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.
2,833
datasets available to search
ShareScore release 0.9.0
Dataset results
2,833 results for “utility”
Data for: Triose phosphate utilization stress during photosynthesis addressed with dynamic assimilation measurements
<p>Oscillations in CO2 assimilation rate and associated fluorescence parameters have been observed alongside the triose phosphate utilization (TPU) limitation of photosynthesis for nearly 50 years. However, the mechanics of these oscillations are poorly understood. Here we utilize the recently developed Dynamic Assimilation Techniques (DAT) for measuring the rate of CO2 assimilation to increase our understanding of what physiological condition is required to cause oscillations. We found that TPU limiting conditions alone were insufficient, and that plants must enter TPU limitation quickly to cause oscillations. We found that ramps of CO2 caused oscillations proportional in strength to the speed of the ramp, and that ramps induce oscillations with worse outcomes than oscillations induced by step change of CO2 concentration. An initial overshoot is caused due to a temporary excess of available phosphate. During the overshoot, the plant out-performs steady state TPU and ribulose 1,5-bisphosphate regeneration limitations of photosynthesis but cannot exceed the rubisco limitation. We performed additional optical measurements which support the role of photosystem I reduction and oscillations in availability of NADP+ and ATP in supporting oscillations.</p>
Data used in "The Utility of RGB Color for Discrimination of Lunar Maturity and Composition"
<p>Datasets from the paper "The Utility of RGB Color for Discrimination of Lunar Maturity and Composition", By D. T. Blewett, T. X. Choi, Y.-C. Zheng, and E. A. Cloutis, to be published in the journal <em>Earth and Space Science</em>.</p> <p>Reflectance spectra for <em>Apollo</em> lunar samples 14003, 15601, 70011, 14310, and 65015 were published by Wagner et al. (1987), <em>Icarus 69</em>, 14–28. The spectra were digitized by Amanda Hendrix and Faith Vilas (see Hendrix and Vilas (2006), <em>Astron. J. 132</em>, 1396–1404). I took the spectra that Hendrix and Vilas supplied to me and resampled them to RELAB wavelengths. The spectra are in a tab-delimited text file.</p> <p>The responsivities of the <em>Chang'E-3</em> PCAM RGB channels were published by X. Ren et al. (2014), <em>Res. Astron. Astrophys. 14</em>, 1557–1566. We digitized the RGB curves from Fig. 2 of the Ren paper. The curves are in tab-delimited text files.</p> <p> </p>
Utilizing traditional and remote sensing techniques to assess Colorado potato beetle host preference in the Columbia Basin -- 2021 Data
<p>This is a remote sensing dataset collected in 2021 that contains orthomosaic images, shape files, analysis scripts, and derived numerical data from each plot. Data was collected using the protocol described here:</p> <p><a href="https://www.protocols.io/view/usda-ars-potato-genetics-lab-drone-data-collection-bp2l6148dvqe/v1">https://www.protocols.io/view/usda-ars-potato-genetics-lab-drone-data-collection-bp2l6148dvqe/v1</a></p> <p>Provided are "field map" files that denote the location and contents of each plot, a folder from each date that contains the 10 band orthomosiac, surface model image, a cropped and rotated image, shape files indicating the location of each plot, and derived data. The analysis can be replicated by following along with workflow listed in file named: rondon_cpb_2021.R. Derived data from this experiment can be found it the file named: "Rondon_CPB_data_2021_UAS_all.csv"<br> <br> If you have any questions or comments regarding this dataset please contact Dr. Max Feldman via email: max.feldman@usda.gov</p> <p> </p>
GPS and hydraulic head measurement utilized in North China Plain research
<p>This dataset contains the raw data of the GPS and hydraulic head measurement <br> utilized in North China Plain research.</p> <p>## Included files</p> <p>The `gps_cmonoc.dat` file contains the horizontal and vertical velocities of <br> the 35 continuous GPS stations from the Crustal Movement Observation Network of <br> China (CMONOC) project.</p> <p>The `gps_bjcors.dat` file contains the horizontal and vertical velocities of <br> the 14 continuous GPS stations from the Beijing Continuously Operating Reference <br> Station (BJCORS) network.</p> <p>The `gps_campaign.dat` file contains the horizontal and vertical velocities of <br> the 432 campaign GPS stations from the Crustal Movement Observation Network of <br> China (CMONOC) project.</p> <p>The `hydraulic_datacenter.xlsx` file contains the 559 measurements from confined <br> well accessed from the National Earth System Science Data Center, National Science <br> & Technology Infrastructure of China (http://www.geodata.cn), recording during 2005-2018.</p> <p>The `hydraulic_yearbook.xlsx` file contains the 130 measurements from both confined<br> and unconfined well compiled from the yearbook 'the China Groundwater Level Yearbook <br> for Geo-environmental Monitoring', recoding during 2005-2016.</p>
The equation of state for neutron star matter has been obtained through Bayesian inference utilizing a relativistic mean field model with a non-linear mesonic interaction.
