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.
10,016
datasets available to search
ShareScore release 0.9.0
Dataset results
10,016 results for “Observation”
Spatiotemporal Dataset on Moon Jellyfish (Aurelia aurita) Incidental Observations in the Gulf of Riga and Eastern Gotland Basin, Baltic Sea
<p>This data article describes the occurrences of the moon jelly <em>Aurelia aurita </em>medusae in the Eastern Gotland basin and the Gulf of Riga (Baltic Sea) between 1998 and 2023. All data are incidental observations obtained during Latvian national monitoring cruises. </p>
Direct observation of electron density reconstruction at the metal-insulator transition in NaOsO3
<p>Open access data set for manuscript "Direct observation of electron density reconstruction at the metal- insulator transition in NaOsO3" published in Physical Review B, 98, 115116 (2018)</p>
11 May 2024 Superstorm Ionospheric Observations in the Continental US
<p><strong>May11_150km.mat: </strong> MATLAB save file - Ionospheric total electron content data from worldwide GNSS receivers, processed by MIT Haystack Observatory through the Millstone Hill Geospace Facility. Data is line of sight and conversion assumes a non-standard 150 km ionospheric pierce point. </p> <p>Direct permanent link: Anthea Coster, MIT/Haystack Observatory. (2024) Data from the CEDAR Madrigal database. Available from https://w3id.org/cedar?experiment_list=experiments4/2024/gps/11may24&file_list=los_20240511.001.h5</p> <p>GPS TEC data products and access through the Madrigal distributed data system are provided to the community by the Massachusetts Institute of Technology under support from US National Science Foundation grant AGS-1952737. Data for the TEC processing is provided from the following organizations: UNAVCO, Scripps Orbit and Permanent Array Center, Institut Geographique National, France, International GNSS Service, The Crustal Dynamics Data Information System (CDDIS), National Geodetic Survey, Instituto Brasileiro de Geografia e Estatística, RAMSAC CORS of Instituto Geográfico Nacional de la República Argentina, Arecibo Observatory, Low-Latitude IonosphericSensor Network (LISN), Canadian High Arctic Ionospheric Network, Institute of Geology and Geophysics, Chinese Academy of Sciences, China Meteorology Administration, Centro di Ricerche Sismologiche, Système d'Observation du Niveau des Eaux Littorales (SONEL), RENAG: REseau NAtional GNSS permanent - https://doi.org/10.15778/resif.rg, GeoNet - the official source of geological hazard information for New Zealand, Finnish Meteorological Institute, SWEPOS - Sweden, Hartebeesthoek Radio Astronomy Observatory, TrigNet Web Application, South Africa, Australian Space Weather Services, RETE INTEGRATA NAZIONALE GPS, Estonian Land Board, TU Delft, Western Canada Deformation Array, EUREF Permanent GNSS Network, GeoDAF: Geodetic Data Archiving Facility, African Geodetic Reference Frame (AFREF), Kartverket - Norwegian Mapping Authority, Geoscience Australia, IGS Data Center of Wuhan University, Pacific Northwest Geodetic Array, Nevada Geodetic Laboratory, Earth Observatory of Singapore, National Time and Frequency Standard Laboratory - Taiwan, and Korea Astronomy and Space Science Institute.</p> <p><strong>teczero.mat:</strong> MATLAB save file - 5-minute averaged background median TEC map, derived from May11_150km.mat file.</p> <p><strong>TECmaps0.m:</strong> MATLAB script - plotting script, employing teczero.mat, and used for Figure 3 and Figure 4B of manuscript.</p> <p><strong>gps_map.mat:</strong> MATLAB save file - source data for Figure 4A of manuscript: median vertical TEC map over 2-minute interval 0207-0208 UTC on 2024-05-11. </p> <p><strong>Fig4a_map.m: </strong>MATLAB script - plotting script, employing gps_map.mat, and used for FIgure 4A of manuscript.</p> <p><strong>cntr.m</strong>: MATLAB script - coastline plotting function.</p> <p><strong>burst.m</strong>: MATLAB script - plots individual LOS TEC data showing TEC bursts. Saves to "bursts.mat". Inputs "May11_150km.mat".</p> <p><strong>bursts.mat</strong>: MATLAB save file - individual TEC burst results.</p> <p><strong>1 Missouri_Skies_-_All-Sky_Fisheye_Missouri_Skies_Fishey_2030UT_2127UT.mp4: </strong>MP4 image file. Fisheye lens image of aurora from Missouri during 2024-05-11 storm. Used in Figure 2 of manuscript.</p> <p><strong>{amt,att,bmt,bot,het,hmt,nmt,omt,pat,sum,vmt}240511.g.001.hdf5</strong> - MagStar magnetometer measurements in HDF5 self-documenting file format, used for magnetometer figures in manuscript. Downloaded from the Madrigal database system. Direct permanent link: Jenn Gannon, Paul E. Meade, Eframir Franco-Diaz, Computational Physics. (2024) Data from the CEDAR Madrigal database. Available from https://w3id.org/cedar?experiment_list=experiments/2024/het/11may24&file_list=het240511g.001.hdf5. (Replace "het" with appropriate 3-letter site name from above list).<br><br></p>
