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1,477 results for “Earth”
Earth - Venus Low-Thrust Optimal Transfers / Database E
<p>A database of mass optimal trajectories of a low thrust spacecraft from Earth to Venus' orbit starting on the date 7th of May 2005 and arriving at Venus' orbit. </p> <p>This database was generated with a perturbation size of 20.0 and contains 409,076 trajectories with 100 samples along each trajectory.</p> <p>The database is in the HDF5 format with 4 dataframes included. These are the 'nominal', 'train', 'val' and 'test' dataframes each of which contains rows of entries in the following format:</p> <pre>['t', 'p', 'f', 'g', 'h', 'k', 'L', 'm', 'lp', 'lf', 'lg', 'lh', 'lk', 'lL', 'lm', 'T', 'ux', 'uy', 'uz', 'traj_id', 'sampl_id', 'vf']</pre> <p>which are the time, equinoctial elements (6 of them), the mass, the costates of the Optimal Control Problem Hamiltonian (7 of them), the thrust magnitude, the thrust directions (ux, uy, uz correspond to fr, ft, fn), the unique trajectory id, the sample id (nth sample from the start), and the value function.</p> <p>We are in the process of writing a paper titled "Real-Time Optimal Guidance for Interplanetary Transfers Using Deep Networks" that uses this dataset for the training of a neural network. The details on how we generated this data can be found in the paper, but it is essentially done using the (famous) "Backward Generation of Optimal Samples" method.</p>
Earth - Venus Low-Thrust Optimal Transfers / Database D
<p>A database of mass optimal trajectories of a low thrust spacecraft from Earth to Venus' orbit starting on the date 7th of May 2005 and arriving at Venus' orbit. </p> <p>This database was generated with a perturbation size of 5.0 and contains 265,603 trajectories with 128 samples along each trajectory.</p> <p>The database is in the HDF5 format with 4 dataframes included. These are the 'nominal', 'train', 'val' and 'test' dataframes each of which contains rows of entries in the following format:</p> <pre>['t', 'p', 'f', 'g', 'h', 'k', 'L', 'm', 'lp', 'lf', 'lg', 'lh', 'lk', 'lL', 'lm', 'T', 'ux', 'uy', 'uz', 'traj_id', 'sampl_id', 'vf']</pre> <p>which are the time, equinoctial elements (6 of them), the mass, the costates of the Optimal Control Problem Hamiltonian (7 of them), the thrust magnitude, the thrust directions (ux, uy, uz correspond to fr, ft, fn), the unique trajectory id, the sample id (nth sample from the start), and the value function.</p> <p>We are in the process of writing a paper titled "Real-Time Optimal Guidance for Interplanetary Transfers Using Deep Networks" that uses this dataset for the training of a neural network. The details on how we generated this data can be found in the paper, but it is essentially done using the (famous) "Backward Generation of Optimal Samples" method.</p>
Earth - Venus Low-Thrust Optimal Transfers / Database C
<p>A database of mass optimal trajectories of a low thrust spacecraft from Earth to Venus' orbit starting on the date 7th of May 2005 and arriving at Venus' orbit. </p> <p>This database was generated with a perturbation size of 0.4 and contains 764,479 trajectories with 100 samples along each trajectory.</p> <p>The database is in the HDF5 format with 4 dataframes included. These are the 'nominal', 'train', 'val' and 'test' dataframes each of which contains rows of entries in the following format:</p> <pre>['t', 'p', 'f', 'g', 'h', 'k', 'L', 'm', 'lp', 'lf', 'lg', 'lh', 'lk', 'lL', 'lm', 'T', 'ux', 'uy', 'uz', 'traj_id', 'sampl_id', 'vf']</pre> <p>which are the time, equinoctial elements (6 of them), the mass, the costates of the Optimal Control Problem Hamiltonian (7 of them), the thrust magnitude, the thrust directions (ux, uy, uz correspond to fr, ft, fn), the unique trajectory id, the sample id (nth sample from the start), and the value function.</p> <p>We are in the process of writing a paper titled "Real-Time Optimal Guidance for Interplanetary Transfers Using Deep Networks" that uses this dataset for the training of a neural network. The details on how we generated this data can be found in the paper, but it is essentially done using the (famous) "Backward Generation of Optimal Samples" method.</p>
Earth - Venus Low-Thrust Optimal Transfers / Database B
