Find research datasets worth reusing

opencc-by-4.0Jan 2022View details →

Paleomagnetic data for Beaver, Kent & Dalziel in Tectonics (2022), "Paleomagnetic Constraints From South Georgia On The Tectonic Reconstruction Of The Early Cretaceous Rocas Verdes Marginal Basin System Of Southernmost South America"

Text data files of paleomagnetic data from Tables in: Beaver, D. G., D. V. Kent, and I. W. D. Dalziel (2022), Paleomagnetic Constraints From South Georgia On The Tectonic Reconstruction Of The Early Cretaceous Rocas Verdes Marginal Basin System Of Southernmost South America: Tectonics, in press. Table 1. Site Mean Stable Paleomagnetic Directions from South Georgia. Table 2. Site Mean Stable Directions for Differential Tilt Test of South Georgia Sites With Structural Control. Table 3. Tectonic Rotations Inferred from Available Paleomagnetic Results from Rocas Verde Rock Units of Late Cretaceous Age in Fuegian Andes and South Georgia.

opencc-by-4.0Oct 2024View details →

Data, Analytical Code, and Model Outputs From: Restoration Treatments Enhance Tree Growth and Alter Climatic Constraints During Extreme Drought

This archive includes data (forest inventories, tree ring measurements, climate variables), statistical code, model outputs, and a preprint copy of Rodman et al. (2024). For more information on specific information, processing methods, and data formats, see "README.md" or "README.html" files associated with this archive

opencc-by-4.0Jun 2023View details →

Data and simulation files for "Constraints on the intergalactic magnetic field using Fermi-LAT and H.E.S.S. blazar observations"

