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4,479 results for “hybridization”
Dataset for Accuracy of Grid-Connected Photovoltaic Power Plant: A Novel Approach Using Hybrid Variational Mode Decomposition and CNN-LSTM Model
<p>This research paper introduces a deep learning hybrid model employing Convolutional Neural Network Long Short-Term Memory (CNN-LSTM) for short-term photovoltaic (PV) solar energy forecasting.The proposed method integrates the Variational Mode Decomposition (VMD) algo-rithm with the CNN-LSTM model to predict PV power generation from a solar farm in Boussada, Algeria, from January 1, 2019, to December 31, 2020. The performance of the developed model is benchmarked against other deep learning models (VMD-CNN, VMD-LSTM, CNN-LSTM) across various time horizons (15, 30, and 60 minutes) to provide a comprehensive evaluation. Our findings exhibit greater performance of the developed model compared to other architectures, showcasing promising results in solar power forecasting. This research contributes to the main goal of enhancing EMS by providing accurate solar energy forecasts.</p>
Growth parameters and resistance to Sphaerulina musiva-induced canker are more important than wood density for increasing genetic gain from selection of Populus spp. hybrids for northern climates
<p>The data was collected from a common garden genetics trial established in 2008 in northern Alberta, Canada. The trial represents 1978 (initial number) hybrid poplar clones from 63 families and includes interspecific crosses between <em>Populus deltoides</em> (D), <em>Populus nigra</em> (N), <em>Populus balsamifera</em> (B), <em>P. maximowiczii</em> (M), and <em>P. × petrowskyana</em> (<em>P. laurifolia</em> × <em>P. nigra</em>). Female clone 24 (‘Walker’ = (<em>Populus deltoides </em>× (<em>P. laurifolia × P. nigra</em>))) and male progeny clone 2403 (‘Okanese’ = (‘Walker’ × (<em>P. laurifolia × P. nigra</em>))) were used as reference clones. The study design was a randomized complete block design, with one ramet per clone in each of four blocks. Measurements were carried out after three, eight, and 10 growing seasons on the genetics trial. Results presented in ‘HybridPoplarsTrial.csv’ file, show is the raw data, while ‘Summary data.csv’ contains the mean values for clones obtained from the four blocks. Measured and calculated traits include: DBH (diameter at breast height; 1.3 m); H (height); canker (canker severity caused by <em>Sphaerulina musiva</em> (scale 0-3)); MAI (mean annual increment), V (volume).</p> <p>Description of headings:</p> <p>Trait [unit] - Description</p> <p>DBH_Age_3 [cm] - diameter at breast height at age 3</p> <p>H_Age_3 [m] - height at age 3</p> <p>DBH_Age_8 [cm] - diameter at breast height at age 8</p> <p>H_Age_8 [m] - height at age 8</p> <p>H_Age_10 [m] - height at age 10</p> <p>DBH_Age_10 [cm] - diameter at breast height at age 10</p> <p>Canker_Age_8 - canker severity caused by <em>Sphaerulina musiva</em> (scale 0-3)</p> <p>Canker_Age_10 - canker severity caused by <em>Sphaerulina musiva</em> (scale 0-3)</p> <p>V_Age_8 [m<sup>3</sup> ha<sup>-1</sup>] - volume at age 8</p> <p>MAI_Age_8 [m<sup>3</sup> ha<sup>-1</sup> yr<sup>-1</sup>] - mean annual increment at age 8</p> <p>V_Age_10 [m<sup>3</sup> ha<sup>-1</sup>] - volume at age 10</p> <p>MAI_Age_10 [m<sup>3</sup> ha<sup>-1</sup> yr<sup>-1</sup>] - mean annual increment at age 10</p> <p>WD_Age_10 [kg m<sup>-3</sup>] - wood density at age 10</p> <p> </p>
Hybrid gridded demographic data for the world, 1950-2020
