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186 results for “Application method”
Fig. 4 in Use of stereophotography in insect science - methods and applications
Fig. 4: Light-microscopic 3D images of the terminal sections of the female accessory glands in the Mediterranean field cricket (a) and Australian field cricket (b) (image widths: 2 mm).
Fig. 2 in Use of stereophotography in insect science - methods and applications
Fig. 2: Stereoscopic macro-photographs (red-green anaglyphs) of different insects: (a) Australian field cricket; (b) hoverfly. All images have to be watched by using red-green glasses.
Fig. 1 in Use of stereophotography in insect science - methods and applications
Fig. 1: Methodds for the generation of stereo-ppairs in microsccopy (STURM 20016a, STURM 20016b): (a) procedure wwith slight rotatioon of the object by an angle α; ((b) displacementt of the focus plaane by the value d andd resulting produuction of an imagge stack.
Sociodemographic predictors of bureaucratic violence in the residency application process: findings from a mixed methods cross-sectional survey of migrant women in Spain.
<p>Dataset contains the data used for the quantitative analyses presented in the paper titled "Sociodemographic predictors of bureaucratic violence in the residency application process: findings from a mixed methods cross-sectional survey of migrant women in Spain.".</p> <p>Data were collected from a cross-sectional online survey of migrant women living in the Valencian Community of Spain between January and March 2023. The dataset contains demographic information about participants, as well as the barriers that the reported facing in the process of applying for residency within Spain.</p>
Generalising an outbreak cluster detection method for two groups: An application to rabies
Open the record for dataset details and reuse information.
Data from: A novel laboratory method to simulate climatic stress with successful application to experiments with medically relevant ticks
Open the record for dataset details and reuse information.
Supplementary Information for "Resolution and the Detection of Cultural Dispersals: development and application of spatiotemporal methods in Lowland South America"
<p>Data and code to reproduce the analyses in "Resolution and the Detection of Cultural Dispersals: development and application of spatiotemporal methods in Lowland South America"</p> <p> </p> <p><strong>Abstract</strong></p> <p>Inferring episodes of expansion, admixture, diffusion, and/or migration in prehistory is at present undergoing a resurgence in macro-scale archaeological interpretation. In parallel to this renewed popularity, expanding access to computational tools and datasets has seen the use of aggregated radiocarbon datasets for the study of dispersals also increasing. This paper advocates for developing reflexive practice in the application of radiocarbon dates to prehistoric dispersals, by reflecting on the quality and qualities of the underlying data, particularly chronometric uncertainty, and framing dispersals explicitly in terms of hypothesis testing. This paper draws on cultural expansions within South America and employs two emblematic examples, the Arauquinoid and Tupiguarani traditions, to develop an analytical solution that not only incorporates chronometric uncertainty in bivariate regression but, importantly, tests whether the datasets provide statistically significant evidence for a dispersal process. The analysis, which the paper provides the means to replicate, identifies fundamental issues with resolution and data quality that impede identification of pre-Columbian cultural dispersals through simple spatial gradients of radiocarbon data. The results suggest that reflexivity must be fed back into theoretical frameworks of prehistoric mobility for the study of dispersals, in turn informing the construction of more critical statistical null models. As a first step, alternative models of cultural expansion should be formally considered alongside demographic models.Inferring episodes of expansion, admixture, diffusion, and/or migration in prehistory is at present undergoing a resurgence in macro-scale archaeological interpretation. In parallel to this renewed popularity, expanding access to computational tools and datasets has seen the use of aggregated radiocarbon datasets for the study of dispersals also increasing. This paper advocates for developing reflexive practice in the application of radiocarbon dates to prehistoric dispersals, by reflecting on the quality and qualities of the underlying data, particularly chronometric uncertainty, and framing dispersals explicitly in terms of hypothesis testing. This paper draws on cultural expansions within South America and employs two emblematic examples, the Arauquinoid and Tupiguarani traditions, to develop an analytical solution that not only incorporates chronometric uncertainty in bivariate regression but, importantly, tests whether the datasets provide statistically significant evidence for a dispersal process. The analysis, which the paper provides the means to replicate, identifies fundamental issues with resolution and data quality that impede identification of pre-Columbian cultural dispersals through simple spatial gradients of radiocarbon data. The results suggest that reflexivity must be fed back into theoretical frameworks of prehistoric mobility for the study of dispersals, in turn informing the construction of more critical statistical null models. As a first step, alternative models of cultural expansion should be formally considered alongside demographic models.</p>
The ‘SAR Matrix’ method and its extensions for applications in medicinal chemistry and chemogenomics
<p>SD files of data sets reported in the manuscript that are used to generate the SARMs and CSMs are uploaded. The given data sets are represented by the corresponding figure numbers (as reported in the publication). The file format is provided in the file 'description.txt'.</p>
Application of high-throughput sequencing (HTS) metabarcoding to diatom biomonitoring: Do DNA extraction methods matter?
