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.
1,243
datasets available to search
ShareScore release 0.7.1
Dataset results
1,243 results for “Statistics”
Figure 4 in Host Plant Records for Fruit Flies (Diptera: Tephritidae: Dacini) in the Pacific Islands: 2. Infestation Statistics on Economic Hosts
Figure 4. Mean number of flies emerged per kg fruit and percent samples infested for the most infested hosts for Bactrocera facialis.
Figure 3 A–C in Host Plant Records for Fruit Flies (Diptera: Tephritidae: Dacini) in the Pacific Islands: 2. Infestation Statistics on Economic Hosts
Figure 3 A–C. Mean number of flies emerged per kg fruit and percent samples infested for the most infested hosts for Pacific fruit fly (Bactrocera xanthodes) (A), B. curvipennis (B), B. psidii (B), and B. trilineola (C).
Text-fig. 7. Projection of the declinations and inclinations of primary component of the DRM vectors and a mean direction based on Fisher statistics A – samples with normal polarity (down - projection on the lower hemisphere), B – samples with reversed polarity (up - projection on the upper hemisphere). in New Updated Results Of Paleomagnetic Dating Of Cave Deposits Exposed In Za Hájovnou Cave, Javoříčko Karst
Text-fig. 7. Projection of the declinations and inclinations of primary component of the DRM vectors and a mean direction based on Fisher statistics A – samples with normal polarity (down - projection on the lower hemisphere), B – samples with reversed polarity (up - projection on the upper hemisphere).
Statistical Identification of Coherent and Incoherent Random Lasing from Carbon Dots
<p><span>Here we demonstrate RL emission at ~565 nm from green emitting CDs (gCDs), and we use the emitted light as a light source for speckle-free microscopy of biological tissues and microparticles. The emission of the CD-based RL is thoroughly studied as a function of experimental conditions, and well-established mathematical tools in the field are used to perform a detailed statistical study of the lasing output. The CD-based RL displays ultra-narrow (~ 0.70 nm) emission lines over a comparatively broader (~10 nm) background. These two emissions are due to, so-called, coherent and incoherent RL. Their relative weight can be controlled by an appropriate choice of experimental conditions, allowing to tune the characteristics of RL light. The results demonstrate the potential of gCDs as a viable alternative to environmental unfriendly, scarce, or chemically unstable nanomaterials as gain media for RL with customizable emissions.</span></p>
Fig. 2. Statistical parsimony cladogram network representing relationships among the 45 haplotypes for a 615 in Genetic diversity of Halyomorpha halys (Hemiptera, Pentatomidae) in Korea and comparison with COI sequence datasets from East Asia, Europe, and North America
Fig. 2. Statistical parsimony cladogram network representing relationships among the 45 haplotypes for a 615 bp fragment of the COI gene of Halyomorpha halys. Each circle is labeled with haplotype number, and the size of each circle is proportional to the frequency of each haplotype [H3 (n = 353); H1 (n = 285); H22 (n = 43); H8 (n = 34); H33 (n = 23); H2 (n = 16); H32 (n = 8); H7, H9–H13, and H43 (n = 3); H6, H14, H34, H39, and H40 (n = 2); H4–H5, H12, H15–H21, H23, H30–H31, H35–H38, H41, H42, and H44–H51 (n = 1)]. Differing colors indicate countries in which samples were collected.
Figure 2. - A in High statistics measurement of the positron fraction in primary cosmic rays of 0.5-500 GeV with the alpha magnetic spectrometer on the international space station
Figure 2. - A: Sea lamprey (Petromyzon marinus); B: shad (either Alosa fallax or A. algeriensis). Scale bars = 10 cm. Photographs: M. JácomeFlores and B. Adrados.
Figure 1 in High statistics measurement of the positron fraction in primary cosmic rays of 0.5-500 GeV with the alpha magnetic spectrometer on the international space station
Figure 1. - Map showing previously known records of the sea lamprey (Petromyzon marinus) in north-western Africa (grey squares) and the record from the mouth of Oued Moulouya (black square). References for previous records: 1: Furnestin et al. (1958); 2: Boutellier (1918), adjacent records; 3: Dollfus (1955); 4: Bacha and Amara (2007); 5: records compiled by Renaud (2011).
