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10,735 results for “dependability”

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zenodo32/100

Results from "The Field-Dependent Nature of PageRank Values in Citation Networks"

<p>This repo contains a gzipped archive of the resulting dataframes from the analyses discussed in our manuscript</p>

opencc-by-4.0Dec 2022View details →
dryad32/100

Context-dependent substitution matrices for coding regions of angiosperm cpDNA

<p>The dataset contains 4x4 substitution matrices generated from fourfold degenerate sites within coding regions of flowering plant chloroplast DNA. Substitution data was generated from multiple sets of 3 taxa sequence comparisons, one taxon serving as the outgroup to root the substitution. There is one matrix for each of the possible 192 tetranucleotide contexts consisting of the two bases immediately flanking the substitution on the 5' side and the two bases immediately flanking the substitution on the 3' side. These are count matrices but can be converted to Markov transition matrices. The analysis presented in the paper examines variation in substitution dynamics as a function of context.</p>

opencc-zeroJan 2023View details →
zenodo32/100

Modules for Experiments in Stellar Astrophysics (MESA): Time-Dependent Convection, Energy Conservation, Automatic Differentiation, and Infrastructure

<p>We update the capabilities of the open-knowledge software instrument Modules for Experiments in Stellar Astrophysics (MESA). The new auto_diff module implements automatic differentiation in MESA, an enabling capability that alleviates the need for hard-coded analytic expressions or finite difference approximations. We significantly enhance the treatment of the growth and decay of convection in MESA with a new model for time-dependent convection, which is particularly important during late-stage nuclear burning in massive stars and electron degenerate ignition events. We strengthen MESA&#39;s implementation of the equation of state, and we quantify continued improvements to energy accounting and solver accuracy through a discussion of different energy equation features and enhancements. To improve the modeling of stars in MESA we describe key updates to the treatment of stellar atmospheres, molecular opacities, Compton opacities, conductive opacities, element diffusion coefficients, and nuclear reaction rates. We introduce treatments of starspots, an important consideration for low-mass stars, and modifications for superadiabatic convection in radiation-dominated regions. We describe new approaches for increasing the efficiency of calculating monochromatic opacities and radiative levitation, and for increasing the efficiency of evolving the late stages of massive stars with a new operator split nuclear burning mode. We close by discussing major updates to MESA&#39;s software infrastructure that enhance source code development and community engagement.</p>

opencc-by-4.0Aug 2022View details →
zenodo32/100

Aridity-dependent sequence of water potentials for wilting, stomatal closure, and embolism in woody plants

<p>The dataset of the paper &quot;Aridity-dependent sequence of water potentials for wilting, stomatal closure, and embolism in woody plants&quot;</p>

opencc-by-4.0Jan 2023View details →
zenodo32/100

Data for: Torii et al.,Observed Kinetics of Enterovirus Inactivation by Free Chlorine Are Host Cell-Dependent, Environmental Science and Technology, 10.1021/acs.est.2c07048

<p>This folder contains the experimental data to the figures shown in the main manuscript and Supporting Information.</p> <p>- Figure 1 (Inactivation curves for E11 by free chlorine, UV, and heat)</p> <p>- Figure 2 (Inactivation curves for CVA9, CVB1, E7, E9, and E13)</p> <p>- Figure S1 (Loss of attachment and the PCR-target by free chlorine treatment)</p> <p>- Figure S2 (Flow cytometric&nbsp;analysis)</p> <p>&nbsp;</p> <p>&nbsp;</p> <p>&nbsp;</p>

opencc-by-4.0Jan 2023View details →
dryad32/100

Explosive Cenozoic origin and diversity-dependent diversification dynamics shaped the evolution of Australian skipper butterflies