High-level Data Products for NEID Solar Observations (January 1, 2021 - June 30, 2024)
<p>High-level data products derived from Sun-as-a-star observations by the NEID Solar Telescope at WIYN Observatory, spanning January 1, 2021 through June 30, 2024. We identify 117,060 observations which are unlikely to be significantly affected by weather, hardware or major calibration issues. This repository provides several high-level data products to the community to aid in the interpretation and inter comparisons of NEID solar observations. These include individual radial velocities (RV) measurements from the Penn State Research Pipeline, RVs binned over solar oscillation timescales, and daily-binned RVs, activity indicators, and cross correlation functions, as well as estimated corrections for differential extinction and the apparent solar rotation velocity. For additional information, see the manscript, "Earths within Reach: Evaluation of Strategies for Mitigating Solar Variability using 3.5 years of NEID Sun-as-a-Star Observations". </p>
Observation files of CG-5 gravity meters for the Zhetygen calibration line for five years
<p>Observation files of three Scintrex CG-5 gravimeters obtained during six field campaigns for calibration of these meters. Measurements were carried out at seven points of the Zhetygen calibration line, located 20 km north of Almaty in Kazakhstan. Also attached is a file with the coordinates of the stations.</p>
ULTRACAM observations of PSR J1622-0315
<p>Optical observations of PSR J1622-0315 taken by the high-speed triple-beam CCD camera ULTRACAM on the New Technology Telescope. As presented in Sen et al. 2024 (ApJ, in press). The first letter in each file name represents the observing filter for that specific file. The raw data for creating the light curves can be requested by contacting Rene Breton or Vik Dhillon.</p> <p>The columns in each file are:<br>1) MJD time<br>2) orbital phase (in rotations, with phase = 0 corresponding to pulsar's ascending node),<br>3) flux density (in erg cm^-2 s^-1 Hz^-1)<br>4) flux density uncertainty (in erg cm^-2 s^-1 Hz^-1)</p>
HI line observations of 290 evolved stars made with the Nancay Radio Telescope - I. Data: online Tables
<p>--- Table B.1: Clear NRT HI detections - basic data </p> <p>Description of the columns:</p> <p>(1) Name: common catalogue name of the target. <br> An ^n after a name indicates that it is clearly not an AGB star, <br> a ^d that we consider its classification as an AGB to be dubious, <br> and a ^* indicates that notes on the object can be found in Appendix A;<br>(2,3) RA,DEC: literature right ascension and declination of the target from Gaia EDR 3, <br> for epoch J2000.0;<br>(4) Type: target type. <br> Primarily the variability type as listed in Version 5.1 of the General Catalogue of Variable Stars, <br> GCVS (A description of GCVS types is given in https://cdsarc.u-strasbg.fr/ftp/cats/B/gcvs/vartype.txt),<br> but if an object is not included in the GCVS, other identifiers are listed in brackets: <br> HPM = high proper motion star, (OH/IR) = OH/IR maser, LPVc = long-period variable candidate, <br> PN = planetary nebula, pPN = proto-planetary nebula, and post-AGB star;<br>(5,6) Spec & ref: spectral type of the star, followed by its literature reference, <br> as retrieved from the SIMBAD database. If none was listed there, the reference is noted as 'SIMBAD';<br>(7,8) Teff & ref: effective temperature of the star, in K, followed by its literature reference; <br>(9) d: distance of the target, based on its parallax (mainly from the Gaia EDR3), in pc.<br> If no Gaia parallax was available a reference to the distance we adopted is given in Appendix A<br> (for RAFGL 3099, mu Cep, and V Peg);<br>(10) Vlit: published radial velocity of the target in the LSR reference frame, in km/s;<br>(11) Vexp: literature expansion velocity measured from CO or OH 1612 MHz line observations, in km/s. <br> If a pair of values was published for a two-velocity component CO line fit, the largest value is listed here;<br>(12) ref: literature references to the published Vlit and Vexp values; <br>(13) line: spectral line on which the published radial velocity measurement (Vlit) was based;<br>(14,15) Mdot & ref: literature mass loss rates, in solar masses per year, <br> followed by its literature reference.</p> <p>Notes to Table B.1:</p> <p>References: see Table B.1 in the Astronomy & Astrophysics paper.</p> <p><br>--- Table B.2: Clear NRT HI detections - HI data </p> <p>Description of the columns:</p> <p>(1) Name: common catalogue name of the target. <br> A ^T after a name indicates that HI line parameters are based on a 'total' spectrum, whereas <br> a ^P indicates that a 'peak' spectrum was used. <br> An ^n indicates that it is clearly not an AGB star, <br> a ^d that we consider its classification as an AGB to be dubious, <br> and a ^* indicates that notes on the object can be found in Appendix A;<br>(2) VHI: our central radial velocity in the LSR reference frame of the Gaussian fitted <br> to the HI profile, in km/s.