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.
1,067
datasets available to search
ShareScore release 0.9.0
Dataset results
1,067 results for “perturbation”
World map presentation of water vapour perturbation emitted by hypersonic aircraft at 32-38 km altitude
<p>World map presentation of water vapour perturbation by hypersonic aircraft in a layer at 32-38 km altitude. In this movie emitted water vapour accumulates for one month in one-hour steps (interpolated from data with five-hour time intervals for a more fluid film). Transport of perturbation dominated by horizontal movement through atmospheric air mass is visible.</p>
An elaborate data set on human gait and the effect of mechanical perturbations
<p>This data set includes measurements during trials of human walking on a treadmill with a movable base intended to provide data rich in content for the identification of the human's control system. The primary experiments were designed to longitudinally perturb the subject at the ground contact by randomly accelerating the belt. The marker locations (treadmill and human), treadmill accelerations, treadmill belt speeds, and the forces and moments from the dual force plates were measured during the trials.</p> <p>PeerJ Article: https://peerj.com/articles/918</p> <p>PeerJ Preprint: https://peerj.com/preprints/700/</p> <p>Paper source repository: https://github.com/csu-hmc/perturbed-data-paper</p>
Inferring and perturbing cell fate regulomes in human cerebral organoids
<p>Supplementary data for the manuscript: Inferring and perturbing cell fate regulomes in human cerebral organoids.</p> <p>Note: Due to the size limit of the repository, this update doesn't include all the relevant data. The following data are available via the same repository but different versions:</p> <ul> <li>Processed data in Seurat objects: <a href="https://doi.org/10.5281/zenodo.7687749" target="_blank" rel="noopener">Version 3</a> - seurat_objects.tar.gz</li> <li>scATAC-seq fragment files by Cell Ranger: <a href="https://zenodo.org/records/13254037" target="_blank" rel="noopener">Version 4</a></li> </ul>
Planetary perturbers: Flaring star-planet interactions in Kepler and TESS
<p>This data set contains:</p> <p>a. almost 13,000 de-trended Kepler and TESS light curves used in the publication with the same title (Ilin et al. 2024). Each light curve is a fits file with the Kepler or TESS identifier, Quarter or Sector, and, if there are multiuple light curves per Quarter/Sector, the number of the light curve. The light curves can be read with any fits file handler (e.g., astropy), or with the lightkurve package. Each light curve contains arrays for the flux, detrended flux, time, and orbital phase of the innermost planet. Note that for transiting planets the orbital phase is set to zero around transit midtime, while for non-transiting planets, the phase zero is set arbitrarily. There is no particular reason for splitting the data in the zip files except for easier upload.</p> <p>b. Tables 1-4 from Ilin et al. (2024). Tables 1 and 3 are combined into one. Each table includes a description of its columns at the top.</p> <p><a href="https://ui.adsabs.harvard.edu/abs/2024MNRAS.527.3395I/abstract"><strong>Ilin et al. (2024)</strong></a> Ilin, E., Poppenhäger, K., Chebly, J., Ilić, N., Alvarado-Gómez, J.~D.</p> <p>Planetary perturbers: flaring star-planet interactions in Kepler and TESS.</p> <p>Monthly Notices of the Royal Astronomical Society 527, 3395–3417.</p> <p>doi:10.1093/mnras/stad3398</p>
Numerical simulation of the equatorial plasma bubble: the effect of seeding by the vertical winds and random background noise perturbations.