<p>A database of mass optimal trajectories of a low thrust spacecraft from Earth to Venus' orbit starting on the date 7th of May 2005 and arriving at Venus' orbit. </p> <p>This database was generated with a perturbation size of 0.4 and contains 382,193 trajectories with 100 samples along each trajectory.</p> <p>The database is in the HDF5 format with 4 dataframes included. These are the 'nominal', 'train', 'val' and 'test' dataframes each of which contains rows of entries in the following format:</p> <pre>['t', 'p', 'f', 'g', 'h', 'k', 'L', 'm', 'lp', 'lf', 'lg', 'lh', 'lk', 'lL', 'lm', 'T', 'ux', 'uy', 'uz', 'traj_id', 'sampl_id', 'vf']</pre> <p>which are the time, equinoctial elements (6 of them), the mass, the costates of the Optimal Control Problem Hamiltonian (7 of them), the thrust magnitude, the thrust directions (ux, uy, uz correspond to fr, ft, fn), the unique trajectory id, the sample id (nth sample from the start), and the value function.</p> <p>We are in the process of writing a paper titled "Real-Time Optimal Guidance for Interplanetary Transfers Using Deep Networks" that uses this dataset for the training of a neural network. The details on how we generated this data can be found in the paper, but it is essentially done using the (famous) "Backward Generation of Optimal Samples" method.</p>
Earth - Venus Low-Thrust Optimal Transfers / Database G
<p>A database of mass optimal trajectories of a low thrust spacecraft from Earth to Venus' orbit starting on the date 7th of May 2005 and arriving at Venus' orbit. </p> <p>This database was generated with a perturbation size of (5.0, 1.0, 1.0, 0.0, 0.0, 0.01) and contains 999,985 trajectories with 100 samples along each trajectory.</p> <p>The database is in the HDF5 format with 4 dataframes included. These are the 'nominal', 'train', 'val' and 'test' dataframes each of which contains rows of entries in the following format:</p> <pre>['t', 'p', 'f', 'g', 'h', 'k', 'L', 'm', 'lp', 'lf', 'lg', 'lh', 'lk', 'lL', 'lm', 'T', 'ux', 'uy', 'uz', 'traj_id', 'sampl_id', 'vf']</pre> <p>which are the time, equinoctial elements (6 of them), the mass, the costates of the Optimal Control Problem Hamiltonian (7 of them), the thrust magnitude, the thrust directions (ux, uy, uz correspond to fr, ft, fn), the unique trajectory id, the sample id (nth sample from the start), and the value function.</p> <p>We are in the process of writing a paper titled "Real-Time Optimal Guidance for Interplanetary Transfers Using Deep Networks" that uses this dataset for the training of a neural network. The details on how we generated this data can be found in the paper, but it is essentially done using the (famous) "Backward Generation of Optimal Samples" method.</p>
A case study on the origin of near-Earth plasma
<p>Summary of data files.</p> <p>This is the dataset for a paper "A case study on the origin of near-Earth Plasma"<br> submitted to JGR-space by Glocer et al</p> <p>The paper conducts multifluid MHD simulations of the magnetosphere by the<br> BATSRUS code, with solar wind input, ionospheric outflow by the PWOM code,<br> and ionospheric potential solver, and inner magnetospehre by the CIMI code.<br> Solutions with and without plasmasphere are considered.</p> <p>All plots in the paper are made with spacepy (specific fork:<br> https://github.com/aglocer/spacepy) or with tecplot</p> <p>The data is organized as follows</p> <p>Top directory:<br> imf.dat: Has the solar wind input as a simple time series. Can be read and<br> plotted with the "ImfInput" tool in spacepy</p> <p>tared Directories with "PS" and "noPS" tags have simulation<br> output with and without plasmasphere. Each has three subdirectories described<br> as follows:</p> <p>GM: Has the "Global Magnetosphere" output from BATSRUS for the images shown.<br> y=0*out are cuts in the y=0 GSM plane and the *log file is the log output<br> containing Dst. Both can be plotted with spacepy pybats. The 3d files are<br> used in a few images and are tecplot binary files and read and plotted with<br> tecplot.</p> <p>PW: Has the "Polar Wind" output from the PWOM code for images shown. The<br> *out files are time dependent binary output for each field line. They can be<br> read and plotted with the pybats.pwom tool in spacepy. North and South<br> indicte northern and southern hemisphere respectively</p> <p>IE: Has the "Ionosphere Electrodynamics" output from the potential solver for<br> plots shown. the *log files have the CPCP data as a function of time. They<br> can be read and plotted with pybats in spacepy</p> <p>IM: Has the "Inner Magnetosphere" output from the CIMI code. The CIMIeq.out<br> file has time dependent snapshots of the solution on the min B surface for<br> plots shown. The *log files have the total energy as a function of time for<br> each species (among other variables). Both files are read and plotted with<br> pybats and pybats.cimi code in spacepy.</p> <p> </p>
