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285 results for “Poles”
ADCP wave and current data at Hog Island and Chimney Pole Marsh, VA 2009
Two acoustic doppler profilers were deployed in on Hog Island and Chimney Pole Marsh in 2009. all quantites (velocity, waves, water elevation, attenuation) are described in the file *.hdr (header file)
Supplementary Data for Wueller et al. (2024): Geologic History of the Amundsen Crater Region Near the Lunar South Pole: Basis for Future Exploration
<p>Supplementary Data for Wueller et al. (2024): Geologic History of the Amundsen Crater Region Near the Lunar South Pole: Basis for Future Exploration</p> <p>Data contains the georeferenced map plate of our geologic map that can be used in any geoinformation system (GIS).</p> <p><strong>If you use these data, please cite BOTH the Planetary Science Journal publication and the Zenodo dataset.</strong></p> <p>Wueller, L., Iqbal, W., Frueh, T., van der Bogert, C. H., & Hiesinger, H. (2024). Geologic history of the Amundsen crater region near the Lunar South Pole: Basis for future exploration. <em>The Planetary Science Journal</em>, <em>5</em>(6), 147. <a href="https://iopscience.iop.org/article/10.3847/PSJ/ad2c04">https://iopscience.iop.org/article/10.3847/PSJ/ad2c04</a></p> <p>Wueller, L., Iqbal, W., Frueh, T., van der Bogert, C. H., & Hiesinger, H. (2024). Supplementary Data for Wueller et al. (2024): Geologic history of the Amundsen crater region near the Lunar South Pole: Basis for future exploration. <em>Zenodo Dataset</em>. <a href="https://doi.org/10.5281/zenodo.10693820" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10693820</a></p> <p>-----------------------------------------------------------------------------------------------------------------------------------------</p> <p>Mapping Scale is 1:100,000</p> <p>Print Scale is 1:1,000,000</p> <p>-----------------------------------------------------------------------------------------------------------------------------------------</p> <p>For further questions contact lwueller@uni-muenster.de</p> <p>Lukas Wueller, Institut für Planetologie, Universität Münster, Germany, June 2024</p>
Cosmic Pi North Pole Dataset 2024
<p>This raw dataset was gathered using a pair of Cosmic Pi detectors carried by the Transglobal Car Expedition across North America, to the North Pole and back to Greenland. To our knowledge this is the first ground level measurement of muons at the North Pole, beating the record previously set by the PolarquEEEst mission, which sailed up to a latitude of 82◦07′ (see https://cds.cern.ch/record/2743921/files/ncc11933(1).pdf for details).</p> <p>The dataset comprises a portable Influxdb 1.8 export, with several sub datasets. </p> <p>We have the following data stored in the CosmicPiV1.8.1 measurement table:</p> <ul> <li>measurement='CosmicPiV1.8.1',</li> <li>DeviceID=cosmicdict['tags']['id'], Unit ID</li> <li>latitude=cosmicdict['fields']['lat'], Latitude</li> <li>longitude=cosmicdict['fields']['lon'], Longitude</li> <li>Temp=cosmicdict['fields']['Temp'], Temperature in C</li> <li>Hum=cosmicdict['fields']['Hum'], Relative Humidity in %</li> <li>Accelx=cosmicdict['fields']['Accelx'], Acceleration, X axis in m/s/s</li> <li>Accely=cosmicdict['fields']['Accely'], Acceleration, Y axis in m/s/s</li> <li>Accelz=cosmicdict['fields']['Accelz'], Acceleration, Z axis in m/s/s</li> <li>Magx=cosmicdict['fields']['Magx'], Magnetic field, X axis in Gauss</li> <li>Magy=cosmicdict['fields']['Magy'], Magnetic field, Y axis in Gauss</li> <li>Magz=cosmicdict['fields']['Magz'], Magnetic field, Z axis in Gauss</li> <li>Pressx=cosmicdict['fields']['Press'], Atmospheric pressure in mbar</li> <li>Altx=cosmicdict['fields']['Alt'], Altitude in meters</li> <li>timestamp=cosmicdict['time'] Time</li> </ul> <p>And we also have the frequency data (how many cosmic rays detected per second) here:</p> <ul> <li>measurement='CosmicPiV1.8.1_freq',</li> <li>DeviceID=cosmicdict['tags']['id'], Unit ID</li> <li>geohash_pos="""+cosmicdict['fields']['geohash']+""", The geohash, a short code that represents lat & lon</li> <li>event_count=cosmicdict['fields']['event_count'], The number of events in this second</li> <li>timestamp=cosmicdict['time']) Time</li> </ul> <p>Some basic analysis code is presented here: https://github.com/CosmicPi/TGCEWorkshop</p>
