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1,477 results for “earth”
Figs 50-52. C in Rare Earth Elements (REE) in Al- and Fe-(Oxy)-Hydroxides in Bauxites of Provence and Languedoc (Southern France): Implications for the Potential Recovery of REEs as By-Products of Bauxite Mining
Figs 50-52. C. (Vitticentrantyx) rouibeti drumonti ssp. nov. 50. mâle holotype, vue dorsale; 51. femelle allotype, vue dorsale; 52. génitalia mâle, édéage: a, paramères vue dorsale; b, apex des paramères.
Fig. 43-49. C in Rare Earth Elements (REE) in Al- and Fe-(Oxy)-Hydroxides in Bauxites of Provence and Languedoc (Southern France): Implications for the Potential Recovery of REEs as By-Products of Bauxite Mining
Fig. 43-49. C. (Vitticentrantyx) rouibeti rouibeti sp. nov. 43. mâle holotype, vue dorsale; 44. mâle holotype, vue ventrale; 45. femelle allotype, vue dorsale; 46. génitalia mâle, édéage: a, paramères vue dorsale; b, apex des paramères; 47. mâle paratype, vue dorsale, forme de couleur; 48. mâle paratype, vue ventrale, forme de couleur; 49. femelle paratype, vue dorsale, forme de couleur.
Figs 39-42. C in Rare Earth Elements (REE) in Al- and Fe-(Oxy)-Hydroxides in Bauxites of Provence and Languedoc (Southern France): Implications for the Potential Recovery of REEs as By-Products of Bauxite Mining
Figs 39-42. C. (Vitticentrantyx) tibialis V.L. 39. mâle alloréférent, vue dorsale; 40. mâle alloréférent, vue ventrale; 41. génitalia mâle, édéage: a, paramères vue dorsale; b, apex des paramères; 42. femelle, vue dorsale.
Figs 35-38. C in Rare Earth Elements (REE) in Al- and Fe-(Oxy)-Hydroxides in Bauxites of Provence and Languedoc (Southern France): Implications for the Potential Recovery of REEs as By-Products of Bauxite Mining
Figs 35-38. C. (Vitticentrantyx) obscuripes V.L. 35. mâle, vue dorsale; 36. mâle, vue ventrale; 37. génitalia mâle, édéage: paramères vue dorsale; 38. femelle, vue dorsale.
Figs 31-34. C in Rare Earth Elements (REE) in Al- and Fe-(Oxy)-Hydroxides in Bauxites of Provence and Languedoc (Southern France): Implications for the Potential Recovery of REEs as By-Products of Bauxite Mining
Figs 31-34. C. (Vitticentrantyx) calogeroi sp. nov. 31. mâle holotype, vue dorsale; 32. mâle holotype, vue ventrale; 33. génitalia mâle, édéage: a, paramères vue dorsale; b, apex des paramères; 34. femelle allotype vue dorsale.
Figs 10-11. C in Rare Earth Elements (REE) in Al- and Fe-(Oxy)-Hydroxides in Bauxites of Provence and Languedoc (Southern France): Implications for the Potential Recovery of REEs as By-Products of Bauxite Mining
Figs 10-11. C. (Nitidocentrantyx) laevicollis V. L. 10. femelle, vue dorsale; 11. femelle, vue ventrale.
Figs 28-30. C in Rare Earth Elements (REE) in Al- and Fe-(Oxy)-Hydroxides in Bauxites of Provence and Languedoc (Southern France): Implications for the Potential Recovery of REEs as By-Products of Bauxite Mining
Figs 28-30. C. (Vitticentrantyx) christineae adelinae ssp. nov. 28. mâle holotype, vue dorsale; 29. femelle allotype vue dorsale; 30. génitalia mâle, édéage: a, paramères vue dorsale; b, apex des paramères.
Figs 19-24. C in Rare Earth Elements (REE) in Al- and Fe-(Oxy)-Hydroxides in Bauxites of Provence and Languedoc (Southern France): Implications for the Potential Recovery of REEs as By-Products of Bauxite Mining
Figs 19-24. C. (Vitticentrantyx) christineae christineae sp. nov. 19. mâle holotype, vue dorsale; 20. mâle holotype, vue ventrale; 21. génitalia mâle, édéage: a, paramères vue dorsale; b, apex des paramères; 22. femelle allotype, vue dorsale; 23. femelle paratype, vue dorsale, forme de couleur; 24. femelle paratype, vue dorsale, forme de couleur.
Figs 12-14. C in Rare Earth Elements (REE) in Al- and Fe-(Oxy)-Hydroxides in Bauxites of Provence and Languedoc (Southern France): Implications for the Potential Recovery of REEs as By-Products of Bauxite Mining
Figs 12-14. C. (Nitidocentrantyx) fairmairei V. L. 12. mâle alloréférent, vue dorsale; 13. mâle alloréférent, vue ventrale; 14. génitalia mâle, édéage: a, paramères vue dorsale; b, apex des paramères.
