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1,956 results for “test data”
Test-Data for RNA-seq HSA Chr21 all protein coding transcripts
<p>Created with ART</p> <p>ART_Illumina (2008-2016) </p> <p>Q Version 2.5.8 (June 6, 2016)</p> <pre><code>art_illumina -ss MSv3 -i chr21_proteincoding.fa -p -l 75 -f 175 -m 200 -s 10 --minQ 30 </code></pre> <p> </p> <p> </p> <p>high:= higher expression</p> <p>low:= lower expression</p> <p>repA/B:=indicates replicates with slightly different sequencing depths</p> <p>R1/2:= forward/reverse read</p>
Data from: Does pH matter for ecosystem multifunctionality? An empirical test in a semi-arid grassland on the Loess Plateau
<p>Date of data collection: 2016-2018</p> <p>Geographic location of data collection: Guyuan, Ningxia, China (106°23′E, 36°15′N)</p> <p>These data were generated to (<em>i</em>) investigate the responses of soil properties, biological communities and multifunctionality to decreased soil pH; (<em>ii</em>) determine the potential biotic and abiotic pathways that soil pH may drive multifunctionality. In 2016, a 17 m × 40 m semi-arid grassland plot with an initial pH value of 8.05 and uniform vegetation was selected. The experiment was granted by the administration of Yunwu Mountain National Nature Reserve. A randomized block design was used with five treatments and six replicates per treatment. A total of 30 plots were established. All plots were 2 m × 2 m and separated by 1 m buffer zones. The treatments included five levels of acid addition rate (0, 0.23, 0.56, 3.60, and 9.01 mol H<sup>+</sup> m<sup>-2</sup>) in the form of sulphuric acid solution. In late August 2017, the plant communities achieved their peak biomass, and were surveyed and harvested in a 0.5 m × 1 m quadrat in each plot to determine the plant community diversity and estimate above-ground biomass. After harvesting the plants, six soil cores (0-15 cm deep, 2.5 cm diameter) per plot in each of the six blocks were collected and pooled by plot as a replicate for further chemical analyses.</p>
Data for: "Above and below ground trait coordination in tree seedlings depend on the most limiting resource: A test comparing a wet and a dry tropical forest in Mexico" by L. Sanaphre-Villanueva, F. Pineda-Garcia, W. Dattilo, L. F. Pinzon-Perez, A. Ricaño Rocha, H. Paz.
<p>These data represent those published in “Above and below ground trait coordination in tree seedlings depend on the most limiting resource: A test comparing a wet and a dry tropical forest in Mexico” by L. Sanaphre-Villanueva, F. Pineda-Garcia, W. Dattilo, L. F. Pinzon-Perez, A. Ricaño Rocha, H. Paz. PeerJ. 2022.</p>
Load, pressure, rubble pile geometry and video data from model-scale tests on shallow water ice-structure interaction
<p>The data is obtained from model-scale experiments on shallow water ice-structure interaction. During the conducted experiments, a ten-meter wide initially intact ice sheet was pushed against a sloping structure of the same width. As the ice failed against the structure, a grounded rubble pile accumulated in front of it. The structure consisted of ten identical one-meter-wide segments and the horizontal load on each of these segments was measured independently with load cells. These measurements are presented as load-time datasets. The horizontal load acting on the false bottom was measured with load cells and are also presented as load-time datasets. Furthermore, the ice pressure on two of the segments was measured with tactile sensors. These pressure measurements are presented as array-based pressure-time datasets. Video footage filmed from two different video angles is included in the data. In addition, the coordinates of the rubble pile geometries at the end of each experiment are published. The data includes the top and side rubble pile geometries. In total, seven experiments were conducted. The data can be used by researchers, engineers and designers who work with ice structure interaction related issues in order to, for instance, optimize the design of offshore structures, improve ice load predictions or develop future experiments and simulations. A full description of the experimental set-up and the published data is submitted to the journal Data in Brief. </p>
Data from: Encoding laboratory testing data: case studies of the national implementation of HHS requirements and related standards in five laboratories