<p>This is a hybrid gridded dataset of demographic data for the world, given as 5-year population bands at a 0.5 degree grid resolution.</p> <p>This dataset combines the NASA SEDAC Gridded Population of the World version 4 (GPWv4) with the ISIMIP Histsoc gridded population data and the United Nations World Population Program (WPP) demographic modelling data.</p> <p>Demographic fractions are given for the time period covered by the UN WPP model (1950-2050) while demographic totals are given for the time period covered by the combination of GPWv4 and Histsoc (1950-2020)</p> <p><strong>Method - demographic fractions</strong></p> <p>Demographic breakdown of country population by grid cell is calculated by combining the GPWv4 demographic data given for 2010 with the yearly country breakdowns from the UN WPP. This combines the spatial distribution of demographics from GPWv4 with the temporal trends from the UN WPP. This makes it possible to calculate exposure trends from 1980 to the present day.</p> <p>To combine the UN WPP demographics with the GPWv4 demographics, we calculate for each country the proportional change in fraction of demographic in each age band relative to 2010 as:</p> <p><span class="math-tex">\(\delta_{year,\ country,age}^{\text{wpp}} = f_{year,\ country,age}^{\text{wpp}}/f_{2010,country,age}^{\text{wpp}}\)</span></p> <p> </p> <p>Where:</p> <p>- <span class="math-tex">\(\delta_{year,\ country,age}^{\text{wpp}}\)</span> is the ratio of change in demographic for a given age and and country from the UN WPP dataset.</p> <p>- <span class="math-tex">\(f_{year,\ country,age}^{\text{wpp}}\)</span> is the fraction of population in the UN WPP dataset for a given age band, country, and year.</p> <p>- <span class="math-tex">\(f_{2010,country,age}^{\text{wpp}}\)</span> is the fraction of population in the UN WPP dataset for a given age band, country for the year 2020.</p> <p> </p> <p>The gridded demographic fraction is then calculated relative to the 2010 demographic data given by GPWv4.</p> <p>For each subset of cells corresponding to a given country <em>c</em>, the fraction of population in a given age band is calculated as:</p> <p><span class="math-tex">\(f_{year,c,age}^{\text{gpw}} = \delta_{year,\ country,age}^{\text{wpp}}*f_{2010,c,\text{age}}^{\text{gpw}}\)</span></p> <p>Where:</p> <p>- <span class="math-tex">\(f_{year,c,age}^{\text{gpw}}\)</span> is the fraction of the population in a given age band for given year, for the grid cell <em>c</em>.</p> <p>- <span class="math-tex">\(f_{2010,c,age}^{\text{gpw}}\)</span> is the fraction of the population in a given age band for 2010, for the grid cell <em>c</em>.</p> <p>The matching between grid cells and country codes is performed using the GPWv4 gridded country code lookup data and country name lookup table. The final dataset is assembled by combining the cells from all countries into a single gridded time series. This time series covers the whole period from 1950-2050, corresponding to the data available in the UN WPP model.</p> <p> </p> <p><strong>Method - demographic totals</strong></p> <p>Total population data from 1950 to 1999 is drawn from ISIMIP Histsoc, while data from 2000-2020 is drawn from GPWv4. These two gridded time series are simply joined at the cut-over date to give a single dataset covering 1950-2020.</p> <p>The total population per age band per cell is calculated by multiplying the population fractions by the population totals per grid cell.</p> <p>Note that as the total population data only covers until 2020, the time span covered by the demographic population totals data is 1950-2020 (not 1950-2050).</p> <p> </p> <p><strong>Disclaimer</strong></p> <p>This dataset is a hybrid of different datasets with independent methodologies. No guarantees are made about the spatial or temporal consistency across dataset boundaries. The dataset may contain outlier points (e.g single cells with demographic fractions >1). This dataset is produced on a 'best effort' basis and has been found to be broadly consistent with other approaches, but may contain inconsistencies which not been identified.</p> <p> </p>
TMY hourly generation profiles for Insolight hybrid Si/III-V planar micro-tracking modules in Madrid
