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4,107 results for “pointing”
Supplementary Data and Code: Change Point Analysis to decode Economic Crisis Information
<p>Raw data, results and Python code of the corresponding publication "Efficient Multi-Change Point Analysis to decode Economic Crisis Information from the S&P500 Mean Market Correlation" (accepted in: Entropy; Section: Complexity; Special Issue: Complexity in Finance). The change point analysis can be performed using the <a href="https://anticpy.readthedocs.io/en/latest/">documented</a> Python package <a href="https://github.com/MartinHessler/antiCPy"><em>antiCPy</em></a>. Some further helpful Python scripts are provided here under a <em>GNU General Public License v3.0.</em></p> <p>In Data_Generation you can find a list of</p> <ol> <li>S&P500 companies which are considered in the analysis,</li> <li>a jupyter notebook to create the correlation time series.</li> </ol> <p>Data_Preprocessing contains the</p> <ol> <li>S&P500 mean market correlation Financial_Time_Series_Centered_Interval__42days.csv,</li> <li>the Python code to thin it,</li> <li>the thinned data saved as .npy file,</li> <li>the time scale is saved <ul> <li>as integer numbers in thinned_time_thinning40.npy,</li> <li>as datetime in TimeScale_FinancialData.npy.</li> </ul> </li> </ol> <p>In Change_Point_Analysis you find the following files:</p> <ol> <li>cp_probs...npy contain the joint probabilities of the corresponding change point configurations (The joint probabilities are saved corresponding to the order in which Python's <em>itertools.combinations() </em>creates the configurations. This holds also for the cp_probs_5_cps.npy for which the whole combinations array is not saved for memory reasons),</li> <li>cp_pdfs...npy contain the marginal probability density functions of the ordinal change point positions averaged over all configurations,</li> <li>cp_configs...npy contain the configurations,</li> <li>segment_fit...npy contain the segment fit data,</li> <li>segment_fit_variance...npy contain corresponding variances,</li> <li>In the case of five change points only the plotted 1st, 13th and 26th most probable configuration in config_ranking_CP1_5.npy for memory reasons.</li> <li>the data and results of figure 3 for each crisis event can be found in the corresponding directory's folders: <ul> <li>blue corresponds to the pre-crisis data segments,</li> <li>green corresponds to the data segments up to the green vertical dotted line,</li> <li>red corresponds to the longest data segments incorporating near and in-crisis data.</li> </ul> </li> </ol> <p> </p> <p> </p>
Synthesis of National projects on Aviation noise gathered by ANIMA National Focal Points
<p>This bundle gathers synthetic informations on more than hundred projects led at national level in EU countries and their neighbourhood (Switzeland, Ukraine, Serbia...) on aviation noise. This projects are partaking to the EU efforts to update the European Research Roadmap on such aviation noise issues.</p> <p>The bundle has been used for elaboration deliverable ANIMA D6.10 - National Focal Points projects</p>
UAV-based multispectral image data of a tree nursery, Eberswalde, Brandenburg, 2023 (Orthomosaics, DEMs, point clouds)
<p>This data set contains three multispectral surveys conducted with an unoccupied aerial vehicle (UAV DJI M300 RTK) of an oak tree nursery experiment</p> <ul> <li>Date of acquisition: 16.08.2023</li> <li>Location: Tree nursery, Eberswalde, Brandenburg, Germany</li> <li>UAV: DJI M300 RTK with active SAPOS connection</li> <li>Flight altitude above ground level: 30 m, 40 m, 60 m</li> <li>Image Overlap forward/side: 80 % / 80 %</li> <li>Camera: MicaSense Altum multispectral</li> <li>EPSG: 32632</li> </ul> <p>Data products: </p> <ul> <li>Orthomosaic multispectral (Resolution: 1.35 cm, 1.79 cm, 2.65 cm)</li> <li>Orthomosaic RGB</li> <li>Orthomosaic thermal LWIR in pseudo °C according to <a href="https://support.micasense.com/hc/en-us/articles/360022446473-Converting-Altum-Thermal-to-degrees-C-after-processing-in-Agisoft-or-Pix4D">Micasense</a> empirical formula</li> <li>DEM</li> <li>Dense point cloud RGB</li> <li>Spectral indices calculated: NDVI, NDRE</li> <li>Agisoft Report</li> </ul> <p><strong>Acknowledgment:</strong></p> <p><strong>Frank Becker</strong></p> <p>Landesbetrieb Forst Brandenburg</p> <p>Landeskompetenzzentrum Forst Eberswalde (LFE)</p>
Cetacean sightings, n T.t.ponticus 59 45 22 4 2 3 1 2 0 0 4 1 June July August September Fig. 4. Sightings per month for each species. in Vantage point surveys of cetaceans (Mammalia, Cetacea) and their interactions with marine birds
Cetacean sightings, n T.t.ponticus 59 45 22 4 2 3 1 2 0 0 4 1 June July August September Fig. 4. Sightings per month for each species.
