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9,113 results for “Sampling”
Fig. 4 in Notes on the Occurrence of Tintinnid Ciliates, and the Nasselarian Radiolarian Amphimelissa setosa of the Marine Microzooplankton, in the Chukchi Sea (Arctic Ocean) Sampled each August from 2011 to 2020
Fig. 4. The distribution of lorica oral diameters (LOD) of the tintinnid species found over 10 years of sampling. The LOD of a species is related positively to the diameter of the preferred prey size, about 25% of the LOD. Note that the most common LODs are 19–30 µm and 35–42 µm, suggesting most species feed on small prey (5–10 µm diameter). Note also that excluding species found only once and species suspected to be morphological variants of another species (see Table 2) yields few changes in the distribution.
ProteinCartography v0.4 analyses for sample of most-studied human proteins
<p>Results from running the ProteinCartography pipeline (v0.4) using analyses on 25 proteins sampled from the most-studied human proteins listed in <a href="https://onlinelibrary.wiley.com/doi/10.1002/pro.4038">Liu and Buck, 2021</a>.<br> <br> The proteins included are listed in the table below. The results for each analysis are provided as individually-zipped folders, with the name of the file reflective of the protein name. To download all of the analyses in one folder, you can download the "25humanprotein_analysis.zip" file.</p> <table> <tbody> <tr> <td> <p><strong>UniProt ID</strong></p> </td> <td> <p><strong>Protein name</strong></p> </td> <td> <p><strong>Protein symbol (in figures)</strong></p> </td> </tr> <tr> <td> <p><strong>Q9UM73</strong></p> </td> <td> <p><strong>ALK tyrosine kinase receptor</strong></p> </td> <td> <p><strong>ALK</strong></p> </td> </tr> <tr> <td> <p><strong>Q96RI1</strong></p> </td> <td> <p><strong>Bile acid receptor</strong></p> </td> <td> <p><strong>NR1H4</strong></p> </td> </tr> <tr> <td> <p><strong>P43235</strong></p> </td> <td> <p><strong>Cathepsin K</strong></p> </td> <td> <p><strong>CATK</strong></p> </td> </tr> <tr> <td> <p><strong>P08603</strong></p> </td> <td> <p><strong>Complement factor H</strong></p> </td> <td> <p><strong>CFAH</strong></p> </td> </tr> <tr> <td> <p><strong>P00374</strong></p> </td> <td> <p><strong>Dihydrofolate reductase</strong></p> </td> <td> <p><strong>DYR</strong></p> </td> </tr> <tr> <td> <p><strong>P98170</strong></p> </td> <td> <p><strong>E3 ubiquitin-protein ligase XIAP</strong></p> </td> <td> <p><strong>XIAP</strong></p> </td> </tr> <tr> <td> <p><strong>P49841</strong></p> </td> <td> <p><strong>Glycogen synthase kinase-3 beta</strong></p> </td> <td> <p><strong>GSK3B</strong></p> </td> </tr> <tr> <td> <p><strong>P01112</strong></p> </td> <td> <p><strong>GTPase HRas</strong></p> </td> <td> <p><strong>RASH</strong></p> </td> </tr> <tr> <td> <p><strong>P68871</strong></p> </td> <td> <p><strong>Hemoglobin subunit beta</strong></p> </td> <td> <p><strong>HBB</strong></p> </td> </tr> <tr> <td> <p><strong>P04439</strong></p> </td> <td> <p><strong>HLA class I histocompatibility antigen, A alpha chain</strong></p> </td> <td> <p><strong>HLAA</strong></p> </td> </tr> <tr> <td> <p><strong>P01834</strong></p> </td> <td> <p><strong>Immunoglobulin kappa constant</strong></p> </td> <td> <p><strong>IGKC</strong></p> </td> </tr> <tr> <td> <p><strong>P14174</strong></p> </td> <td> <p><strong>Macrophage migration inhibitory factor</strong></p> </td> <td> <p><strong>MIF</strong></p> </td> </tr> <tr> <td> <p><strong>P53779</strong></p> </td> <td> <p><strong>Mitogen-activated protein kinase 10</strong></p> </td> <td> <p><strong>MK10</strong></p> </td> </tr> <tr> <td> <p><strong>Q15596</strong></p> </td> <td> <p><strong>Nuclear receptor coactivator 2</strong></p> </td> <td> <p><strong>NCOA2</strong></p> </td> </tr> <tr> <td> <p><strong>Q99497</strong></p> </td> <td> <p><strong>Parkinson disease protein 7</strong></p> </td> <td> <p><strong>PARK7</strong></p> </td> </tr> <tr> <td> <p><strong>P62937</strong></p> </td> <td> <p><strong>Peptidyl-prolyl cis-trans isomerase A</strong></p> </td> <td> <p><strong>PPIA</strong></p> </td> </tr> <tr> <td> <p><strong>Q13451</strong></p> </td> <td> <p><strong>Peptidyl-prolyl cis-trans isomerase