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1,880 results for “MAR”
SuperDARN data in netCDF format (1994-Mar)
<p>1994-Mar SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.</p><p>The RST is available here: https://github.com/SuperDARN/rst</p><p>The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.</p><p>Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (<a href="http://vt.superdarn.org/tiki-index.php?page=Radar+Overview">SuperDARN Radar Overview</a>)</p><p><strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong></p><p>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'</p>
Socioeconomic and climate scenarios for the Mar Menor-Campo de Cartagena socioecosystem
<p>The five scenarios describe plausible changes in 15 external drivers of the socioecosystem of the Mar Menor and surrounding Campo de Cartagena between 1964 and 2070. These scenarios were developed for evaluation of their impacts on Key Performance Indicators of sustainability using a simulation model based on System Dynamics. This model, developed by Martínez-López et al (2022) can be consulted <a href="http://doi.org/10.5281/zenodo.7142764">here</a>.</p> <p>Historic data combined with the Shared Socioeconomic Pathways (SSPs) from the IPCC report ‘Global warming of 1.5°C’ and the Representative Concentration Pathways (RCPs), were used as starting point to develop the model-specific scenarios. The five scenarios are based on SSP 1, SSP2, SSP4 and SSP5 in combination with emission scenarios that will keep global temperature rise below 1.5ºC. The BAU scenario represents a combination of SSP2 without any climate change.</p> <p>The detailed documentation of SSPs from O’Neil et al (<a href="http://dx.doi.org/10.1016/j.gloenvcha.2015.01.004">http://dx.doi.org/10.1016/j.gloenvcha.2015.01.004</a>) and subsequent expert interviews and input received during stakeholder workshops organised in the framework of the COASTAL project were used to prepare the region-specific time-series of the 15 variables for the Mar Menor and surrounding Campo de Cartagena.</p> <p>The 15 external drivers are:</p> <ol> <li>Agricultural revenue per hectare</li> <li>Growth rate of agriculture</li> <li>Percentage of nutrients that are metabolized by the native lagoon ecosystem</li> <li>Average excess of fertilizer use</li> <li>Yearly effectiveness in nutrients reduction of nutrients, soil and water retention measures</li> <li>Electricity Price</li> <li>Mean number of hours per day of photovoltaic electricity production</li> <li>Photovoltaic energy facilities growth rate in Megawatts installed</li> <li>Growth rate of tourism</li> <li>Average percentage of groundwater desalinated</li> <li>Agricultural water demand per hectare</li> <li>Catchment water sources</li> <li>Urban wastewater treatment plant effluents</li> <li>Yearly average of sea water desalination</li> <li>Amount of water transferred from the Tagus river (RCP15ATS)</li> </ol>
Ballistic capture sets at Mars using Differential Algebra with initial epoch December 9, 2023
<p>The data set is made of two items containing initial sub-domains of inconsistent, weakly-stable, unstable, crash, moon-crash, and capture sets at Mars [1]. They are compressed files that contain a classification of the sub-domains of the search-space defined and used to compute the consistency and quality criteria of the differential algebra mapping, as well as graphical representations of the search space. All initial sub-domains have initial epoch 09 DEC 2023 00:45:18.363 (UTC). The search space is built to maximize the capture ratio for Mars (see Figure 10 in [2]). Initial sub-domains are propagated in high-fidelity. The equations of motion of the restricted n-body problem including solar radiation pressure are considered. The first item contains a low-resolution mapping. The search space is divided 32 times along the radius of the periapsis and the argument of the periapsis. The maximum depth of the Automatic Domain Splitting algorithm is 9 splits [3]. The first item contains a high-resolution mapping. The search space is divided 128 times along the radius of the periapsis and the argument of the periapsis. The maximum depth of the Automatic Domain Splitting algorithm is 10 splits [3].