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265 results for “MESSENGER”
Spectral reflectance data of Mercury's surface collected by the Mercury Atmospheric and Surface Composition Spectrometer (MASCS) instrument during orbital observations of the NASA MESSENGER mission between 2011 and 2015 resampled to a [55399 × 396] tabular data format.
<p>MASCS is a three sensor point spectrometer with a spectral coverage from 200 nm to 1450 nm.<br> Single spectra are resamples in to steps to a format useful for our ML application : a datacube with ~400 spectral channel covering the whole surface of Mercury.<br> The final dataset has dimension [N×M] where N is the number of grid cells (360 × 180 = 64, 800) and M is the number of spectral features (396).<br> Due to the incomplete coverage and data filtering, some grid cells are empty.<br> After removing these empty cells, the size of the dataset is [55399 × 396].</p> <p>This specific product is stored as a gzip compressed json, where each element is a grid cell.<br> We are in the process to publish a complete pipeline to produce this product from RAW data on https://github.com/epn-ml/MESSENGER-Mercury-Surface-Cassification-Unsupervised_DLR/ .</p> <p>Spectral reflectance data of Mercury’s surface collected by the Mercury Atmospheric and Surface Composition Spectrometer (MASCS) instrument during orbital observations of the NASA MESSENGER mission between 2011 and 2015.<br> MASCS is a three sensor point spectrometer with a spectral coverage from 200 nm to 1450 nm.<br> Single spectra are resamples in to steps to a format useful for our ML application : a datacube with ~400 spectral channel covering the whole surface of Mercury.<br> The final dataset has dimension [N×M] where N is the number of grid cells (360 × 180 = 64, 800) and M is the number of spectral features (396).<br> Due to the incomplete coverage and data filtering, some grid cells are empty.<br> After removing these empty cells, the size of the dataset is [55399 × 396].</p> <p>0. Pre-filtering<br> We used the most recent dataset that had large-scale photometric corrections and thus was almost free from observation geometry effects.<br> However, extreme geometry are still present and are typically associated with high noise and some residual instrumental effects.<br> Based on our empirical tests, we filtered out observations with an emission/incidence angle ≥80∘.<br> We also calculated the median value per wavelength and per cell grid when constructing the global hyperspectral data cube and filtered out observations falling under the 2nd percentile and above 99.9th percentile to clean some residual geometry effects.<br> With this approach we create an effective noise filter while retaining enough observations to be able to analyse the entirety of the surface of the planet.</p> <p>1. Spectral resmpling<br> Unprocessed MASCS spectra could have 512 or 256 channes, depending on binning.<br> We resampled the data in the spectral dimension to a common wavelength range from 260 nm to 1052 nm with a 4 nm resolution (2 nm spectral sampling), resulting in 396 spectral channels.<br> This approach slightly oversamples the original 4.77 nm spectral resolution and removes some points from the original 200-1050 nm range.<br> The resulting data matrix is expressed in tabular form, with each row representing a single grid cell or pixel on the surface.<br> The elements of each row are the spectral reflectance values from the VIS instrument at 396 (resampled) wavelengths.</p> <p>2. Spatial resmpling<br> The whole dataset of ∼ 5 million spectra is resampled to a planet-wide rectangular grid of 1×1deg in the latitudinal band between ± 80.<br> The cell longitudinal size varies between ∼ 40 km at the equator to a minimum of ∼ 10 km at ±80∘.<br> Thus, the area spanned by each grid cell depends on the latitude. However, the same is true for the acquisition process, where higher spatial resolution is reached near the equator and lower resolution at the poles.</p>
Constraining properties of the next nearby core-collapse supernova with multi-messenger signals: multi-messenger signals
