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.
112
datasets available to search
ShareScore release 0.9.0
Dataset results
112 results for “probability maps”
Iterative Mapping of Probabilities
<p>This repository contains data and scripts for implementing the Iterative Mapping of Probabilities (IMP) algorithm proposed in the preprint submitted to the International Journal of Applied Earth Observation and Geoinformation (JAG). The framework aims to improve the accuracy of land cover mapping by iteratively refining class maps to match independent area statistics. The experiment focuses on generating classification maps for five countries (Belgium, Czechia, Germany, Luxembourg, Netherlands) based on input probability rasters.</p> <h2>Usage</h2> <ol> <li><strong>Create a project folder</strong> where you'll store the files.</li> <li><strong>Download all the files</strong> to the project folder.</li> <li><strong>Extract the countries data</strong> into the project folder (be.zip=Belgium, cz=Czechia, de.zip=Germany, lu.zip=Luxembourg, nl.zip=Netherlands).<br>(Note: ensure the folder structure matches the "Data Description" section provided below) </li> <li><strong>Install Dependencies</strong> by navigating to the project folder in your terminal and install the necessary dependencies by running:<br>(Note: make sure you have Python installed on your system)<br><code>pip install -r ./requirements.txt</code></li> <li><strong>Run the script</strong> using the following command in the terminal:<br><code>python ./main.py</code></li> </ol> <h2>Data Description</h2> <p>After downloading and decompressing the files, the data must have the following structure.</p> <ul> <li><strong>area_estimates.csv: </strong>The area estimates for each land cover provided by Eurostat.</li> <li><strong>[country_code]/</strong><br> <ul> <li><strong>[model]/:</strong><br> <ul> <li><strong>classified_highest_likelihood/: </strong>Contains classification maps generated using the maximum likelihood mapping algorithm.</li> <li><strong>classified_proportional/: </strong>Stores classification maps produced using the Iterative Mapping of Probabilities algorithm.</li> <li><strong>iterations/: </strong>Stores images representing the iteration number in which each pixel was classified using the iterative proportional algorithm.</li> <li><strong>probabilities/: </strong>Contains input probability rasters for both mapping algorithms.</li> </ul> </li> </ul> </li> <li><strong>main.py</strong>: Python script implementing the Iterative Mapping of Probabilities framework.</li> <li><strong>requirements.txt</strong>: List of required libraries to run the script.</li> <li><strong>graphical_abstracl.pdf (optional)</strong>: Illustration on the IMP algorithm.</li> </ul> <h2>Script Explanation:</h2> <p>The script <strong>main.py</strong> implements IMP algorithm and process land use and land cover classification maps from probability rasters. These probabilities were generated for different countries and used two diffrent models (local and general). Please, refer to the paper for more details on how these models were trained.</p> <h3>Script Steps:</h3> <ol> <li><strong>Data Preparation:</strong><br>Loads <code>area_estimate.csv</code> file containing area estimates for different land cover classes in various countries and years.</li> <li><strong>Parameter Setup:</strong><br>Sets up parameters for each country, year, and model combination.<br>Each parameter set includes the country code, year, model type (local or general), and a list of land cover class codes.<br>(Note: you can change this section to set up parametersto process just some countries)</li> <li><strong>Processing Maps:</strong><br>Iterates over each parameter set and:<br> <ol> <li>Loads reference proportions of land cover classes for the specified country, year, and model.</li> <li>Loads probabilities from raster images.</li> <li>Runs the Iterative Mapping of Probabilities algorithm using the loaded probabilities and reference proportions.</li> <li>Saves the resulting land use and land cover classification map as an output.</li> </ol> </li> </ol> <h3>Script Inputs:</h3> <p>- CSV file containing area estimates for land cover classes (<code>./area_estimates.csv</code>).<br>- Probability raster images generated by classification models stored in <code>./[country_code]/[model]/probabilities/</code> folders.