</p> <p>The data set is associated to the following journal article:</p> <ul> <li> Article: Stable sets mapping with Taylor differential algebra with application to ballistic capture orbits around Mars</li> <li> Journal: Celestial Mechanics and Dynamical Astronomy</li> <li> Authors: Thomas Caleb (corresponding author: thomas.caleb@student.isae-supaero.fr), Gianmario Merisio, Pierluigi di Lizia, and Francesco Topputo</li> <li> DOI: <a href="https://www.doi.org/10.1007/s10569-022-10090-8">10.1007/s10569-022-10090-8</a></li> </ul> <p><strong>References</strong><br> [1] F. Topputo and E. Belbruno,'Earth–Mars transfers with ballistic capture', Celestial Mechanics and Dynamical Astronomy, Vol. 121, No. 4, 2015, pp. 329-346. DOI: <a href="https://doi.org/10.1007/s10569-015-9605-8">10.1007/s10569-015-9605-8</a><br> [2] Z.-F. Luo and F. Topputo, 'Analysis of ballistic capture in Sun–planet models', Advances in Space Research, Vol. 56, No. 6, 2015, pp. 1030-1041. DOI: <a href="https://doi.org/10.1016/j.asr.2015.05.042">10.1016/j.asr.2015.05.042</a><br> [3] A. Wittig, P. Di Lizia, et al., Propagation of large uncertainty sets in orbital dynamics by automatic domain splitting', Celestial Mechanics and Dynamical Astronomy, Vol. 122, No. 3, 2015, pp. 239-261. DOI: <a href="https://doi.org/10.1007/s10569-015-9618-3">10.1007/s10569-015-9618-3</a></p>
Fig. 26. Lycosa piochardi Simon, 1876, ten carapace molts shed between 9 Mar. 2018–1 Jun. 2020 in Lycosa Latreille, 1804 (Araneae, Lycosidae) of Israel, with a note on Geolycosa Montgomery, 1904
Fig. 26. Lycosa piochardi Simon, 1876, ten carapace molts shed between 9 Mar. 2018–1 Jun. 2020, by a single specimen (HUJ INV-AR20813) in laboratory conditions. Scale bar = 10 mm. Photo by I. Armiach Steinpress.
Dataset for Cloud Identification in Mars Daily Global Maps with Deep Learning
<p><strong>Overview:</strong></p> <p>This repository stores cloud masks and MDGMS for Martian Years (MYs) 28-33. MDGMs were obtained from Harvard Dataverse (<a href="https://doi.org/10.7910/DVN/U3766S">https://doi.org/10.7910/DVN/U3766S</a>), and cloud masks were created using the cloudmask model (<a href="https://github.com/03kalven/cloudmask">https://github.com/03kalven/cloudmask</a>). The cloud masks contained in the binary folders have already been binarized using the threshold of 0.912. This dataset is considerably smaller in size than the floating-point cloud mask dataset and is suited for researchers that prefer to use the default threshold of 0.912.</p> <p> </p> <p><strong>Quick breakdown of the files and folders:</strong></p> <ul> <li>phase folders contain the complete set of MDGMs and cloud masks for Mars Reconnaisance Orbiter mission phases P, B, G, D, F, and J (MYs 28-33)</li> <li>phase_binary folders contain the complete set of binary MDGMs and cloud masks for Mars Reconnaisance Orbiter mission phases P, B, G, D, F, and J (MYs 28-33)</li> <li>view_masks.ipynb has a few handy methods to plot cloud masks and MDGMs</li> </ul> <p> </p> <p><strong>Phase folders:</strong></p> <p>Each folder is organized based on phase (P, B, G, D, F, J) and subphase (_01 to _23). In each subphase, there are cloudmasks and mdgms folders, as well as a .txt file with MY and solar longitude (Ls) data for each day.</p> <p> </p> <p><strong>MDGM and cloud mask formats:</strong></p> <ul> <li>mdgm: JPEG, 3600x1801</li> <li>cloudmask: (NETCDF4_CLASSIC data model, file format HDF5): <ul> <li>dimensions(sizes): x(3600), y(1801)</li> <li>variables(dimensions): float32 longitude(x), float32 latitude(y), float32/int16 cloudmask(y, x)</li> </ul> </li> </ul> <p>The cloud masks' values for any pixel are -999 for NaN and a float from 0 to 1 reporting the model's confidence in that pixel being a cloud. The cloud masks can be binarized using get_cloudmask() included in view_masks.ipynb. A binarized mask would report -999 for NaN, 0 for no cloud, and 1 for cloud. The default threshold is 0.912, but this value can be adjusted if desired. The cloud masks' (0,0) coordinate is the lower left corner of the map, so it may be needed to flip the cloudmask vertically before plotting on a Martian map. The cloud mask NetCDF files are constructed the same way as Wang and González Abad's (<a href="https://doi.org/10.7910/DVN/WU6VZ8">https://doi.org/10.7910/DVN/WU6VZ8</a>).</p>
