Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
7,228
datasets available to search
ShareScore release 0.9.0
Dataset results
7,228 results for “Modules”
An Image-Based Gamut Analysis of Translucent Digital Ceramic Prints for Coloured Photovoltaic Modules: Supplementary Data
<p>Colouring the frontglass of PV modules via digital ceramic printing aids in concealing the PV when integrated into existing building façades as BIPV, while admitting sufficient light to produce electricity. This promotes the visual acceptance and adoption of PV as a source of renewable energy in urban environments. The effective colour of the PV laminate is a combination of the transparent colour on glass and the colour of the PV cells. This colour should ideally match the architect’s visual expectations in terms of fidelity, but also in terms of relative PV efficiency as a function of print density. In practice, these requirements are often contradictory, particularly for vivid colours, and the visual results may deviate significantly. This paper presents an objective analysis of how colours appear on PV frontglass laminated with a PV module, using an image-based colour acquisition process. Given a set of 1044 nominal colours uniformly distributed in the RGB colour space, each printed in 10 opacities, we quantify the range of effective colours observed when printed on glass and combined with PV, and their deviation from the nominals. Our results confirm that the effective colour gamuts are significantly constrainted and skewed, depending on the ink volume and glass finish used for printing. In particular, blue-magenta hues cannot be reliably rendered with this process. These insights can serve as guidelines for selecting target colours for BIPV that can be well approximated in practice.</p>
Constriction rate modulation can drive cell size control and homeostasis in C. crescentus. Data part 2.
<p>This data corresponds to the publication "Constriction rate modulation can drive cell size control and homeostasis in <em>C. crescentus</em>".</p> <p>This is part 2 out of 2.</p> <p>Part 1: 10.5281/zenodo.1172042</p> <p>See metadata.docx for more information.</p>
Constriction rate modulation can drive cell size control and homeostasis in C. crescentus. Data part 1.
<p>This data corresponds to the publication "Constriction rate modulation can drive cell size control and homeostasis in <em>C. crescentus</em>".</p> <p>This is part 1 out of 2.</p> <p>Part 2: 10.5281/zenodo.1241005 </p> <p>See metadata.docx for more information.</p>
Measurement Results of Single-Bit Sigma-Delta Modulator and Simulation Data for DFT Based RSSI Calculation
<p>Measurement of single-bit output data stream of an low-IF receiver with sigma-delta ADC. Inphase and Quadraturephase signals can be found in raw_rx_*.csv files. Simulation results of DFT based RSSI calculation are found in sim_RSSI_*.csv. Measurement results of the RSSI dynamic range are located in meas_RSSI_dynamicRange.csv.</p>
Data for "Sperm motility in modulated microchannels"
<p>Data for "Sperm motility in modulated microchannels" as published in NJP.</p> <p> </p> <p>Plots are generated by the jupyter notebook sperm_microchannels.ipynb</p> <p>All necessary python packages are specified in requirements.txt</p> <p> </p> <p>SETUP:</p> <p>- use python 2</p> <p> - pip install -r requirements.txt</p> <p> - jupyter notebook sperm_microchannels.ipynb</p> <p> </p>
Dataset, Module and Questionnaire of Teachers' Beliefs About Drug Abuse
<p>This study assessed the effect of electronic module of learning material about drugs and the prevention toward teachers' beliefs in preventing drug abuse. The database including variable dataset, module and questionnaire of teachers' beliefs about drug abuse and the prevention. </p>
Modules for Experiments in Stellar Astrophysics (MESA): Planets, Oscillations, Rotation, and Massive Stars