Earth grazing meteor over Northern Europe, September 22, 2020, 03:53UTC
<p>An earth grazing meteor was observed over Northern Europe on September 22, 2020, around 03:53UTC.</p> <p>From Dwingeloo in the Netherlands, two images were obtained.</p> <ul> <li>2020-09-22T03:53:33.445.fits (FITS format, BGGR bayer matrix)</li> <li>2020-09-22T03:53:33.445.png (Debayered image)</li> <li>2020-09-22T03:53:50.135.fits (FITS format, BGGR bayer matrix)</li> <li>2020-09-22T03:53:50.135.png (Debayered image)</li> </ul> <p>The image times refer to the start of the exposure.</p>
Rare earth-free motor designs for e-mobility
<p>First webinar of the ReFreeDrive's webinar series. This webinar gives an overview of the different motor technologies that will be developed during the project.</p> <p>The webinar is also available on Youtube. Link: <a href="https://www.youtube.com/watch?v=PziGDLfE5_w">https://www.youtube.com/watch?v=PziGDLfE5_w</a></p>
Spectral library of laser-induced fluorescence (LiF) properties from Smithsonian rare-earth element (REE) orthophosphate standards
<p>The spectral library presents a data set of laser-induced fluorescence (LiF) spectra from rare-earth element (REE) orthophosphates provided and distributed as reference material for microbeam analysis by the Smithsonian National Museum of Natural History (sample IDs: 16484 - NMNH 168499; Jarosewich and Boatner, 1991; Donovan et al., 2002 and 2003). The data set delivers high-resolution LiF spectra excited at three standard laser wavelengths (325 nm, 442 nm, 532 nm) recorded in the UV-visible to near-infrared spectral range (340 - 1080nm). Presented LiF spectra represent data from efficient signal excitation conditions and contain the diagnostic emission lines of individual REE including detailed information on splitting into sub-levels. The LiF spectral library data provides a reference for various applications in spectroscopy-based material composition analysis with the scope of REE identification. LiF as a tool can complement the merging technique of reflectance spectroscopy, because LiF is a particularly well suited method for REE detection and can be used to cross-validate results (e.g. Lorenz et al. 2019) The LiF library allows for transparent and reproducible result analysis in scientific studies and promotes further developments of efficient automated algorithms for REE identification and characterisation. This addresses especially the need for innovative, non-invasive techniques of raw material exploration (securing REE supply) and material stream characterisation (e.g. in e-waste recycling) or for manifold applications in other fields of geosciences (e.g. geology) and physics.</p> <p>references:</p> <p>Donovan, J., Hanchar, J., Picolli, P., Schrier, M., Boatner, L., Jarosewich, E., 2002. Contamination in the rare-earth element orthophosphate reference sam- ples. J. Res. National Institute of Standards and Technology 106, 693–701. doi:10.6028/jres.107.056. </p> <p>Donovan, J., Hanchar, J., Piccoli, P., Schrier, M., Boatner, L., Jarosewich, E., 2003. A reexamination of the rare-earth element orthophosphate reference samples for electron microprobe analysis. Canadian Mineralogist 41, 221– 232. doi:10.2113/gscanmin.41.1.221. </p> <p>Jarosewich, E., Boatner, L., 1991. Rare-earth element reference samples for electron microprobe analysis. Geostandards Newsletter 15, 397–399. doi:10. 1111/j.1751-908X.1991.tb00115.x. </p> <p>Lorenz, S., Beyer, J., Fuchs, M., Seidel, P., Turner, D., Heitmann, J., Gloaguen, R., 2019. The Potential of Reflectance and Laser Induced Luminescence Spectroscopy for Near-Field Rare Earth Element Detection in Mineral Ex- ploration. Remote Sensing 11, 21. doi:10.3390/rs11010021.</p>