Figs 25-27. C in Rare Earth Elements (REE) in Al- and Fe-(Oxy)-Hydroxides in Bauxites of Provence and Languedoc (Southern France): Implications for the Potential Recovery of REEs as By-Products of Bauxite Mining
Figs 25-27. C. (Vitticentrantyx) christineae werneri ssp. nov. 25. mâle holotype, vue dorsale; 26. femelle allotype vue dorsale; 27. génitalia mâle, édéage: a, paramères vue dorsale; b, apex des paramères.
Figs 1-5. C in Rare Earth Elements (REE) in Al- and Fe-(Oxy)-Hydroxides in Bauxites of Provence and Languedoc (Southern France): Implications for the Potential Recovery of REEs as By-Products of Bauxite Mining
Figs 1-5. C. (Nitidocentrantyx) nitidus nitidus V. L. 1. mâle alloréférent, vue dorsale; 2. mâle alloréférent, vue ventrale; 3. génitalia mâle, édéage: a, paramères vue dorsale; b, apex des paramères; 4. femelle, vue dorsale; 5. femelle, vue dorsale, forme de couleur;.
Figs 15-18. C in Rare Earth Elements (REE) in Al- and Fe-(Oxy)-Hydroxides in Bauxites of Provence and Languedoc (Southern France): Implications for the Potential Recovery of REEs as By-Products of Bauxite Mining
Figs 15-18. C. (Nitidocentrantyx) raffrayi Fairmaire. 15. néotype mâle, vue dorsale; 16. néotype mâle, vue ventrale; 17. génitalia mâle, édéage: a, paramères vue dorsale; b, apex des paramères; 18. alloréférent femelle, vue dorsale.
Figs 8-9. C in Rare Earth Elements (REE) in Al- and Fe-(Oxy)-Hydroxides in Bauxites of Provence and Languedoc (Southern France): Implications for the Potential Recovery of REEs as By-Products of Bauxite Mining
Figs 8-9. C. (Nitidocentrantyx) nitidus madeleineae ssp. nov. 8. mâle holotype, vue dorsale; 9. femelle allotype, vue dorsale.
Figs 6-7. C in Rare Earth Elements (REE) in Al- and Fe-(Oxy)-Hydroxides in Bauxites of Provence and Languedoc (Southern France): Implications for the Potential Recovery of REEs as By-Products of Bauxite Mining
Figs 6-7. C. (Nitidocentrantyx) nitidus meriteti ssp. nov. 6. mâle holotype, vue dorsale; 7. femelle allotype, vue dorsale.
Variability of Earth's ionospheric outflow in response to the dynamic terrestrial exosphere - dataset and plotting script
<p>This dataset contains all the information discussed and displayed in the paper titled "Variability of Earth's ionospheric outflow in response to the dynamic terrestrial exosphere". The attached Jupyter notebook contains the necessary software to produce the same plots that were used to conduct the analysis.</p>
Data for replication of figures in "In situ imaging of a kleptoplastidic ciliate thin layer indicates traditional sampling underestimates oceanic mixotroph biomass", Barua et al. Communications Earth & Environment (2024).
<p>This dataset contains the relevant information needed to reproduce data plots in Figures 1-5 and Supplementary Figures 2-3 of the following manuscript.</p> <p><em><strong>In situ imaging of a kleptoplastidic ciliate thin layer indicates traditional sampling underestimates oceanic mixotroph biomass</strong></em></p> <p>Ranjoy Barua, Lisa Nyman, Buyu Guo, Matthew D. Johnson, Anvita U. Kerkar, Jiarong Hong, Adam T. Greer, John Lehrter, Malcolm McFarland, Bradley Penta, Aditya R. Nayak</p> <p>Communications Earth & Environment (2024).</p>
Natural Earth Dataset for GRASS GIS
<p><strong>Basic Global Dataset for GRASS GIS</strong><br>This geospatial dataset contains global raster and vector data from <a href="https://www.naturalearthdata.com/">Natural Earth</a>. The top level directory <em>global-dataset</em> is a GRASS GIS location for the World Geodetic System 1984 (WGS84) with <a href="https://epsg.io/4326">EPSG code 4326</a>. Inside the location there is the <em>PERMANENT</em> mapset, a license file, and readme file.</p> <p><strong>Instructions</strong><br>Install <a href="https://grass.osgeo.org/">GRASS GIS</a>, extract this archive, and move the location into your <a href="https://grass.osgeo.org/grass77/manuals/grass_database.html">GRASS GIS database</a> directory. If you are new to GRASS GIS read the <a href="https://grass.osgeo.org/documentation/first-time-users/">first time users guide.</a></p> <p><strong>Data Source</strong></p> <ul> <li><a href="https://www.naturalearthdata.com/">Natural Earth</a></li> </ul> <p><strong>License</strong><br>This dataset is licensed under the <a href="https://opendatacommons.org/licenses/pddl/index.html">ODC Public Domain Dedication and License 1.0 (PDDL)</a> by Brendan Harmon and Paulo van Breugel.<br> </p>
Earth's curvature effect on global warming (1850–2300)