<br>(3) FWHM: our full width half maximum of the Gaussian fitted to the HI line profile, in km/s;<br>(4) Speak: our peak flux density of the HI line profile, in Jy;<br>(5) diam: our estimated angular size of the HI CSE in the east-west direction, in arcmin;<br>(6) FHI: our integrated line flux of the HI profile, in Jy km/s;<br>(7) MHI: our total HI mass, in Msun;<br>(8) HI ref: references to previously published HI studies,<br> see Table B.2 in the Astronomy & Astrophysics paper.</p> <p><br>--- Table B.3: Possible NRT HI detections - basic data</p> <p>Description of the columns:</p> <p>(1) Name: common catalogue name of the target. <br> An ^n after a name indicates that it is clearly not an AGB star, <br> a ^d that we consider its classification as an AGB to be dubious, <br> and a ^* indicates that notes on the object can be found in Appendix A;<br>(2,3) RA,DEC: literature right ascension and declination of the target from Gaia EDR 3, <br> for epoch J2000.0;<br>(4) Type: target type. <br> Primarily the variability type as listed in Version 5.1 of the General Catalogue of Variable Stars, <br> GCVS (A description of GCVS types is given in https://cdsarc.u-strasbg.fr/ftp/cats/B/gcvs/vartype.txt),<br> but if an object is not included in the GCVS, other identifiers are listed in brackets: <br> HPM = high proper motion star, (OH/IR) = OH/IR maser, LPVc = long-period variable candidate, <br> PN = planetary nebula, pPN = proto-planetary nebula, and post-AGB star;<br>(5,6) Spec & ref: spectral type of the star, followed by its literature reference, <br> as retrieved from the SIMBAD database. If none was listed there, the reference is noted as 'SIMBAD';<br>(7,8) Teff & ref: effective temperature of the star, in K, followed by its literature reference; <br>(9) d: distance of the target, based on its parallax (mainly from the Gaia ED33), in pc.<br> If no Gaia parallax was available a reference to the distance we adopted is given in Appendix A<br> (for RAFGL 3099, mu Cep, and V Peg);<br>(10) Vlit: published radial velocity of the target in the LSR reference frame, in km/s;<br>(11) Vexp: literature expansion velocity measured from CO or OH 1612 MHz line observations, in km/s. <br> If a pair of values was published for a two-velocity component CO line fit, the largest value is listed here;<br>(12) Mdot : literature mass loss rates, in solar masses per year, <br>(13) line: spectral line on which the published radial velocity measurement (Vlit) was based;<br>(14) ref: literature references to the published Vlit, Vexp and Mdot values; </p> <p>Notes to Table B.3:</p> <p>References: see Table B.1 in the Astronomy & Astrophysics paper.</p> <p><br>--- Table B.4: Possible NRT HI detections - HI data</p> <p>Description of the columns:</p> <p>See the description of the columns of Table B.2.</p> <p><br>--- Online only Table 5: Upper limits to NRT HI lines </p> <p>Description of the columns:</p> <p>(1) Name: common catalogue name of the target. <br> An ^n after a name indicates that it is clearly not an AGB star, <br> a ^d that we consider its classification as an AGB to be dubious, <br> and a ^* indicates that notes on the object can be found in Appendix A;<br>(2,3) RA,DEC: literature right ascension and declination of the target from Gaia EDR 3, <br> for epoch J2000.0;<br>(4) Type: target type. <br> Primarily the variability type as listed in Version 5.1 of the General Catalogue of Variable Stars, <br> GCVS (A description of GCVS types is given in https://cdsarc.u-strasbg.fr/ftp/cats/B/gcvs/vartype.txt),<br> but if an object is not included in the GCVS, other identifiers are listed in brackets: <br> HPM = high proper motion star, (OH/IR) = OH/IR maser, LPVc = long-period variable candidate, <br> PN = planetary nebula, pPN = proto-planetary nebula, and post-AGB star;<br>(5,6) spec & ref: spectral type of the star, followed by its literature reference, <br> as retrieved from the SIMBAD database. If none was listed there, the reference is noted as 'SIMBAD';<br>(7,8) Teff & ref: effective temperature of the star, in K, followed by its literature reference; <br>(9) d: distance of the target, based on its parallax (mainly from the Gaia EDR3, in pc.