Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
4,107
datasets available to search
ShareScore release 0.9.0
Dataset results
4,107 results for “pointing”
Vertically pointing doppler radar profiles (24 GHz Metek MRR-2) at Mario Zucchelli Station (Terra Nova Bay, Antarctica), aggregated to 5min, monthly netCDF archive
<p>Vertical profiles along the first kilometre of atmosphere above the ground (from 105 to 1050 m AGL) of equivalent radar reflectivity factor (Ze), Doppler velocity (W) and Doppler spectral width (Sw) from a 24-GHz vertically pointing Micro Rain Radar MRR-2 by METEK GmbH positioned at Mario Zucchelli Station (Terra Nova Bay, Antarctica).</p> <p>Metadata available at <a href="https://antarcticdatacenter.cnr.it/geonetwork/srv/api/records/6fe32f1f-247e-493d-9cd3-88714e5b38ef" target="_blank" rel="noopener">https://antarcticdatacenter.cnr.it/geonetwork/srv/api/records/6fe32f1f-247e-493d-9cd3-88714e5b38ef</a></p> <p>--------------------------------------------------------------------</p> <p>Example of netCDF file structure: </p> <h2>File "MZS_MRR_MeK_201912_5min.nc"</h2> <pre><strong> dimensions</strong>: <em>range </em>= 31; <em>time </em>= UNLIMITED; // (8928 currently) <strong>variables</strong>: float <em>Ze</em>(range=31, time=8928); :description = "Equivalent reflectivity factor relative to the most significant peak, dealiased, 5min non-logarithmic average. NaN means clear sky at the specified height."; :units = "dBZ"; :_ChunkSizes = 31U, 1U; // uint long <em>time_UTC</em>(time=8928); :description = "Measurement time. Timestamp indicates the end of the aggregation interval, e.g. 01-Mar-2020 00:05:00 represents the average of the variables between 01-Mar-2020 00:00:01 and 01-Mar-2020 00:05:00."; :time_zone = "UTC"; :units = "Seconds since 1970-01-01 00:00:00 (Unix time)."; :_ChunkSizes = 512U; // uint float <em>W</em>(range=31, time=8928); :description = "Mean Doppler Velocity of the most significant peak, dealiased, 5min average. Value not available in clear-sky conditions."; :units = "m s^-1"; :_ChunkSizes = 31U, 1U; // uint float <em>height</em>(range=31, time=8928); :description = "Height above instrument."; :units = "m"; :_ChunkSizes = 31U, 1U; // uint float <em>spectralWidth</em>(range=31, time=8928); :description = "Doppler Spectral Width of the most significant peak, dealiased, 5min average. Value not available in clear-sky conditions."; :units = "m s^-1"; :_ChunkSizes = 31U, 1U; // uint // <strong>global attributes</strong>: :<em>title </em>= "Micro rain radar data processed with IMProToo (Maahn, M. and Kollias, P., 2012), aggregated to 5min, monthly netCDF archive."; :<em>comment </em>= "IMProToo has been developed for improved snow measurements. Note that this data has been processed regardless of precipitation type."; :<em>time_label </em>= "Dec 2019"; :<em>source </em>= "Micro Rain Radar 2 (MRR-2), METEK GmbH, at MZS (Antarctica), frequency: 24 GHz, power: 50 mW, antenna diameter: 60 cm [https://metek.de/product/mrr-2/]"; :<em>institution </em>= "CNR-ISAC, Rome (IT)"; :<em>contact_person </em>= "Luca Baldini, CNR-ISAC, Rome (IT), l.baldini@isac.cnr.it"; :<em>location </em>= "Mario Zucchelli Station (Terra Nova Bay, Antarctica, 74°42\'S, 164°07\'E, 15 m a.s.l.)"; :<em>author </em>= "Giacomo Roversi, Ca\' Foscari University, Venice (IT) and CNR-ISAC, Rome (IT), g.roversi@isac.cnr.it"; :<em>creation_date </em>= "22-Oct-2024 17:40:46 UTC"; :<em>coverage </em>= "Monthly coverage (Dec 2019): 100 %"; :<em>time_resolution </em>= "5 minutes"; :<em>history </em>= "Created with IMProToo v0.107 [https://github.com/maahn/IMProToo], aggregated to 5 minutes temporal resolution with an average of the 1-minute values if least 3 out of 5 are not NaN."; </pre>