<p>1D FLASH simulations with STIR, for alpha_lambda = 1.23, 1.25, and 1.27. Run with SFHo EOS, M1 with 12 energy groups.</p> <p>For more information on these simulations, see Warren, Couch, O'Connor, & Morozova (arXiv:1912.03328) and Couch, Warren, & O'Connor (2020).</p> <p>Includes the multi-messenger data from the STIR simulations. The filename indicates the turbulent mixing parameter a and progenitor mass m of the simulation. Columns are time [s], shock radius [cm], explosion energy [ergs], electron neutrino mean energy [MeV], electron neutrino rms energy [MeV], electron neutrino luminosity [10^51 ergs/s], electron antineutrino mean energy [MeV], electron antineutrino rms energy [MeV], electron antineutrino luminosity [10^51 ergs/s], x neutrino mean energy [MeV], x neutrino rms energy [MeV], x neutrino luminosity [10^51 ergs/s], gravitational wave frequency from eigenmode analysis of the protoneutron star structure [Hz]. Note that the x neutrino luminosity is for <strong>one</strong> neutrino flavor - to get the total mu/tau neutrino and antineutrino luminosities requires multiplying this number by 4.</p> <p>v1.1 - removed unnecessary duplicate files</p> <p>v1.2 - upload failed. Obsolete.</p> <p>v1.3 - packaging alpha values as separate tar files for easy download.</p>
Automated MESSENGER Plasma Region Classifications via Unsupervised Transfer Learning
<p>This file contains the 1-minute resolution dataset (“labeled_sunside_data_3labels.csv”) for Toy-Edens et al.’s Automated Classification of MESSENGER Plasma Observations via Unsupervised Transfer Learning. The 1-minute resolution file contains the rolled up 1-minute epoch, features that go into clustering and post-cleaning methods, spacecraft positions (in MSO), total magnetic field, raw and cleaned clustering labels, and raw and cleaned transition name.</p> <p>We ask that if you use any parts of the dataset that you cite Toy-Edens et al.’s Automated Classification of MESSENGER Plasma Observations via Unsupervised Transfer Learning (DOI: 10.3389/fspas.2025.1608091).</p> <p>This work was supported by NASA grants 80NSSC19K0789 and 80NSSC22K0993.</p> <p> </p> <p>The following tables detail the contents of the described files:</p> <p><strong>labeled_sunside_data_3labels.csv description</strong></p> <table style="width: 100.063%; height: 851.2px;"> <tbody> <tr style="height: 47.6px;"> <td style="width: 17.3792%; height: 47.6px;"> <p><strong>Column Name</strong></p> </td> <td style="width: 78.9512%; height: 47.6px;"> <p><strong>Description</strong></p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 17.3792%; height: 47.6px;"> <p> Epoch</p> </td> <td style="width: 78.9512%; height: 47.6px;"> <p>Epoch in datetime (YYYY-MM-DD HH:MM:SS)</p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 17.3792%; height: 47.6px;"> <p> x_mso</p> </td> <td style="width: 78.9512%; height: 47.6px;"> <p>x position of the spacecraft in MSO [km]</p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 17.3792%; height: 47.6px;"> <p> y_mso</p> </td> <td style="width: 78.9512%; height: 47.6px;"> <p>y position of the spacecraft in MSO [km]</p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 17.3792%; height: 47.6px;"> <p> z_mso</p> </td> <td style="width: 78.9512%; height: 47.6px;"> <p>z position of the spacecraft in MSO [km]</p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 17.3792%; height: 47.6px;"> <p> btot_mso</p> </td> <td style="width: 78.9512%; height: 47.6px;"> <p>Total magnetic field [nT]</p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 17.3792%; height: 47.6px;"> <p> norm_Btot</p> </td> <td style="width: 78.9512%; height: 47.6px;"> <p>Magnitude of the total magnetic field normalized to 150nT. See paper for more information</p> </td> </tr> <tr style="height: 67.2px;"> <td style="width: 17.3792%; height: 67.2px;"> <p> ratio_max_width</p> </td> <td style="width: 78.9512%; height: 67.2px;"> <p>Ratio of the width of the most prominent ion spectra peak (in number of energy channels) to max number of energy channels. See paper for more information</p> </td> </tr> <tr style="height: 67.2px;"> <td style="width: 17.3792%; height: 67.2px;"> <p> ratio_high_low</p> </td> <td style="width: 78.9512%; height: 67.2px;"> <p>Ratio of the mean of the log intensity of high energies in the ion spectra to the mean of the log intensity of low energies in the ion spectra. See paper for more information</p> </td> </tr> <tr