In this repository, we provide data files in connection to our paper “Constraints on the intergalactic magnetic field using Fermi-LAT and H.E.S.S. blazar observations” accepted for publication in the Astrophysical Journals and soon available on Arxiv. In the publication, we perform a joint analysis of observations of five blazars with the Fermi Large Area Telescope (LAT) and the High Energy Stereoscopic System (H.E.S.S.) in order to search for signatures of a gamma-ray halo around these sources. The non-detection of such extended emission allows us to place lower limits on the intergalactic magnetic field (IGMF). In this repository, we provide our data analysis products of both H.E.S.S. and LAT data for the case when a template for the halo flux is not included in the data. Furthermore, we provide files that contain the log likelihood profiles as functions of the IGMF in case the halo emission is included. Lastly, we also provide our template files for the halo, generated with <a href="https://crpropa.github.io/CRPropa3/">CRPropa 3</a>. Below, we provide minimal code examples to demonstrate how to read in the specific files. H.E.S.S. observational results We provide the best-fit spectral parameters as well as the flux points (spectral energy distribution; SED) for the H.E.S.S. observations of the five blazars under consideration. The corresponding files are: <ul> <li>hess_fit_result_*.fits which contain the best-fit parameters,</li> <li>hess_sed_file_*.fits which contain the flux points.</li> </ul> In the file names above, the '*' should be replaced with a the corresponding source name, e.g. 1ES0229+200. The files can be read in using astropy: <pre><code class="language-python">from astropy.table import Table src = "1ES0229+200" best_fit_pars = Table.read("hess_fit_result_1ES0229+200.fits") sed = Table.read("hess_sed_file_1ES0229+200.fits")</code></pre> Fermi observational results For Fermi-LAT, we provide the SED files as well as the best-fit models for the region of interests. These files are called: <ul> <li>fermi_avg_file_*.npy provides the best-fit ROI model</li> <li>fermi_sed_file_*.npy provides the SED.</li> </ul> Both of these files are generated with <a href="https://fermipy.readthedocs.io/en/latest/">fermipy</a> and can be read-in the following way: <pre><code class="language-python">import numpy as np # first a little helper function since the # fermipy analysis was run under python 2.7 def convert(data): if isinstance(data, bytes): return data.decode('ascii') if isinstance(data, dict): return dict(map(convert, data.items())) if isinstance(data, tuple): return map(convert, data) return data # Load the ROI fit roi_fit_file = "fermi_avg_file_1ES0229+200.npy" roi_fit = np.load(avg_file, allow_pickle=True, encoding="latin1").flat[0] # if you want to inspect the dictionaries in python 3, you need to run the convert function. # For example, to inspect the central source of the ROI # you would first get the source name src_fgl_name = roi_fit['config']['selection']['target'] # and then you can get the dictionary for the central source src_dict = convert(roi_fit['sources'])[src_fgl_name] # Load the SED sed_file = "fermi_sed_file_1ES0229+200.npy" sed = np.load(sed_file, allow_pickle=True, encoding='latin1').flat[0] # to plot the SED, you can use the SEDPlotter class from fermipy from fermipy.plotting import SEDPlotter SEDPlotter.plot_sed(sed)</code></pre> Likelihood profiles The likelihood profiles as function of the IGMF strengths are provided in the files logl_profile_*_*yr.npz. Their are provided for all five sources and all tested blazar activity times of 10, 104, and 107 years. They can be read in with the following code snippet: <pre><code class="language-python">import numpy as np logl = dict(np.load("logl_profile_1ES0229+200_1.0e+07yr.npz")) b_fields = np.array([1.00000e-16, 3.16228e-16, 1.00000e-15, 3.16228e-15, 1.00000e-14, 3.16228e-14, 1.00000e-13]) for k, v in logl.items(): print(k,v)</code></pre> As the print command shows, the python dictionary contains 3 entries: "fermi_only" are the likelihood values for the Fermi data as a function of magnetic field, "combined" are the likelihood values from Fermi and H.E.S.S. combined, and "ps" is the likelihood value of the Fit without halo to the H.E.S.S. data only. Halo simulations Lastly, we also provide the output simulations files from CRPropa. For details how the simulations were run, please consult the accompanying paper, in particular Section 3.1 and Appendix C. For each source redshift, a tar file is provided, which in itself contains 7 hdf5 files with the simulation outputs for each tested magnetic field strength. The name of the files is casc_file_z*.tar.gz. After unpacking the files, they can be read in with your favorite hdf5 library; in python you would need to install h5py. We recommend that you check out <a href="https://github.com/me-manu/simCRpropa">this github repository</a> which provides an advanced python wrapper for CRPropa and functions to read in the files. In particular, you can use <a href="https://github.com/me-manu/simCRpropa/blob/b3f39b5c77c6b97d19f7db387427d857690444d2/simCRpropa/cascmaps.py#L28">this function</a> to read in the files. It also writes a new hdf5 file with parallel transport applied. The written data is also returned together with the configuration dictionary. <pre><code class="language-python">from simCRpropa.cascmaps import stack_results_lso data, config = stack_results_lso("casc_file_z0.140_B1.00e-16.hdf5", "casc_file_z0.140_B1.00e-16_theta_obs0.0.hdf5" )</code></pre> You can provide arbitrary angles between the observer and the jet angles using the theta_obs keyword. Note, however, that the simulations used a jet opening angle of 3 degrees and going beyond that value will return zero halo photons.

opencc-by-4.0Dec 2022View details →

Observational constraints of fire, environmental and anthropogenic on pantropical tree cover - Data