<p>A wide variety of small-amplitude waves widely exist in the ionosphere and have significant effects on the evolution of equatorial plasma bubbles. In this paper, we simulated equatorial plasma bubbles (EPB) seeded by vertical neutral wind perturbations with wavelengths of 125 km and 250 km, and compared the morphology characteristics of plasma bubble structures with those under random noise perturbations in the background density. The numerical results showed that both vertical winds and random background noise perturbations can contribute to the growth of plasma bubbles, and the perturbations under additional random background noise can promote the growth of the plasma bubble structures faster. Additionally, several processes of the nonlinear behavior of bifurcated EPB structures, including bifurcation, pinching, and small-scale turbulent structures, were successfully obtained. Our simulation captured supersonic flows within the low-density plasma structures characterized by vertical velocities of about 1.5 km/s, which is consistent with experimental studies found in the literature.</p>
Perturbative effective field theory expansions for cosmological phase transitions, dataset
<p>This dataset is the work of Oliver Gould and Tuomas V.I. Tenkanen. It collects the numerical data from the paper <a href="https://arxiv.org/abs/2309.01672">"Perturbative effective field theory expansions for cosmological phase transitions"</a> (2023). It primarily contains data from perturbative calculations of the thermal evolution of the real-triplet extended Standard Model at two benchmark parameter points.</p><p>In addition, for comparison to the perturbative results, we have included data of the scalar quadratic condensates as a function of temperature from the lattice Monte-Carlo simulations of Lauri Niemi, Michael J. Ramsey-Musolf, Tuomas V.I. Tenkanen and David J. Weir, from the paper <a href="https://doi.org/10.1103/PhysRevLett.126.171802">"Thermodynamics of a Two-Step Electroweak Phase Transition"</a> (2020). We thank the authors for granting permission to reproduce this data here.</p><p>Everything is contained within the archive file <a href="https://zenodo.org/api/records/10353066/draft/files/triplet_two_step_data.tar.gz/content">triplet_two_step_data.tar.gz</a>, a tarball compressed with Gzip. For further details and for the context of this dataset, see the above papers. Details of the conventions used in the dataset can be found in the accompanying README.md file.</p>
Shelf temperature and salinity in the ACCESS-OM2-01 RYF90-91 and Future perturbation simulations
<p>The time-mean ocean temperature and salinity (averaged over the final 10 years of the simulation) in the ACCESS-OM2-01 model for isobaths shallower than 3000m in the Antarctic. T and S in the Repeat-Year Forced (RYF) 1990-1991 simulation and future perturbation run (from Li et al., 2023) are provided in this dataset. This dataset is used in the unsupervised classification work done by Sohail & Zika, 2023. The code necessary to reproduce all figures except figures 1 and 2 is also provided. </p>
Dataset: Impact of early Danian environmental perturbations on mid-latitude planktic foraminiferal assemblages from the ODP Site 1262 (South Atlantic Ocean)
<div> <div> <div> <p><span>After the mass extinction at the Cretaceous/Paleogene (K/Pg) boundary (~66 Ma), life forms quickly radiated to occupy ecological niches in the world’s oceans. Nevertheless, the aftermath of the Chicxulub impact, the massive volcanism of the Deccan Traps and climatic perturbations endured during the early Danian. The impact of consequent carbon cycle perturbations on nascent plankton communities, such as the Dan-C2 event (~65.80 to ~65.71 Ma), is still poorly known. In this work, we present a detailed study of planktic foraminiferal assemblages from Ocean Drilling Program (ODP) Site 1262 (Walvis Ridge, South Atlantic Ocean), spanning the first ~400 kyr of the Danian. No relevant perturbations in planktic foraminiferal assemblages and carbonate preservation indices have been identified at Site 1262 during the Dan-C2 event. Approximately 50 kyr before the beginning of the Dan-C2 event, a Hg-rich interval, potentially linked to the emplacement of the Ambenali Formation of the Deccan Traps massive volcanism, is recorded between ~65.95 and 65.82 Ma. It coincides with an increase in aberrant planktic foraminifera (~65.93 to ~65.82 Ma), allowing to establish a cause-effect relationship. Additionally, a bloom of triserial guembelitriids was recognized between ~65.87 and ~65.78 Ma, also preceding the Dan-C2 event but lagging the Hg-rich interval and the bloom of aberrant planktic foraminifera. The lag time between the first volcanic episode and ecosystem response may be due to factors such as an inefficient biological pump and increases in temperature, microbial activity and food supply at the ocean surface. A second Hg-rich interval identified between ~65.70 and ~65.65 Ma has been tentatively tied to the Mahabaleswar Formation of the Deccan Traps. Neither the second volcanic episode nor the Dan-C2 event resulted in relevant environmental perturbations at Site 1262. This suggests that, although volcanism may have had an impact on early plankton communities in the early Danian through metal contamination, marine ecosystems likely became progressively more stable and resistant to changes in volcanic emissions and the carbon cycle. </span></p> </div> </div> </div>