<p>Hourly energy density (1 m<sup>2</sup>) generation profiles for Insolight hybrid Si/III-V planar micro-tracking modules installed in Madrid (40.5°N, -3.75°E), synthetically generated using <a href="https://github.com/isi-ies-group/cpvlib">CPVLIB library</a> (based on <a href="https://pvlib-python.readthedocs.io/en/stable/">PVLIB Python</a>) and ERA5 typical meteorological year. Performance model parameters were empirically fitted using several outdoor monitoring campaigns and indoor characterization at the <a href="https://www.ies.upm.es/Investigacion/Research_Lines/Concentrator_photovoltaics/CPV_characterization">collimated-light solar simulator</a> available at IES-UPM.</p> <p><strong>Location</strong>: 40.5°N, -3.75°E</p> <p><strong>Format</strong>: CSV (separator: semicolon); headers in first row.</p> <p><strong>Parameters </strong>(ordered from first column): </p> <ul> <li>Time: YYYY-MM-DD HH:MM:SS+TimeZoneOffset</li> <li>Latitude: latitude of the installation in °N</li> <li>Longitude: longitude of the installation in °E</li> <li>Wind speed [m/s]: average wind speed</li> <li>Tair [°C]: average ambient temperature</li> <li>precipitable_water [mm]: average precipitable water in the atmosphere</li> <li>GHI [Wh/m2]: global horizontal irradiation</li> <li>DHI [Wh/m2]: diffuse horizontal irradiation</li> <li>DNI [Wh/m2]: direct (beam) normal irradiation</li> <li>CPV submodule [kWh/m2]: energy generated per m<sup>2</sup> by the III-V CPV submodule</li> <li>Flat-plate submodule [kWh/m2]: energy generated per m<sup>2</sup> by the Si flat-plate submodule</li> <li>Hybrid [kWh/m2]: energy generated per m<sup>2</sup> by the whole Insolight hybrid module</li> </ul>
Strong sequence dependence in RNA/DNA hybrid strand displacement kinetics supplementary data and code
<p>Supplementary data and code needed to replicate figures and results for the paper: Strong sequence-dependence in RNA/DNA hybrid strand displacement kinetics - Francesca G. Smith, John P. Goertz, Molly M. Stevens and Thomas E. Ouldridge. README is included to explain each folder and file in the repository.</p>
EXIOBASE HYBRID v3 - 2011
<p>The hybrid version of EXIOBASE, which is part of wider input-output database , is a multi-regional supply and use table. Here the term hybrid indicates that physical flows are accounted in mass units, energy flows in TJ and services in millions of euro (current prices).</p> <p><strong>EXIOBASE 3 </strong>provides a time series of environmentally extended multi-regional input‐output (EE MRIO) tables ranging from 1995 to a recent year for 44 countries (28 EU member plus 16 major economies) and five rest of the world regions. EXIOBASE 3 builds upon the previous versions of EXIOBASE by using rectangular supply‐use tables (SUT) in a 163 industry by 200 products classification as the main building blocks. The tables are provided in current, basic prices (Million EUR).</p> <p>EXIOBASE 3 is the culmination of work in the <a href="http://fp7desire.eu/">FP7 DESIRE project</a> and builds upon earlier work on EXIOBASE 2 in the <a href="http://www.creea.eu/">FP7 CREEA</a> project, EXIOBASE 1 of the <a href="http://www.feem-project.net/exiopol/">FP6 EXIOPOL project</a> and FORWAST project. </p> <p>A <a href="https://onlinelibrary.wiley.com/toc/15309290/2018/22/3">special issue of Journal of Industrial Ecology (Volume 22, Issue 3)</a> describes the build process and some use cases of EXIOBASE 3. </p>
Hybrid Metrology for Nanostructured Optical Metasurfaces - Dataset
<p>This is the dataset of "Hybrid Metrology for Nanostructured Optical Metasurfaces", https://doi.org/10.1021/acsami.3c13923. </p>
Merging Morphological and Genetic Evidence to assess hybridization in Eurasian Late Pleistocene hominins
<pre>Previous scientific consensus saw human evolution as defined by adaptive differences (behavioural and/or biological) and the emergence of Homo sapiens as the ultimate replacement of non-modern groups by a modern, adaptively more competitive one. However, recent research has shown that the process underlying our origins was considerably more complex. While archaeological and fossil evidence suggests that behavioural complexity may not be confined to the modern human lineage, recent paleogenomic work shows that gene flow between distinct lineages (e.g., Neanderthals, Denisovans, early H. sapiens) occurred repeatedly in the Late Pleistocene, likely contributing elements to our genetic make-up that might have been crucial to our success as a diverse, adaptable species. Following these advances, the prevailing human origins model has shifted from one of near-complete replacement to a more nuanced view of partial