<p>The equation of state for matter in neutron stars has been obtained through Bayesian inference utilizing a relativistic mean field model with a non-linear mesonic interaction.</p> <p>----------------------------<br> Dr. Tuhin Malik<br> Department of Physics, University of Coimbra<br> tm@uc.pt<br> Date: 22 Apr 2023<br> -----------------------------<br> The high density behavior of nuclear matter is analyzed within a relativistic mean field description with non-linear meson interactions. To assess the model parameters and their output, a Bayesian inference technique is used. The Bayesian setup is limited only by a few nuclear saturation properties, the neutron star maximum mass larger than 2 M$_\odot$, and the low-density pure neutron matter equation of state (EOS) produced by an accurate N$^3$LO calculation in chiral effective field theory. Depending on the strength of the non-linear scalar vector field contribution, we have found three distinct classes of EOSs, each one correlated to different star properties distributions. If the non-linear vector field contribution is absent, the gravitational maximum mass and the sound velocity at high densities are the greatest. However, it also gives the smallest speed of sound at densities below three times saturation density. On the other hand, models with the strongest non-linear vector field contribution, predict the largest radii and tidal deformabilities for 1.4 M$_\odot$ stars, together with the smallest mass for the onset of the nucleonic direct Urca processes and the smallest central baryonic densities for the maximum mass configuration. {These models have the largest speed of sound below three times saturation density, but the smallest at high densities, in particular, above four times saturation density the speed of sound decreases approaching approximately $\sqrt{0.4}c$ at the center of the maximum mass star. On the contrary, a weak non-linear vector contribution gives a monotonically increasing speed of sound.} {A 2.75 M$_\odot$ NS maximum mass was obtained in the tail of the posterior with a weak non-linear vector field interaction. This indicates that the secondary object in GW190814 could also be an NS. {The possible onset of hyperons and the compatibility of the different sets of models with pQCD are discussed. It is shown that pQCD favors models with a large contribution from the non-linear vector field term or which include hyperons.}}</p> <p>The article e-Print: <a href="https://arxiv.org/abs/2301.08169">2301.08169</a></p> <p>We release model parameters, its nuclear saturation properties, equation of state, and TOV solutions derived from Bayesian Inference with Prior Set 0, 1, 2, and 3. We also share Set 0 with Hyperon. <br> <br> For every Set, our data release packet contains four CSV files, namely "set{X}_prop.csv", "set{X}_eos.csv", "set{X}_tov.csv", and "set{X}_cs2.csv", where X in [0,1,2,3 and 0_hyp].<br> <br> set{X}_prop.csv:<br> The file contains the parameters for the RMF model, as well as a few NS properties and nuclear saturation properties. It has the following columns:<br> model name,gs,gv,gr,B,C,xi,lam,rho0,e0,k0,q0,z0,jsym0,lsym0,<br> ksym0,qsym0,zsym0,m_max,r_max,r14,lam14,cs2_max, ec,rhoc,rho_durca.<br> It is to be noted that the parameter B and C are the 10^3*b and 10^3 c (see article for details). <br> <br> set{X}_eos.csv:<br> For those models in set{X}_prop.csv, it is the NS matter EOS file. It has the following columns: model name, baryon number density, energy density and pressure. The units for baryon number density is fm-3 and MeV/fm3 is for both energy density and pressure. The EOS is for the core only. The crust is not added. <br> <br> set{X}_tov.csv:<br> For those models in set{X}_prop.csv, it is the TOV solution. It has the following columns: model name, ns radius (km), ns mass (msun), and dimensionless tidal deformability lambda. </p> <p>set{X}_cs2.csv:<br> For those models in set{X}_prop.csv, it is the square of the speed of sound over density. It has the following columns: model name, number density fm-3, and square of the speed of sound c2. <br> -------------------------------------------------------------------------</p>