An ensemble of 48 physically perturbed model estimates of the 1/8° terrestrial water budget over the conterminous United States, 1980–2015
<p>This dataset contains the 1980–2015 monthly terrestrial water budget simulated by a 48-member perturbed-physics ensemble configured from the Noah LSM with multi-physics options (Noah‑MP v3.6). Simulation outputs include total evapotranspiration and its constituents (canopy evaporation, soil evaporation, and transpiration), runoff (the surface and subsurface components), as well as terrestrial water storage (snow water equivalent, four-layer soil water content from the surface down to 2 m, and the groundwater storage anomaly). The file name has four parts: the abbreviation for "terrestrial water budget", the used parameterization, the time scale, and the suffix.</p> <p>The 48 physics configurations are generated by combining four runoff parameterizations (run1: SIMGM, run2: SIMTOP, run3: NOAHR, run4: BATS), two parameterizations of stomatal conductance (can1: Ball–Berry, can2: Jarvis), three parameterizations of soil moisture stress factor (btr1:NOAHB, btr2: CLM, btr3: SSiB), and two parameterizations of near-surface atmospheric turbulence (tub1: M-O, tub2: Chen97).</p> <p>The simulation domain covers the all of conterminous United States (25°–53°N, 125°–67°W), which is also called the NLDAS-2 testbed (Xia et al., 2012a, b). The simulations were performed at a spatial resolution of 0.125°, which is the same as for NLDAS-2 models. Details of the simulation settings and spin-up run can be found in Section 2.3 of Zheng et al. (2019) and Section 2.2 of Fei et al. (2021).</p>
Simulation and laboratory eddy current testing data - modelling compound defects via perturbation theory
<p>This dataset serves to fit and validate a perturbation approach to the modelling eddy current signals of compound defects. It was obtained during the AIFRI project (Artificial Intelligence for Rail Inspection). The simulation data was generated with the Faraday software by INTEGRATED Engineering Software, using its BEM Solver. The simulation data is supplied as csv. The laboratory data was gathered by Rainer Pohl in the eddy current laboratory of BAM, section 8.4. It is supplied in the DICONDE data format. The first frame of the pixel array in the DICONDE files corresponds to the real part and the second frame corresponds to the imaginary part of the signal. The data set is analyzed in an upcoming article.</p> <p><span> </span></p>
An Evaluation of Equatorial Perturbation Electric Fields Using Empirical Vertical Drift Models
<p>Dataset for the article titled "An Evaluation of Equatorial Perturbation Electric Fields Using Empirical Vertical Drift Models", submitted to <em>Space Weather</em>. The dataset includes the outputs from the four empirical vertical drift models used in the article: Fejer and Scherliess (1997), Kelley and Retterer (2008), Manoj and Maus (2012), and Scherliess and Fejer (1999).</p>
A collection of draft gene regulatory networks and perturbation transcriptomics data
<p>These are collections of previously published gene regulatory networks and perturbation transcriptomics data analyzed in our manuscript "A systematic comparison of computational methods for expression forecasting". For more information and related code, see https://github.com/ekernf01/perturbation_benchmarking . </p>
An ensemble of 48 perturbed-physics model (Noah-MP) estimates of the 1/8° runoff over the conterminous United States, 1980–2015
<p>This dataset contains the 1980–2015 monthly evapotranspiration simulated by a 48-member perturbed-physics ensemble configured from the Noah LSM with multi-physics options (Noah‑MP v3.6). Simulation outputs include the surface and subsurface runoff. The file name has four parts: the variable collection, the used parameterization, the time scale, and the suffix.</p> <p>The 48 physics configurations are generated by combining four runoff parameterizations (run1: SIMGM, run2: SIMTOP, run3: NOAHR, run4: BATS), two parameterizations of stomatal conductance (can1: Ball–Berry, can2: Jarvis), three parameterizations of soil moisture stress factor (btr1:NOAHB, btr2: CLM, btr3: SSiB), and two parameterizations of near-surface atmospheric turbulence (tub1: M-O, tub2: Chen97).</p> <p>The simulation domain covers the all of conterminous United States (25°–53°N, 125°–67°W), which is also called the NLDAS-2 testbed (Xia et al., 2012a, b). The simulations were performed at a spatial resolution of 0.125°, which is the same as for NLDAS-2 models. Details of the simulation settings and spin-up run can be found in Section 2.3 of Zheng et al. (2019) and Section 2.2 of Fei et al. (2021).</p>
