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.
1,646
datasets available to search
ShareScore release 0.9.0
Dataset results
1,646 results for “plot”
The data and analysis code used to produce the nanomechanical plots in "Stress control of tensile-strained In_{1-x}Ga_{x}P nanomechanical string resonators"
<p>Data and code for figures in "Stress control of tensile-strained In_{1-x}Ga_{x}P nanomechanical string resonators"</p>
Chronicle of Nature - Phenology of Plants of Bryansk Forest Nature Reserve (plot based)
Plants Phenology dataset compiled in the context of Chronicle of Nature program in Bryansk Forest Nature Reserve (Russian Federation) consisting of 14084 records collected during 20 years between 1994 and 2013.
Fig. 5. Violin plots for height experiment 2 in A Baited Time Sorting Pitfall Trap Allowing More Temporal Fidelity of Dung Beetle (Coleoptera: Scarabaeidae) Activity
Fig. 5. Violin plots for height experiment 2 comparing: A) Abundance per trap type, B) Species richness per trap type, C) Shannon index for each trap type, D) Pielou evenness per trap type.
Data for producing agreement circle plot and Jaccard index for six child violence exposure measures
<p>Data used in</p> <p><span>Elklit, A.; Pettersson, M.; Salbøg Petersen, A. & Nielsen, T.<em> (2024)</em>. </span><span>Is there consensus on child violence exposure measures? A study of six recommended instruments. <em>European Journal of Psychotraumatology</em>. <span> </span></span></p> <p><strong><span><span> </span></span></strong></p>
Variability of Earth's ionospheric outflow in response to the dynamic terrestrial exosphere - dataset and plotting script
<p>This dataset contains all the information discussed and displayed in the paper titled "Variability of Earth's ionospheric outflow in response to the dynamic terrestrial exosphere". The attached Jupyter notebook contains the necessary software to produce the same plots that were used to conduct the analysis.</p>
FIGURE 4. Concordia Age plot for sample PCM3Aug14-04. Data have been corrected for 230 in PE Note: Corrigenda to Murphey et al. 2018
FIGURE 4. Concordia Age plot for sample PCM3Aug14-04. Data have been corrected for 230Th disequilibrium.
Forest plots of pairwise comparisons from original studies for the primary outcomes
<p>Supplemental File #9 for the Cochrane review entitled: "<span>Non-invasive respiratory support in preterm infants as primary mode: a network meta-analysis"</span></p>
Comparison-adjusted Funnell plots for the Primary outcomes: Treatment Failure, Endotracheal Ventilation and Moderate-Severe CLD
<p>Supplementary File #10 for Cochrane review entitled: "Non-invasive respiratory support in preterm infants as primary mode: a network meta-analysis"</p>
Tables of direct, indirect and networks estimates and Forests plots of pairwise comparisons for all secondary outcomes
<p>Supplementary File #12 for Cochrane review entitled: "Non-invasive respiratory support in preterm infants as primary mode: a network meta-analysis"</p>
FIGURE 1. Box-plots regarding A in Agrimonia eupatoria subsp. major stat. nov. (Rosaceae) and notes on the Agrimonia eupatoria aggregate
FIGURE 1. Box-plots regarding A) angle hypanthium/lowest bristles (measure in degree, axis y), and B) length of hypanthium + crown (measures in mm, axis y).
FIGURE 1. Box plot for leaf and stem variables with p-value less than 0.0001 in A new species of Oxytropis (Fabaceae) from Central Apennines (Italy)
FIGURE 1. Box plot for leaf and stem variables with p-value less than 0.0001 in Kruskal-Wallis test. O.o.: Oxytropis ocrensis, O.h.: O. halleri, O.k.: O. korabensis; O.x.: O. xerophila.
FIGURE 2. Box plot for leaf and stem variables with p-value less than 0.0001 in A new species of Oxytropis (Fabaceae) from Central Apennines (Italy)
FIGURE 2. Box plot for leaf and stem variables with p-value less than 0.0001 in Kruskal-Wallis test. O.o.: Oxytropis ocrensis, O.h.: O. halleri, O.k.: O. korabensis; O.x.: O. xerophila.
