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.
204
datasets available to search
ShareScore release 0.9.0
Dataset results
204 results for “fundamental”
The physiological effects of non-invasive brain stimulation fundamentally differ across the human cortex
Open the record for dataset details and reuse information.
Experimental determination of the hafnium L-subshell fundamental parameters using the holistic approach
<p>This file contains the experimentally determined atomic fundamental parameters for the hafnium L-shells as described in "Experimental determination of the hafnium L-subshell fundamental parameters using the holistic approach" by N. Wauschkuhn, H. Gundlach and P. Hönicke. Fundamental parameters in this file: L-subshell fluorescence yields, L-shell Coster-Kronig factors, L-shell Auger yields, L-subshell photo ionization cross sections up to 23 keV, L-subshell fluorescence prodution cross sections up to 23 keV </p>
Experimental determination of the sulfur K-shell fundamental parameters employing the holistic approach
<p>This dataset contains the experimentally determined fundamental parameters for the sulfur K-subshells from as shown in the publication with the title "Experimental determination of the sulfur K-shell fundamental parameters employing the holistic approach". The paper will be published soon in a peer-reviewd journal.</p> <p>This file contains the following fundamental parameters for sulfur: K-subshell fluorescence yield, K-shell Auger yield, Ka and Kb transition probabilities, K-subshell photo ionization cross sections up to 10 keV, K-subshell fluorescence prodution cross sections up to 10 keV</p>
A novel and holistic approach for experimental X-ray fundamental parameter determination - the Ru L-shell
<p>This dataset contains the experimentally determined fundamental parameters for the ruthenium L-subshells from our paper with the title "A novel and holistic approach for experimental X-ray fundamental parameter determination - the Ru L-shell". The paper will be published soon in a peer-reviewd journal.</p> <p>This file contains L-subshell fluorescence yields, L-shell Coster-Kronig factors, L-shell Auger yields, <br> Mass attenuation coefficients in energy range from 2.41 keV to 8 keV, L-subshell photo ionization cross sections up to 8 keV and <br> L-subshell fluorescence prodution cross sections of Ru.</p>
Fundamental hydrogen storage properties of TiFe-alloy with partial substitution of Fe by Ti and Mn - Dataset related to publication
<p>Data type: Experimental measurements, correlations and Van't Hoff plot. Date format: .opj. Origin of the data: Experimental pressure composition isotherm measurements. Data generated by a home-made Sieverts’ type apparatus from CNRS, ICMPE, Thiais, France. Software needed to plot the data: Origin.</p>
Table of Ultracool Fundamental Properties
<p>The Table of Ultracool Fundamental Properties is a subset of The UltracoolSheet (version 2.0.0, in preparation) associated with <a href="https://ui.adsabs.harvard.edu/abs/2023arXiv230903082S/abstract">Sanghi et al. (2023)</a>: The Hawaii Infrared Parallax Program. VI. The Fundamental Properties of 1000+ Ultracool Dwarfs and Planetary-mass Objects Using Optical to Mid-IR SEDs and Comparison to BT-Settl and ATMO 2020 Model Atmospheres.</p> <p>It is a comprehensive compilation of the astrometric, photometric, spectroscopic, age properties, and fundamental parameters (bolometric luminosities, masses, radii, surface gravities, and effective temperatures) of all objects in the above paper's sample.</p> <p>Three CSV files are provided here: (1) Ultracool_Fundamental_Properties_Table.csv (with all object information); (2) AgeValues.csv (with age values for the different age assignment categories); and (3) References.csv (with citation information). The README.txt file contains a detailed description of the contents of the three CSV files.</p> <p>When using data from the Table of Ultracool Fundamental Properties, please cite the individual papers from which the data comes. Citations codes are included for all the data in the tables, and the References table translates the citation codes into ADS bibcodes, Papers citekeys, and publication titles. For research that benefits from this compilation, please cite this Zenodo post and include the following acknowledgment:</p> <p>"This work has benefitted from The UltracoolSheet at http://bit.ly/UltracoolSheet, maintained by Will Best, Trent Dupuy, Michael Liu, Aniket Sanghi, Rob Siverd, and Zhoujian Zhang, and developed from compilations by <a href="https://ui.adsabs.harvard.edu/abs/2012ApJS..201...19D/abstract">Dupuy & Liu (2012, ApJS, 201, 19)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2013Sci...341.1492D/abstract">Dupuy & Kraus (2013, Science, 341, 1492)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2016ApJ...833...96L/abstract">Liu et al. (2016, ApJ, 833, 96)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2018ApJS..234....1B/abstract">Best et al. (2018, ApJS, 234, 1</a>), <a href="https://ui.adsabs.harvard.edu/abs/2021AJ....161...42B">Best et al. (2021, AJ, 161, 42)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2023arXiv230903082S/abstract">Sanghi et al. (2023)</a>, and <a href="https://ui.adsabs.harvard.edu/abs/2023AJ....166..103S/abstract">Schneider et al. (2023, AJ, 166, 103)</a>."</p>
