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897 results for “cone”
Cone production of upper slope conifers in the Cascade Range of Oregon and Washington, 1959 to 2022
Seed supply is a key feature of tree population dynamics, and seed production may be indicative of environmental and biological drivers. This study examines cone production in upper-slope, true fir-hemlock forests of the Pacific Northwest, starting in 1959 to the present. Annual surveys of cone counts of Abies spp. (A. amabilis, A. concolor, A. grandis, A. lasiocarpa, A. magnifica, A. procera), Pinus spp. (P. engelmannii, P. lamberti, P. monticola), and Tsuga spp. (T mertsiana) have been conducted at sixty-one plots in 37 locations in nine national forests in Washington and Oregon (originally 10 national forests, but Mt. Baker and Snoqualimie were combined). At each site, a visual count is made of cone production in each of a number (20-30) trees in a stand of one tree species. At some plots, additional trees were added in the 1980s. Primary data include numbers of counts per tree per year, periodic measurements of tree diameter, and the names of the sites. These data illustrate the periodicity of cone production cycles, as well as longer trends associated with climate change and variability in the region.
Ecosystem nutrient cycling in northern hardwood and conifer stands at Cone Pond, Hubbard Brook, and Sleepers River
This dataset provides comprehensive measurements of nutrient concentrations and fluxes in foliage, fine roots, wood, litterfall, and throughfall in hardwood and conifer stands across temperate forest stands at three long-term ecological research sites in the northeastern United States: Cone Pond, NH, Hubbard Brook, NH, and Sleepers River, VT. These sites vary in bedrock composition, parent material, and soil chemistry, but share similar climatic characteristics. Tissue nutrient concentrations were determined in leaves, fine roots, wood, and branches using site- and tissue-specific methods, with additional quality control through certified standards and duplicate sampling. Nutrient fluxes via litterfall and throughfall were measured over multiple years. Nutrient fluxes in roots were estimated from minirhizotron-based turnover rates and fine root biomass. Annual nutrient accumulation and uptake were calculated by integrating biomass production and nutrient concentrations. This dataset supports cross-site comparisons of forest biogeochemistry and provides a basis for evaluating nutrient limitations, cycling processes, and ecosystem responses to environmental gradients in northeastern temperate forests.
Dynamic Contrast Enhanced MRI Raw Data Acquired with 3D Cones Trajectory
<p>This repository contains the raw data for the second dynamic contrast enhanced (DCE) MRI in <a href="https://arxiv.org/abs/1909.13482">Extreme MRI: Large-Scale Volumetric Dynamic Imaging from Continuous Non-Gated Acquisitions</a>. The data is stored as numpy arrays, containing k-space data (ksp.npy), coordinates (coord.npy), and density compensation factors (dcf.npy). Code to process and reconstruct the data is available here: <a href="https://github.com/mikgroup/extreme_mri">https://github.com/mikgroup/extreme_mri</a></p> <p>For more information about how the data is acquired, please see the linked paper.</p>
Localizing Spherical Fiducials in C‐arm Based Cone‐Beam CT
<p>This dataset was acquired as part of the work described in: "Localizing spherical fiducials in C-arm based cone-beam CT", Z. Yaniv, Med. Phys., Vol. 36(11), pp. 4957-4966, 2009, <a href="https://doi.org/10.1118/1.3233684">doi.org/10.1118/1.3233684</a>.</p> <p>The data includes two phantom imaging studies acquired with a Cone-Beam CT (CBCT) system. Each of the two datasets includes projection images (cine loops) and 3D reconstructions. Additionally, the data includes the CBCT system's projection matrices and MATLAB code for reading the projection images and overlaying epipolar lines onto them. All images are stored in the DICOM format.</p> <p>The full MATLAB code for fiducial localization described in the manuscript is available <a href="https://www.yanivresearch.info/software/cbctSphericalFiducialLocalization.zip">here</a>.</p> <p> </p>
Multi-year temperature time series from cinder cones on the Mauna Kea summit plateau, Hawaii
<p>Multi-year time series from data loggers in several cinder cones on the Mauna Kea summit plateau, Hawaii. The variable measured is primarily ground temperature, but several time series for air temperature, relative humidity, and light intensity have also been obtained. </p> <p>Study sites, time period:<br>Puu Wekiu (Summit Cone Crater), 2012-2025<br>Puu Hau Kea (Goodrich Cone), 2012-2025<br>Puu Waiau, 2013-2016<br>Puu Pohaku (Douglas Cone), 2014-2016<br>Puu Makanaka, 2016</p> <p>The raw data are downloads from HOBO data loggers. The reduced data are merged time series without time gaps and corrected for known issues. A list of data loggers with time periods and sensor depths is provided in loggers_summary_mk.csv. For file formats and further information, see README.TXT. For context see references.</p>