<p><strong>Data Set</strong></p> <ul> <li>Model : CMIP6 MRI-ESM2-0 (Japan) </li> <li>Scenario : historical + SSP5-8.5 experiment</li> <li>Area : global (-90°S~90°N, -180°W~180°E)</li> <li>Period : 1850-01-01 ~ 2300-12-31</li> <li>Horizontal resolution : 1.125° × 1.125° </li> <li>Temporal resolution : daily</li> <li>Variable : daily maximum near-surface air temperature</li> <li>Source:<strong> </strong>https://doi.org/10.24381/cds.c866074c</li> </ul> <p> </p> <p><strong>Data1</strong></p> <ul> <li><strong>Data1.xlsx</strong>: <ul> <li>(a) Year 1850: Annual average temperature in 1850</li> <li>(b) Year 2000−Year 1850: Difference between the present (Year 2000) and 1850</li> <li>(c) Year 2150−Year 1850: Difference between the near future (Year 2150) and 1850</li> <li>(d) Year 2300−Year 1850: Difference between the future (Year 2300) and 1850</li> </ul> </li> <li><strong>Longitude and Latitude</strong>: The first column and row represent longitude and latitude, containing global temperature data.</li> <li><strong>Code1.py</strong>: Source code for processing Data1.xlsx</li> </ul> <p><strong>Data 2</strong></p> <ul> <li><strong>Weighted Analysis</strong>: To analyze the Earth's curvature effect, geodetic weights were applied using cosine weighting based on latitude (w(ϕ) = cos(ϕ), where ϕ represents latitude).</li> <li><strong>Dataset </strong>: <ul> <li>weighted: Using cosine weighting</li> <li>unweighted: Without using cosine weighting</li> <li>Difference: Difference between the two (Difference (weighted – unweighted)).</li> </ul> </li> <li><strong>Code2.py </strong>: Source code for processing and analyzing the data contained in Data2.xlsx.</li> </ul> <p><strong>Data 3</strong></p> <ul> <li><strong>Data3.xlsx </strong>: <ul> <li>(a) Year 1850: Occurrence frequency of daily maximum near-surface air temperatures equal to or exceeding 35°C in the year 1850</li> <li>(b) Year 2000 – Year 1850: Difference in occurrence frequency between the present (Year 2000) and 1850</li> <li>(c) Year 2150 – Year 1850: Difference in occurrence frequency between the near future (Year 2150) and 1850</li> <li>(d) Year 2300 – Year 1850: Difference in occurrence frequency between the future (Year 2300) and 1850.</li> </ul> </li> <li><strong>Longitude and Latitude</strong>: The first column and row represent longitude and latitude, encompassing global temperature occurrence data.</li> <li><strong>Code3.py</strong>: Source code for processing and analyzing the data contained in Data3.xlsx.</li> </ul> <p><strong>Data 4</strong></p> <ul> <li><strong>(a) Frequency (weighted)</strong>: Counts the occurrence of temperatures between 30°C and 60°C from 1850 to 2300 using cosine weighting.</li> <li><strong>(b) Frequency (unweighted)</strong>: Counts the occurrence of temperatures between 30°C and 60°C without using cosine weighting.</li> <li><strong>(c) Difference (weighted – unweighted)</strong>: The difference between (a) Frequency (weighted) and (b) Frequency (unweighted).</li> <li><strong>(d) Difference (weighted – unweighted) mean</strong>: The mean of (c) Difference (weighted – unweighted) across all years.</li> </ul> <p><strong>Acknowledgments</strong></p> <p>This work was also supported by Global - Learning & Academic research institution for Master’s·PhD students, and Postdocs (LAMP) Program of the National Research Foundation of Korea (NRF) grant funded by the Ministry of Education (No. RS-2023-00301914).</p> <p> </p>
First global machine learning model to predict solar flare impact on Earth's ionosphere
Open the record for dataset details and reuse information.
Earth's tectonic and plate boundary evolution over 1.8 billion years
<p>This is a new full-plate tectonic reconstruction from 1.8 Ga to the present, based on this paper: </p> <p><span>Cao, X., Collins, A.S., Pisarevsky, S., Flament, N., Li, S., Hasterok, D. and Müller, R.D., 2024. Earth’s tectonic and plate boundary evolution over 1.8 billion years. </span><strong><em><span>Geoscience Frontiers</span></em></strong><span>, p.101922.</span></p> <p> </p> <p>Change Log:</p> <p>v2.4: Fixed topology issue at 410 Ma; adjusted the appearance time of two small continent polygons in "shapes_continents.gpmlz".<br>v2.3: Fixed a hole east of Kamchatka in "shapes_continents.gpmlz".<br>v2.2: Merged the 1000-410 Ma boundary files into a single file.<br>v2.1: Added "static_polygons.gpmlz"; fixed holes in northern Europe and west of Kamchatka in "shapes_continents.gpmlz".<br>v2: Fixed some subduction zones with no convergence; added sample site positions to "Paleomagnetic_poles.gpml".</p>
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.