Skip to main content
ConductScienceScienceDex
Powered by ShareScore

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.

6,334

datasets available to search

ShareScore release 0.7.1

Reset

Dataset results

6,334 results for “Directivity”

Learn how ShareScore rates datasets ↗
zenodo44/100

Cascading effects augment the direct impact of CO2 on phytoplankton growth in a biogeochemical model, links to model results

<p>This dataset provides the output of eight model simulations with the global ocean biogeochemical model FESOM-REcoM necessary to reproduce the findings of Seifert et al. (2022). In addition to information on the mesh, the dataset contains 1) 5-year means of global phytoplankton biomass, chlorophyll, net primary production, growth rates, limitations, calcification, grazing rates, calcite concentrations, zooplankton biomass, export fluxes as well as CO<sub>2(aq)</sub>, HCO<sub>3</sub><sup>-</sup> and nutrient concentrations, and 2) a time series of global and North Atlantic coccolithophore biomass, temperature, and CO<sub>2(aq)</sub> concentrations from 1958 to 2018.</p> <p>File names refer to the Figures and Tables in the paper where the respective data are used. See &ldquo;readme&rdquo; for detailed information on the dataset and separate files.</p>

opencc-by-4.0Jun 2022View details →
zenodo44/100

direct RNA seq data, triplicate of RSV - strain A2 in Calu-3 cells at 48 hours post infection

<p>Raw fastq data from Calue-3 cells infected with RSV strain A2</p>

opencc-by-4.0Sep 2022View details →
zenodo44/100

Elliptical Alignment Holes Enabling Accurate Direct Assembly of Microchips to Standard Waveguide Flanges at sub-THz Frequencies - Dataset

<p>Current waveguide flange standards do not allow for the accurate fitting of microchips, due to the large mechanical tolerances of the flange alignment pins and the brittle nature of Silicon, requiring greatly oversized alignment holes on the chip to fit worst-case fabrication tolerances, resulting in unacceptably large misalignment error for sub-THz frequencies. This paper presents, for the first time, a new method for directly aligning micromachined Silicon chips to standard, i.e. unmodified, waveguide flanges with alignment accuracy significantly better than the waveguide-flange fabrication tolerances, through the combination of a tightly-fitting circular and an elliptical alignment hole on the chip. A Monte Carlo analysis predicts the reduction of the mechanical assembly margin by a factor of 5.5 compared to conventional circular holes, reducing the potential chip misalignment from 46 μm to 8.5 μm for a probability of fitting of 99.5%. For experimental verification, micromachined waveguide chips using either conventional (oversized) circular or the proposed elliptical alignment holes were fabricated and measured. A reduction in the standard deviation of the reflection coefficient by a factor of up to 20 was experimentally observed from a total of 200 measurements with random chip placement, exceeding the<br> expectations from the Monte Carlo analysis. To our knowledge, this paper presents the first solution for highly accurate assembly<br> of micromachined waveguide chips to standard waveguide flanges, requiring no custom flanges or other tailor-made split blocks.<br>  </p>

opencc-by-nc-4.0Jun 2017View details →
zenodo44/100

Data set for "Reward-based learning drives rapid sensory signals in medial prefrontal cortex and dorsal hippocampus necessary for goal-directed behavior"

