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7,195 results for “SET”
Python Time Normalized Superposed Epoch Analysis (SEAnorm) Example Data Set
<p>Solar Wind Omni and SAMPEX ( Solar Anomalous and Magnetospheric Particle Explorer) datasets used in examples for <a href="https://github.com/samwalton7645/SEA_Code">SEAnorm</a>, a time normalized superposed epoch analysis package in python.</p> <p>Both data sets are stored as either a HDF5 or a compressed csv file (csv.bz2) which contain a Pandas DataFrame of either the Solar Wind Omni and SAMPEX data sets. The data sets where written with pandas.DataFrame.to_hdf() and pandas.DataFrame.to_csv() using a compression level of 9. The DataFrames can be read using pandas.DataFrame.read_hdf( ) or pandas.DataFrame.read_csv( ) depending on the file format. </p> <p>The Solar Wind Omni data sets contains solar wind velocity (V) and dynamic pressure (P), the southward interplanetary magnetic field in Geocentric Solar Ecliptic System (GSE) coordinates (B_Z_GSE), the auroral electrojet index (AE), and the Sym-H index all at 1 minute cadence. </p> <p>The SAMPEX data set contains electron flux from the Proton/Electron Telescope (PET) at two energy channels 1.5-6.0 MeV (ELO) and 2.5-14 MeV (EHI) at an approximate 6 second cadence.</p> <p> </p>
H2020 773782-COASTAL MAL03 Management set for the Norrström-Baltic region
<p>The sets of measures relate to different Business Road Map (BRM) alternatives prioritized by stakeholders in the Norrström-Baltic (MAL3) case:</p> <ul> <li><strong>Current management</strong>: Base case with no change in nutrient concentrations for nitrogen (N) and phosphorous (P) (same results as for the base case scenario in D19).</li> <li><strong>Agricultural set of measures</strong>: Considers the example of 25% reduction in nutrient concentrations leaching from currently active agriculture with associated reduction in agricultural nutrient contributions to subsurface water (SSW) and surface water (SW) nutrient concentrations. Such reductions may result, e.g., from improved/optimized agricultural fertilization practices and drainage facilities, and restoration/construction of wetlands that can capture local nutrient leakage. This set of measures relates to the stakeholder-prioritized BRM alternative “Integrated risk assessment of nutrient losses from agricultural soils to surface waters”.</li> <li><strong>WWTP set of measures</strong>: Considers the example of 25% reduction of nutrient concentrations in discharges from currently active municipal and industrial wastewater treatment plants (WWTPs) and unconnected wastewater facilities with associated reduction in their contributions to SW nutrient concentrations. Such reductions may result, e.g., from improved nutrient removal in WWTPs and recovery in smart water and sanitation systems, related to technological advancements and, more widely, e.g., creation of a nutrient market that makes such capture and reuse worthwhile. This set of measures relates to the stakeholder-prioritized BRM alternatives “Nutrient recovery in wastewater treatment plants” and “Smart water and sanitation systems”.</li> <li><strong>Legacy set of measures</strong>: Considers the example of 25% reduction in total SSW and SW nutrient concentrations. Such reductions may result from catchment-wide mitigation (removal/capture and possible reuse) of nutrients released from diffuse wide-spread legacy sources that still remain in soil, groundwater and sediments from different types of earlier nutrient inputs (past agricultural leakage, municipal and industrial wastewater discharges). Such mitigation may be achieved, e.g., by restoration/construction of wetlands and construction of reactive barriers that are well-placed and distributed to effectively capture considerable parts of the overall legacy nutrient releases throughout