Conf example 48^3 x 12 T=230 MeV fullQCD 2+1 at physical point
<p>Full QCD</p> <p>2+1 rooted stout staggered fermions at physical point</p> <p>V = 48^3 x 12</p> <p>a = 0.0715 fm</p> <p>Gauge configuration saved after 5460 RHMC step (run 1) in the presence of bias multicanonic topotential.</p>
Raw Data and PCA Filtering of Apache Point Observatory NMSU 1m StellaCam Observations of LCROSS
<p>This archive contains the raw data and data products from observations of the 2009-10-09 impact of the Lunar CRater Observation and Sensing Satellite (LCROSS) spacecraft on the Moon by the StellaCam instrument on the Apache Point Observatory NMSU 1m telescope.</p> <p>Full details about the raw data are available in Chanover, N. J. et al. Results from the NMSU-NASA Marshall Space Flight Center LCROSS observational campaign. <em>J. Geophys. Res. (Planets)</em> <strong>116</strong>, E08003 (2011). <a href="https://doi.org/10.1029/2010JE003761">https://doi.org/10.1029/2010JE003761</a></p> <p>We use principal component analysis (PCA) filtering both to coregister the raw time series and to effectively remove a static background signal that is spatially and temporally modified by atmospheric and instrumental effects. We iteratively remove principal components from the data through cumulative sequential elimination (CSE) resulting in a non-detection of the LCROSS ejecta plume signal.</p> <p>Full details are available in the published journal article:</p> <p>Strycker, Paul D., Nancy J. Chanover, Ruth L. Temme, Jonathan M. Schotte, Payton L. Mueller, and Emily L. Karls. 2023. "Time Series Analysis Methods and Detectability Factors for Ground-Based Imaging of the LCROSS Impact Plume" <em>Remote Sensing</em> <strong>15</strong>, no. 1: 37. <a href="https://doi.org/10.3390/rs15010037">https://doi.org/10.3390/rs15010037</a></p> <p>This work was supported by NASA’s Lunar Data Analysis Program through grant number NNX15AP92G.</p>
PCA Filtering of Apache Point Observatory 3.5m Agile Observations of LCROSS
<p>This archive contains data products from observations of the 2009-10-09 impact of the Lunar CRater Observation and Sensing Satellite (LCROSS) spacecraft on the Moon by the Agile instrument on the Apache Point Observatory 3.5m telescope. We use principal component analysis (PCA) filtering both to improve the coregistration of the raw time series and to effectively remove a static background signal that is spatially and temporally modified by atmospheric and instrumental effects. We iteratively remove principal components from the data through cumulative sequential elimination (CSE) to find a maximum signal-to-noise ratio of the LCROSS ejecta plume signal.</p> <p>Full details are available in the published journal article:</p> <p>Strycker, Paul D., Nancy J. Chanover, Ruth L. Temme, Jonathan M. Schotte, Payton L. Mueller, and Emily L. Karls. 2023. "Time Series Analysis Methods and Detectability Factors for Ground-Based Imaging of the LCROSS Impact Plume" <em>Remote Sensing</em> <strong>15</strong>, no. 1: 37. <a href="https://doi.org/10.3390/rs15010037">https://doi.org/10.3390/rs15010037</a></p> <p>This work was supported by NASA’s Lunar Data Analysis Program through grant number NNX15AP92G.</p>
UAV RGB imagery dataset captured at nadir and oblique angles over pistachio trees in Spain, including images, GCPs, 3D point cloud and orthomosaic.