<p><strong>Objective</strong>: Assess the effectiveness of providing Logical Observation Identifiers Names and Codes (LOINC®)-to-In Vitro Diagnostic (LIVD) coding specification, required by the United States Department of Health and Human Services for SARS-CoV-2 reporting, in medical center laboratories and utilize findings to inform future United States Food and Drug Administration policy on the use of real-world evidence in regulatory decisions.</p> <p><strong>Materials and Methods</strong>: We compared gaps and similarities between diagnostic test manufacturers' recommended LOINC® codes and the LOINC® codes used in medical center laboratories for the same tests.</p> <p><strong>Results</strong>: Five medical centers and three test manufacturers extracted data from laboratory information systems (LIS) for prioritized tests of interest. The data submission ranged from 74 to 532 LOINC® codes per site. Three test manufacturers submitted 15 LIVD catalogs representing 26 distinct devices, 6956 tests, and 686 LOINC® codes. We identified mismatches in how medical centers use LOINC® to encode laboratory tests compared to how test manufacturers encode the same laboratory tests. Of 331 tests available in the LIVD files, 136 (41%) were represented by a mismatched LOINC® code by the medical centers (chi-square 45.0, 4 df, P &lt; .0001).</p> <p><strong>Discussion</strong>: The five medical centers and three test manufacturers vary in how they organize, categorize, and store LIS catalog information. This variation impacts data quality and interoperability. </p> <p><strong>Conclusion</strong>: The results of the study indicate that providing the LIVD mappings was not sufficient to support laboratory data interoperability. National implementation of LIVD and further efforts to promote laboratory interoperability will require a more comprehensive effort and continuing evaluation and quality control.</p>
Datasets and Code for "Hypothesis Tests with Functional Data for Surface Quality Change Detection in Surface Finishing Processes"
<p>This is the set of data and computer code used for reproducing the results in Jin, Tuo, Tiwari, Bukkapatnam, Aracne-Ruddle, Lighty, Hamza, and Ding, 2022, “Hypothesis tests with functional data for surface quality change detection in surface finishing processes,” <em>IISE Transactions</em>, in press.</p>
Data and models in Support of "Joint and Constrained Inversion as Hypothesis Testing Tools"
<p>The model and data files as well as the plotting and run scripts to reproduce the examples in "Joint and Constrained Inversion as Hypothesis Testing Tools".</p>
Oscillatory Flow Testing Data Collected at Field Site for Research in Fractured Sedimentary Rock (FSR)^2
<p>This dataset contains raw and processed pressure data collected in 2019 during oscillatory flow testing experiments at the Field Site for Research in Fractured Sedimentary Rock (FSR)^2 near Madison, WI. The included ReadMe file describes the data and code used in data processing. The companion processing and analysis code is included as a separate upload (doi:10.5281/zenodo.6584777)</p>
synthetic 3DBOS test data
<p>This data is test data for the code accompanying the paper</p> <p><strong>Atcheson, Ihrke, Heidrich, Tevs, Bradley, Magnor, Seidel<br> "Time-resolved 3d capture of non-stationary gas flows"<br> Siggraph Asia 2008</strong></p> <p><br> Author: Ivo Ihrke (2007)</p> <p>Contact: ivo [dot] ihrke [at] uni-siegen [dot] de</p> <p>Note that the format is custom; the necessary code for processing it is in preparation of open-sourcing.</p>
Training and test data, plus saved models for the paper "Top-down effects in an early visual cortex inspired hierarchical Variational Autoencoder" submitted to the SVRHM 2022 Workshop @ NeurIPS
<p>Each .pkl file contains a training or test dataset in the form of a Python dictionary (generated with Python 3.8.5) with the following fields:</p><ul><li>'train_images': 640,000 float32 images used for model training. 20px images contain 400 pixel intensities, 40px images contain 1600 pixel intensities each.</li><li>'train_labels': float32 labels for each image in 'train_images'. All natural images are labeled with 0.0. Texture images are labeled with 0.0, 1,0, 2.0, 3.0, or 4.0, according to their texture family.</li><li>'test_images': 64,000 float32 images used for model testing. 20px images contain 400 pixel intensities, 40px images contain 1600 pixel intensities each.</li><li>'test_labels': float32 labels for each image in 'test_images'. All natural images are labeled with 0.0. Texture images are labeled with 0.0, 1,0, 2.0, 3.0, or 4.0, according to their texture family.</li></ul><p>Each .zip file contains a saved model. Details on these are coming soon.</p><p>For more details, see the paper "Top-down effects in an early visual cortex inspired hierarchical Variational Autoencoder" published at the SVRHM 2022 Workshop @ NeurIPS (<a href="https://openreview.net/forum?id=8dfboOQfYt3">link</a>).</p>
Data of durability test
<p>1) For the effect of temperature, the ID of samples are 0048-CEA-New and 0047-CEA-New (for ageing at 850°C and 750°C at -0.75 A/cm² respectively). These data have been published in F. Monaco D. Ferreira-Sanchez, M. Hubert, B. Morel, D. Montinaro, D. Grolimund, J. Laurencin, Oxygen Electrode Degradation in Solid Oxide Cells Operating in Electrolysis and Fuel Cell Modes: LSCF Destabilization and Inter-Diffusion at the Electrode/Electrolyte Interface, Inter. J. of Hydrogen Energy, 46(62) (2021) 31533-31549 (with AD Astra in the acknowledgment)</p> <p>2) For the effect of humidity at the air side: 0050-CEA-New (750°C, -0.75 A/cm², 8% of steam in the air flow) vs 0049-CEA-New (dry condition).</p>