</p> <h3>Script Outputs:</h3> <p>Land use and land cover classification maps obtained by running the Iterative Mapping of Probabilities algorithm for each parameter set. The outputs are stored in <code>./[country_code]/[model]/classified_proportional_user/</code> folders.</p> <h2>Citation</h2> <p>If you use this code or data in your research, please cite the corresponding paper:</p> <p><em>Witjes, M., Herold, M., & de Bruin, S. (2024). Iterative Mapping of Probabilities: A data fusion framework for generating accurate land cover maps that match area statistics. Journal of Applied Earth Observation and Geoinformation (JAG), in review.</em></p> <h2>License</h2> <p>The code in this repository is licensed under the MIT License.</p> <p>The data provided in this repository is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).</p>
Damage Absolute Probability map
<p>Damage Absolute Probability map is a layer in support to Area of Interest (AOI) definition for an earthquake event. It provides the spatial distribution, in the examined area, of the absolute values of probability of damage derived considering the most severe damage classes provided by loss assessment data</p>
Annual carbon density and annual forest probability maps
<p>The annual carbon density (file name is GBRF+year) shows aboveground woody biomass and uses boosted regression trees, which were trained with a static global benchmark map of carbon density of woody vegetation for 2018, using MODIS (MCD43A4 7 bands; NDII, EV12, MCD43A3 shortwave albedo) and STRM data. The carbon density was mapped at 1-ha spatial grid cells and the unit is MgC/ha.</p> <p>The forest probability maps (file name is LT_nbr_paramset01_fitted.+year) were created from annual MODIS data at the resolution of 500m x 500m. The forest maps take values ranging from 0 to1, and the probability shows the likeliness if a pixel belongs to the forest (1) or non-forest (0) class. The higher the value, the higher the probability that the pixel is forest land.</p>
Probability maps of exceeding six soil thickness in mainland France
<p>This is the dataset of probability maps of exceeding six soil thickness (i.e. 5, 15, 30, 60, 100, 200 cm) in mainland France produced in the paper "Probability mapping of soil thickness by random survival forest at a national<br> scale" by Chen et al. (2019).</p> <p>Manuscript citation: Chen, S., Mulder, V.L., Martin, M.P., Walter, C., Lacoste, M., Richer-de-Forges, A.C., Saby, N.P., Loiseau, T., Hu, B. and Arrouays, D., 2019. Probability mapping of soil thickness by random survival forest at a national<br> scale. Geoderma, 344, 184-194.</p> <p>When using the data, please cite repositories as well as the original manuscript.</p> <p>For any questions on the data, please contact Dr. Songchao Chen (chensongchao@zju.edu.cn).</p>
Offshore wind turbine damage probability maps and hub height TC wind speeds for U.S. Atlantic and Gulf Coasts exposed to historical and future tropical cyclones
<p>Damage probability maps for offshore wind turbines exposed to tropical cyclones (TCs) under both historical and future climate scenarios along the U.S. Atlantic and Gulf Coasts are presented in this dataset. TCs are generated using <a href="../records/10392725" target="_blank" rel="noopener">The Risk Analysis Framework for Tropical Cyclones (RAFT)</a>, forced by <a href="https://pcmdi.llnl.gov/CMIP6/" target="_blank" rel="noopener">CMIP6</a> historical and future global climate simulations. Maximum wind speeds for 20- and 50-year TCs are processed through a <a href="https://www.sciencedirect.com/science/article/pii/S0960148120311423">fragility function</a> specific to offshore wind (OSW) turbines in order to estimate the probability of damage – specifically yielding and buckling – based on wind speed intensity. </p> <p><strong>Included data:</strong></p> <ul> <li><strong>TC wind speeds:</strong> Peak 10-min mean hub height (90m) TC wind speed maps</li> <li><strong>Damage states:</strong> Yielding and Buckling probability maps for OSW turbines</li> <li><strong>Geographic coverage:</strong> U.S. Atlantic and Gulf Coasts (up to 200km from the shoreline)</li> <li><strong>Time periods:</strong> Historic (1980-2014) and Future (2066-2100)</li> </ul> <p><strong>Methodology:</strong></p> <ul> <li><strong>Tropical cyclone simulation:</strong> The RAFT TC model is used to simulate storms for historical and future climates using CMIP6 environmental conditions.</li> <li><strong>TC impact metric:</strong> Wind speeds associated with 20- and 50-year return period TCs are used to estimate the aerodynamic and sea wave loading on OSW turbines.