<p>The dataset comprises 248 images taken in two flights on 29 July 2021 over a pistachio orchard in Spain. In addition, GCPs (ground control points) were collected to improve the photogrammetric process accuracy. The photos were taken using a UAV DJI Phantom Advance quadcopter equipped with a DJI FC6310 RGB 20-megapixel camera. The first flight mission was planned to take nadir images (-90º gimbal pitch degree), whereas the second flight was scheduled to take oblique images (-60º gimbal pitch degree), both at 55 metres above the ground. In addition, the images were used to generate a 3D point cloud, DEM and orthomosaic, which were included in the dataset.This dataset is useful for precision agriculture researchers interested in photogrammetric reconstruction.</p>
Health Record Hiccups - 5526 real-world time series with change points labelled by crowd-sourced visual inspection
<p>5526 real-world time series with labels for the location of all abrupt changes in level, variability, trend, presence/absence of data points, and irregular outliers. The time series were produced from a range of electronic health record data extracts from a large UK hospital group. Values in each data field were aggregated by day/week/month, and numeric summary values calculated for each timepoint from the (often non-numeric) data by applying simple functions (e.g. number of values present, percentage of missing values, number of distinct values, median value). Labels were produced by visual inspection of time series plots from ~2000 volunteers, via the Health Record Hiccups project on the Zooniverse platform (https://www.zooniverse.org/projects/phuongquan/health-record-hiccups). Volunteers drew a vertical line on the image wherever they saw a change point (green line if they were certain, yellow line if they were unsure). Consensus labels per image were calculated using density based clustering with noise (R v3.6.3, dbscan v1.1-5), and converted back to a date.</p>
Puma concolor occurrence points (filtered data)
<p>Puma concolor occurrence points (duplicates removed) in Canada until December 2021. Used in Maxent habitat suitability model (performed in R).</p>
Supporting biophysical data for "The ecosystem wilting point defines drought response and recovery of a Quercus-Carya forest"
<p>The data in this product support analyses in the paper titled "The ecosystem wilting point defines drought response and recovery of a Quercus-Carya forest".</p> <p>The data in this submission include leaf area index (LAI_MOFLUX_2008_2009_2012.csv) and soil water retention curves (MOFLUX_SWRC_data.csv).</p> <p><strong>Leaf area index</strong></p> <p>Each data record in LAI_MOFLUX_2008_2009_2012.csv consists of transect level mean single-sided leaf area index (LAI) values for 5 transects measured during the growing seasons of years 2008, 2009, and 2012. Records are uniquely identified by Year and DOY (day of year). Data were collected using a plant canopy analyzer (model LAI-2000). Data columns are defined by headers. Missing data are represented by values of -9999.</p> <p>Table 1. Header definitions of LAI_MOFLUX_2008_2009_2012.csv data file.</p> <table> <tbody> <tr> <td>Column</td> <td>Header name</td> <td>Definition</td> </tr> <tr> <td>1</td> <td>Year</td> <td>Year of observation</td> </tr> <tr> <td>2</td> <td>DOY</td> <td>Day of year of observation</td> </tr> <tr> <td>3</td> <td>LAI_SE</td> <td>Mean LAI of southeast transect</td> </tr> <tr> <td>4</td> <td>LAI_S</td> <td>Mean LAI of south transect</td> </tr> <tr> <td>5</td> <td>LAI_SW</td> <td>Mean LAI of southwest transect</td> </tr> <tr> <td>6</td> <td>LAI_W</td> <td>Mean LAI of west transect</td> </tr> <tr> <td>7</td> <td>LAI_NW</td> <td>Mean LAI of northwest transect</td> </tr> </tbody> </table> <p><strong>Soil water retention curves</strong></p> <p>Soil water retention curves for two soil depth ranges (0-30 cm and >30 cm) are reported in MOFLUX_SWRC_data.csv. Soil samples were collected near the MOFLUX tower. Samples were evaluated periodically for soil water potential using a dewpoint potentiometer (Decagon Devices, Model WP4C) and a pressure plate as they dried over time.