4D cone beam computed tomography phantom data set
<p>This data set accompanies the following Medical Physics publication: <a href="https://doi.org/10.1002/mp.14441"><i>Madesta, F., Sentker, T., Gauer, T., & Werner, R. (2020). Self‐contained deep learning‐based boosting of 4D cone‐beam CT reconstruction. Medical Physics, 47(11), 5619-5631</i></a><i>.</i></p><p>It comprises 6 time-resolved (4D) cone-beam computed tomography scans with the following scan configurations:</p><ul><li>4D CBCT Scanner: Varian TrueBeam (the detailed scan geometry and further details can be found in Scan.xml included in each scan)</li><li>Phantom: <a href="https://www.cirsinc.com/products/radiation-therapy/dynamic-thorax-motion-phantom/">Dynamic Thorax Phantom: Model 008A</a></li><li>The following motion patterns are included:<ol><li>SI amplitude of insert: ±10mm, pattern: sin, period: 5.0s</li><li>SI amplitude of insert: ±10mm, pattern: cos**4, period: 5.0s</li><li>SI amplitude of insert: ±10mm, pattern: sin, period: 2.5s</li><li>SI amplitude of insert: ±10mm, pattern: cos**4, period: 2.5s</li><li>SI amplitude of insert: ±10mm, pattern: sin, period: 7.5s</li><li>SI amplitude of insert: ±10mm, pattern: cos**4, period: 7.5s</li></ol></li></ul>
Supplementary materials for "Experimental verification for self-organization process on the spatial distribution and edifice size of rootless cone"
<p>This is a ReadMe for the supplementary material for "Experimental verification for self-organization process on the spatial distribution and edifice size of rootless cone" written by Rina Noguchi and Wataru Nakagawa.</p> <p>----------------------<br>[ReadMe.txt]<br>ReadMe text file.</p> <p>[FigS1.png]<br>This figure is a supplementary figure which appeared as "Figure S1" in the main text.<br>Caption: Figure S1. Examples of conduits (dashed green lines) and loser conduits (solid magenta lines) were observed in the experiments with original and contrast-enhanced images.</p> <p>[FigS2.png]<br>This figure is a supplementary figure which appeared as "Figure S2" in the main text.<br>Caption: Figure S2. Relationships between the thickness of poured heated syrup and (A) mass losses caused by baking soda decomposition, (B) number of conduits, (C) total conduit area, (D) average conduit area, (E) number of failed conduits, and (F) sum number of conduits and failed conduits. Each plot and error bar represents the average and standard deviation in three repeated experiments, respectively. The red plots and error bars show the 350 g of heated syrup case, which performed ten repeated experiments to verify the reproducibility. Note that horizontal error bars are derived from the difficulty of strict heated syrup-pouring control.</p> <p>[Experimental_datasheet.xlsx]<br>This EXCEL file includes two sheets: a mass loss change log and a summary of experimental results.</p> <p>[movie/SSS_X_x15.mp4]<br>These MP4 files are fast-forward movies (x15) for each experiment. SSS = the amount of poured hearty syrup (g), and X = round in each condition.<br>----------------------</p> <p>For more details, please refer to a research paper "Experimental verification for self-organization process on the spatial distribution and edifice size of rootless cone".</p> <p>If you have any questions, please send an e-mail to:<br>r-noguchi@env.sc.niigata-u.ac.jp<br>or<br>flugel555@gmail.com<br>.<br>(R. Noguchi)</p>
FAST loss-cone electron precipitation database
<p>A FAST electron precipitation database derived from FAST EESA observations, covering beginning of mission (October 1996) through 2009.</p> <p>Number flux ('j') and energy flux ('je') moments are produced by integrating EESA measurements over all energies above 70 eV<br> up to the EESA detector limit (30 keV), and over all pitch angles within the earthward portion of the loss cone (see references). </p> <p>================================<br> EXPLANATION OF DATAFRAME COLUMNS<br> ================================</p> <p>'j' : units of #/cm^2-s (ALL QUANTITIES ARE POSITIVE, WHERE I HAVE USED THE CONVENTION 'POSITIVE' == 'EARTHWARD')<br> 'je' : units of mW/m^2 (ALL QUANTITIES ARE POSITIVE, WHERE I HAVE USED THE CONVENTION 'POSITIVE' == 'EARTHWARD')<br> <br> 'jerr' : units of #/cm^2-s (Number flux uncertainty, calculated using the Gershman et al (2015) method)<br> 'jeerr' : units of mW/m^2 (Energy flux uncertainty, calculated using the Gershman et al (2015) method)<br> <br> 'orbit' : FAST orbit number<br> 'alt' : FAST geodetic altitude, in km. (FAST altitude ranges from ~300-4180 km)<br> 'apexmlt' : Magnetic local time in Apex-110 coordinates (see Laundal and Richmond (2016))<br> 'apexmlat' : Magnetic latitude in Apex-110 coordinates (see Laundal and Richmond (2016))</p> <p>'shadowRegion110' : Integer indicator of the region of the Earth's shadow that FAST's field-line footpoint at 110-km altitude lands ind.