Brazilian Earth System Model: CMIP5 Sea ice concentration and Air Temperature data
<p>The Brazilian Earth System Model, Version 2.5 (BESM-OAV2.5) used here is a global climate coupled ocean-atmosphere-sea ice model, and is part of CMIP5 project. The atmospheric component of BESM-OAV2.5 is BAM (Brazilian Atmospheric Model) and was described in detail by Figueroa et al., (2016). BAM, developed at Center for Weather Forecasting and Climate Studies of the National Institute for Space Research CPTEC-INPE has been constantly reformulated over the last years (Figueroa et al., 2016; Nobre et al., 2013). The lastest version, used here and described by Veiga et al., (2019), has spectral horizontal representation truncated at triangular wave number 62, grid resolution of approximately 1.875∘×1.875∘, and 28 sigma levels in the vertical, with unequal increments between the vertical levels (i.e., a T62L28). The oceanic component of BESM-OAV2.5 is the Modular Ocean Model, Version 4p1, from National Oceanic and Atmospheric Administration-Geophysical Fluid Dynamics Laboratory (MOM4p1/NOAA-GFDL), described in detail by Griffies, (2009). The MOM4p1 includes a Sea Ice Simulator (SIS) built-in ice model (Winton 2000). The SIS has five ice thickness categories and three vertical layers (one snow and two ice). To calculate ice internal stresses are used the elastic-viscous-plastic technique described by Hunke and Dukowicz, (1997). The thermodynamics is given by a modified Semtner’s three-layer scheme (Semtner, 1976). SIS is able to calculate sea ice concentration, snow cover, thickness, brine content and temperature. Furthermore, SIS calculates ice-ocean fluxes and transmits fluxes between atmosphere and ocean. The horizontal grid resolution of MOM4p1 in the longitudinal direction is a set to 1˚. The latitudinal direction varies uniformly, in both hemispheres, from 1∕4<sup>o </sup>between 10<sup>o</sup> S and 10<sup>o </sup>N to 1<sup>o </sup>of resolution at 45<sup>o</sup> and to 2<sup>o</sup> of resolution at 90<sup>o</sup>. The vertical axis has 50 levels (upper 220m, has 10 m resolution, increasing to about 360 at deeper levels. The MOM4p1 and BAM models were coupled using FMS coupler. FMS coupled was developed by NOAA-GFDL. The BAM model receives SST and ocean albedo from MOM4p1 and SIS (hour by hour). The MOM4p1 receives momentum fluxes, specific humidity, pressure, heat fluxes, vertical diffusion of velocity components and freshwater. </p> <p>This study used two numerical experiments from CMIP5: (i) piControl: it runs for 700 years, forced by invariant pre-industrial atmospheric CO<sub>2</sub> concentration level (280ppmv) and (ii) Abrupt 4xCO<sub>2</sub>: it runs for 460 years, comprising an abrupt instantaneous quadrupling of atmospheric CO<sub>2 </sub>level concentration from the piControl simulation. The design of both experiments follows the CMIP5 protocol (Taylor et al., 2012).</p> <p> </p>
User requirements of Big Earth Data - Survey 2019
<p>The survey was conducted between November 2018 and May 2019 with the aim to find out how users working with large volumes of environmental data interact with data, what challenges they face and how they would like to use cloud-based data services in the future.</p> <p>The term Big Earth Data in this context refers to digital information about Earth, including observations, imagery, derived higher-level products, forecasts and analyses produced by computer models.</p> <p>The survey was conducted in collaboration with the European Centre for Medium-Range Weather Forecasts (ECMWF) and as part of a PhD thesis on "Big Data technologies for environmental and climate data" at University of Marburg, Germany.</p> <p>The results are published in form of two articles:</p> <ul> <li>Wagemann, J., Siemen, S., Seeger, B. and J. Bendix (2021): Users of open Big Earth data - An analysis of the current state. Computers and Geosciences 2021. <a href="https://doi.org/10.1016/j.cageo.2021.104916">doi:10.1016/j.cageo.2021.104916</a></li> <li>Wagemann, J. Siemen, S., Seeger, B. and J. Bendix (2021): A user perspective on future cloud-based services for Big Earth data. International Journal of Digital Earth 2021. doi: <a href="http://doi.org/10.1080/17538947.2021.1982031">10.1080/17538947.2021.1982031</a></li> </ul> <p> </p>