<br> If no Gaia parallax was available a reference to the distance we adopted is given in Appendix A<br> (for RAFGL 3099, mu Cep, and V Peg);<br>(10) Vlit: published radial velocity of the target in the LSR reference frame, in km/s;<br>(11) Vexp: literature expansion velocity measured from CO or OH 1612 MHz line observations, in km/s. <br> If a pair of values was published for a two-velocity component CO line fit, the largest value is listed here;<br>(12) Mdot : literature mass loss rates, in solar masses per year, <br>(13) ref: literature references for Vlit, Vexp and Mdot, as applicable;<br>(14) line: spectral line on which the published radial velocity measurement (Vlit) was based;<br>(15) Speak: peak flux density of our HI line profile, in Jy;<br>(16) notes: 'old data' indicates objects observed only in 1992/1993, before the renovation of the NRT; <br> 'blue/red side' indicates that either the blue or red side of the HI profile could be used to measure <br> an upper limit to the line flux;<br>(17) HI ref: references to previously published HI studies;</p> <p>Notes to online only Table 5: </p> <p>HI references: see Table B.2 in the Astronomy & Astrophysics paper.<br>Other references: see Table B.1 in the Astronomy & Astrophysics paper.</p> <p><br>--- Online only Table 6: Confused NRT HI spectra</p> <p>Description of the columns:</p> <p>(1) Name: common catalogue name of the target. <br>(2,3) RA, DEC: literature right ascension and declination of the target from Gaia EDR3, <br> for epoch J2000.0; <br>(4) type: target type. Primarily the variability type as listed in Version 5.1 of the <br> General Catalogue of Variable Stars, GCVS; but if an object is not included in the GCVS, <br> other identifiers are listed in brackets: HPM = high proper motion star, (OH/IR) = OH/IR maser, <br> LPVc = long-period variable candidate, PN = planetary nebula, pPN = proto-planetary nebula, <br> and post-AGB star; <br>(5,6) spec & ref spectral type of the star, followed by its literature reference, <br> as retrieved from the SIMBAD database. If none was listed there, the reference is noted as 'SIMBAD'; <br>(7,8) Vlit & ref: published radial velocity of the target in the LSR reference frame, in km/s; <br>(9) line: spectral line on which the published radial velocity measurement (Vlit) was based; <br>(10) notes: 'old data' denotes objects observed only in 1992/1993, before the renovation of the NRT<br> (see Section 3), for which no observations in digital form are available; <br>(11) HI ref: references to previously published HI studies.</p> <p>Notes to online only Table 6:</p> <p>HI references: see Table B.2 in the Astronomy & Astrophysics paper.<br>Other references: see Table B.1 in the Astronomy & Astrophysics paper.</p>
Dataset for: Importance of satellite observations for high-resolution mapping of near-surface NO2 by machine learning
<p>Dataset for: Importance of satellite observations for high-resolution mapping of near-surface NO<sub>2 </sub>by machine learning</p> <p>This dataset is uploaded as a part of the article by Kim et al. (2021). The dataset is the hourly maps of near-surface nitrogen dioxide (NO<sub>2</sub>) concentrations at 100 m resolution for an Alpine domain (Switzerland and northern Italy, 6-12 °E, 42-48 °N). The dataset is provided per day (24 hours) in a netcdf (*.nc ~550MB). In this work, we have generated NO<sub>2 </sub>hourly maps for Feb. 2019 to May 2020 and, here, we upload for March 2019 only (~16 GB). If you need data for another period of time, please contact Gerrit Kuhlmann (gerrit.kuhlmann@empa.ch) or Minsu Kim (minsu.kim@empa.ch). </p>
Meteor observations and wind estimates from the northern Germany SIMONe radar network on November 5, 2018
<p>This dataset includes meteor observations and wind estimates taken as part of the SIMONe 2018 campaign in northern Germany on November 5, 2018. The files are in netCDF-4 format and follow CF conventions (https://cfconventions.org/). We recommend loading the data using the xarray Python package.</p> <p>The SIMONe 2018 campaign ran from November 2, 2018 through November 9, 2018 in northern Germany. The radar network consisted of two pulsed transmitters in Juliusruh and Collm and a five-element interferometric MIMO-CW transmitter located in Kühlungsborn. Monostatic receiver stations co-located with the pulsed transmitters and six additional receiver stations located in Mechelsdorf, Breege, Neustrelitz, Guderup, Salzwedel, and Bornim were used to form a total of two monostatic and ten bistatic links. The data from these individual links were then processed to detect specular meteor echoes and estimate their parameters, including Doppler shift. The Doppler shifts, imposed by movement of the meteor trail due to the neutral winds, were then used to estimate the 4-D wind field. More details about the campaign can be found in Vierinen et al. (2019). Details for the wind field estimation can be found in Volz et al. (submitted).</p>
The Thousand-Pulsar-Array program on MeerKAT -- IX. The time-averaged properties of the observed pulsar population: data set