Endmember spectra and classified maps derived from CRISM targeted data at the south pole of Mars
<p><strong>Overview</strong></p> <p>Current maps of compositional variation across south polar ice exposures on Mars do not resolve the meter-scales at which erosional processes are most active, ultimately limiting our understanding of how the deposits form and evolve and how they can be used to interpret long-term climate records. In this study, we use <em>k</em>-means clustering and random forest classification to identify and map a set of universal spectral endmembers across 167 high-resolution observations acquired during southern summer by the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM). The 21 endmembers show distinct combinations and strengths of key infrared absorption features reflecting diverse mixtures of CO<sub>2</sub> ice, H<sub>2</sub>O ice, and dust. The resulting compositional framework can be used to characterize the nature of both seasonal CO<sub>2</sub> frost and the residual ices it overlies across a variety of terrains. </p> <p> </p> <p><strong>Contents</strong></p> <p>The repository contains three .zip files, which can be expanded to access the files described below:</p> <ul> <li><strong>classified_maps.zip</strong> <ul> <li>lookup_files <ul> <li><em>SP_CRISM_RF_ColorMap.clr</em> : An ESRI-formatted color map file that can be used to apply the endmember color scheme to random forest-classified maps in ArcGIS (see Symbology settings).</li> <li><em>SP_CRISM_RF_ColorMap.txt</em> : A text file that can be loaded into Python as a numpy array and used to generate a matplotlib color ramp. Row indices correspond to endmember numbers (see below) and columns are red, green, and blue values scaled from 0 to 1.</li> <li><em>SP_CRISM_RF_Endmember_Lookup.csv</em> : A lookup table that can be used to cross-reference between endmember numbers (as stored in GeoTiffs or the indices of the colormaps above) and corresponding endmember names (C1, Dc2, etc.).</li> </ul> </li> <li>morphologic_reference <ul> <li>Contains GeoTiffs of the R1330 spectral parameter (reflectance at 1330 nm) for each processed CRISM observation. Filenames indicate the observation ID, Mars Year, and solar longitude (Ls) of acquisition ("Ls314-07" = Ls 314.07º). These images can be used as a reference for the surface morphology and albedo of each scene. Tie points used to georeference these images to other datasets can be applied to the random forest classification maps to properly align endmember mapping results.</li> </ul> </li> <li>random_forest_classification <ul> <li>Contains GeoTiffs of the random forest classification results for each processed CRISM observation. Filenames indicate the observation ID, Mars Year, and solar longitude (Ls) of acquisition ("Ls314-07" = Ls 314.07º). These images are not rendered to display colors consistent with the publication figures, but instead store the endmember classification for each pixel as a number from 0 to 21; use the contents of <em>lookup_files</em> to find the corresponding endmember name or render the image with the color scheme from the publication. To view rendered summary plots of each observation, see the contents of <em>observation_info</em>. To georeference these images to other datasets, use the corresponding morphologic reference (see above) to set tie points.</li> </ul> </li> </ul> </li> <li><strong>observation_info.zip</strong> <ul> <li>footprint_shapefile <ul> <li>Contains the components of an ESRI shapefile that outlines the surface footprint/coverage of each processed CRISM observation. The attributes associated with each observation are the same as those in <em>observation_lookup</em> below. Note that the polygons extend slightly beyond the area shown in maps in <em>random_forest_classification</em> due to the inclusion of border pixels.