Data used for analysis in "Explainable Clustering Applied to the Definition of Terrestrial Biome" - using Decision Tree and Clustering techniques to identify biomes. Land surface properties: <ul> <li>TreeCover - Vegetation Continuous Fields (VCF) collection 6 fractional tree cover from 1, regridded as per 2.</li> <li>urban cover from the History Database of the Global Environment, Version 3.1 (HYDE)  3,4</li> <li>crop cover (from HYDE)</li> </ul> <ul> <li>pas - Pasture Cover (from HYDE)PopDen (population density from HYDE)</li> <li>BurntArea_xxxxx - Burnt area with xxxx denoting different products, provided by fireMIP 5–7: <ul> <li>GFED_four: Global Fire Emissions Database, Version 4 (GFED4)  8</li> <li>GFED_four_s: Global Fire Emissions Database, Version 4.1, including small fires (GFEDv4.1) 9</li> <li>MCD_forty_five: MCD45 10</li> <li>Meris: Fire_CCI4.0 11</li> <li>MODIS: Fire_CCI5.1 12</li> </ul> </li> </ul> Climate: <ul> <li>MAP_xxx - Mean annual precipitation where xxx denotes data source: <ul> <li>CMORPH 13,14</li> <li>CRU from version 4.03 of the Climatic Research Unit Time Series high-resolution gridded dataset (CRU TS v4.01) 15</li> <li>GPCC: 16</li> <li>MSWEP: 17</li> </ul> </li> <li>MAT - Mean annual temperature from CRU)</li> <li>MConc_xxx – Mean annual concentration of rainfall as defined by 18, where xxx denotes precip data source (see “MAP_xxx”)</li> <li>MADD_xxx- Mean annual fractional dry days from CRU - i.e. seasonality of rainfall), where xxx denotes precip data source (see “MAP_xxx”)</li> <li>MDDM_xxx – Mean fractional dry days of the driest month.</li> <li>MADM_xxx – Mean annual precipitation of the driest month.</li> <li>MTWM - Mean Maximum Temperature of the warmest month from CRU</li> <li>MTCM - Mean minimum temperature of the coldest month from CRU</li> <li>SW1 - direct downwards SW simulated using the SLASH model using CRU cloud cover</li> <li>SW2 - diffuse downwards SW simulated using the SLASH model using CRU cloud cover</li> <li>MaxWind (Mean Max Windspeed from CRU-(National Centers for Environmental Prediction 15</li> </ul> ‘output_summary’ contains framework output. There are several directories for different experiments, each containing a netcdf file. Along with standard latitude and longitude,each file contains ‘model_level_number’ dimension, with each layer representing the 1, 5, 10, 25, 50, 75, 90, 95 and 99% quantiles of the model posterior. The folder represents the experiment: <ul> <li>Control – standard full model reconstruction</li> <li>noHumans – without human influence (from crop, pasture, population density or urban influence)</li> <li>noMortality – without disturbance stress (burnt area, wind, heat stress, rainfall seasonality</li> <li>noMAP – without mean annual precip influence.</li> <li>noNoneMAT – without mean annual temperature influence.</li> <li>noFire – tree cover without the influence of fire</li> <li>noDrought – without the influence of rainfall distribution</li> <li>noTasMort – without mortality from heat stress</li> <li>noWind – without influence from max. windspeed</li> <li>noPas – without exclusion from pasture</li> <li>noCrop – without exclusion from crop</li> <li>noPop – without reduction from population density</li> <li>noUrban – without exclusion from urban</li> <li>firePlus1pc – tree cover with burnt area was 1% higher.</li> </ul>   References   1.      Dimiceli, C. & Others. MOD44B MODIS/Terra Vegetation Continuous Fields Yearly L3 Global 250m SIN Grid V006 (NASA EOSDIS Land Processes DAAC, 2015). Preprint at (2015). 2.      Kelley, D. I. et al. How contemporary bioclimatic and human controls change global fire regimes. Nat. Clim. Chang. 9, 690–696 (2019). 3.      Klein Goldewijk, K., Goldewijk, K. K., Beusen, A., Van Drecht, G. & De Vos, M. The HYDE 3.1 spatially explicit database of human-induced global land-use change over the past 12,000 years. Glob. Ecol. Biogeogr. 20, 73–86 (2010). 4.      Hurtt, G. C. et al. Harmonization of land-use scenarios for the period 1500–2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands. Climatic Change vol. 109 117–161 Preprint at https://doi.org/10.1007/s10584-011-0153-2 (2011). 5.      Hantson, S., Arneth, A., Harrison, S. P. & Kelley, D. I. The status and challenge of global fire modelling. (2016). 6.      Hantson, S. et al. Quantitative assessment of fire and vegetation properties in simulations with fire-enabled vegetation models from the Fire Model Intercomparison Project. Geoscientific Model Development vol. 13 3299–3318 Preprint at https://doi.org/10.5194/gmd-13-3299-2020 (2020). 7.      Rabin, S. S., Melton, J. R. & Lasslop, G. The Fire Modeling Intercomparison Project (FireMIP), phase 1: experimental and analytical protocols with detailed model descriptions. Geoscientific Model (2017). 8.      Giglio, L., Randerson, J. T. & van der Werf, G. R. Analysis of daily, monthly, and annual burned area using the fourth-generation global fire emissions database (GFED4). J. Geophys. Res. Biogeosci. 118, 317–328 (2013). 9.      van der Werf, G. R. et al. Global fire emissions estimates during 1997–2016. Earth Syst. Sci. Data 9, 697–720 (2017). 10.    Roy, D. P., Boschetti, L., Justice, C. O. & Ju, J. The collection 5 MODIS burned area product — Global evaluation by comparison with the MODIS active fire product. Remote Sensing of Environment vol. 112 3690–3707 Preprint at https://doi.org/10.1016/j.rse.2008.05.013 (2008). 11.    Alonso-Canas, I. & Chuvieco, E. Global burned area mapping from ENVISAT-MERIS and MODIS active fire data. Remote Sens. Environ. 163, 140–152 (2015). 12.    Chuvieco, E. et al. Generation and analysis of a new global burned area product based on MODIS 250 m reflectance bands and thermal anomalies. Earth System Science Data vol. 10 2015–2031 Preprint at https://doi.org/10.5194/essd-10-2015-2018 (2018). 13.    Joyce, R. J., Janowiak, J. E., Arkin, P. A. & Xie, P. CMORPH: A Method that Produces Global Precipitation Estimates from Passive Microwave and Infrared Data at High Spatial and Temporal Resolution. J. Hydrometeorol. 5, 487–503 (2004). 14.    Marthews, T. R., Blyth, E. M., Martínez-de la Torre, A. & Veldkamp, T. I. E. A global-scale evaluation of extreme event uncertainty in the eartH2Observe project. Hydrol. Earth Syst. Sci. 24, 75–92 (2020). 15.    Harris, I. C. & Jones, P. D. CRU TS4.03: Climatic Research Unit (CRU) Time-Series (TS) version 4.03 of high-resolution gridded data of month-by-month variation in climate (Jan. 1901- Dec. 2018). (2019) doi:10.5285/10D3E3640F004C578403419AAC167D82. 16.    Schneider, U., Becker, A., Finger, P., Meyer-Christoffer, A. & Ziese, M. GPCC Full Data Monthly Product Version 2018 at 0.5◦: Monthly Land-Surface Precipitation from Rain-Gauges Built on GTS-Based and Historical Data. Deutscher Wetterdienst: Offenbach am Main, Germany (2018). 17.    Beck, H. E., Van Dijk, A. & Levizzani, V. MSWEP: 3-hourly 0.25 global gridded precipitation (1979-2015) by merging gauge, satellite, and reanalysis data. Hydrol. Earth Syst. Sci. (2017). 18.    Kelley, D. I., Harrison, S. P., Wang, H. & Simard, M. A comprehensive benchmarking system for evaluating global vegetation models. (2013).

