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
Dataset results
6,334 results for “Directivity”
5D-NP-FABTECH_SALV - Open Dataset for "3D Micropatterned Functional Surface Inspired by Salvinia Molesta via Direct Laser Lithography for Air Retention and Drag Reduction"
<p>This is the open dataset for the paper: "Omar Tricinci*, Francesca Pignatelli, Virgilio Mattoli*, 3D Micropatterned Functional Surface Inspired by Salvinia Molesta via Direct Laser Lithography for Air Retention and Drag Reduction, On line (2023) [DOI: 10.1002/adfm.202206946] "</p> <p>This include the Supplementary Information file ("SI-PaperSalvinia3_PostRevOKV2.pdf.pdf"), all the source material used for the paper preparation and more. </p> <p>For each folder (sub-dataset) there is a corresponding readme file describing the content and including metadata</p>
Reproduction package for the paper "Measuring the variability of directly imaged exoplanets using vector Apodizing Phase Plates combined with ground-based differential spectrophotometry"
<p>This is a basic reproduction package for the paper <a href="https://doi.org/10.1093/mnras/stad249">"Measuring the variability of directly imaged exoplanets using vector Apodizing Phase Plates combined with ground-based differential spectrophotometry" by Sutlieff et al. (2023)</a>. It aims to provide the most important data products to check and reproduce the main results of the paper.</p>
Direct synthesis of a stable radical doped electrically conductive coordination polymer
<ul> <li><strong>Data type</strong>: Experimental spectroscopic measurements.</li> <li>Files are with filename extensions: <strong>DSC</strong>, <strong>DAT</strong>,<strong>txt</strong></li> <li>Information on <strong>origin of the data</strong>:</li> </ul> <ul> <li>EPR spectroscopic measurements with filename extensions <strong>DSC</strong>, <strong>DTA.</strong></li> <li>EPR spectra are exported as <strong>txt</strong> files in ASCII format.</li> </ul> <ul> <li>X-band CW-EPR spectroscopic measurements were generated by EMX spectrometer equipped with SHQ cavity produced by Bruker.</li> <li><strong>If the dataset includes multiple files that relate to each other:</strong> <ul> <li>Files in <strong>PARACAT_WP4_20220202_ULEI_01_ONDICathedral_RT </strong>folder includes X-band CW-EPR spectroscopic measurements; original data are in DTA/DSC and txt formats.</li> </ul> </li> <li><strong>Information on</strong>: <ul> <li>specialized abbreviations: <strong>ONDICatechol– </strong>ONDI with Catechol, <strong>ONDIDMF– </strong>ONDI with DMF, <strong>ONDI_piperidine– </strong>ONDI with piperidine,<strong> ONDICdCl– </strong>ONDI with CdCl, <strong>ONDIDMSO– </strong>ONDI with DMSO.</li> <li>_RT– measured at 300 K</li> <li>definitions of variables: <strong>Magnetic field, Temperature.</strong></li> <li>units of measurement: <strong>Gauss (G), K, degree (°), milliTesla (mT)</strong>.</li> </ul> </li> </ul>
Master and Landsat-8 simultaneous acquisition datacubes for the quantification of directional anisotropy in Thermal Infra-Red domain
<p>‎</p> <p>This dataset contains datacubes of simultaneous Landsat-8 and Master<sup><a href="#fn.1">1</a></sup> data as listed in table <a href="#org4c9ba67">1</a>. Those pairs have been identified by cross-searching Landsat-8 and Master archive for Master flight tracks with a Landsat-8 overpass during the flight. The dataset has been collected and analysed in the following paper:</p> <p><em>Julien Michel, Olivier Hagolle, Simon J Hook, Jean-Louis Roujean, Philippe Gamet. Quantifying Thermal Infra-Red directional anisotropy using Master and Landsat-8 simultaneous acquisitions. 