style="height: 67.2px;"> <td style="width: 17.3792%; height: 67.2px;"> <p> high_intensity</p> </td> <td style="width: 78.9512%; height: 67.2px;"> <p>Boolean if there is a peak with a higher minimum intensity threshold. See paper for more information</p> </td> </tr> <tr style="height: 67.2px;"> <td style="width: 17.3792%; height: 67.2px;"> <p> spectra_counts</p> </td> <td style="width: 78.9512%; height: 67.2px;"> <p>A ratio of spectra bins with non-zero counts to all possible spectra bins (i.e. way to determine if too much missing spectra data). See paper for more information</p> </td> </tr> <tr style="height: 67.2px;"> <td style="width: 17.3792%; height: 67.2px;"> <p> raw_named_label</p> </td> <td style="width: 78.9512%; height: 67.2px;"> <p>Raw cluster assigned plasma region label (allowed values: magnetosheath, magnetosphere, solar wind)</p> </td> </tr> <tr> <td style="width: 17.3792%;"> <p>intermediate_named_label</p> </td> <td style="width: 78.9512%;"> <p>Cleaned cluster assigned plasma region label with only relabeling rules applied. See paper for more information</p> </td> </tr> <tr style="height: 67.2px;"> <td style="width: 17.3792%; height: 67.2px;"> <p> named_label</p> </td> <td style="width: 78.9512%; height: 67.2px;"> <p>Cleaned cluster assigned plasma region label with relabeling rules and post-processing applied (use these unless have a specific reason to use raw labels). See paper for more information</p> </td> </tr> <tr style="height: 47.6px;"> <td style="width: 17.3792%; height: 47.6px;"> <p> raw_transition_name</p> </td> <td style="width: 78.9512%; height: 47.6px;"> <p>Raw transition names (e.g. bow shock, magnetopause) based on "raw_named_label" cluster labels. See paper for more information</p> </td> </tr> <tr style="height: 67.2px;"> <td style="width: 17.3792%; height: 67.2px;"> <p> transition_name</p> </td> <td style="width: 78.9512%; height: 67.2px;"> <p>Cleaned transition names (e.g. bow shock, magnetopause) after removing likely transient transitions based on "named_label" cluster labels. See paper for more information</p> </td> </tr> </tbody> </table> <p> </p>
MESSENGER magnetometer and coordinates prepared dataset
<p>This dataset is based on the original MESSENGER mission magnetometer and coordinate data as made available at PDS PPI. It introduces a number of improvements upon the original [1 sec temporal resolution]:</p> <ol> <li>Magnetometer calibration signals have been removed.</li> <li>Coordinates and magnetic field measurements have been merged together.</li> <li>Additional fields, such as model dipole magnetic field and planetary position in a heliocentric coordinate system have been added.</li> <li>The dataset has been split up into files by orbit numbers, with each file centered on the periapsis.</li> <li>Data for a number of partially recorded orbits has been removed.</li> </ol>
Cerebral 3-[18F]fluoro-5-(2-pyridinylethynyl)benzonitrile ([18F]FPEB) uptake and fragile X messenger ribonucleoprotein in men with fragile X syndrome
<p>Legends. Cerebral3-[<sup>18</sup>F]fluoro-5-(2-pyridinylethynyl)benzonitrile([<sup>18</sup>F]FPEB)uptake and fragileX messenger ribonucleoprotein in men with fragile X syndrome</p> <p>Table S1. Concomitant medications of participants from the Institute for Neurodegenerative Disorders (IND)</p> <p>Table S2. Demographic and clinical characteristics of participants from the Institute for Neurodegenerative Disorders (IND)</p> <p>Table S3. Demographic and clinical characteristics of participants from the Johns Hopkins University (JHU)</p> <p>Table S4. Genetic and psychological assessments of participants from the Institute for Neurodegenerative Disorders (IND)</p> <p>Table S5. Genetic and psychological assessments of participants from the Johns Hopkins University (JHU)</p> <p>Table S6. Fragile X Mental Retardation Protein (FMRP) in nanogram per microgram total protein for ten healthy people with typical development (TD) with normal CGG repeat sizes [range (20, 37)] utilizing the same analyses as the other participants</p> <p>Table S7. Positron emission tomography (PET) data and analyses for participants from the Institute for Neurodegenerative Disorders (IND)</p> <p>Table S8. Positron emission tomography (PET) data and analysis of participants from