replacement with considerable reticulation. Here we provide a brief introduction to the current genetic evidence for hybridization among hominins, its prevalence in, and effects on, comparative mammal groups, and especially how it manifests in the skull. We then explore the degree to which cranial variation seen in the fossil record of Late Pleistocene hominins from Western Eurasia corresponds with our current genetic and comparative data. We are especially interested in understanding the degree to which skeletal data can reflect admixture. Our findings indicate some correspondence between these different lines of evidence, flag individual fossils as possibly admixed, and suggest that different cranial regions may preserve hybridisation signals differentially. We urge further studies of the phenotype in order to expand our ability to detect the ways in which migration, interaction and genetic exchange have shaped the human past, beyond what is currently visible with the lens of ancient DNA. </pre>
Annual Article Processing Charges (APCs) and number of gold and hybrid open access articles in Web of Science indexed journals published by Elsevier, Sage, Springer-Nature, Taylor & Francis and Wiley 2015-2018
<p><strong>Dataset of annual Article Processing Charges (APCs) for 6,252 journals from 2015 to 2018. </strong>The dataset contains annual APCs for journals indexed in the Web of Science (WoS) and published by the oligopoly of academic publishers (Elsevier, Sage, Springer-Nature, Taylor & Francis, Wiley). It also includes an estimate of the total APCs paid by the academic community based on the number of gold and hybrid articles published between 2015 and 2018. The dataset was created using publication data from WoS, OA status from Unpaywall and annual APC prices from open datasets (<a href="https://doi.org/10.5281/ZENODO.3841568">Matthias, 2020</a>; <a href="https://doi.org/10.5683/SP2/84PNSG">Morrison, 2021</a>) and historical fees retrieved via the Internet Archive Wayback Machine. </p> <p>Detailed methods and findings are reported in the following journal article</p> <p>Butler, L.-A., Matthias, L., Simard, M.-A., Mongeon, P., & Haustein, S. (2023). The Oligopoly's Shift to Open Access. How the Big Five Academic Publishers Profit from Article Processing Charges. <em>Quantitative Science Studies</em>. Preprint: <a href="https://doi.org/10.5281/zenodo.8322555">https://doi.org/10.5281/zenodo.8322555</a></p> <p><strong>Description of included files (v1):</strong></p> <p><em>APCs.csv: </em>contains the annual APCs for gold and hybrid OA journals indexed in Web of Science published by the oligopoly of academic publishers (Elsevier, Sage, Springer-Nature, Taylor & Francis, Wiley) between 2015 and 2018 including the total estimate of APCs paid per journal per year. It contains APC data for 18,846 journal-year-OA status combinations.</p> <p><em>countries.csv</em>: contains the fractionalized number of annual gold and hybrid OA articles by oligopoly publishers between 2015 and 2018 and the total estimate of fractionalized APCs paid per country per journal per year.</p> <p><em>oecd.csv</em>: contains the fractionalized number of annual gold and hybrid OA articles by oligopoly publishers between 2015 and 2018 and the total estimate of fractionalized APCs per discipline per journal per year.</p> <p><em>ReadMe.csv</em>: contains a description of the variables used in <em>APCs.csv</em>, <em>countries.csv</em> and <em>oecd.csv</em>.</p> <p> </p>
MicroCT scans of a hybrid poplar leaf dehydrating, with annotated slices for model training