<p>The 8 benthic samples from Mainland France (stream Edian, stream Aire, lake Geneva), Sweden (stream Dåmman, Agricultural stream, lake Båtkåjaure) and Mayotte (stream Dapani, stream Majimbini) were collected by scraping material from the surface of stones, following the French standard (AFNOR 2007) used in routine biomonitoring programs.DNA was extracted from each sample (2 replicates) using five DNA extraction methods, followed by the amplification of a short rbcL DNA barcode (312bp) specific to diatoms. PCR products were then sequenced in one random direction using the Ion Torrent™ Personal Genome Machine® (PGM) System according to the manufacturer’s instructions. The data file contains one fastq file per library sequenced with the raw DNA reads, as provided by the sequencing platform (demultiplexing performed by the sequencing platform). An excel file is also provided to make the link between the fastq file number and the sample information (sample origin, DNA extraction method used, number of raw reads).</p>
Replication Package for: A Configurable Method for Benchmarking Scalability of Cloud-Native Applications
<p>This repository contains a replication package and experimental results for our study <em>A Configurable Method for Benchmarking Scalability of Cloud-Native Applications</em>.</p> <p>It provides benchmark execution files for repeating our experiments as well as the collected data from our experiments and Jupyter notebooks for reproducing our analysis.</p> <p>Instructions for repeating our experiments and reproducing our analysis can be found in the Readme.md file.</p>
Water application method influences survival or growth of Escherichia coli on bulb onions during field curing.
<p>These data are presented in figure 2, 3, 4 and 5 of the publication entitled "Water application method influences survival or growth of Escherichia coli on bulb onions during field curing" submitted to Journal of Food Protection</p> <p> </p> <p> </p> <p> </p>
Semi-empirical error ellipsoid clustering for identifying the second-order structural features from a laboratory AE source location cloud—method, validation, and application to a hydraulic fracturing test [DATA]
<p>Data and metadata for the publication "Semi-empirical error ellipsoid clustering for identifying the second-order structural features from a laboratory AE source location cloud—method, validation, and application to a hydraulic fracturing test", published in Earth and Space Science.</p>
A systematic assessment of deep learning methods for drug response prediction: from in-vitro to clinical application
<p>https://github.com/LihongLab/Suppl-data-Benchmark</p> <p>## GDSC dataset</p> <p>**Table S3.** GDSC gene expression profiles for 966 cancer cell lines, where each column represents a cell line in the form of its name and tissue collection site, and each row represents a gene in the form of the HGNC symbol.</p> <p> </p> <p>**Table S4.** GDSC gene mutation profiles for 966 cancer cell lines, where each column represents a cell line in the form of its name and tissue collection site, and each row represents a gene in the form of the HGNC symbol. The wild type is coded as 1 and the wild type as 0.</p> <p> </p> <p>**Table S5.** GDSC copy number variation profiles for 966 cancer cell lines, where each column represents a cell line in the form of its name and tissue collection site, and each row represents a gene in the form of the HGNC symbol. The copy-neutral is coded as 0 and the deletion or amplification as 1.