2023. <a href="https://hal.science/hal-04073733">⟨hal-04073733⟩</a></em></p> <table> <caption>Table 1: List of valid Master and Landsat-8 pairs</caption> <thead> <tr> <th scope="col"><strong>Id</strong></th> <th scope="col"><strong>Master track id</strong></th> <th scope="col"><strong>Landsat L2 product id</strong></th> </tr> </thead> <tbody> <tr> <td>1</td> <td><code>2013-03-29_18:06:53</code></td> <td><code>LC08_L2SP_038037_20130329_20200912_02_T1</code></td> </tr> </tbody> <tbody> <tr> <td>2</td> <td><code>2013-04-11_18:14:46</code></td> <td><code>LC08_L2SP_041036_20130411_20200912_02_T1</code></td> </tr> </tbody> <tbody> <tr> <td>3a</td> <td><code>2013-05-22_18:13:09</code></td> <td><code>LC08_L2SP_040036_20130522_20200913_02_T1</code></td> </tr> <tr> <td>3b</td> <td><code>2013-05-22_18:13:09</code></td> <td><code>LC08_L2SP_040037_20130522_20200913_02_T1</code></td> </tr> </tbody> <tbody> <tr> <td>4</td> <td><code>2013-12-05_18:23:35</code></td> <td><code>LC08_L2SP_043035_20131205_20200912_02_T1</code></td> </tr> </tbody> <tbody> <tr> <td>5a</td> <td><code>2014-03-31_18:11:16</code></td> <td><code>LC08_L2SP_039035_20140331_20200911_02_T1</code></td> </tr> <tr> <td>5b</td> <td><code>2014-03-31_18:11:16</code></td> <td><code>LC08_L2SP_039036_20140331_20200911_02_T1</code></td> </tr> </tbody> <tbody> <tr> <td>6a</td> <td><code>2014-04-14_18:27:14</code></td> <td><code>LC08_L2SP_041036_20140414_20200911_02_T1</code></td> </tr> <tr> <td>6b</td> <td><code>2014-04-14_18:27:14</code></td> <td><code>LC08_L2SP_041037_20140414_20200911_02_T1</code></td> </tr> </tbody> <tbody> <tr> <td>7</td> <td><code>2014-04-28_18:22:43</code></td> <td><code>LC08_L2SP_043035_20140428_20200911_02_T1</code></td> </tr> </tbody> <tbody> <tr> <td>8a</td> <td><code>2014-06-06_18:25:35</code></td> <td><code>LC08_L2SP_044033_20140606_20200911_02_T1</code></td> </tr> <tr> <td>8b</td> <td><code>2014-06-06_18:25:35</code></td> <td><code>LC08_L2SP_044034_20140606_20200911_02_T1</code></td> </tr> </tbody> <tbody> <tr> <td>9a</td> <td><code>2014-10-21_18:35:15</code></td> <td><code>LC08_L2SP_043034_20141021_20200910_02_T1</code></td> </tr> <tr> <td>9b</td> <td><code>2014-10-21_18:35:15</code></td> <td><code>LC08_L2SP_043035_20141021_20200911_02_T1</code></td> </tr> </tbody> <tbody> <tr> <td>10a</td> <td><code>2015-05-28_18:13:05</code></td> <td><code>LC08_L2SP_040036_20150528_20200909_02_T1</code></td> </tr> <tr> <td>10b</td> <td><code>2015-05-28_18:13:05</code></td> <td><code>LC08_L2SP_040037_20150528_20200909_02_T1</code></td> </tr> </tbody> <tbody> <tr> <td>11</td> <td><code>2018-06-19_18:28:30</code></td> <td><code>LC08_L2SP_042034_20180619_20200831_02_T1</code></td> </tr> </tbody> <tbody> <tr> <td>12a</td> <td><code>2021-03-30_18:32:40</code></td> <td><code>LC08_L2SP_043033_20210330_20210409_02_T1</code></td> </tr> <tr> <td>12b</td> <td><code>2021-03-30_18:32:40</code></td> <td><code>LC08_L2SP_043034_20210330_20210409_02_T1</code></td> </tr> </tbody> </table> <p>Variables of interest are resampled on a common UTM grid at 100m. The resulting datacubes are distributed as netCDF files, and contains the variables listed in table <a href="#org09b0cd2">2</a>. Landsat-8 pixels flagged as cloud and missing pixels are set to NaN.</p> <table> <caption>Table 2: Description of variables in netCDF files</caption> <thead> <tr> <th scope="col"><strong>Variable Name</strong></th> <th scope="col"><strong>Description</strong></th> </tr> </thead> <tbody> <tr> <td><code>ls8_lst</code></td> <td>Landsat-8 Land Surface Temperature (K)</td> </tr> <tr> <td><code>ls8_bt</code></td> <td>Landsat-8 Surface Brightness temperature (K)</td> </tr> <tr> <td><code>ls8_b2</code></td> <td>Landsat-8 B2 Surface reflectance (unitless)</td> </tr> <tr> <td><code>ls8_b3</code></td> <td>Landsat-8 B2 Surface reflectance (unitless)</td> </tr> <tr> <td><code>ls8_b4</code></td> <td>Landsat-8 B2 Surface reflectance (unitless)</td> </tr> <tr> <td><code>ls8_b5</code></td> <td>Landsat-8 B2 Surface reflectance (unitless)</td> </tr> <tr> <td><code>ls8_emis</code></td> <td>Landsat-8 emissivity (unitless)</td> </tr> <tr> <td><code>ls8_water</code></td> <td>Landsat-8 water mask (1 = water, 0 = no water)</td> </tr> <tr> <td><code>ls8_snow</code></td> <td>Landsat-8 snow mask (1 = snow, 0 = no snow)</td> </tr> <tr> <td><code>ls8_view_zenith</code></td> <td>Landsat-8 view zenith angle (degrees)</td> </tr> <tr> <td><code>ls8_view_azimuth</code></td> <td>Landsat-8 view azimuth angle (degrees)</td> </tr> <tr> <td> </td> <td>(0 = north, positive to the east, negative to the west)</td> </tr> <tr> <td><code>ls8_sun_zenith</code></td> <td>Landsat-8 sun zenith angle (degrees)</td> </tr> <tr> <td><code>ls8_sun_azimuth</code></td> <td>Landsat-8 sun azimuth angle (degrees)</td> </tr> <tr> <td> </td> <td>(0 = north, positive to the east, negative to the west)</td> </tr> </tbody> <tbody> <tr> <td><code>master_lst</code></td> <td>Master Land Surface Temperature (K)</td> </tr> <tr> <td><code>master_bt</code></td> <td>Master Surface Brightness Temperature (K)</td> </tr> <tr> <td><code>master_emis3</code></td> <td>Master B47 emissivity (unitless)</td> </tr> <tr> <td><code>master_emis4</code></td> <td>Master B48 emissivity (unitless)</td> </tr> <tr> <td><code>master_emis</code></td> <td>Master interpolated emissivity (unitless)</td> </tr> <tr> <td><code>master_view_zenith</code></td> <td>Master view zenith angle (degrees)</td> </tr> <tr> <td><code>master_view_azimuth</code></td> <td>Master view azimuth angle (degrees)</td> </tr> <tr> <td> </td> <td>(0 = north, positive to the east, negative to the west)</td> </tr> <tr> <td><code>master_sun_zenith</code></td> <td>Master sun zenith angle (degrees)</td> </tr> <tr> <td><code>master_sun_azimuth</code></td> <td>Master sun azimuth angle (degrees)</td> </tr> <tr> <td> </td> <td>(0 = north, positive to the east, negative to the west)</td> </tr> </tbody> </table> <p>Landsat-8 products were downloaded from the collection 2 level 2 archive from the EarthExplorer portal<sup><a href="#fn.2">2</a></sup>. Master L1B products, containing radiances and viewing angles, as well as L2 products, containing LST and geo-location grids, were requested on the Master website<sup><a href="#fn.1">1</a></sup>. Landsat-8 viewing angles have been computed by using a C program publicly available on USGS website<sup><a href="#fn.3">3</a></sup>.</p> <p>Footnotes:</p> <p><sup><a href="#fnr.1">1</a></sup></p> <p><a href="https://masterprojects.jpl.nasa.gov/">https://masterprojects.jpl.nasa.gov/</a>, consulted on 2023.03.01</p> <p><sup><a href="#fnr.2">2</a></sup></p> <p><a href="https://earthexplorer.usgs.gov/">https://earthexplorer.usgs.gov/</a>, consulted on 2023.03.01</p> <p><sup><a href="#fnr.3">3</a></sup></p> <p><a href="https://www.usgs.gov/landsat-missions/solar-illumination-and-sensor-viewing-angle-coefficient-file">https://www.usgs.gov/landsat-missions/solar-illumination-and-sensor-viewing-angle-coefficient-file</a>, consulted on 2022.09.12</p>
Inverse design of metal-organic frameworks for direct air capture of CO2 via deep reinforcement learning
<p>The combination of several interesting characteristics makes metal-organic frameworks (MOFs) a highly sought-after class of nanomaterials for a broad range of applications like gas storage and separation, catalysis, drug delivery, and so on. However, the ever-expanding and nearly infinite chemical space of MOFs makes it extremely challenging to identify the most optimal materials for a given application. In this work, we present a novel approach using deep reinforcement learning for the inverse design of MOFs, our motivation being designing promising materials for the important environmental application of direct air capture of CO2 (DAC). We demonstrate that the reinforcement learning framework can successfully design MOFs with critical characteristics important for DAC. Our top-performing structures populate two separate subspaces of the MOF chemical space: the subspace with high CO2 heat of adsorption and the subspace with preferential adsorption of CO2 from humid air, with few structures having both characteristics. Our model can thus serve as an essential tool for the rational design and discovery of materials for different target properties and applications.</p>
Direct and indirect effects of climate and land use change on food webs in lakes and streams