<p><span>Australia was predominantly tropical for most of the early Cenozoic, then transitioned to a cooler and drier climate in the Oligocene. In response to this increasing aridity, some lineages adapted to more xeric ecosystems, contracted, or became restricted to increasingly fragmented mesic refugia, or went extinct. Yet, the lack of macroevolutionary studies at a continental scale precludes a better understanding of Australian biodiversity patterns and processes during the Cenozoic. Here, we infer a robust dated phylogenomic tree for a radiation of Australian endemic butterflies, the Trapezitinae skippers, to test the impact of biotic and abiotic drivers on Cenozoic diversification dynamics in Australia. These butterflies originated during the Eocene (<em>ca</em>. 42 Ma) in the mesic biome of Australia. Trapezitinae exploded in diversity during a cool, dry period in the late Oligocene and early Miocene, then experienced a sharp deceleration in speciation. Xeric ecosystems appear to have been colonized more recently, supporting the hypothesis of arid and semi-arid biomes as evolutionary sinks. Temperature-dependent and phytophagy-dependent diversification models received little support. Instead, we find evidence for diversity-dependent processes with a declining diversification in Trapezitinae likely linked to limited ecological opportunities following a rapid initial burst of diversification.</span></p>

opencc-zeroJan 2023View details →
dryad32/100

Tradeoff between pre and postcopulatory traits depends on locomotor activity in male Tribolium castaneum beetles

<p><span>Locomotor performance is an indicator of dynamic exercise; thus, it is a central trait in many animal behaviors. Although higher locomotor endurance may increase male reproductive success (e.g., in mate searching and male–male contests), investment in other male reproductive traits (e.g., male attractiveness and sperm competition) may be decreased through energy consumption due to higher activity levels. Here, I investigated male attractiveness, mating success, and paternity success using males of the red flour beetle Tribolium castaneum selected for higher (H) and lower (L) locomotor endurance. Although there was no difference in male attractiveness between the selection regimes, H males had significantly higher mating success than L males. Conversely, L males had significantly higher paternity success than H males. Therefore, there was a tradeoff between mating success and paternity success among the selection regimes, suggesting that locomotor endurance affects male reproduction in T. castaneum, and individual variation of locomotor endurance may be maintained within a population.</span></p>

opencc-zeroJan 2023View details →
zenodo32/100

Database for "A New Four-Component L*-dependent Model for Radial Diffusion based on Solar Wind and Magnetospheric Drivers of ULF Waves"