Landslide Removal Experiment vegetation cover and pole touches
The purpose of this data set is to document recovery of vegetation in removal plots in landslides in the Luquillo Experimental Forest (LEF.) Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Glacial Lake Dataset for the Third Pole
<p>A complete glacial lake dataset compiled for the whole Third Pole, in which 63,727 glacial lakes (≥900 m<sup>2</sup>) with a combined area of 2,122.01 ± 1.26 km<sup>2</sup> were included. Please see data description document for more information.</p>
Lightning Potential Index Using ICON Simulation at the km-scale over the Third Pole Region: ISS-LIS events and ICON-CLM simulated LPI
<p>This dataset contains records of lightning events recorded by the International Space Station (ISS) Lightning Imaging Sensor (LIS) from October 2019 to September 2022 in the Third Pole region. Furthermore, the Icosahedral Nonhydrostatic Weather and Climate Model in Climate Limited-Area Mode (ICON-CLM) was utilized to simulate the hourly Lightning Potential Index (LPI) over the Third Pole region for the same duration. The aforementioned dataset was utilized in the creation of the research article titled "Modeling Lightning Activity in the Third Pole Region: Performance of a km-scale ICON-CLM Simulation" authored by Prashant Singh and Bodo Ahrens. The paper has been submitted to the journal Atmosphere. In CORDEX-FPS-CPTP contribution no. 17 (GUF), you can find more data from ICON-CLM, such as precipitation, CAPE, wind vectors, and more.</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>
study of second harmonic generation in periodically poled fiber in double pass configuration
<p>This dataset includes the experimental measurements and the numerical simulations of the power of second harmonic generated inside a periodically poled fiber traversed in single and double pass by a fundamental signal whose wavelength is included in a certain range of values. This measurements are the preliminary study for situation where the PPSF can be exploited in multiple pass configuration, such as in a cavity. </p>
The molecular architecture of the yeast spindle pole body core determined by Bayesian integrative modeling
<p>This repository pertains to the molecular architecture of the yeast spindle pole body (SPB), the structural and functional equivalent of the metazoan centrosome. Data from in vivo FRET and yeast two-hybrid, along with SAXS, X-ray crystallography, and electron microscopy were integrated by a Bayesian structure modeling approach.</p> <p>For more information about how to reproduce this modeling, see the <a href="https://salilab.org/spb/">Sali lab website</a> or the README file.</p>
Cryogenically Cooled Periodically Poled Lithium Niobate Wafer Stacks for Multi-Cycle Terahertz Pulses
<p>Dataset used for the figures in the paper: "Cryogenically Cooled Periodically Poled Lithium Niobate Wafer Stacks for Multi-Cycle Terahertz Pulses" by Dalton et al. Accepted for publication in Applied Physics Letters on the 6th September 2024.</p>
Supplementary material to: Highly resolved topography and illumination at Mercury south pole from MESSENGER MDIS-NAC
<p>We produced a new higher-resolution topographic map of Mercury’s south polar region (75°-90° South, covering ~1.3 million km<sup>2</sup>) by using data collected by the NASA MESSENGER spacecraft’s Mercury Dual Imaging System (MDIS; Hawkins et al, 2007) over the years 2011-2015. This new map enables, <em>e.g.</em>, the first detailed modeling of illumination and thermal conditions in these southern radar-bright locations and the first constraints on the nature and history of volatiles residing there, but it is also intended as a resource for other geophysical analyses and for the preparation of the BepiColombo mission, currently en-route to the planet.</p> <p>For more details, please visit <a href="https://pgda.gsfc.nasa.gov/products/88">https://pgda.gsfc.nasa.gov/products/88</a>.</p> <p><strong>Products:</strong></p> <p>DEM (interpolated), DEM (filled), Slopes, PSR masks</p> <p>All these files (except the PSR masks shapefile) are 250 m/pix GeoTiffs with south polar stereographic X/Y coords in meters.</p> <p><br> <em>If using these products, please cite:</em><br> Bertone, S., E. Mazarico, M.K. Barker, M. Siegler, J. M. Martinez Camacho, C. Hamill, A. Glatzenberg, N. L. Chabot, 2022: <em>Highly resolved topography and illumination at Mercury south pole from MESSENGER MDIS-NAC</em>. The Planetary Science Journal, 02/2023, <a href="http://dx.doi.org/10.3847/PSJ/acaddb">doi:10.3847/PSJ/acaddb</a></p>