Fig. 1 in Vantage point surveys of cetaceans (Mammalia, Cetacea) and their interactions with marine birds
Fig. 1. Cetacean sightings at Cape Emine (white dot) between June-September, 2021 (blue – T. t. ponticus, green – P. p. relicta, red – D. d. ponticus, yellow - vantage - point).
Fig. 2. Feeding activity during the 2021 in Vantage point surveys of cetaceans (Mammalia, Cetacea) and their interactions with marine birds
Fig. 2. Feeding activity during the 2021 observations at Cape Emine - white dot; blue – T. t. ponticus, green – P. p. relicta, red – D. d. ponticus, yellow - vantage - point.
Data points for "Modelling sorption of hydrocarbons in polyethylene with the SAFT-γ Mie approach combined with a statistical-mechanical model to describe semi-crystalline polymers"
<p>A variety of thermodynamic calculations (VLE, sorption isotherms, etc.) performed with a combination of the SAFT-γ equation of state and a novel model to account for the constraints affecting the amorphous domains in semi-crystalline polyethylene (PE). Please refer to the original article (published in Macromolecules) for the bibliography and more details.</p>
Replication data for: Effect of Regional Marine Cloud Brightening Interventions on Climate Tipping Points
<p>Data for reproduction of Hirasawa, H., Hingmire, D., Singh, H., Rasch, P. J., & Mitra, P. (2023). Effect of regional marine cloud brightening interventions on climate tipping elements. Geophysical Research Letters, 50, e2023GL104314. https://doi.org/10.1029/2023GL104314</p> <p>Includes:</p> <ul> <li>Raw monthly 2m temperature (TREFHT) and precipitation (PRECT) data from CESM2 MCB simulations.</li> <li>Ensemble mean data from CESM2 Large Ensemble</li> <li>Tipping point metric timeseries from CESM2 LE Historical and SSP2-4.5 and CESM2 MCB simulations.</li> <li>Jupyter notebook displaying plotting script</li> <li>Scripts showing procedure for computing tipping point metrics</li> <li>CAM6 SourceMod changes to apply cloud droplet number concentration perturbations</li> <li>Top of atmosphere long and shortwave anomalies from fixed sea surface temperature simulations for computing effective radiative forcing</li> </ul>
Dissecting glial scar formation by spatial point pattern and topological data analysis
<p>These data were generated by the Laboratory of Neurovascular Interactions (https://elalilab.com/) at University Laval (Quebec, Canada), and reported in "Dissecting glial scar formation by spatial point pattern and topological data analysis". </p> <p>Please refer to the Open Science Framework (OSF) repository (https://osf.io/3vg8j/) or GitHub (https://github.com/elalilab/GlialScar_PPA-TDA_2022) to see the processing pipeline.</p> <p><strong>AUTHORS</strong><br> Manrique-Castano, Daniel; Bhaskar, Dhananjay; ElAli, Ayman</p> <p><strong>KEYWORDS</strong><br> Stroke, cerebral ischemia, brain injury, glial scar, reactive astrocytes, reactive microglia, </p> <p><br> <strong>1. STUDY DESCRIPTION </strong> <br> This research provides a quantitative analysis of reactive glia and glial scar formation in a mouse model of cerebral ischemia. The dataset in this repository consists of raw widefield microscopy images from healthy and ischemic animals. </p> <p><strong>2. EXPERIMENTAL CONDITIONS</strong><br> Six-month-old C57BL/6 mice were subjected to 30 minutes of cerebral ischemia by middle cerebral artery occlusion (MCAO). Brains were harvested at 5, 15, and 30 days post-ischemia (DPI) (see 10.5281/zenodo.3559570). 5 sham animals were included as controls. The full protocol for brain harvesting is available at 10.17504/protocols.io.4r3l27q5pg1y/v1. Brain sections were stained with NeuN, Gfap, and Iba1 antibodies to detect neurons and reactive glia after injury. Full protocol available at 10.17504/protocols.io.yxmvmk94og3p/v1 <br> <br> <strong>3. FILE DESCRIPTION</strong></p> <p><strong>- GT5X_Gfap_Iba1_NeuN.rar: </strong>Contain widefield (5x magnification) .tif images grouped by animals (5-7 images per animal; see research article for further details). The images were taken with the following parameters.</p> <p>Objective: Fluar 5x/0.25 M27<br> Scaling per pixel: 1.300 x 1.300 µm<br> Bit depth: 16 bit </p> <p>Stainings:<br> Neun Channel AF647; Excitation 653; Emission 668; Exposure 3 s<br> IBA1 Channel AFCy3; Excitation 458; Emission 561; Exposure 4 s<br> GFAP Channel AF488; Excitation 493; Emission 517; Exposure 1 s<br> DAPI Channel AF405; Excitation 353; Emission 465; Exposure 50 ms</p> <p>We used a FIJI script to pre-process the original .czi files. The script is shared in the GitHub repository under the name GT_Exp2_5x_GenerateTiffs.jim.