</p> <p>There are 4 columns of data in MOFLUX_SWRC_data.csv. The first two columns contain data for the 0-30 cm depth range, and the second columns contain data for the >30 cm depth range. Each curve is defined by volumetric water content (VWC, %) and soil water potential (SWP, MPa). Missing data are represented by values of -9999.</p> <p> </p>
IODP Expedition 350 Magnetic susceptibility (point or contact system)
<p>Magnetic susceptibility was measured on section halves on the Section Half Multisensor Logger (SHMSL) using a Bartington MS2 meter and either a MS2E or MS2K probe. Because all JRSO cores meet minimum size requirements for these two probes, MSPOINT data are corrected for volume and recorded in SI susceptibility units (x10<sup>-5</sup>).</p>
IODP Expedition 376 Magnetic susceptibility (point or contact system)
<p>Magnetic susceptibility was measured on section halves on the Section Half Multisensor Logger (SHMSL) using a Bartington MS2 meter and either a MS2E or MS2K probe. Because all JRSO cores meet minimum size requirements for these two probes, MSPOINT data are corrected for volume and recorded in SI susceptibility units (x10<sup>-5</sup>).</p>
Water Supply Tipping Point Data and Code
<p>This publication provides code, WaterSupplyTippingPoints.R, to calculate tipping points in water supply by using a time series of deficit data. The code uses data files that are produced from an agent-based model that simulates water supply dynamics. The code uses deficit data to calculate a water supply performance metric, Sustainability Index, and implements a decision tree change point detection method detects change points. The code implements a tipping point rule that is applied to select a change point as a tipping point. Data files of deficits that are used as input are described and published at https://zenodo.org/record/3971213.</p> <p>The code can be executed for four management scenarios and 16 shifting climate scenarios. Management Strategy 1 is no management. Management Strategy 2 is retrofitting. Management Strategy 3 is retrofitting with reactive drought restrictions. Management Strategy 4 is retrofitting with proactive drought restrictions. The 16 shifting climate scenarios correspond to shifting factors equal to 0.25 - 1.0, increasing in 0.05 increments. For each of the 64 modeling scenarios (16 climate settings X 4 management strategies), 100 simulations were run to capture stochasticity.</p> <p>The output from the code are provided in a set of .csv files, attached here. Data files (TippingPoints-mgmt1.csv, TippingPoints-mgmt2.csv, TippingPoints-mgmt3.csv, TippingPoints-mgmt4.csv) list the 100 tipping points that were identified for each shifting factor and each management strategy. Columns (V1, V2, ... V100) are 100 unique and randomized simulations for each modeling scenario. Rows correspond to shifting factors, where Row 1 is shifting factor 0.25, and Row 16 is shifting factor 1.00.</p>