FKBP5</strong></p> </td> <td> <p><strong>FKBP5</strong></p> </td> </tr> <tr> <td> <p><strong>P27986</strong></p> </td> <td> <p><strong>Phosphatidylinositol 3-kinase regulatory subunit alpha</strong></p> </td> <td> <p><strong>P85A</strong></p> </td> </tr> <tr> <td> <p><strong>O75530</strong></p> </td> <td> <p><strong>Polycomb protein EED</strong></p> </td> <td> <p><strong>EED</strong></p> </td> </tr> <tr> <td> <p><strong>P28074</strong></p> </td> <td> <p><strong>Proteasome subunit beta type-5</strong></p> </td> <td> <p><strong>PSB5</strong></p> </td> </tr> <tr> <td> <p><strong>P19793</strong></p> </td> <td> <p><strong>Retinoic acid receptor RXR-alpha</strong></p> </td> <td> <p><strong>RXRA</strong></p> </td> </tr> <tr> <td> <p><strong>P50120</strong></p> </td> <td> <p><strong>Retinol-binding protein 2</strong></p> </td> <td> <p><strong>RET2</strong></p> </td> </tr> <tr> <td> <p><strong>P00441</strong></p> </td> <td> <p><strong>Superoxide dismutase [Ccu-Zn]</strong></p> </td> <td> <p><strong>SODC</strong></p> </td> </tr> <tr> <td> <p><strong>Q93009</strong></p> </td> <td> <p><strong>Ubiquitin carboxyl-terminal hydrolase 7</strong></p> </td> <td> <p><strong>UPB7</strong></p> </td> </tr> <tr> <td> <p><strong>P40337</strong></p> </td> <td> <p><strong>von Hippel -–Lindau disease tumor suppressor</strong></p> </td> <td> <p><strong>VHL</strong></p> </td> </tr> </tbody> </table> <p>A TSV file, "most_studied_human_proteins.tsv", is included for use with the analysis notebook ("pub/most_studied_human_proteins.ipynb") found in our <a href="https://github.com/Arcadia-Science/ProteinCartography">GitHub repository</a> for the Arcadia Science Pub "<a href="https://doi.org/10.57844/arcadia-a5a6-1068">ProteinCartography: Comparing proteins with structure-based maps for interactive exploration</a>". The notebook produces the outputs, "sampled_proteins.tsv", and. "most_studied_human_proteins_analysis.tsv", which are used by the analysis notebook ("pub/cluster_quality_human_proteins.ipynb"), which uses the contents of this deposition.</p> <p> </p>
Sample Face Q.Dsent Database (SFQ2D)
<p>This database is named Sample Face Q.Dsent Database (SFQ2D) and is used to test the face recognition model in making a face-based student attendance system taken from 5 Informatics students of Muhammadiyah University Semarang with 5 shooting positions, namely front, right, left, top, and bottom.</p>
QPI mapping of PTCDA on Ag111 with sparse sampling
<p>This measurement was initiated by Danyang Liu and implemented together with Jens Oppliger, Dr. Taner Esat, and Valentin Maissen in the lab of Prof. Ruslan Temirov at PGI-3, Forschungszentrum Jülich, co-supervised by Prof. Fabian Natterer from the University of Zurich.</p> <p>Copyright: danyang.liu@physik.uzh.ch</p> <p> </p>
Monitoring wildlife population trends with sample counts: A case study on the Alpine ibex (Capra ibex)
<p><span>Monitoring population dynamics is of fundamental importance in conservation but assessing trends in abundance can be costly, especially in large and rough areas. Obtaining trend estimations from counts performed in only a portion of the total area (sample counts) can be a cost-effective method to improve the monitoring and conservation of species difficult to count. </span></p> <p><span>We tested the effectiveness of sample counts in monitoring population trends of wild animals, using as a model population the Alpine ibex (<em>Capra ibex</em>) in the Gran Paradiso National Park (Italy), both with computer simulations and using historical count data collected over the last 65 years. Despite sample counts failed to correctly estimate the true population abundance, sampling half of the target area could reliably monitor the trend of the target population. In case of strong changes in abundance, an even lower proportion of the total area could be sufficient to identify the direction of the population trend. However, when there is a high yearly trend variability, the required number of samples increases and even counting in the entire area can be ineffective to detect population trends. The effect of other parameters, such as which portion of the area is sampled and detectability, was lower, but these should be tested case by case. </span></p> <p><span>Sample counts could therefore constitute a viable alternative to assess population trends, allowing for important, cost-effective improvements in the monitoring of wild animals of conservation interest. </span></p>