the Johns Hopkins University (JHU)</p> <p>Table S9. Correlation coefficients between Fragile X Mental Retardation Protein (FMRP) (Table S4) and [18F]FPEB uptake (Wong DF et al. 2013) for participants with fragile X syndrome (Table S7)</p> <p>Table S10. Correlation coefficients between Fragile X Mental Retardation Protein (FMRP) (Table S4) and [18F]FPEB uptake (Wong DF et al. 2013) for participants with fragile X syndrome and fragile X syndrome-mosaicism (Table S7)</p> <p>Poster. Cerebral3-[<sup>18</sup>F]fluoro-5-(2-pyridinylethynyl)benzonitrile([<sup>18</sup>F]FPEB)uptake and fragileX messenger ribonucleoprotein in men with fragile X syndrome</p> <p>Video. Cerebral3-[<sup>18</sup>F]fluoro-5-(2-pyridinylethynyl)benzonitrile([<sup>18</sup>F]FPEB)uptake and fragileX messenger ribonucleoprotein in men with fragile X syndrome</p> <p>The authors thank Flora Tassone, Ph.D., Department of Biochemistry and Molecular Medicine, School of Medicine, UC Davis Health, Sacramento, California, for providing genetic and protein data about participants.</p>
Supplementary material to: Highly resolved topography and illumination at Mercury south pole from MESSENGER MDIS-NAC
<p>We produced a new higher-resolution topographic map of Mercury’s south polar region (75°-90° South, covering ~1.3 million km<sup>2</sup>) by using data collected by the NASA MESSENGER spacecraft’s Mercury Dual Imaging System (MDIS; Hawkins et al, 2007) over the years 2011-2015. This new map enables, <em>e.g.</em>, the first detailed modeling of illumination and thermal conditions in these southern radar-bright locations and the first constraints on the nature and history of volatiles residing there, but it is also intended as a resource for other geophysical analyses and for the preparation of the BepiColombo mission, currently en-route to the planet.</p> <p>For more details, please visit <a href="https://pgda.gsfc.nasa.gov/products/88">https://pgda.gsfc.nasa.gov/products/88</a>.</p> <p><strong>Products:</strong></p> <p>DEM (interpolated), DEM (filled), Slopes, PSR masks</p> <p>All these files (except the PSR masks shapefile) are 250 m/pix GeoTiffs with south polar stereographic X/Y coords in meters.</p> <p><br> <em>If using these products, please cite:</em><br> Bertone, S., E. Mazarico, M.K. Barker, M. Siegler, J. M. Martinez Camacho, C. Hamill, A. Glatzenberg, N. L. Chabot, 2022: <em>Highly resolved topography and illumination at Mercury south pole from MESSENGER MDIS-NAC</em>. The Planetary Science Journal, 02/2023, <a href="http://dx.doi.org/10.3847/PSJ/acaddb">doi:10.3847/PSJ/acaddb</a></p>
Lists of Magnetopause and Bow Shock Crossings of Mercury by MESSENGER Spacecraft
<p>The dataset titled “Lists of Magnetopause and Bow Shock Crossings of Mercury by MESSENGER Spacecraft” employs the measurements from the MESSENGER spacecraft’s Magnetometer (MAG) and Fast Imaging Plasma Spectrometer (FPIS) instruments to identify magnetopause and bow shock crossings during MESSENGER's orbit of Mercury. MESSENGER's data orbiting Mercury were collected between 23-03-2011 and 30-04-2015 and are available from the Planetary Data System’s Planetary Plasma Interactions (PDS/PPI) Node at https://pds-ppi.igpp.ucla.edu.</p> <p> </p> <p>The dataset includes four lists:</p> <p>a, Bow_Shock_Out_Time_Duration_public_version_WeijieSun_20230829.txt</p> <p>b, Bow_Shock_In_Time_Duration__public_version_WeijieSun_20230829.txt</p> <p>c, MagPause_In_Time_Duration__public_version_WeijieSun_20230829.txt</p> <p>d, MagPause_Out_Time_Duration_public_version_WeijieSun_20230829.txt</p> <p> </p> <p>Here are examples for the time in the list:</p> <p>Example A</p> <p>2011 03 23 15 39 10.5 2011 03 23 16 24 02.4 BSO m </p> <p>This entry represents multiple bow shock crossings. The first six columns indicate the time of the first boundary crossing, while the next six columns indicate the time of the last boundary crossing. “BSO” stands for outbound crossing of the bow shock, and “m” indicates that this is a multiple bow shock crossing made by MESSENGER. Only the first and last boundaries were selected out, we did not identify the boundary crossings in between.