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.
741
datasets available to search
ShareScore release 0.9.0
Dataset results
741 results for “Decay”
Figure 1 in Comparison of Coleoptera emergent from various decay classes of downed coarse woody debris in Great Smoky Mountains National Park, USA
Figure 1. Map of collection locations in Great Smoky Mountains National Park. Primary forest sites: 1) Laurel Falls; 2) Porters Creek; 3) Albright Grove. Secondary forest sites: 4) Tremont; 5) Sugarlands Quiet Walkway; 6) Greenbrier.
Figures 15–20. Habitus images. 15 in Comparison of Coleoptera emergent from various decay classes of downed coarse woody debris in Great Smoky Mountains National Park, USA
Figures 15–20. Habitus images. 15) Urographis fasciatus (Degeer) (Cerambycidae: Lamiinae). 16) Analeptura lineola Say (Cerambycidae: Lepturinae). 17) Trachysida mutabilis (Newman) (Cerambycidae: Lepturinae). 18) Cerylon castaneum Say (Cerylonidae). 19) Mychocerus striatus (Sen Gupta and Crowson) (Cerylonidae). 20) Philothermus glabriculus (LeConte) (Cerylonidae).
Fungal community composition and genetic potential regulate fine root decay in northern temperate forests
<p>Understanding how genetic differences among soil microorganisms regulate spatial patterns in litter decay remains a persistent challenge in ecology. Despite fine root litter accounting for ~50% of total litter production in forest ecosystems, far less is known about the microbial decay of fine roots relative to aboveground litter. Here, we evaluated whether fine root decay occurred more rapidly where fungal communities have a greater genetic potential for litter decay. Additionally, we tested if linkages between decay and fungal genes can be adequately captured by delineating saprotrophic and ectomycorrhizal fungal functional groups based on whether they have genes encoding certain ligninolytic class II peroxidase enzymes, which oxidize lignin and polyphenolic compounds. To address these ideas, we used a litterbag study paired with fungal DNA barcoding to characterize fine root decay rates and fungal community composition at the landscape scale in northern temperate forests, and we estimated the genetic potential of fungal communities for litter decay using publicly available genomes. Fine root decay occurred more rapidly where fungal communities had a greater genetic potential for decay, especially of cellulose and hemicellulose. Fine root decay was positively correlated with ligninolytic saprotrophic fungi and negatively correlated with ECM fungi with ligninolytic peroxidases, likely because these saprotrophic and ectomycorrhizal functional groups had the highest and lowest genetic potentials for plant cell wall degradation, respectively. These fungal variables overwhelmed direct environmental controls, suggesting fungal community composition and genetic variation are primary controls over fine root decay in temperate forests at regional scales.</p>
Data of publication: "Many-Body Radiative Decay in Strongly Interacting Rydberg Ensembles"
<p>The uploaded files contain the data of the simulations presented in the figures in <a href="https://doi.org/10.1103/PhysRevLett.129.243202">https://doi.org/10.1103/PhysRevLett.129.243202</a>.</p>
The source data for "Inductively shunted transmon: A superconducting qubit with flux noise insensitive plasmon states and a protected fluxon decay exceeding 3 hours"
<p>The following folder contains all the raw data, analysis Mathematica notebook and ScQubits python codes used to generate the results in “Inductively shunted transmon: A superconducting qubit with flux noise insensitive plasmon states and a protected fluxon decay exceeding 3 hours” in nature communications. Please follow the instruction below for proper navigation through the data:</p> <p>Fig. 1 folder:</p> <ol> <li>Run “Color map of Matrix element Vs energy parameters” to generate “x.dat”, “y.dat”, ”M.dat”(respectively EJ/EL, EJ/EC and the matrix element of the first flux transition). <strong>Make sure to correct the address where these file should be saved</strong>.</li> <li>The mathematica notebook plots the dispersion in IST limit and the matrix element gray scale color map contours separately and the full image was constructed in illustrator later. The green dots on the dispersion plot represent the position of other qubits on the color map. </li> </ol> <p>Fig. 2&3 folder:</p> <ol> <li>The python file “paper figures” uses ScQubits to generate different studies in IST limit presented in Fig. 2&3 and generates the following files:</li> </ol> <p>Fig. 2a:</p> <p>“fluxonium.hdf5”: The spectrum of a typical fluxonium</p> <p>“fluxoniumME.hdf5”: The matrix element of all transition in “fluxonium.hdf5”</p> <p> </p> <p>Fig. 2d:</p> <p>“case1.hdf5”: The spectrum of fluxonium with EJ/EC=6.6</p> <p>“ME1.hdf5”: The matrix element of transition in “case1.hdf5”</p> <p>“case2.hdf5”: The spectrum of fluxonium with EJ/EC=13</p> <p>“ME2.hdf5”: The matrix element of transition in “case2.hdf5”</p> <p>.