Data from: "Inventory of Earth's Ice Loss and Associated Energy Uptake from 1979 to 2017"
<p>Earth’s cryosphere is a buffer to the warming of the planet and its loss must be accounted for in planetary energy budgets. Yet, even as melting ice is an evident manifestation of climate change, inventories of its energy uptake are largely lacking, based on inconsistent methods, or limited to the fraction that contributes to sea level rise. By combining recent syntheses, we undertake a systematic estimate of ice loss to show that Earth lost 40700 ± 5800 Gt of ice with a corresponding energy uptake of 13.8 ± 2.0 ZJ, from 1979 to 2017, larger than previous estimates and equivalent to the energy uptake by the deep ocean, the land and the atmosphere. The total loss is due to approximately equal contributions from Arctic sea-ice, the Antarctic and Greenland ice sheets, and glaciers. Only half of it contributed to sea level rise. From the 1980s to the 2010s, the rate of ice loss has almost tripled.</p> <p>In this HDF5 dataset, we provide cumulative annual estimates of energy uptake for three components of the cryosphere in Zetajoules (10<sup>21</sup> Joules):</p> <p>1) Antarctica<br> 2) Greenland<br> 2) Glaciers<br> 3) Sea Ice</p> <p>For 1–3, we separate energy uptake contributions for the grounded and floating components. We also provide a Matlab file with code to read the fields in the dataset.</p> <p>Python code to read the data is available at: <a href="https://github.com/sioglaciology/energy_imbalance_cryosphere">https://github.com/sioglaciology/energy_imbalance_cryosphere</a></p>
On the airburst of large meteoroids in the Earth's atmosphere. The Lugo bolide: reanalysis of a case study
<p>Seismic data of the Lugo bolide of January 19, 1993.<br /> </p>
Data for "Hydroclimate Volatility on a Warming Earth"
<p><strong>The data archived here represents data files and code used in the publication “Hydroclimate Volatility on a Warming Earth” by Swain et al. 2025. </strong>Here, we include the derived "hydroclimate whiplash" variable used in the summary analysis described in the published paper as calcuated for the historical period (ending in 2023) the ERA5 reanalysis (ERA5), and the NOAA-CIRES-DOE 20CRv3 reanalysis (NCD20C), as well as the future period (2014-2100 using SSP3-7.0 forcing) from the CESM2 Large Ensemble (CESM2-LE). Additionally, code used in the analysis discussed in the associated published manuscript is also available in this repository.</p>
Sunburned plankton: Ultraviolet radiation inhibition of phytoplankton photosynthesis in the Community Earth System Model version 2
<p>Climate model output for paper describing CESM2-UVphyto.</p>
Supplementary files for: A dynamic 2000–540 Ma Earth history: From cratonic amalgamation to the age of supercontinent cycle
<p>Supplementary materials for the Earth-science Reviews paper 'A dynamic 2000–540 Ma Earth history: From cratonic amalgamation to the age of supercontinent cycle'. </p> <p>Supplementary Material 1 – Palaeomagnetic pole list for the ca. 2000–540 Ma interval.<br> Supplementary Material 2 – IGCP 440 pre-700 Ma geotectonic database (with minor corrections made) in shapefiles format<br> Supplementary Material 3 – Neoproterozoic sedimentary facies point data of Li et al. (2013) in shapefile format<br> Supplementary Material 4 – Generalised global large igneous province (LIP) database for 2010–0 Ma (after Ernst et al., 2021) in both shapefile and Excel formats<br> Supplementary Material 5 – Global passive margin database of (Bradley, 2008) in shapefile format<br> Supplementary Material 6 – Global orogen database of Condie et al. (2021) with minor modifications and in shapefile format<br> Supplementary Material 7 – Global 2000–540 Ma full-plate animation following the extended orthoversion principle, Scenario Ia (0-90W-0)<br> Supplementary Material 8 – Global 2000–540 Ma full-plate animation following the extended orthoversion principle, Scenario Ib (0-90E-0)<br> Supplementary Material 9 – 2000–540 Ma global animation highlighting the occurrence of LIP events in time and space, including possible plume centres.<br> Supplementary Material 10 – GPlates project files for the two alternative global 2000–540 Ma full-plate animations with associated geotectonic databases</p>