<br> Takes on values [0,1,2], corresponding to ['Umbra','Penumbra','Sunlit']. <br> Calculated by mapping FAST's location to 110-km altitude in Apex coordinates and then following the methodology of Jia et al. (2015).</p> <p>'mono' = 0,1,2 : 'not monoenergetic','weak monoenergetic','strict monoenergetic'<br> 'broad' = 0,1,2 : 'not broadband','weak broadband','strict broadband'<br> 'diffuse' = 0,1 : 'not diffuse','diffuse'</p> <p>'mono', ' broad', and 'diffuse' follow the Hatch et al. (2016) FAST adaptation of the Newell et al. (2009) classification scheme<br> *NOTE: I do NOT force 'mono' and 'broad' to be exclusive categories! I consider it fine for precipitation to be identified as both 'broad' and 'mono'</p> <p>'drop' : Boolean indicating whether a row should be dropped from the DataFrame</p> <p>==========<br> REFERENCES<br> ==========<br> Gershman, D. J., Dorelli, J. C., F.-Viñas, A., & Pollock, C. J. (2015). The calculation of moment uncertainties from velocity distribution functions with random errors. Journal of Geophysical Research A: Space Physics, 120(8), 6633–6645. https://doi.org/10.1002/2014JA020775</p> <p>Hatch, S. M., Chaston, C. C., & LaBelle, J. (2016). Alfvén wave-driven ionospheric mass outflow and electron precipitation during storms. Journal of Geophysical Research: Space Physics, 121(8), 7828–7846. https://doi.org/10.1002/2016JA022805</p> <p>Hatch, S. M., Labelle, J., Lotko, W., Chaston, C. C., & Zhang, B. (2017). IMF control of Alfvénic energy transport and deposition at high latitudes. Journal of Geophysical Research: Space Physics, 122(12). https://doi.org/10.1002/2017JA024175</p> <p>Jia, X., Xu, M., Pan, X., & Mao, X. (2017). Eclipse Prediction Algorithms for Low-Earth-Orbiting Satellites. IEEE Transactions on Aerospace and Electronic Systems, 53(6), 2963–2975. https://doi.org/10.1109/TAES.2017.2722518</p> <p>Laundal, K. M., & Richmond, A. D. (2016). Magnetic Coordinate Systems. Space Science Reviews, 1–33. https://doi.org/10.1007/s11214-016-0275-y</p> <p>Newell, P. T., Sotirelis, T., & Wing, S. (2009). Diffuse, monoenergetic, and broadband aurora: The global precipitation budget. Journal of Geophysical Research, 114, A09207. https://doi.org/http://dx.doi.org/10.1029/2009JA014326<br> </p>
Icing Wind Tunnel Measurements of Supercooled Large Droplets Using the 12 mm Total Water Content Cone of the Nevzorov Probe: Measurement Data
<p>This repository contains the measurement data that was used for the publication "Icing Wind Tunnel Measurements of Supercooled Large Droplets Using the 12 mm Total Water Content Cone of the Nevzorov Probe".</p>
Cone-Beam Computed Tomography Dataset of a Seashell
<p><strong>Summary</strong></p> <p>This dataset is a collection of X-ray projection images of a seashell imaged in a cone-beam computed tomography (CBCT) scanner. The dataset also includes a metadata file, specifying the scan geometry and other important scan parameters, and photographs of the sample and the measurement setup.</p> <p> </p> <p><strong>Description</strong></p> <p><em>Sample Information</em></p> <p>The sample is an empty seashell of an unknown species, approximately 4.3 cm in length and 2.5 cm in diameter. The sample was placed in a plastic tube filled with cotton wool to prevent unwanted motion during the scan.</p> <p><em>Scanner</em></p> <p>The measurements were acquired using a cone-beam computed tomography scanner designed and constructed in-house in the Industrial Mathematics Computed Tomography Laboratory at the University of Helsinki. The scanner consists of a molybdenum target X-ray tube (Oxford Instruments XTF5011), a motorized rotation stage (Thorlabs CR1-Z7), and a 12-bit, 2240x2368 pixel, energy-integrating flat panel detector (Hamatsu Photonics C7942CA-22).</p> <p><em>Scan Settings </em></p> <p>721 X-ray projections were acquired using an angle increment of 0.5 degrees. The X-ray source voltage and tube current were set at 50 kV and 1 mA, respectively. The exposure time of the flat panel detector was set to 1000 ms.</p> <p><em>Data Post-Processing</em></p> <p>Two correction images were acquired before scanning the sample. A dark current image was created by averaging 100 images taken with the X-ray source off. A flat-field image was created by averaging 100 images taken with the X-ray source switched on with no sample placed in the scanner. After the scan, dark current and flat-field corrections were applied to each projection image using the Hamamatsu HiPic imaging software version 9.3.