</li> </ul> </li> <li>observation_lookup <ul> <li><em>SP_CRISM_Classified_Obs_Info.csv</em> : Information on each processed CRISM observation; this is the same file as Table S1 in the publication. Includes the MY and Ls of acquisition and (where applicable) the figure panel where the observation appears in the publication. The location of each observation is indicated with Center Latitude/Longitude and the assigned Spatial Domain (see Figure 1 in the publication). The Observation ID can be used to locate the source Targeted Reduced Data Record (TRDRs, Version 3) on the Geosciences Node of the Planetary Data System. The spatial extent of each observation is provided in the <em>footprint_shapefile</em> described above. </li> </ul> </li> <li>summary_plots <ul> <li>Contains summary plots of the endmember map generated for each processed CRISM observation. Each plot notes the observation ID, Mars Year, and Ls and displays the morphologic reference map, random forest classification map, and a breakdown of the endmembers that are present. To access the maps rendered here, see the contents of <em>classified_maps.zip</em>.</li> <li>Also contains the full-resolution version of Figure S3 from the publication (<em>All_Obs_Unprojected.png</em>), which can be used to lookup observations of interest via small labels above each map. </li> </ul> </li> </ul> </li> <li><strong>spectral_library.zip</strong> <ul> <li>Contains spectral libraries with the median spectrum of each endmember as presented in Figure 3 in the publication. A basic text file listing the wavelength (WVL) and normalized reflectance values for each endmember is included (<em>SP_CRISM_EndmemberMedians.txt</em>) as well as an ENVI-formatted spectral library (S<em>P_CRISM_EndmemberMedians_ENVI.sli</em>). Note that a subset of the 438 wavelengths sampled in the source CRISM data were removed around the longest and shortest wavelengths and the filter boundary to avoid error-prone bands, leaving these spectral libraries with 404 bands.</li> </ul> </li> </ul>
Geophysical evidence for an active mantle plume underneath Elysium Planitia on Mars
<p>Data files for Broquet & Andrews-Hanna, Geophysical evidence for an active mantle plume underneath Elysium Planitia on Mars, <em>Nature Astronomy</em>.</p> <p>Some relevant model results shown in Figures 2C and D, and 5A, B, and C.</p>
Dataset: Mars Acquisition Corp. (MARX) 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.
Dataset: Marriott International, Inc. (MAR) 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.
Dataset: Mars Acquisition Corp. (MARXU) 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.
Dataset: Mars Acquisition Corp. (MARXR) 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.
FIGURE 1 in Reproductive biology of Parona signata (Actinopterygii: Carangidae), a valuable economic resource, in the coastal area of Mar del Plata, Buenos Aires, Argentina
FIGURE 1 | Monthly variation of the gonadosomatic index (GSI) of females (black continuous line), standard deviation (dashed lines) and temperature (in Celsius degrees) (gray continuous line) based on an annual cycle.
FIGURE 3 in Reproductive biology of Parona signata (Actinopterygii: Carangidae), a valuable economic resource, in the coastal area of Mar del Plata, Buenos Aires, Argentina
FIGURE 3 | Photomicrographs of Parona signata. A: primary growth oocytes (P); B: cortical alveoli oocytes (CA); C: yolked oocytes (YO) and a post-ovulatory follicle (large arrow); D: details of a yolked oocyte (r: radiate zone; g: granulosa cells; t: theca cells); E: hydrated oocyte (arrow); F: details of a post-ovulatory follicle (arrow); G: primary growth oocytes in an adult ovary (notice the thick wall of the ovary – OW); H: atresic follicle (arrow).
FIGURE 2 in Reproductive biology of Parona signata (Actinopterygii: Carangidae), a valuable economic resource, in the coastal area of Mar del Plata, Buenos Aires, Argentina
FIGURE 2 | Monthly relative frequency of the different gonadal development stages observed in females of Parona signata on the annual cycle.
