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1,880 results for “mars”
Linked collectors and determiners for: Cnidarios del Golfo de México y mar Caribe.
Natural history specimen data linked to collectors and determiners held within, "Cnidarios del Golfo de México y mar Caribe". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/94a7bb14-d0bc-4082-b4eb-b300bcbdd058">https://bionomia.net/dataset/94a7bb14-d0bc-4082-b4eb-b300bcbdd058</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/94a7bb14-d0bc-4082-b4eb-b300bcbdd058">https://gbif.org/dataset/94a7bb14-d0bc-4082-b4eb-b300bcbdd058</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Colección de peces marinos del mar argentino y aguas adyacentes del INIDEP.
Natural history specimen data linked to collectors and determiners held within, "Colección de peces marinos del mar argentino y aguas adyacentes del INIDEP". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/cc47cd0b-7cdb-452d-b41c-c3e340092c09">https://bionomia.net/dataset/cc47cd0b-7cdb-452d-b41c-c3e340092c09</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/cc47cd0b-7cdb-452d-b41c-c3e340092c09">https://gbif.org/dataset/cc47cd0b-7cdb-452d-b41c-c3e340092c09</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: MAR - Herbário do Maranhão.
Natural history specimen data linked to collectors and determiners held within, "MAR - Herbário do Maranhão". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/86aea6d0-e340-4745-a596-3fc20f90a021">https://bionomia.net/dataset/86aea6d0-e340-4745-a596-3fc20f90a021</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/86aea6d0-e340-4745-a596-3fc20f90a021">https://gbif.org/dataset/86aea6d0-e340-4745-a596-3fc20f90a021</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Aix-Marseille Université - MARS herbarium – Cytogenetic data-base.
Natural history specimen data linked to collectors and determiners held within, "Aix-Marseille Université - MARS herbarium – Cytogenetic data-base". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/4521e9af-e6c4-4f0c-9701-f2cd0c279b32">https://bionomia.net/dataset/4521e9af-e6c4-4f0c-9701-f2cd0c279b32</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/4521e9af-e6c4-4f0c-9701-f2cd0c279b32">https://gbif.org/dataset/4521e9af-e6c4-4f0c-9701-f2cd0c279b32</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: The "Martian" flora: new collections of vascular plants, lichens, fungi, algae, and cyanobacteria from the Mars Desert Research Station, Utah.
Natural history specimen data linked to collectors and determiners held within, "The "Martian" flora: new collections of vascular plants, lichens, fungi, algae, and cyanobacteria from the Mars Desert Research Station, Utah". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/3c5f4a3d-7d3b-48f3-8146-0d26f2a65d67">https://bionomia.net/dataset/3c5f4a3d-7d3b-48f3-8146-0d26f2a65d67</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/3c5f4a3d-7d3b-48f3-8146-0d26f2a65d67">https://gbif.org/dataset/3c5f4a3d-7d3b-48f3-8146-0d26f2a65d67</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Aix-Marseille Université - MARS herbarium – Herbier Jacob de Cordemoy, La Réunion.
Natural history specimen data linked to collectors and determiners held within, "Aix-Marseille Université - MARS herbarium – Herbier Jacob de Cordemoy, La Réunion". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/763fb11e-061f-45a4-a24c-4dd4659b3a24">https://bionomia.net/dataset/763fb11e-061f-45a4-a24c-4dd4659b3a24</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/763fb11e-061f-45a4-a24c-4dd4659b3a24">https://gbif.org/dataset/763fb11e-061f-45a4-a24c-4dd4659b3a24</a>. Formatted as a Frictionless Data package.