Chimney Pole Marsh Erosion-Camera Images and Video 2009-2012
This data consists of a time series of image and video files showing erosion at the western edge of Chimney Pole Marsh, in Northampton Co. Virginia. Images depict the edge of a salt marsh as it erodes. Images and videos have a time stamp (YYYYmmdd_HHMMss) embedded in their file name and also in the upper left of the images themselves. All dates and times are in Eastern Standard Time. Images and videos are taken once ever 30 minutes at 25 and 55 minutes after the hour. Still image JPEG (.jpg) files are 1600x1200 pixels in size. Videos are encoded as MPEG-4 (.mp4) files with a resolution of 400x304 at 8.05 frames per second. They were collected by a 2 mega-pixel Vivotek IP7161 security camera attached to a post (approximately 3-m above the marsh surface). The camera was removed when the marsh was sufficiently eroded that the camera platform was imperiled. There are some gaps in the data caused by network and electrical problems.
CFHT MegaPrime/MegaCam u-band source catalogue of the AKARI North Ecliptic Pole Wide field
<p>This is the CFHT MegaPrime/MegaCam u-band catalogue of the AKARI North Ecliptic Pole Wide field. The observations were performed by the CFHT MegaPrime/MegaCam with its u-band filter (u.MP9302; range 3041-4014 angstrom; effective wavelength at 3610 angstrom) covering a 3.6-square-degree area. Observation periods were 2015 May 22 to 26, and 2016 July 6 to 7. The limiting magnitude is 25.4 mag, while reaches 25.8 mag in a deeper 1-square-degree area. 351,635 sources have been detected.</p> <p>The details of the data reduction and quality are described in the paper: <a href="https://doi.org/10.1093/mnras/staa2459">https://doi.org/10.1093/mnras/staa2459</a> (or <a href="http://arxiv.org/abs/2008.05224">https://arxiv.org/abs/2008.05224</a>). </p>
Effects of Tide-Induced Mixing on the Surface Temperature Gradients Between the Equator and Poles During the Middle Miocene Climate Optimum -- Dataset
<p>The files contain the data related to the figures in this paper.</p><p>-- Fig.1 The topographic roughness of the PI and MMCO before and after reconstruction</p><p>-- Fig.2 The 300-year time series of the annual mean SAT and SST</p><p>-- Fig.3 The data of SSH for PI_TF experiment</p><p>-- Fig.4 The tidal dissipation and mixing for MMCO_TM, and the ocean vertical mixing</p><p>-- Fig.5 The annual mean SAT and SST for the MMCO_TM and<i> </i>MMCO<i>_</i>noTM</p><p>-- Fig.6 The global meridional heat transport for the MMCO_TM and<i> </i>MMCO<i>_</i>noTM</p><p>-- Fig.7 The net sea surface heat flux for the MMCO_TM and<i> </i>MMCO<i>_</i>noTM</p><p>-- Fig.8 The GMOC and AMOC for the MMCO_TM and<i> </i>MMCO<i>_</i>noTM</p>
Source code and simulation results: Poles and zeros of electromagnetic quantities in photonic systems
<h4><strong>Summary</strong></h4> <p>This publication supplements the article "Poles and zeros of electromagnetic quantities in photonic systems" with tabulated data and matlab code that allows to reproduce the results. The article elaborates how evaluating resonances based on contour integrals of scalar electromagnetic quantities extends to computing zeros. Furthermore, direct differentiation of underlying scattering problems is used to compute sensitivities with respect to design parameters.</p> <h4><strong>Structure</strong></h4> <p>The script 'main_text.m' can be used to reproduce the results provided in the paper. In tabulated form the results are contained in the directory <strong>tabulated</strong>. Furthermore, the script 'supplement.m' can be used to reproduce results presented in the supplement. The directory <strong>RPExpand </strong>contains the software RPExpand v2, which is available on <a href="https://doi.org/10.5281/zenodo.10371002">Zenodo</a> with additional examples. </p> <h4><strong>Compute residues</strong></h4> <p>The modal expansion of the Fourier transform is based on its residues at the dominant resonances. If the poles are simple, which often is the case, the