Text-fig. 49. Scanning electron microscope (SEM, a, b) and synchrotron radiation X-ray tomographic microscopy (SRXTM, c–e) images of "Foveolate seed sp. 1"; Catefica locality, Portugal. a) Lateral view of seed showing foveolate surface; note the slightly pointed hilar-micropylar region with the preservation of a presumed secretion (arrow) from the micropyle; b) Detail of seed surface showing shallow pitting and very faint outlines of the undulate anticlinal walls of the exotestal cells; c) Longitudinal section (volume rendering cut between orthoslices yz0450 and yz0460) through the middle of the seed showing the slightly pointed hilarmicropylar region and the rounded chalazal region; note the thick exotesta of the outer integument composed of thick-walled palisade-like cells (oi); note partial preservation of large cells of the nutritive tissue (nu) and the smaller cells of the embryo (em) at the micropylar end of the seed; d, e) Longitudinal sections (d, orthoslice xz0750, e, orthoslice yz0485) through middle of the seed perpendicular to each other showing the hilar-micropylar region with the preservation of a presumed secretion from micropyle (e, arrow), thick palisade-like cells of outer integument (oi), larger cells of the nutritive tissue (nu) and remains of the smaller cells of the embryo (em). Specimen, Catefica 49-S172316 (a–e). Scale bars = 300 Μm (a, c–e), 50 Μm (b). in The Early Cretaceous Mesofossil Flora Of Catefica, Portugal: Angiosperms
Text-fig. 49. Scanning electron microscope (SEM, a, b) and synchrotron radiation X-ray tomographic microscopy (SRXTM, c–e) images of "Foveolate seed sp. 1"; Catefica locality, Portugal. a) Lateral view of seed showing foveolate surface; note the slightly pointed hilar-micropylar region with the preservation of a presumed secretion (arrow) from the micropyle; b) Detail of seed surface showing shallow pitting and very faint outlines of the undulate anticlinal walls of the exotestal cells; c) Longitudinal section (volume rendering cut between orthoslices yz0450 and yz0460) through the middle of the seed showing the slightly pointed hilarmicropylar region and the rounded chalazal region; note the thick exotesta of the outer integument composed of thick-walled palisade-like cells (oi); note partial preservation of large cells of the nutritive tissue (nu) and the smaller cells of the embryo (em) at the micropylar end of the seed; d, e) Longitudinal sections (d, orthoslice xz0750, e, orthoslice yz0485) through middle of the seed perpendicular to each other showing the hilar-micropylar region with the preservation of a presumed secretion from micropyle (e, arrow), thick palisade-like cells of outer integument (oi), larger cells of the nutritive tissue (nu) and remains of the smaller cells of the embryo (em). Specimen, Catefica 49-S172316 (a–e). Scale bars = 300 Μm (a, c–e), 50 Μm (b).
Text-fig. 5. Scanning electron microscope images of the seed of Canrightia sp.; Catefica locality, Portugal. a) Seed in ventral view showing the slightly protruding chalaza (ch) at the apex, pointed micropyle (mi) at the base and pitted surface of the endotesta; b) Detail of endotesta surface showing larger pits in longitudinal rows and the finer pits created by crystals in the cells of the endotesta. Specimen, Catefica MM158-P0289 (a, b). Scale bars = 300 Μm (a), 100 Μm (b). in The Early Cretaceous Mesofossil Flora Of Catefica, Portugal: Angiosperms
Text-fig. 5. Scanning electron microscope images of the seed of Canrightia sp.; Catefica locality, Portugal. a) Seed in ventral view showing the slightly protruding chalaza (ch) at the apex, pointed micropyle (mi) at the base and pitted surface of the endotesta; b) Detail of endotesta surface showing larger pits in longitudinal rows and the finer pits created by crystals in the cells of the endotesta. Specimen, Catefica MM158-P0289 (a, b). Scale bars = 300 Μm (a), 100 Μm (b).