each hydrological catchment. This set of measures also relates to possible changes in socio-economic drivers, such as creation of a nutrient market that can make capture and reuse of nutrients worthwhile, along with improved knowledge transfer between sectors and some shift in the municipal water (quality) management monopoly that current applies in Sweden. With regard to the stakeholder-prioritized BRM alternatives, it relates to: (i) “Improved knowledge transfer between sectors” that may drive better system understanding with more efficient nutrient mitigation measures taken and well/optimally placed in each hydrological catchment for targeting and mitigating diffuse legacy sources; and (ii) “Change of municipal monopoly” that may enhance collaboration and communication between different municipalities within the same hydrological catchment toward more overarching efficiency and circular principles on whole catchment-scale.</li> </ul> <p>The .xslx data are organized as follows.</p> <p>One sheet per set of measure: </p> <ul> <li>Base case (current management)</li> <li>Agricultural set of measures</li> <li>Wastewater treatment plants set of measures</li> <li>Legacy set of measures</li> </ul> <p>For each sheet, the following data are included. </p> <p>Columns:</p> <ul> <li><strong>A: Variable name</strong></li> <li><strong>B: Input variables</strong> - full name and units of the variable</li> <li><strong>C: Initial</strong></li> <li><strong>D: current climate and socio-economy</strong></li> <li><strong>E: scenarios</strong>: RCP4.5+SSP1, RCP4.5+SSP2, RCP4.5+SSP4, RCP4.5+SSP5</li> </ul> <p>For each column, the values are organized with the following variables (rows 2-16): “Concentration of N in SSW”, “Concentration of N in SW”, "Concentration of N in WWTP-input", "Concentration of N in WWTP-output", “Concentration of P in SSW”, “Concentration of P in SW”, "Concentration of P in WWTP-input", "Concentration of P in WWTP-output", "Concentration of P-SW to agriculture", "Concentration of N-SW to agriculture", "Concentration of P-agriculture to SW", "Concentration of N-agriculture to SW", "Concentration of N-agriculture to SSW", "Concentration of P-agriculture to SSW"</p> <ul> </ul> <p> </p>
On the formulation and implementation of extrinsic cohesive zone models with contact - data set
<p>This data set contains data relating to the paper "On the formulation and implementation of extrinsic cohesive zone models with contact", <a href="https://doi.org/10.1016/j.cma.2022.115545">https://doi.org/10.1016/j.cma.2022.115545</a> , specifically:<br> 1. the meshes used to conduct finite element analyses,<br> 2. the results of those finite element analyses (in the form of vtk files and numpy pickles), and<br> 3. some images of the meshes and the total displacement at the end of the analyses.<br> <br> The corresponding code to generate and read the data is available at https://github.com/nickcollins-craft/On-the-formulation-and-implementation-of-extrinsic-cohesive-zone-models-with-contact (which is the preferred method), or alternatively via https://doi.org/10.5281/zenodo.6939391.</p>
Dialexical set of Proto-Tai simple onsets
<p>A dialexical set for the study of variation and sound change in the simple onsets of Proto-Tai. Includes columns for the proto-onset, reconstructed form from Pittayaporn (2009), the posited Proto-Tai gloss, the standard Thai reflex in Thai script (if one exists), the cognate ID number from Pittayaporn's reconstructions, and the matching cognate ID number from Hudak (2008), which compiles Gedney's field data (if a match exists).</p>
Data set used in glacier algae and filamentous cyanobacteria models