Mapa de Localização da Escola Municipal de Ensino Fundamental Santo Antônio, Linha dos Pomeranos, Agudo, Rio Grande do Sul
<p>Mapa de Localização da Escola Municipal de Ensino Fundamental Santo Antônio, Linha dos Pomeranos, Agudo, Rio Grande do Sul, realizado com o software QGIS, versão 3.12.3, com técnica de sombreamento utilizando o relevo sombreado do TOPODATA do Instituto Nacional de Pesquisas Espaciais (INPE).</p>
Resources for The Fundamental Limit of Jet Tagging
<p>Resources related to The Fundamental Limit of Jet Tagging (arxiv:2411.02628)</p> <p>Includes:</p> <p>-In total 11,200,0000 qcd and 11,200,0000 top jets generated from corresponding transformer-based models (Tmodels) trained using the JetClass Dataset.</p> <p>-The trained Tmodels.</p> <p>-The LLR predictions from the Tmodels, for different number of constituents.</p> <p>-The predictions corresponding to the classifier-based jet taggers.</p>
Lattice studies of the Sp(4) gauge theory with two fundamental and three antisymmetric Dirac fermions—data release
<p>This dataset contains the raw data and metadata used to prepare the publication <a href="https://arxiv.org/abs/2202.05516">Lattice studies of the Sp(4) gauge theory with two fundamental and three antisymmetric Dirac fermions</a>. </p> <p>Included are:</p> <ul> <li>The raw log output from the configuration generation, correlation function calculation, and Dirac eigenvalue computation, as well as metadata describing the ensembles used for the mass spectrum calculation, in `raw_data.zip`. These include all numbers used in the publication (aside from fit parameters) in plaintext form.</li> <li>All numbers included in the above logs, restructured into HDF5 format for convenience, in `data.h5`.</li> <li>The fit parameters used to compute the spectrum, including the thermalisation length, and the plateau start and end points, in `fit_params.zip`.</li> <li>The data underlying tables 2–6 of the publication above, in CSV format.</li> </ul> <p>More details can be found in the file README.md.</p> <p>Version history:</p> <ul> <li>v1.1: Replace out_corr_48x24x24x24b6.5mas-1.01mf-0.71 due to a mistake where the wrong version of the measurement code was used.</li> <li>v1.0: Initial release</li> </ul>
RRR/RAPID input and output files corresponding to "Underlying Fundamentals of Kalman Filtering for River Network Modeling"
<p><strong>Corresponding peer-reviewed publication</strong></p> <p>This dataset corresponds to all the RRR/RAPID input and output files that were used in the study reported in:</p> <ul> <li> <p>Emery, C. M., C. H. David, K. M. Andreadis, M. J. Turmon, J. T. Reager, and J. M. Hobbs (2020), Underlying Fundamentals of Kalman Filtering for River Network Modeling, Journal of Hydrometeorology, 21, 453-474, DOI: 10.1175/JHM-D-19-0084.1.</p> </li> </ul> <p>When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein. </p> <p><strong>Known bugs and limitations in this dataset or the associated manuscript.</strong></p> <p>In the final version of the published manuscript, Figure 5a, Figure 5b, Figure 5c, and Figure SF1 are inaccurate. The issue in these figures is that they were all prepared with an incorrect indexing relating observed and simulated discharge, hence observations at any one location were consistently being compared to simulations at another different location. As a result, all values of "measured" discharge errors (i.e. Bias, STDE, and RMSE) are incorrect. This issue did not affect the values of "estimated" errors, nor did it affect all values of the Nash-Sutcliffe effeciency that are presented. The figures published in the manuscript can all be recreated using the files in which "BUG_DO_NOT_USE" was appended to the name. Correct figures can also be created using corresponding file names that were not so appended. </p> <p>Note that corrected versions of Figure 5a, Figure 5b, Figure 5c, and Figure SF1 all retain the same strong linear relationships that are discussed in the paper. The slope of the daily discharge STDE trend initially reported as <span class="math-tex">\(\alpha = 0.3876\)</span> in Figure 5c changes to <span class="math-tex">\(\alpha = 0.4507\)</span> after correction. The resulting value of the ideal inflation factor hence changes from <span class="math-tex">\(I = {1 \over 0.3876} \approx 2.58\)</span> to <span class="math-tex">\(I = {1 \over 0.4507} \approx 2.22\)</span>. This updated ideal inflation factor has no impact on the conclusions reached in the manuscript because it remains closer to <span class="math-tex">\(I = 2.58\)</span> than to <span class="math-tex">\(I = 1\)</span> or <span class="math-tex">\(I = 5\)</span>, <em>i.e.