To design, or not to design? Comparison of beetle ultraconserved element probe set utility based on phylogenetic distance, breadth, and method of probe design
<p>This repository contains Materials and designed UCE probe sets for the manuscript entitled "To design or not to design? Comparison of beetle ultraconserved element probe set utility based on phylogenetic distance, breadth, and method of probe design".</p>
Exploring the utility of regulatory network-based machine learning for gene expression prediction in maize
<p>Relevant Data and Code for <em>Exploring the utility of regulatory network-based machine learning for gene expression prediction in maize </em>by Taylor Ferebee and Edward Buckler.</p> <p><strong>Input Data</strong></p> <p>The inputs of the models are enclosed in <em>Input_data-2022-001.zip</em></p> <p><strong>Output Data</strong></p> <p>The outputs of the models are enclosed in <em>Output_Results-2022-001.zip</em></p> <p><strong>Relevant Code </strong></p> <p>The code for all analyses is enclosed in <em>Code_Archive.zip </em></p>
Tailoring magnetic hysteresis of Fe-Ni additive manufactured permalloy via multiphysics-multiscale simulations: Temperature-dependent parameters, thermodynamic database, results, and utilities
<p>This dataset contains temperature-dependent parameters and thermodynamic database, supplementary data and utilities of the publication "Tailoring magnetic hysteresis of additive manufactured Fe-Ni permalloy via multiphysics-multiscale simulations of process-property relationships" (<a href="http://doi.org/10.1038/s41524-023-01058-9">Yang et al., 2023</a>).</p> <p>We performed non-isothermal phase-field simulations of SLS process of the Fe<sub>21.5</sub>Ni<sub>78.5</sub> permalloy and subsequential mesoscopic thermo-elasto-plastic calculations and nanoscopic chemical order-disorder (<span>\(\gamma/\gamma'\)</span>) transition simulations as well as micromagnetic hysteresis calculations on nanostructures. Temperature-dependent parameters are employed. We then investigate the dependence of the fusion zone size, the residual stress and plastic strain, and the magnetic hysteresis of AM-produced Fe<sub>21.5</sub>Ni<sub>78.5 </sub>on beam power and scan speed.</p> <p>This dataset contains:</p> <ul> <li><em>feni_cac.tdb</em>: Thermodynamic database of the Fe-Ni binary system based on <a href="https://doi.org/10.1016/j.intermet.2010.02.026">Cacciamani et al., 2010</a></li> <li><em>average_values.csv</em>: Average quantities for creating the contours in Fig. 6a, 6b, 7a, 7b, 8a, and Supp. Fig. 10a, 10b.</li> <li><em>mesostructures.zip</em>: Containing resampled mesostructures from SLS single scan simulations (final timestep) with associated temperature, stress, and strain evolution. Nodal values are explained in Table 1. Naming pattern is <ul> <li>SLS-TEP__<power>-<scan_speed>__.e</li> </ul> </li> <li><em>parameters.zip</em>: Containing temperature-dependent parameters for performing SLS simulations and thermo-elasto-plastic calculations with fine (1K) temperature increments. The same temperature-dependent parameters with coarse temperature increments are already listed as Supp. Table 1, 2.