Data for "Workhorse minimally-empirical dispersion-corrected density functional, with tests for weakly-bound systems: r2 SCAN + rVV10"
<p>VASP inputs and outputs for the:</p> <ul> <li>Ar2 binding energy curve (Ar2_bpara.tar.gz)</li> <li>L28 set of layered-solid geometries and interlayer binding energies (L28.tar.gz)</li> <li>S22 set of interaction energies of weakly-bound complexes (S22.tar.gz)</li> </ul> <p>All POTCAR files have been replaced by "potcar.txt" files containing the title(s) of the POTCAR(s) needed to reproduce the calculations. A preprint is available from the arXiv:2204.11717 (<a href="https://arxiv.org/abs/2204.11717">link</a>).</p>
GlottisNetV2 - Time variant training and testing data
<p>Here we provide time variant training and testing data for the GlottisNetV2 study. In particular, this dataset relies on the benchmark for automatic glottis segmentation (BAGLS dataset). This three-dimensional data (time, y, x) is used for experiments involving deep neural networks capable of processing time-variant data. </p> <p>Each folder contains videos each with the following data:</p> <ul> <li>Endoscopic video as mp4 (*.mp4)</li> <li>Glottis segmentation as mask-file (hdf5 container, *.mask)</li> <li>Glottis segmentation as mp4 file (*_mask.mp4)</li> <li>Metadata as JSON file (*.meta)</li> <li>Glottal midline annotation as JSON file (*.points)</li> </ul>
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>
Test data for RonaQC - mapped SARS-CoV-2 reads
<p>This dataset includes test data for <a href="https://ronaqc.netlify.app/">RonaQC</a></p> <p>RonaQC accepts mapped SARS-CoV-2 reads (BAM format), generated from the SARS-CoV-2 bioinformatic pipelines like ARTIC, and any control samples from the respective sequencing run (negative/positive) as input. It will then assess the levels of cross contamination and primer contamination in the samples, and determine if the samples are reliable for detecting SARS-CoV-2, phylogenetic analysis, and/or submission to public databases.</p> <p><br> The dataset includes SARS-CoV-2 sequenced reads compiled by <a href="https://github.com/CDCgov/datasets-sars-cov-2">CDCgov/datasets-sars-cov-2</a> [1]. </p> <p>These were reads were processed using the <a href="https://github.com/connor-lab/ncov2019-artic-nf">ncov2019-artic-nf pipelines</a>, which is a Nextflow pipeline for running the <a href="https://github.com/artic-network/fieldbioinformatics">ARTIC network's fieldbioinformatics tools</a>, with a focus on ncov2019. </p> <p><br> This dataset includes: </p> <ul> <li><strong>FailedQC </strong>- A cohort of 24 samples failed basic QC metrics, covering 8 possible failure scenarios, Illumina platform, amplicon-based approach </li> <li><strong>VOCRepresentatives </strong>- A cohort of 16 samples from 10 representative CDC defined VOI/VOC lineages as of 06/15/2021, Illumina platform, amplicon-based approach </li> <li><strong>Test </strong>- Smaller test samples, including sequenced negative controls of varying quality</li> </ul> <p>[1] Timme, Ruth E., et al. "Benchmark datasets for phylogenomic pipeline validation, applications for foodborne pathogen surveillance." PeerJ 5 (2017): e3893. </p>
Experimental data: Low-velocity out-of-plane impact tests on double-wythe unreinforced brick masonry walls instrumented with optical measurements
<p>This dataset includes the results of laboratory impact tests conducted on natural-scale double-wythe unreinforced brick masonry walls. The walls were spanning vertically between two reinforced concrete slabs and were subjected to low-velocity drop-weight pendulum tests in which they were repeatedly hit until the opening of a breach in the center of the wall. The tests were instrumented with both hard-wired and optical measurements, the latter consisting of high-speed cameras and digital image correlation techniques. Investigated in these tests were the out-of-plane response of the walls and their capacity to resist the impacts. The axial load applied on the top of the walls was varied for two wall configurations and monitored throughout the tests to study the effect of arching on the failure mechanism produced and number of repeated hits needed to open the breach. Of interest was also the evidence of cracking, more specifically the way it initiated on the undamaged walls and next propagated upon consecutive hits. </p> <p>The data generated from the tests is made here available and documented to support further investigations on masonry structures subjected to extreme actions. The dataset includes four ZIP files, ordered from 01 to 05, along with an auxiliary PDF document describing the content and organization of the data. </p> <p>Test implementation and test results that are built upon this data are presented and discussed in the following research article:</p> <blockquote> <p><a href="https://www.sciencedirect.com/science/article/pii/S0734743X23001082?via%3Dihub">Godio M, Flansbjer M, Williams Portal N (2023) Low-velocity out-of-plane impact tests on double-wythe unreinforced brick masonry walls instrumented with optical measurements, International Journal of Impact Engineering</a></p> </blockquote> <p>To cite this data in your work please refer to the article.</p> <p>The Authors</p>