<p>Here, we provide the data and code necessary to reproduce the workflow and analysis in: Barbosa and Siqueira. Direct and indirect effects of climate and land use change on food webs in lakes and streams. A preprint is available at https://doi.org/10.1101/2022.04.18.488700</p> <p>We compiled multicontinental data to investigate how climate and land use change are related to the structure of freshwater food webs, considering the inherent differences in lentic and lotic ecosystems. We analyzed the direct and indirect relationships between land use intensity, and temperature and precipitation changes, and food webs using multi-group structural equation modeling. Freshwater food webs were obtained from three sources: the Mangal interaction database, using the rmangal package in R, the GlobAl databasE of traits and food Web Architecture (GATEWAY) version 1.0, and the Interaction Web Data Base (IWDB). We also included food webs acquired from a search in the Web of Science Core Collection. Land use data was compiled from the global ESA CCI database, an annually generated land cover product at 300 m resolution for the period 1992 – 2015. Climate data was compiled from the TerraClimate database, a monthly generated product for climate and climatic water balance for global terrestrial surfaces at ~ 4 km for the period 1958 – 2015. </p>
Direct observation of geometric phase in dynamics around a conical intersection
<p>The CSV files contain experimental and theoretical data corresponding to figures 3 and A1 of the paper "Direct observation of geometric phase in dynamics around a conical intersection", available at <a href="http://arxiv.org/abs/2211.07320">arxiv:2211.07320</a>. </p> <p>The contents of the files are described in README.txt.</p>
Datasets for "Placebo effects of transcranial direct current stimulation on motor skill acquisition"
<p>The following two .csv files contain the participant level data for the primary analyses conducted within the research study:</p> <p>"Placebo effects of transcranial direct current stimulation on motor skill acquisition"</p> <p>Data are formatted in long format for ease of analysis</p> <p>Dataset used in first analysis - Estimation of TDCS effect and Placebo effect including a NO TDCS control group</p> <p>ALLGROUPS.csv</p> <p>subid = Participant specific identifier<br> Age = Participant age in years<br> Sex = Participant sex (M/F)<br> RASex = Sex of research assistant that conducted the study for the participant<br> TrialNum = Trial number for the reaching task<br> Performance = Total trial time of the trial in seconds<br> AssignGrp = Group participant was assigned: Active = Active TDCS, Sham = Sham TDCS, Ctrl = No TDCS</p> <p>Dataset used in second analysis - Estimation of expectancy effects on Performance among TDCS groups ONLY</p> <p>TDCSGroupsONLY.csv</p> <p>subid = Participant specific identifier<br> Age = Participant age in years<br> Sex = Participant sex (M/F)<br> RASex = Sex of research assistant that conducted the study for the participant<br> TrialNum = Trial number for the reaching task<br> Performance = Total trial time of the trial in seconds<br> AssignGrp = Group participant was assigned: Active = Active TDCS, Sham = Sham TDCS, Ctrl = No TDCS<br> PostExp = Expectancy score post practice<br> PreExp = Expectancy score pre practice<br> Suggestibility = Suggestibility score<br> Prior Know = Prior knowledge of TDCS (Yes/No)<br> Prior Study = Participation in a study using TDCS (Yes/No)</p>
Data and scripts for reproducing "Quantifying Uncertainties in Direct Numerical Simulations of a Turbulent Channel Flow"
<p>This is the accompanying data and Python scripts to reproduce the figures in "Quantifying Uncertainties in Direct Numerical Simulations of a Turbulent Channel Flow", currently under review.</p>
Platinum-Iron(II) Oxide Sites Directly Responsible for Preferential Carbon Monoxide Oxidation at Ambient Temperature: An Operando X-ray Absorption Spectroscopy Study