</p> <p> </p> <p> </p> <p>Example B</p> <p>2011 03 25 13 04 24.2 2011 03 25 13 04 24.0 BSI s </p> <p>This entry represents a single bow shock crossing. The first six columns and the next six columns are identical, indicating that this is a single event. “BSI” stands for inbound crossing of the bow shock, and “s” indicates that this is a single bow shock crossing made by MESSENGER.</p> <p><br> </p> <p>The dataset does not include magnetopause and bow shock crossings during the following time intervals:</p> <p>a. From 03:02 to 20:00 on 05-04-2011</p> <p>b. From 24-05-2011 to 03-06-2011</p> <p>c. From 17:50 to 22:53 on 16-04-2012</p> <p>d. From 09-06-2012 to 13-06-2012</p> <p>e. From 07:30 on 08-01-2013 to 16:00 on 09-01-2013</p> <p>f. From 07:55 to 18:33 on 28-02-2013</p> <p>g. From 14:22 to 17:38 on 26-12-2014</p> <p> </p> <p>The current version is updated on 29 August 2023.</p> <p> </p> <p>This work was supported by NASA Discovery Data Analysis Program (DDAP) Grant #80NSSC22K1061 (PI Weijie Sun).</p>
Constraining properties of the next nearby core-collapse supernova with multi-messenger signals: gravitational wave frequency fits
<p>1D FLASH simulations with STIR, for alpha_lambda = 1.23, 1.25, and 1.27. Run with SFHo EOS, M1 with 12 energy groups.</p> <p>For more information on these simulations, see Warren, Couch, O'Connor, & Morozova (arXiv:1912.03328) and Couch, Warren, & O'Connor (2020).</p> <p>Includes fit to the gravitational wave peak frequency versus time post-bounce, for a functional fit of the form f = A*sqrt(t) + B*t + C, where the frequency f is in Hz and the time t is in seconds. The columns are: progenitor mass [M_sun], fit coefficient A, fit coefficient B, fit coefficient C, and the R^2 of the fit.</p>
MESSENGER_FNN_IMF_Predictions_27_03_2024
<p>This dataset contains MESSENGER magnetosheath measurements and predictions made by a feed-forward neural network. The parameters are in the aberrated Mercury magnetospheric coordinate system (MSM'). The time cadence for these measurements is 40 s.</p> <p>The columns are as follows:</p> <ol> <li>magx: x component of the magnetosheath magnetic field (nT)</li> <li>magy: y component of the magnetosheath magnetic field (nT)</li> <li>magz: z component of the magnetosheath magnetic field (nT)</li> <li>magamp: amplitude of the magnetosheath magnetic field (nT)</li> <li>time: time of magnetosheath measurement (YEAR-MONTH-DAY HOUR:MINUTE:SECOND)</li> <li>x: position of spacecraft along the X_MSM' axis</li> <li>r: cylindrical radial position (sqrt(Y_MSM'^2+Z_MSM'^2))</li> <li>theta: angular position tan^(-1)(Y_{MSM'}/Z_{MSM'})</li> <li>bswx_pred: x component of the FNN prediction for the upstream IMF</li> <li>bswy_pred: y component of the FNN prediction for the upstream IMF</li> <li>bswz_pred: z component of the FNN prediction for the upstream IMF</li> <li>magsw_pred: amplitude of the FNN prediction for the upstream IMF</li> <li>bswx_err: uncertainty in the x component of the FNN prediction for the upstream IMF</li> <li>bswy_err: uncertainty in the y component of the FNN prediction for the upstream IMF</li> <li>bswz_err: uncertainty in the z component of the FNN prediction for the upstream IMF</li> <li>magsw_err: uncertainty in the amplitude of the FNN prediction for the upstream IMF</li> </ol> <p> </p>
Bow shock crossing list at Mercury (from MESSENGER magnetic field data)
<div> <div> <div> <div> <p><strong>Version 2 (latest): </strong></p> <p>This file contains a list of bow shock crossings at Mercury, identified from magnetic field data of the MESSENGER mission with 1 second resolution (not aberrated), (https://pds-ppi.igpp.ucla.edu/). </p> <p>For a pre-selection of intervals containing bow shock crossings, we use the list of Philpott (2020): "MESSENGER bowshock and magnetopause crossings", https://doi.org/10.5683/SP2/1U6FEO. There, the first and last crossings of the inbound/outbound orbit segments are listed (boundary numbers 1 and 2 for inbound, 7 and 8 for outbound segments). For the bow shock identification, an automatic detection algorithm is applied to these intervals, extended by <strong>45</strong> seconds in either direction.