</p> <p><em>Data Format</em></p> <p>The X-ray projections are stored in .tif format. The metadata is contained in .txt file with formatting that is both human-readable and machine-readable.</p> <p><em>Notes</em></p> <p>Due to a slightly misaligned center of rotation in the scanner, the CT reconstructions can appear blurry. It was empirically observed that this problem can be compensated for quite well by shifting each projection left by 4 pixels, using circular boundary conditions, before performing any other operations on the projections.</p> <p> </p> <p><strong>Research Group</strong></p> <p>This dataset was produced by the Inverse Problems research group at the Department of Mathematics and Statistics at the University of Helsinki, Finland: <a href="https://www2.helsinki.fi/en/researchgroups/inverse-problems">https://www2.helsinki.fi/en/researchgroups/inverse-problems</a>.</p> <p> </p> <p><strong>Additional Links</strong></p> <p>To get started with the data, we recommend looking at the HelTomo toolbox, specifically created for working with CBCT data collected in the Industrial Mathematics Computed Tomography Laboratory, and available at <a href="https://github.com/Diagonalizable/HelTomo">https://github.com/Diagonalizable/HelTomo</a></p> <p> </p> <p><strong>Contact Details</strong></p> <p>For more information or guidance in using these datasets, please contact alexander.meaney [at] helsinki.fi.</p>
Cone-Beam Computed Tomography Dataset of a Walnut
<p><strong>Summary</strong></p> <p>This dataset is a collection of X-ray projection images of a walnut imaged in a cone-beam computed tomography (CBCT) scanner. The dataset also includes a metadata file, specifying the scan geometry and other important scan parameters.</p> <p> </p> <p><strong>Description</strong></p> <p><em>Sample Information</em></p> <p>The sample is a walnut in its shell. For the scanning process double-sided tape was used to attach the sample to a plastic tube placed into the rotation stage.</p> <p><em>Scanner</em></p> <p>The measurements were acquired using a cone-beam computed tomography scanner designed and constructed in-house in the Industrial Mathematics Computed Tomography Laboratory at the University of Helsinki. The scanner consists of a molybdenum target X-ray tube (Oxford Instruments XTF5011), a motorized rotation stage (Thorlabs CR1-Z7), and a 12-bit, 2240x2368 pixel, energy-integrating flat panel detector (Hamatsu Photonics C7942CA-22).</p> <p><em>Scan Settings</em></p> <p>721 X-ray projections were acquired using an angle increment of 0.5 degrees. The X-ray source voltage and tube current were set at 40 kV and 1 mA, respectively. The exposure time of the flat panel detector was set to 1000 ms.</p> <p><em>Data Post-Processing</em></p> <p>Two correction images were acquired before scanning the sample. A dark current image was created by averaging 100 images taken with the X-ray source off. A flat-field image was created by averaging 100 images taken with the X-ray source switched on with no sample placed in the scanner. After the scan, dark current and flat-field corrections were applied to each projection image using the Hamamatsu HiPic imaging software version 9.3.</p> <p><em>Data Format</em></p> <p>The X-ray projections are stored in .tif format. The metadata is contained in .txt file with formatting that is both human-readable and machine-readable.</p> <p><em>Notes</em></p> <p>Due to a slightly misaligned center of rotation in the scanner, the CT reconstructions can appear blurry. It was empirically observed that this problem can be compensated for quite well by shifting each projection left by 5 pixels, using circular boundary conditions, before performing any other operations on the projections.</p> <p> </p> <p><strong>Research Group</strong></p> <p>This dataset was produced by the Inverse Problems research group at the Department of Mathematics and Statistics at the University of Helsinki, Finland: <a href="https://www2.helsinki.fi/en/researchgroups/inverse-problems">https://www2.helsinki.fi/en/researchgroups/inverse-problems</a>.</p> <p> </p> <p><strong>Additional Links</strong></p> <p>This dataset was originally created as part of a tutorial on working with measured X-ray data in computed tomography. A video tutorial on the measurement process can be found on the Inverse Problems Channel on YouTube at <a href="https://www.youtube.com/watch?v=CWUomAmUDys">https://www.youtube.com/watch?v=CWUomAmUDys</a>.</p> <p>To get started with the data, we recommend looking at the HelTomo toolbox, specifically created for working with CBCT data collected in the Industrial Mathematics Computed Tomography Laboratory, and available at <a href="https://github.com/Diagonalizable/HelTomo">https://github.com/Diagonalizable/HelTomo</a>.</p> <p>Please note that this is a an entirely separate dataset from the Walnut dataset accessible at <a href="https://zenodo.org/record/1254206">https://zenodo.org/record/1254206</a>, although both datasets have been created by the same research group.</p> <p> </p> <p><strong>Contact Details</strong></p> <p>For more information or guidance in using these datasets, please contact alexander.meaney [at] helsinki.fi.</p>