<p>This archive contains pulsar data presented as part of the MNRAS paper: <em>"The Thousand-Pulsar-Array program on MeerKAT -- IX. The time-averaged properties of the observed pulsar population"</em>.</p> <p>Folded, time-averaged pulse profiles (4 Stokes parameters, 8 frequency channels, 1024 time bins across the period) of the 1271 pulsars listed in Table 1 of the MNRAS paper are included in the ar_files.zip. Ephemerides of these pulsars (as used in the MNRAS paper) are included in the eph_files.zip. The pulsar data are readable by the PSRCHIVE package, see e.g. van Straten et al., Astronomical Research and Technology 9, 237 (2012).</p> <p>Tables 1, 5, and 6 from the MNRAS paper are included in tables_files.zip as .csv files. The file column_descriptions.txt describes the quantities in columns of these tables.<br> </p>
In situ observations of Ganymede's outflowing ionosphere
<p>The file contains values for ionospheric moments from the Juno flyby of Ganymede on 7 June 2021, during the period between 16:50 to 17:00, UTC. A description of the flyby and the data set is found in Valek et al. (2022), <em>In situ ion composition observations of the Ganynmede's outflowing ionosphere</em>, GRL DOI:10.1029/2022GL100281.</p>
SO-WISE South Atlantic Ocean and Indian Ocean Observational Constraints
<p>This dataset contains an initial set of curated and processed oceanographic observations collected as part of a joint effort between the EU SO-CHIC project and a UKRI Future Leaders Fellowship. It is partly intended to be used as a set of observational constraints for a Weddell Gyre region state estimate, although it can be used for more general analysis purposes as well. It has been used as part of an unsupervised clustering analysis [see Jones (2022) for software and Jones and Zhou (2022) for labelled dataset, see references]. </p> <p><strong>Overall spatial and temporal coverage</strong></p> <ul> <li>Latitude: 85°S-30°S</li> <li>Longitude: 65°W-80°E</li> <li>Time: 1974-2020</li> </ul> <p><strong>Contents</strong></p> <ul> <li>CPOM_SSH: sea-ice corrected sea surface height </li> <li>CTD: temperature and salinity profiles from ship-based CTD casts </li> <li>FLOATS: temperature and salinity profiles from Argo floats </li> <li>SEALS: temperature and salinity profiles from seal-mounted profilers</li> <li>Stress_and_EKE: sea-ice corrected surface stress and EKE </li> <li>XBT: temperature and salinity profiles from expendable bathythermographs (XBTs)</li> </ul> <p><strong>Profile quality control</strong></p> <p>We only consider profiles with good position and time flags, as well as good temperature, salinity, and pressure measurements with good flags. Duplicated profiles are identified when multiple profiles are found within 24 hours over the same 2 km x 2 km grid cell, and only one profile within the spatio-temporal window is used. We then used the MITprof toolbox (Forget, G., 2017) to pre-process the selected profiles, re-gridding them onto 72 standard pressure levels; the vertical interval varies from 20 dbar at the surface to 100 dbar in the deep ocean. </p> <p><strong>SSH processing</strong></p> <p>SSH data is sea-ice corrected version provided by the Centre for Polar Observation and Modelling (CPOM) in the UK. It is composed by two satellite missions, Envisat (2004/05-2012/03) and Cryosat-2 (2010/07-2020/04). The data is available in montly along-track format. A gaussian 300km filter, ±3 std outliner removal and 0.5x0.25 deg interpolation is applied to grid the data. Intersatellite offset is removed using the overlapped period between two missions using the mean difference map. SSH is referenced to EIGEN6C4 geoid to obtain the dynamic ocean topography feild for the computation of geostrophic velocity. See the README in the Stress_and_EKE directory for more information. </p> <p><strong>Sources</strong></p> <ul> <li>Argo floats: <a href="http://argo.ucsd.edu">http://argo.ucsd.edu</a></li> <li>World Ocean Database: <a href="https://www.ncei.noaa.gov/products/world-ocean-database">https://www.ncei.noaa.gov/products/world-ocean-database</a></li> <li>MEOP-CTD Database (seal profilers): <a href="https://www.meop.net/">https://www.meop.net/</a></li> <li>CDRv4 available via NSIDC: <a href="https://nsidc.org/data/G02202">https://nsidc.org/data/G02202</a></li> <li>Polar Pathfinder sea ice drift data via NSIDC: <a href="https://nsidc.org/data/nsidc-0116">https://nsidc.org/data/nsidc-0116</a></li> </ul> <p><strong>Version</strong></p> <p>This is a pre-production version, in that it has not yet been used with a state estimate. </p>
Data supporting: Microscopic observation of two-level systems in a metallic glass model