</p> <p>The algorithm calculates running averages and variances of the magnetic field magnitude within adjacent 15 second intervals, separated by two seconds (gap interval). Whenever the ratio between the average magnetic fields exceed a threshold (default: 1.4), a bow shock crossing is selected. Should there be more than one ratio maximum within 10 seconds, then only the maximum is selected that corresponds to the minimal sum of the variances within the adjacent intervals. Crossings are classified with respect to detection quality depending on the ratios of the average magnetic fields and the corresponding variances. Details can be found in the algorithm script that is published alongside this file.</p> <p>Columns of the list file: <br>['time', 'orbit_number', 'x_mso_km', 'y_mso_km', 'z_mso_km', 'jump_ratio', 'indicator']</p> <p>'time': date and time in YYYY-MM-DD HH:mm:SS<br>'orbit_number': orbit number (MESSENGER Mission)<br>'x_mso_km': position (x-coordinate) in MSO coordinate system in km, not aberrated<br>'y_mso_km': position (y-coordinate) in MSO coordinate system in km, not aberrated<br>'z_mso_km': position (z-coordinate) in MSO coordinate system in km, not aberrated<br>'in/out': inbound or outbound segment of the orbit (apoherm towards periherm or periherm towards apoherm)<br>'jump_ratio': ratio of the average magnetic fields before and after selected crossings<br>'indicator': 1, 2 or 3 (1: very clear crossings, high quality, 2: clear crossings, good quality, 3: unclear crossings, poor quality)</p> <p>For further analysis it is recommended to only use the crossings with the indicators 1 and 2.<br>The uncertainty in the determination of the times is +/- 2 seconds. </p> <p>Number of analyzed orbits: 3982<br>Number of total crossings found: 13502<br>Number of crossings with indicator 1 (very clear crossings, best quality): 1765<br>Number of crossings with indicator 2 (clear crossings, good quality): 5027<br>Number of crossings with indicator 3 (unclear crossings, poor quality): 6710</p> <p> </p> <p><strong>Version 1: </strong></p> <p><br>This file contains a list of bow shock crossings at Mercury, identified from magnetic field data of the MESSENGER mission with 1 second resolution (not aberrated), (https://pds-ppi.igpp.ucla.edu/). </p> <p>For a pre-selection of intervals containing bow shock crossings, we use the list of Philpott (2020): "MESSENGER bowshock and magnetopause crossings", https://doi.org/10.5683/SP2/1U6FEO. There, the first and last crossings of the inbound/outbound orbit segments are listed (boundary numbers 1 and 2 for inbound, 7 and 8 for outbound segments). For the bow shock identification, an automatic detection algorithm is applied to these intervals, extended by 15 seconds in either direction.</p> <p>The algorithm calculates running averages and variances of the magnetic field magnitude within adjacent 15 second intervals, separated by two seconds (gap interval). Whenever the ratio between the average magnetic fields exceed a threshold (default: 1.4), a bow shock crossing is selected. Should there be more than one ratio maximum within 10 seconds, then only the maximum is selected that corresponds to the minimal sum of the variances within the adjacent intervals. Crossings are classified with respect to detection quality depending on the ratios of the average magnetic fields and the corresponding variances. Details can be found in the algorithm script that is published alongside this file.</p> <p>Columns of the list file: <br>['time', 'orbit_number', 'x_mso_km', 'y_mso_km', 'z_mso_km', 'in/out', 'jump_ratio', 'indicator']</p> <p>'time': date and time in YYYY-MM-DD HH:mm:SS<br>'orbit_number': orbit number (MESSENGER Mission)<br>'x_mso_km': position (x-coordinate) in MSO coordinate system in km, not aberrated<br>'y_mso_km': position (y-coordinate) in MSO coordinate system in km, not aberrated<br>'z_mso_km': position (z-coordinate) in MSO coordinate system in km, not aberrated<br>'in/out': inbound or outbound segment of the orbit (apoherm towards periherm or periherm towards apoherm)<br>'jump_ratio': ratio of the average magnetic fields before and after selected crossings<br>'indicator': 1, 2 or 3 (1: very clear crossings, high quality, 2: clear crossings, good quality, 3: unclear crossings, poor quality)</p> <p>For further analysis it is recommended to only use the crossings with the indicators 1 and 2.<br>The uncertainty in the determination of the times is +/- 2 seconds. </p> <p>Number of analyzed orbits: 3982<br>Number of total crossings found: 65666<br>Number of crossings with indicator 1 (very clear crossings, best quality): 4291<br>Number of crossings with indicator 2 (clear crossings, good quality): 16652<br>Number of crossings with indicator 3 (unclear crossings, poor quality): 44723</p> </div> <div> </div> <div> </div> </div> </div> </div>