</p> <p><strong>- GT10X_Gfap_Iba1_NeuN.rar:</strong> Contain a single widefield (10x magnification) .tif image per animal at the level of the MCA territory (see research article for further details). The images were taken with the following parameters.</p> <p>Objective: ECM paln-NeoFluar 10x/0.30 M27<br> Scaling per pixel: 0.45 x 0.45 µm<br> Bit depth: 16 bit </p> <p>Stainings:<br> Neun Channel AF647; Excitation 653; Emission 668; Exposure 200 ms<br> IBA1 Channel AFCy3; Excitation 458; Emission 561; Exposure 250 ms<br> GFAP Channel AF488; Excitation 493; Emission 517; Exposure 100 ms<br> DAPI Channel AF405; Excitation 353; Emission 465; Exposure 10 ms</p> <p><br> We used a FIJI script to pre-process the original .czi files. The script is shared in the GitHub repository under the name GT_Exp2_10x_GenerateTiffs.jim.<br> <br> For 5x and 10x images, the following naming strings apply:</p> <p>GT5x: Research project identifier indicating the magnification<br> M01(n): Animal ID<br> 5D(n): Days post-ischemia. 0D refers to healthy (naive) animals. <br> Scene1(n): Bregma level. Scene 1 corresponds to the most anterior area sampled, while Scene 6 or 7 is the most posterior.</p> <p><strong>- PointPatterns_10x.rds: </strong>2D point patterns of GFAP, IBA1, and NeuN generated by the r-package <em>spatstat</em>. The observation window comprises a horizontal ROI from the ventricular area to the outer border of the dorsolateral cerebral cortex. The point patterns were generated from the files and coordinates contained in the <strong>QupathProjects_10x.rar</strong> file in this repository. To reproduce the generation of point patterns please refer to the associated GitHub repository (https://github.com/elalilab/Stroke_GlialScar_PPA-TDA). </p> <p><strong>- PointPatterns_5x.rds: </strong>2D point patterns of GFAP, IBA1, and NeuN generated by the r-package <em>spatstat</em>. The observation window comprises the ischemic hemisphere. The point patterns were generated from the files and coordinates contained in the <strong>QupathProjects_5x.rar</strong> file in this repository. To reproduce the generation of point patterns please refer to the associated GitHub repository (https://github.com/elalilab/Stroke_GlialScar_PPA-TDA). </p> <p><strong>- QupathProjects_5x.rar: </strong>QuPath project folder for 5x images (GT5X_Gfap_Iba1_NeuN.rar). Each subfolder (per animal) contains the necessary files to import annotations (alignment to the Allen Brain Atlas) generated by ABBA (https://biop.github.io/ijp-imagetoatlas/). Please see the research article for further details. </p> <p><strong>**NOTE** </strong>Gfap, Iba1, and NeuN folders contain raw .tsv data originated by QuPath (cell counting). These folders are read in the R processing pipeline to extract the coordinates of each cell. Please make sure the whole folder is in the R working directory. The file "project.qpproj" in each folder opens the QuPath project in QuPath and reads the classifiers and data folders. Each folder also contains "_Alignement.json" and "_Registration_json" files generated during the alignment and annotation procedures in ABBA. However, when the route of the source images is changed, the plugin does not allow rerouting, and the files are of no practical use. The issue has been reported to the ABBA Github repository. </p> <p><strong>- QupathProjects_10x.rar:</strong> QuPath project folder for 10x images (GT5X_Gfap_Iba1_NeuN.rar). The folder contains the necessary files to import annotations (Alignment to the Allen Brain Atlas) generated by ABBA (https://biop.github.io/ijp-imagetoatlas/). Please see the research article for further details. </p> <p><strong>**NOTE** </strong>Gfap, Iba1, NeuN, and DAPI folders contain raw .tsv data originated by QuPath (cell counting). These folders are read in the R processing pipeline to extract the coordinates of each cell. Please make sure the whole folder is in the R working directory. The file "project.qpproj" opens the QuPath project in QuPath and reads the classifiers and data folders. </p>
UVA laser scanning labelled las data over tropical moist forest classified as leaf or wood points