residues can be obtained directly from the eigenvectors of the generalized eigenvalue problem used to obtain the poles or the zeros. Introducing the Vandermonde matrix</p> <p>\(V = \begin{bmatrix} 1 & \dots & 1 \\ w_1 & \dots & w_M \\ \vdots & & \vdots \\ w_1^{M-1} &\dots & w_M^{M-1} \end{bmatrix}\),</p> <p>the Hankel matrix \(H\) can be written as \(H = V A V^T\) with \(A\) being the diagonal matrix \(\mathrm{diag}(a_1,\dots,a_M)\) containing the residues \(a_m \). This decomposition is a consequence of the Cauchy's reisdue theorem if the poles are simple. Furthermore, we now that \(V^{-T}\) solves the generalized eigenproblem \(H^<X = HX\Omega\) (Eq. 2 in the original paper) and hence the eigenvectors we get from Matlabs eig routine are \(X = V^{-T}D\) where \(D\) is some scaling. It follows that we obtain the residues using \(A = X^T H X (X V^T)^{-2}\)</p> <h4><strong>Derivatives</strong></h4> <p>Similarly, our framework provides a straight forward approach to the derivatives of zeros and poles if they are simple. Using direct differentiation we have access to partial derivatives of the quantity \(q(\omega)\) and hence the derivatives of the moments \(s_k = \frac{1}{2\pi i} \oint_C \omega^k q(\omega) \mathrm{d}\omega\). For the zeros the inverse \(1/q(\omega)\) and the respective derivative are considered. Using Cauchy's residue theorem the derivatives \(\frac{\partial w_m}{\partial p}\)are solutions of the linear system of equations \(\frac{\partial s_k}{\partial p} = \sum_{m = 1}^{M}\left[k\omega_m^{k-1}\frac{\partial w_m}{\partial p} a_m + \omega_m^k\frac{\partial a_m}{\partial p} \right]\).</p> <h4><strong>Higher order singularities</strong></h4> <p>Finding higher order poles and zeros is possible without further adaptation. Computing derivatives and residues requires some special care. The moments are then given by \(s_k = \sum_{m=1}^{M} \sum_{n = 1}^{N_m} a_{m,n} \frac{k! \, \omega^{k-n+1}}{(k-n+1)!(n-1)!}\)with \(a_{m,n}\) being the residue of the pole \(\omega_m\) and \(n \) refers to the order. Accordingly expressions for the derivatives are available.</p> <h4><strong>Error estimates</strong></h4> <p>The estimated errors in Table 1 refer to the number of integration points, i.e. we are interested in the question how close we get with a given number of integration points to the exact solution of the chosen approximate model of the physical system. Due to propagation of the error the convergence of the derivatives is shifted towards a larger number of integration points.</p> <h4><strong>Requirements</strong></h4> <ul> <li>JCMsuite (version 5.4.3 or newer)</li> <li>MATLAB (tested with version R2019b)</li> </ul> <p>In order to run the scripts you must replace the corresponding place holder in 'zeros_poles.m' by a path to your installation of JCMsuite. Free trial licenses are available, please refer to the homepage of <a href="https://jcmwave.com/">JCMwave</a>.</p> <h4><strong>References</strong></h4> <p>[1] Felix Binkowski, Fridtjof Betz, Rémi Colom, Patrice Genevet, Sven Burger, Poles and zeros of electromagnetic quantities in photonic systems, https://doi.org/10.48550/arXiv.2307.04654</p> <p>[2] Anthony P. Austin, Peter Kravanja, Lloyd N. Trefethen, Numerical algorithms based on analytic function values at roots of unity, SIAM Journal of Numerical Analysis 52, 1795 (2014), https://doi.org/10.1137/130931035</p> <p>[3] Felix Binkowski, Fridtjof Betz, Martin Hammerschmidt, Philipp-Immanuel Schneider, Lin Zschiedrich, Sven Burger, Computation of eigenfrequency sensitivities using Riesz projections for efficient optimization of nanophotonic resonators, Communications Physics <strong>5</strong>, 202 (2022), https://doi.org/10.1038/s42005-022-00977-1</p>
Supplementary material for: "Who gets the pole position? Spatial and social behaviour of snowfinches at winter feeders in Alpine habitat"