<p>Dataset of a leaf segment of a hybrid poplar (<em>P. maximowiczii x P. nigra</em> ‘Max3’) leaf scanned using microcomputed tomography (microCT) over time as it dehydrates.</p> <p> </p> <p><strong>Data acquisition methodology</strong></p> <p>Plants were brought to the TOMCAT tomographic beamline of the Swiss Light Source at the Paul Scherrer Institute (Villigen, Switzerland). Before microCT scanning, a young fully expanded leaf was detached from the plant and a short strip (0.4 x 1.5 cm) was cut between second-order veins. The base of the strip was wrapped in polyimide tape and inserted into a styrofoam block fixed on a sample holder. The strip was immediately scanned by imaging 1801 projections of 100 ms under a beam energy of 21 keV and a magnification of 40x, yielding a final voxel size of 0.1625 µm (field of view: ~416x416x312 µm). The leaf was left to dehydrate in the holder and additional scans were taken 10, 20, 25, and 30 minutes after the initial scan. Scanned projections were reconstructed to a transverse view using both absorption (gridrec; Marone <em>et al.</em> 2012) and phase contrast enhancement (Paganin <em>et al.</em> 2002) reconstruction.</p> <p> </p> <p><strong>Dataset description</strong></p> <p>On the reconstructed images a region of interest was identified using a paradermal view (i.e. top to bottom of the leaf) and used to manually align the scans of each time step. Thereafter, all images were cropped to that ROI, ensuring that the same region of the leaf was present in all image stacks.</p> <p>For all stacks, files start with:<br> <em>DEHYDRATION_small_Leaf4_time_N_</em><br> where N is the time point, with values from 1 to 5 equaling 0, 10, 20, 25, and 30 minutes.</p> <p>Following this prefix is either GRID (gridrec reconstruction), PAGANIN (phase contrast enhancement reconstruction), or LABELLED (hand labelled slices or ground truth). For GRID and PAGANIN, 8-bit grayscale stacks are provided. The AOI suffix indicates the region of interest.</p> <p>Stacks have been hand labelled over three orientations (for visual examples of the orientations see <a href="https://zenodo.org/api/files/6f06d15b-3ee9-412d-82ca-a20336c4bffa/Labeled_Sections_order_time1.png?versionId=26fc15aa-702e-4052-b162-702cc567634c">Labeled_Sections_order_time1.png</a> and <a href="https://zenodo.org/api/files/6f06d15b-3ee9-412d-82ca-a20336c4bffa/Labeled_Sections_order_time2.png">Labeled_Sections_order_time2.png</a>):</p> <ol> <li>CROSS (cross sectional, or transverse, view)</li> <li>LONGI (longitudinal view: similar to cross sectional view but starting normal to it, i.e. along the depth of the stack starting from the left of the cross-sectional view)</li> <li>PARADERMAL (top to bottom view: starting at the upper epidermis)</li> </ol> <p>A general idea of the slice range within one LABELLED stack is presented after the orientation, as:<br> <em>STARTtoENDbyRANGE</em><br> The exact position of the labelled slices for each time point can be found in the <a href="https://zenodo.org/api/files/6f06d15b-3ee9-412d-82ca-a20336c4bffa/Labeled_slices_positions.txt?versionId=93d7e22f-9f07-4f98-8c49-d93e9a2e1ce5">Labeled_slices_positions.txt </a>file. <strong>Note that one-based indexing is used (as in ImageJ), not zero-based indexing (as in e.g. Python).</strong></p> <p> </p> <p><strong>References</strong></p> <p>Marone F, Stampanoni M. 2012. Regridding reconstruction algorithm for realtime tomographic imaging. Journal of Synchrotron Radiation 19: 1029–1037.</p> <p>Paganin D, Mayo SC, Gureyev TE, Miller PR, Wilkins SW. 2002. Simultaneous phase and amplitude extraction from a single defocused image of a homogeneous object. Journal of Microscopy 206: 33–40.</p>
Short-term traffic flow prediction based on secondary hybrid decomposition and deep echo state networks
<p>The publication titled "Short-term traffic flow prediction based on secondary hybrid decomposition and deep echo state networks" is supported by the STRIDE K3 project. The dataset used in the publication is uploaded here.</p>
Mixed ionic-electronic conduction in Ruddlesden-Popper and Dion-Jacobson layered hybrid perovskites with aromatic organic spacers
<p>Characterisation dataset for “Mixed ionic-electronic conduction in Ruddlesden-Popper and Dion-Jacobson layered hybrid perovskites with aromatic organic spacers”, DOI:10.1039/d4tc01010h. Data provided as *.xlsx, *.csv, *tif and *.png files.</p> <p> </p> <p> </p> <p> </p>
data for Tunable magnonic crystal in a hybrid superconductor–ferrimagnet nanostructure