Text-fig. 2. Scanning electron microscope (SEM, a, c–f) and synchrotron radiation X-ray tomographic microscopy (SRXTM, b, g) images of the flower of Mugideiriflora portugallica (a, b) and fruits, seeds and pollen of Canrightia resinifera (c–g); Catefica locality, Portugal. a) Oblique, apical view of flower showing multiparted organization with numerous laminar tepals, stamens that are rhomboidal and flattened in transverse section and carpels that are borne on the short conical apex of the receptacle; b) Transverse section (orthoslice xy0800) through basal part of flower showing the elongate bases of the laminar tepals and the flattened rhomboidal bases of the stamens; c) Fruit in lateral view showing irregular surface resulting from the abundant resin bodies in the fruit and hypanthium wall, scars from stamens on the rim of the hypanthium (arrowheads) and the lobed apical stigmatic region (st); d) Broken fruit with one or two seeds missing but showing three pendant, orthotropous seeds with pointed micropylar regions (mi) and a finely pitted crystalliferous endotesta; note the remains of the apical vascular bundles (vb); e) Single seed isolated from a fruit showing two distinct bundles (vb) still attached apically to the chalazal region of the seed, the pointed micropyle (mi) and the finely pitted surface of the crystalliferous endotesta; f) Monocolpate pollen from stigmatic region of fruit showing the long colpus and coarse reticulum; g) Transverse section (orthoslice xy0705) through a fruit showing four seeds all with radially elongated endothelium cells formed from the inner epidermis of the tegmen (asterisks). Specimens, Catefica 150-S174254 (a, b), Catefica 49-S170377 (c), Catefica 49-S170372 (d), Catefica 50-S170401 (e), Catefica 50-S170404 (f), Catefica 50-S174906 (g). Scale bars = 300 Μm (a–e, g), 6 Μm (f). in The Early Cretaceous Mesofossil Flora Of Catefica, Portugal: Angiosperms
Text-fig. 2. Scanning electron microscope (SEM, a, c–f) and synchrotron radiation X-ray tomographic microscopy (SRXTM, b, g) images of the flower of Mugideiriflora portugallica (a, b) and fruits, seeds and pollen of Canrightia resinifera (c–g); Catefica locality, Portugal. a) Oblique, apical view of flower showing multiparted organization with numerous laminar tepals, stamens that are rhomboidal and flattened in transverse section and carpels that are borne on the short conical apex of the receptacle; b) Transverse section (orthoslice xy0800) through basal part of flower showing the elongate bases of the laminar tepals and the flattened rhomboidal bases of the stamens; c) Fruit in lateral view showing irregular surface resulting from the abundant resin bodies in the fruit and hypanthium wall, scars from stamens on the rim of the hypanthium (arrowheads) and the lobed apical stigmatic region (st); d) Broken fruit with one or two seeds missing but showing three pendant, orthotropous seeds with pointed micropylar regions (mi) and a finely pitted crystalliferous endotesta; note the remains of the apical vascular bundles (vb); e) Single seed isolated from a fruit showing two distinct bundles (vb) still attached apically to the chalazal region of the seed, the pointed micropyle (mi) and the finely pitted surface of the crystalliferous endotesta; f) Monocolpate pollen from stigmatic region of fruit showing the long colpus and coarse reticulum; g) Transverse section (orthoslice xy0705) through a fruit showing four seeds all with radially elongated endothelium cells formed from the inner epidermis of the tegmen (asterisks). Specimens, Catefica 150-S174254 (a, b), Catefica 49-S170377 (c), Catefica 49-S170372 (d), Catefica 50-S170401 (e), Catefica 50-S170404 (f), Catefica 50-S174906 (g). Scale bars = 300 Μm (a–e, g), 6 Μm (f).