</p> <p> </p> <p>**Table S6.** GDSC drug response data for 966 cancer cell lines and 282 drugs in the form of the natural logarithm of the IC50 readout. The first column shows the cell line name and tissue collection site, the second column shows the drug name, and the third column shows the drug response readout.</p> <p> </p> <p>**Table S7.** GDSC annotations for 282 drugs include drug name, PubChem CID, PubChem canonical SMILES, Rdkit canonical SMILES, Target Pathway, standard deviation, bimodality coefficient and density coverage.</p> <p>## TCGA dataset</p> <p>**Table S8.** TCGA gene expression profiles, where each column represents a patient in the form of TCGA patient ID, and each row represents a gene in the form of the HGNC symbol.</p> <p> </p> <p>**Table S9.** TCGA gene mutation profiles, where each column represents a patient in the form of TCGA patient ID, and each row represents a gene in the form of the HGNC symbol. The wild type is coded as 1 and the wild type as 0.</p> <p> </p> <p>**Table S10.** TCGA copy number variation profiles, where each column represents a patient in the form of TCGA patient ID, and each row represents a gene in the form of the HGNC symbol. The copy-neutral is coded as 0 and the deletion or amplification as 1.</p> <p> </p> <p>**Table S11.** TCGA clinical response data. The first column shows the TCGA patient ID, the second column shows the drug name, the third column shows the clinical response category, the fourth column shows the cancer type, and the last column shows the clinical label as responder or non-responder.</p>
Data supplementing the article "Avoiding quantification bias in metabarcoding: application of a cell biovolume correction factor in diatom molecular biomonitoring" V. Vasselon, A. Bouchez, F. Rimet, S. Jacquet, R. Trobajo, M. Corniquel, K. Tapolczai, I. Domaizon submitted to Methods in Ecology and Evolution journal
<p>These data supplement the article "Avoiding quantification bias in metabarcoding: application of a cell biovolume correction factor in diatom molecular biomonitoring" V. Vasselon, A. Bouchez, F. Rimet, S. Jacquet, R. Trobajo, M. Corniquel, K. Tapolczai, I. Domaizon submitted to Methods in Ecology and Evolution journal</p> <p>The directory contains the following files:</p> <p>1<strong>5 fastq files raw reads (5 mock communities, 3 replicates)</strong><strong>.rar </strong>- contains the 15 fastq files provided by the sequencing platform with demultiplexed DNA reads (raw data prior any bioinformatics treatments).</p> <p><strong>15 fastq files information.xlsx</strong> :</p> <p>- contains the information relative to the 15 fastq files corresponding to the PGM raw data of the 5 mock communities (sequenced with 3 replicates), including: the ID of the fastq files, the mock community name, the replicate number, the final sample Id and the number of raw reads per fastq file.</p> <p>- contains the information of the proportion of the 8 diatoms species (%) used to create the 5 mock communities (estimated from microscopy).</p>
Fig.2 in Conceptual Application Of Dytiscus Latissimus Linnaeus, 1758 (Dytiscidae, Coleoptera) Gathering Methods In Natural Habitat
Fig.2. Fishing tackle bought in a shop: A – standard; B – modernized
Raw coordinates of 3D landmarks related to the article 'A new zooarchaeological application for geometric morphometric methods: Distinguishing Ovis aries morphotypes to address connectivity and mobility of prehistoric Central Asian pastoralists' by Haruda et al.