<p>Database&nbsp;for <strong>&quot;A New Four-Component L*-dependent Model for Radial Diffusion based on Solar Wind and Magnetospheric Drivers of ULF Waves&quot; </strong>submitted to Space Weather&nbsp;by Murphy et al.&nbsp;</p> <p>The repository contains 2 datasets:</p> <ul> <li>The power spectral density of the compressional&nbsp;magnetic field from THEMIS, Van Allen Probes, and GOES along with accompanying position (MLT, L, L* TS05), solar wind, and geomagnetic data</li> <li>The power spectral density of the azimuthal electric&nbsp;field from THEMIS and Van Allen Probes along with accompanying position (MLT, L, L* TS05), solar wind, and geomagnetic data</li> </ul> <p>Both datasets are provided as an IDL save file and as an HDF5 file.</p> <p>The IDL data can be loaded using:&nbsp;</p> <pre><code>filename='electric_field_psd.sav' filename='magnetic_field_psd.sav' restore, filename, /verbose</code></pre> <p>The HDF5 file can be opened and investigate using (small changes will be required to store each variable):</p> <pre><code class="language-python">import h5py filename = 'magnetic_field_psd.h5' # magnetic field data filename = 'magnetic_field_psd.h5' # electric field data with h5py.File(filename, "r") as f:     # loop through all keys (data)     #print key and key attribute and size     for i in f.keys():     print(f"{i} - {f[i].attrs['attributes']}, shape - {f[i].shape}")     ds_obj = f[i]      # returns as a h5py dataset object     ds_arr = f[i][()]  # returns as a numpy array</code></pre> <p>Below is a description of unique and common&nbsp;variables in each file. The psd variables have a shape [f,t], indicating the first dimension is frequency and the second is time,&nbsp;the f_mhz variable has shape [f], and all time series have shape [t]; here [f] and [t]&nbsp;denotes the number of elements in frequency and time arrays.&nbsp;&nbsp;</p> <p>------------------------------------</p> <p><strong>Magnetic field data set:</strong></p> <p><strong><em>Files</em></strong></p> <ul> <li>magnetic_field_psd.h5</li> <li>magnetic_field_psd.sav</li> </ul> <p><strong><em>Unique Data (variable in file)</em></strong></p> <ul> <li>psd&nbsp; <ul> <li>Power spectral density of the compressional magnetic field from THEMIS, Van Allen Probes, and GOES</li> <li>Units -&nbsp;nT<sup>2</sup>/mHz</li> <li>Shape - [f, t]</li> </ul> </li> </ul> <p>------------------------------------</p> <p><strong>Electric field data set:</strong></p> <p><strong><em>Files</em></strong></p> <ul> <li>electric_field_psd.h5</li> <li>electric_field_psd.sav</li> </ul> <p><strong><em>Unique Data (variable in file)</em></strong></p> <ul> <li>psd&nbsp; <ul> <li>Power spectral density of the azimuthal electric field from THEMIS, Van Allen Probes, and GOES</li> <li>Units -&nbsp;(mV/m)<sup>2</sup>/mHz</li> <li>Shape - [f, t]</li> </ul> </li> </ul> <p>------------------------------------</p> <p><strong>Common Data in the Magnetic and Electric Field Datasets (variable in file):</strong></p> <ul> <li>probe <ul> <li>Corresponding satellite of each time stamp</li> <li>Shape [t]</li> </ul> </li> <li>t <ul> <li>Time stamp of each time series; number of seconds since 1970 &nbsp;(UNIX time), [t]</li> <li>Units - s</li> <li>Shape [t]</li> </ul> </li> <li>f_mhz <ul> <li>Frequency of psd data, [f]</li> <li>Units - mHz</li> <li>Shape [f] - (19)</li> </ul> </li> <li>ae <ul> <li>OMNI AE index of each time stamp</li> <li>Units - nT&#39;</li> <li>Shape [t]</li> </ul> </li> <li>al <ul> <li>OMNI AL index of each time stamp, units - nT</li> <li>Shape [t]</li> </ul> </li> <li>au <ul> <li>OMNI AU index of each time stamp&nbsp;</li> <li>Units - nT</li> <li>Shape [t]</li> </ul> </li> <li>b_t <ul> <li>OMNI IMF B of each time stamp</li> <li>Units - nT</li> <li>Shape [t]</li> </ul> </li> <li>b_x <ul> <li>OMNI IMF Bx (GSM) of each time stamp</li> <li>Units - nT</li> <li>Shape [t]</li> </ul> </li> <li>b_y <ul> <li>OMNI IMF By (GSM) of each time stamp</li> <li>Units - nT</li> <li>Shape [t]</li> </ul> </li> <li>b_z <ul> <li>OMNI IMF Bz (GSM) of each time stamp</li> <li>Units - nT</li> <li>Shape [t]</li> </ul> </li> <li>dst <ul> <li>OMNI Dst of each time stamp</li> <li>Units - nT</li> <li>Shape [t]</li> </ul> </li> <li>kp - <ul> <li>OMNI Kp (Kp*10) of each time stamp</li> <li>units - NA</li> <li>Shape [t]</li> </ul> </li> <li>l_sh <ul> <li>L-shell of each time stamp</li> <li>Shape [t]</li> </ul> </li> <li>ls_t05 <ul> <li>L* from TS05 of each time stamp</li> <li>Shape [t]</li> </ul> </li> <li>mlt <ul> <li>Magnetic Local Time of each time stamp</li> <li>Unit - hour</li> <li>Shape&nbsp;[t]</li> </ul> </li> <li>n <ul> <li>OMNI Solar Wind Proton Density of each time stamp</li> <li>Units - n/cc</li> <li>Shape [t]</li> </ul> </li> <li>pdyn <ul> <li>OMNI Solar Wind Dynamic Pressure (flow pressure) of each time stamp</li> <li>Units - nPa</li> <li>Shape [t]</li> </ul> </li> <li>symh - b&#39;OMNI Sym-H of each time stamp, units - nT&#39;, shape - (477205,)</li> <li>v_t <ul> <li>OMNI Solar wind V of each time stamp</li> <li>Units - km/s</li> <li>Shape [t]</li> </ul> </li> <li>v_x <ul> <li>OMNI Solar wind Vx (GSE) of each time stamp</li> <li>Units - km/s</li> <li>Shape [t]</li> </ul> </li> <li>v_y <ul> <li>OMNI Solar wind Vy (GSE) of each time stamp</li> <li>Units - km/s</li> <li>Shape [t]</li> </ul> </li> <li>v_z <ul> <li>OMNI Solar wind Vz (GSE) of each time stamp</li> <li>Units - km/s</li> <li>Shape [t]</li> </ul> </li> </ul> <p>&nbsp;</p> <p>&nbsp;</p> <p>&nbsp;</p>

opencc-by-4.0Jan 2023View details →
dryad32/100

Density-dependent patterns of multivariate selection on sperm motility and morphology in a broadcast spawning mussel