Credit scoring with class imbalance data: An out-of-sample and out-of-time perspective
<p>The raw datasets provided here are intended for use in a Data in Brief article. These comprehensive files, sourced from the Freddie Mac website, offer quarterly snapshots of mortgage loans that have been originated in the USA since 1999, along with details of their subsequent repayment behaviours. This data remains current and is updated every three months. Specifically, the loan origination data present here encompasses amortized fixed-rate mortgage loans from 1999 up to June 2022. In contrast, the performance data is presented on a monthly basis, detailing loan repayment profiles from 1999 until September 30, 2022. Both the origination and performance datasets feature a unique loan ID, which can be utilized to integrate the data on loan originations with that of loan repayments.</p>
User preference optimization for control of ankle exoskeletons using sample efficient active learning
<p>A major challenge to the widespread success of augmentative exoskeletons is accurately adjusting the controller to provide cooperative assistance with their wearer. Often, the controller parameters are ``tuned'' to optimize a physiological or biomechanical objective. However, these approaches are resource-intensive, while typically only enabling optimization of a single objective. In reality, the exoskeleton user experience is derived from many factors, including comfort and stability, among others. This work introduces an approach to conveniently tune four parameters of the exoskeleton controller that maximize user preference. We use an evolutionary algorithm to recommend potential parameters, which are ranked by a neural network that is pre-trained with previously collected preference data. The controller parameters that have the highest preference ranking are provided to the exoskeleton, and the wearer provides feedback as forced-choice comparisons. Our approach was able to converge on controller parameters preferred by the wearer compared to randomized parameters with an accuracy of 88% on average. The result indicates that the proposed algorithm was able to identify users' preferences while requiring less than 50 queries to users. This work demonstrates user preference can be used to tune high-dimensional controller spaces easily and accurately, which shows the potential of translating lower-limb wearable technologies into our daily lives.</p>
SeaPaCS sampling log and samples
<p>The file contains two sheets: one is the sample log for the sampling performed on the 14th and 15th of July, in Anzio (RM), Italy (harbour and coastal samples), as well as a first description of the samples collected. </p> <p>The two tows with the neuston net allowed an approximate calculation of the average plastic concentration (particles > 300 µm) per cubic meter of seawater along the coast of Anzio. </p> <p>This is the updated version as there was a mistake in the number of plastics found (68 instead of 72 as wrongly reported previously). </p>
Phtometry measurements using spectroscopic ellipsometer of PAAO containing samples in air and water (AoI 45 deg., 60 deg., varying polarizer and analyser angles)
<p>The file contains raw intensity data measured in reflection mode for various material combinations: porous anodized aluminium oxide (PAAO), gold nanoparticles (Au NPs), diamond-like carbon with silver nanoparticles composite (DLC:Ag), quartz, and silicon.