<p>This is a data set for using the glacier algae and filamentous cyanobacteria models (Onuma et al., 2022). The content is as below.</p> <p>- data: observed data (bio-volume, cell count, mineral weight, EC, pH and meteorological conditions) on the bare ice surface in Qaanaaq Ice Cap (CSV files). And, model input and output data (CSV files). About the detailed information on each file, please see the readme files.</p> <p>- python: programs for the visualization (python scripts)</p> <p>- figure: png files created by the python scripts</p> <p>The codes of the glacier algae model can be downloaded below.<br> https://github.com/YukihikoOnuma/SnowAlgaeModel<br> <br> The article regarding the models is as below.<br> https://doi.org/10.1017/jog.2022.76</p>
Point cloud data sets of real and virtual Chenopodium alba
<p>This data set contains:</p> <p>- 5 annotated point clouds of real Chenopodium alba plants obtained from multi-view 2D camera imaging. Annotations consist of 5 classes: leaf blade, petiole, apex, main stem, branch. .txt files contain both 3D coordinates and annotations. .ply files are also provided for raw 3D point data without annotations.</p> <p>- 24 annotated point clouds of virtual Chenopodium alba that were generated by a L-system simulation program. Annotations consist of 3 classes: leaf blade, petiole, stem. 3D coordinates and annotations are in separated .txt files. </p> <p>These files have been used in a companion paper.</p> <p> </p>
Figure Sets and Data Associated with AJ Publication: "NICMOS Kernel-Phase Interferometry I: Catalogue of Brown Dwarfs Observed in F110W and F170M"
<p>Images for Figure Sets 4, 5, 6, 7, and 9 and data behind the figure for Figure 15 from the AJ publication "NICMOS Kernel-Phase Interferometry I: Catalogue of Brown Dwarfs Observed in F110W and F170M" (Currently accepted and in press.). Figure sets and file names are described in the fsREADME file. Data behind the figure is described in the dbfREADME file.</p>
Data set: Average daily minimum temperature in January and February in Corsica
<p>Raster providing the average of the daily minimum temperature in Celsius degrees over January and February in Corsica from 1995 to 2003 with a 0.016667x0.0166671 resolution in latitude and longitude.</p> <p>Construction: This raster was constructed from the freely available database (PVGIS © European Communities, 2001-2008) providing, in particular, monthly averages of the daily minimum temperature reconstructed over a grid with 1$\times$1km spatial resolution (Huld et al., 2006). These monthly averages correspond to the period 1995-2003 and were used by Abboud et al. (2019, 2020) to model Xylella fastidious dynamics in South Corsica.</p> <p>Load the raster in the R statistical software (v4.1.2):<br> library(raster)<br> ADMT=raster("average-daily-minimum-temperature_Corsica_Abboud-et-al_Forecasting.grd")<br> print(ADMT)<br> plot(ADMT)</p> <p>Summary information:<br> class : RasterLayer <br> dimensions : 108, 78, 8424 (nrow, ncol, ncell)<br> resolution : 0.016667, 0.016667 (x, y)<br> extent : 8.400708, 9.700734, 41.30018, 43.10021 (xmin, xmax, ymin, ymax)<br> crs : +proj=longlat +datum=WGS84 +no_defs <br> source : average-daily-minimum-temperature_Corsica_Abboud-et-al_Forecasting.grd <br> names : layer <br> values : -0.6748945, 6.75789 (min, max)</p> <p>References:<br> - Abboud, C., Bonnefon, O., Parent, E., and Soubeyrand, S. (2019). Dating and localizing an invasion from post-introduction data and a coupled reaction–diffusion–absorption model. Journal of Mathematical Biology 79, 765–789.<br> - Abboud, C., Parent, E., Bonnefon, O., and Soubeyrand, S. (2022). Forecasting pathogen dynamics with Bayesian model-averaging: Application to Xylella fastidiosa. Preprint.<br> - Huld, T. A., Suri, M., Dunlop, E. D., and Micale, F. (2006). Estimating average daytime and daily temperature profiles within Europe. Environmental Modelling & Software 21, 1650–1661.</p>
Data set from: Rates of Compact Object Coalescences