<p>Data set for: Le Merre P, Esmaeili V, Charri&egrave;re E, Galan K, Salin P-A, Petersen CCH, Crochet S (2018) Reward-based learning drives rapid sensory signals in medial prefrontal cortex and dorsal hippocampus necessary for goal-directed behavior. Neuron, https://doi.org/10.1016/j.neuron.2017.11.031</p> <p>There are 44 files in this data upload:<br> 1.&nbsp;&nbsp; &nbsp;&#39;2018_LeMerre_Neuron.pdf&#39; - this is a pdf version of the online publication.<br> 2.&nbsp;&nbsp; &nbsp;&#39;Chronic_LFP_data.mat&#39; - this is a Matlab data structure, which contains all the chronic LFP data for the publication.<br> 3.&nbsp;&nbsp; &nbsp;&#39;Silicon_Probe_data.mat&#39; - this is a Matlab data structure, which contains all the mPFC silicon probe recording data for the publication.<br> 4.&nbsp;&nbsp; &nbsp;&#39;Opto_Inactivation_data.mat&#39; - this is a Matlab data structure, which contains all the optogenetic inactivation data for the publication.<br> 5.&nbsp;&nbsp; &nbsp;&#39;Mus_Inactivation_data.mat&#39; - this is a Matlab data structure, which contains all the pharmacological (Muscimol) inactivation data for the publication.<br> 6.&nbsp;&nbsp; &nbsp;&#39;Learning_Days_Mtrx.mat&#39; - this is a Matlab data file, which contains the selected training days analyzed for the Trained condition in the Detection Task.<br> 7.&nbsp;&nbsp; &nbsp;&#39;Exposed_Days_Mtrx.mat&#39; - this is a Matlab data file, which contains the selected days analyzed for the Exposed condition in the Neutral Exposure.<br> 8.&nbsp;&nbsp; &nbsp;&#39;p_value_colormap.mat&#39; - this is a Matlab data file, which contains the color map used to display the p value in the Matlab codes &#39;plot_fig2A_SEP_D1_vs_Trained.m&#39;; &#39;plot_fig2B_Amplitude_D1_vs_Trained.m&#39;; &#39;plot_fig3A_SEP_D1_vs_Exposed.m&#39;; &#39;plot_fig4A_SEP_H_vs_M.m&rsquo;.<br> 9.&nbsp;&nbsp; &nbsp;&#39;p_value_colormap2.mat&#39; - this is a Matlab data file, which contains the color map used to display the p value in the Matlab code &#39;plot_figS3B_Stim_vs_Catch_for_significantly_inc_dec_units.m&rsquo;; &rsquo;plot_figS4A_H_vs_M_for_inc_dec_units_and_zscored_PSTH.m&rsquo;.<br> 10.&nbsp;&nbsp; &nbsp;&#39;scatterplot_colormap.mat&#39; - this is a Matlab data file, which contains the color map used to display the p value in the Matlab code &#39;plot_fig2C_Scatterplot_Amplitude_vs_dprime.m&#39;.<br> 11.&nbsp;&nbsp; &nbsp;&#39;SEP_colormtrx.mat&#39; - this is a Matlab data file, which contains the color map used to display the p value in the Matlab code &#39;plot_fig1B_Sensory_Evoked_Potentials.m&#39;; &#39;plot_figS3A_SEP_EMG_amplitude_ReactionTime.m&#39;.<br> 12.&nbsp;&nbsp; &nbsp;&#39;zscore_colormap.mat&#39; - this is a Matlab data file, which contains the color map used to display the p value in the Matlab code &#39;plot_fig3D_mPFC_PSTH_and_zscore_DT_vs_NE.m&#39;; &#39;plot_figS4A_H_vs_M_for_inc_dec_units_and_zscored_PSTH.m&rsquo;.<br> 13.&nbsp;&nbsp; &nbsp;&#39;Chronic_LFP_dataViewer.fig&#39; - this is a Matlab Figure file, which is the GUI layout for &#39;Chronic_LFP_dataViewer.m&#39;.<br> 14.&nbsp;&nbsp; &nbsp;&#39;Chronic_LFP_dataViewer.m&#39; - this is a Matlab code, which displays the data contained in &#39;Chronic_LFP_data.mat&#39;.<br> 15.&nbsp;&nbsp; &nbsp;&#39;Silicon_Probe_dataViewer.fig&#39; - this is a Matlab Figure file, which is the GUI layout for &#39;Silicon_Probe_dataViewer.m&#39;.<br> 16.&nbsp;&nbsp; &nbsp;&#39;Silicon_Probe_dataViewer.m&#39; - this is a Matlab code, which displays the data contained in &#39;Silicon_Probe_data.mat&#39;.