Data from: Empirically testing the influence of light regime on diel activity patterns in a marine predator reveals complex interacting factors shaping behaviour
<p>Diel cycles in marine predator diving behaviour centre around the light-mediated diel vertical migration (DVM) of prey, and are considered critical for optimizing foraging and limiting competition across global seascapes. Yet our understanding of predator diel behaviour is based primarily on examining relative depth usage between constant day/night cycles with no formal investigation of how varying light regimes interact with abiotic factors to shape diel activity. The extreme seasonal light regimes (midnight sun, polar night, day/night cycle) in the Arctic provide a unique natural experimental setting to empirically investigate the occurrence and intensity of diel behaviour in marine predators relative to changing light levels while concomitantly assessing interacting abiotic factors. Depth time series data from satellite-linked tags deployed on six belugas for up to 12 months were used to quantify diel behaviour by calculating dissimilarity in time-at-depth between periods of low and high solar altitude on each day. Generalized additive mixed effects models were used to examine the influence of hours of daylight across extreme light cycles, coupled with bathymetry and sea ice concentration; focal diel patterns were further examined relative to the thermal structure of the water column. As predicted, belugas exhibited cathemerality during the midnight sun, and initiated diel behaviour with the onset of the fall day/night cycle, with a marked increase in its intensity with the progression to equal day/night length. Occurrence of diel patterns, however, was complex; ceasing in regions with seafloor depths < 700 m, and occurring with greatest intensity when the water column was thermally homogeneous within the upper 150 m. Through empirical investigation, this study demonstrates that the onset of day/night light cycles and presumably associated prey DVM can modulate predator diel dive behaviour under certain circumstances, but highlights how the complex interaction of abiotic factors with light regime shape dynamic spatiotemporal patterns. These findings, building on a body of recent work, emphasize that the traditional view of the ubiquitous occurrence of diel behaviour tied to DVM at the base of the food web oversimplifies vertical predator-prey interactions, identifying the need for more structured investigation. </p>
Test data for the Large Genome Assembly tutorial
<p>A set of test data to use for the Galaxy Training Network tutorial, Large genome assembly. This data is publicly available in other sources, but has been combined here and subsampled for easier use in the tutorial. We do not claim ownership of this data - please see the full attribution to each of the data sources explained below.</p> <p><strong>Sequencing reads:</strong></p> <p>From the Snow gum: <em>Eucalyptus pauciflora</em>. From NCBI BioProject number: PRJNA450887; Paper: Wang W, Das A, Kainer D, Schalamun M, Morales-Suarez A, Schwessinger B, Lanfear R; 2020, doi: 10.1093/gigascience/giz160.</p> <p>From NCBI, three read files were imported into Galaxy for this tutorial: nanopore reads (SRR7153076), and paired Illumina reads (SRR7153045). For the test data set: these were randomly subsampled to 10% of the original file size, and reads mapping to related chloroplast gene sequences (rbcL sequence: accession KM360776.1; matK sequence: accession KT632904.1) were excluded. </p> <p>Files: Nanopore reads; Illumina reads, R1 and R2</p> <p><strong>Reference genome: </strong></p> <p><em>Arabidopsis thaliana. </em>Although this is not the same species as above, we can use it as an example for a comparison step in the tutorial. This has been downloaded from The Arabidopsis Information Resource at <a href="https://www.arabidopsis.org/index.jsp">https://www.arabidopsis.org/index.jsp</a> from Genes: Download: TAIR10 genome release: TAIR10 chromosome files: file TAIR10_chr_all.fas.gz. Then unzipped into a fasta file. </p>
Frictionless Data Test Dataset Without Descriptor
<p>This is a test dataset</p>
Frictionless Data Test Dataset
<p>This is a test dataset</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.