opencc-by-4.0Dec 2020View details →

Supporting Data Sets for "New Constraints on the Lunar Optical Space Weathering Rate"

Data Sets supporting "New Constraints on the Lunar Optical Space Weathering Rate" submitted to Geophysical Research Letter on 12/18/2020. See Supporting Information (link TBD).

opencc-by-4.0Oct 2024View details →

Data from: Evolutionary potential and constraints in an aposematic species: Genetic correlations between warning coloration and fitness components in wood tiger moths

Phenotypic data and pedigrees of two laboratory populations of wood tiger moths (Arctia plantaginis) of Finnish (=FIN) and Estonian (=EST) ancestry. Pedigree:  ID: individual identifier sire = Father dam=mother Pheno.data:  ID: individual identifier Sex: 1=male; 2=female hatchingdate: date when larva hatched pupadate: date of pupation adultdate: date of exclusion Pupa.Weight: weight of pupa [mg] Female.Colour = hindwing colour of females. In this species hindwing colour in females varies continuously from yellow to red. It was quantified by visual matching of hinwdings against a colour scale ranging from  1 = yellow to 6 = red.  Signal.Size = larva signal size. Larvae show an orange patch of variable size on the back of their black body. The size is given as number of segments Egg.N = egg number produced by the individual Off.N = offspring number. Larvae were counted 2-3 weeks after egg laying

opencc-by-4.0May 2022View details →

Supplementary Data: Cosmological constraints on decaying axion-like particles: a global analysis

