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268 results for “Mercury”
High Resolution Digital Terrain Models of Mercury
<p>Supplementary material of the article:<br> Tenthoff, M.; Wohlfarth, K.; Wöhler, C. High Resolution Digital Terrain Models of Mercury. <em>Remote Sens.</em> <strong>2020</strong>, <em>12</em>, 3989.</p> <p><br> Abstract:<br> We refined our Shape from Shading (SfS) algorithm, which has previously been used to<br> create digital terrain models (DTMs) of the Lunar and the Martian surface, to generate high-resolution<br> DTMs of Mercury from MESSENGER imagery. To adapt the reconstruction procedure to the specific<br> conditions of Mercury and the available imagery, we introduced two methodic innovations. First, we<br> extended the SfS algorithm to enable the 3D-reconstruction from image mosaics. Because most mosaic<br> tiles were acquired at different times and under various illumination conditions, the brightness of<br> adjacent tiles may vary. Brightness variations that are not fully captured by the reflectance model may<br> yield discontinuities at tile borders. We found that the relaxation of the constraint for a continuous<br> albedo map improves the topographic results of an extensive region removing discontinuities at<br> tile borders. The second innovation enables the generation of accurate DTMs from images with<br> substantial albedo variations, such as hollows. We employed an iterative procedure that initializes the<br> SfS algorithm with the albedo map that was obtained by the previous iteration step. This approach<br> converges and yields a reasonable albedo map and topography. With these approaches, we generated<br> DTMs of several science targets such as the Rachmaninoff basin, Praxiteles crater, fault lines, and<br> several hollows. To evaluate the results, we compared our DTMs with stereo DTMs and laser altimeter<br> data. In contrast to coarse laser altimetry tracks and stereo algorithms, which tend to be affected by<br> interpolation artifacts, SfS can generate DTMs almost at image resolution. The root mean squared<br> errors (RMSE) at our target sites are below the size of the lateral image resolution. For some targets,<br> we could achieve an effective resolution of less than 10 m/pixel, which is the best resolution of<br> Mercury to date. We critically discuss the limitations of the evaluation methodology.<br> <br> </p>
Data for Tekran Model 3425 performance evaluation report for elemental mercury
<p>During the SI-Hg performance evaluation of elemental mercury gas generators on the market three generators were tested, e.g., PSA 10.536 elemental Hg generator, bell-jar and Tekran Model 3425. Key characteristics were determined e.g.; the stabilisation period, short-term drift, precision, i.e., reproducibility and repeatability of the concentration generated, linearity, bias, sensitivity to sample gas pressure, sensitivity to surrounding temperature and sensitivity to electrical voltage. All three generators could be tested according to the calibration protocol developed within the project. The results obtained with the different gas generator clearly shows the importance of a metrological calibration. All three candidate generators show a different bias for the setpoint compared to the calibrated output. </p><p>The data obtained during the performance evaluation of the Tekran Model 3425 is published in this repository. The files of the following experiments can be found here:</p><ul><li>m1<ul><li>Calibration Tekran mercury gas generator m1 20230612</li><li>Calibration_Tekran_m1</li></ul></li><li>m2<ul><li>Calibration Tekran mercury gas generator m2 20230619</li><li>Calibration_Tekran_m2</li></ul></li><li>m3<ul><li>Calibration Tekran mercury gas generator m3 20230626</li><li>Calibration_Tekran_m3</li></ul></li><li>m4<ul><li>Calibration Tekran mercury gas generator m4 20230629</li><li>Calibration_Tekran_m4</li></ul></li><li>short-term drift<ul><li>m2<ul><li>Calibration Tekran mercury gas generator short term drift m2</li><li>Tekran_Short_Term_M2</li></ul></li><li>m3<ul><li>Calibration Tekran mercury gas generator short term drift m3</li><li>Tekran_Short_Term_M3</li></ul></li><li>m4<ul><li>Calibration Tekran mercury gas generator short term drift m4</li><li>Tekran_Short_Term_M4</li></ul></li><li>m5<ul><li>Calibration Tekran mercury gas generator short term drift m5</li><li>Tekran_Short_Term_M5</li></ul></li></ul></li><li>stability<ul><li>Calibration Tekran mercury gas generator 20230609 stability</li></ul></li></ul>