An ensemble of 48 perturbed-physics model (Noah-MP) estimates of the 1/8° ET over the conterminous United States, 1980–2015
<p>This dataset contains the 1980–2015 monthly evapotranspiration simulated by a 48-member perturbed-physics ensemble configured from the Noah LSM with multi-physics options (Noah‑MP v3.6). Simulation outputs include total evapotranspiration and its constituents (canopy evaporation, soil evaporation, and transpiration). The file name has four parts: the variable collection, the used parameterization, the time scale, and the suffix.</p> <p>The 48 physics configurations are generated by combining four runoff parameterizations (run1: SIMGM, run2: SIMTOP, run3: NOAHR, run4: BATS), two parameterizations of stomatal conductance (can1: Ball–Berry, can2: Jarvis), three parameterizations of soil moisture stress factor (btr1:NOAHB, btr2: CLM, btr3: SSiB), and two parameterizations of near-surface atmospheric turbulence (tub1: M-O, tub2: Chen97).</p> <p>The simulation domain covers the all of conterminous United States (25°–53°N, 125°–67°W), which is also called the NLDAS-2 testbed (Xia et al., 2012a, b). The simulations were performed at a spatial resolution of 0.125°, which is the same as for NLDAS-2 models. Details of the simulation settings and spin-up run can be found in Section 2.3 of Zheng et al. (2019) and Section 2.2 of Fei et al. (2021).</p>
An ensemble of 48 perturbed-physics model (Noah-MP) estimates of the 1/8° terrestrial water storage over the conterminous United States, 1980–2015
<p>This dataset contains the 1980–2015 monthly terrestrial water storage simulated by a 48-member perturbed-physics ensemble configured from the Noah LSM with multi-physics options (Noah‑MP v3.6). Simulation outputs include the total terrestrial water storage and its constituents (snow water equivalent, four-layer soil water content from the surface down to 2 m, and the groundwater storage anomaly). The file name has four parts: the variable collection, the used parameterization, the time scale, and the suffix.</p> <p>The 48 physics configurations are generated by combining four runoff parameterizations (run1: SIMGM, run2: SIMTOP, run3: NOAHR, run4: BATS), two parameterizations of stomatal conductance (can1: Ball–Berry, can2: Jarvis), three parameterizations of soil moisture stress factor (btr1:NOAHB, btr2: CLM, btr3: SSiB), and two parameterizations of near-surface atmospheric turbulence (tub1: M-O, tub2: Chen97).</p> <p>The simulation domain covers the all of conterminous United States (25°–53°N, 125°–67°W), which is also called the NLDAS-2 testbed (Xia et al., 2012a, b). The simulations were performed at a spatial resolution of 0.125°, which is the same as for NLDAS-2 models. Details of the simulation settings and spin-up run can be found in Section 2.3 of Zheng et al. (2019) and Section 2.2 of Fei et al. (2021).</p>
An ensemble of 48 perturbed-physics model (Noah-MP) estimates of the 1/8° runoff over the conterminous United States, 1980–2015 (time-merged version)
<p>This dataset contains the 1980–2015 monthly evapotranspiration simulated by a 48-member perturbed-physics ensemble configured from the Noah LSM with multi-physics options (Noah‑MP v3.6). Simulation outputs include the surface and subsurface runoff. The file name has four parts: the variable collection, the used parameterization, the time period, the suffix, and the compression format.</p> <p>The 48 physics configurations are generated by combining four runoff parameterizations (run1: SIMGM, run2: SIMTOP, run3: NOAHR, run4: BATS), two parameterizations of stomatal conductance (can1: Ball–Berry, can2: Jarvis), three parameterizations of soil moisture stress factor (btr1:NOAHB, btr2: CLM, btr3: SSiB), and two parameterizations of near-surface atmospheric turbulence (tub1: M-O, tub2: Chen97).</p> <p>The simulation domain covers the all of conterminous United States (25°–53°N, 125°–67°W), which is also called the NLDAS-2 testbed (Xia et al., 2012a, b). The simulations were performed at a spatial resolution of 0.125°, which is the same as for NLDAS-2 models. Details of the simulation settings and spin-up run can be found in Section 2.3 of Zheng et al. (2019) and Section 2.2 of Fei et al. (2021).</p>
An ensemble of 48 perturbed-physics model (Noah-MP) estimates of the 1/8° ET over the conterminous United States, 1980–2015 (time-merged version)