<br> 17.&nbsp;&nbsp; &nbsp;&#39;plot_fig1B_Sensory_Evoked_Potentials.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the results published in figure 1, panel B (Le Merre et al., 2018).<br> 18.&nbsp;&nbsp; &nbsp;&#39;plot_fig1C_Silicon_Probe_Hit_trials.m&#39; - this is a Matlab code, which analyses the data in &#39;Silicon_Probe_data.mat&#39;, and displays the results in the same way as the published figure 1, panel C (Le Merre et al., 2018).<br> 19.&nbsp;&nbsp; &nbsp;&#39;plot_fig2A_SEP_D1_vs_Trained.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the results in the same way as the published figure 2, panel A (Le Merre et al., 2018).<br> 20.&nbsp;&nbsp; &nbsp;&#39;plot_fig2B_Amplitude_D1_vs_Trained.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the results in the same way as the published figure 2, panel B (Le Merre et al., 2018).<br> 21.&nbsp;&nbsp; &nbsp;&#39;plot_fig2C_Scatterplot_Amplitude_vs_dprime.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the results in the same way as the published figure 2, panel C (Le Merre et al., 2018).<br> 22.&nbsp;&nbsp; &nbsp;&#39;plot_fig3A_SEP_D1_vs_Exposed.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the results in the same way as the published figure 3, panel A (Le Merre et al., 2018).<br> 23.&nbsp;&nbsp; &nbsp;&#39;plot_fig3B_Amplitude_D1_vs_Exposed.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the results in the same way as the published figure 3, panel B (Le Merre et al., 2018).<br> 24.&nbsp;&nbsp; &nbsp;&#39;plot_fig3C_ROC_Trained_vs_Exposed.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the ROCs in the same way as the published figure 3, panel C (Le Merre et al., 2018).<br> 25.&nbsp;&nbsp; &nbsp;&#39;plot_fig3C_ROC_Randomization.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the label shuffled ROCs in the same way as the published figure 3, panel C (Le Merre et al., 2018).<br> 26.&nbsp;&nbsp; &nbsp;&#39;plot_fig3D_mPFC_PSTH_and_zscore_DT_vs_NE.m&#39; - this is a Matlab code, which analyses the data in &#39;Silicon_Probe_data.mat&#39;, and displays the results in the same way as the published figure 3, panel D (Le Merre et al., 2018).<br> 27.&nbsp;&nbsp; &nbsp;&#39;plot_fig4A_SEP_H_vs_M.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the results in the same way as the published figure 4, panel A (Le Merre et al., 2018).<br> 28.&nbsp;&nbsp; &nbsp;&#39;plot_fig4B_Amplitude_ H_vs_M.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the results in the same way as the published figure 4, panel B (Le Merre et al., 2018).<br> 29.&nbsp;&nbsp; &nbsp;&#39;plot_fig4C_mPFC_PSTH_Hit_vs_Miss.m&#39; - this is a Matlab code, which analyses the data in &#39;Silicon_Probe_data.mat&#39;, and displays the results in the same way as the published figure 4, panel C, left panel (Le Merre et al., 2018).<br> 30.&nbsp;&nbsp; &nbsp;&#39;plot_fig4C_Scatterplot_modulation_Hit_vs_Miss.m&#39; - this is a Matlab code, which analyses the data in &#39;Silicon_Probe_data.mat&#39;, and displays the results in the same way as the published figure 4, panel C, right panel (Le Merre et al., 2018).<br> 31.&nbsp;&nbsp; &nbsp;&#39;plot_fig4D_Photoinhibitions.m&#39; - this is a Matlab code, which analyses the data in &#39;Opto_Inactivation_data.mat&#39;, and displays the results published in figure 4, panel D (Le Merre et al., 2018).<br> 32.&nbsp;&nbsp; &nbsp;&#39;plot_figS2D_Performance_DetectionTask_NeutralExposition.