<h3><strong>General description</strong></h3> <p>The data set designed for the reconstruction of the graphs identified in the manuscript as Fig. 2, Fig. 4 and Fig. 6 has been compiled. The data were respectively described as <strong>calculations</strong> relating to the method of obtaining the data (Brandt's method then plane wave method - PWM) and <strong>simulations</strong> (finite element method - FEM). </p> <h3><strong>Fig. 2</strong></h3> <p>In the case of Fig. 2(a-f), the data marked with a black solid line have been included. The data set includes information on the (x,y) components of the magnetic field induction generated by the superconductor (expressed in millitesla) in the x-direction (expressed in metres). </p> <h3><strong>Fig. 4</strong></h3> <p>The data set for each subsection of Fig. 4 comprises the results of the simulations and calculations. The resulting data from the simulations are classified according to their respective modes. For each mod, the wave vector (expressed as part of the first Brillouin zone) and frequency (GHz) are determined. The data sets resulting from the semi-analytical calculations comprise the common axis (1st column) of the wave vector (expressed as part of the Brillouin zone) followed by columns containing frequency for each mode (GHz).</p> <p> </p> <h3><strong>Fig. 6</strong></h3> <p>The data pertaining to the subsections of Fig. 6(a) and (b) are stored in a separate set of files. The data set for Fig. 6a comprises frequencies that indicate the boundaries of bands in relation to the external magnetic field (B_{0}). Each column has been assigned a number. The numbering is from the lowest frequencies to the highest for k_{x}=0. Each line is comprised of two columns, the first of which describes the magnetic field induction (expressed in millitesla) and the second of which describes the frequency (expressed in GHz). <br>Subsection (b) contains the dependence of the band boundaries (frequencies) on the separation between superconductors (d). The lines are also numbered from lowest frequency to highest for k_{x}=0. Each line is described by two columns, named by a number. The first column is the separation (expressed in nanometres), and the second is the frequency (expressed in GHz). </p>
Data from: "Damage deflection and subsequent damage diffusion in carbon-boron fibre hybrid composites under longitudinal compression"
<p>The datset contains raw data used for the work presented in the journal paper "Damage deflection and subsequent damage diffusion in carbon-boron fibre hybrid composites under longitudinal compression".<br>Specifically, it contains machine recorded data and video recordings (either SEM or with optical microscope) of the compression tests on small scale single edge notched specimens made of IM7/8552 (carbon/epoxy) and HyBor 52 FPI (carbon-boron fibre hybrid composite). It also contains specimens pictures taken during and after the tests (including SEM and optical micrographs).</p> <p>For more details, please refer to the full paper.</p>
Variant Data from Pooled Sequencing of Hybrid Kiwifruit
<p>Variant data from pooled sequencing of hybrid <em>Actinidia</em> families segregating for fruit size and Vitamin C Content.</p>
Hybrid Approaches to Detect Comments Violating Macro Norms on Reddit
<p>[<strong>Content warning: </strong><em>Files may contain instances of highly inflammatory and offensive content.]</em></p> <p><br> This dataset was generated as an extension of our <a href="https://www.cc.gatech.edu/~eshwar3/uploads/3/8/0/4/38043045/eshwar-norms-cscw2018.pdf">CSCW 2018 paper</a>:</p> <p><em>Eshwar Chandrasekharan, Mattia Samory, Shagun Jhaver, Hunter Charvat, Amy Bruckman, Cliff Lampe, Jacob Eisenstein, and Eric Gilbert. 2018. The Internet’s Hidden Rules: An Empirical Study of Reddit Norm Violations at Micro, Meso, and Macro Scales. Proceedings of the ACM on Human-Computer Interaction 2, CSCW (2018), 32.</em></p> <p><strong>Description:</strong></p> <p>Working with over 2M removed comments collected from 100 different communities on Reddit (subreddit names listed in data/study-subreddits.csv), we identified <strong>8 macro norms</strong>, i.e., norms that are widely enforced on most parts of Reddit. We extracted these macro norms by employing a hybrid approach—classification, topic modeling, and open-coding—on comments identified to be norm violations within at least 85 out of the 100 study subreddits. Finally, we labelled over 40K Reddit comments removed by moderators according to the specific type of macro norm being violated, and make this dataset publicly available (also available on <a href="https://github.com/ceshwar/reddit-norm-violations">Github</a>).