</p> <p>.</p> <p>“case6.hdf5”: The spectrum of fluxonium with EJ/EC=200</p> <p>“ME6.hdf5”: The matrix element of transition in “case6.hdf5”</p> <p>“IST.hdf5”: The spectrum of the IST qubit</p> <p>“ISTME.hdf5”: The matrix element of transition in “IST.hdf5”</p> <p>“transmon.hdf5”: The spectrum of a transmon with the same EJ and EC as IST qubit</p> <p>Fig. 3a</p> <p>“Waveamp.hdf5”: The wave functions and eigenenergies of the IST qubit</p> <p>“WaveampT.hdf5”: The wave functions and eigenenergies of the transmon</p> <p> </p> <p>Fig. 3b:</p> <p>“ELcase1.hdf5”: The spectrum of IST qubit with EL=2 GHz</p> <p>“ELME1.hdf5”: The matrix element of transition in “ELcase1.hdf5”</p> <p>“ELcase2.hdf5”: The spectrum of IST qubit with EL=1.5 GHz</p> <p>“ELME2.hdf5”: The matrix element of transition in “ELcase2.hdf5”</p> <p>.</p> <p>.</p> <p>“ELcase6.hdf5”: The spectrum of IST qubit with EL=0.25 GHz</p> <p>“ELME6.hdf5”: The matrix element of transition in “ELcase6.hdf5”</p> <p> </p> <p>Fig. 3b inset:</p> <p>“WaveampEL.hdf5”contains the wave functions for El={2,1.5,1,0.75,0.5,0.25}GHz.</p> <p> </p> <p>Fig. 3c:</p> <p>“EC.hdf5” contains numerical simulation of an IST qubit with fixed EJ and Ec while EL is changing to calculate anharmonicity.</p> <ol> <li>The Mathematica notebook “Theory_figures” runs based on the files above and plot the result presented in the paper.</li> </ol> <p>Fig. 5 folder:</p> <ol> <li>Fig. 5a&b folder contains the raw data of spectroscopy of the IST qubit with different temperature and the Mathematica notebook “Tempsweeps_figa&b” simply plots the data. In the data set the I and Q quadrature as well as the amplitude and power of the signal coming back from cavity is provided.</li> <li>Fig. 5c folder contains several sweeps of both spectroscopy and resonator performed at fridge base temperature (7mK) labeled as “specge#.txt” and “Res_VNA_*.txt” respectively. The ScQubits python code “IST_Device” provides a fit for the data using the fit procedure explained in Supplementary Note 4 and generates the bare spectrum of the device saved in “Fit.h5”. The Mathematica notebook “spec_analysis” uses all spectroscopy data and the fit file to plot Fig. 5c.</li> </ol> <p>Fig. 6 folder: Contains all the raw data of T1 and T2 experiment at different flux positions across a flux quantum. The Mathematica notebook “T1&2” performs all the analysis presented in Fig. 6 for devices A, B and C.</p> <p>Fig. 7 folder:</p> <ol> <li>Fig. 7a: In this folder the we provide the raw data for fidelity experiment. The data is in the “*.mat” format and contains 40000 single shot I&Q bins collected with measurement band width of 2MHz and integration time of 500ns. The files names indicate whether the data was taken with qubit prepared in ground/excited state by having “_g_”/”_e_”. Following the state preparation condition, the measurement power at which the data was taken is indicated. The Mathematica notebook “fidelity_sweep” takes the data and extract the fidelities shown in Fig. 7a and the 2D histogram plots presented in Supplementary Figure 5d.</li> <li>Fig. 7b: The raw data for QND-ness experiment is presented in this folder. Each file contains 500 time traces of the two consecutive pulses applied to the resonator to study the non-QND effects of the IST qubit in high power. The Qubit preparation condition is apparent in the file name along with the power at which the measurement was performed. The Mathematica notebook “QND_ness” extracts the QND_ness and plots the results shown in Fig. 7b</li> </ol> <p> </p> <p>Fig. 8 folder:</p> <ol> <li>Fig. 8a: This folder contains the spectroscopy sweeps conditions by the fluxon state using a strong microwave pulse applied to the resonator. The Mathematica notebook “sweeps” plots the data.</li> <li>Fig. 8c: This folder contains the raw data for long fluxon decays collected using quantum machines (QM). In this experiment the fluxon excitation pulse was applied and repeated until a successful fluxon state is detected. Afterwards, the experiment enters monitoring stage where every 30s we check the fluxon state until a tunneling to fluxon ground state is detected. This event is logged and the QM repeats the fluxon excitation immediately followed by a monitoring stage and logging the time it took for tunneling to occur. The raw data of every 30 second monitoring stage is saved in files with “_raw_” in their labels. The files containing “_taus_” in their names have only the logged tunneling time events. The Mathematica notebook “qubit analysis” takes the data for three external flux bias and, by loading the “_taus_” files, reconstructs the quasi quantum jump traces and finally the decay traces presented in Fig. 8c.</li> </ol>