<p>This dataset contains the 1980–2015 monthly evapotranspiration simulated by a 48-member perturbed-physics ensemble configured from the Noah LSM with multi-physics options (Noah‑MP v3.6). Simulation outputs include total evapotranspiration and its constituents (canopy evaporation, soil evaporation, and transpiration). The file name has four parts: the variable collection, the used parameterization, the time period, the suffix, and the compression format.</p> <p>The 48 physics configurations are generated by combining four runoff parameterizations (run1: SIMGM, run2: SIMTOP, run3: NOAHR, run4: BATS), two parameterizations of stomatal conductance (can1: Ball–Berry, can2: Jarvis), three parameterizations of soil moisture stress factor (btr1:NOAHB, btr2: CLM, btr3: SSiB), and two parameterizations of near-surface atmospheric turbulence (tub1: M-O, tub2: Chen97).</p> <p>The simulation domain covers the all of conterminous United States (25°–53°N, 125°–67°W), which is also called the NLDAS-2 testbed (Xia et al., 2012a, b). The simulations were performed at a spatial resolution of 0.125°, which is the same as for NLDAS-2 models. Details of the simulation settings and spin-up run can be found in Section 2.3 of Zheng et al. (2019) and Section 2.2 of Fei et al. (2021).</p>
An ensemble of 48 perturbed-physics model (Noah-MP) estimates of the 1/8° terrestrial water storage over the conterminous United States, 1980–2015 (time-merged version)
<p>This dataset contains the 1980–2015 monthly terrestrial water storage simulated by a 48-member perturbed-physics ensemble configured from the Noah LSM with multi-physics options (Noah‑MP v3.6). Simulation outputs include the total terrestrial water storage and its constituents (snow water equivalent, four-layer soil water content from the surface down to 2 m, and the groundwater storage anomaly). The file name has four parts: the variable collection, the used parameterization, the time period, the suffix, and the compression format.</p> <p>The 48 physics configurations are generated by combining four runoff parameterizations (run1: SIMGM, run2: SIMTOP, run3: NOAHR, run4: BATS), two parameterizations of stomatal conductance (can1: Ball–Berry, can2: Jarvis), three parameterizations of soil moisture stress factor (btr1:NOAHB, btr2: CLM, btr3: SSiB), and two parameterizations of near-surface atmospheric turbulence (tub1: M-O, tub2: Chen97).</p> <p>The simulation domain covers the all of conterminous United States (25°–53°N, 125°–67°W), which is also called the NLDAS-2 testbed (Xia et al., 2012a, b). The simulations were performed at a spatial resolution of 0.125°, which is the same as for NLDAS-2 models. Details of the simulation settings and spin-up run can be found in Section 2.3 of Zheng et al. (2019) and Section 2.2 of Fei et al. (2021).</p>
Outputs from Isca perturbed parameter ensemble (PPE) simulations (Part II)
<p>Outputs from Isca perturbed paramter ensemble (PPE) simulations under 1xCO2 and 4xCO2, in which the simulations are prescribed with Q-flux. The output is the extracted data for the first 20-year simulations for the surface temperature and radiative fluxes at the top of the atmosphere (TOA).</p> <p>This is Part II, and Part I can be found at: <a href="https://doi.org/10.5281/zenodo.5150241">10.5281/zenodo.5150241</a></p>
An ensemble of 48 physically perturbed model estimates of the 1/8° terrestrial water budget over the conterminous United States, 1980–2015
<p>This dataset contains the 1980–2015 monthly terrestrial water budget simulated by a 48-member perturbed-physics ensemble configured from the Noah LSM with multi-physics options (Noah‑MP v3.6). Simulation outputs include total evapotranspiration and its constituents (canopy evaporation, soil evaporation, and transpiration), runoff (the surface and subsurface components), as well as terrestrial water storage (snow water equivalent, four-layer soil water content from the surface down to 2 m, and the groundwater storage anomaly). The file name has four parts: the abbreviation for "terrestrial water budget", the used parameterization, the time scale, and the suffix.</p> <p>The 48 physics configurations are generated by combining four runoff parameterizations (run1: SIMGM, run2: SIMTOP, run3: NOAHR, run4: BATS), two parameterizations of stomatal conductance (can1: Ball–Berry, can2: Jarvis), three parameterizations of soil moisture stress factor (btr1:NOAHB, btr2: CLM, btr3: SSiB), and two parameterizations of near-surface atmospheric turbulence (tub1: M-O, tub2: Chen97).</p> <p>The simulation domain covers the all of conterminous United States (25°–53°N, 125°–67°W), which is also called the NLDAS-2 testbed (Xia et al., 2012a, b). The simulations were performed at a spatial resolution of 0.125°, which is the same as for NLDAS-2 models. Details of the simulation settings and spin-up run can be found in Section 2.3 of Zheng et al. (2019) and Section 2.2 of Fei et al. (2021).</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.