The Return of the Templates: Revisiting the Galactic Center Excess with Multi-Messenger Observations
<p>We provide here the galactic diffuse emission maps used in "The Return of the Templates: Revisiting the Galactic Center Excess with Multi-Messenger Observations". </p>
NMMA: A nuclear-physics and multi-messenger astrophysics framework to analyze binary neutron star mergers
<p>Data release associated with the preprint "<em>NMMA: A nuclear-physics and multi-messenger astrophysics framework to analyze binary neutron star mergers</em>"</p> <p>Data includes:</p> <p>EOS files:</p> <ul> <li>5000 eos files with radius (km), mass (Msun), and tidal deformability as columns stored under eos/eos_data</li> <li>prior probabilities for the EOSs are stored in eos/eos_prior_probability.dat</li> </ul> <p>Posterior samples:</p> <ul> <li>Posterior samples based on the analysis of GW170817 and AT2017gfo stored in posterior_samples/GW170817-AT2017gfo_posterior_samples.dat</li> <li>Posterior samples based on the analysis of GW170817, AT2017gfo, and the afterglow of GRB170817A are stored in posterior_samples/GW170817-AT2017gfo-GRB170817A_afterglow_posterior_samples.dat</li> </ul> <p> </p>
Dataset from: Multi-messenger observations of binary neutron star mergers in the O4 run
<p>The binary neutron stars population data from the paper <strong><em>"Multi-messenger observations of binary neutron star mergers in the O4 run" </em>(<a href="https://arxiv.org/abs/2204.07592">https://arxiv.org/abs/2204.07592</a>).</strong></p> <p>Details of how to use the data, as well as the scripts to reproduce the figures of the main text of the paper are given in the accompanying Github repository <a href="https://github.com/acolombo140/O4NSNS">https://github.com/acolombo140/O4NSNS</a></p> <p>If you use this data, please cite the above manuscript.</p> <p> </p>
MESSENGER magnetic field data with Mercury's magnetic main field removed through application of the Chapman-Miller method
<p>The MESSENGER (Mercury Surface, Space Environment, Geochemistry and Ranging) spacecraft followed a highly elliptical orbit about Mercury. Therefore, attenuation with radial distance of the dipole and higher order terms of Mercury’s core-generated, steady main field led to MESSENGER’s low-noise, triaxial ring-core fluxgate magnetometer registering magnetic field variations of several hundred nanoteslas. These variations swamp Mercury’s significantly smaller time-varying induction signal. Generally, the steady main field of a planetary body can be removed using a model derived through spherical harmonic analysis. However, MESSENGER’s highly eccentric orbit with near-polar perihermian leads to models of Mercury’s magnetic main field that are inadequately characterised for this purpose. Instead, novel application of the Chapman-Miller method, a geophysical processing technique, better models and removes Mercury’s magnetic main field from MESSENGER data. Three-component magnetic field time series sampled at 10 s intervals were downloaded from NASA’s Planetary Data System (Korth and Anderson, 2016) and processed by applying the Chapman-Miller method to 20 pairs of MESSENGER orbits, yielding 40 events of 256 data points per magnetic component that provide a basis for studying electromagnetic induction in Mercury’s deep crust and mantle.</p>
Gravitational lensing: towards combining the multi-messengers (data sharing)