</p> <p>For each of the labeled topics, we identified the top 5000 removed comments that were best fit by the LDA topic model. In this way, we identified over 5000 removed comments that are examples of each type of macro norm violation described in the paper. The removed comments were sorted by their topic fit, stored into respective files based on the type of norm violation they represent, and are made available on this repo.</p> <p>Here we make the following datasets publicly available:</p> <p>* <strong>1 file</strong> containing the log of over 2M removed comments obtained from the top 100 subreddits between May 2016 to March 2017, after filtering out the following comments: 1) comments by u/AutoModerator, 2) replies to removed comments (i.e., children of the poisoned tree - refer to the paper for more information), and 3) non-readable comments (not utf-8 encoded).</p> <p>* <strong>8 files</strong>, each containing 5000+ removed comments obtained from Reddit, are stored in: data/macro-norm-violations/ , and they are split into different files based on the macro norm they violated. Each new line in the files represent a comment that was posted on Reddit between May 2016 to March 2017, and subsequently removed by subreddit moderators for violating community norms. All comments were preprocessed using the script in code/preprocessing-reddit-comments.py , in order to do the following: 1. remove new lines, 2. convert text to lowercase, and 3. strip numbers and punctuations from comments.</p> <p><strong>Description of 1 file</strong> containing over<em> 2M removed comments </em>from <em>100 subreddits.</em></p> <ul> <li>"reddit-removal-log.csv" - all comments that were removed from the 100 study subreddits during the study period described above (post-filtering).</li> </ul> <p><strong>Descriptions of each file</strong> containing <em>5059 comments</em> (that were removed from Reddit, and preprocessed)<strong> violating macro norms </strong>present in data/macro-norm-violations/:</p> <ul> <li>"macro-norm-violations-n10-t0-misogynistic-slurs.csv" - Comments that use misogynistic slurs.</li> <li>"macro-norm-violations-n15-t2-hatespeech-racist-homophobic.csv" - Comments containing hate speech that is racist or homophobic.</li> <li>"macro-norm-violations-n10-t3-opposing-political-views-trump.csv", "macro-norm-violations-n15-t10-opposing-political-views-trump.csv" - Comments with opposing political views around Trump (depends on originating sub).</li> <li>"macro-norm-violations-n10-t4-verbal-attacks-on-Reddit.csv" - Comments containing verbal attacks on Reddit or specific subreddits.</li> <li>"macro-norm-violations-n10-t5-porno-links.csv" - Comments with pornographic links.</li> <li>"macro-norm-violations-n10-t8-personal-attacks.csv", "macro-norm-violations-n10-t9-personal-attacks.csv"- Comments containing personal attacks.</li> <li>"macro-norm-violations-n15-t3-abusing-and-criticisizing-mods.csv" - Comments abusing and criticisizng moderators.</li> <li>"macro-norm-violations-n15-t9-namecalling-claiming-other-too-sensitive.csv" - Comments with name-calling, or claiming that the other person is too sensitive.</li> </ul> <p>More details about the dataset can be found on arXiv: <a href="https://arxiv.org/abs/1904.03596">https://arxiv.org/abs/1904.03596</a></p>
Research data supporting "Block copolymer-directed single diamond hybrid structures derived from X-ray nanotomography"
<p>Research data supporting "Block copolymer-directed single diamond hybrid structures derived from X-ray nanotomography"</p>
FLOATECH WP3 experimental dataset : wave-tank hybrid testing of a 10 MW turbine based on a spar platform (ECN)