Supplementary Data Cosmological constraints on decaying axion-like particles: a global analysis This record contains the supplemetary data for the GAMBIT article, "Cosmological constraints on decaying axion-like particles: a global analysis".

opencc-by-4.0Oct 2017View details →

2746-node Polish Energy System Data of Transmission and Voltage Constraints Contribution to the Formation of LMP

This is the dataset that is used for the original article: "Contribution of Transmission and Voltage Constraints in the Formation of Locational Marginal Prices"   MATPOWER is required. The dataset obtained by MATPOWER ver6.0 tool [1]. Run run_matpower_acopf.m in MATLAB to start AC OPF. Offers should be saved in the MATLAB search path. Results of AC OPF are saved in bus, branch and gen files. Column names correspond to MATPOWER case file. Price-bonding factors are saved in lambda_P_PBF and lambda_Q_PBF. Ones in MP, MQ columns correspond to marginal nodes for real and reactive power respectively. Ones in CV, CD columns correspond to controlled voltage magnitude and phase respectively.  Acronyms in column names: <ul> <li>C(N) - bidding price of a marginal generator at node N </li> <li>Fmax(N) - maximum allowable real power throught line N, where N - index number of a line in branch.csv with binding constraint</li> <li>LAMP_P - LMP for real power</li> <li>LAM_Q - LMP for reactive power</li> <li>PBF - price-bonding factor</li> <li>TC - transmission constraint</li> <li>VC - voltage constraint</li> <li>V(N) - maximum (minimum) allowed voltages at node N</li> </ul>

opencc-by-4.0Jul 2024View details →

Data accompanying the manuscript "Biogeochemical cycling of trace elements and nutrients in ferruginous waters – constraints from a deep oligotrophic ancient lake", published in Limnology and Oceanography (doi: 10.1002/lno.12687)

CTD and geochemical data accompanying the publication: Biogeochemical cycling of trace elements and nutrients in ferruginous waters – constraints from a deep oligotrophic ancient lake in Limnology & Oceanography (doi: 10.1002/lno.12687).

opencc-by-4.0Sep 2024View details →

Data for "Constraints on the Observability of Energetic Neutral Atoms from the Magnetosphere-Atmosphere Interactions at Callisto and Europa" by Haynes et al.

Accompanying data products for publication entitled "Constraints on the Observability of Energetic Neutral Atoms from the Magnetosphere-Atmosphere Interactions at Callisto and Europa". The manuscript was submitted to JGR Space Physics shortly after upload. Data includes all simulation outputs that are depicted in this work, both for the AIKEF hybrid model (i.e., Figure 4) and the model used to produce synthetic ENA images (Figures 3, 6, 8, 9, 11, A1, and B1). All other figures in the work are used for illustrative purposes and were not generated with simulation output.  Information regarding the organization and file structure can be found in H24_data_readme.txt , as well as which dataset corresponds to which figure. Any inquiries, questions, or comments may be addressed through the email associated with this data publication.

opencc-by-4.0Feb 2021View details →

Data for paper "Parametric schedulability analysis of a launcher flight control system under reactivity constraints"

This is the data set (models, sources and results) for the paper "Parametric schedulability analysis of a launcher flight control system under reactivity constraints" published in Informatica Fundamentae in 2021.

opencc-by-4.0Mar 2023View details →

Supplementary Data: Collider constraints on electroweakinos in the presence of a light gravitino

This record contains the full dataset generated for the study "Collider constraints on electroweakinos in the presence of a light gravitino" by the GAMBIT Collaboration. The record also includes a GAMBIT configuration file and example scripts for plotting. See the README.md file for further details.

opencc-zeroJan 2016View details →

Supporting Data from Ecological Opportunities and Constraints in the Evolution of Diatoms