Data for SI-Hg D2 validation report for the calibration of elemental mercury gas generators including information on repeatability, reproducibility and uncertainty evaluation at emission and ambient levels extended to the sub ng/m3 level
<p>In deliverable 2 of the SI-Hg project the first validation results of the SI-Hg calibration protocol are reported. Within the SI-Hg project a protocol for the metrological calibration of elemental mercury gas generators used in the field was developed. For the validation the output of two different mercury gas generators was calibrated according to the protocol. As metrological reference standard the primary mercury gas standard from the Van Swinden Laboratory (VSL) was used. The measurements described in the protocol could be performed during the validation and the data was processed using a script to determine the output of the candidate generator and the uncertainty of the mercury concentration. Based on the validation measurements and data processing several improvements for the calibration protocol were identified and were used to improve the calibration protocol. </p><p>In this repository data obtained during the validation is published. The files of the following comparisons between reference generator and candidate generator can be found in this repository:</p><ul><li>VSL vs VSL<ul><li>m1<ul><li>09022022 calibration mercury gas generator VSL vs VSL m1</li><li>VSL_vs_VSL_m1</li></ul></li><li>m2 <ul><li>05072022 calibration mercury gas generator VSL vs VSL m2</li><li>VSL_vs_VSL_m2</li></ul></li><li>m3<ul><li>07072022 calibration mercury gas generator VSL vs VSL m3</li><li>VSL_vs_VSL_m3</li></ul></li></ul></li><li>VSL vs PSA before modification<ul><li>m1<ul><li>15032022 calibration mercury gas generator VSL vs PSA fixed m1</li><li>single_point_VSL_vs_PSA_fixed_m1_4</li><li>single_point_VSL_vs_PSA_fixed_m1_6</li><li>single_point_VSL_vs_PSA_fixed_m1_8</li><li>single_point_VSL_vs_PSA_fixed_m1_12</li></ul></li><li>m2<ul><li>28032022 calibration mercury gas generator VSL vs PSA fixed m2</li><li>single_point_VSL_vs_PSA_fixed_m2_4</li><li>single_point_VSL_vs_PSA_fixed_m2_6</li><li>single_point_VSL_vs_PSA_fixed_m2_8</li><li>single_point_VSL_vs_PSA_fixed_m2_12</li></ul></li><li>m3 <ul><li>06042022 calibration mercury gas generator VSL vs PSA fixed m3</li><li>single_point_VSL_vs_PSA_fixed_m3_4</li><li>single_point_VSL_vs_PSA_fixed_m3_6</li><li>single_point_VSL_vs_PSA_fixed_m3_8</li><li>single_point_VSL_vs_PSA_fixed_m3_12</li></ul></li><li>m4 <ul><li>12042022 calibration mercury gas generator VSL vs PSA fixed m4</li><li>single_point_VSL_vs_PSA_fixed_m4_4</li><li>single_point_VSL_vs_PSA_fixed_m4_6</li><li>single_point_VSL_vs_PSA_fixed_m4_8</li><li>single_point_VSL_vs_PSA_fixed_m4_12</li></ul></li><li>less tubing <ul><li>14042022 calibration mercury gas generator VSL vs PSA fixed less tubing</li><li>single_point_VSL_vs_PSA_fixed_less_tubing</li></ul></li><li>less tubing and air as complementary gas <ul><li>19042022 calibration mercury gas generator VSL vs PSA fixed less tubing in air</li><li>single_point_VSL_vs_PSA_fixed_less_tubing_air</li></ul></li></ul></li><li>VSL vs PSA after modification<ul><li>m1 air as complementary gas <ul><li>Calibration PSA fixed mercury gas generator air m1 20230324</li><li>PSA_fixed_air_m1_9</li><li>PSA_fixed_air_m1_11</li><li>PSA_fixed_air_m1_14</li></ul></li><li>m2 air as complementary gas <ul><li>Calibration PSA fixed mercury gas generator air m2 20230327</li><li>PSA_fixed_air_m2_9</li><li>PSA_fixed_air_m2_11</li><li>PSA_fixed_air_m2_14</li></ul></li><li>m3 air as complementary gas <ul><li>Calibration PSA fixed mercury gas generator air m3 20230329</li><li>PSA_fixed_air_m3_9</li><li>PSA_fixed_air_m3_11</li><li>PSA_fixed_air_m3_14</li></ul></li><li>m4 air as complementary