<p>This dataset presents the experimental measurements made in the Hydrodynamic and Ocean Engineering wave tank of Ecole Centrale de Nantes, in France, with the model of a 10 MW turbine supported by a spar platform at a scale 1:40. </p> <p>The tests were performed using a real-time hybrid testing method (or software-in-the-loop) called SoftWind presented and published in Ocean Engineering (the paper is available at this <a title="Paper SoftWind" href="https://doi.org/10.1016/j.oceaneng.2024.118390">link</a>). </p> <p> </p> <p><strong>Presentation of the experimental model:</strong></p> <p>The model is presented in details in the provided Excel file "FLOATECH_C3_Project data and model description.xlsx". </p> <p> </p> <p><strong>In the dataset:</strong></p> <p>The measurement files of the tests are gathered in folders by "series", and each test file has a test number. The series and the test conditions of each run are detailed in the provided Excel file "FLOATECH_C3_Database_Matrix.xlsx". </p> <p>Decay tests, pull-out tests and hammer tests were performed and are given in the dataset. </p> <p> </p> <p><strong>Real-time simulation models</strong></p> <p>The numerical models used in the real-time OpenFAST simulations are also provided in the compressed file "RT Simulations files.zip". </p> <p> </p> <p><strong>Data used in the published paper:</strong></p> <p>Some of the tests were used in the paper (see <a title="Paper SoftWind" href="https://doi.org/10.1016/j.oceaneng.2024.118390">link</a>). The corresponding test numbers are given in the table below. </p> <table> <tbody> <tr> <td><strong>Load cases</strong></td> <td><strong>Hs (m)</strong></td> <td><strong>Tp (s)</strong></td> <td><strong>Uhub (m/s)</strong></td> <td><strong>TI (%)</strong></td> <td><strong>Wave dir. (°)</strong></td> <td><strong>Wind dir(°)</strong></td> <td><strong>TestNum 1C</strong></td> <td><strong>TestNum 3C</strong></td> <td><strong>TestNum 5C</strong></td> </tr> <tr> <td>1.2</td> <td>7</td> <td>12</td> <td>14</td> <td>13.8</td> <td>0</td> <td>0</td> <td>269</td> <td>268</td> <td>270</td> </tr> <tr> <td>2.1</td> <td>7</td> <td>12</td> <td>14</td> <td>13.8</td> <td>0</td> <td>25</td> <td>275</td> <td>307</td> <td>281</td> </tr> </tbody> </table> <p> </p> <p> </p>
5D-NP-FABTECH_ALD - Open Dataset for: "ZnO vapor phase infiltration into photo-patternable polyacrylate networks for the microfabrication of hybrid organic-inorganic structures"
<p>This is the open dataset for the paper: "L. Demelius, L. Zhang, A. M. Coclite and M. D. Losego, ZnO vapor phase infiltration into photo-patternable polyacrylate networks for the microfabrication of hybrid organic–inorganic structures, <em>Mater. Adv.</em>, 2024, <strong>5</strong>, 8464–8474."</p> <p>This includes the supplementary information and all the source material that was used for the paper preparation.</p> <p>For each folder (sub-dataset), there exists a corresponding readme file describing the content and including material.</p>
CAMISIM hybrid time series data
<p>CAMISIM was used to simulate Illumina and Nanopore reads for a time series based on the genome sources from the “CAMI II challenge toy mouse gut dataset” (Meyer et al., 2021), containing 791 genomes. For this, the most recent development version of CAMISIM at the time of preparing this data was used (available at <a href="https://doi.org/10.5281/zenodo.5137751">https://doi.org/10.5281/zenodo.5137751</a>). Two groups of samples were generated by using different CAMISIM seeds, each comprising a time series of four samples.</p> <p>The sample sheet file (samplesheet.CAMISIM_hybrid.csv) can be used as direct input for the nf-core/mag pipeline, e.g. with the command:</p> <p>> nextflow run nf-core/mag -r 2.1.0 -profile <docker/singularity/podman/shifter/charliecloud/conda/institute> --input https://zenodo.org/record/5155395/files/samplesheet.CAMISIM_hybrid.csv --coassemble_group</p> <p>Note, in case of download problems, restarting the pipeline run with `-resume` or downloading the files beforehand and adjusting the paths in the sample sheet file should help.</p> <p>See <a href="https://nf-co.re/mag">https://nf-co.re/mag</a> for a comprehensive usage documentation.</p> <p> </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.