Raw data of XRD and geochemical composition for plots for the lagoonal to littoral K-bentonites
Open the record for dataset details and reuse information.
Total Electron Content and Magnetic Field TIme Series Survey Plots during THEMIS/CMO/FAIR Magnetic Conjunctions
<p>These survey plots are for time intervals corresponding to magnetic conjunctions between (1) NASA's Time History of Events and Macroscale Interactions during Substorms (THEMIS) satellites, (2) USGS College Alaska (CMO) magnetic observatory, and (3) the FAIR GNSS receiver near Fairbanks, Alaska. Magnetic field measurements are used from THEMIS (~3s sampling interval) and CMO (1s sampling interval), while 1s Total Electron Content (TEC) measurements are used from high-rate RINEX data for FAIR obtained from the NASA Crustal Dynamics Data Information System (CDDIS) archive of space geodesy data. The plots also contain solar wind measurements and geomagnetic activity indices from NASA's OMNIWeb (https://omniweb.gsfc.nasa.gov/) database for the same magnetic conjunction intervals. </p> <p>The time series stackplots during each magnetic conjunction show the three components of Interplanetary Magnetic Field, Sym-H index, THEMIS satellite magnetic field perturbation, CMO ground-based magnetometer magnetic field perturbation, TEC perturbation from different GPS satellites-FAIR receiver pairs (specific GPS satellite varies from event to event), and elevation angle for the same GPS satellites.</p>
Dataset for the plots in paper: 'A Kronecker product accelerated efficient sparse Gaussian Process (E-SGP) for flow emulation' in 'Journal of Computational Physics'
<p>The .xlsx file contains the data used for the plots Fig. 3, 4, 7, 8 and 9 in the paper 'Kronecker product accelerated efficient sparse Gaussian Process (E-SGP) for flow emulation'.</p>
Supplementary Figure S2.The funnel plots of the association between insomnia and MetS components genetically in the reverse MR analysis.
<p>This figure described the funnel plots of the association between insomnia and MetS components genetically in the reverse MR analysis.</p>
Results and plotting scripts for the manuscript 'SuCCESs – a global IAM for exploring the interactions between energy, materials, land-use and climate systems in long-term scenarios'
<p><br>This archives the results for the manuscript 'SuCCESs – a global IAM for exploring the interactions between energy, materials, land-use and climate systems in long-term scenarios'</p> <p>For the model version used to create these results, please see: https://doi.org/10.5281/zenodo.13981520</p> <p>Files to reproduce the figures, in R language:<br>SuCCESs validation.R - Reads GDX files and produces plots for energy and emissions.<br>SuCCESs validation MC.R - The same, but with the Monte Carlo GDXs.</p> <p>External data sources:</p> <p>***<br>GHG emissions are from IGCC and PRIMAP</p> <p>IGCC: https://climatechangetracker.org/igcc (CC-BY license)</p> <p>PRIMAP:<br>Gütschow, Johannes; Jeffery, M. Louise; Gieseke, Robert; Gebel, Ronja; Stevens, David; Krapp, Mario; Rocha, Marcia (2016): The PRIMAP-hist national historical emissions time series, Earth Syst. Sci. Data, 8, 571-603, https://doi.org/10.5194/essd-8-571-2016<br>Gütschow, Johannes ; Busch, Daniel ; Pflüger, Mika (2024): The PRIMAP-hist national historical emissions time series (1750-2023) v2.6. Zenodo. https://doi.org/10.5281/zenodo.13752654<br>https://primap.org/primap-hist/ (CC-BY-4.0 license)</p> <p>***<br>Historical energy production and use data are from IEA Energy Statistics Data Browser (CC BY 4.0 licence).