Find research datasets worth reusing

opencc-by-4.0Dec 2022View details →

Data for manuscript "Modes of Variability in E3SM and CESM Large Ensembles"

An adequate characterization of internal modes of climate variability (MoV) is prerequisite for both accurate seasonal predictions and the attribution and detection of forced climate change in nature. Assessing the fidelity of climate models in simulating MoV is therefore essential; however, doing so is complicated by the large intrinsic variations in MoV and the limited span of the observational record. Large ensembles (LEs) provide a unique opportunity to assess model fidelity in simulating MoV and quantify inter-model contrasts. In this work, these goals are pursued in four recently produced LEs: the Energy Exascale Earth System Model (E3SM) versions 1 and 2 LEs, and the Community Earth System Model (CESM) versions 1 and 2 LEs. In general, the representation of MoV is found to improve across successive E3SM and CESM versions concurrent with improved simulation of the base state climate. The patterns of global coupled modes and many extratropical modes are well simulated by both E3SM2 and CESM2, though various persistent shortcomings are identified. The results both demonstrate the successes of these recent model versions and suggest the potential for continued improvement in the representation of MoV with advances in model physics.

opencc-by-4.0Oct 2023View details →

Data, software, and Figures used in a manuscript submitted to Geoscientific Model Development

Data, software, and Figures used in 'A General Comprehensive Evaluation Method for Cross-Scale Precipitation Forecast' submitted to Geoscientific Model Development.

Plasim dataset for manuscript titled: "Extreme heatwave sampling and prediction with analog Markov chain and comparisons with deep learning"

This dataset was originally generated by running a Plasim general circulation model for 8000 years. We extract a small subset of this dataset which is only 500 years long with temperature, soil moisture and 500 hPa Geopotential extracted over the northern hemisphere above 30 North. This dataset can be used to reproduce the results in https://arxiv.org/abs/2307.09060 concerning training CNN and SWG with cross-validation length of 500 years.

Dataset package for the manuscript "Topological aspects of multi-$\bm{k}$ antiferromagnetism in cubic rare-earth compounds"

Dataset 1:Title: Magnetic phase diagrams of HoCu, ErCu, and TmCu for magnetic field along the [111] axis.Description:Maxima and points of inflection observed in AC magnetic susceptibility data are stored in "HoCu111_1.dat", "HoCu111_2.dat", "HoCu111_3.dat", "HoCu111_4.dat", "ErCu111_FS.dat", "ErCu111_TS.dat", "TmCu111_FS.dat", and "TmCu111_TS.dat". The first and second columns correspond to temperature and field values of these points, respectively. Dataset 2:Title: Magnetization and electrical transport properties of bulk TmCu at T = 2K for magnetic field along [111].Description:The field dependence of the magnetization, longitudinal electrical resistivity, and transverse electrical resistivity are stored in the files "TmCuMagnetization.dat", "TmCuRhoxx.dat", and "TmCuRhoxy.dat", respectively. Contributions to the transverse resistivity that are associated with the normal Hall effect are stored in "TmCuRhoxynormalHall.dat". Anomalous Hall effect contributions that are linear in the magnetization are stored in "TmCuRhoxyAHE.dat". Dataset 3:Title:  Magnetic neutron diffraction intensity as a function of temperature in HoCu at zero magnetic field.Description:Magnetic neutron diffraction intensity, as recorded by means of rocking-scans of the sample at the single-crystal diffractometer HEiDi (FRMII, Garching), centered around the reciprocal-space position Q0 = (1.5, 1.5, 1), are presented in "HoCuNeutronDiffraction_Int.dat" in terms of a 81 times 25 matrix. The presented intensity is normalized to the monitor. The first index enumerates the momentum transfer difference with respect to the reciprocal-space position Q0 and the second index the temperatures. The respective values are stored in "HoCuNeutronDiffraction_Q.dat" and "HoCuNeutronDiffraction_T.dat".

Supplementary Data for Manuscript 'Observing impacts on luminescence depth profile evolutions from surface altered quartzite using OSL laser scanning and controlled light exposed rock sampling techniques'

The attached file contains supplementary documents offered in the Quaternary Geochronology manuscript 'Observing impacts on luminescence depth profile evolutions from surface altered quartzite using OSL laser scanning and controlled light exposed rock sampling techniques' for the LED2023 special issue.