</li> <li><strong>Fragility functions:</strong> Wind speeds are input into a fragility function developed for OSW turbines, estimating the probability of yielding and buckling damage.</li> <li><strong>Damage probability maps:</strong> The results consist of eight (8) gridded damage probability maps representing the likelihoods of yielding and buckling to OSW turbines from 20- and 50-year TCs under historical and future climatic conditions.</li> </ul> <p><strong>Potential Uses:</strong></p> <ul> <li>Assessing the spatial vulnerability of OSW infrastructure to TCs</li> <li>Supporting decision-making for the design and siting of turbines</li> <li>Evaluating the impact of climate change on the risk of damage to OSW infrastructure</li> </ul> <p>For further insights into this dataset, users are encouraged to refer to the associated paper: <a href="https://www.nature.com/articles/s43247-024-01887-6">https://www.nature.com/articles/s43247-024-01887-6</a></p> <p>This dataset offers valuable insights into the potential impact of TCs on offshore wind infrastructure, aiding in risk assessment and resilience planning for the renewable energy sector.</p> <p> </p>
L4D - Probability map of giant trees occurrence (> 70 m) in the Brazilian Amazon
<p>The probability of giant trees occurrence (> 70m) based on environmental conditions. The observations higher than 70 m were filtered out and used to adjust an envelope model based on maximum entropy. In its optimization routine, the algorithm tracked how much the model gain was improved when small changes were made to each coefficient value associated with a particular variable. The resulting map of predicted occurrence of the tallest trees in the Amazon from the MaxEnt model shows that the probability of maximum tree height occurrence is highest in the northeastern Amazon (Fig. 6), more specifically in the Roraima and Guianan Lowlands. We considered 18 environmental variables: (1) fraction of absorbed photosynthetically active radiation (FAPAR; in %); (2) elevation above sea level (Elevation; in m); (3) the component of the horizontal wind towards east, i.e. zonal velocity (u-speed ; in m s<sup>-1</sup>); (4) the component of the horizontal wind towards north, i.e. meridional velocity (v-speed ; in m s<sup>-1</sup>); (5) the number of days not affected by cloud cover (clear days; in days yr<sup>-1</sup>); (6) the number of days with precipitation above 20 mm (days > 20mm; in days yr<sup>-1</sup> ); (7) the number of months with precipitation below 100 mm (months < 100mm; in months yr<sup>-1</sup> ) ; (8) lightning frequency (flashes rate); (9) annual precipitation (in mm); (10) potential evapotranspiration (in mm); (11) coefficient of variation of precipitation (precipitation seasonality; in %); (12) amount of precipitation on the wettest month (precip. wettest; in mm); (13) amount of precipitation on the driest month (precip. driest; in mm); (14) mean annual temperature (in °C); (15) standard deviation of temperature (temp. seasonality; in °C); (16) annual maximum temperature (in °C); (17) soil clay content (in %); and (18) soil water content (in %). </p>
Spatial probability maps of the superior parietal sulcus in the human brain
<p><span>The superior parietal sulcus (SPS) is the defining sulcus within the superior parietal lobule. The morphological variability of the SPS was examined in individual magnetic resonance imaging (MRI) scans of the human brain that were registered to the Montreal Neurological Institute (MNI) standard stereotaxic space. Two primary morphological patterns were consistently identified across hemispheres: 1) the SPS was identified as a single sulcus, separating the anterior from the posterior part of the superior parietal lobule and 2) the SPS was found as a complex of multiple sulcal segments. These morphological patterns were subdivided based on whether the SPS or SPS complex remained distinct or merged with surrounding parietal sulci. The morphological variability and spatial extent of the SPS were quantified using volumetric and surface spatial probabilistic mapping. The current investigation e</span><span>stablished consistent morphological patterns in a common anatomical space, the MNI stereotaxic space, to facilitate structural and functional analyses within the superior parietal lobule. </span></p>
Global heat map of probable importance of terrestrial ecosystems on meeting local demand of freshwater services