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/?#abs/2013ApJS..208....4P">Modules for Experiments in Stellar Astrophysics (MESA): Planets, Oscillations, Rotation, and Massive Stars</a></p>
Modules for Experiments in Stellar Astrophysics (MESA): Convective Boundaries, Element Diffusion, and Massive Star Explosions
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/#abs/2018ApJS..234...34P/abstract">Modules for Experiments in Stellar Astrophysics (MESA): Convective Boundaries, Element Diffusion, and Massive Star Explosions</a></p>
Modules for Experiments in Stellar Astrophysics (MESA): Binaries, Pulsations, and Explosions
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/?#abs/2015ApJS..220...15P">Modules for Experiments in Stellar Astrophysics (MESA): Binaries, Pulsations, and Explosions</a></p>
Dataset from: "The possibility to make choices modulates feature-based effects of reward"
<p>Dataset from the following publication:</p> <p>Heuer, A., Wolf, C., Schütz, A. C., & Schubö, A. (2019). The possibility to make choices modulates feature-based effects of reward. <em>Scientific Reports,</em> 9:5749.</p>
Choice-induced inter-trial inhibition is modulated by idiosyncratic choice-consistency
<p>Dataset from the following publication:</p> <p>Wolf, C. & Schütz, A.C. Choice-induced inter-trial inhibition is modulated by idiosyncratic choice-consistency</p> <p><br> There is a csv and txt file containing the data. An additional csv file ('*_columnDescription') gives a description of the columns.</p> <p><br> For further questions, please contact:<br> chr.wolf[at]uni-muenster.de or a.schuetz[at]uni.marburg.de</p> <p> </p>
SR load modulation by AP morphology
<p>Data in this data set was used to determine SR Ca load during cell pacing with different AP waveforms.</p>
Meridional and zonal eddy-induced heat and salt transport in the Bay of Bengal and their seasonal modulation
<p>CTD (conductivity-temperature-depth) and ADCP (Acoustic Doppler Current Profiler ) data in the Bay of Bengal.</p>
Serotonergic modulation of walking in Drosophila
<p>This repository includes the data and code associated with Howard et al 2019.</p>
Data and R code used in Baudson et al (2019) Developmental plasticity of Brachypodium distachyon in response to P deficiency: modulation by inoculation with phosphate-solubilizing bacteria
<p>This repository contains the raw data and R code used for the following paper: Baudson et al (2019) Developmental plasticity of <em>Brachypodium distachyon</em> in response to P deficiency: modulation by inoculation with phosphate-solubilizing bacteria</p>
Data set and data processing software of: Bacterial cell size modulation along the growth curve across nutrient conditions
<div>In Repository.zip it is possible to find the following folders:</div> <div> </div> <div>ImageProcess: Shows an example of the studied phtos, the segmentation mask obtained using Ilastik and the scripts used to estimate the cell dimensions.</div> <div> </div> <div>DataProcessing: Includes the raw data for cells size in all the studied conditions, a script showing the filtering and the data processing for plotting most of the figures of the article.</div> <div> </div> <div>CFUod: Includes the dataset of CFU and OD measurements studied in the article. The inered trends over different biological replica and the data processing for plotting the Figures in the main text. </div> <div> </div> <div> </div> <div>_______________________________________________________________</div> <div> </div> <div>ImageProces:</div> <div> </div> <div>This folder contains:</div> <div> </div> <div>* IMAGES folder: Contains a 10 arbitrary folders of images, one for different OD conditions for the experiment of M9 + 0.25% CAS. Each image is a .tif file. The pixel size is 0.07 micrometers per pixel and they were obtained using bright field microscopy imaging. </div> <div> </div> <div>* SEG folder: Contains the masks for the same number of folders and photos equivalent photos in the IMAGES folder. Masks are also in .tif format.