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the results in the same way as the published figure S2, panel D (Le Merre et al., 2018).<br> 33.&nbsp;&nbsp; &nbsp;&#39;plot_figS3A_SEP_EMG_amplitude_ReactionTime.m&#39; - this is a Matlab code, which analyses the data in &#39;Chronic_LFP_data.mat&#39;, and displays the results in the same way as the published figure S3, panel A (Le Merre et al., 2018).<br> 34.&nbsp;&nbsp; &nbsp;&#39;plot_figS3B_Stim_vs_Catch_for_significantly_inc_dec_units.m&#39; - this is a Matlab code, which analyses the data in &#39;Silicon_Probe_data.mat&#39;, and displays the results in the same way as the published figure S3, panel B (Le Merre et al., 2018).<br> 35.&nbsp;&nbsp; &nbsp;&#39;plot_figS4A_H_vs_M_for_inc_dec_units_and_zscored_PSTH.m&#39; - this is a Matlab code, which analyses the data in &#39;Silicon_Probe_data.mat&#39;, and displays the results in the same way as the published figure S4, panel A (Le Merre et al., 2018).<br> 36.&nbsp;&nbsp; &nbsp;&#39;plot_figS4B_Pharmacological_Inactivations.m&#39; - this is a Matlab code, which analyses the data in &#39;Mus_Inactivation_data.mat&#39;, and displays the results published in figure S4 (Le Merre et al., 2018).<br> 37.&nbsp;&nbsp; &nbsp;&#39;Load_LFP_Multisite_database.m&#39; - this is a Matlab code, which is called in the Matlab codes that analyze the data in &#39;Chronic_LFP_data.mat&#39;.<br> 38.&nbsp;&nbsp; &nbsp;&#39;Load_Silicon_Probe_database.m&#39; - this is a Matlab code, which is called in the Matlab codes that analyze the data in &#39;Silicon_Probe_data.mat&#39;.<br> 39.&nbsp;&nbsp; &nbsp;&#39;Load_Optogenetic_Inactivation_database.m&#39; - this is a Matlab code, which is called in the Matlab code that analyzes the data in &#39;Opto_Inactivation_data.mat&#39;.<br> 40.&nbsp;&nbsp; &nbsp;&#39;Load_Pharmacological_Inactivation_database.m&#39; - this is a Matlab code, which is called in the Matlab code that analyzes the data in &#39;Mus_Inactivation_data.mat&#39;.<br> 41.&nbsp;&nbsp; &nbsp;&#39;bonf_holm.m&#39; - this is a Matlab code developed by D. M. Groppe, which is called in the Matlab code &#39;plot_figS4B_Pharmacological_Inactivations.m&#39;:<br> https://ch.mathworks.com/matlabcentral/fileexchange/28303-bonferroni-holm-correction-for-multiple-comparisons<br> 42.&nbsp;&nbsp; &nbsp;&#39;boundedline.m&#39; - this is a Matlab code developed by K. Kearney, which is called in the Matlab codes &#39;plot_fig1C_Silicon_Probe_Hit_trials.m&#39;; &#39;plot_fig2A_SEP_D1_vs_Trained.m&#39;; &#39;plot_fig3A_SEP_D1_vs_Exposed.m&rsquo;; &#39;plot_fig3C_ROC_Trained_vs_Exposed.m&#39;; &#39;plot_fig3D_mPFC_PSTH_and_zscore_DT_vs_NE.m&#39;; &#39;plot_fig4A_SEP_H_vs_M.m&#39;; &#39;plot_fig4C_mPFC_PSTH_Hit_vs_Miss.m&#39;; &#39;plot_figS3B_Stim_vs_Catch_for_significantly_inc_dec_units.m&#39;; &#39;plot_figS4A_H_vs_M_for_inc_dec_units_and_zscored_PSTH.m&#39;:<br> https://ch.mathworks.com/matlabcentral/fileexchange/27485-boundedline-m<br> 43.&nbsp;&nbsp; &nbsp;&#39;inpaint_nans.m&#39; - this is a Matlab code, which is called in the Matlab code &#39;boundedline.m&#39;.<br> 44.&nbsp;&nbsp; &nbsp;&#39;PSTH_Simple.m&#39; - this is a Matlab code developed by V. Esmaeili, which is called in the Matlab codes &#39;plot_fig1C_Silicon_Probe_Hit_trials.m&#39;; &#39;plot_fig3D_mPFC_PSTH_and_zscore_DT_vs_NE.m&#39;; &#39;plot_fig4C_mPFC_PSTH_Hit_vs_Miss.m&#39;; &#39;plot_figS3B_Stim_vs_Catch_for_significantly_inc_dec_units.m&#39;; &#39;plot_figS4A_H_vs_M_for_inc_dec_units_and_zscored_PSTH.m&rsquo;.</p>