<p>This map (raster dataset, single layer) uses existing datasets to map globally “How important point x is likely to be for meeting the demand of a reliable & useable source of water on a scale of 0 to 1?” This relatively simple approach uses estimated water demand in a given basin as weight to identify pressure for flow regulation and water provisioning services. Precipitation and land cover estimates are then combined with it to give some insight into the hydrologic attributes of “location” and “timing” of flow that the ecosystems may influence. The underlying assumption here is that undisturbed ecosystems everywhere are performing the ecohydrological functions leading to freshwater services. The question is more (at the global scale): how dependent are the populations in the basin on the continued functioning of these services.</p> <p><strong>Input datasets:</strong></p> <ol> <li>Annual surface & groundwater (“blue”) water consumption estimates. URL: <a href="http://waterfootprint.org/en/resources/water-footprint-statistics/">http://waterfootprint.org/en/resources/water-footprint-statistics/</a></li> <li>HydroBasins watershed outline.</li> <li>European Space Agency (ESA) global land cover 2015.</li> <li>WorldClim annual average precipitation (Version 2.0).</li> </ol> <p><strong>Process:</strong></p> <p>Step 1: Calculate average annual water consumption estimates over HydroBasin outlines. This step spreads the demand laterally (in case of small basins) and upstream to the headwaters from (typically) downstream consumer concentration.</p> <p>Step 2: Normalize the demand globally and map the normalized values on to “natural” land cover classes from the land cover dataset [forests, grasslands, etc].</p> <p>Step 3: Normalize annual precipitation layer within basins on the scale 0-1 where 1 is the maximum annual precipitation in that basin. This is also mapped on the “natural” land cover. Precipitation is thus acting as ‘weight’ for importance within the basin. Example, upland headwaters will typically receive more rainfall and can be argued to be important for the flow regulation in the basin.</p> <p>Step 4: Combine the layers from 2 and 3.</p> <p><strong>Caveats:</strong></p> <ol> <li>Identification of what constitutes a “natural” land cover is not trivial, especially from global land cover maps. Example: Forests and plantations are hard to distinguish from these products.</li> <li>Improvement of quality of water is assumed to be implicit for functioning ecosystems.</li> </ol>
The superior frontal sulcus in the human brain – Morphology and probability maps
<p><span>The superior frontal sulcus (SFS) is a major sulcus on the dorsolateral frontal cortex that defines the lateral limit of the superior frontal gyrus. Caudally, it originates </span>near the superior precentral sulcus (SPRS) and rostrally, it terminates near the frontal pole. T<span>he advent of structural neuroimaging has revealed that this primary sulcus shows significant variability below the surface which is not captured by the classic sulcal maps. The present investigation examined the morphological variability of the SFS in 50 individual magnetic resonance imaging (MRI) scans of the human brain that were registered to the Montreal Neurological Institute (MNI) standard stereotaxic space. Two primary morphological patterns were identified: i) the SFS was classified as either a continuous sulcus or ii) the SFS was a complex of sulcal segments. Further, the SFS showed a high probability of merging with neighboring sulci on the superior and middle frontal gyri, and these patterns were documented. In addition, the morphological variability and spatial extent of the SFS were quantified using volumetric and surface spatial probability maps. The results from the current investigation provide an anatomical framework for understanding the sulcal morphology of the SFS, which is critical for the interpretation of structural and functional neuroimaging data in the dorsolateral frontal region, as well as for improving the accuracy of neurosurgical interventions. </span></p>
The superior frontal sulcus in the human brain – Morphology and probability maps
Open the record for dataset details and reuse information.
Global maps of current (1979-2013) and future (2061-2080) habitat suitability probability for 1,485 European endemic plant species
Open the record for dataset details and reuse information.
Spatial probability maps of the superior parietal sulcus in the human brain
Open the record for dataset details and reuse information.
Microstructure images, ilastik labels and probability maps.