</div> <div> </div> <div>* "Dataset.csv": Is a typical dataset obtained from the images using the script of image processing. The data consists on the following columns:</div> <div>a. OD: Label of the OD measurement. Following experimental arbitrary notation, this number was the time in hours times 10. </div> <div>b. Photo: The label of the segmented photo.</div> <div>c. Area: Area of the segmenteated contour (squared micrometers).</div> <div>d. Len: Cell size length (Micrometers).</div> <div> </div> <div>* "ImageProcesing.ipynb": Jupyter notebook for procesing the images and their masks. The output is "Dataset.csv"</div> <div> </div> <div>____________________________________________________________________________________________________</div> <div> </div> <div> </div> <div>DataProcessing:</div> <div> </div> <div>This folder contains:</div> <div> </div> <div>* RawData.csv: comma separated values file with the dimensions of different cells in for the studied conditions. The data consists on the following columns:</div> <div>a. Strain: Represents the experimental condition. It has the following values:</div> <div>M9= E.coli Growth in minimal M9</div> <div>M9cas25= E.coli in M9 + 0.25% Casaminoacids</div> <div>LBSS= E.coli in LB in steady growth</div> <div>SalLB= S. enterica in LB.</div> <div>SalM9=S. enterica in M9</div> <div>M9cas50= E.coli in M9 + 0.5% Casaminoacids</div> <div>LB2= E. coli in LB</div> <div>b. Photo: label for the studied photo.</div> <div>c. Time: Time in hours after resuspension.</div> <div>d. OD: Optical density of the studied population.</div> <div>e. Len: Cell length of the situdied contour (micrometers).</div> <div>f. Area: Projected area of the cell contour (squared micrometers).</div> <div>g. Area: Volume of the cell (cubic micrometers).</div> <div>h. SAV surface/volume ratio.</div> <div>i. Width: Cell width </div> <div>j. Aspect; Aspect ratio length/width</div> <div> </div> <div>*Stats.csv: Results of the statistical moments of cell size dimensions calculated from "Rawdata.csv" using "Plotter.ipynb". These data consists on the following columns:</div> <div> </div> <div>a. Time: Time (hours)</div> <div>b. OD: Optical density </div> <div>c. MnVol: Mean cell volume (cubic micrometers)</div> <div>d. MnVolErr: 95% confidence interval of the mean volume.</div> <div>e. CV2Vol: squared coefficient of variation of the volume.</div> <div>f. CV2VolErr: 95% confidence interval squared coefficient of variation of the volume.</div> <div>g. Mnw: Mean cell width (micrometers)</div> <div>h. MnwErr: 95% confidence interval of the mean width.</div> <div>i. CV2w: squared coefficient of variation of the cell width.</div> <div>j. CV2wErr: 95% confidence interval squared coefficient of variation of the width.</div> <div>k. MnLen: Mean cell length (micrometers)</div> <div>l. MnLenErr: 95% confidence interval of the mean length.</div> <div>m. CV2Len: squared coefficient of variation of the cell length.</div> <div>n. CV2LenErr: 95% confidence interval of the squared coefficient of variation of the cell length.</div> <div>o. Strain: Nutrient conditions</div> <div> </div> <div>*Ploter.ipnyb: Jupyter notebook which using "RawData.csv" calculates the moments in "Stats.csv" and plots most of the figures of the main article. </div> <div> </div> <div> </div> <div>__________________________________________________________________________________ </div> <div> </div> <div>CFUod: </div> <div> </div> <div>This folder contains:</div> <div> </div> <div>* resultsOD.csv: OD values for different biology replicas. The columns are as follows:</div> <div>a. t: Time (hours)</div> <div>b. log(OD): Natural logarithm of the bets fit for the optical density</div> <div>c. log(OD) error: 95% confidence interval for the best fit of the natural logarithm of the optical density.</div> <div>d. gr: best fit growth rate in units of 1/hours.</div> <div>e. gr error: 95% confidence interval of the growth rate.