<p>Dataset of double well potentials sampled from energy landscape exploration of a ternary Lennard-Jones model supporting: "Microscopic observation of two-level systems in a metallic glass model"</p> <p>Thermalised configurations of the ternary Lennard-Jones model are given in the archive (configs.zip) of 1200 atoms at <span class="math-tex">\(T_f\)</span> 0.488, 0.509, 0.558 and 0.617 in the lammps (https://www.lammps.org/) data file format (https://docs.lammps.org/read_data.html).</p> <p>The two datasets each provided as (.zip) archives named dataset1.zip and dataset2.zip</p> <p>Datafiles (.csv) are named nebdf_{:3.3f}_{:05d}.csv where the float is <span class="math-tex">\(T_f\)</span> and the integer is <span class="math-tex">\(\tilde{m}\)</span>. </p> <p>columns of each .csv file are:</p> <p>'transitions', 'forward barriers', 'reverse barriers', 'asymmetry', 'barrier', 'euclidean distance', 'distance along string', 'n_intermediates', 'deltas', 'splittings', 'delta_zeroes', 'gammas', 'PR', 'glass', 'omegas1', 'omegas2', 'omegasts', 'Index 1', 'Index 2', 'Frequency 1>2', 'Frequency 2>1', 'e_1', 'e_2', 'dc', 'Tprep'</p> <p>'glass' is the index of the glassy metabasin sampled</p> <p>omegas1', 'omegas2', 'omegasts' are the curvatures of the minimum energy oaths near the first minimum, second minimum and transition state</p> <p>'e_1', 'e_2' are the energy per atom of the two glass minima </p> <p>'dc' is the typical particle displacement corresponding to <span class="math-tex">\(\sqrt{\dfrac{d^2}{PR}}\)</span></p> <p> </p>
Spatial and Temporal Availability of Cloud-free Optical Observations in the Tropics
<p>These data comprise three layers describing the spatial and temporal distribution of cloud-free optical observations over the tropics. The test datasets shared here are derived from the combination of Landsat and Sentinel-2 satellite data and represent a portion of the full datasets. The test data correspond to year 2020 over Mesoamerica. </p> <ul> <li>Spatial data: the Meso2020_validObs contains one band 'valid_obs' indicating the number of cloud-free observations at the pixel level. </li> <li>Temporal data: the maximumWaitDate2020 contains two bands 'max' and 'maxDay' corresponding to the number of maximum consecutive days without data in a year and final date in which the maximum number of consecutive days without data occurred, respectively.</li> </ul>
Dataset for manuscript "Thermal infrared dust optical depth and coarse-mode effective diameter over oceans retrieved from collocated MODIS and CALIOP observations"
<p>This is the long-term satellite retrieval dataset of dust aerosol optical depth at 10 μm (DAOD<sub>10μm</sub>) and dust coarse mode effective diameter (D<sub>eff</sub>) based on collocated MODIS and CALIOP observations from July 2006 to August 2018. The full description is in the manuscript "<strong>Thermal infrared dust optical depth and coarse-mode effective diameter over oceans retrieved from collocated MODIS and CALIOP observations" </strong>by Zheng, Jianyu, et al. The readme file for the data is in "readme_dust_aod_size_product.txt". The variable list of Level-2 data is in "variable_list_L2.txt". The variable list of Level-3 data is in "variable_list_L3.txt".</p>
Data and simulation files for "Constraints on the intergalactic magnetic field using Fermi-LAT and H.E.S.S. blazar observations"
<p>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.</p> <p>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).</p> <p>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 <em>not</em> included in the data. Furthermore, we provide files that contain the log likelihood profiles as functions of the IGMF in case the halo emission <em>is</em> included. Lastly, we also provide our template files for the halo, generated with <a href="https://crpropa.github.io/CRPropa3/">CRPropa 3</a>.</p> <p>Below, we provide minimal code examples to demonstrate how to read in the specific files.</p> <p><strong>H.E.S.S. observational results</strong></p> <p>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:</p> <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> <p>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:</p> <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> <p><strong>Fermi observational results</strong></p> <p>For Fermi-LAT, we provide the SED files as well as the best-fit models for the region of interests. These files are called:</p> <ul> <li>fermi_avg_file_*.npy provides the best-fit ROI model</li> <li>fermi_sed_file_*.npy provides the SED.</li> </ul> <p>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:</p> <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> <p><strong>Likelihood profiles</strong></p> <p>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, 10<sup>4</sup>, and 10<sup>7</sup> years. They can be read in with the following code snippet:</p> <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> <p>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.</p> <p><strong>Halo simulations</strong></p> <p>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.</p> <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> <p>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.</p>
Fruit-bearing plant species observations in Brazilian cities