Cone-Beam Computed Tomography Dataset of a Pine Cone
<p><strong>Summary</strong></p> <p>This dataset is a collection of X-ray projection images of a pine cone imaged in a cone-beam computed tomography (CBCT) scanner. The dataset also includes a metadata file, specifying the scan geometry and other important scan parameters, and a photograph of the sample.</p> <p> </p> <p><strong>Description</strong></p> <p><em>Sample Information</em></p> <p>The sample is an open cone of a Baltic pine (<em>Pinus sylvestris</em>), approximately 3 cm in diameter. For the scanning process sticky tack was used to attach the sample to a plastic tube placed into the rotation stage.</p> <p><em>Scanner</em></p> <p>The measurements were acquired using a cone-beam computed tomography scanner designed and constructed in-house in the Industrial Mathematics Computed Tomography Laboratory at the University of Helsinki. The scanner consists of a molybdenum target X-ray tube (Oxford Instruments XTF5011), a motorized rotation stage (Thorlabs CR1-Z7), and a 12-bit, 2240x2368 pixel, energy-integrating flat panel detector (Hamatsu Photonics C7942CA-22).</p> <p><em>Scan Settings</em></p> <p>721 X-ray projections were acquired using an angle increment of 0.5 degrees. The X-ray source voltage and tube current were set at 40 kV and 1 mA, respectively. The exposure time of the flat panel detector was set to 1000 ms.</p> <p><em>Data Post-Processing</em></p> <p>Two correction images were acquired before scanning the sample. A dark current image was created by averaging 100 images taken with the X-ray source off. A flat-field image was created by averaging 100 images taken with the X-ray source switched on with no sample placed in the scanner. After the scan, dark current and flat-field corrections were applied to each projection image using the Hamamatsu HiPic imaging software version 9.3.</p> <p><em>Data Format</em></p> <p>The X-ray projections are stored in .tif format. The metadata is contained in .txt file with formatting that is both human-readable and machine-readable.</p> <p><em>Notes</em></p> <p>Due to a slightly misaligned center of rotation in the scanner, the CT reconstructions can appear blurry. It was empirically observed that this problem can be compensated for quite well by shifting each projection left by 5 pixels, using circular boundary conditions, before performing any other operations on the projections.</p> <p> </p> <p><strong>Research Group</strong></p> <p>This dataset was produced by the Inverse Problems research group at the Department of Mathematics and Statistics at the University of Helsinki, Finland: <a href="https://www2.helsinki.fi/en/researchgroups/inverse-problems">https://www2.helsinki.fi/en/researchgroups/inverse-problems</a>.</p> <p> </p> <p><strong>Additional Links</strong></p> <p>This dataset was originally created as part of a tutorial on working with measured X-ray data in computed tomography. A video tutorial on the measurement process can be found on the Inverse Problems Channel on YouTube at <a href="https://www.youtube.com/watch?v=CWUomAmUDys">https://www.youtube.com/watch?v=CWUomAmUDys</a>.</p> <p>To get started with the data, we recommend looking at the HelTomo toolbox, specifically created for working with CBCT data collected in the Industrial Mathematics Computed Tomography Laboratory, and available at <a href="https://github.com/Diagonalizable/HelTomo">https://github.com/Diagonalizable/HelTomo</a></p> <p> </p> <p><strong>Contact Details</strong></p> <p>For more information or guidance in using these datasets, please contact alexander.meaney [at] helsinki.fi.</p>
Data set: UAS-based optical- and thermal infrared remote sensing of the fumarole field of La Fossa cone, Vulcano Island (Italy), reveals the degassing and hydrothermal alteration structure