<div> <div># Gravitational lensing: towards combining the multi-messengers (data sharing)</div> <br> <div>This repository contains the data and code to plot Fig. 1 and Fig. 4 from the article titled - "Gravitational lensing : towards combining the multi messengers". The two folders included here are described below.</div> <br> <div>## 1. delta-psi</div> <br> <div>- This folder contains the data and code to plot Fig. 1 in the paper.</div> <div>- To generate plot for Fig. 1, run python plot_fig1.py</div> <div>- It computes the y-axis ($\delta \Delta \psi$ : uncertainty in the determination of the relative Fermat potential) for each combination of image pairs (x-axis), for 3 mock lenses, for each of the 4 configuration which are stored in "PLpert_fig_data"</div> <br> <div>## 2. grb-gw-lensing</div> <br> <div>- This folder contains the data and code to plot Fig. 4 in the paper.</div> <div>- data-gererator.ipynb generates the data for the plot. This data is stored in "ler_data" folder. Parameters for the GRB-GW lensing system are stored there.</div> <div>- grb-gw-lensing-plot.ipynb generates the plot for Fig. 4 (stored as 'combined-final.png'). It uses the data generated by data-gererator.ipynb. This plot shows, i) detectable lensed GRBs and the associated detectable lensed GW events, and ii) detectable lensed GW events and their</div> <div>associated detectable lensed GRBs.</div> </div>
One scaffold, two conformations: The ring-flip of the messenger InsP8 occurs under cytosolic conditions
<p>Inositol poly- and pyrophosphates (InsPs and PP-InsPs) are central eukaryotic messengers. These very highly phosphorylated molecules can exist in two distinct conformations, a canonical one with five phosphoryl groups in equatorial positions, and a “flipped” axial conformation. Using <sup>13</sup>C-labeled InsPs / PP-InsPs, the behavior of these molecules was investigated by 2D-NMR under solution conditions reminiscent of a cytosolic environment. Remarkably, the most highly phosphorylated messenger InsP<sub>8</sub> readily adopts both conformations at physiological conditions. Environmental factors - such as pH, metal cation composition, and temperature - strongly influence the conformational equilibrium. Thermodynamic data revealed that the transition of InsP<sub>8</sub> from the equatorial to the axial conformation is, in fact, an exothermic process. The speciation of InsPs and PP-InsPs also affects their interaction with protein binding partners; addition of Mg<sup>2+</sup> decreased the binding constant K<sub>d</sub> of InsP<sub>8</sub> to an SPX protein domain. The results illustrate that PP-InsP speciation reacts very sensitively to solution conditions, suggesting it might act as an environment-dependent molecular switch.</p>
Seasonal Variability of Mercury's Sodium Exosphere Deduced from MESSENGER Data and Numerical Simulation
<p>This is the dataset used in "Suzuki et al. (2020). Seasonal variability of Mercury's sodium exosphere deduced from MESSENGER data and numerical simulation. <em>Journal of Geophysical Research: Planets</em>, 125, e2020JE006472. doi:10.1029/2020JE006472".</p>
Model Output for: "MESSENGER observations of Mercury's planetary ion escape rates and their dependence on true anomaly angle"
<p>In the manuscript titled ‘MESSENGER Observations of Mercury’s Planetary Ion Escape Rates and Their Dependence on True Anomaly Angle’, submitted to Geophysical Research Letters, we provide an analysis of test sodium ion (Na+) particles. These test particles, integral to our research, are illustrated in Figure 4. This dataset contains an ASCII file with detailed information and descriptions of these test particles.</p>
Messenger magentometer and coordinates dataset for Machine learning
<p>This dataset is based on the original MESSENGER mission magnetometer and coordinate data as made available at PDS PPI. It introduces a number of improvements upon the original [1 sec temporal resolution]:</p> <ol> <li>Magnetometer calibration signals have been removed.</li> <li>Coordinates and magnetic field measurements have been merged together.</li> <li>Additional fields, such as model dipole magnetic field and planetary position in a heliocentric coordinate system have been added.</li> <li>The dataset has been split up into files by orbit numbers, with each file centered on the periapsis.</li> <li>Data for a number of partially recorded orbits has been removed.</li> </ol>
Pheromone relay networks in the honeybee: messenger workers distribute the queen's fertility signal throughout the hive
<p>This resource contains two items:</p><p>1. Dataset; Within-hive trajectories of individually-tagged honeybee workers.</p><p>2. Model; A C++ script for simulating queen pheromone transmission via physical contacts between bees. </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.