Decayed Isotopic Yields for Keegans et al 2023
<p>Supplementary material for the paper 'Type Ia Supernova Nucleosynthesis: Metallicity-Dependent Yields' comprising decayed isotopic abundances from 39 models.</p>
Decaying Turbulence in Molecular Clouds
<p><strong>Introduction</strong></p> <p>This data release contains a suite of 15 AREPO simulations of a spherical molecular clouds of mass <span class="math-tex">\(M=10^4\text{M}_\odot\)</span> and radius <span class="math-tex">\(R \approx 9\text{pc}\)</span> seeded with different types of decaying turbulence. The initial conditions for each simulation are different, with different turbulent modes: mixed turbulence, purely compressive, and purely solenoidal; and different Virial ratios: gravitationally underbound and overbound clouds.</p> <ul> </ul> <p><strong>Simulations</strong></p> <p>Each of the .zip archives contains a simulation. The simulation name is as follows: <em><Turbulent mode><RNG seed><Virial ratio></em> where:</p> <ul> <li>Turbulent mode can be: M = mixed, C = compressive, S = solenoidal.</li> <li>RNG seed used to generate the turbulence can be: 26, 57 and 90.</li> <li>The Virial ratio can be: <em>none</em> = Virial equilibrium (<span class="math-tex">\(\alpha_\text{vir}=1.0 \)</span>), O = overbound (<span class="math-tex">\(\alpha_\text{vir}=0.6\)</span>), U = underbound (<span class="math-tex">\(\alpha_\text{vir}=2.0\)</span>).</li> </ul> <p>Each simulation consists of:</p> <ul> <li>Four simulation snapshots, which are AREPO binary files, corresponding to the four epochs of interest: when total mass in sinks/stars is <span class="math-tex">\(250\text{M}_\odot\)</span>, <span class="math-tex">\(500\text{M}_\odot\)</span>, <span class="math-tex">\(750\text{M}_\odot\)</span>, <span class="math-tex">\(1000\text{M}_\odot\)</span>.</li> <li>Four density grids, corresponding to the four snapshots, which are ray-casted from the AREPO simulations.</li> <li>Four filament networks identified in the density grids using the tool DisPerSE.</li> </ul> <p><strong>Reading in the files</strong></p> <p>The simulations are easily read and analyzed using the code released as part of our study, called <span class="math-tex">\(\texttt{fiesta}\)</span>: <a href="http://fiesta-astro.readthedocs.io">fiesta-astro.readthedocs.io</a>.</p> <p><strong>Supplementary content</strong></p> <p>The data release also contains two animations: <a href="https://zenodo.org/api/files/8b207605-0b06-4a19-96ee-52a949578c77/2D_temporal_animation.mp4?versionId=40527056-ee88-4d31-91ca-5f0ead23557c">2D_temporal_animation.mp4</a> and <a href="https://zenodo.org/api/files/8b207605-0b06-4a19-96ee-52a949578c77/3D_sinkepoch_animation.mp4?versionId=5dbe181d-8963-4536-8667-938fda0ddcda">3D_sinkepoch_animation.mp4</a>, which are useful for visualizing the simulations.</p>
Data for: Drivers of wood decay in tropical ecosystems: Termites vs. microbes along spatial, temporal and experimental precipitation gradients
Open the record for dataset details and reuse information.
Imprints of latitude, host taxon and decay stage on fungus-associated arthropod communities
Open the record for dataset details and reuse information.
Wood trait–decay relationships vary with topography and rainfall seasonality in a subtropical forest in China
Open the record for dataset details and reuse information.
Rapid biphasic decay of intact and defective HIV DNA reservoir during acute treated HIV disease
Open the record for dataset details and reuse information.
Data for: Spatial variability in the contribution of termites to the decay of plant detritus
Open the record for dataset details and reuse information.
Fungal community composition and genetic potential regulate fine root decay in northern temperate forests
Open the record for dataset details and reuse information.
Data from: Biogeographical variation in termite distributions alters global deadwood decay
Open the record for dataset details and reuse information.