<p>This is the data set supporting the paper "Anatomy of a fumarole field; drone remote sensing and petrological approaches reveal the degassing and alteration structure at La Fossa cone, Vulcano Island, Italy" (DOI: <a href="https://doi.org/10.5194/egusphere-2023-1692" target="_blank" rel="noopener noreferrer">10.5194/egusphere-2023-1692</a>).</p> <p> </p> <p><strong>Short description of the study:</strong> Hydrothermal alteration is common on actively degassing volcanoes and can lead to significant changes in the physical and chemical properties of the volcanic rocks, such as changes in permeability or rock strength. Despite the potentially far-reaching consequences of hydrothermal alteration for volcano stability, less is known about the detailed structures and dynamics of degassing and alteration systems. In this study, we use UAS-derived high-resolution data to analyze the fumarole field at La Fossa cone, Vulcano Island (Italy), aiming to better understand the structures and dynamics of volcanic degassing and alteration systems. By combining Principal Component Analysis, image analysis, and classification applied to high-resolution optical data and analysis of thermal infrared data, we resolve the detailed structure of the surficial degassing and alteration system based on optical and thermal anomalies. We identified characteristic anomaly patterns that indicate local degassing and alteration variability, and larger units of diffuse activity that, next to high-temperature fumaroles, contribute significantly to the total activity. We compared the observed anomaly patterns with the mineralogical and geochemical composition of representative rock samples, and with the surface degassing activity, and are able to provide the anatomy of the La Fossa fumarole field at great resolution. We show local alteration gradients, the presence of larger diffuse active complexes, and evidence for dynamic processes associated with the hydrothermal alteration. For more details, please read on: "<em>Müller, D., Walter, T. R., Troll, V. R., Stammeier, J., Karlsson, A., De Paolo, E., ... & De Jarnatt, B. (2023). Anatomy of a fumarole field; drone remote sensing and petrological approaches reveal the degassing and alteration structure at La Fossa cone, Vulcano Island, Italy. EGUsphere, 2023, 1-45. </em> https://doi.org/10.5194/egusphere-2023-1692".</p> <p> </p> <p> </p> <p><strong>Data set:</strong> We provide a UAS-based high-resolution dataset covering the whole La Fossa cone, including aerial Orthomosaic, Digital Elevation Model, and a Temperature Map derived from an airborne optical- and thermal infrared sensor (acquired in 2018 and 2019). </p> <p>The dataset is organized in 1) photogrammetric data, and 2) relevant processing results and related data. <strong>Filenames</strong> are written in bold letters and are a composite of the file type and the date (YYYYMMDD). </p> <p> </p> <p> </p> <p><strong>1) Photogrammetric data: </strong></p> <ul> <li><strong>Orthomosaic_20191114.tif</strong> is the in Agisoft Metashape processed orthomosaic of a 150 m (above fumarole field) optical overflight (DJI Phantom 4 Pro camera). </li> <li><strong>DigitalElevationModel_20191114.tif</strong> is the in Agisoft Metashape processed Digital Elevation Model (DEM) from the above-mentioned 150 m overflight. </li> <li><strong>Hillshade_20191114.tif</strong> is the 2.5-D representation of the DigitalElevationModel_20191114. Note, for viewing use a stretched (black to white) color scale.</li> <li><strong>TemperatureMap_20181115.tif</strong> is showing the apparent surface temperature for the La Fossa cone, acquired by a Flir Tau 2 thermal infrared camera at ~150 m (above fumarole field) flight altitude in the early morning hours (before sunrise) of 15 November 2018. Note that apparent temperatures shown may underestimate real in situ fumarole temperatures due to pixel-to-vent size ratios and atmospheric- or gas-plume distortion effects. Note further that the data has some processing artifacts, due to blind pixels of our IR camera system. For more detailed information or an updated data set please contact dmueller@gfz-potsdam.de.</li> <li><strong>T_20to40C.tif</strong> shows the diffuse thermally active surface at the fumarole field of the La Fossa cone (units a-g, see Fig. 4 in "Anatomy of a fumarole field...", https://doi.org/10.5194/egusphere-2023-1692). This raster shows the extracted pixels from TemperatureMap_20181115 in the range of 22 - 40 °C.</li> <li><strong>T_higher40C.tif</strong> outlines the high-temperature fumarole locations of the La Fossa fumarole field (HTF, see Fig. 4 in "Anatomy of a fumarole field...", https://doi.org/10.5194/egusphere-2023-1692), based on the extracted pixels with temperatures > 40 °C from TemperatureMap_20181115.</li> </ul> <p>Shapefiles for temperatures > 40 °C representing the high-temperature fumarole locations (HTF) and for temperatures of 20 - 40 °C representing diffuse active units, are attached at the end of the upload list and named <strong>T_higher40C_polygon</strong> and <strong>T_20_40C_polygon</strong> and consist of multiple files per shapefile with the file extensions .CPG, .dbf, .prj, .sbn, .sbx, .shp, .shp.xml, .shx. </p> <p>The coordinate system of the data sets is WGS84 EPSG:4326. For nadir projection use WGS 84 / UTM zone 33N - EPSG:32633. Note that the data might have horizontal and vertical offsets in the typical range of SfM-derived products with single-band GPS accuracy.