<p><strong>Data from: Rates of Compact Object Coalescence </strong></p> <p><strong>Brief overview: </strong><br> This Zenodo entry contains the data that has been used to make the figures for the living review <a href="https://ui.adsabs.harvard.edu/abs/2021arXiv210714239M/abstract">"Rates of Compact Object Coalescence" by Ilya Mandel & Floor Broekgaarden (2021)</a>. To reproduce the figures, download all the <strong>*.csv</strong> files and run the jupyter notebook created to reproduce the results in the publicly available Github directory <a href="https://github.com/FloorBroekgaarden/Rates_of_Compact_Object_Coalescence">https://github.com/FloorBroekgaarden/Rates_of_Compact_Object_Coalescence</a> (the exact jupyter notebook can be found <a href="https://github.com/FloorBroekgaarden/Rates_of_Compact_Object_Coalescence/tree/main/plottingCode/Make_figures_Mandel_and_Broekgaarden_2021_COC_rates_review.ipynb">here</a>)</p> <p>For any suggestions, questions or inquiry, please email one, or both, of the authors: </p> <ul> <li><strong>Ilya Mandel</strong>: <em>ilya.mandel@monash.edu</em> </li> <li><strong>Floor Broekgaarden</strong>: <em>floor.broekgaarden@cfa.harvard.edu</em></li> </ul> <p>We very much welcome suggestions for additional/missing literature with rate predictions or measurements. </p> <p> </p> <p><strong>Extra figures:</strong><br> Extra figures that can be used can be found here:</p> <p><strong>Vertical figures: <a href="https://docs.google.com/presentation/d/1GqJ0k2zpnxBGwIYNeQ0BfsLSU7H2942gspL-PN_iaJY/edit?usp=sharing">https://docs.google.com/presentation/d/1GqJ0k2zpnxBGwIYNeQ0BfsLSU7H2942gspL-PN_iaJY/edit?usp=sharing</a> </strong></p> <p><br> The authors are currently working on making an interactive tool for plotting the rates that will be available soon. In the mean time, feel free to send requests for plots/figures to the authors. </p> <p><strong>Reference</strong><br> If you use this data/code for publication, please cite both the paper: <a href="https://ui.adsabs.harvard.edu/abs/2021arXiv210714239M/abstract">Mandel & Broekgaarden (2021)</a> (<a href="https://ui.adsabs.harvard.edu/abs/2021arXiv210714239M/abstract">https://ui.adsabs.harvard.edu/abs/2021arXiv210714239M/abstract</a>) and the dataset on Zenodo through it's doi (see tabs on the right of this zenodo entry) <br> <br> <strong>Details datafiles: </strong></p> <p>The PDF <strong>COC_rates_supplementary_material.pdf</strong> attached (and in the Github repository) describes how each of the rates in the data files of this Zenodo entry are retrieved. The other 26 files are .csv files, where each csv file contains the rates from one specific double compact object type: NS-NS, NS-BH or BH-BH, and specific rate group (isolated binary evolution, gravitational wave observations etc.). The files in this entry are: </p> <p> </p> <ul> <li><strong>Data_Mandel_and_Broekgaarden_2021.zip </strong>all the files below conveniently in one zip file so that you only have to do 1 download. <br> </li> <li><strong>COC_rates_supplementary_material.pdf </strong> # PDF document describing how the rates are retrieved and quoted rom each study<br> </li> <li><strong>BH-BH_rates_CHE.csv</strong> # BH-BH rates for chemically homogeneous evolution </li> <li><strong>BH-BH_rates_flybys.csv </strong> # BH-BH rates for formation from wide isolated binaries with dynamical interactions from flybys</li> <li><strong>BH-BH_rates_globular-clusters.csv</strong> # BH-BH rates for dynamical formation in globular clusters </li> <li><strong>BH-BH_rates_isolated-binary-evolution.csv</strong> # BH-BH rates for isolated binary evolution </li> <li><strong>BH-BH_rates_nuclear-clusters.csv</strong> # BH-BH rates for (dynamical )formation in (active) nuclear star clusters</li> <li><strong>BH-BH_rates_observations-GWs.csv</strong> # BH-BH rates for observations from gravitational waves</li> <li><strong>BH-BH_rates_population-III.csv</strong> # BH-BH