Non-Abelian anyon statistics through AC conductance of a Majorana interferometer
<p>We provide the raw data used to produce Fig.3 and Fig.4 of our paper "Non-Abelian anyon statistics through AC conductance of a Majorana interferometer". We also provide a s<span>hematic three-dimensional view of the consid</span><span>ered experimental device for observing non-Abelian braiding </span><span>of Ising anyons via the AC conductance.</span></p>
-0.2 0.0 0.2 0.4 0.6 PC1 (29.8% of total variance) Fig. 8. Morphospace plot of the first two PCO axes generated in the R statistical environment (Claddis package). Branches are superimposed from a single representative topology selected from amongst the 48 MPTs. in The sauropodomorph biostratigraphy of the Elliot Formation of southern Africa: Tracking the evolution of Sauropodomorpha across the Triassic-Jurassic boundary
-0.2 0.0 0.2 0.4 0.6 PC1 (29.8% of total variance) Fig. 8. Morphospace plot of the first two PCO axes generated in the R statistical environment (Claddis package). Branches are superimposed from a single representative topology selected from amongst the 48 MPTs.
FIGURE 2 in RNames, a stratigraphical database designed for the statistical analysis of fossil occurrences - the Ordovician diversification as a case study
FIGURE 2. Structure of algorithm for time binning of stratigraphical units of the RNames Database (available under https://github.com/bjoekroe/RNames). Time bins are selected via three correlation routes (colour codes) and six rules resulting in six tables with referenced bins from which only those are selected which are most precise (i.e., range through lowest number of bins). Abbreviations: bio.unit, biostratigraphic unit; non-bio. unit, non-biostratigraphic unit. Colour code: red, correlation exclusively based on biostratigraphy; orange; correlation indirectly based on biostratigraphy; yellow, correlation based on direct or indirect assignments to time bins. -> arrow refers to referenced relations in RNames.
FIGURE 1 in RNames, a stratigraphical database designed for the statistical analysis of fossil occurrences - the Ordovician diversification as a case study
FIGURE 1. Simplified structure of the RNames Database (rnames.luomus.fi/). The database contains eight related tables (blue and red objects) of which the object "Relations" is central. In "Relations" correlated stratigraphic units are listed by reference. Three output tables (yellow objects) list time binned stratigraphic units based on a search algorithm that uses "Relations" via R-Package RMySQL (the scripts are available under https://github.com/bjoekroe/ RNames). Global Stages after Cooper et al. (2012). Abbreviations: ID, identifier; StS, Stage Slice (Bergström et al., 2009); TS, Time Slice (Webby et al., 2004)
FIGURE 5 in RNames, a stratigraphical database designed for the statistical analysis of fossil occurrences - the Ordovician diversification as a case study
FIGURE 5. Quality of PaleobioDB data used for diversity calculations. 1. Number of collections available per time bin. 2. Mean stratigraphic range of collections through time bins. Diamonds, two-time-bin resolution; triangles, one-time bin resolution; squares, all collections. Red, Global Stages after Cooper et al. (2012), green; Stage Slices, Bergström et al. (2009); blue, Time Slices, Webby et al. (2004).
FIGURE 4 in RNames, a stratigraphical database designed for the statistical analysis of fossil occurrences - the Ordovician diversification as a case study
FIGURE 4. Ordovician genus-level diversity trends of PaleobioDB occurrence data, based on three different time binning approaches. 1. Total mean standing diversity (after Cooper, 2004). 2. Rarefied diversity with time bins of <100 collections culled, with quota 600. Diamonds, two-time-bin resolution; triangles, one-time bin resolution; stars, all collections. Red, Global Stages after Cooper et al. (2012), green; Stage Slices, Bergström et al. (2009); blue, Time Slices, Webby et al. (2004). Error bars reflect 95% confidence interval.