</em> the three values that were evaluated.</p> <p>Additionally, a faulty version 1.3.1 of the Python toolbox netCDF4 led to incorrect interpretation of _FillValue in which every data point of value greater than _FillValue was interpreted as masked. This created discrepancies in the following three files, which were updated between V1 and V2 of this dataset: "timeseries_rap_exp01.csv", "timeseries_rap_exp18.csv", and "stats_rap_exp18.csv". Faulty versions of the same files have "BUG_NETCDF4" appended to their names. Correct files have been recreated with file names that were not so appended. </p>
Antibiotic resistant pathogen outbreak investigation: an interdisciplinary module to teach fundamentals of evolutionary biology
<p>The evolution of resistance to antibiotics provides a timely and relevant topic for teaching undergraduate students evolutionary biology. Here, we present a module incorporating modified sequencing data from eight antibiotic resistant pathogen outbreaks in hospital settings with bioinformatics and phylogenetic analyses. This module uses whole genome sequencing data from hospital outbreaks investigated by the Centers for Disease Control and Prevention to provide examples of antibiotic resistance spread. Students work in groups to analyze outbreak data to identify the bacterial species and antibiotic resistance genes, to infer a phylogenetic tree examining relatedness among isolates, and to determine a possible source of the outbreak. Students then compile their results in individual reports and provide recommendations for preventing the further spread of antibiotic resistant organisms. In addition to providing genomic outbreak data, we include a teaching concepts guide discussing three integral components of the module: how evolutionary biology concepts of natural selection and competition impact antibiotic resistance; outbreak investigation information to aid in phylogenetic analysis and creation of recommendations; and instructions for the bioinformatics protocol. Completion of this module provides students an opportunity to think critically about the evolution of resistance, practice bioinformatics techniques, and relate evolutionary biology to current events.</p>
Supporting data for "Fundamental limitations of cavity-assisted atom interferometry"
<p>Supporting data with code to generate Fig. 2 Cavity-induced deformation of a Gaussian input. Publication: DOI:https://doi.org/10.1103/PhysRevA.96.053820</p> <p>arXiv:1710.02448</p> <p>This dataset contains a zip file with raw data sets of all relevant measurements to plot figure 2.</p> <p>Figure 2. Envelope functions of the intracavity field for a 1 m cavity injected with<br> a 1μs pulse for different cavity finesses. All areas are normalized<br> to the input pulse area for comparison. When the pulse duration is<br> comparable to the photon lifetime of the cavity, its envelope function<br> is elongated. Inset: Envelopes without normalization.</p> <p> </p> <p>Further data and information are available from Miguel Dovale <mdovale@star.sr.bham.ac.uk> at reasonable request.</p> <p>School of Physics and Astronomy and Institute of Gravitational Wave Astronomy, University of Birmingham, Edgbaston, Birmingham B15 2TT, United Kingdom</p>
Rethinking the fundamental unit of ecological remote sensing: Estimating individual level plant traits at scale
<p>derived data of leaf and plant structural traits for two National Ecological Observatory Network (NEON) Airborne Observatory Platform (AOP) sites. Dataset contains spatial explicit information for 4.5 million trees, and include: Nitrogen (%mass), Phosphorus (%mass), Leaf mass per area (g m<sup>-2</sup>), diameter at breast height (cm), crown area (m2), tree height (m) and other physical topographic variables (Albedo, Elevation, Slope, Aspect). data are associated to the </p>
Supporting Material for article "The ELIXIR Core Data Resources: fundamental infrastructure for the life sciences"
<p>This data set is the Supporting Material referred to in the Supplementary Data for the article "The ELIXIR Core Data Resources: fundamental infrastructure for the life sciences" (Drysdale, et al.) submitted for publication in April 2019.</p> <p> </p>