rates for population-III stars </li> <li><strong>BH-BH_rates_primordial.csv </strong> # BH-BH rates for primordial formation</li> <li><strong>BH-BH_rates_triples.csv</strong>. # BH-BH rates for formation in (hierarchical) triples </li> <li><strong>BH-BH_rates_young-stellar-clusters.csv</strong> # BH-BH rates for dynamical formation in young/open star clusters <br> </li> <li><strong>NS-BH_rates_CHE.csv</strong> # NS-BH rates for chemically homogeneous evolution </li> <li><strong>NS-BH_rates_flybys.csv </strong> # BH-BH rates for formation from wide isolated binaries with dynamical interactions from flybys</li> <li><strong>NS-BH_rates_globular-clusters.csv</strong> # NS-BH rates for dynamical formation in globular clusters </li> <li><strong>NS-BH_rates_isolated-binary-evolution.csv. </strong># NS-BH rates for isolated binary evolution </li> <li><strong>NS-BH_rates_nuclear-clusters.csv</strong> # NS-BH rates for (dynamical )formation in (active) nuclear star clusters</li> <li><strong>NS-BH_rates_observations-GWs.csv</strong> # NS-BH rates for observations from gravitational waves</li> <li><strong>NS-BH_rates_population-III.csv</strong> # NS-BH rates for population-III stars </li> <li><strong>NS-BH_rates_triples.csv</strong> # NS-BH rates for formation in (hierarchical) triples </li> <li><strong>NS-BH_rates_young-stellar-clusters.csv</strong> # BH-BH rates for dynamical formation in young/open star clusters<br> </li> <li><strong>NS-NS_rates_globular-clusters.csv </strong># NS-NS rates for dynamical formation in globular clusters </li> <li><strong>NS-NS_rates_isolated-binary-evolution.csv </strong> # NS-NS rates for isolated binary evolution </li> <li><strong>NS-NS_rates_nuclear-clusters.csv </strong># NS-NS rates for (dynamical )formation in (active) nuclear star clusters</li> <li><strong>NS-NS_rates_observations-GWs.csv</strong> # NS-NS rates for observations from gravitational waves</li> <li><strong>NS-NS_rates_observations-kilonovae.csv</strong> # NS-NS rates for observations from kilonovae</li> <li><strong>NS-NS_rates_observations-pulsars.csv</strong> # NS-NS rates for observations from Galactic pulsars</li> <li><strong>NS-NS_rates_observations-sGRBs.csv </strong># NS-NS rates for observations short gamma-ray bursts</li> <li><strong>NS-NS_rates_triples.csv </strong># NS-NS rates for formation in (hierarchical) triples </li> <li><strong>NS-NS_rates_young-stellar-clusters.csv</strong> # NS-NS rates for dynamical formation in young/open star clusters </li> </ul> <p> </p> <p><strong>Each csv file contains the following header: </strong><br> ADS year # year of the paper in the ADS entry<br> ADS month # month of the paper in the ADS entry <br> ADS abstract link # link to the ADS abstract <br> ArXiv link # link to the ArXiv version of the paper <br> First Author # name of the first author<br> label string # label of the study, that corresponds to the label in the figure<br> code (optional) # name of the code used in this study <br> type of limit (for plotting, see jupyter notebook for a dictionary) # integer, that is used to map to a certain limit visualization in the plot (e.g. scatter points vs upper limit). </p> <p>Each entry takes two columns in the csv files. One for the rates (quoted under the header 'rate [Gpc^-3 yr^-1]') and one for "notes" where we sometimes added notes about the rates (such as whether it is an upper or lower limit). </p> <p> </p>
Data Set for_Integrating torrefaction of pulp industry sludge with anaerobic digestion to produce biomethane and volatile fatty acids: An example of industrial symbiosis for circular bioeconomy