</p> <p> </p> <p> </p> <p><strong>2) Relevant processing steps and related data:</strong></p> <ul> <li>Step 1) Principal Component Analysis applied to Orthomosaic_20191114 results in the following 3 Principal Components (decorrelated variance representations of the initial RGB bands): <ul> <li><strong>1_PCA_PC1.tif </strong>1st principal component </li> <li><strong>1_PCA_PC2.tif</strong> 2nd principal component</li> <li><strong>1_PCA_PC3.tif</strong> 3rd principal component - highlights well the effects of concentrated and diffuse degassing, resulting in different alteration effects from a simple shift from reddish oxidized surface to gray, up to strong silicic alteration effects. This can be used to extract the data of interest, the hydrothermally altered surface, and to create a new alteration sub-dataset. </li> </ul> </li> <li>Step 2) Extraction of hydrothermally altered surface / alteration sub-dataset <ul> <li><strong>2_alteration_subdata_RGB.tif</strong> The alteration sub-data set was extracted from the original Orthomosaic_20191114 based on a mask obtained from Principal Component 3 (1_PCA_PC3) for values > 85. The resulting raster data set is an extract of the original RGB data.</li> </ul> </li> <li>Step 3) PCA applied to 2_alteration_subdata_RGB will adjust to the reduced spectral range of the alteration sub-data set, provide a more sensitive variance representation, and highlight variability within the hydrothermally altered surface. <ul> <li><strong>3_PCA_PC1.tif</strong> 1st principal component of 2_alteration_subdata_RGB</li> <li><strong>3_PCA_PC2.tif</strong> 2nd principal component of 2_alteration_subdata_RGB</li> <li><strong>3_PCA_PC3.tif</strong> 3rd principal component of 2_alteration_subdata_RGB</li> </ul> </li> <li>Step 4) Unsupervised classification <ul> <li><strong>4_classification.tif</strong> is the unsupervised classification result of 3_PCA (all Principal Components), classified into 32 classes to achieve a high class resolution. When combining different classes, they form larger spatial units / surface types with similar spectral characteristics. This way, we divide the alteration surface into 3 surface types (see Fig. 4B in "Anatomy of a fumarole field..." DOI: 10.5194/egusphere-2023-1692) representing different alteration gradients and important structural units. To achieve the same results, combine classes 1 -19 (surface type 3), 20 - 25 (surface type 2), 26 - 30 (surface type 1), and 31 - 32 for sulfur/fumarole plume. See Image <strong>optical_structure.jpg</strong> for comparison. </li> </ul> </li> </ul> <p>Note that Principal Components and Classification of Principal Components highlight data variability along the axes of highest data variance. Results have to be evaluated carefully and may be valid only locally. They are efficient for identifying variability in degassing and alteration areas, but at the same time may also highlight certain fractions of vegetation or settlements for instance. We evaluated the structure defined by our classification results by analyzing the thermal structure (<strong>thermal_structure.jpg</strong>) of the fumarole field and additional geochemical- and mineralogical investigations (XRD and XRF) of rock samples and by measuring the diffuse degassing from surface (see "Anatomy of a fumarole field..." DOI: 10.5194/egusphere-2023-1692) to prove that the observed degassing/alteration units are true.</p> <p>To highlight alteration effects throughout the entire La Fossa cone, including the southern inner and outer crater rim, the alteration zones of La Forgia, or alteration on the outer flanks of La Fossa e.g. the 1988 Landslide, we provide the raster <strong>La_Fossa_alteration.tif </strong>and image <strong>La_Fossa_alteration.jpg (</strong>Note that the color scale for strong alteration (classes 31 - 32) was changed from white to purple for highlighting purpose).</p> <p> </p> <p>In case of further questions about the dataset, please contact dmueller@gfz-potsdam.de.</p> <p> </p> <p> </p> <p> </p> <p> </p>
Tree regeneration after fire: Wickersham Dome long-term vegetation study, bl. spruce cone analysis
These data represent the most recent set of observations (made in 2002 by J. Johnstone) for several long-term vegetation monitoring plots near Wickersham Dome that were set up by Les Viereck and Joan Foote following the 1971 wildfire and 1978 experimental burns. Earlier records are available in the BNZ long-term vegetation database. This dataset documents tree seedling/sapling and shrub measurements made in 2002. Contains counts of total numbers of cones on individual trees (black spruce only), along with tree height.