Dataset package for the manuscript "A microscopic Kondo lattice model for the heavy fermion antiferromagnet CeIn3"

Figure 2: Derivation of the Kondo-Heisenberg model.Description:The electronic structure of CeIn3 on the reciprocal-space path RGXMG (G=Gamma) is stored in "fbands.dat" and in "allotherbands.dat". The file "fbands.dat" has 15 columns, where the first column provides the wave-vector projection onto the path RGXMG and columns 2-15 the energies of the 14 f-bands. In the file "allotherbands.dat" the first column corresponds to the wave-vector values and the other columns contain all remaining bands.The wave-vector dependent interactions are stored in "InteractionSpectrum.dat". Here, the first column provides the wave-vector projection onto the path RGXMG, the second column the RKKY-interaction, the third column the superexchange, the fourth column the particle-particle interaction and the last column the sum of all interactions. Figure 3: Calculated and measured magnon dispersion and dynamic magnetic susceptibility in the antiferromagnetic state of CeIn3.Description:The imaginary part of the dynamic magnetic susceptibility, as inferred from high-energy inelastic neutron scattering intensity, on the reciprocal space-path RG is stored in the file "HighEnergy_Int_RG.dat" in terms of a 59 x 68 matrix. Here, the first index enumerates the wavevector-projection onto the path RG and the second index the energy transfer. The respective wave-vector and energy values are stored in "HighEnergy_Q_RG.dat" and "HighEnergy_E_RG.dat", respectively. Similarly, inelastic neutron scattering intensity, wave-vector values, and energy-transfer values for the paths GX, XM, and MG are stored in the files "HighEnergy_Int_GX.dat", "HighEnergy_Q_GX.dat", "HighEnergy_E_GX.dat", "HighEnergy_Int_XM.dat", "HighEnergy_Q_XM.dat", "HighEnergy_E_XM.dat", "HighEnergy_Int_MG.dat", "HighEnergy_Q_MG.dat", and "HighEnergy_E_MG.dat".The imaginary part of the dynamic magnetic susceptibility on the path RGXMG, as inferred from theory, is stored in the files "Theory_Q_RGXMG.dat", "Theory_E_RGXMG.dat", and "Theory_Int_RGXMG.dat", where the first, second, and third file provide the wave-vector projections, the energy transfers, and the values of the dynamic magnetic susceptibility, respectively.The dispersion on the path RGXMG inferred from MOPAM calculations is stored in "MOPAM-Dispersion_RGXMG.dat" and the dispersion of the J1-model in "J1Model-Dispersion_RGXMG.dat". In both files, the first column corresponds to the wave-vector projection onto the path RGXMG and the second column to the energy.Cuts at the constant wave-vectors Q1 and Q2, as inferred from experiments, are stored in "Experiment_ConstQ1.dat" and "Experiment_ConstQ2.dat", respectively. The cuts from theory are stored in "Theory_ConstQ1.dat" and "Theory_ConstQ2.dat".The integral over the dynamic magnetic susceptibility on the path RGXMG inferred from theory is stored in "Theory_IntegralOverchi.dat", where the first and second columns provide the wave-vector projection and the integrated values, respectively. The values inferred from experiment are stored in "Experiment_IntegralOverchi.dat". The first and second columns provide the wave-vector projection and the integrated values, respectively. The last column provides the error bars. Figure 4: Signature of long-range RKKY interactions in CeIn3,Description:High-resolution inelastic neutron scattering data on the path RG are presented in "HighResolution_Int_RG.dat". The first and second index enumerates the wave-vector projection onto the path RG and energy transfer, respectively. The respective values are stored in "HighResolution_Q_RG.dat" and "HighResolution_E_RG.dat".Similarly, data for the paths RX and RM are stored in the files "HighResolution_Int_RX.dat", "HighResolution_Q_RX.dat", "HighResolution_E_RX.dat", "HighResolution_Int_RM.dat", "HighResolution_Q_RM.dat", and "HighResolution_E_RM.dat".The dispersion inferred from MOPAM calculations on the paths RG, RX, and RM, is stored in "MOPAM-Dispersion_RG.dat", "MOPAM-Dispersion_RX.dat", and "MOPAM-Dispersion_RM.dat", respectively. Similarly, the dispersions of the J1 model on the paths RG, RX, and RM, are stored in "J1Model-Dispersion_RG.dat", "J1Model-Dispersion_RX.dat", and "J1Model-Dispersion_RM.dat", respectively.Cuts at constant energies 0.6 meV and 1.4 meV along RG are stored in "HighResolution_RG_ConstEcut_0p6meV.dat" and "HighResolution_RG_ConstEcut_1p4meV.dat", respectively. Similarly, cuts along RX are stored in "HighResolution_RX_ConstEcut_0p6meV.dat" "and HighResolution_RX_ConstEcut_1p4meV.dat" and along RM in "HighResolution_RM_ConstEcut_0p4meV.dat" "and HighResolution_RM_ConstEcut_1p4meV.dat".