<p>Open data for "Platinum-Iron(II) Oxide Sites Directly Responsible for Preferential Carbon Monoxide Oxidation at Ambient Temperature: An Operando X-ray Absorption Spectroscopy Study" Angew. Chem.Int. Ed. 2023,62, e202214032(1 of 11) <a href="https://doi.org/10.1002/anie.202214032">https://doi.org/10.1002/anie.202214032</a></p>
Exposure fusion applied to enable wider-angle transmission Kikuchi diffraction with direct electron detectors
<p>Raw dataset for "<strong>Exposure fusion applied to enable wider-angle transmission Kikuchi diffraction with direct electron detectors</strong>" by T.Zhang, T.B.Britton.</p> <ul> <li>ArXiv: https://doi.org/10.48550/arXiv.2306.14167</li> </ul> <p>An excel file with metadata of the patterns is included. </p> <p> </p> <p>Details will be updated after acceptance.</p> <p>Processing with the proposed methodology in the paper above requires the AstroEBSD toolbox in MATLAB. This is available on GitHub at https://zenodo.org/record/8078806</p>
Airborne Infrasound Data from The AtmoSOFAR Channel: First Direct Observations of an Elevated Acoustic Duct
<p>Airborne infrasound data including waveform recordings from two payloads attached to a single 6 m heliotrope that was launched at dawn (~0700 local) out of Belen Regional Airport, NM, USA. Balloon trajectory is also included. This data accompanies the publication titled, "The AtmoSOFAR Channel: First Direct Observations of an Elevated Acoustic Duct" submitted to Earth & Space Science.</p>
Seed dispersal data for Warneke et al "Habitat fragmentation alters the distance of abiotic seed dispersal through edge effects and direction of dispersal"
This csv file contains seed dispersal data for five species (Carphephorus bellidifolius, Aristida beyrichiana, Liatris squarrulosa, Sorghastrum secundum, and Anthenantia villosa). Data were collected at the Savannah River Site, near Aiken, South Carolina, United States. Data were collected between November 17, 2009, to January 22, 2010 and were collected using the methods outlined in this document.
Summary of measured and modeled light curve parameters for diffuse, direct, and intermediate light curves for 14 whole-canopy 1mx1m plots sampled near the shrub LTER sites at Toolik Field Station, Alaska, summer 2012.
14 1m x 1m shrub plots were sampled the summer of 2012 under direct and diffuse light conditions. Light response curves were measured under each light condition for each plot using a Li-Cor 6400 to measure net ecosystem exchange (NEP); these measurements were modelled using a saturatingMichaelis-Menton formula. The best fit parameters for those models are contained here (Pmax, K, RE, Eo, and light compensation point) for each individual NEP light response curve (direct, diffuse, and intermediate light conditions) measured with corresponding NDVI , LAI, diffuse light fraction, and average temperature. Sorting variables and curve ID numbers for each curve match the corresponding data in the flux data file.
Flow direction grid at 1 kilometer resolution for North Slope drainage basins, Alaska
We derived and evaluated a 1 kilometer spatial resolution flow direction grid for the terrestrial drainage of the North Slope of Alaska. The region is resolved by 182,722 grid cells and the associated connectivity. It is bounded by the Brooks Range and Beaufort Sea coast, and extends from the northern Chukchi Sea coast eastward to the small rivers near 140 degrees West. The dataset is provided in raster and tabular format, with the latter including coordinates, river basin identifier, and downstream reach and direction for each grid cell in the region. Over three dozen river basins are identified by name in an associated lookup table. This new mapping resolves the terrestrial drainages for rivers exporting freshwater, nutrients, and other materials to Elson, Simpson, Jago, Kaktovik, and other coastal lagoons at a resolution that captures important processes linked to surface and subsurface hydrological flows. Our analysis suggests that the mapping exhibits notable similarity in basin area boundaries relative to the benchmark USGS National Hydrography Dataset.