<p>Industrial symbiosis, which allows the sharing of resources between different industries, could help to improve the overall feasibility of bio-based chemicals production. In that regard, this study focused on integrating the torrefaction of pulp industry sludge with anaerobic digestion. More specifically, anaerobic digestion (AD) of pulp sludge-derived torrefaction condensate (TC) was studied to evaluate the biomethane and volatile fatty acid (VFA) potential. The torrefaction condensate produced at 275 and 300 °C was used in AD. The volatile solid content (VS) was 6.69 and 9.01% for the condensate produced at 275 and 300 °C, respectively. The organic fraction of TC mainly contained acetic acid, 2-furanmethanol, and syringol. The methane yield was in the range of 481–772 mL/g VS for the mesophilic and 401–746 mL/g VS for the thermophilic process, respectively. The VFA yield was in the range of 1.1 to 3.4 g/g VS for mesophilic and from 1.5 to 4.7 g/g VS in thermophilic conditions, when methanogenesis was inhibited. Finally, pulp sludge TC is a feasible feedstock to produce platform chemicals like VFA. However, at higher substrate loading, signs of process inhibition were observed because of the relatively increasing concentration of microbial inhibitors</p>
A data set of monthly global ocean vertical velocity from 1950-2014
<p>This data set provides monthly global ocean vertical velocity from 1950-2014. It was constructed from 41 CMIP6 models (historical experiment). It may be used for investigating the large-scale upwelling and downwelling.</p> <p>Note that this data set has not been widely tested. Please feel free to contact the author if you had any questions or concerns.</p> <p>It will be greatly appreciated if you could send the author an email when you used this data set, so that the author can better improve this data set, and more importantly, provide you with updated data sets or any modifications.</p> <p> </p> <p> </p>
hiPSC 3D immunofluorescence images, test data set 2x2, 10Z
<p>Example dataset of human induced pluripotent stem cells, imaged at 40x magnification with a Yokogawa CV7000. This is a small subset of a larger experiment intended as a test dataset for Fractal: https://github.com/fractal-analytics-platform/fractal</p> <p>3 Channels were imaged:</p> <p>- C01: DAPI, nuclear stain</p> <p>- C02: nanog, antibody staining with Bio-Techne AG, AF1997-SP, Lot KKJ0617121 for the stemness marker nanog</p> <p>- C03: Lamin B1, antibody staining with Abcam, ab16048, Lot GR3244890-2 for the nuclear envelope marker Lamin B1</p> <p> </p> <p>This dataset contains 10 Z levels for 4 field of views for those 3 channels, as well as (manually adjusted) metadata files from the Yokogawa CV7000.</p> <p> </p> <p>The data was acquired in the Pelkmans lab in August 2020. The images have been converted from TIFF into PNG (lossless). </p>
Data set for the manuscript 'Polarization-controlled chromo-encryption'
<p>In this dataset, there are 1 pdf and 2 zip files.</p> <p>The manuscript (<strong><em>Zenodo OpenData for chromo encryption<em>.</em>pdf</em></strong>) contains the figures of simulated and measured spectra.</p> <p>The corresponding raw data can refer to the 2 zip files (<strong><em>Simulated.zip </em></strong>and<strong><em> </em></strong><strong><em>Measured<em>.</em>pdf</em></strong>).</p>
Data set for the publication entitled "Azithromycin alters spatial and temporal dynamics of airway microbiota in idiopathic pulmonary fibrosis"
<p>Set of files containing data used for microbiota analysis by 16S rRNA amplicon sequencing.</p> <p>The study cohort included patients with idiopathic pulmonary fibrosis from four centres in Switzerland, treated with azithromycin or placebo, sampled sequentially by oropharyngeal swab.</p> <p>This work is available in medRxiv and has been submitted</p>
Data set for "The annual-hydrogen-yield-climatic-response ratio: evaluating the real-life performance of integrated solar water splitting devices"