<p>This data set, extracted from iNaturalist, compiles observations from the capitals of all 27 Brazilian federative units and specifically focuses on the species listed on https://doi.org/10.5281/zenodo.10212850. A backup of this data set was obtained from iNaturalist on August 22nd, 2023. The dataset features 47 columns, capturing details such as observation time, location, license, and taxonomic identification. It provides an extensive taxonomic breakdown, covering classifications from kingdom and phylum down to species, subspecies, and variety (in some cases). </p>
Observational constraints of fire, environmental and anthropogenic on pantropical tree cover - Data
<p>Data used for analysis in "Explainable Clustering Applied to the Definition of Terrestrial Biome" - using Decision Tree and Clustering techniques to identify biomes.</p> <p>Land surface properties:</p> <ul> <li><strong>TreeCover </strong>- Vegetation Continuous Fields (VCF) collection 6 fractional tree cover from <sup>1</sup>, regridded as per <sup>2</sup>.</li> <li><strong>urban </strong>cover from the History Database of the Global Environment, Version 3.1 (HYDE) <sup>3,4</sup></li> <li><strong>crop </strong>cover (from HYDE)</li> </ul> <ul> <li><strong>pas - Pasture </strong>Cover (from HYDE)<strong>PopDen </strong>(population density from HYDE)</li> <li><strong>BurntArea_xxxxx </strong>- Burnt area with xxxx denoting different products, provided by fireMIP <sup>5–7</sup>: <ul> <li>GFED_four: Global Fire Emissions Database, Version 4 (GFED4) <sup>8</sup></li> <li>GFED_four_s: Global Fire Emissions Database, Version 4.1, including small fires (GFEDv4.1) <sup>9</sup></li> <li>MCD_forty_five: MCD45 <sup>10</sup></li> <li>Meris: Fire_CCI4.0 <sup>11</sup></li> <li>MODIS: Fire_CCI5.1 <sup>12</sup></li> </ul> </li> </ul> <p>Climate:</p> <ul> <li><strong>MAP_xxx </strong>- Mean annual precipitation where xxx denotes data source: <ul> <li><strong>CMORPH </strong><sup>13,14</sup></li> <li><strong>CRU </strong>from version 4.03 of the Climatic Research Unit Time Series high-resolution gridded dataset (CRU TS v4.01) <sup>15</sup></li> <li><strong>GPCC: </strong><sup>16</sup></li> <li><strong>MSWEP: </strong><sup>17</sup></li> </ul> </li> <li><strong>MAT </strong>- Mean annual temperature from CRU)</li> <li><strong>MConc_xxx </strong>– Mean annual concentration of rainfall as defined by <sup>18</sup>, where xxx denotes precip data source (see “MAP_xxx”)</li> <li><strong>MADD_xxx</strong>- Mean annual fractional dry days from CRU - i.e. seasonality of rainfall), where xxx denotes precip data source (see “MAP_xxx”)</li> <li><strong>MDDM_xxx </strong>– Mean fractional dry days of the driest month.</li> <li><strong>MADM_xxx – </strong>Mean annual precipitation of the driest month<strong>.</strong></li> <li><strong>MTWM </strong>- Mean Maximum Temperature of the warmest month from CRU</li> <li><strong>MTCM </strong>- Mean minimum temperature of the coldest month from CRU</li> <li><strong>SW1 </strong>- direct downwards SW simulated using the SLASH model using CRU cloud cover</li> <li><strong>SW2 </strong>- diffuse downwards SW simulated using the SLASH model using CRU cloud cover</li> <li><strong>MaxWind </strong>(Mean Max Windspeed from CRU-(National Centers for Environmental Prediction <sup>15</sup></li> </ul> <p>‘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:</p> <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> <p> </p> <p><strong>References</strong></p> <p> </p> <p>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).</p> <p>2. Kelley, D. I. <em>et al.</em> How contemporary bioclimatic and human controls change global fire regimes. <em>Nat. Clim. Chang.</em> <strong>9</strong>, 690–696 (2019).</p> <p>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. <em>Glob. Ecol. Biogeogr.</em> <strong>20</strong>, 73–86 (2010).</p> <p>4. Hurtt, G. C. <em>et al.</em> 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. <em>Climatic Change</em> vol. 109 117–161 Preprint at https://doi.org/10.1007/s10584-011-0153-2 (2011).</p> <p>5. Hantson, S., Arneth, A., Harrison, S. P. & Kelley, D. I. The status and challenge of global fire modelling. (2016).</p> <p>6. Hantson, S. <em>et al.</em> Quantitative assessment of fire and vegetation properties in simulations with fire-enabled vegetation models from the Fire Model Intercomparison Project. <em>Geoscientific Model Development</em> vol. 13 3299–3318 Preprint at https://doi.org/10.5194/gmd-13-3299-2020 (2020).</p> <p>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. <em>Geoscientific Model</em> (2017).</p> <p>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). <em>J. Geophys. Res. Biogeosci.</em> <strong>118</strong>, 317–328 (2013).</p> <p>9. van der Werf, G. R. <em>et al.</em> Global fire emissions estimates during 1997–2016. <em>Earth Syst. Sci. Data</em> <strong>9</strong>, 697–720 (2017).