List of insects in the Natura 2000 site "Fiumi Giardino-Aterno-Sagittario, Riserva Sorgenti del Pescara", with focus on protected species under the Habitats Directive
<p>The dataset contains records of insects surveyed in the Special Area of Conservation "Fiumi Giardino-Aterno-Sagittario, Riserva Sorgenti del Pescara" (IT7110097), Abruzzo Region, Italy. The species were collected during a field campaign aimed at updating the information about the presence of species protected under the Habitats Directive for the purposes of the site management plan.</p> <p>The survey was carried out in 2013 with different sampling methods: visual inspection (Odonata and Lepidoptera), entomological net (Odonata and Lepidoptera), black cross window traps baited with specific pheromone (<em>Osmoderma eremita</em>).</p> <p>The study area was divided into 5 sampling areas which corresponds to different habitat types: 1. Sorgenti del Pescara (river spring), 2. Parco Fluviale Giardino (urban park with old trees), 3. Orti di Popoli (agricultural landscape), 4. Fiume Aterno (river), 5. Fiume Sagittario (river). Among them, a total of 11 sampling sites were identified.</p> <p>The target taxa were: Odonata, Lepidoptera and <em>Osmoderma eremita</em> (Coleoptera). The information collected on <em>Osmoderma eremita</em> were included in the paper by Giangregorio et al. (2015), a study on the presence of this species in the Abruzzo Region.</p> <p>The dataset contains 37 species, the taxonomy follows Fauna europaea (https://fauna-eu.org/).</p>
Data and R code from: Pollination interactions reveal direct costs and indirect benefits of plant–plant facilitation for ecosystem engineers
Ecosystem engineers substantially modify the environment via their impact on abiotic conditions and the biota, resulting in facilitation of associated species that would not otherwise grow. Yet, reciprocal effects are poorly understood as studies of plant–plant interactions usually estimate only benefits for associated species while hardly considering how another trophic level may mediate direct and indirect effects for ecosystem engineers. We run a field experiment with ecosystem engineers blooming either alone or with associated plants to decompose net effects and to test the hypothesis that pollinator-mediated interactions provide benefits which balance costs of facilitation by ecosystem engineers. We found that net costs of facilitation are accompanied by pollinator-mediated benefits. Despite ecosystem engineers producing less flowers per plant, they were visited by more and more diverse pollinators per flower when blooming with associated plants than when blooming alone. However, fruit set was unaffected by the presence of associated plants and seed production per plant was higher when ecosystem engineers bloomed alone. Our findings suggest that besides experiencing direct costs, ecosystem engineers can also benefit from facilitating other species via increasing their own visibility to pollinators. This study illuminates how the outcome of direct plant–plant interactions might be mediated by indirect interactions including third players.
Raw data for "Examining the effects of transcranial direct current stimulation on human episodic memory with machine learning"
<p>This is the raw dataset for "Examining the effects of transcranial direct current stimulation on human episodic memory with machine learning". Each .xlsx file represents an experimental results of a single participant.</p> <p>Directory description:</p> <p>Eng_sham - the results from experiments without stimulation on English sample from Medvedeva, 2019.</p> <p>Eng_vlPFC - the results from experiments with vLPFC stimulation on English sample from Medvedeva, 2019.</p> <p>enc_off_new - the results from experiments with dLPFC offline encoding stimulation on Russian sample.</p> <p>enc_on_new - the results from experiments with dLPFC online encoding stimulation on Russian sample.</p> <p>sham_no stimulation - the results from experiments without stimulation offline encoding stimulation on Russian sample.</p> <p>vlPFC_stimulation - the results from experiments with vLPFC stimulation on Russian sample.</p> <p>Age.xlsx - the ages of the participants</p> <p> </p>
Variation of texture anisotropy and hardness with build parameters and wall height in directed-energy-deposited 316L steel
<p>Raw data associated with a paper submission.<br> " Variation of texture anisotropy and hardness with build parameters and wall height in directed-energy-deposited 316L steel" submitted to Additive Manufacturing.</p> <p>Contained are all the raw images used in figures, as well as csv's of any data pltoted in graphs.</p> <p>Raw images captured during printing of various processing parameters<br> EBSD scans (.ctf) of all disucssed samples </p> <p>Wall definitions (EBSD compared to paper)<br> Wall 1 - Wall A1 300 W 2750 mm/s<br> Wall 2 - Wall D 500 W 2250 mm/s<br> Wall 3 - Wall B 300 W 2250 mm/s<br> Wall 4 - Wall C 500 W 2750 mm/s<br> Wall 5 - Wall A2 300 W 2750 mm/s</p>
Dataset for English Health-Related Advice Directed to the General Public on Twitter During the Early Spread of COVID-19 [Dataset]
<p>Health-related advice directed to the public on twitterprovides insight into the use of social media duringa pandemic. This paper describes our data collection, sampling, and analysis of 44 million tweets in English in March 2020. We make reference to a parallel dataset and analysis of tweets in Arabic during thesame period. The contribution of this paper is a description of our dataset, our coding process to indicate tweets with health related advice, and our analysis and comparisons of the characteristics of the tweets with and without health-related advice. These contributions providethe basis for future research on semi-automated classifiers for health-related advice and efforts to reduce thespread of harmful health advice.</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.