Samples of individual trees (cones, age) within sites across an age since fire range of 6-338 years for 30 sites in northern Yukon and central Alaska
This dataset describes individual trees within site attributes including cohort of cones (2006-2009), whether cones are opened or closed, the number of cones and seeds and viability of each cohort and the measured age of two perpendicular sections at the base of tree. <br><br> **In Prep Puplication** Viglas, Jayme, Carissa Brown, and Jill Johnstone. Stand age effects on seed productivity of north
Cone Pond Watershed: Soil Profiles (Pedons), 1988-2023
Cone Pond Watershed: Soil Profiles (Pedons), 1988-2023 This dataset documents pedons (soil profiles) sampled at Cone Pond Watershed, Thornton, New Hampshire. Since the early 1980’s, Cone Pond, on the Pemigewasset District of the White Mountain National Forest, has been an active research satellite site to the nearby Hubbard Brook Experimental Forest. In 1988, intensive monitoring began as part of a comprehensive watershed scale ecosystem study. A weir was built on the main inlet stream, just above its mouth at Cone Pond, to monitor streamflow. Two rain gages were installed to monitor atmospheric deposition and multiple studies of major ecosystem components were initiated. Soil profiles documented in this dataset were sampled between 1988 and 2003. All descriptive profile and horizon data as well as chemical analyses of samples of genetic horizons are included in this dataset. Samples from 123 of these horizons have been accessioned into the Hubbard Brook physical sample archive; the archived mass of each is included in the horizon table. In addition to these pedons sampled in detail, a number of reconnaissance observations, made at a lower level of detail, and without sampling, were made in 2023 in order to validate a hydropedologic soil model created at Hubbard Brook. These data are included in a separate table. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Figure 5. Pupal cone curvature differences between Colorado A in A review of three species-level taxa of the Anthocharis sara complex (Lepidoptera: Pieridae: Pierinae: Anthocharidini)
Figure 5. Pupal cone curvature differences between Colorado A. julia prestonorum (Garfield County) and A. thoosa colorado (Montezuma County).
Fig. 6. A. Conus exiguus var. optimus Sowerby, 1913, 35.3 in Conus hughmorrisoni, a new species of cone snail from New Ireland, Papua New Guinea (Gastropoda: Conidae)
Fig. 6. A. Conus exiguus var. optimus Sowerby, 1913, 35.3 mm. Point Parme, New Caledonia. FL. B. Conus exiguus var. bougei Sowerby, 1907, 20.3 mm. Poum, N. New Caledonia. FL. C. Conus exiguus var. cabritii Bernardi, 1858, 22.0 mm. Northern New Caledonia. FL. D. Conus sp. cf. exiguus, 18.0 mm. Apia, Western Samoa. From Röckel et al. (1995), pl. 72, figs 14–15. E–F. Conus hanshassi (Lorenz & Barbier, 2012). E. 23.4 mm. Siargao Is., Philippines. Holotype, MNHN-IM-2000-24814. F. 22.9 mm. Siargao Is., Philippines. Paratype 1, FL.
Fig. 3 in Conus hughmorrisoni, a new species of cone snail from New Ireland, Papua New Guinea (Gastropoda: Conidae)
Fig. 3. Conus hughmorrisoni sp. nov. A–B. Paratype 2, 12.55 mm. C. Operculum of paratype 5. D–F. Radula of paratype 3.
Fig. 2 in Conus hughmorrisoni, a new species of cone snail from New Ireland, Papua New Guinea (Gastropoda: Conidae)
Fig. 2. Conus hughmorrisoni sp. nov. A. Holotype, 20.4 mm. B–C. Paratype 1, 13.25 mm. B. Teleoconch. C. Protoconch (scale bar = 0.5 mm). D. Paratype 2, 12.55 mm. E. Paratype 3, 16.5 mm. F–G. Paratype 4, 10.8 mm. F. Teleoconch. G. Top view. H. Paratype 5, 14.15 mm. All pictures by Manuel Caballer Gutierrez (credits project E-Recolnat, MNHN).
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.