</p> <p>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. <em>Remote Sensing of Environment</em> vol. 112 3690–3707 Preprint at https://doi.org/10.1016/j.rse.2008.05.013 (2008).</p> <p>11. Alonso-Canas, I. & Chuvieco, E. Global burned area mapping from ENVISAT-MERIS and MODIS active fire data. <em>Remote Sens. Environ.</em> <strong>163</strong>, 140–152 (2015).</p> <p>12. Chuvieco, E. <em>et al.</em> Generation and analysis of a new global burned area product based on MODIS 250 m reflectance bands and thermal anomalies. <em>Earth System Science Data</em> vol. 10 2015–2031 Preprint at https://doi.org/10.5194/essd-10-2015-2018 (2018).</p> <p>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. <em>J. Hydrometeorol.</em> <strong>5</strong>, 487–503 (2004).</p> <p>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. <em>Hydrol. Earth Syst. Sci.</em> <strong>24</strong>, 75–92 (2020).</p> <p>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.</p> <p>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. <em>Deutscher Wetterdienst: Offenbach am Main, Germany</em> (2018).</p> <p>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. <em>Hydrol. Earth Syst. Sci.</em> (2017).</p> <p>18. Kelley, D. I., Harrison, S. P., Wang, H. & Simard, M. A comprehensive benchmarking system for evaluating global vegetation models. (2013).</p>
Journey North - Red-winged blackbird observations by volunteer community scientists across Central and North America (1999-2020)
This data package contains Red-winged Blackbird migration data consisting of 9,352 total observational reports from 1999 - 2020 across North and Central America. These data were collected by 4,647 community scientists for Journey North, a crowdsourced participatory science program of the University of Wisconsin-Madison Arboretum. The Journey North Red-winged Blackbird Project is an ongoing study of Red-winged Blackbird phenology conducted at broad spatial and temporal scales. Since 1999, community scientists have tracked first arrival dates and breeding and feeding behavior as well as the onset of fall migration and presence of Red-winged blackbird species throughout the winter months in the United States. The focal species is the Red-winged Blackbird (Agelaius phoeniceus). Observers also provide estimates of the number of birds sighted. However, observers do not follow standardized methods for counting species observed. Observers do not observe at set times of the day, do not repeat observations regularly, and are not required to provide the length of time during which a specified number of species observed were counted. Therefore, it is recommended that this dataset be analyzed to indicate presence not abundance. Researchers are encouraged to read the rich information provided by volunteers in their comments. These comments provide qualitative information about observational reports. Researchers are also encouraged to refer to submitted photographs that also provide context for observational reports. The Journey North Red-winged Blackbird Project dataset is hosted by the University of Wisconsin-Madison Shared Web Hosting Service.
Journey North - American Robin observations by volunteer community scientists across Central and North America (1996-2020)
This data package contains American Robin migration data consisting of 39,260 total observational reports from 1996 - 2020 across North and Central America. These data were collected by 17,619 community scientists for Journey North, a crowdsourced participatory science program of the University of Wisconsin-Madison Arboretum. The Journey North American Robin Project is an ongoing study of American Robin phenology conducted at broad spatial and temporal scales. Since 1996, community scientists have tracked first arrival dates and breeding and feeding behavior as well as the onset of fall migration and presence of American Robin species throughout the winter months in the United States. The focal species is the American Robin (Turdus migratorius). Observers also provide estimates of the number of birds sighted. However, observers do not follow standardized methods for counting species observed. Observers do not observe at set times of the day, do not repeat observations regularly, and are not required to provide the length of time during which a specified number of species observed were counted. Therefore, it is recommended that this dataset be analyzed to indicate presence not abundance. Researchers are encouraged to read the rich information provided by volunteers in their comments. These comments provide qualitative information about observational reports. Researchers are also encouraged to refer to submitted photographs that also provide context for observational reports. The Journey North American Robin Project dataset is hosted by the University of Wisconsin-Madison Shared Web Hosting Service.
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.