Dissolved oxygen decay rate and ambient condition data, Waccamaw River Watershed, SC, Summer 2020
Ambient conditions of various dissolved and particulate biogeochemical parameters were measured within replicate station types (Waccamaw River proper, stormwater detention ponds, and forested wetlands) within the watershed of the Waccamaw River, SC in the summer of 2020. Additionally, 5-day dark bottle incubations of dissolved oxygen allowed the calculation of decay rates using an exponential curve fit. Q10 temperature coefficients were calculated using...
Ambient nutrients, carbon, and DOM absorbance metrics along with experimental dissolved oxygen decay rates for 5-day incubations of water within the Waccamaw River Watershed, SC, Summer 2020.
Dissolved oxygen (DO) impairment within coastal waters is widespread. Rising temperatures may exacerbate low DO levels by enhancing organic matter (OM) degradation. Here, the temperature sensitivity of OM degradation was investigated as DO decay rates determined during standard five-day biochemical oxygen demand (BOD) measurements conducted under different incubation temperatures. Sampling was conducted in the Waccamaw River watershed, South Carolina, a blackwater river with extensive forested wetland that also receives drainage from stormwater detention ponds associated with coastal development, thus providing contrasting sources of OM composition. Temperature sensitivities were measured as Q10 temperature coefficients, which define how DO decay rates change with 10 degrees of warming. The average Q10 value for the wetland sites (2.14 ± 0.41) was significantly greater (p = 0.004) than those measured in either the river (1.49 ± 0.36) or stormwater ponds (1.41 ± 0.21). Furthermore, using Intergovernmental Panel on Climate Change intermediate-to-very high temperature estimates for 2100 of +2.7 – 4.4 °C, average predicted increases in DO decay rates for wetlands (~22-39 %) are more than double the River (~11-18 %) and stormwater pond rates (~9-16 %). Our findings for inland, coastal waters agree with previous results for soils, suggesting that temperature sensitivities are variable across sites and increase with more complex, lower quality OM. Future modeling scenarios of DO utilization must therefore consider the influence of OM heterogeneity and the temperature sensitivity response of OM degradation across sources and region to better predict how climate change may impact oxygen impairment in aquatic ecosystems.
MESA files for paper "Discovery of an Exceptionally Strong β-Decay Transition of 20F and Implications for the Fate of Intermediate-Mass Stars"
<p>Files required to reproduce MESA results in the paper "Discovery of an Exceptionally Strong β-Decay Transition of 20F and Implications for the Fate of Intermediate-Mass Stars" (https://doi.org/10.1103/PhysRevLett.123.262701).</p>
Dataset for paper "Decaying dark matter signal across the Milky Way?"
<p>Summary of observations after cleaning.</p> <p>File naming: mw-xmm-obs-<region>.csv, where <region> identifies corresponding region bounds in arcmin.</p> <p>List of fields:<br> ObsId - XMM-Newton observation id<br> l - galactic longitude, degrees<br> b - galactic latitude, degrees<br> off-center - an angular distance of the instrument pointing direction from the Galactic center, arcmin<br> Exposure - cleaned exposure of the MOS1/MOS2/PN cameras, ksec<br> FoV - spectra extraction regions of the MOS1/MOS2/PN spectra (field-of-view), arcmin^2</p>
Niche differentiation and evolution of the wood decay machinery in the invasive fungus Serpula lacrymans
<p>Ecological niche breadth and the mechanisms facilitating its evolution are fundamental to understanding adaptation to changing environments, persistence of generalist and specialist lineages and the formation of new species. Woody substrates are structurally complex resources utilized by organisms with specialized decay machinery. Wood-decaying fungi represent ideal model systems to study evolution of niche breadth, as they vary greatly in their host range and preferred decay stage of the substrate. In order to dissect the genetic basis for niche specialization in the invasive brown rot fungus <i>Serpula lacrymans</i>, we used phenotyping and integrative analysis of phylogenomic and transcriptomic data to compare this species to wild relatives in the Serpulaceae with a range of specialist to generalist decay strategies. Our results indicate specialist species have rewired regulatory networks active during wood decay towards decreased reliance on enzymatic machinery, and therefore nitrogen-intensive decay components. This shift was likely accompanied with adaptation to a narrow tree line habitat and switch to a pioneer decomposer strategy, both requiring rapid colonization of a nitrogen-limited substrate. Among substrate specialists with narrow niches, we also found evidence for pathways facilitating reversal to generalism, highlighting how evolution may move along different axes of niche space.</p>
Neutrino emission by plasmon decay in a strong magnetic field: Data material
<p>This data reference paper summarizes the fundamental calculated results obtained analytically for the case of a neutron star (NS) crust: it’s cooling rate, time-scale evolution and neutrino luminosity, based on two different theories- Photo-Neutrino (PN) interaction and conventional weak interaction. Considering the NS environment as a degenerate plasmon composition the calculation tables are given. </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.