<p>Microstructure images of several Al alloys reinforced with quasicrystals. Images were obtained using a Zeiss Axio Imager.A1m with AxioCam ERc 5s. Original images were enhanced using FIJI and analysed using ilastik. Included is the ilastik training set, labels used and probability maps.</p>
Subspecies and Distribution. O.c.cuniculusLinnaeus,1758—N,NE&EIberianPeninsula(Spain). O.c.algirusLoche,1858—S,SW&WIberianPeninsula(Spain,Portugal),NMorocco,NAlgeria(includingHabibasI). O.c.brachyotusTrouessart,1917—SFrance. O.c.cnossiusBate,1906—CreteI. O.c.habetensisCabrera,1923—Tanger-Tetouan-AlHoceimaRegion(NMorocco). O. c. huxleyi Haeckel, 1874 — Mediterranean Is (Balearic Is, Corsica, Sardinia, Sicily and Macaronesia (Azores, Madeira, and Canary Is). Original distribution after last Ice Age restricted to Iberian Peninsula, W France, and N Africa. Ancient introductions of the nominate subspecies probably during the Ro- man period have spread it throughout Europe, and now it is present in most of W, C & E Europe and the Mediterranean and Macaronesian Is (these mostly old introductions are also shaded on the map). During the 20" century it has been released into the steppes of the Black Sea in Ukraine and Russia (N Caucasus); introduced into Australia in 1788 and again in 1859 where it is now widespread; it is found on many Pacific Is, islands off the coast of South Africa and Namibia, and in New Zealand; successfully introduced only since 1936 into South America, nowadays with a limited range in Chile, Argentina, and Falkland Is, it is also present in the Caribbean Is (all these modern introductions not shaded in the map). Worldwide as domesticated forms. in Leporidae
Subspecies and Distribution. O.c.cuniculusLinnaeus,1758—N,NE&EIberianPeninsula(Spain). O.c.algirusLoche,1858—S,SW&WIberianPeninsula(Spain,Portugal),NMorocco,NAlgeria(includingHabibasI). O.c.brachyotusTrouessart,1917—SFrance. O.c.cnossiusBate,1906—CreteI. O.c.habetensisCabrera,1923—Tanger-Tetouan-AlHoceimaRegion(NMorocco). O. c. huxleyi Haeckel, 1874 — Mediterranean Is (Balearic Is, Corsica, Sardinia, Sicily and Macaronesia (Azores, Madeira, and Canary Is). Original distribution after last Ice Age restricted to Iberian Peninsula, W France, and N Africa. Ancient introductions of the nominate subspecies probably during the Ro- man period have spread it throughout Europe, and now it is present in most of W, C & E Europe and the Mediterranean and Macaronesian Is (these mostly old introductions are also shaded on the map). During the 20" century it has been released into the steppes of the Black Sea in Ukraine and Russia (N Caucasus); introduced into Australia in 1788 and again in 1859 where it is now widespread; it is found on many Pacific Is, islands off the coast of South Africa and Namibia, and in New Zealand; successfully introduced only since 1936 into South America, nowadays with a limited range in Chile, Argentina, and Falkland Is, it is also present in the Caribbean Is (all these modern introductions not shaded in the map). Worldwide as domesticated forms.
DL-FRONT MERRA-2 weather front probability maps over North America, 1980-
<p>DL-FRONT is a Deep Learning Neural Network (DLNN) that was trained to detect weather fronts using spatial grids of near-surface atmospheric variables. The dataset is composed of hourly spatial grids containing probability maps for each of five front-type categories—cold front, warm front, stationary front, occluded front, and no front.</p> <p>This dataset is the product of processing data from the National Aeronautics and Space Administration (NASA) <a href="https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/">Modern-Era Retrospective analysis for Research and Applications, Version 2</a> (MERRA-2). DL-FRONT processed MERRA-2 hourly data grids of instantaneous measures of air pressure reduced to mean sea level, air temperature at 2 meters, specific humidity at 2 meters, and wind velocity at 10 meters over the time span 1980 - 2018 to produce this dataset. The original MERRA-2 data were resampled at 1 degree resolution over the spatial range 31W - 171W x 10N - 77N using bicubic interpolation.</p> <p>At each hourly time step the network produced a set of spatial grids with the same resolution and spatial range as the input, one for each of the five categories mentioned above. Each cell in a spatial grid for a given category records the network-assigned probability (from 0.0 to 1.0) that the cell is in a weather front boundary region of that category (or, for the "no front" category, the probability that the cell is not in any weather front boundary region).</p> <p>The DLNN was trained using MERRA-2 data and human-identified fronts from the NOAA National Weather Service (NWS) Weather Prediction Center (WPC) <a href="https://www.wpc.ncep.noaa.gov/html/sfc2.shtml">Coded Surface Bulletin</a> dataset. The training datasets covered the years 2003-2007.</p> <p>The dataset contains two sets of files. The first set contains the original front probability maps. The second set contains "one hot" versions of the front probability maps. In the one hot version the five front-type probabilities for a spatial grid cell for a given time step are replaced by the value 1 for the largest front-type probability, and by 0 for the others.</p> <p>The front probability files have names that follow the form merra2_merra2-1deg_fronts_<year>.nc. The one hot files have names that follow the form merra2_merra2-1deg_onehot_<year>.nc. Each file contains one year of hourly spatial data grids.</p>
Data from: Modeling and mapping the probability of occurrence of invasive wild pigs across the contiguous United States
Open the record for dataset details and reuse information.