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (SAI_2001_2005)
<p>Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change. </p> <p>This data repository is linked to <a href="../records/10815170" target="_blank" rel="noopener">https://zenodo.org/records/10815170</a></p>
Global 1km Land Surface Parameters for Kilometer-Scale Earth System Modeling (SAI_2006_2010)
<p>Earth system models (ESMs) are progressively advancing towards the kilometer scale (k-scale). However, the surface parameters for Land Surface Models (LSMs) within ESMs running at the k-scale are typically derived from coarse resolution and outdated datasets. This study aims to develop a new set of global land surface parameters with a resolution of 1 km for multiple years from 2001 to 2020, utilizing the latest and most accurate available datasets. Specifically, the datasets consist of parameters related to land use and land cover, vegetation, soil, and topography. Differences between the newly developed 1k land surface parameters and conventional parameters emphasize their potential for higher accuracy due to the incorporation of the most advanced and latest data sources. To demonstrate the capability of these new parameters, we conducted 1 km resolution simulations using the E3SM Land Model version 2 (ELM2) over the contiguous United States. Our results demonstrate that land surface parameters contribute to significant spatial heterogeneity in ELM2 simulations of soil moisture, latent heat, emitted longwave radiation, and absorbed shortwave radiation. On average, about 31% to 54% of spatial information is lost by upscaling the 1 km ELM2 simulations to a 12 km resolution. Using eXplainable Machine Learning (XML) methods, the influential factors driving the spatial variability and spatial information loss of ELM2 simulations were identified, highlighting the substantial impact of the spatial variability and information loss of various land surface parameters, as well as the mean climate conditions. The comparison against four benchmark datasets indicates that ELM generally performs well in simulating soil moisture and surface energy fluxes. The new land surface parameters are tailored to meet the emerging needs of k-scale LSMs and ESMs modeling with significant implications for advancing our understanding of water, carbon, and energy cycles under global change.</p> <p>This data repository is linked to <a href="../records/10815170" target="_blank" rel="noopener">https://zenodo.org/records/10815170</a></p>
Supporting data for "Global Model of Atmospheric Chlorate on Earth" by Chan et al.
<p>Model code, simulation outputs, observation tables, and Python scripts for reproducing the analysis results/ figures presented in "Global Model of Atmospheric Chlorate on Earth" by Yuk-Chun Chan et al. Please refer to the publication and readme.txt for more information.</p>
A geospatial dataset of lichen key attributes in the Earth's three poles
<p>To develop the geospatial dataset, we initially defined two lichen attributes: color type and growth form. These attributes were chosen due to their significant correlation with lichen physiological and biochemical characteristics, as well as their association with reflection spectra. Each record of this geospatial dataset consists of information such as scientific name, longitude, latitude, ecoregion name, biome name, color type, growth form, and the occurranceID belongs to the GBIF original dataset.</p> <p>This dataset serves as a foundational resource for extensive investigations into the intricate interplay between lichen physiology and the environment, addressing a significant knowledge gap in the field. Furthermore, our dataset holds the potential to address challenges associated with remote sensing monitoring of lichens, a longstanding issue in vegetation remote sensing. Precise in situ observation records, as provided by our dataset, can facilitate the development of remote sensing techniques tailored for lichen monitoring.</p> <p>"Program" is the code used in the process of establishing the geospatial dataset of lichen key attributes in the Earth’s three poles.</p> <p> </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.