gas <ul><li>Calibration PSA fixed mercury gas generator air m4 20230907</li><li>PSA_fixed_air_m4_9</li><li>PSA_fixed_air_m4_11</li><li>PSA_fixed_air_m4_14</li></ul></li><li>m5 air as complemantary gas <ul><li>Calibration PSA fixed mercury gas generator air m5 20230911</li><li>PSA_fixed_air_m5_9</li><li>PSA_fixed_air_m5_11</li><li>PSA_fixed_air_m5_14</li></ul></li><li>m1 nitrogen (N2) as complementary gas<ul><li>Calibration PSA fixed mercury gas generator nitrogen m1 20230330</li><li>PSA_fixed_N2_m1_9</li><li>PSA_fixed_N2_m1_11</li><li>PSA_fixed_N2_m1_14</li></ul></li><li>m2 N2 as complementary gas <ul><li>Calibration PSA fixed mercury gas generator nitrogen m2 20230331</li><li>PSA_fixed_N2_m2_9</li><li>PSA_fixed_N2_m2_11</li><li>PSA_fixed_N2_m2_14</li></ul></li><li>m3 N2 as complementary gas <ul><li>Calibration PSA fixed mercury gas generator nitrogen m3 20230405</li><li>PSA_fixed_N2_m3_9</li><li>PSA_fixed_N2_m3_11</li><li>PSA_fixed_N2_m3_14</li></ul></li><li>measurement at TUV<ul><li>PSA_Fixed_at_TUV</li></ul></li></ul></li></ul>
Venus and Mercury Trails
<p>Honorable mention in the 2023 IAU OAE Astrophotography Contest, category Still images of phases of Venus: Venus and Mercury Trails, by Marcella Giulia Pace.</p> <p>In this composite image, both Mercury (left) and Venus (right) can be seen heading into the sunset. The phases of each are beautifully captured as they descend. Not all planets or moons in the Solar System show phases as viewed from Earth. This phenomenon occurs because the orbits of Venus and Mercury are positioned between Earth’s orbit and the Sun, sometimes allowing us to see only part of the illuminated portion of each planet. These phases are similar to the phases we see of our own Moon.</p> <p>Credit: Marcella Giulia Pace (<a href="https://creativecommons.org/licenses/by/4.0/legalcode">CC BY 4.0</a>)</p>
BepiColombo Mio MIA and MEA2 Data During the Third Mercury Flyby
<p>This data set includes energy-time spectrograms of low-energy ions and electrons obtained by MIA and MEA2, respectively, during BepiColombo Mio's third Mercury flyby. The "eflux" files contain the differential energy flux in units of eV/cm^2/s/sr/eV, and the "energy" files contain the corresponding energies in units of eV/q for ions and eV for electrons.</p>
Total mercury (Hg) concentrations in beaks and muscle of the giant warty squid Moroteuthopsis longimana (Southern Ocean)
<p>In this study we used beaks and buccal masses (muscle) of the giant warty squid <em>Moroteuthopsis</em> <em>longimana</em> to evaluate if squid beaks can be used as a proxy for mercury (Hg) concentrations in the muscle. Beaks and respective buccal masses were collected from the diet of Antarctic toothfish <em>Dissostichus mawsoni </em>captured at the South Sandwich Islands (Atlantic Sector of the Southern Ocean; CCAMLR Subarea 48.4) in 2019 (ML1 to ML12) and 2020 (ML13 to ML21).</p> <p>We analysed Total Hg concentrations in the wing of the lower beak, hood tip of the upper beak, both upper and lower beaks, and in muscle sampled from buccal masses.</p> <p>This dataset includes the size of the beaks, the estimated mantle length, the estimated mass, and total Hg concentrations analysed in beaks and buccle mass in 21 individuals of <em>M. longimana</em>.</p> <p>This dataset is associated with the article: Lopes-Santos S, Xavier JC, Seco J, Coelho JP, Hollyman PR, Pereira E, Phillips RA, Queirós JP (2025) Squid beaks as a proxy for mercury concentrations in muscle of the giant warty squid <em>Moroteuthopsis longimana</em>. Marine Environmental Research 204:106841. Doi 10.1016/j.marenvres.2024.106841</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>
Fig. 3 in Mercury bioaccumulation in fish of commercial importance from different trophic categories in an Amazon floodplain lake
Fig. 3. Bioconcentration factor (Bf) among trophic categories in the Lago Grande de Manacapuru, in the Amazon floodplain. DET, Detritivores; HER/FRU, Herbivores/Frugivores; ONI, Omnivores; ONI/FRU, Omnivores/Frugivores; ONI/INS, Omnivores/Insectivores; PLA, Planktivores; CAR/PIS, Carnivores/Piscivores; PIS, Piscivores; CAR/NEC, Carnivores/Necrophagous.