</li> <li><em>sampled_point_data.zip</em>: Containing mechanical quantities on sampled points and corresponding results of nanoscopic <span>\(\gamma'\)</span> phase fraction (<span>\(\Psi_{\gamma'}\)</span>) and magnetic coercivity <span>\(H_\mathrm{c}\)</span>. Naming pattern is <ul> <li>mech__<power>-<scan_speed>__.csv</li> <li>Psi__<power>-<scan_speed>__.csv</li> <li>Hc__<power>-<scan_speed>__.csv</li> </ul> </li> <li><em>utilities.zip</em>: Containing Python utilities to perform calculations of free energy density and related thermodynamic quantities, extracting parameters from <em>feni_cac.tdb. </em><br><strong>Notice: </strong><a href="https://pycalphad.org/docs/latest/">pyCALPHAD</a> (ver 0.8.4) is requested for performing the Python utilities.</li> </ul> <p>Table 1. Nodal values in an exodus file Nodal value name Symbol Meaning Unit T <span>\(T\)</span> Normalized Temperature by <span>\(T_\mathrm{M}\)</span> - c <span>\(\rho\)</span> Substance order parameter - pb <span>\(\xi\)</span> Fusion zone indicator - eps (eps_11, eps_12, eps_13, eps_22, eps_23, eps_33) <span>\({\varepsilon}\)</span> Strain - epsp (epsp_11, epsp_12, epsp_13, epsp_22, epsp_23, epsp_33) <span>\({\varepsilon}_\mathrm{pl}\)</span> Plastic Strain - peeq <span>\(p_\mathrm{e}\)</span> Accumulated plastic strain - sigma (sigma_11, sigma_12, sigma_13, sigma_22, sigma_23, sigma_33) <span>\({\sigma}\)</span> Stress MPa vonmises <span>\(\sigma_\mathrm{e}\)</span> von Mises stress MPa u (u_X, u_Y, u_Z) <span>\(\mathbf{u}\)</span> Displacement μm</p> <p> </p> <p><strong>Notice</strong>: The raw transient outputs are not cured in this dataset due to the vast file size. Please contact the authors to acquire related files/utilities.</p>
Fig. 44 in Description of five new species of frog-biting midges (Diptera, Corethrellidae) from Brazil and examination of new morphological characters with utility for taxonomic and phylogenetic studies
Fig. 44. Detail of pupal metathorax of Corethrella sp. showing spherical structure, presumably a rudimentary spiracle.
Fig. 42 in Description of five new species of frog-biting midges (Diptera, Corethrellidae) from Brazil and examination of new morphological characters with utility for taxonomic and phylogenetic studies
Fig. 42. Corethrella cambirela Amaral, Mariano & Pinho, 2019, adult. A. Cranial setae, anterior and posterior views, and female clypeus in anterior view. B. Thoracic setae, lateral view.
Fig. 38 in Description of five new species of frog-biting midges (Diptera, Corethrellidae) from Brazil and examination of new morphological characters with utility for taxonomic and phylogenetic studies
Fig. 38. Corethrella longituba Belkin, Heinemann & Page, 1970, male adult. A. Cranial setae, anterior and posterior views, and clypeus in anterior view. B. Thoracic setae, lateral view. C. Hind leg claw and empodium, lateral view.
Fig. 34. Corethrella edwardsi Lane, 1942. A in Description of five new species of frog-biting midges (Diptera, Corethrellidae) from Brazil and examination of new morphological characters with utility for taxonomic and phylogenetic studies
Fig. 34. Corethrella edwardsi Lane, 1942. A. Adult cranial setae, anterior and posterior views, and female clypeus in anterior view. B. Adult thoracic setae, lateral view. C. Hind leg claw and empodium, lateral view. D. Larval exuvia, ventral view. E. Larval head, ventral view, except mandible in dorsal view. F. Larval siphon, dorsal view. G. Pupal exuvia, dorsal view. H. Pupal respiratory organ, dorsal view. I. Pupal metathorax and abdomen, dorsal and ventral views. Scale bars: 0.2 mm. Abbreviations: 1, 6, 9-S = siphon setae.