</p> <p>List of investigated samples:</p> <table> <thead> <tr> <th scope="col">Sample name</th> <th scope="col">Sample description</th> </tr> </thead> <tbody> <tr> <td>AJ1</td> <td>Aluminium substrate, ~241 nm thickness PAAO (anodized in 0.3 mol/L oxalic acid at 40 V for 3 min 16 s), 60 nm diameter Au NPs (dip-coated at a speed of 1 μm/s)</td> </tr> <tr> <td>AJ2</td> <td>Aluminium substrate, ~259 nm thickness PAAO (anodized in 0.3 mol/L oxalic acid at 40 V for 3 min 37 s), 60 nm diameter Au NPs (dip-coated at a speed of 1 μm/s)</td> </tr> <tr> <td>AJ3</td> <td>Aluminium substrate, ~293 nm thickness PAAO (anodized in 0.3 mol/L oxalic acid at 40 V for 4 min 8 s), 60 nm diameter Au NPs (dip-coated at a speed of 1 μm/s)</td> </tr> <tr> <td>AJ4</td> <td>Aluminium substrate, ~317 nm thickness PAAO (anodized in 0.3 mol/L oxalic acid at 40 V for 4 min 30 s), 60 nm diameter Au NPs (dip-coated at a speed of 1 μm/s)</td> </tr> <tr> <td>AJ5</td> <td>Aluminium substrate, ~345 nm thickness PAAO (anodized in 0.3 mol/L oxalic acid at 40 V for 4 min 57 s), 60 nm diameter Au NPs (dip-coated at a speed of 1 μm/s)</td> </tr> <tr> <td>AJ6</td> <td>Aluminium substrate, ~276 nm thickness PAAO (anodized in 0.3 mol/L oxalic acid at 40 V for 3 min 42 s), ~55 nm thickness, 20 V.% Ag concentration DLC:Ag (magnetron sputtered using silver target, 80 sccm argon gas flow, 5.4 sccm C<sub>2</sub>H<sub>2</sub> gas flow, 405 V voltage, 0.09-0.10 A current, 7·10<sup>-3</sup> mbar work pressure, 2 minutes 32 seconds process duration)</td> </tr> <tr> <td>AJ7</td> <td>Aluminium substrate, ~316 nm thickness PAAO (anodized in 0.3 mol/L oxalic acid at 40 V for 4 min 25 s), ~55 nm thickness, 20 V.% Ag concentration DLC:Ag (magnetron sputtered using silver target, 80 sccm argon gas flow, 5.4 sccm C<sub>2</sub>H<sub>2</sub> gas flow, 405 V voltage, 0.09-0.10 A current, 7·10<sup>-3</sup> mbar work pressure, 2 minutes 32 seconds process duration)</td> </tr> <tr> <td>AJ8</td> <td>Aluminium substrate, ~345 nm thickness PAAO (anodized in 0.3 mol/L oxalic acid at 40 V for 5 min 4 s), ~55 nm thickness, 20 V.% Ag concentration DLC:Ag (magnetron sputtered using silver target, 80 sccm argon gas flow, 5.4 sccm C<sub>2</sub>H<sub>2</sub> gas flow, 405 V voltage, 0.09-0.10 A current, 7·10<sup>-3</sup> mbar work pressure, 2 minutes 32 seconds process duration)</td> </tr> <tr> <td>Q140</td> <td>Quartz substrate, ~55 nm thickness, 20 V.% Ag concentration DLC:Ag (magnetron sputtered using silver target, 80 sccm argon gas flow, 5.4 sccm C<sub>2</sub>H<sub>2</sub> gas flow, 405 V voltage, 0.09-0.10 A current, 7·10<sup>-3</sup> mbar work pressure, 2 minutes 32 seconds process duration)</td> </tr> <tr> <td>Si140</td> <td>Silicon substrate, ~55 nm thickness, 20 V.% Ag concentration DLC:Ag (magnetron sputtered using silver target, 80 sccm argon gas flow, 5.4 sccm C<sub>2</sub>H<sub>2</sub> gas flow, 405 V voltage, 0.09-0.10 A current, 7·10<sup>-3</sup> mbar work pressure, 2 minutes 32 seconds process duration)</td> </tr> </tbody> </table> <p>Ellipsometer: rotating compensator GES5-E (Semilab).</p> <p>Light source: 75 W xenon short arc lamp with 185-2000 nm wavelength spectrum.</p> <p>Detector: UV-Vis CCD with 0.8 nm resolution.</p> <p>Spectral range: approximately 230-960 nm.</p> <p>Mode: photometry.</p> <p>Light incidence angles: 45°, 60°.</p> <p>Polarizer angles: 0°, 90°.</p> <p>Analyzer angles: 0°, 45°, 90°.</p> <p>Light beam size: microspot (365 × 470 μm<sup>2</sup> at 75° angle of incidence).</p> <p>The reference was measured from aluminum mirror provided with the ellipsometer for each variation of measurement set-up.</p> <p>Each sample was first measured in air and then after immersion in water. Focusing was performed before each measurement.</p> <p>.smdx and .mup files are generated by the ellipsometer and can be opened using Semilab software. Their file names contain sample name, polarizer angle (Pol), analyzer angle (Ana), incidence angle (ang), and surrounding medium (air/water). .csv files are exported data related to each sample. .jpg files are for illustrative purposes to show the spectra. .rar file contains all the files in this upload.</p> <p> </p>
Sample data for analysis of demographic potential of the 15-minute city in northern and southern France