opencc-by-4.0Dec 2017View details →
zenodo44/100

Dataset for "A simple and accurate method to determine fluid-crystal phase boundaries from direct coexistence simulations"

<p>This is a dataset for the article "A simple and accurate method to determine &nbsp;fluid-crystal phase boundaries from direct coexistence simulations", available at https://arxiv.org/abs/2403.10891&nbsp;&nbsp;&nbsp; (Full citation data will be added upon final publication of the article.)</p> <p>This package provides figure data and representative configuration files associated with the systems studied in the article above. Additionally, for the hard sphere system, this package includes direct coexistence data for all reported system sizes and crystal orientations.</p> <p>&nbsp;</p> <p>&nbsp;</p>

opencc-by-4.0May 2024View details →
zenodo44/100

2005_2018_Wind_Speed_Direction

<p><strong>Abstract:</strong></p> <p>European Wind characteristics at 10m in height derived from the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalyses (ERA) data. The data defines characteristics such as windspeed direction from and direction.&nbsp; Monthly mean values for the years 2005-2018 at 0.125 of a degree Clipped to the E4warning extent.&nbsp;</p> <table> <tbody> <tr> <td><strong>PROJECTION:</strong></td> <td>Geographic</td> </tr> <tr> <td><strong>DATUM:</strong></td> <td>WGS84</td> </tr> </tbody> </table> <p><strong>File Names:&nbsp;</strong></p> <p>The last 4 digits of the file name present Month and Year of file.&nbsp;</p> <p>dir in file names refer to direction.</p> <p>speed in file names refer to windspeed</p> <p>&nbsp;</p> <p>&nbsp;</p>

opencc-by-4.0May 2024View details →
zenodo44/100

Reproduction package for the paper "Exploring the directly imaged HD 1160 system through spectroscopic characterization and high-cadence variability monitoring"

<p>This is a basic reproduction package for the paper&nbsp;<a href="https://doi.org/10.1093/mnras/stae1315">"Exploring the directly imaged HD 1160 system through spectroscopic characterization and high-cadence variability monitoring" by Sutlieff et al. (2024)</a>. It aims to provide the most important data products to check and reproduce the main results of the paper.</p>

opencc-by-4.0Jun 2024View details →
zenodo44/100

Data for Dodds et al., The direction of core solidification in asteroids: implications for dynamo generation

<p>Numerical dataset for the data presented in Dodds et al., The direction of core solidification in asteroids: implications for dynamo generation, manuscript submitted to Icarus journal.</p>

opencc-by-4.0Jun 2024View details →
zenodo44/100

Compression tests and direct shear test of two types of railway ballast

<p>This data set contains measurement data from uniaxial compression tests and direct shear tests<br> &nbsp;conducted on two types of railway ballast.<br> For a detailed description of the experiments see:</p> <p>B. Suhr, S. Marschnig and K. Six:<br> &quot;Comparison of two different types of railway ballast in compression and direct shear tests:<br> experimental results and DEM model validation&quot;<br> Granular Matter (2018)<br> Doi: 10.1007/s10035-018-0843-9</p> <p>For the uniaxial compression tests, measured normal forces and vertical paths are provided.<br> The direct shear tests are conducted directly afterwards, i.e. the information for<br> one compression and one shear test are contained in only one file.<br> For the shear tests shear paths, shear forces and vertical path are provided.</p> <p>At first the uniaxial compression test is carried out. At the end of this test, the normal load is equal to zero.<br> For the following direct shear test, the normal load is constant (according to the load specified in the file name)<br> and not recorded in the file.<br> The direct shear test starts, when the measured shear path is greater than zero.</p> <p>Check the README.txt file for more information.&nbsp;</p>

opencc-by-4.0Sep 2018View details →
zenodo44/100

Directional wave data collected by R/V Aranda in the Baltic Sea

<p>Data source: Finnish Meteorological Institute</p> <p>This is wave and meteorological data collected on board R/V Aranda in July 2015 in the Baltic Sea. Each netcdf-file contains the data and metadata from one station.</p> <p>The experimental setup is described in the paper: Bj&ouml;rkqvist, J.-V., Pettersson, H., Drennan, W. M., and Kahma, K. K., 2019: A new inverse phase speed spectrum of nonlinear gravity wind waves, Journal of Geophysical Research: Oceans, 124, 6097&ndash;6119, DOI: 10.1029/2018JC014904</p>

opencc-by-4.0Dec 2018View details →
zenodo44/100

Data set for "Distinct contributions of whisker sensory cortex and tongue-jaw motor cortex in a goal-directed sensorimotor transformation"