<p>UAV Laser Scanning data collected over neotropical forest (Paracou French Guiana). Four flights conducted over one ha plot in 2021 and 2022.</p> <p>Leaf wood labels were transferred from contemporaneous (2021) TLS acquisition, for which segmentation was done using LeWoS and onscreen post correction.</p> <p>Predicted values using SOUL model (see ref below) for a single UAV-LS acquisition are provided as separate file.</p> <p> </p>
Magnetic Bloch point and vortex configurations
<p>This dataset is associated to the arxiv publication "Energetics and Dynamics of a stable Bloch point" by Winkler et al., arxiv:2303.10091 (2023). Link: https://arxiv.org/abs/2303.10091</p> <p>The set contains stable Bloch point and vortex configurations in various discretizations, disk shapes and models (Heisenberg or micromagnetic), as well as snapshots of dynamics after shifting the structures out of equilibrium. One jupyter notebook is added to plot the data directly within this repository.</p> <p>The github repsitory to produce this states and to verify the results of the study can be found here: https://github.com/WinklerTB/BP_paper_2023</p> <p>The simulation code (MicMag2) can be found here: https://github.com/WinklerTB/MicMag2</p> <p>Also the ubermag framework was partly used to evaluate the data: https://ubermag.github.io/</p>
Vegetation Density Across NYC: Analysis of Land Cover Data (2017) within 200 meter Buffers of Points
<p><strong>Summary:</strong></p><p>This repository contains spatial data files representing the density of vegetation cover within a 200 meter radius of points on a grid across the land area of New York City (NYC), New York, USA based on 2017 six-inch resolution land cover data, as well as SQL code used to carry out the analysis. The 200 meter radius was selected based on a study led by researchers at the NYC Department of Health and Mental Hygiene, which found that for a given point in the city, cooling benefits of vegetation only begin to accrue once the vegetation cover within a 200 meter radius is at least 32% (Johnson et al. 2020). The grid spacing of 100 feet in north/south and east/west directions was intended to provide granular enough detail to offer useful insights at a local scale (e.g., within a neighborhood) while keeping the amount of data needed to be processed for this manageable. </p><p>The contained files were developed by the NY Cities Program of <a href="https://www.nature.org/newyork">The Nature Conservancy</a> and the <a href="https://nyc-eja.org/">NYC Environmental Justice Alliance</a> through the <a href="https://medium.com/gage-nyc/introducing-the-just-nature-nyc-partnership-513612e8c3b4">Just Nature NYC Partnership</a>. Additional context and interpretation of this work is available in a <a href="https://medium.com/gage-nyc/looking-at-cooling-benefits-of-plants-through-nyc-vegetation-data-ccdeb33cbe17">blog post</a>.</p><p> </p><p><i>References:</i></p><p>Johnson, S., Z. Ross, I. Kheirbek, and K. Ito. 2020. Characterization of intra-urban spatial variation in observed summer ambient temperature from the New York City Community Air Survey. <i>Urban Climate</i> 31:100583. <a href="https://doi.org/10.1016/j.uclim.2020.100583">https://doi.org/10.1016/j.uclim.2020.100583</a></p><p> </p><p><strong>Files in this Repository:</strong></p><p>Spatial Data (all data are in the New York State Plane Coordinate System - Long Island Zone, North American Datum 1983, <a href="https://epsg.io/2263">EPSG 2263</a>):</p><p>Points with unique identifiers (<i>fid</i>) and data on proportion tree canopy cover (<i>prop_canopy</i>), proportion grass/shrub cover (<i>prop_grassshrub</i>), and proportion total vegetation cover (<i>prop_veg</i>) within a 200 meter radius (same data made available in two commonly used formats, Esri File GeoDatabase and GeoPackage):</p><p><i>nyc_propveg2017_200mbuffer_100ftgrid_nowater.gdb.zip</i></p><p><i>nyc_propveg2017_200mbuffer_100ftgrid_nowater.gpkg</i> </p><p>Raster Data with the proportion total vegetation within a 200 meter radius of the center of each cell (pixel centers align with the spatial point data)</p><p><i>nyc_propveg2017_200mbuffer_100ftgrid_nowater.tif</i></p><p>Computer Code:</p><p>Code for generating the point data in PostgreSQL/PostGIS, assuming the data sources listed below are already in a PostGIS database.