<p>This data set was used for the modelling in the article M. Kölbach, O. Höhn, K. Rehfeld, M. Finkbeiner, J. Barry, and M. M. May, “The annual-hydrogen-yield-climatic-response ratio: evaluating the real-life performance of integrated solar water splitting devices”<strong><em>,</em></strong> <em>Sustainable Energy Fuels</em>, <strong>2022</strong>, <strong>6</strong>, 4062-4074, <a href="https://doi.org/10.1039/D2SE00561A">https://doi.org/10.1039/D2SE00561A</a>.</p> <p>It contains the External Quantum Efficiency (EQE) data of a wafer-bonded AlGaAs//Si dual-junction solar cell for several top absorber compositions, angle of incidences, and temperatures modelled using the OPTOS formalism (see <a href="https://doi.org/10.1364/OE.24.0A1083">https://doi.org/10.1364/OE.24.0A1083</a> , <a href="https://doi.org/10.1364/OE.23.0A1720">https://doi.org/10.1364/OE.23.0A1720</a> , and <a href="http://doi.org/10.1109/JPHOTOV.2021.3064562"> https://doi.org/10.1109/JPHOTOV.2021.3064562</a>). Moreover, the data set includes hourly resolved direct and diffuse solar spectra for a location near the Neumayer station in Antarctica (-70.67°/-8.28°) that were modelled using the libRadtran software package for the year 2021 (see <a href="https://doi.org/10.1140/epjconf/e2009-00912-1">https://doi.org/10.1140/epjconf/e2009-00912-1</a> and <a href="http://doi.org/10.5194/acp-5-1855-2005">https://doi.org/10.5194/acp-5-1855-2005</a>). The modelling of the spectra was performed employing the predefined “subarctic summer” and “subarctic winter” atmosphere datasets assuming a tilt angle of 70° and 1-axis tracking. For the sake of simplicity, no cloud cover was assumed over the course of the whole year. Finally, the input files required for modelling the climatic response of solar water splitting devices for the selected location in Antarctica using the “climatic_response_function” of YaSoFo (see <a href="http://doi.org/10.5281/zenodo.5257492">https://doi.org/10.5281/zenodo.5257492</a> for an extended example) are included in the data set.</p>
Set of simulated overexposured PSF's
<p>This archive contains synthetic data for calculations supporting the presentation of a new phase retrieval method in SPIE BiOS 2022 conference (<a href="https://doi.org/10.1117/12.2609697">https://doi.org/10.1117/12.2609697</a>). <br> <br> The set contains representatives of three different types of pupil phases and the corresponding point-spread functions (PSFs), reflecting various noise and hardware parameters. Please refer to the proceedings paper for details,</p>
Monitoring open access publishing of NWO funded research (2015-2021) data set
<p>This is the dataset underlying the report "Monitoring open access publishing of NWO funded research" (<a href="https://doi.org/10.5281/zenodo.7041897">https://doi.org/10.5281/zenodo.7041897</a>)</p> <p>The report presents statistics on the extent to which publications from the period 2015–2021 funded by NWO are available in Open Access. The analyses presented in this report also cover publications funded by the Netherlands Organisation for Health Research and Development ZonMw. This report builds on two earlier reports, published in <a href="https://zenodo.org/record/4446042">2020</a> and <a href="https://zenodo.org/record/5056043">2021</a>, covering publications from the period 2015–2018 and 2015-2020, respectively.</p>
Diagnostic accuracy of a set of clinical and radiological criteria for screening of COVID-19 using RT-PCR as the reference standard - Dataset