<p>Sperm cells exhibit extraordinary phenotypic variation, both among taxa and within individual species, yet our understanding of the adaptive value of sperm trait variation across multiple contexts is incomplete. For species without the opportunity to choose mating partners, such as sessile broadcast spawning invertebrates, fertilisation depends on gamete interactions, which in turn can be strongly influenced by local environmental conditions that alter the concentration of sperm and eggs. However, the way in which such environmental factors impact phenotypic selection on functional gamete traits remains unclear in most systems. Here, we analyse patterns of linear and nonlinear multivariate selection under experimentally altered local sperm densities (densities within the capture zone of eggs) on a range of functionally important sperm traits in the broadcast spawning marine mussel, <i>Mytilus galloprovincialis</i>. Specifically, we assay components of sperm motility and morphology across two fertilisation environments that simulate either sperm limitation (when there are too few sperm to fertilise all available eggs), or sperm saturation (when there are many more sperm than required for fertilisation, and the risk of polyspermy and embryonic failure is heightened). Our findings reveal that the strength, form, and targets of selection on sperm depend on the prevailing fertilisation environment. In particular, our analyses revealed multiple significant axes of nonlinear selection on sperm motility traits under sperm limitation, but only significant negative directional selection on flagellum length under sperm saturation. These findings highlight the importance of local sperm densities in driving the adaptation of sperm phenotypes, particularly those related to sperm motility, in broadcast spawning invertebrates.</p>

opencc-zeroJan 2023View details →
zenodo32/100

Data for Sleep cycle-dependent vascular dynamics and the predicted effects on perivascular cerebrospinal fluid flow and solute transport

<p>Datasets containing derived data and results&nbsp;used by code in:&nbsp;https://github.com/AlexandraVallet/PVSflow&nbsp;for &quot;Sleep cycle-dependent vascular dynamics and the predicted effects on perivascular cerebrospinal fluid flow and solute transport&quot;, Bojarskaite et al. 2023. Please see github for instructions.&nbsp;</p>

opencc-by-4.0Jan 2023View details →
zenodo32/100

Leveraging the Crowd to Assess the Risk of Automated Dependency Updates: A Study on the Compatibility Score - Replication Package

<p>Replication package for FSE 2023 submission:&nbsp;Leveraging the Crowd to Assess the Risk of Automated Dependency Updates: A Study on the Compatibility Score</p> <p>Includes datasets used for the study&nbsp;and additional appendix material.</p>

opencc-by-4.0Jan 2023View details →
zenodo32/100

Meridional wave power distribution data for article "Latitudinal Dependence of Ground VLF Transmitter Wave Power in the Inner Magnetosphere"

<p>The data are the meridional wave power distribution for the four VLF transmitters: NWC, NAA, NML, and NLK in the inner magnetosphere. Each transmitter has a .mat file, and in each data file, we provide the meridional wave power distribution for both ray tracing simulation (parameter &quot;rt&quot;) and ERG observation (parameter &quot;erg&quot;). In each distribution data, the variables &quot;x2d&quot; and &quot;z2d&quot; are the cartesian coordinates in the meridional plane in the unit of Re (the Eearth&#39;s radius). x2d=r*cos(theta) and z2d=r*sin*(theta), where r is the radius from the Earth&#39;s center, and theta is the magnetic latitude. The variable &quot;power&quot; is the corresponding transmitter wave power in the unit of V^2/m^2.</p>

opencc-by-4.0Feb 2023View details →
zenodo32/100

Supplementary data for "The fitness of beta-lactamase mutants depends nonlinearly on resistance level at sublethal antibiotic concentrations."