Fig. 2 in Mercury bioaccumulation in fish of commercial importance from different trophic categories in an Amazon floodplain lake
Fig. 2. Mean levels of total mercury in fish from different trophic categories in the Lago Grande de Manacapuru, in the Amazon floodplain. DET, Detritivores; HER/FRU, Herbivores/ Frugivores; ONI, Omnivores; ONI/FRU, Omnivores/ Frugivores; ONI/INS, Omnivores/Insectivores; PLA, Planktivores; CAR/PIS, Carnivores/Piscivores; PIS, Piscivores; CAR/NEC, Carnivores/Necrophagous.
gmap - qgis training material: Beagle Rupes (Mercury)
<p>This dataset part of the Geology and Planetary Mapping Winter School 2022 featuring Beagle Rupes as a study area.<br> Beagle Rupes is lobate scarp at Mercurys surface with a length of more than 600km cross-cutting an oval shaped crater. <br> We compiled a beginners – intermediate level training package for the area. The package includes several basemaps such as Map Projected Basemap Reduced Data Record (BDR) (Hash 2013a), High-incidence East-illumination Basemap (HIE), Map-projected High-incidence West-illumination (HIW) (Hash 2015a), Map Projected Low-Incidence Angle Basemap Reduced Data Record (LOI) (Hash 2013b), Map Projected Multispectral Reduced Data Record (MDR) Hash 2015b) and digital terrain model (DTM) (Becker et al., 2016). The data is cut to the area of interest and a training project is set up for QGIS. </p> <p>The training package is designed as a group exercise with four adjacent tiles covering the Beagle Rupes area. </p>
BepiColombo Mio MIA Data During the First Mercury Flyby
<p>This data set includes ion energy-time spectrograms obtained by MIA during BepiColombo Mio's first Mercury flyby in units of differential energy flux (eV/cm^2/s/sr/eV).</p>
Geological Map of the Derain (H10) Quadrangle of Mercury (3 crater class version)
<p>Geological (morphostratigraphic) map recognising 3 crater degradation classes. We also have a 5 crater class version that is otherwise identical. This version is slightly revised after review for publication in J Maps (3 Aug 2022).</p>
Geological Map of the Derain (H10) Quadrangle of Mercury (5 crater class version)
<p>Geological (morphostratigraphic) map recognising 5 crater degradation classes. We also have a 3 crater class version, that is otherwise identical. This version is slightly revised after review for publication in J Maps 3 Aug 2022.</p>
Supplementary data for global distribution of mercury in foliage predicted by machine learning
<p>Global distribution of foliar mercury concentrations and pools with a spatial resolution of 0.25 latitude by 0.25 longitude, predicted by machine learning.</p>
Data sample for Mercury simulator
<p><strong>Sample data to run Mercury</strong></p> <p>Mercury is available at:<strong> </strong><a href="https://github.com/UoW-ATM/Mercury">https://github.com/UoW-ATM/Mercury</a></p> <p>This file contains a sample of input data for the open-source air mobility simulator Mercury. Please use use version 3 of the dataset for Mercury 3.0 and version X.Y.Z for Mercury X.Y.</p> <p>The dataset is structured as follows:</p> <ul> <li><strong>input</strong>: Input folder for Mercury <ul> <li><strong>input/scenario=-1</strong>: Folder containing scenario -1 with about 1000 flights anonymised.</li> <li><strong>input/scenario=-1/scenario_config.toml</strong>: Configuration file for the scenario</li> <li><strong>input/scenario=-1/data</strong>: data provided is organised as follows: <ul> <li><strong>ac_performance</strong>: aircraft performance dataset. Requires BADA files (not provided, BADA files need to be structured inside provided folders (see Mercury Readme)</li> <li><strong>airlines</strong>: static information on airlines used in the scenario</li> <li><strong>airports</strong>: static information on airports, including capacity declarations and minimum turnaround time. Two subfolders included: taxi (with taxi-in and taxi-out times) and curfew (with curfew times)</li> <li><strong>costs</strong>: data required to define cost functions</li> <li><strong>delay</strong>: delay parameters (non-ATFM)</li> <li><strong>eaman</strong>: definition of EAMAN in scenario (scope)</li> <li><strong>flight_plans</strong>: information on the flight plans, contains: <ul> <li><strong>crco</strong>: not provided as not needed to run Mercury (used for FP