opencc-by-4.0Jun 2023View details →

Dataset to manuscript Excitation's lifetime extracted from electron-photon (EELS-CL) nanosecond-scale temporal coincidences

The datasets provided are raw data after the initial electron-photon temporal lists have been analyzed to determine the electron-photon pairs and their temporal delay. These lists are too long and not very meaningful to be provided in full.The datasets were analyzed with the following Python libraries: Numpy 1.23.5, Matplotlib 3.6.2, Scipy 1.10.0, Hyperspy 1.7.3.The provided jupyter notebooks were used to generate the figures in the manuscript.

Dataset for plots and results in manuscript: "Reliance on fossil fuels increases during extreme temperature events in the continental United States"

These are the dataset for plots and results in the manuscript titled "Reliance on fossil fuels increases during extreme temperature events in the continental United States".Figure 1,2,3,4,5 are the dataset used for analysis and plotting the figures in the manuscript.Other dataset are the 34 years' air temperature and population-weighted air temperature thresholds for detecting extreme temperature events in each U.S. states. For example, Ta_threshold(1990-2023)_99th_percentiles.csv is the 99th percentiles for each U.S. states.Any questions and further assitance or collaborations are welcome to contact through: wz2481@columbia.edu, happystillwaterzhao@gmail.com

Data for manuscript: "East Antarctic warming forced by ice loss during the Last Interglacial".

This repository contains data accompanying the manuscript: David K. Hutchinson, Laurie Menviel, Katrin J. Meissner, Andrew McC. Hogg, 2024: East Antarctic warming forced by ice loss during the Last Interglacial, Nature Communications, 15, 1026. doi: <a href="https://doi.org/10.1038/s41467-024-45501-x">10.1038/s41467-024-45501-x</a> The data correspond to Figures 1-5 in the main manuscript and Figures S1-S12 in the Supplementary material. Generally the surface fields are provided in both monthly and annual frequency, while the deep ocean metrics are annual only. Topography and grids used in each simulation are provided in topog_grids, and the runoff folder contains the freshwater forcing applied as a perturbation in each case. Data are provided in self-describing netcdf format. Experiment details are provided in the manuscript and will be available upon publication.

Data Associated with Manuscript Titled "Conflicted Emotions Following Trust-Based Interaction"

This is a dataset used in analyses of trust-based interaction behavior and emotional reactions described and interpreted in the associated manuscript titled "Conflicted Emotions Following Trust-Based Interaction".

Forecasts, score summary files, target observational data and meteorological driver files to accompany the manuscript "Skill of process-based forecasts relative to multiple null models varies across time and depth for water temperature and dissolved oxygen"

This data publication includes raw ensemble forecast output (forecasts.zip), as well as summary score files (scores.zip) for process-based forecasts produced with the Forecasting Lake and Reservoir Ecosystems (FLARE) framework. In addition, it includes scores for climatology (climatology_scores.csv) and random walk (RW_scores.csv) null forecasts, formatted observational data of target variables (sunp-targets-insitu.csv), and meteorological driver files required for analysis to accompany the manuscript "Skill of process-based forecasts relative to multiple null models varies across time and depth for water temperature and dissolved oxygen". Forecasts were made of water temperature and dissolved oxygen at Lake Sunapee, NH in 2021 and 2022.