<p>Raw coordinates from 3D landmarks of <em>Ovis aries </em>astragali. These bones originate from Final Bronze Age archaeological contexts from central and southeastern Kazakhstan. These relate to the article 'A new zooarchaeological application for geometric morphometric methods: Distinguishing <em>Ovis aries</em> morphotypes to address connectivity and mobility of prehistoric Central Asian pastoralists' by Haruda et al. </p>
Applicability of the inverse dispersion method to measure emissions from animal housings - data set & R scripts
<h2>Data availability</h2> <p>Provided are:<br>- raw data of the instruments<br>- R Scripts to reproduce the findings in the publication<br>- R outputs</p> <h2>Scripts</h2> <p>In total, there are 10 scripts provided, of which most of them are needed to reproduce the data in the publication.</p> <p>Below, a brief explanation of the content of the different scripts.</p> <ul> <li>01_Datatreatment_01_Weatherstation.r ## This script reads in the weather station data and makes it ready for further use.</li> <li>01_Datatreatment_02_Sonics.r ## This script reads in the 3D ultrasonic data and makes it ready for further use.</li> <li>01_Datatreatment_03_GasFinder.r ## This script reads in the GasFinder data and makes it ready for further use.</li> <li>01_Datatreatment_04_MFC_Pressuresensor.r ## This script reads in the mass flow controller (MFC) and pressure sensor data and makes it ready for further use.</li> <li>02_Calculation_01_bLS.r ## This script is made to run the bLSmodelR and tailored to the number cruncher of the University of Applied Sciences BFH. The code should also work on your computer but you have to adopt the number of cores.</li> <li>02_Calculation_02_Concentration.r ## This script treats the unprocessed concentration data. It removes false concentrations, applies an intercalibration, and makes the data ready for further use.</li> <li>02_Calculation_03_Emissions.r ## This script calculates emissions and makes it ready for further use.</li> <li>02_Calculation_04_contourXYZ_Plume.r ## This script calculates the plume contours in the XY and XZ plane. This script is not necessary to reproduce the findings of the publication.</li> <li>03_Apply_filter.r ## This script applies the quality filtering and makes the data ready for further use.</li> <li>04_Plots_Tables.r ## With this script one can recreate all the plots and values in the tables of the publication, the supplement, and the initial submission.</li> </ul> <p>Note, for the geometry, there is no script provided. The coordinates of the different sensors and the source are solely provided as R output.</p> <h3>Naming of instruments</h3> <p>The instruments in the publication have different names than in the scripts. In some scripts the final names are also provided but throughout the evaluation the original device names are used. Only in the script 04_Plots_Tables.r are the final names introduced. Below is an overview of what original name corresponds to the final name of the devices:</p> <h4><strong>GasFinder instruments called 'OP' in the publication</strong></h4> <ul> <li>OP-UW = GF26</li> <li>OP-2.0h = GF17</li> <li>OP-5.3h = GF18</li> <li>OP-6.8h = GF16</li> <li>OP-12h = GF25</li> </ul> <p><strong>3D ultrasonic anemometer instruments called 'UA' in the publication</strong></p> <ul> <li>UA-UW = SonicC</li> <li>UA-2.0h = SonicA</li> <li>UA-5.3h = Sonic2</li> <li>UA-6.8h = SonicB</li> </ul> <p><strong>Source</strong><br>In some of the scripts, the source might be called 'Schopf' which is a local term for 'shed'.</p> <h2>Note</h2> <p>This code was written by Marcel Bühler (minor code chunks were originally written by Christoph Häni) and is intended to reproduce the findings of the linked publication. Please feel free to use and modify it (e.g., use it to run different dispersion models), but attribution is appreciated.</p> <h2>Disclaimer</h2> <p>I do not guarantee that everything works. It might be that not all variables were changed to English for better understanding correctly. Unfortunately, it is not possible to provide all the catalogs of the bLS run, as the total size is several 100s of GB. In case you run the bLS model on your own, the result will have a minimal difference, as no bLS run produces the same result twice. This should, however, not alter the findings.</p> <h2>Contact</h2> <p>In case you have questions, please contact Marcel Bühler (mb@bce.au.dk). In case this does not work, Christoph Häni might also be able to help (christoph.haeni@bfh.ch).</p>