Fig. 33. Corethrella vittata Lane, 1939, adult. A in Description of five new species of frog-biting midges (Diptera, Corethrellidae) from Brazil and examination of new morphological characters with utility for taxonomic and phylogenetic studies
Fig. 33. Corethrella vittata Lane, 1939, adult. A. Cranial setae, anterior and posterior views, and female clypeus in anterior view. B. Pedicel and flagellomeres, lateral view. C. Thoracic setae, lateral view. D. Hind leg claw and empodium, lateral view.
Fig. 35 in Description of five new species of frog-biting midges (Diptera, Corethrellidae) from Brazil and examination of new morphological characters with utility for taxonomic and phylogenetic studies
Fig. 35. Corethrella quadrivittata Shannon & Del Ponte, 1928, female adult. A. Cranial setae, anterior and posterior views, and clypeus in anterior view. B. Thoracic setae, lateral view. C. Hind leg claw and empodium, lateral view.
Fig. 31. Corethrella fulva Lane, 1939. A in Description of five new species of frog-biting midges (Diptera, Corethrellidae) from Brazil and examination of new morphological characters with utility for taxonomic and phylogenetic studies
Fig. 31. Corethrella fulva Lane, 1939. A. Adult cranial setae, anterior and posterior views, and female clypeus in anterior view. B. Adult thoracic setae, lateral view. C. Hind leg claw and empodium, lateral view. D. Larval exuvia, ventral view. E. Exuvia of larval head, ventral view, except mandible in dorsal view. F. Pupal exuvia, ventral view. G. Pupal respiratory organ, dorsal view. H. Pupal metathorax and abdomen, dorsal and ventral views. Scale bars: 0.2 mm.
Fig. 32. Corethrella appendiculata Grabham, 1906, adult. A in Description of five new species of frog-biting midges (Diptera, Corethrellidae) from Brazil and examination of new morphological characters with utility for taxonomic and phylogenetic studies
Fig. 32. Corethrella appendiculata Grabham, 1906, adult. A. Cranial setae, anterior and posterior views, and female clypeus in anterior view. B. Thoracic setae, lateral view. C. Hind leg claw and empodium, lateral view.
Fig. 29. Corethrella lepida Borkent, 2008, female adult. A in Description of five new species of frog-biting midges (Diptera, Corethrellidae) from Brazil and examination of new morphological characters with utility for taxonomic and phylogenetic studies
Fig. 29. Corethrella lepida Borkent, 2008, female adult. A. Cranial setae, anterior and posterior views, and clypeus in anterior view. B. Thoracic setae, lateral view. C. Hind leg claw and empodium, lateral view.
Fig. 28. Corethrella aurita Borkent, 2008, female adult. A in Description of five new species of frog-biting midges (Diptera, Corethrellidae) from Brazil and examination of new morphological characters with utility for taxonomic and phylogenetic studies
Fig. 28. Corethrella aurita Borkent, 2008, female adult. A. Cranial setae, anterior and posterior views, and clypeus in anterior view. B. Thoracic setae, lateral view. C. Hind leg claw and empodium, lateral view.
Fig. 43 in Description of five new species of frog-biting midges (Diptera, Corethrellidae) from Brazil and examination of new morphological characters with utility for taxonomic and phylogenetic studies
Fig. 43. Corethrella yanomami Amaral, Mariano & Pinho, 2019, female adult. A. Cranial setae, anterior and posterior views, and clypeus in anterior view. B. Thoracic setae, lateral view.
Fig. 40. Corethrella atricornis Borkent, 2008, female adult. A in Description of five new species of frog-biting midges (Diptera, Corethrellidae) from Brazil and examination of new morphological characters with utility for taxonomic and phylogenetic studies
Fig. 40. Corethrella atricornis Borkent, 2008, female adult. A. Cranial setae, anterior and posterior views, and clypeus in anterior view. B. Thoracic setae, lateral view. C. Hind leg claw and empodium, lateral view.
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.