Products and Models for "Detection of carbon monoxide's 4.6 micron fundamental band structure in WASP-39b's atmosphere with JWST NIRSpec G395H"
<p>Overview:</p> <p>Carbon monoxide (CO) is predicted to be the dominant carbon-bearing molecule in giant planet atmospheres, and, along with water, is important for discerning the oxygen and therefore carbon-to-oxygen ratio of these planets. The fundamental absorption mode of CO has a broad double-branched structure composed of many individual absorption lines from 4.3 to 5.1 µm, which can now be spectroscopically measured with JWST. Here we present a technique for detecting the rotational sub-band structure of CO at medium resolution with the NIRSpec G395H instrument. We use a single transit observation of the hot Jupiter WASP-39b from the JWST Transiting Exoplanet Community Early Release Science (JTEC ERS) program at the native resolution of the instrument (R ~ 2700) to resolve the CO absorption structure. We robustly detect absorption by CO, with an increase in transit depth of 264 <span>\(\pm\)</span> 68 ppm, in agreement with the predicted CO contribution from the best-fit model at low resolution. This detection confirms our theoretical expectations that CO is the dominant carbon-bearing molecule in WASP-39b's atmosphere, and further supports the conclusions of low C/O and super-solar metallicities presented in the JTEC ERS papers for WASP-39b. </p>
The APO-K2 Catalog. I. ~7,500 Red Giants with Fundamental Stellar Parameters from APOGEE DR17 Spectroscopy and K2-GAP Asteroseismology
<p><strong>Abstract: </strong>We present a catalog of fundamental stellar properties for ~7,500 evolved stars, including stellar radii and masses, determined from the combination of spectroscopic observations from the Apache Point Observatory Galactic Evolution Experiment (APOGEE), part of the Sloan Digital Sky Survey IV (SDSS), and asteroseismology from K2. The resulting APO-K2 catalog provides spectroscopically derived temperatures and metallicities, asteroseismic global parameters, evolutionary states, and asteroseismically-derived masses and radii. Additionally, we include kinematic information from <em>Gaia</em>. We investigate the multi-dimensional space of abundance, stellar mass, and velocity with an eye toward applications in Galactic archaeology. The APO-K2 sample has a large population of low metallicity stars (~288 at [M/H] ≤ -1), and their asteroseismic masses are larger than astrophysical estimates. We argue that this may reflect offsets in the adopted fundamental temperature scale for metal-poor stars rather than metallicity-dependent issues with interpreting asteroseismic data. We characterize the kinematic properties of the population as a function of α-enhancement and position in the disk and identify those stars in the sample that are candidate components of the <em>Gaia-Enceladus</em> merger. Importantly, we characterize the selection function for the APO-K2 sample as a function of metallicity, radius, mass, νmax, color, and magnitude referencing Galactic simulations and target selection criteria to enable robust statistical inferences with the catalog.</p> <p><strong>Included Files:</strong></p> <ul> <li>The publicly available APO-K2 catalog, the is provided in the publication.</li> <li>The APO-K2 catalog without truncation to any numbers. </li> <li>The selection function relative density tables for the mass-radius parameter space. </li> <li>The selection function relative density tables for the metallicity-mass parameter space. </li> <li>The selection function relative density tables for the magnitude-color parameter space. </li> <li>The selection function relative density tables for the ν<sub>max</sub>-mag parameter space. </li> </ul>
Data for: A fundamental tradeoff among resilience, resistance, efficiency, and redundancy in tidal wetlands
<p>We filtered the raw NASA-MODIS (MOD13Q1) Enhanced Vegetation Index (EVI) dataset to only inlcude pixels with high tidal wetland class purity and Quality Assurance (QA) reliability scores. We filtered 782,693 tidal wetland pixels with coverage spanning the entire contiguous United States to only include those with greater than 90% tidal wetland class purity. We then further filtered these pixels to only include those where data dropouts in the EVI or QA layer occured fewer than 10% of the time. In the end, we used 145,871 pixels in our analysis. Tidal wetland GPP was calculated by pixel for the dates 3/5/2000 to 12/2/2020 at 16-day intervals using the algorithms published in: </p> <p>R. A. Feagin, I. Forbrich, T.P. Huff, J.G. Barr, J. Ruiz-plancarte, J.D Fuentes, R.G. Najjar, R. Vargas, A. Vazquez-lule, L. Windham-Myers, K. Kroeger, E.J. Ward, G.W. Moore, M. Leclerc, K.W. Krauss, C.L. Stagg, M. Alber, S.H. Knox, K.V.R. Schafer, T.S., Bianchi, J.A. Hutchings, H.B. Nahrawi, A. Noormets, B. Mitra, A. Jaimes, A.L. Hinson, B. Bergamaschi, J. King, and G. Miao., Tidal wetland gross primary production across the continental United States, 2000–2019. Global Biogeochemical Cycles 34, e2019GB006349 (2020).