generation)</li> <li><strong>en_route_wind</strong>: not provided as not needed to run Mercury (used for FP generation)</li> <li><strong>flight_plans_pool</strong>: pool of flight plans for o-d ac type triplets</li> <li><strong>flight_uncertainty</strong>: distributions to model uncertainty on the realisation of the flight plans</li> <li><strong>routes</strong>: routes available between o-d pairs (used only for HMI and for FP generation (not needed to run Mercury))</li> <li><strong>trajectories</strong>: trajectories available from o-d ac type triplets (used only for HMI and for FP generation (not needed to run Mercury))</li> <li><strong>network_manager</strong>: ATFM probabilities, distributions and definition</li> </ul> </li> <li><strong>pax</strong>: passenger itineraries for flights provided</li> <li><strong>scenario</strong>: static information on scenario (to be deprecated in subsequent updates)</li> <li><strong>schedules</strong>: flight schedules provided (note these will determine which airports, flight plans, etc. are provided in the dataset)</li> </ul> </li> <li><strong>input/scenario=-1/case_sdudies</strong>: folder to contain the definition of case studies <ul> <li><strong>case_study=0</strong>: default case study with the configuration file (case_study_config.toml)</li> </ul> </li> </ul> </li> </ul> <p> </p>
Less than one weight percent of graphite on the surface of Mercury
<p>These are BD600 Global map, LRM global distribution map, python codes for radiative transfer modeling, and source codes and data for Figures.</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>
Summary of anthropogenic mercury emission inventories
<p>Streets: Streets2019_Hg.nc<br>Annual (2000-2010) emissions of Hg0, Hg2, Hgp, from all sectors. Years 2001-2009 are a linear interpolation of years 2000 and 2010. See Streets et al. (2019) and http://geoschemdata.wustl.edu/ExtData/HEMCO/MERCURY/v2020-07/Streets/ for more details.<br>References: D.G. Streets, H.M. Horowitz, Z. Lu, L. Levin, C.P. Thackray, E.M. Sunderland. 2019. Global and regional trends in mercury emissions and concentrations, 2010-2015. Atmospheric Environment. 201: 417-427.</p> <p>EDGAR: EDGAR_totals_$YYYY_Hg.nc<br>Annual (1970-2012) EDGARv4tox2 emissions of Hg0, Hg2, Hgp, from all sectors. See Muntean et al. (2018), https://edgar.jrc.ec.europa.eu/dataset_4tox2 and http://geoschemdata.wustl.edu/ExtData/HEMCO/MERCURY/v2020-07/EDGAR/ for more details.<br>References: Muntean M, Janssens-Maenhout G, Song S, Giang A, Selin NE, Zhong H, Zhao Y, Olivier JG, Guizzardi D, Crippa M, Schaaf E. Evaluating EDGARv4.tox2 speciated mercury emissions ex-post scenarios and their impacts on modelled global and regional wet deposition patterns. Atmospheric Environment. 2018; 184:56-68.</p> <p>AMAP: AMAP_comb.0.5x0.5.2010.nc, AMAP_inds.0.5x0.5.2010.nc and AMAP_intw.0.5x0.5.2010.nc<br>Annual (2010) AMAP/UNEP emissions of Hg0, Hg2, Hgp, from stationary combustion sources. See AMAP documentation, https://www.amap.no/mercury-emissions and https://doi.org/10.34894/SZ2KOI for more details.<br>References: Technical Background Report to the Global Mercury Assessment 2013;<br>AMAP/UNEP: Oslo, Norway and Geneva, Switzerland, 2013.<br>http://www.amap.no/mercury-emissions/datasets</p> <p>WHET: WHET_Hg0.geos.1x1.2010_final.nc, WHET_Hg2.geos.1x1.2010_final.nc, WHET_HgP.geos.1x1.2010_final.nc<br>Annual (2010) WHET emissions of Hg0, Hg2, Hgp, from all sectors. See Zhang et al. (2016) and http://geoschemdata.wustl.edu/ExtData/HEMCO/MERCURY/v2018-04/ for more details.<br>References: Zhang, Y.; Jacob, D. J.; Horowitz, H. M.; Chen, L.; Amos, H. M.; Krabbenhoft, D. P.; Slemr, F.; St. Louis, V. L.; Sunderland, E. M., Observed decrease in atmospheric mercury explained by global decline in anthropogenic emissions. Proceedings of the National Academy of Sciences 2016, 113 (3), 526-531.</p>
Dataset: Mercury Systems, Inc. (MRCY) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Fig. 3 in Mercury and stable isotopes ( N and C) as tracers during the ontogeny of Trichiurus lepturus
Fig. 3. Relationship between δ15N and δ13C in the muscle of sub-adult and adult specimens of Trichiurus lepturus. Bars represent the standard deviation.
ScienceDex guides
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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.