basic types. To create the dataset, we monitored the test suites of 15 Python Standard Libraries. TestDossier contains 1,234 distinct argument/variable names and 133,169 dis- tinct values, leading to a total of 12,9M individual values.</p>
Long-term stability of dental arch widths after extraction and non-extraction orthodontic treatment: a retrospective cohort study
<p>Dataset for all analyses. Initially, access will be granted to the editor and the peer reviewers. After acceptance, unrestricted access will be granted to everyone.</p>
Recopliación de datos cuantitativos extraídos del análisis textural de la colección de referencia experimental sobre tratamiento de superficie en cerámica a mano mediante microscopía óptica confocal / Compilation of quantitative data extracted from the textural analysis of the experimental reference collection on surface treatment of handmade pottery by confocal optical microscopy.
<p>This repository contains the quantitative data and stadistical test extracted from the textural analysis of the experimental reference collection on surface treatment of handmade pottery by confocal optical microscopy.</p>
Empirical Data, Survey and Letter of Consent of the Study: Brouillet C. et al. "Soil extraction as an adaptation strategy to climate change - a focus on urban ecosystem services"
<ul> <li>Empirical Data (quantitative part of the results), Survey and Letter of Consent</li> <li>From the study entitled "Soil extraction as an adaptation strategy to climate change - a focus on urban ecosystem services" Brouillet C. et al. </li> <li>All documents are in French.</li> </ul>
Extracts North Caucuses
<p>Extracts of interviews from North Caucuses case study. Extracted using Atlas.ti software.</p>
Extracts Estonia
<p>Extracts of interviews from Estonian case study. Extracted using Atlas.ti software.</p>
Extracts Russia
<p>Extracts of interviews from Russian case study. Extracted using Atlas.ti software.</p>
Extracts Uzbekistan
<p>Extracts of interviews from Russian case study. Extracted using Atlas.ti software.</p>
Facilitating high throughput collections-based genomics: a comparison of DNA extraction and library build methods
<p>Dataset for submission: "Facilitating high throughput collections-based genomics: a comparison of DNA extraction and library build methods"</p>
Argument Graphs Extracted From the Online Debate Portal Kialo
<p>For our paper "Towards an Argument Mining Pipeline Transforming Texts to Argument Graphs" published at COMMA 2020, we extracted debates from the online debate portal Kialo. We used it to train multiple classifiers and evaluate our pipeline, but the data may be useful in other areas as well. The argument graphs are saved in a format similar to the one used by OVA (<a href="https://ova.arg-tech.org">https://ova.arg-tech.org</a>). They can be parsed using our Python library (<a href="https://pypi.org/project/recap-argument-graph/">https://pypi.org/project/recap-argument-graph/</a>).</p> <p>If you use the data, please cite our work as follows:</p> <pre><code>@inproceedings{Lenz2020ArgumentMiningPipeline, address = {{Perugia, Italy}}, archiveprefix = {arXiv}, author = {Lenz, Mirko and Sahitaj, Premtim and Kallenberg, Sean and Coors, Christopher and Dumani, Lorik and Schenkel, Ralf and Bergmann, Ralph}, booktitle = {Proceedings of the 8th {{International Conference}} on {{Computational Models}} of {{Argument}}}, doi = {10.3233/FAIA200510}, editor = {Prakken, Henry and Bistarelli, Stefano and Santini, Francesco and Taticchi, Carlo}, eprint = {2006.04562}, eprinttype = {arxiv}, pages = {263--270}, publisher = {{IOS Press}}, series = {Frontiers in {{Artificial Intelligence}} and {{Applications}}}, title = {Towards an {{Argument Mining Pipeline Transforming Texts}} to {{Argument Graphs}}}, volume = {326}, year = {2020}, Bdsk-Url-1 = {https://doi.org/10.3233/FAIA200510}} </code></pre> <p> </p>
Model America - WRF Grid Cell Archetype extract from ORNL's AutoBEM