Supplementary Data to Pontesilli et al 2023 manuscript

Supplementary Data for the manuscript entitled "Magma differentiation in dynamic mush domains from the perspective of multivariate statistics: open- vs closed-system evolution" and submitted for review on Geochemistry, Geophysics, Geosystems. Matrix glass major and trace element compositions, including original variables, analytical precision and accuracy based on secondary standards, as well as principal component scores, data clustering for each point analysis, partition coefficient values from literature including the complete reference list and results of differentiation modeling. Upload of the dataset on Zenodo under a CC4 Public License on restricted access on 06-12-2023, the dataset will be made publicly available upon eventual acceptance and publication.

opencc-by-4.0May 2023View details →

Shine Dalgarno strength data for manuscript "Occlusion of the anti-Shine-Dalgarno on the 30S platform reduces the efficiency of prfB programmed frameshifting in Flavobacterium johnsoniae"

This data set contains Shine Dalgarno strengths for every gene in 4362 bacterial genomes that were analyzed in the manuscript titled "Analysis of programmed frameshifting during translation of prfB in Flavobacterium johnsoniae". There is one comma separated file per genome. They are split between six zip archives. Each line in these files represents one gene. The columns are the genome ID, the name of the gene, the SD strength in kcal/mol as determined by free_scan, the mock SD strength in kcal/mol as determined by free_scan, the anti Shine Dalgarno sequence identified from the 16S ribosomal RNAs of the organism, the sequence of the SD region that was scanned, and the sequence of the mock SD region that was scanned. The mock SD region is further upstream of the gene and should not contain a true SD sequence and is provided as a control.

opencc-by-4.0Jul 2023View details →

UKESM1 data supporting manuscript "Marine isoprene emissions in the Southern Ocean are significantly larger than expected"

Model output and README from simulations performed for study "Marine isoprene emissions in the Southern Ocean are significantly larger than expected" (Ferracci and Weber et al)

Experimental data to manuscript "Resonance-Induced Anomalies in Temperature-Dependent Raman Scattering of PdSe2"

Experimental data to manuscript  "Resonance-Induced Anomalies in Temperature-Dependent Raman Scattering of PdSe2"

Bonneville Salt Flats InSAR Pairs and Coherence Statistics Data for Radwin et al 2024 Manuscript

This data repository is a supplemental dataset to a manuscript submitted to IEEE TGRS, titled "Assessing C-Band InSAR Coherence Data for Application to Study the Bonneville Salt Flats, Utah: Harnessing Cloud-Based InSAR Data Processing" by Mark Radwin, Brenda Bowen, and Richard Forster.  This repository hosts two files:  1) <table> <tbody> <tr> <td> <div><a href="../api/records/10436440/draft/files/InSAR_PAIRS_LIST.xlsx/content" target="_blank" rel="noopener noreferrer">InSAR_PAIRS_LIST.xlsx</a> (list of InSAR pairs used in total for the study)</div> </td> </tr> </tbody> </table> 2)  <table> <tbody> <tr> <td> <div><a href="../api/records/10436440/draft/files/Radwin_etal_BSF_InSAR_Complete_Coherence_Data.xlsx/content" target="_blank" rel="noopener noreferrer">Radwin_etal_BSF_InSAR_Complete_Coherence_Data.xlsx</a> (organized collection of 12, 24, and 36 day data - seperated by tabbed sheets)</div> </td> </tr> </tbody> </table> Both files provide sufficient information for others to reproduce the work. If anything is desired that is not included, please email mark.radwin@utah.edu or markradwin@gmail.com

Dataset and Code for Manuscript "Cell sorting based on pulse shapes from angle resolved detection of scattered light"

Dataset and code for the cell cycle analysis and cluster selection for sorting: <ul> <li>ReadMe file with explanations of the data set and analysis</li> <li>Python scripts for converting the data and to reproduce the sort cluster selection</li> <li>binary data files containing pulse shapes and wavelet transform coefficients</li> <li>FSC data files containing the respective common flow cytometry parameters</li> <li>text files with event indices that represent the gating</li> </ul>

opencc-by-4.0Jul 2023View details →

Input parameters and output trajectory files for manuscript "Inhibitory Activity of Flavonoid Scaffolds on SARS-CoV-2 3CLPro: Insights from the Computational and Experimental Investigations"

Input parameters used for molecular dynamics simulations on GROMACS 2022 software and the output trajectory files for calculating binding free energy, in the manuscript with the title: "Inhibitory Activity of Flavonoid Scaffolds on SARS-CoV-2 3CLPro: Insights from the Computational and Experimental Investigations"

Code and Dataset used for assessment of key variables in manuscript titled "Equity Assessment of Global Mitigation Pathways in the IPCC Sixth Assessment Report".

This repository contains the code used for extraction of data from the IPCC scenarios database for key variables that are assessed in the manuscript titled "Equity Assessment of Global Mitigation Pathways in the IPCC Sixth Assessment Report". It also contains data for key variables for scenario categories C1, C2, C3, and C4