<p><strong>Abstract</strong></p> <p><span>In collective foraging, an individual’s ability to compete with conspecifics can considerably influence its foraging strategy and success. Strong competitors can fight for prioritised access to food, while weak competitors may avoid aggressions and rather seek out less contested foraging opportunities. However, insecure access to food can be detrimental to weak competitors, especially during periods of low and unpredictable resource availability. Here we investigated individual foraging behaviour of White-winged snowfinches <em>Montifringilla nivalis</em>, specialists of alpine habitats and their strategies for coping with competition for food resources in winter. A difference in condition dependence of survival rates between males and females in this species has made us hypothesise that males may be more dominant in accessing food resources than females. We set up artificial experimental food patches in the Swiss Alps to observe foraging male and female snow finches and recorded their behaviour in interactions with conspecifics and their spatial position relative to the food. We found that individuals frequently occupying positions close to the food resources were more involved in agonistic interactions than individuals staying in more distant positions.</span><span> </span><span>This result suggests that different individuals use different foraging strategies, probably depending on their level of competitiveness. Furthermore, we observed males to forage slightly closer to the food source than females,</span><span> </span><span>which could imply that females may have less access to artificial feeders than males. </span></p>
Crater Populations on the Walls of Complex Craters found near the Lunar Southern Pole
<p>Date: June 23, 2022<br> Authors: C. L. Talkington and P. E. Montalvo<br> Notes: Data sets contain crater counts on floors of 16 lunar south polar complex craters and surface slopes at all counted craters. </p> <p>Data sets in crater_data.xlsx are updated from the previous version to improve the model age calculation. The current version uses an approach using Poisson’s statistics (Michael and Neukum, 2010; Michael et al., 2016).</p> <p>ArcMap 10.7.1 was used in addition to the CraterTools Add in toolset (Kniessl et al., 2011) and DEM data from the PDS Geosciences Node (https://pds-geosciences.wustl.edu/lro/lro-l-lola-3-rdr-v1/lrolol_1xxx/data/lola_gdr/polar/jp2/) to visualize the craters.Complex craters were selected from previous studies (Cannon et al., 2020; Deutsch et al., 2020; Tye et al., 2015) as regions of interest. The counts from this work span the walls of 16 complex craters found near the lunar southern pole, while those cited refer to the floor regions (Cannon et al., 2020; Deutsch et al., 2020), or the entire crater (Tye et al., 2015). Craters were included within the analysis from definitions described by Deutsch et al., (2020) as circular features with central depressions. We considered primary craters only, and those with morphologies reflecting secondary crater populations (crater chains or clusters) were omitted. Crater sizes range from sub km range to 35 km in diameter. Their slope conditions range from very shallow to very steep. </p> <p>PDS Geoscience Node DEM used ldem_80s_20m. This DEM has a resolution of 20 m/pixel and was updated 6/2/2017.<br> Data format, .csv files:<br> - Column A: Diameter. Crater diameter [km]<br> - Column B: Slope angle of crater [deg] determined by the 500 m buffer size from the determined crater radius. </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>
Рис. 7. ГнезΑо ΑаΛьневосточного аиста, распоΛоженное на гнезΑовой пΛатформе, установΛенной на опоре ΑЭП-500 в Серышевском районе Амурской обΛасти Fig. 7. The Oriental stork nest, built on the special platform installed on a 500 kV power transmission pole in the Seryshevsky district of the Amur region in Oriental stork (Ciconia boyciana Swinhoe) breeding population survey in the Amur region in 2018-2019
Рис. 7. ГнезΑо ΑаΛьневосточного аиста, распоΛоженное на гнезΑовой пΛатформе, установΛенной на опоре ΑЭП-500 в Серышевском районе Амурской обΛасти Fig. 7. The Oriental stork nest, built on the special platform installed on a 500 kV power transmission pole in the Seryshevsky district of the Amur region
Video abstract for "Communicating climate change in a 'post-factual' society: Lessons learned from the Pole to Paris campaign"
<p>MPEG-4 video containing the video abstract for the publication "Communicating climate change in a 'post-factual' society: Lessons learned from the Pole to Paris campaign" in Geoscience Communication by Erlend Moster Knudsen and Oria Jamar de Bolsée, published in 2019.</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.