Data of soil infiltration tests and soil samples, Los Arenales MAR Systems, Santiuste and La Laguna del Señor infiltration basins
<p><span>Infiltration test and soil sample data utilised in the article "a nature-based solution to enhance aquifer recharge: combining trees and infiltration basins"</span></p>
Data from: Brine driven destruction of clay minerals in Gale crater, Mars
<p><span><span><span><span><span><span><span><span><span><span><span>This repository contains files and non-commercial software associated with the journal article "Brine Driven Destruction of Clay Minerals in Gale Crater, Mars.<strong>" </strong></span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>The article presents mineralogical, geochemical, and sedimentological observations made by the Mars Science Laboratory rover <em>Curiosity </em>in an area called Glen Torridon, Gale crater, Mars. Rocks exposed in Glen Torridon were deposited in a lake that occupied the floor of Gale crater about 3.5 billion years ago and are stratigraphic and depositional equivalents of rocks exposed ~ 400m away on Vera Rubin ridge. The mineralogy of rocks in these two areas are different despite forming in the same lake at the same time. Glen Torridon rocks contain about 30 wt % clay minerals and 2 wt % or less of the mineral hematite (an iron oxide). In contrast, Vera Rubin ridge rocks contain 5 to 13 wt % clay minerals, with larger quantities (between 9 and 16 wt %) of iron oxide and oxyhydroxide minerals. The observed differences in mineralogy are attributed to preferential post-depositional alteration of Vera Rubin ridge rocks by silica-poor brines. These brines are thought to have formed during the deposition of sedimentary strata of the 'sulfate-bearing unit' that overlie Glen Torridon and Vera Rubin ridge rocks. Orbital spacecraft have detected magnesium sulfates in the sulfate-bearing unit. The presence of these highly soluable salts imply that changing climate and/or hydrological conditions in Gale crater resulted in the formation of dense brines during deposition of the sulfate-bearing unit. It is hypothesized that brines infiltrated older clay-bearing sediments, converting iron-rich clay minerals to iron oxides and oxyhydroxides. Glen Torridon rocks also contain a mineral phase not previously identified on the mission. This mineral gives rise to a distinctive x-ray diffraction peak represents a interplanar spacing of 9.22 angstroms. This phase is identified as a mixed-layer serpentine-talc and is thought to have been transported into the crater floor by rivers.</span></span></span></span></span></span></span></span></span></span></span></p> <p>This repository contains:</p> <p>- Files needed to perform mineral search and Rietveld refinement of measured x-ray diffraction data using <span><span><span><span><span><span><span><span><span><span><span>BGMN and MDI Jade software.</span></span></span></span></span></span></span></span></span></span></span> </p> <p>- A non-commerical Excel-based program called FULLPAT, used for mineral and x-ray amorphous quantification of x-ray diffraction patterns collected by the CheMin instrument aboard <em>Curiosity. </em></p> <p><em>- </em>Python code that was used to identify the 9.22 angstrom phase through automated search the American Mineralogist Crystal Structure Database.</p> <p>- Collection times of Alpha Particle X-ray Spectrometer analyses of bulk rock geochemical presented in the article that can be used to retrieve raw data from NASA's Planetary Data system (<span><span><span><span><span><span><span><span><span><span><span>https://pds-geosciences.wustl.edu/msl/msl-m-apxs-4_5-rdr-v1/mslapx_1xxx/extras/)</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span>- A compilation of Li abundances across Vera Rubin ridge and Glen Torridon measured by the ChemCam that were presented in the article and used as a proxy for rock clay mineral content.</span></span></span></span></span></span></span></span></span></span></span></p>
Helium Ion Density Ratios Upstream from Mars
<p>This data set contains ratios of singly to doubly ionized helium computed from MAVEN STATIC measurements made upstream from Mars in the magnetosheath and upstream solar wind. </p>
Comparison of radiative transfer schemes for the calculation of aerosol radiative forcing in Mars' atmosphere
<p>Output datasets for Figures (fig. 1 to 10) for the intercomparison of radiative transfer algorithms for the calculation of aerosol radiative forcing in the Martian atmosphere.</p>
Map Shapefiles of Regional Geology of the Hypanis Valles System, Mars
<p>This dataset consists of shapefiles for the manuscript Regional Geology of the Hypanis Valles System, Mars by Adler et al. (under review). Inside the zipfile is a README explaining how to view the data and sort shape orders. The data represent a geomorphic map of the Hypanis Valles watershed and a geomorphic map of the Hypanis deposit region at its terminus. We mapped these two regions at different scales: 1:2,000,000 for the catchment map (-5-10°N, 300-315°E) and 1:500,000 for the Hypanis deposit map (10-13.0°N, 313-316.5°E). Our mapping provides new morphologic insights beyond previous efforts which used lower spatial resolution data. We defined units based on morphology, albedo, thermal inertia, elevation, and spectral parameters.</p>
A laboratory study of the photometric properties of Mars Global Soil Simulant MGS-1 and its variants
<p>Figures 2 and 5-16 of "A laboratory study of the photometric properties of Mars Global Soil Simulant MGS-1 and its variants" submitted to Planetary & Space Science.</p>
Diurnal waves forced by horizontal convergence of near-surface winds on Mars