Spatial probability maps of the main morphological patterns of the inferior frontal sulcus in fsaverage space
Open the record for dataset details and reuse information.
Raw motif mapping bedfile data and model training set class probabilities
<p>Leveraging prior viral genome sequencing data to make predictions on whether an unknown, emergent virus harbors a 'phenotype-of-concern' has been a long-sought goal of genomic epidemiology. A predictive phenotype model built from nucleotide-level information alone is challenging with respect to RNA viruses due to the ultra-high intra-sequence variance of their genomes, even within closely related clades. We developed a degenerate k-mer method to accommodate this high intra-sequence variation of RNA virus genomes for modeling frameworks. By leveraging a taxonomy-guided 'group-shuffle-split' cross validation paradigm on complete coronavirus assemblies from prior to October 2018, we trained multiple regularized logistic regression classifiers at the nucleotide k-mer level. We demonstrate the feasibility of this method by finding models accurately predicting withheld SARS-CoV-2 genome sequences as human pathogens and accurately predicting withheld Swine Acute Diarrhea Syndrome coronavirus (SADS-CoV) genome sequences as non-human pathogens. Feature selection using L1 regularization identified several degenerate nucleotide predictor motifs with high model coefficients for the human pathogen class that were present across widely disparate clades of coronaviruses. However, these motifs differed in which genes they were present in, what specific codons were used to encode them, and what the translated amino acid motif was. This emphasizes the importance of a phenetic view of emerging pathogenic RNA viruses, as opposed to the canonical phylogenetic interpretations most commonly used to track and manage viral zoonoses. Applying our model to more recent Orthocoronavirinae genomes deposited since October 2018 yields a novel contextual view of pathogen potential across bat-related, canine-related, porcine-related, and rodent-related coronaviruses and critical adaptations which may have contributed to the emergence of the pandemic SARS-CoV-2 virus. Finally, we discuss the next steps to achieve robust predictive ensembles and the utility of these models (and their associated predictor motifs) to novel biosurveillance protocols that substantially increase the 'pound-for-pound' information content of field-collected sequencing data and make a strong argument for the necessity of routine collection and sequencing of zoonotic viruses. </p>
Raw motif mapping bedfile data and model training set class probabilities
Open the record for dataset details and reuse information.