</p><p><i>nyc_point_buffer_vegetation_overlay.sql</i></p><p> </p><p><strong>Data Sources and Methods:</strong></p><p>We used two openly available datasets from the City of New York for this analysis:</p><p>Borough Boundaries (Clipped to Shoreline) for NYC, from the NYC Department of City Planning, available at <a href="https://www.nyc.gov/site/planning/data-maps/open-data/districts-download-metadata.page">https://www.nyc.gov/site/planning/data-maps/open-data/districts-download-metadata.page</a> </p><p>Six-inch resolution land cover data for New York City as of 2017, available at <a href="https://data.cityofnewyork.us/Environment/Land-Cover-Raster-Data-2017-6in-Resolution/he6d-2qns">https://data.cityofnewyork.us/Environment/Land-Cover-Raster-Data-2017-6in-Resolution/he6d-2qns</a> </p><p>All data were used in the New York State Plane Coordinate System, Long Island Zone (<a href="https://epsg.io/2263">EPSG 2263</a>). Land cover data were used in a polygonized form for these analyses.</p><p>The general steps for developing the data available in this repository were as follows:</p><p>Create a grid of points across the city, based on the full extent of the Borough Boundaries dataset, with points 100 feet from one another in east/west and north/south directions</p><p>Delete any points that do not overlap the areas in the Borough Boundaries dataset.</p><p>Create circles centered at each point, with a radius of 200 meters (656.168 feet) in line with the aforementioned paper (Johnson et al. 2020).</p><p>Overlay the circles with the land cover data, and calculate the proportion of the land cover that was grass/shrub and tree canopy land cover types. Note, because the land cover data consistently ended at the boundaries of NYC, for points within 200 meters of Nassau and Westchester Counties, the area with land cover data was smaller than the area of the circles.</p><p>Relate the results from the overlay analysis back to the associated points.</p><p>Create a raster data layer from the point data, with 100 foot by 100 foot resolution, where the center of each pixel is at the location of the respective points. Areas between the Borough Boundary polygons (open water of NY Harbor) are coded as "no data."</p><p>All steps except for the creation of the raster dataset were conducted in PostgreSQL/PostGIS, as documented in <i>nyc_point_buffer_vegetation_overlay.sql</i>. The conversion of the results to a raster dataset was done in QGIS (version 3.28), ultimately using the <a href="https://gdal.org/programs/gdal_rasterize.html">gdal_rasterize</a> function.</p>
Figure 8 Relationship between host parasitism rate and mean parasitoid load per host. Each data point represents 1 in Infection behavior, life history, and host parasitism rates of Emblemasoma erro (Diptera: Sarcophagidae), an acoustically hunting parasitoid of the cicada Tibicen dorsatus (Hemiptera: Cicadidae)
Figure 8 Relationship between host parasitism rate and mean parasitoid load per host. Each data point represents 1 year of host population sampling data for a single study site. The solid line (blue in the color figure) represents the linear regression model for the data.
Point-of-care Pharmacogenomic Testing to Optimize Isoniazid Dosing for Tuberculosis Prevention
ClinicalTrials.gov study NCT05413551. IPD Sharing: YES. Countries: 1. Publications: 1.
Approaching a thermal tipping point in the Eurasian boreal forest at its southern margin
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Amazona obscured occurrences, background points, and environmental data
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Autistic traits relate to reduced reward sensitivity in learning from social point-light displays (PLDs)
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Reducing Hepatitis C diagnostic disparities with a point of care assay for HCV antigen detection
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Vegetation and vantage point influence visibility across diverse ecosystems: implications for animal ecology
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Data from: Time series of bird abundances, land cover and temperature from standardized breeding bird monitoring schemes (line transects and point count routes) from Norway, Sweden and Finland, for 1975-2016
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ScienceDex guides
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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.