<p>This site provides public access to data used in the following journal article: </p> <p>D. Hinson and J. Wilson (2023). Diurnal waves forced by horizontal convergence of near-surface winds on Mars, Icarus 394, 115420, doi: 10.1016/j.icarus.2022.115420 </p> <p><a href="https://ntrs.nasa.gov/api/citations/20230001896/downloads/20230001896-Hinson_2022_Diurnal_waves%5B1%5D.pdf">https://ntrs.nasa.gov/api/citations/20230001896/downloads/20230001896-Hinson_2022_Diurnal_waves%5B1%5D.pdf</a></p> <p> </p>
Mars Dust Activity Database
<table> <tbody> <tr> </tr> <tr> <td> <p>Version 1.1 of the Mars Dust Activity Database (MDAD, v1.1) includes storm boundaries for every instance from the Mars Color Imager (MARCI) era of <a href="http://doi.org/10.7910/DVN/F8R2JX">Version 1.0 of the MDAD</a> (Mars Years 28–32). The dataset is organized into five tar files, one for each Mars year. Two formats are provided in every tar file and provide equivalent data. A netcdf file (*.nc) contains a mask of the dust events on each sol of the Mars Year (soy) in an array called "MDAD," along with a string vector of the name of the sol from the Mars Daily Global Map (MDGM) database in a variable called "dayList," and an array of values in a variable called "timeList" of the time of the sol containing: start soy, end soy, mean soy, median soy, start areocentric longitude (Ls), end Ls, mean Ls, median Ls. The MDAD mask has dimensions time x longitude (3600 points) x latitude (1801 points). The length of the time dimension varies across years: MY 28 (340), MY 29 (551), MY 30 (616), MY 31 (623), MY 32 (358). The dayList and timeList variables have the same time dimension length as the MDAD masks. The mask variable is in uint8 (unsigned byte) format and may be converted directly to unsigned int upon reading. Values of (255) indicate missing pixels of the MDGM. A value of (0) indicates no dust instances. Values ranging 1–133 are the number portion of the member ID to connect the mask back to MDAD v1.0. Values ranging 134–250 are splitting members with the member ID of (mask value - 133)b. </p> <p>Each Mars-year bundle also contains a folder of *.csv files, each of which corresponds to a single dust storm instance on a single sol. The csv files are named with the following convention: SUB_dayXX_MEM_ZZZb.csv, where “SUB” is the MARCI mission subphase and XX is the sol number ranging 01–34. The last seven characters are the member ID for the storm from MDAD v1.0, with ZZZ ranging 001–133 and "MEM" the subphase on which the member starts. For storms that have merged, only the member ID with the smallest ZZZ is included in the file name, and the character "b" is only included in the file name for splitting storms. Each *.csv file consists of a two column matrix. Column 1 is east longitude, and column 2 is latitude. Each longitude, latitude point is a pixel on the boundary of the storm instance. The boundaries have a resolution of 1/10°, so each row of the matrix maps to a unique pixel of a MDGM. The total set of longitude, latitude points is the outline of the dust instance, which traces the outside of the storm masks provided in the *.nc files.</p> <p>An additional comma separated value file with the name SUB.list provides a copy of the "timeList" array included in the *.nc files. For each subphase of MDGMs, there is a single *.list, and each row is a single day with the following information in csv format: subphase_day, start MY, end MY, start sol-of-year (soy), end soy, mean soy, median soy, start areocentric longitude (Ls), end Ls, mean Ls, median Ls. Additionally, a folder entitled "list" contains an additional *.list comma separated value file for each day of the subphase, where each row describes a single swath in that sol's MDGM. It has the following format: Swath name, Earth time collected, MY, Mars month, soy, Ls, center west longitude.</p> <p> </p> <p> </p> </td> </tr> </tbody> </table> <div> </div>
Modèle Atmosphérique Régional (MAR) v3.12.0 15km output for Greenland (JJA 1980-2020, 1-hourly)
<p>The Modèle Atmosphérique Régional (MAR) is a regional climate model especially developed for studying the near surface climate and surface mass balance of both polar ice sheets. This dataset includes MAR outputs from version 3.12.0 of the model (MARv3.12) forced by the ERA5 reanalysis for a domain surrounding Greenland. The data are provided at a spatial resolution of 15km and temporal resolution of 1 hour, for the June-July-August (JJA) summer months during 1980-2020.</p> <p>With respect to MARv3.11, the main improvements of MARv3.12 are the geographical projection used by MAR which is now the standard Polar Stereographic EPSG 3413, a correction of an important bug impacting the snow temperature at the base of the snowpack, a conservation of water mass in the soil impacting water fluxes over the tundra, and a continuous conversion from rainfall to snowfall from 0°C to -2°C as input of the snow model instead as a fixed one at -1°C.</p> <p>The following MAR variables are included in this dataset: lat/lon coordinates, ice sheet mask, surface height, meltwater, 10-meter u/v-wind components, and relative humidity.</p>
SuperDARN Grid data in netCDF format (2013-Mar)
<p>2013-Mar SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, 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>
SuperDARN Grid data in netCDF format (2014-Mar)
<p>2014-Mar SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, 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>
SuperDARN Grid data in netCDF format (2015-Mar)
<p>2015-Mar SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, 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>
SuperDARN Grid data in netCDF format (1996-Mar)
<p>1996-Mar SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, 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>
SuperDARN Grid data in netCDF format (1995-Mar)
<p>1995-Mar SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, 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>
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