Fig. 6. A and C in Reproductive studies of Anchoa marinii Hildebrand, 1943 (Actinopterygii: Engraulidae) in the nearby-coastal area of Mar Chiquita coastal lagoon, Buenos Aires, Argentina
Fig. 6. A and C: Batch fecundity as a function of total length and total weight (without ovary), respectively. B and D: relative fecundity as a function of total length and total weight (without ovary), respectively.
Fig. 8 in Reproductive studies of Anchoa marinii Hildebrand, 1943 (Actinopterygii: Engraulidae) in the nearby-coastal area of Mar Chiquita coastal lagoon, Buenos Aires, Argentina
Fig. 8. Proportion of mature individuals observed for each length classes of Anchoa marinii. A. Females, B. Males.
Fig. 7 in Reproductive studies of Anchoa marinii Hildebrand, 1943 (Actinopterygii: Engraulidae) in the nearby-coastal area of Mar Chiquita coastal lagoon, Buenos Aires, Argentina
Fig. 7. Monthly variation of the gonadosomatic index (GSI), based on an annual cycle. Boxplots with median, 75th percentile and 25th percentile. Bars denote standard deviation. Open circles= outlier values; asterisk= extreme outliers.
Fig. 4 in Reproductive studies of Anchoa marinii Hildebrand, 1943 (Actinopterygii: Engraulidae) in the nearby-coastal area of Mar Chiquita coastal lagoon, Buenos Aires, Argentina
Fig. 4. Stages of oocyte development of Anchoa marinii. A, oogonias (o) and primary growth (p) oocytes; B, cortical alveoli stage oocyte (arrow); C, yolked oocytes (arrow); D, details of a yolked oocyte (r: radiata zone; g: granulosa cells; t: teca cells); E, migration of the nucleus (n); F, hydrated oocytes; G, atretic follicle; H, post- ovulatory follicle (arrow). Scale bars: A, B, D, 20 µm; C, E, F, G, H, 70 µm.
Fig. 3 in Reproductive studies of Anchoa marinii Hildebrand, 1943 (Actinopterygii: Engraulidae) in the nearby-coastal area of Mar Chiquita coastal lagoon, Buenos Aires, Argentina
Fig. 3. Captures per unite effort (CPUE), temperature and salinity values obtained for Anchoa marinii. Black triangles: CPUE; open squares: temperature; circles with dotted line: salinity.
Dataset for the manuscript: "Elevated-Mn ChemCam Targets Illuminating Mn Redox Cycling and Diagenesis in the Bradbury Rise, Gale Crater, Mars"
<p><span>The dataset for the manuscript titled, “Elevated-Mn ChemCam Targets Illuminating Mn Redox Cycling and Diagenesis in the Bradbury Rise, Gale Crater, Mars” consists of a single CSV file. This CSV contains 1,539 rows, with one ChemCam observation point per row. The observation points in this file are from the ChemCam rock targets between martian solar days (sols) 1 and 600 of the MSL <em>Curiosity</em> rover mission that have at least one observation point with > 0.2 wt% MnO. Metadata and compositional data are provided for each row. The metadata includes the LIBS spectrum filename; the name of the target to which the observation point belongs; the class into which we grouped the target; the spacecraft clock value (timestamp) for the observation point; the sol on which the observation was taken; and the ChemCam sequence identifier; the observation point number within the sequence; the ChemCam-to-target distance (in meters); the laser power used for the LIBS measurements; the spectrum totals; and a binary column indicating whether the observation point has > 0.2 wt% MnO. The compositional data includes the oxide chemistry (oxide wt.%), RMSEP accuracy, and shot-to-shot standard deviation, for the major oxides SiO2, TiO2, Al2O3, FeOT, MgO, CaO, Na2O, K2O, as well as for MnO; the sum of oxides for each observation point is also provided.</span></p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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