<br>https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser?country=WORLD&fuel=CO2%20emissions&indicator=CO2BySource</p> <p>***<br>IAM results are from the SSP database: https://tntcat.iiasa.ac.at/SspDb </p> <p>Keywan Riahi, Detlef P. van Vuuren, Elmar Kriegler, Jae Edmonds, Brian C. O’Neill, Shinichiro Fujimori, Nico Bauer, Katherine Calvin, Rob Dellink, Oliver Fricko, Wolfgang Lutz, Alexander Popp, Jesus Crespo Cuaresma, Samir KC, Marian Leimbach, Leiwen Jiang, Tom Kram, Shilpa Rao, Johannes Emmerling, Kristie Ebi, Tomoko Hasegawa, Petr Havlík, Florian Humpenöder, Lara Aleluia Da Silva, Steve Smith, Elke Stehfest, Valentina Bosetti, Jiyong Eom, David Gernaat, Toshihiko Masui, Joeri Rogelj, Jessica Strefler, Laurent Drouet, Volker Krey, Gunnar Luderer, Mathijs Harmsen, Kiyoshi Takahashi, Lavinia Baumstark, Jonathan C. Doelman, Mikiko Kainuma, Zbigniew Klimont, Giacomo Marangoni, Hermann Lotze-Campen, Michael Obersteiner, Andrzej Tabeau, Massimo Tavoni.<br>The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview, Global Environmental Change, Volume 42, Pages 153-168, 2017,<br>DOI:110.1016/j.gloenvcha.2016.05.009</p> <p>Rogelj, J., Popp, A., Calvin, K.V., Luderer, G., Emmerling, J., Gernaat, D., Fujimori, S., Strefler, J., Hasegawa, T., Marangoni, G., Krey, V., Kriegler, E., Riahi, K., van Vuuren, D.P., Doelman, J., Drouet, L., Edmonds, J., Fricko, O., Harmsen, M., Havlik, P., Humpenöder, F., Stehfest, E., Tavoni, M., Scenarios towards limiting global mean temperature increase below 1.5 °C. Nature Climate Change 8, 2018, 325-332.<br>DOI:10.1038/s41558-018-0091-3</p>
Simulation Code for Plotting the Range-Doppler Map
<p>This file contains simulation code for plotting a Range-Doppler map. Key parameters have been left blank and should be populated with your specific data before running the code. Ensure all required fields are correctly set to generate accurate simulation results.</p>
Species and plot characteristics for River restoration effects on dispersal and the development of riparian seed bank: Do poor seed banks limit restoration of boreal riparian zones?
<p>Vegetation composition in boreal streams in the standing vegetation and the seed bank, </p>
The global distribution and drivers of wood density across angiosperms and gymnosperms and their impact on forest carbon stocks (Data and Plots)
<p>Abstract:</p> <div>The density of wood is a key indicator of trees’ carbon investment strategies, impacting productivity and carbon storage. Despite its importance, the global variation in wood density and its environmental controls remain poorly understood, preventing accurate predictions of global forest carbon stocks. Here, we analyze information from 1.1 million forest inventory plots alongside wood density data from 10,703 tree species to create a spatially-explicit understanding of the global wood density distribution and its drivers. Our findings reveal a pronounced latitudinal gradient, with wood in tropical forests being up to ~30% denser than that in boreal forests. In both angiosperms and gymnosperms, hydrothermal conditions represented by annual mean temperature and soil moisture emerged as the primary factors influencing the variation in wood density globally. This indicates similar environmental filters and evolutionary adaptations among distinct plant groups, underscoring the essential role of abiotic factors in determining wood density in forest ecosystems. Additionally, our study highlights the prominent role of disturbance, such as human modification and fire risk, in influencing wood density at more local scales. Factoring in the spatial variation of wood density notably changes the estimates of forest carbon stocks, leading to differences of up to 21% within biomes. Therefore, our research contributes to a deeper understanding of terrestrial biomass distribution and how environmental changes and disturbances impact forest ecosystems.</div> <div> </div> <p>This repository only provides the folders of 'Data' and 'Plots' that are needed for for the repository on GitHub: https://github.com/LidongMo/GlobalWoodDensityProject</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.