IBEX High Energy Neutral Atom Imager (ENA-Hi) Data Release 10, not Compton-Getting corrected, Survival Probability corrected, Ram direction, West Longitude Ecliptic Maps, 1 year averaged data, Level H3
This IBEX-Hi data set is from Release 10 of all-sky map data for the first seven years, 2009-2015, in the form of ram direction Hydrogen, H, energetic neutral atom fluxes with no Compton-Getting corrections for spacecraft motion and with corrections for ENA survival probability between 1 and 100 AU. All-sky maps have been compiled for each consecutive 1 year time interval. The Interstellar Boundary Explorer, IBEX, has operated in space since 2008 updating our knowledge of the outer heliosphere and its interaction with the local interstellar medium. Start-time: 2008-12-25. There are currently 14 releases of IBEX-Hi and/or IBEX-Lo data covering 2009-2017. The data consist of all-sky maps in Solar Ecliptic Longitude, east and west, and Latitude angles for Energetic Neutral Atom, ENA, Hydrogen fluxes from IBEX-Hi from energy band 2 through energy band 6, see the first table below, in numerical data form. This particular data set is from IBEX Release 10 which includes observation from the first seven years, 2009-2015, of the IBEX mission. Details of the data and enabled science from Release 10 are given in the following journal publication: McComas, D.J., et al. (2017), Seven Years of Imaging the Global Heliosphere with IBEX, Astrophys. J. Supp. Ser., 229(2), 41 (32 pp.), http://doi.org/10.3847/1538-4365/aa66d8 The IBEX-Hi band/channel center energies and full width half maximum, FWHM, energy ranges are listed in a table below: +-----------------------------------------------------+ Energy Band Center Energy Energy Range ----------------------------------------------------- Channel 2 ~0.71 keV 0.52 keV to 0.95 keV Channel 3 ~1.11 keV 0.84 keV to 1.55 keV Channel 4 ~1.74 keV 1.36 keV to 2.50 keV Channel 5 ~2.73 keV 1.99 keV to 3.75 keV Channel 6 ~4.29 keV 3.13 keV to 6.00 keV +-----------------------------------------------------+ This particular IBEX-Hi CDF data product was constructed from the original ascii files named using the pattern hvset_tabular_ram_yearN for N=1,7, includes pixel map data from the ram direction, with no corrections, nocg, for the Compton-Getting effect corrections, sp, for ENA survival probability between 1 AU and 100 AU, and a map compilation cadence equal to 1 year. In all, there are 12 IBEX-Hi Release 10 CDF data products resulting from the multiplication of options for two Compton-Getting correction settings by two survival probability settings by three directional settings: antiram, ram, omni. The table below defines how the file naming pattern is constructed for each data product. Note that "ibex_h3_ena_hi_r10" is the file naming pattern root for all twelve of these IBEX-Hi CDF data products. The asterisk symbols in the last column of the table shows the line corresponding to this CDF data product within the expanded file naming pattern schema. +-----------------------------------------------------------------------------------------------------+ C-G Corr. SP Corr. Dir. Acronym Map Cadence File Naming Pattern for 1 yr Skymaps ----------------------------------------------------------------------------------------------------- cg nosp antiram 1 year ibex_h3_ena_hi_r10_cg_nosp_antiram_1yr cg sp antiram 1 year ibex_h3_ena_hi_r10_cg_sp_antiram_1yr nocg nosp antiram 1 year ibex_h3_ena_hi_r10_nocg_nosp_antiram_1yr nocg sp antiram 1 year ibex_h3_ena_hi_r10_nocg_sp_antiram_1yr ----------------------------------------------------------------------------------------------------- cg nosp ram 1 year ibex_h3_ena_hi_r10_cg_nosp_ram_1yr cg sp ram 1 year ibex_h3_ena_hi_r10_cg_sp_ram_1yr nocg nosp ram 1 year ibex_h3_ena_hi_r10_nocg_nosp_ram_1yr nocg sp ram 1 year ibex_h3_ena_hi_r10_nocg_sp_ram_1yr *** ----------------------------------------------------------------------------------------------------- cg nosp omni 6 months ibex_h3_ena_hi_r10_cg_nosp_omni_6mo cg sp omni 6 months ibex_h3_ena_hi_r10_cg_sp_omni_6mo nocg nosp omni 6 months ibex_h3_ena_hi_r10_nocg_nosp_omni_6mo nocg sp omni 6 months ibex_h3_ena_hi_r10_nocg_sp_omni_6mo +-----------------------------------------------------------------------------------------------------+ The first column in the above table shows whether Compton-Getting, C-G, corrections have been applied to the data. C-G corrections account for how ENA measurements are affected by the the orientation of the IBEX spacecraft velocity vector relative to the arrival direction of the ENAs. cg: Compton-Getting corrections applied nocg: Compton-Getting corrections not applied The second column in the above table shows whether Survival Probability, SP, corrections have been applied to the data. SP corrections account for the loss of ENAs due to radiation pressure, photoionization and ionization via charge exchange with solar wind protons as they
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.