Clinically relevant or statistically significant: effects, p-values and confidence intervals
<p><span>When evaluating and interpreting medical studies, a distinction must be made as to whether the result is clinically relevant and/or statistically significant. For this decision, it is best to calculate effects, p-values and confidence intervals. A decision can be made depending on the effects and the p-values or confidence intervals (e.g. 95% CI). </span></p> <p><span>The diagram illustrates the different scenarios of a clinically relevant and/or statistically significant result. </span></p>
Luka Dončić Rookie Year Statistics (2018-2019 NBA Season)
<p>This dataset contains key performance statistics for Luka Dončić during his rookie season with the Dallas Mavericks in the 2018-2019 NBA season. The dataset includes per-game statistics such as points per game (PPG), assists per game (APG), rebounds per game (RPG), and shooting percentages. The data is focused on analyzing Dončić's impactful first season in the NBA.</p>
Great Lakes monthly water balance components from the Large Lakes Statistical Water Balance Model (L2SWBM)
<p>**Note that an updated version of the data (v3.0) was uploaded on October 2, 2024, which supersedes earlier versions**</p> <p>These data sets are the results of leveraging bi-national data and the Large Lakes Statistical Water Balance Model (L2SWBM) specifically tailored for the Laurentian Great Lakes to produce value-added time series of water supply components, including expressions of uncertainty, that ultimately close the water balance across the interconnected Great Lakes system. </p> <p>The model serves as a new cornerstone for bi-national coordination of hydrologic data throughout this international transboundary basin, providing an improved means of capturing data patterns, revealing seasonal variabilities, as well as short-term and long-term trends.</p> <p>This repository includes monthly output from the L2SWBM. Output datasets include over-lake precipitation, over-lake evaporation, lateral tributary inflow (runoff), connecting channel flow (cms and also included in mm normalized to lake area), diversion flow (cms and also included in mm normalized to lake area), and component net basin supply. Data is included for lakes Superior, Michigan-Huron, Erie, and Ontario.</p> <p>This version contains data from 1950 to 2022.</p>
Dataset for publication: Statistically Equivalent Virtual Microstructures for Modeling of Complex Polycrystalline Alloys Using a Generative Adversarial Network (GAN)-Enabled Computational Platform
<p>This dataset provides the necessary data to get the images and results shown in the paper "Statistically Equivalent Virtual Microstructures for Modeling of Complex Polycrystalline Alloys Using a Generative Adversarial Network (GAN)-Enabled Computational Platform". </p> <p>Source Data Raw.zip has the entire data set used to generate the images.</p> <p>Source Data.zip contains the processed data from "Source Data Raw.zip". </p> <p>Files with extension .dream3d are accompained by a file with extension .xdmf. This files can be opened with Paraview. And their data can be accesible using python or matlab.</p> <p>For more information contact Proffesor Somnath Ghosh at Johns Hopkins University, Civil and Systems Engineering Department.</p>
Output from Linear Inverse Models (LIMs) emulating the observed spatiotemporal statistics of Australian precipitation and global sea surface temperatures
<p><strong>Data repository for <em>How unusual was Australia's 2017–2019 Tinderbox Drought?</em></strong></p> <p>This repository contains LIM data underpinning the paper <em>How unusual was Australia's 2017–2019 Tinderbox Drought?</em> [doi: 10.1016/j.wace.2024.100734 <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.wace.2024.100734" target="_blank" rel="noopener">available online in <em>Weather and Climate Extremes</em> 17 October 2024</a>]. All other datasets used in the paper are freely available online (see Data Availability statement in the paper for details). </p> <p>The repository contains 12 netcdf files, which together comprise the Linear Inverse Model (LIM) outputs described in the paper. <strong>In all cases, please see the paper for important details on the data and how they were produced.</strong> </p> <p><em>Global LIMs</em></p> <ul> <li>`LIM5000_COBE-globalSST_prec-anoms-aus_monthly.nc` <ul> <li>contains 5000 years of emulated Australian precipitation variability, modelled using Australian rainfall data from the Australian Gridded Climate Dataset v2 (AGCD) and global SST data from 'Centennial in situ Observation-Based Estimates of the Variability of SST and Marine Meteorological Variables version 2' (COBE)</li> </ul> </li> <li>`LIM5000_ERSST-globalSST_prec-anoms-aus_monthly.nc` <ul> <li>contains 5000 years of emulated Australian precipitation variability, modelled using Australian rainfall data from the AGCD and global SST data from US National Oceanic and Atmospheric Administration 'Extended Reconstruction SST version 5’ (ERSST)</li> </ul> </li> <li>`LIM5000_COBE-globalSST_SST-anoms-global_monthly.nc` <ul> <li>contains 5000 years of emulated global SST variability, modelled using global SST data from COBE</li> </ul> </li> <li>`LIM5000_ERSST-globalSST_SST-anoms-global_monthly.nc` <ul> <li>contains 5000 years of