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Dataset results
315 results for “Zeros”
Stranded RNA-seq were performed on total RNA following ribosomal RNAs depletion (Ribo-zero removal kit, illumina) for 3 brain , 8 IDHwt and 5 IDHmut glioma samples.
GEO Series GSE123892. Homo sapiens. 16 samples. Type: Expression profiling by high throughput sequencing.
Stranded RNA-seq were performed on total RNA following ribosomal RNAs depletion (Ribo-zero removal kit, illumina) for glioblastoma stem cell
GEO Series GSE161438. Homo sapiens. 2 samples. Type: Expression profiling by high throughput sequencing.
IGH VDJ-RNA-seq of circulating plasma cells at day zero and seven relative to experimental vaccination dosing
GEO Series GSE131090. Homo sapiens. 22 samples. Type: Expression profiling by high throughput sequencing.
RNA-seq of circulating Tfh like cells at day zero and seven and 28 relative to experimental vaccination dosing
GEO Series GSE131088. Homo sapiens. 45 samples. Type: Expression profiling by high throughput sequencing.
Intermediate- and low-methylation epigenotypes do not correspond to CpG island methylator phenotype (low and -zero) in colorectal cancer.
GEO Series GSE37740. Homo sapiens. 21 samples. Type: Methylation profiling by array.
5-ALA photodynamic metabolite-powered zero-waste “ferroptosis amplifier” for enhanced hypertrophic scar therapy
GEO Series GSE300586. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing.
Physiological and cell morphology adaptation of Bacillus subtilis at near-zero specific growth rates: a transcriptome analysis.
GEO Series GSE55690. Bacillus subtilis; Bacillus subtilis subsp. subtilis str. 168. 24 samples. Type: Expression profiling by array.
Patient-derived response estimates from zero-passage organoids of luminal breast cancer
GEO Series GSE262110. Homo sapiens. 35 samples. Type: Expression profiling by high throughput sequencing.
A functional genomics predictive network model identifies regulators of inflammatory bowel disease: Ribo-zero RNAseq mouse distal colon DSS Colitis model of genetically engineered mice
GEO Series GSE83550. Mus musculus. 194 samples. Type: Expression profiling by high throughput sequencing.
How Magic Is Zero? An Empirical Analysis of Initial Development Releases in Three Software Package Distributions
<p>This is the replication package for "How Magic Is Zero? An Empirical Analysis of Initial Development Releases in<br> Three Software Package Distributions" accepted for publication in SoHeal 2020.</p> <p>The notebooks in "notebooks/" require the dependencies specified in "requirements.txt" to be installed.<br> They rely on data files in "data/". These data can be obtained by running the "convert.py" scripts in that folder.<br> The script requires data files from "data-raw/". These files can be generated by running "extract.py" in that folder.<br> This script requires the libraries.io data dump, as explained in the README file contained in this folder.</p> <p>If you don't have these data, or if you don't want to download them, you can ask for the required data/*.csv.gz files by email.</p> <p> </p>
Helium Zero Age Main Sequence tracks in the HR diagram
<p>This entry provides models computed using the MESA code for the location of the He-ZAMS at three different metallicities, Z=Zsun, Z=Zsun/2 (representative of the LMC) and Z=Zsun/5 (representative of the SMC).</p>
Data from: Numerical ordering of zero in honey bees
Some vertebrates demonstrate complex numerosity concepts—including addition, sequential ordering of numbers, or even the concept of zero—but whether an insect can develop an understanding for such concepts remains unknown. We trained individual honey bees to the numerical concepts of "greater than" or "less than" using stimuli containing one to six elemental features. Bees could subsequently extrapolate the concept of less than to order zero numerosity at the lower end of the numerical continuum. Bees demonstrated an understanding that parallels animals such as the African grey parrot, nonhuman primates, and even preschool children.
Full data for 'Ubiquitous Non-Majorana Zero-Bias Conductance Peaks in Nanowire Devices'
<p>This repository contains experimental data for the following paper:<br> Ubiquitous Non-Majorana Zero-Bias Conductance Peaks in Nanowire Devices.<br> Authors: J. Chen, B. D. Woods, P. Yu, M. Hocevar, D. Car, S. R. Plissard, E. P. A. M. Bakkers, T. D. Stanescu, and S. M. Frolov.<br> Content of this repository: </p> <p>Readme file. </p> <p>/RawData/<br> Original data obtained at the time of measurement for devices 0520-840-NW5,0520-840-2,0520-840-NW1,0510-197.</p> <p>/Measurement notes/<br> All the measurement data was summarized in powerpoints, catagorized by the name of the device. Data in the main text was measured on devices 0524-840-NW5 and 0520-840-2, Data in the supplementary information was measured on all the devices.</p> <p>/Data of paper figures/<br> All the organized data files for the figures in the main text and supplementary information.</p> <p>Data file types:<br> data_NNN.dat - the original data file obtained at the time of the experiment<br> dataNNN.py - the original QTLab data acquisition script saved with data<br> data_NNN.set - settings of measurement instruments at the time of measurement<br> data_NNN.meta - auxillary file necessary for plotting data using SpyView (see below) <br> data_NNN.MTX - a simple 2D/3D matrix format developed for Spyview</p> <p>NNN stands for dataset number, automatically indexed by QTLab</p> <p>How to plot data:</p> <p>1) Spyview - a free data plotting program written by Gary Steele</p> <p>Data in this repository can be simply dropped into Spyview for plotting. </p> <p>Spyview also produces and can read .mtx files which are available for some of the data in this repository.</p> <p>https://nsweb.tn.tudelft.nl/~gsteele/spyview/</p> <p><br> 2) QTPlot - a Python plotter written by Ruben van Gulik</p> <p>Data in this repository can be directly opened with QTPlot, which will read axis labels.</p> <p>https://github.com/Rubenknex/qtplot</p> <p>Note: requires PyQT4</p>