<p>Data set for: Mayrhofer JM, El-Boustani S, Foustoukos G, Auffret M, Tamura K, Petersen CCH (2019) Distinct contributions of whisker sensory cortex and tongue-jaw motor cortex in a goal-directed sensorimotor transformation. Neuron https://doi.org/10.1016/j.neuron.2019.07.008</p> <p>There are 2 files in this upload:</p> <p>1. The file named &quot;2019_Mayrhofer_Neuron.pdf&quot; is the Open Access pdf file of the manuscript published in Neuron.</p> <p>2. The file named &quot;Mayrhofer_data_code.zip&quot; (~20 GB) is a zipped version of a folder &quot;Mayrhofer_data_code&quot; (~57 GB), which contains the data analysed in the study along with the Matlab code used to generate the published figures. The analysis code is in a subfolder named &quot;MatlabCode&quot;, and the specific code for generating each figure panel is in a sub-subfolder named &quot;Figures_tjM1_paper&quot;. When running the code, you need to set the Matlab file path to be &quot;Mayrhofer_data_code&quot;. In addition, you should add the folder&nbsp;&quot;Mayrhofer_data_code&quot; with subfolders in Matlab &quot;Set Path&quot;. The figures will be saved in a subfolder named &quot;Figures&quot;. Some parts of the code rely upon previous results, and need to be executed sequentially in the order of the figure panels in the journal publication.</p>

opencc-by-4.0Aug 2019View details →
zenodo44/100

LMDZOR-INCA global model simulations diagnostics for mineral dust direct radiative effet calculations

<p>This dataset contains the diagnostic variable used to estimate the mineral dust aerosol direct radiative effect from LMDZOR-INCA global simulations using different refractive index data and different size modes and a multimodal size distribution.</p> <p>The NetCDF files provide the radiation fields shortwave all sky (solswad, topswad) and clear sky (solswad0, topswad0) (sol is for the surface and top for the top of the atmosphere) and the longwave all sky (sollwad, toplwad)&nbsp;and clear sky (sollwad0, toplwad0), as monthly means over global grids.</p> <p>Data are provided for the mean, minimum and maximum of the complex refractive index from Di Biagio et al. (2017) ( https://doi.org/10.5194/acp-17-1901-2017 ) and the refractive index by Volz et al. (1973) ( <a href="https://doi.org/10.1364/AO.12.000564">https://doi.org/10.1364/AO.12.000564</a> ) in the longwave spectral range and for the refractive index by Balkanski et al. (2007) ( https://doi.org/10.5194/acp-7-81-2007) corresponding to 1.5% hematite by volume in the shortwave range.</p> <p>Simulations are performed for four lognormal size distributions with mass median diameters (sigma) of 1 &micro;m (1.8), 2.5 &micro;m (2), 7 &micro;m (1.9), 22 &micro;m (2). The multimodal run is performed on the size distribution obtained as the sum of the four modes combined follwing the mass fractions of 0.6%, 4.3%, 31.5%, and 63.6% for the four modes, respectively.</p> <p>Variables for the dust atmospheric load and optical depth at 550 nm for each mode are in the mean run for each mode.</p> <p>Input mass extinction efficiency (Ext, m2/g), absorption exitinction efficiency (Abs, m2/g), single scattering albedo (w) and asymmetry factor (g) for the different radiative bands at at some wavelengths used in the MODIS sensor are provided in the 1MODE_xxum_dust_optical_data_1.5dielectric_mixture.</p>

opencc-by-4.0Nov 2019View details →
zenodo44/100

Photon absorption in direct bandgap semiconductor

<p>Video illustrating a photon absorption process in a direct bandgap semicondutor. The video has been created using Blender 2.81. The source file is also attached.</p>

opencc-by-4.0Nov 2019View details →
zenodo44/100

Detailed insight into gillnet catches: fish directivity and micro distribution

<p>This dataset contains data for gillnets that were deployed in Ř&iacute;mov reservoir, South Bohemia, Czech Republic (48&deg;50'55.0"N 14&deg;29'14.0"E). The sampling dates were recorded from July 30 to August 2, 2019. This experiment was conducted to test the bias of gillnets in relation to fish direction capture. To determine if this is a random pattern or if it follows a directional pattern. The dataset includes various terms such as eventID, eventDate, country, countryCode, geodeticDatum, decimalLatitude, decimalLongitude, coordinateUncertaintyInMeters, habitat, waterBody, locality, DEIMS.iD, basisOfRecord, minimumDepthInMeters, maximumDepthInMeters, samplingEffort, samplingProtocol, dynamicProperties, occurrenceStatus, organismQuantity, organismQuantityType, measurementValue, measurementUnit, measurementType, measurementRemarks, organismRemarks, acceptedNameUsageID, scientificName, taxonRank, class, order, family.</p>

opencc-by-4.0Sep 2024View details →
zenodo44/100

Revised direct band gap and band parameters for AlP: hybrid-functional first-principles calculations vs. experiment