emulated global SST variability, modelled using global SST data from ERSST</li> </ul> </li> </ul> <p><em>Tropical Pacific Ocean LIMs</em></p> <ul> <li>`LIM5000_COBE-TropicalPacificSST_prec-anoms-aus_monthly.nc` <ul> <li>contains 5000 years of emulated Australian precipitation variability, modelled using Australian rainfall data from the AGCD and tropical Pacific Ocean SST data from COBE</li> </ul> </li> <li>`LIM5000_ERSST-TropicalPacificSST_prec-anoms-aus_monthly.nc` <ul> <li>contains 5000 years of emulated Australian precipitation variability, modelled using Australian rainfall data from the AGCD and tropical Pacific Ocean SST data from ERSST</li> </ul> </li> <li>`LIM5000_COBE-TropicalPacificSST_SST-anoms-TropicalPacific_monthly.nc` <ul> <li>contains 5000 years of emulated global SST variability, modelled using tropical Pacific Ocean SST data from COBE</li> </ul> </li> <li>`LIM5000_ERSST-TropicalPacificSST_SST-anoms-TropicalPacific_monthly.nc` <ul> <li>contains 5000 years of emulated global SST variability, modelled using tropical Pacific Ocean SST data from ERSST</li> </ul> </li> </ul> <p><em>Indian Ocean LIMs</em></p> <ul> <li>`LIM5000_COBE-IndianOceanSST_prec-anoms-aus_monthly.nc` <ul> <li>contains 5000 years of emulated Australian precipitation variability, modelled using Australian rainfall data from the AGCD and Indian Ocean SST data from COBE</li> </ul> </li> <li>`LIM5000_ERSST-IndianOceanSST_prec-anoms-aus_monthly.nc` <ul> <li>contains 5000 years of emulated Australian precipitation variability, modelled using Australian rainfall data from the AGCD and Indian Ocean SST data from ERSST</li> </ul> </li> <li>`LIM5000_COBE-IndianOceanSST_SST-anoms-TropicalPacific_monthly.nc` <ul> <li>contains 5000 years of emulated global SST variability, modelled using Indian Ocean SST data from COBE</li> </ul> </li> <li>`LIM5000_ERSST-IndianOceanSST_SST-anoms-TropicalPacific_monthly.nc` <ul> <li>contains 5000 years of emulated global SST variability, modelled using Indian Ocean SST data from ERSST</li> </ul> </li> </ul> <p><strong>How to cite this</strong> <strong>repository</strong></p> <p>If using this data, please cite the original publication, available from <a href="https://www.sciencedirect.com/science/article/pii/S2212094724000951" target="_blank" rel="noopener">https://www.sciencedirect.com/science/article/pii/S2212094724000951.</a> </p>
Statistical analysis code for output from a model used to simulate foot-and-mouth disease dynamics in the United Kingdom
<p>Epidemics can sometimes be managed through reductions of host density, such as social distancing for human diseases, reducing plant density through cultural and genetic means, and host culling for epizootics. These approaches allow for a certain density of hosts to remain within a targeted area. By contrast, total ring depopulation is often used as a management strategy for emerging infectious diseases in livestock. In this study, we explore the trade-offs of a density-based culling strategy to determine if fewer livestock farms can be culled within rings while maintaining a decrease in disease transmission. To do so, we evaluated a farm-density-based ring culling strategy to control foot-and-mouth disease (FMD) in the United Kingdom. This strategy may allow for some farms within rings around infected premises (IPs) to escape depopulation, with the aim to prevent over-culling during outbreaks. Using a spatially-explicit, stochastic, state-transition simulation algorithm originally developed by Keeling et al. 2001 to model FMD spread in the United Kingdom, we simulated this reduced-farm-density, or "target density" strategy. We modeled FMD disease spread in four counties in the UK (Aberdeenshire, Cumbria, Devon, and North Yorkshire) that have different farm demographies. We ran 740,000 simulations in a full-factorial analysis of epidemic impact measurements (i.e. culled animals, culled farms, epidemic length) and cull strategy parameters (i.e. target farm density, daily farm cull capacity, cull radius). We found that all of the cull strategy parameters were drivers of epidemic impact. We found that outbreaks in Cumbria had higher epidemic impacts and were more likely to take off compared with other counties with more outbreaks being likely to take off in Cumbria. Most importantly, in all counties, our proposed target density strategy was more effective at combatting FMD compared with traditional 'total ring depopulation' when considering average culled animals and culled farms. The differences in epidemic impact between the counties are likely driven by farm demography, especially differences in cattle and farm density. This target density strategy can be applied to many different systems, including other livestock and agricultural systems, to reduce host density as opposed to over-culling hosts.</p>
Figure 7 in Taxonomic notes on the Asian frogs of the tribe Paini (Ranidae, Dicroglossinae): 1. Morphology and synonymy of Chaparana aenea (Smith, 1922), with proposal of a new statistical method for testing homogeneity of small samples
Figure 7. Chaparana aenea (Smith, 1922), MNHN 1999.5821, topotype of Rana (Chaparana) fansipani Bourret, 1939, adult male from Fan Si Pan, Vietnam: ventral view of chest and throat showing shape of patch of nuptial spines.
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.