Zero-derived nouns and deverbal nominalization: Database for German
<p>This is a collection of deverbal zero-derived nouns with various information on their date of attestation, etymology, frequency, possible interpretations and their ability to realize verbal argument structure. Most of this information is extracted from lexical resources, in particular dictonaries. Examples with argument structure are extracted from natural text corpora. There are four collections in total for English, Italian, Spanish and German. The English and the Italian collections follow a parallel structure, while the Spanish and the German one are more targeted on the research purposes of the students who created them.</p>
Less Training, More Repairing Please: Revisiting Automated Program Repair via Zero-shot Learning
<p>Code used for the paper along with the generated outputs</p>
Data availability Optimized inventory control for zero waste in new product development
<p>This data elucidates the research focus using the ABC method, the distribution of lead times based on material types, and the comparative analysis between initial and proposed conditions.</p>
Index of supplementary files from "Perils of Zero-Interaction Security in the Internet of Things"
<p>This record serves an an index to the other dataset releases that are part of the paper "Perils of Zero Interaction Security in the Internet of Things" by Mikhail Fomichev, Max Maass, Lars Almon, Alejandro Molina, Matthias Hollick, in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 3, Issue 1.</p> <p>We have chosen to split the dataset into several parts to meet Zenodo size requirements and make it easier to find specific pieces of data. In total, the following datasets exist:</p> <ol> <li><strong>Raw data</strong><br> These datasets contain raw data, as collected directly from the devices doing the recording. It includes readings from several different sensors, as well as observed WiFi and BLE signals with their signal strength, and in one case, audio recordings. This raw data can be used to repeat our own experiments, or to apply different schemes to it to have a baseline for comparisons. Four datasets exist, mapped to the three scenarios discussed in the paper: <ol> <li><a href="http://dx.doi.org/10.5281/zenodo.2537699">Car Scenario</a></li> <li><a href="http://dx.doi.org/10.5281/zenodo.2537701">Office Scenario</a></li> <li><a href="http://dx.doi.org/10.5281/zenodo.2537703">Mobile Scenario</a> + <a href="http://dx.doi.org/10.5281/zenodo.2537984">audio data in separate deposit</a> (with access control)</li> </ol> </li> <li><strong>Processed Data</strong><br> The processed data is generated from the raw data using the processing code (which can be found in <a href="https://dx.doi.org/10.5281/zenodo.2543721">the code repository</a>). The resulting data contains computed features from the five papers under investigation plus derived machine learning datasets, and can be used to see in detail how the schemes behave in specific situations. These datasets tend to be fairly large. Three datasets exist: <ol> <li><a href="http://dx.doi.org/10.5281/zenodo.2537705">Car Scenario</a></li> <li><a href="http://dx.doi.org/10.5281/zenodo.2537707">Office Scenario</a></li> <li><a href="http://dx.doi.org/10.5281/zenodo.2537709">Mobile Scenario</a></li> </ol> </li> <li><strong>Result Data</strong><br> Finally, the result datasets contain the results of the evaluation (i.e., the computed error rates and generated plots, plus associated caches). The code used to derive these results can once again be found in the <a href="http://dx.doi.org/10.5281/zenodo.2543721">source code repository</a>. Here, five datasets exist, one for each investigated paper: <ol> <li><a href="http://dx.doi.org/10.5281/zenodo.2537711">Karapanos et al.</a></li> <li><a href="http://dx.doi.org/10.5281/zenodo.2537713">Schürmann and Sigg</a></li> <li><a href="http://dx.doi.org/10.5281/zenodo.2537715">Miettinen et al.</a></li> <li><a href="http://dx.doi.org/10.5281/zenodo.2537717">Truong et al.</a></li> <li><a href="http://dx.doi.org/10.5281/zenodo.2537719">Shrestha et al.</a></li> </ol> </li> </ol>
Raw data from Car scenario from "Perils of Zero-Interaction Security in the Internet of Things"
<p>This deposit contains the raw data from the Car scenario in the paper "Perils of Zero Interaction Security in the Internet of Things" by Mikhail Fomichev, Max Maass, Lars Almon, Alejandro Molina, Matthias Hollick, in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 3, Issue 1. See the <a href="https://dx.doi.org/10.5281/zenodo.2537721">index of all related datasets</a> for more details on the paper, and see the included README for details on this dataset.</p>
Raw data from Office scenario from "Perils of Zero-Interaction Security in the Internet of Things"
<p>This deposit contains the raw data from the Office scenario in the paper "Perils of Zero Interaction Security in the Internet of Things" by Mikhail Fomichev, Max Maass, Lars Almon, Alejandro Molina, Matthias Hollick, in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 3, Issue 1. See the <a href="https://dx.doi.org/10.5281/zenodo.2537721">index of all related datasets</a> for more details on the paper, and see the included README for details on this dataset.</p>
Processed data from Car scenario from "Perils of Zero-Interaction Security in the Internet of Things"
<p>This deposit contains the processed data from the Car scenario in the paper "Perils of Zero Interaction Security in the Internet of Things" by Mikhail Fomichev, Max Maass, Lars Almon, Alejandro Molina, Matthias Hollick, in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 3, Issue 1. See the <a href="https://dx.doi.org/10.5281/zenodo.2537721">index of all related datasets</a> for more details on the paper, and see the included README for details on this 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.