</div> <div>f. three columns called "od": each represents the optical density for each experimental replica.</div> <div> </div> <div> </div> <div>* resultscfu.csv: cfu values for different biology replicas. The columns are as follows:</div> <div>a. t: Time (hours)</div> <div>b. log(OD): Natural logarithm of the bets fit for the cfu</div> <div>c. log(OD) error: 95% confidence interval for the best fit of the natural logarithm of the cfu.</div> <div>d. gr: best fit growth rate in units of 1/hours.</div> <div>e. gr error: 95% confidence interval of the growth rate.</div> <div>f. three columns called "od": each represents the cfu for each experimental replica.</div> <div> </div> <div> </div> <div>*ODGrowthRate.ipynb: jupyter notebook that uses "resultsOD.csv" and "resultscfu.csv" for plotting the ratio OD/cfu.</div> <div> </div> <div> </div> <div>Any question please ask cnieto@udel.edu</div> <div> </div> <div>Cesar Augusto Nieto Acuna</div> <div> </div> <div>Newark, Delaware, USA</div> <div> </div> <div>08/05/2024</div>
Supporting Information for "The polarity of IMF By strongly modulates particle precipitation during high-speed streams"
<p>This data file contains start times of 485 high-speed stream events, used in the manuscript (for Geophysical Research Letters) "The polarity of IMF By strongly modulates particle precipitation during high-speed streams". </p>
HPTLC Data of "Metal Ion Cofactors Modulate Integral Enzyme Activity By Varying Differential Membrane Curvature Stress"
<p>Lipid hydrolysis by the integral membrane protein OmpLA (outer membrane phospholipase). The enzymatic degradation of the proteoliposomes was determined by TLC. After lipid extraction against organic solvent (2:1 vol/vol chloroform/methanol) based on the Folch extraction method, the samples were spotted on a silica plate (Sigma-Aldrich, Steinheim, Germany) with the automatic TLC sampler 4 (CAMAG, Muttenz, Switzerland). The mobile phase in the developing chamber was a solvent mixture composed of 32.5:12.5:2 vol/vol/vol CHCl_3/MeOH/H_2O. After drying, the plate was immersed in a developing bath (5.08 g MnCl_2 dissolved in 480 ml H_2O, 480 ml EtOH and 32 ml H_2SO_4), which is sensitive to double bonds, and dried for 15 min at 120°C. To quantify the lipid concentrations the plate was scanned with the TLC scanner 3 (CAMAG, Muttenz, Switzerland) and further analyzed with WinCats software.</p> <p>Lipids:</p> <ul> <li>1-palmitoyl-2-oleoyl-<em>sn</em>-glycero-3-phosphocholine (POPC)</li> <li>1-palmitoyl-2-oleoyl-<em>sn</em>-glycero-3-phosphoethanolamine (POPE)</li> <li>1-palmitoyl-2-oleoyl-<em>sn</em>-glycero-3-phosphoglycerol (POPG)</li> </ul>
Dataset (Part II) accompanying the paper "In the brain of the beholder: bi-stable motion reveals mesoscopic-scale feedback modulation in V1"
<p><strong>This is Part II of the dataset (from sub-06 to sub-09).</strong> Due to size constraints, the remaining subjects can be found in the following records: <em>Dataset (Part I) accompanying the paper 'In the Brain of the Beholder: Bi-Stable Motion Reveals Mesoscopic-Scale Feedback Modulation in V1'</em> (from sub-01 to sub-05) and <em>Dataset (Part III) accompanying the paper 'In the Brain of the Beholder: Bi-Stable Motion Reveals Mesoscopic-Scale Feedback Modulation in V1'</em> (sub-10)</p>
Dataset (Part III) accompanying the paper "In the brain of the beholder: bi-stable motion reveals mesoscopic-scale feedback modulation in V1"
<p><strong>This is Part III of the dataset (sub-10). </strong> Due to size constraints, the remaining subjects can be found in the following records: <em>Dataset (Part I) accompanying the paper 'In the Brain of the Beholder: Bi-Stable Motion Reveals Mesoscopic-Scale Feedback Modulation in V1'</em> (from sub-01 to sub-05) and <em>Dataset (Part II) accompanying the paper 'In the Brain of the Beholder: Bi-Stable Motion Reveals Mesoscopic-Scale Feedback Modulation in V1'</em> (from sub-06 to sub-09) </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.