<p>Oak Ridge National Laboratory (ORNL) has developed the Automatic Building Energy Modeling (AutoBEM) software suite to process multiple types of data, extract building-specific descriptors, generate building energy models, and simulate them on High Performance Computing (HPC) resources. For more information, see AutoBEM-related publications (<a href="https://bit.ly/AutoBEM">bit.ly/AutoBEM</a>).</p> <p><strong>Critical note: Building multipliers and models will be updated soon.</strong></p> <p>Two sets of data are provided for 298,084 building <strong>archetypes</strong> located within the boundary of WRF (Weather Research and Forecasting) model in and around Los Angeles (United States):</p> <ol> <li><strong>Data (46.4MB *.csv) - minimalist list of each building (rows) for the following fields (columns)</strong> <ol> <li>ID - unique building ID</li> <li>Area - estimate of total conditioned floor area (ft<sup>2</sup>)</li> <li>CZ - ASHRAE Climate Zone designation</li> <li>Height - building height (ft)</li> <li>NumFloors - number of floors (above-grade)</li> <li>BuildingType - DOE prototype building designation (IECC=residential) as implemented by OpenStudio-standards</li> <li>Standard - building vintage</li> <li>WWR_surfaces - percent of each facade (pair of points from Footprint2D) covered by fenestration/windows (average 14.5% for residential, 40% for commercial buildings)</li> <li>Area2D - footprint area (ft<sup>2</sup>)</li> <li>Num_build_per_zone - Number of this building type/vintage in WRF zone</li> <li>Total_zone_area - Total area of this building type/vintage in WRF zone (ft<sup>2</sup>)</li> <li>Area_multiplier - Scaling factor for building type/vintage for building in WRF zone</li> <li>Vintage - Generalized "Standard"</li> <li>BuildingType_general - Generalized "BuildingType"</li> <li>Zone_ID - ID of WRF zone</li> </ol> </li> <li><strong>Models (6.3GB *.zip) - EnergyPlus building energy models named according to ID</strong> <ul> <li>Each model has approximately 3,000 building input descriptors that can be extracted. Please see the EnergyPlus (v9.4) 2,784-page <a href="https://energyplus.net/sites/all/modules/custom/nrel_custom/pdfs/pdfs_v9.4.0/InputOutputReference.pdf">Input/Output Reference Guide </a>for everything that can be retrieved or simulated from these models.</li> </ul> </li> </ol> <p> </p>
TSEM image of 30 nm Latex particles taken with the (oldish) Zeiss Supra - for testing metadata extraction
TSEM image of 30 nm Latex particles taken with the (oldish) Zeiss Supra - for testing metadata extraction
Material extraction (used) (construction minerals) from Global Material Flows Database
<p>The following data was used in the paper "Schiller & Roscher (2023). Impact of Urbanization on construction material consumption: A global analysis" to calculate material consumption of non-metallic mineral construction materials.</p> <p>This dataset provided data on the extraction of mineral construction materials that are used for further processes. The data represent the extraction of raw materials from the environment by country. They are based on reported data on the one hand and estimated data on the other. The specific assumptions and factors for the estimates can be found in Krausmann et al. (2009). Growth in global material use, GDP and population during the 20th century. Ecological Economics 68(10) 2696-2705. doi: 10.1016/j.ecolecon.2009.05.007.<br> In the MFA methodology, some categories of materials are defined as "unused extraction" because they are not economically used or further processed. For example, unused materials include overburden from mining activities and unused residues from biomass extraction (OECD, 2008). These are not included in presented data.</p> <p>The following materials belong to the group of construction minerals: asphalt, chert and flint, common clay, clay for bricks etc., crushed stone, igneous rock, lava sand, limestone, marl, shell, loam, marble, travertines, sand and gravel, sandstone, slate and turfaceous rock (see Lutter, S., Lieber, M., & Giljum, S. (2016). Global Materialflow database. Material extraction data. Technical Report, Version 2015.1. retrieved February 2018 from www.materialflows.net).<br> The status of the given data is spring 2018 and was downloaded from www.materialflows.net at this time. Due to restructuring of the website, 2018 data is no longer available online. Current data on material extraction and consumption can be found on United Nations Environment Programme, International Resource Panel (IRP). (2023). Global Material Flows Database. https://www.resourcepanel.org/global-material-flows-database.</p>
Extracted Data of Releases from Projects
<p>Extracted Data of Release notes and related artifacts from GitHub repositories. We uploaded small data size.</p>
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.