</p> <p>The file named "SWR_90percentFinal.csv" contains the filtered SWR database used to calculate GPP. File named "temp_90percentFinal_rounded.csv" contains the filtered air temperature database used to calculate GPP. The file named "EVI(gapped_filled)_90percentFinal.csv" contains the final gap-filled EVI time series database used to calculate GPP. File named "QA_90%Final.csv" contains the filtered quality assurance (QA) values. Dates in the EVI database where QA = 3 were determined to be of poor quality, removed from the database, and replaced with "NA". Single NA gaps in the EVI database were gap-filled by taking the mean of the dates flanking the NA gap. File named "myGPP(gap_filled)_FINAL.csv" contains the calculated GPP estimates used throughout the study analysis. </p> <p>File named "rawEVI.csv" contains the raw EVI database prior to filtering and gap-filling. File named "rawSWR.csv" contains the raw SWR database prior to filtering. File named "rawAirTemp.csv" contains the air temperature database prior to filtering. File named "rawQA.csv" contains the QA layer of the MOD13 satellite product prior to filtering. These files contain the raw data for all 782,693 tidal wetland pixel locations. These raw files can also be accessed at daac.ornl.gov. </p>
Data and code for: The centrality of the Huanan market among early COVID-19 cases is robust to fundamental misconceptions about epidemiology and misrepresentations of Worobey et al. (2022)
<p>Data and R code for <br>The centrality of the Huanan market among early COVID-19 cases is robust to fundamental misconceptions about epidemiology and misrepresentations of Worobey et al. (2022)</p>
Data and code from: Predicting the fundamental thermal niche of ectotherms
<p>Climate warming is predicted to increase mean temperatures and thermal extremes on a global scale. Because their body temperature depends on the environmental temperature, ectotherms bear the full brunt of climate warming. Predicting the impact of climate warming on ectotherm diversity and distributions requires a framework that can translate temperature effects on ectotherm life history traits into population- and community-level outcomes. Here we present a mechanistic theoretical framework that can predict the fundamental thermal niche and climate envelope of ectotherm species based on how temperature affects the underlying life history traits. The advantage of this framework is two-fold. First, it can translate temperature effects on the phenotypic traits of individual organisms to population-level patterns observed in nature. Second, it can predict thermal niches and climate envelopes based solely on trait response data and hence completely independently of any population-level information. We find that the temperature at which the intrinsic growth rate is maximized exceeds the temperature at which abundance is maximized under density-dependent growth. As a result, the temperature at which a species will increase the fastest when rare is lower than the temperature at which it will recover from a perturbation the fastest when abundant. We test model predictions using data from a native-invasive interaction to identify the temperatures at which the invader can most easily invade the native's habitat, and the native species is most likely to resist the invader. The framework is sufficiently mechanistic to yield reliable predictions for individual species, and sufficiently broad to apply across a range of ectothermic taxa. This ability to predict the thermal niche before a species encounters a new thermal environment is essential to mitigating some of the major effects of climate change on ectotherm populations around the globe.</p>
Dataset for article "W18O49 Nanowhiskers Decorating SiO2 Nanofibers: Lessons from In Situ SEM/TEM Growth to Large Scale Synthesis and Fundamental Structural Understanding" in Crystal Growth & Design, Des. 2024, 24, 1, 378–390
<p>Supported dataset for publication "W18O49 Nanowhiskers Decorating SiO2 Nanofibers: Lessons from In Situ SEM/TEM Growth to Large Scale Synthesis and Fundamental Structural Understanding" in Crystal Growth & Design Des. 2024, 24, 1, 378–390, https://doi.org/10.1021/acs.cgd.3c01094.</p> <p>The directory Data contains the figures (png, jpg), and vido (AVI) source data files for the manuscript.</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.