DAS Data for the figure in the paper entitled "A Hybrid Earthquake Detection Method for Distributed Acoustic Sensing Array Data and Its Application to the 2022 Menyuan Earthquake Sequence"
<p>The DAS data can be loaded using numpy. The sampling rate is 100 Hz, and each row is a time series for that channel.</p>
A systematic assessment of deep learning methods for drug response prediction: From in vitro to clinical applications
<p>## GDSC dataset</p> <p>**GDSC_EXP.csv** GDSC gene expression profiles for 966 cancer cell lines, where each column represents a cell line in the form of its name and tissue collection site, and each row represents a gene in the form of the HGNC symbol.</p> <p> </p> <p>**GDSC_MUT.csv** GDSC gene mutation profiles for 966 cancer cell lines, where each column represents a cell line in the form of its name and tissue collection site, and each row represents a gene in the form of the HGNC symbol. The wild type is coded as 1 and the wild type as 0.</p> <p> </p> <p>**GDSC_CNV.csv** GDSC copy number variation profiles for 966 cancer cell lines, where each column represents a cell line in the form of its name and tissue collection site, and each row represents a gene in the form of the HGNC symbol. The copy-neutral is coded as 0 and the deletion or amplification as 1.</p> <p> </p> <p>**GDSC_DR.csv** GDSC drug response data for 966 cancer cell lines and 282 drugs in the form of the natural logarithm of the IC50 readout. The first column shows the cell line name and tissue collection site, the second column shows the drug name, and the third column shows the drug response readout.</p> <p> </p> <p>**GDSC_DrugAnnotation.csv** GDSC annotations for 282 drugs include drug name, PubChem CID, PubChem canonical SMILES, Rdkit canonical SMILES, Target Pathway, standard deviation, bimodality coefficient and density coverage.</p> <p>## TCGA dataset</p> <p>**TCGA_EXP.csv** TCGA gene expression profiles, where each column represents a patient in the form of TCGA patient ID, and each row represents a gene in the form of the HGNC symbol.</p> <p> </p> <p>**TCGA_MUT.csv** TCGA gene mutation profiles, where each column represents a patient in the form of TCGA patient ID, and each row represents a gene in the form of the HGNC symbol. The wild type is coded as 1 and the wild type as 0.</p> <p> </p> <p>**TCGA_CNV.csv** TCGA copy number variation profiles, where each column represents a patient in the form of TCGA patient ID, and each row represents a gene in the form of the HGNC symbol. The copy-neutral is coded as 0 and the deletion or amplification as 1.</p> <p> </p> <p>**TCGA_DR.csv** TCGA clinical response data. The first column shows the TCGA patient ID, the second column shows the drug name, the third column shows the clinical response category, the fourth column shows the cancer type, and the last column shows the clinical label as responder or non-responder.</p> <p>## PMID17185464 (Bortezomib) dataset</p> <p>**PMID17185464_EXP.csv** Bortezomib clinical trial gene expression profiles, where each column represents a patient in the form of patient ID, and each row represents a gene in the form of the HGNC symbol.</p> <p>**PMID17185464_DR.csv** Bortezomib clinical trial clinical response data. The first column shows the TCGA patient ID, the second column shows the drug name, the third column shows the clinical response category, and the last column shows the clinical label as responder or non-responder (NR: Non-responder, R: Responder).</p>
Data for "Absolute binding free energy calculation based on the fragment molecular orbital method and its application in designing novel SHP-2 allosteric inhibitors"
<p>Data for publication "Absolute binding free energy calculation based on the fragment molecular orbital method and its application in designing novel SHP-2 allosteric inhibitors".All structures of complex and input files for FMO , FMO/SMD , FMO/PCM , and COSMO calculation are provided .</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.