2016-02-01 Teofil Nakov Data and code for Nakov, Ruck, Alverson: Ecological opportunities and constraints in the evolution of diatom This packet contains the following files: 1. Sequence alignment for 540 diatoms and 2 Bolidomonas: Nakov-etal-542-taxon-alignment-clean.fasta 2. The constraint topology used in tree searches: Nakov-etal-constraint-small-clades.phylip 3. Alignment partition file: Nakov-etal-tiger-partitions-rax 4. Character data for 540 diatoms and 2 Bolidomonas: Nakov-etal-542-taxon-discrete-characters.csv 5. The best tree from 420 RAxML optimizations: Nakov-etal-RAxML_bestTree.opt_89 6. 99 additional trees used in the analyses: Nakov-etal-99-randomly-sampled-unique-trees.newick 7. All 100 trees converted into chronograms: Nakov-etal-100-chronograms-smooth-1000.newick 8. Script with random seeds for RAxML optimizations: Nakov-etal-RAxML-calls-for-420-optimizations 9. R scripts with random seeds: Nakov-etal-R-scripts.zip (three scripts: equal-rates, independent, and coordinated + paint regimes models) 10. TreePL control file: Nakov-etal-TreePL-control-file.txt

opencc-by-4.0Dec 2023View details →

Investigation of the post-2007 methane renewed growth with high-resolution 3-D variational inverse modelling and isotopic constraints - Input data

This dataset contains all the input data utilized to perform the inversions in Thanwerdas et al. (2023). First, we store here some data used in the paper but originally generated for other studies. Because these original datasets did not have any DOI, the authors have graciously agreed to store their dataset here. Note that the paper associated to each dataset must be properly referenced if utilized. <ul> <li>Cl Concentrations - Wang et al. (2021).zip: Original Cl concentrations field from Wang et al. (2021). </li> <li>CH4 Fluxes - Saunois et al. (2020).zip: Original CH4 fluxes used as prior data for the inversions performed as part of the Global Methane Budget 2000-2017 (Saunois et al., 2020).</li> </ul> Second, we store the processed input data generated for the purpose of our study. <ul> <li>CH4 Fluxes - LMDz9696.zip: Aggregated CH4 fluxes remapped on LMDz horizontal resolution for the five emission categories used in the paper.</li> <li>d13C Signatures - LMDz9696.zip: δ(13C, CH4) at LMDz horizontal resolution for the five emission categories used in the paper.</li> <li>dD Signatures - LMDz9696.zip: δ(D, CH4) at LMDz horizontal resolution for the five emission categories used in the paper.</li> <li>OH O1D Concentrations - LMDz9696-INCA.zip: OH and O1D monthly concentrations simulated with LMDz-INCA.</li> <li>Masks regions.zip: Masks for the regions used for the input data and the analysis.</li> </ul>

opencc-by-4.0Apr 2024View details →

Data for "Improved constraints on hematite refractive index for estimating climatic effects of dust aerosols"

This repository contains calculated/simulated data on the imaginary part of the complex refractive index, single scattering albedo, and/or optical depth for dust aerosols in the visible band or at the wavelength of 550 nm. For detailed information on (1) the acquisition and utilization of this data, (2) comprehensive configurations for model simulations, (3) the principal findings, and (4) the methodology employed to achieve these findings, please refer to the article authored by Li, Mahowald et al. (2024; Commun. Earth Environ). Other datasets, including the code and laboratory observations presented in the paper, can be found elsewhere (refer to the Data and Code Availability sections of the paper). For any clarification regarding the data and code, inquiries related to the publication, or potential collaboration, please contact Longlei Li (<a href="mailto:ll859@cornell.edu">ll859@cornell.edu</a>) or Natalie M. Mahowald (<a href="mailto:mahowald@cornell.edu">mahowald@cornell.edu</a>).

opencc-by-4.0Nov 2024View details →

Multiwavelength Constraints on the Origin of a Nearby Repeating Fast Radio Burst Source in a Globular Cluster (Public Data Release)