<p>Dataset of a cohort whose summary is described below.</p> <p>Abstract</p> <p><strong>Objective:</strong> To evaluate the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of a set of clinical-radiological criteria for COVID-19 screening in patients with severe acute respiratory failure (SARF) admitted to intensive care units (ICUs), using reverse-transcriptase polymerase chain reaction (RT-PCR) as the reference standard. <strong>Method: </strong>Diagnostic accuracy study including a historical cohort of 1009 patients consecutively admitted to ICUs across six hospitals in Curitiba (Brazil) from March to September, 2020. The sample was stratified into groups by the strength of suspicion for COVID-19 (strong <em>versus</em> weak) using parameters based on three clinical and radiological (chest computed tomography) criteria. The diagnosis of COVID-19 was confirmed by RT-PCR (referent). <strong>Results:</strong> With respect to RT-PCR, the proposed criteria had 98.5% (95% confidence interval [95% CI] 97.5–99.5%) sensitivity, 70% (95% CI 65.8–74.2%) specificity, 85.5% (95% CI 83.4–87.7%) accuracy, PPV of 79.7% (95% CI 76.6–82.7%) and NPV of 97.6% (95% CI 95.9–99.2%). <strong>Conclusion: </strong>The proposed set of clinical-radiological criteria were accurate in identifying patients with strong <em>versus</em> weak suspicion for COVID-19 and had high sensitivity and considerable specificity with respect to RT-PCR. These criteria may be useful for screening COVID-19 in patients presenting with SARF.</p>
Data set: Australia's hidden radiation - phylogenomic analysis reveals rapid Miocene radiation of blindsnakes
<p>This repository contains the additional raw data to accompany our paper entitled "Australia’s hidden radiation: phylogenomic analysis reveals rapid Miocene radiation of blind snakes."</p> <p>This project is part of the AusARG Initiative funded by BioPlatforms Australia.</p> <p>Raw sequences data can be downloaded from the BioPlatforms downloads portal: https://data.bioplatforms.com/dataset?q=ticket%3ABPAOPS-1196</p> <p><strong>Information about files</strong></p> <ol> <li>ASTRAL_tree_SqCL_AHE.tre - output from ASTRAL-III just with SqCL data + outgroups</li> <li>ASTRAL_tree_SqCL_AHE_Ramphotyphlops.tre - same with above but also including additional <em>Ramphotyphlops </em>genes.</li> <li>mcmctree_1.txt - mcmcfile output from MCMCTree analysis using all SkewT or SkewNormal distribution priors.</li> <li>mcmctree_2.txt - mcmcfile output from MCMCTree analysis using SkewT, SkewNormal, and cauchy distribution priors. **This is the tree used in our publication**</li> <li>mcmctree_strategy1.tre - output phylogeny 1</li> <li>mcmctree_strategy2.tre - output phylogeny 2</li> <li>IQTREE_gcf_scf.nex - gene concordance and site factors for mcmctree_strategy2.tre</li> </ol> <p>tree_data/ folder contains concatenated gene trees (IQTREE) and corresponding shortcut coalescent method (ASTRAL-III) tree.</p> <p>Should there be questions regarding the code and data set, please contact the corresponding author.</p>
Data set for "Optical multiplexing of metrological time and frequency signals in a single 100 GHz-grid optical channel"
<p>Here we share the relevant data of the manuscript “Optical multiplexing of metrological time and frequency signals in a single 100 GHz-grid optical channel”.</p> <p>Files:</p> <ul> <li>Opt_Fr_stability_part1.txt</li> <li>Opt_Fr_stability_part2.txt</li> </ul> <p>contain the data used for evaluation of optical frequency transfer stability (Fig. 7 in the paper). The measurements were done with 8-channels K+K phase/frequency recorder. Column 1 contains date, col. 2: time, col. 5: in-loop beatnote phase, col. 6: out-of-loop beatnote phase. The phase is recorded in cycles. In case of out-of-loop beatnote it was divided by factor of two before recording, therefore the data from col. 6 should be multiplied by two to obtain true values of the optical phase fluctuations.</p> <p>File:</p> <ul> <li>RF_stability.txt</li> </ul> <p>contains the data used for evaluation of RF frequency transfer stability (Fig. 8 in the paper). Column 1 contains time in hours, and col. 2 RF phase fluctuations in seconds.</p>
ScienceDex guides
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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.