<p>Supplementary data files and scripts, notes and figures accompanying the manuscript with working title &quot;The fitness of beta-lactamase mutants depends nonlinearly on resistance level at sublethal antibiotic concentrations&quot;. The figures and tables this dataset underpins is indicated in the name of each file of the dataset, or in the section &quot;Code and data accessibility&quot; of the manuscript.</p>

opencc-by-4.0Dec 2021View details →
zenodo32/100

Human Study of Demystifying Dependency Bugs in Deep Learning Stack

<p>A docker of human study which is the reproduce of id 215(a.tar) 319(b.tar) 389(c.tar) in our dataset.</p>

opencc-by-4.0Feb 2023View details →
zenodo32/100

Anonymized data for paper "RepeatNPR: Improving Neural Program Repair using Program Dependence Analysis and Filter Mechanism" submitted to ICSE 2024

<p>The project includes the dataset and source&nbsp;code used in the submitted ICSE&nbsp;2024&nbsp;paper titled &quot;RepeatNPR: Improving Neural Program Repair using Program Dependence Analysis and Filter Mechanism&quot;</p>

opencc-by-4.0Feb 2023View details →
zenodo32/100

Data for study "Device-dependent click-through rate estimation in Google organic search results based on clicks and impressions data"

<p>Data for the study "Device-dependent click-through rate estimation in Google organic search results based on clicks and impressions data".</p>

opencc-by-4.0Feb 2023View details →
zenodo32/100

Model output for analyses in Qian et al. (2023) "Region and cloud regime dependences of parametric sensitivity in E3SM Atmosphere Model".

<p>This archive contains&nbsp;post-processed data for analyses in Qian et al. (2023) &quot;<strong>Region and cloud regime dependences of parametric sensitivity in E3SM Atmosphere Model</strong>&quot;.&nbsp;&nbsp;These data are model output from the&nbsp;Perturbed Parameters Ensemble (PPE) simulations conducted with DOE&#39;s E3SM Atmosphere Model&nbsp;Version 1 (EAMv1).&nbsp; There are&nbsp;256x12 PPE simulations with each simulation run for 5 days&nbsp;following the approach of Cloud Associated Parameterization Testbed (CAPT) and transpose Atmosphere Model Intercomparison Project (AMIP) paradigm. The diagnostic variables associated with&nbsp;the parameterizations of turbulence and shallow convection, deep convection, cloud microphysics, and gravity wave drag at day 5 are saved and used for analyses in this paper.&nbsp;</p>

opencc-by-4.0Feb 2023View details →
zenodo32/100

Supplementary material 1 from: Woch MW, Kapusta P, Stanek M, Możdżeń K, Grześ IM, Rożej-Pabijan E, Stefanowicz AM (2023) Effects of invasive Rosa rugosa on Baltic coastal dune communities depend on dune age. NeoBiota 82: 163-187. https://doi.org/10.3897/neobiota.82.97275

Detailed information and results of supplementary analyses on the structure of resident plant communities

opencc-zeroMar 2023View details →
zenodo32/100

Graph of the dependence of the pH of the aqueous extract of the leather tissue of sheepskins in the canning process

<p>Graph of the dependence of the pH of the aqueous extract of the leather tissue of sheepskins in the canning process</p>

opencc-by-4.0Mar 2023View details →
zenodo32/100

Clinically used broad-spectrum antibiotics compromise inflammatory monocyte-dependent antibacterial defense in the lung

<p>Code and data for the preprint &quot;Clinically used broad-spectrum antibiotics compromise inflammatory monocyte-dependent antibacterial defense in the lung&quot;.&nbsp;</p> <p><strong>Metagenomic analysis</strong>&nbsp;</p> <p>- uses `<a href="https://github.com/ropensci/targets">targets</a>` (workflow manager) and `<a href="https://github.com/rstudio/renv">renv</a>` (environment manager) R packages.&nbsp;</p> <p>- calling `targets::tar_make()`, once the environment is configured using renv and a separate project has been created (with the exact file structure including the data), should run the entire pipeline, creating a _targets folder with all the .rds files&nbsp;</p> <p>- independent objects can be explored using `targets::tar_load(obj)` or `targets::tar_read(plot)`&nbsp;</p> <p><a href="https://books.ropensci.org/targets/">Targets user manual </a>for more information. Preprocessing/QC scripts can be provided upon reasonable request.&nbsp;</p> <p>&nbsp;</p>

opencc-by-4.0Apr 2023View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record

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Your academic CV and NIH biosketch

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