This Zenodo dataset contains the data for radio bursts B1-B9 from FRB 20200120E, as described in A. B. Pearlman et al., Nature Astronomy (2024) (see: https://doi.org/10.1038/s41550-024-02386-6). The following data products are included: <ul> <li>Channelized total intensity (Stokes I) data containing radio bursts B1-B5 from FRB 20200120E, recorded using the Effelsberg radio telescope during Pinpointing Repeating CHIME Sources with the EVN (PRECISE) VLBI observations. These data have a time resolution of 8 μs and were used in Figure 1 in A. B. Pearlman et al., Nature Astronomy (2024). <ul> <li>frb20200120e_b1_8us_burst_data.npy</li> <li>frb20200120e_b2_8us_burst_data.npy</li> <li>frb20200120e_b3_8us_burst_data.npy</li> <li>frb20200120e_b4_8us_burst_data.npy</li> <li>frb20200120e_b5_8us_burst_data.npy</li> </ul> </li> <li>Channelized total intensity (Stokes I) data containing radio bursts B6-B9 from FRB 20200120E, recorded using the Effelsberg radio telescope. These data have a time resolution of 64 μs and were used in Figure 1 in A. B. Pearlman et al., Nature Astronomy (2024). <ul> <li>frb20200120e_b6_64us_burst_data.npz</li> <li>frb20200120e_b7_64us_burst_data.npz</li> <li>frb20200120e_b8_64us_burst_data.npz</li> <li>frb20200120e_b9_64us_burst_data.npz</li> </ul> </li> <li>Frequency-summed total intensity (Stokes I) burst profiles of radio burst B4. The frequency range and time resolution of the data are listed below. These data were used in Extended Data Figure 2 (panels b, c, and d) in A. B. Pearlman et al., Nature Astronomy (2024). <ul> <li>frb20200120e_b4_8us_1254-1510mhz_burst_profile.npz; (frequency range, time resolution) = (1254-1510 MHz, 8 μs)</li> <li>frb20200120e_b4_1us_1302-1478mhz_burst_profile.npy; (frequency range, time resolution) = (1302-1478 MHz, 1 μs)</li> <li>frb20200120e_b4_31.25ns_1398-1414mhz_burst_profile.npy; (frequency range, time resolution) = (1398-1414 MHz, 31.25 ns)</li> </ul> </li> </ul> We also provide the following Python code containing functions that can be used to load and plot the radio data. The plots generated by this code are similar to those shown in Figure 1 and Extended Data Figure 2 (panels b, c, and d) in A. B. Pearlman et al., Nature Astronomy (2024). <ul> <li>plot_frb20200120e_radio_data_pearlman+2024_nature_astronomy.py</li> </ul> The X-ray data (from NICER, XMM-Newton, Chandra, and NuSTAR) used in A. B. Pearlman et al., Nature Astronomy (2024) are publicly available and can be accessed through NASA's High Energy Astrophysics Science Archive Research Center (HEASARC) archive. If the data or Python code included in this Zenodo repository are used, please include the following two citations in your work: <ol> <li>Pearlman, A. B., Scholz, P., Bethapudi, S. et al. Multiwavelength constraints on the origin of a nearby repeating fast radio burst source in a globular cluster. Nature Astronomy (2024). <a href="https://doi.org/10.5281/zenodo.13359005">https://doi.org/10.1038/s41550-024-02386-6</a></li> <li>Pearlman, A. B., Scholz, P., Bethapudi, S. et al. Multiwavelength constraints on the origin of a nearby repeating fast radio burst source in a globular cluster (public data release). Zenodo (2024). <a href="https://doi.org/10.5281/zenodo.13359005">https://doi.org/10.5281/zenodo.13359005</a></li> </ol> If you have questions about the contents of this Zenodo repository, please contact the lead author: Dr. Aaron B. Pearlman (<a href="mailto:aaron.b.pearlman@physics.mcgill.ca">aaron.b.pearlman@physics.mcgill.ca</a>)

opencc-by-4.0Nov 2021View details →

Data distribution of Constraints on the cosmic expansion history from the GWTC-3

This is the data distribution associated with the publication "Constraints on the cosmic expansion history from the GWTC--3". Please, refer to the README_icarogw.md and README_gwcosmo.md files for a description of the data distribution files.

opencc-by-4.0Mar 2022View details →

Data of glaciological property constraints for fractures in FRIS

This dataset is applied to set the glaciological property-based constraints for fractures extracted in FRIS. The dataset includes: (1) Outer boundaries of ice rises and ice rumples in FRIS and the corresponding buffer zones, which is modified from Inventory of Antarctic ice rises and rumples (Matsuoka et al., 2015). (2) Ice velocity maps of FRIS, including x and y component of ice velocity, which is generated based on MEaSUREs InSAR-Based Antarctica Ice Velocity Map (Version 2) (Mouginot et al., 2017).