<pre>This upload contains two Geopackage files of raw data used for urban analysis in the outskirts of Lille and Nice, France. <br>The data include building footprints (layer "building"), roads (layer "road"), and administrative boundaries (layer "adm_boundaries")<br>extracted from version 3.3 of the French dataset BD TOPO®3 (IGN, 2023) for the municipalities of Santes, Hallennes-lez-Haubourdin,<br>Haubourdin, and Emmerin in northern France (Geopackage "DPC_59.gpkg") and Drap, Cantaron and La Trinité in southern France <br>(Geopackage "DPC_06.gpkg").</pre> <pre> </pre> <pre>Metadata for these layers is available here: https://geoservices.ign.fr/sites/default/files/2023-01/DC_BDTOPO_3-3.pdf</pre> <pre> </pre> <pre>Additionally, this upload contains the results of the following algorithms available in GitHub (<a href="https://github.com/perezjoan/emc2-WP2?tab=readme-ov-file">https://github.com/perezjoan/emc2-WP2?tab=readme-ov-file</a>)</pre> <pre><code> </code></pre> <pre>1. The<code> </code>identification<code> </code>of<code> </code>main<code> </code>streets using the QGIS plugin Morpheo (layers "road_morpheo" and "buffer_morpheo") <br><a href="https://plugins.qgis.org/plugins/morpheo/">https://plugins.qgis.org/plugins/morpheo/</a> </pre> <pre><code>2. </code>The<code> </code>identification of main streets in local contexts – connectivity locally weighted<code> </code>(layer "road_LocRelCon")</pre> <pre><code>3. </code>Basic morphometry<code> </code>of<code> </code>buildings<code> </code>(layer "building_morpho")</pre> <pre><code>4. </code>Evaluation<code> </code>of<code> </code>the<code> </code>number<code> </code>of<code> </code>dwellings<code> </code>within<code> </code>inhabited<code> </code>buildings<code> </code>(layer "building_dwellings")</pre> <pre>5. Projecting<code> </code>population<code> </code>potential<code> </code>accessible from<code> </code>main<code> </code>streets<code> </code>(layer "road_pop_results")</pre> <pre> </pre> <pre>Project website: <a href="http://emc2-dut.org/">http://emc2-dut.org/</a></pre> <pre> </pre> <pre>Publications using this sample data: <br>Perez, J. and Fusco, G., 2024. Potential of the 15-Minute Peripheral City: Identifying Main Streets and Population Within Walking Distance. In: O. Gervasi, B. Murgante, C. Garau, D. Taniar, A.M.A.C. Rocha and M.N. Faginas Lago, eds. <em>Computational Science and Its Applications – ICCSA 2024 Workshops. ICCSA 2024</em>. Lecture Notes in Computer Science, vol 14817. Cham: Springer, pp.50-60. <a href="https://doi.org/10.1007/978-3-031-65238-7_4" target="_blank" rel="noopener noreferrer">https://doi.org/10.1007/978-3-031-65238-7_4</a>.</pre> <p><strong>Acknowledgement.</strong> <a name="_Hlk162443883"></a>This work is part of the emc2 project, which received the grant ANR-23-DUTP-0003-01 from the French National Research Agency (ANR) within the DUT Partnership.</p>
Data from: Reduced sampling intensity through key sampling site selection for optimal characterization of riverine fish communities by eDNA metabarcoding
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Data from: Flying high: Sampling savanna vegetation with UAV-lidar
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Testing the effectiveness of genetic monitoring using genetic non-invasive sampling
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Calibrating phylogenies assuming bifurcation or budding alters inferred macroevolutionary dynamics in a densely sampled phylogeny of bivalve families
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Code for: A century of wild bee sampling: historical data and neural network analysis reveal ecological traits associated with species loss
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Sampling bias exaggerates a textbook example of a trophic cascade
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SSP: An R package to estimate sampling effort in studies of ecological communities
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Evaluating the usefulness of Protection Motivation Theory for predicting climate change mitigation behavioral intentions among a US sample of climate change deniers and acknowledgers
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Data for: PickMe: Sample selection for species tree reconstruction using coalescent weighted quartets
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What you sample is what you get: ecomorphological variation in Trithemis (Odonata, Libellulidae) dragonfly wings reconsidered
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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.