<p>Raw data and plotting scripts associated with the paper:<br><br>C&oacute;nal Murphy, Eoin P. O'Reilly and Christopher A. Broderick, "Revised direct band-gap and band parameters for AlP: hybrid-functional first-principles calculations vs. experiment", <em>APL Mater.</em> (2024) (undergoing revision)<br><br>Tyndall National Institute, Lee Maltings, Dyke Parade, University College Cork, Cork T12 R5CP, Ireland<br>School of Physics, University College Cork, Cork T12 YN60, Ireland</p>

opencc-by-4.0Nov 2024View details →
zenodo44/100

Direct molecular evidence for an ancient, conserved developmental toolkit controlling post-transcriptional gene regulation in land plants

<p>In plants, miRNA production is orchestrated by a suite of proteins that control transcription of the pri-miRNA gene, post-transcriptional processing and nuclear export of the mature miRNA. Post-transcriptional processing of miRNAs is controlled by a pair of physically-interacting proteins, HYL1 and DCL1. However, the evolutionary history and structural basis of the HYL1-DCL1 interaction is unknown. Here we use ancestral sequence reconstruction and functional characterization of ancestral HYL1 <em>in vitro</em> and in <em>Arabidopsis thaliana </em>to better understand the origin and evolution of the HYL1-DCL1 interaction and its impact on miRNA production and plant development. We found the ancestral plant HYL1 evolved high affinity for both double-stranded RNA (dsRNA) and its DCL1 partner before the divergence of mosses from seed plants (~500 Ma), and these high-affinity interactions remained largely conserved throughout plant evolutionary history. Structural modeling and molecular binding experiments suggest that the second of two double-stranded RNA-binding motifs (DSRMs) in HYL1 may interact tightly with the first of two C-terminal DCL1 DSRMs to mediate the HYL1-DCL1 physical interaction necessary for efficient miRNA production. Transgenic expression of the nearly 200 Ma-old ancestral flowering-plant HYL1 in <em>A. thaliana</em> was sufficient to rescue many key aspects of plant development disrupted by HYL1<sup>-</sup> knockout and restored near-native miRNA production, suggesting that the functional partnership of HYL1-DCL1 originated very early in and was strongly conserved throughout the evolutionary history of terrestrial plants. Overall, our results are consistent with a model in which miRNA-based gene regulation evolved as part of a conserved plant &lsquo;developmental toolkit&rsquo;.</p>

opencc-by-4.0Jun 2021View details →
zenodo44/100

xPore: Identification of differential RNA modifications from nanopore direct RNA sequencing - SGNEx data

<p>xPore is&nbsp;a Python package for identification and quantification of differential RNA modifications from direct RNA sequencing.</p> <p>The detailed usage&nbsp;is&nbsp;documented at&nbsp;<a href="https://xpore.readthedocs.io/en/latest/">https://xpore.readthedocs.io/en/latest</a>, while all&nbsp;scripts and source code are available at&nbsp;<a href="https://github.com/GoekeLab/xpore">https://github.com/GoekeLab/xpore</a>.</p> <p>All the preprocessed&nbsp;datasets&nbsp;used in the paper are provided here.&nbsp;</p> <p>Please cite our paper below&nbsp;when using these data.<br> Ploy N. Pratanwanich et al. &quot;Detection of differential RNA modifications from direct RNA sequencing of human cell lines.&quot; bioRxiv (2020).</p>

opencc-by-4.0Mar 2021View details →
zenodo44/100

Direct sun retrievals of nitrogen dioxide (NO2) total columns from Brewer #067, Rome, Italy (reprocessed with algorithm BNALG2)

<p>Cloud-screened and quality-filtered direct sun retrievals of nitrogen dioxide (NO2) vertical column densities (VCDs) derived from MkIV Brewer #067 measurements in Rome (wavelengths 425.02, 431.40, 437.35, 442.83, 448.08, and 453.20 nm) and processed using the Brewer Nitrogen Dioxide Algoritm BNALG2. Calibration is carried out with Bootstrap Estimation techniques. The values represent averages of 5 samples.</p> <p>In the latest version, days with obviously erroneous data (NO2 VCD &gt; 99.9% percentile) have been removed.</p> <p>A detailed description of the method has been accepted as a research article by the ESSD journal (H. Di&eacute;moz et al., Advanced NO2 retrieval technique for the Brewer spectrophotometer applied to the 20-year record in Rome, Italy, Earth Syst. Sci. Data, 2021).</p>

opencc-by-4.0Apr 2021View details →
zenodo44/100

Whole-genome capture and sequencing of Mycobacterium tuberculosis directly from clinical samples - Design of RNA oligonucleotide baits for Agilent Technologies' SureSelect target enrichment

<p>This dataset comprises the sequence of <strong>44&nbsp;278&nbsp;RNA oligonucleotide &quot;baits&quot; (120 bp each) </strong>designed to perform&nbsp;<strong>whole-genome capture and sequencing of <em>Mycobacterium tuberculosis</em>&nbsp;directly from clinical samples</strong>&nbsp;(DNA)&nbsp;using Agilent Technologies&rsquo; SureSelect target enrichment system following the Illumina paired-end multiplexed sequencing library protocol.&nbsp;</p> <p>RNA oligonucleotide &ldquo;baits&rdquo; were designed to span the &sim;4.5 Mb of the <em>M. tuberculosis</em> genome. In brief, the reference genome sequence of the MTBC H37Rv strain (Genbank #AL123456) was <em>in silico</em> fragmented into 120 bp sequences twice, to ensure an overlap of 60 bp between sequences. Due to their rich GC content, which could interfere with DNA capture, all MTBC genes of the PE, PPE and PE-PGRS family were also independently fragmented into 120 bp sequences, in order to increase capture sensitivity. All resulting sequences were BLASTn searched against the Human Genomic + Transcript database to excluded homologous sequences to the human genome. Overall, a total of 42,278 RNA probes were generated and this custom bait library was then uploaded to the SureDesign software (https://earray.chem.agilent.com/suredesign) and synthesized by Agilent Technologies. During synthesis, the 2198 sequences complementary to the PE, PPE and PE-PGRS family were unbalanced 8:1 to potentiate capture.</p> <p>More details can be found in the following publication:</p> <p>- Macedo, R., Isidro, J., Ferreira, R., Pinto, M., Borges, V., Duarte, S., Vieira, L., &amp; Gomes, J. P. (2023). Molecular Capture of&nbsp;<em>Mycobacterium tuberculosis</em>&nbsp;Genomes Directly from Clinical Samples: A Potential Backup Approach for Epidemiological and Drug Susceptibility Inferences.&nbsp;<em>International journal of molecular sciences</em>,&nbsp;<em>24</em>(3), 2912. https://doi.org/10.3390/ijms24032912</p>

opencc-by-4.0Jan 2023View details →
zenodo44/100

Synthetic proton radiographs for testing direct inversion algorithms

<p>Proton radiographs generated by particle tracing in specified radial force profiles in cylinders and spheres saved in pradformat (github.com/phyzicist/pradformat) in a zipped folder intended as tests for direct inversion algorithms. For details see:&nbsp;J. R. Davies, and P. V. Heuer, https://arxiv.org/abs/2203.00495</p> <p>Version 2 includes 3 additional radiographs for a spherical Gaussian potential with a reduced bin width and more bins (0.02R and 200x200 bins)</p> <p>Version 3 corrects an error in the x values given for the original spherical Gaussian potentials with negative mu values sphGauss_mum0p25 and sphGauss_mum0p5. The bin widths were half that of the spherical Gaussian results with positive mu values.&nbsp;</p> <p>Version 4 corrects an&nbsp;error in the x values given for the mesh run&nbsp;and adds a smoothed version of the&nbsp;source intensity (mu0)</p>

opencc-by-4.0Jun 2022View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record