Text-fig. 19. Synchrotron radiation X-ray tomographic microscopy (SRXTM, a–c) of Aristospermum huberi and scanning electron microscope (SEM, d, e) images of Choffaticarpus compactus; Catefica locality, Portugal. a) Volume rendering of strongly flattened, triangular seed with pointed micropylar region; note thin-walled cells of outer integument preserved along the margins of the seed and pitted surface of the crystalliferous inner cells of outer integument where the outer cells are abraded and the narrow, lateral funicle/raphe; b) Volume rendering of seed showing surface of inner integument (endotesta) with cells showing clear imprints of crystals (arrows); c) Longitudinal section (orthoslice yz0241) of seed showing crystalliferous cells of endotesta (white arrows) and the two fiber layers of the tegmen that are perpendicular to each other (inner integument, ii-f, black arrows); d) Fragment of multiparted, apocarpous fruiting structure showing several helically-arranged, laterally flattened, fruitlets; e) Fruitlet in lateral view showing the prominent ventral face with its lateral groove, short attachment scar, and sunken regions of the fruit wall that indicate the probable presence of oil cells. Specimens, Catefica 49-S266049 (a–c), Catefica 49-S172558 (d), Catefica 49-S118675 (e). Scale bars = 300 Μm (a, c–e), 100 Μm (b). in The Early Cretaceous Mesofossil Flora Of Catefica, Portugal: Angiosperms
Text-fig. 19. Synchrotron radiation X-ray tomographic microscopy (SRXTM, a–c) of Aristospermum huberi and scanning electron microscope (SEM, d, e) images of Choffaticarpus compactus; Catefica locality, Portugal. a) Volume rendering of strongly flattened, triangular seed with pointed micropylar region; note thin-walled cells of outer integument preserved along the margins of the seed and pitted surface of the crystalliferous inner cells of outer integument where the outer cells are abraded and the narrow, lateral funicle/raphe; b) Volume rendering of seed showing surface of inner integument (endotesta) with cells showing clear imprints of crystals (arrows); c) Longitudinal section (orthoslice yz0241) of seed showing crystalliferous cells of endotesta (white arrows) and the two fiber layers of the tegmen that are perpendicular to each other (inner integument, ii-f, black arrows); d) Fragment of multiparted, apocarpous fruiting structure showing several helically-arranged, laterally flattened, fruitlets; e) Fruitlet in lateral view showing the prominent ventral face with its lateral groove, short attachment scar, and sunken regions of the fruit wall that indicate the probable presence of oil cells. Specimens, Catefica 49-S266049 (a–c), Catefica 49-S172558 (d), Catefica 49-S118675 (e). Scale bars = 300 Μm (a, c–e), 100 Μm (b).
Text-fig. 29. Scanning electron microscope (SEM) images of stamens and pollen grains of cf. Endressistemon sp. 1 (a, b), cf. Endressistemon sp. 2 (c, d) and cf. Endressistemon sp. 3 (e–g); Catefica locality, Portugal. a) Two adhering stamens, each with a long, pointed extension of the connective; b) Monocolpate, reticulate pollen in situ in stamen from stamen pair in (a); c) Fragment of stamen with prominent apical extension of the connective; d) Reticulate, apparently monocolpate, pollen in situ in stamen fragment in (c); e) Stamen with basifixed anther, perhaps sessile, and with prominent, wing-like apical extensions of the connective; note the dehisced thecae with the anther wall curved back; f, g) Monocolpate, reticulate pollen in situ in stamen in (e). Specimens, Catefica 49-S107780 (a, b), Catefica 49-S107784 (c, d), Catefica 49-S107781 (e–g). Scale bars = 600 Μm (a, c, e), 6 Μm (b, d, f, g). in The Early Cretaceous Mesofossil Flora Of Catefica, Portugal: Angiosperms
Text-fig. 29. Scanning electron microscope (SEM) images of stamens and pollen grains of cf. Endressistemon sp. 1 (a, b), cf. Endressistemon sp. 2 (c, d) and cf. Endressistemon sp. 3 (e–g); Catefica locality, Portugal. a) Two adhering stamens, each with a long, pointed extension of the connective; b) Monocolpate, reticulate pollen in situ in stamen from stamen pair in (a); c) Fragment of stamen with prominent apical extension of the connective; d) Reticulate, apparently monocolpate, pollen in situ in stamen fragment in (c); e) Stamen with basifixed anther, perhaps sessile, and with prominent, wing-like apical extensions of the connective; note the dehisced thecae with the anther wall curved back; f, g) Monocolpate, reticulate pollen in situ in stamen in (e). Specimens, Catefica 49-S107780 (a, b), Catefica 49-S107784 (c, d), Catefica 49-S107781 (e–g). Scale bars = 600 Μm (a, c, e), 6 Μm (b, d, f, g).
Data used in the paper (Two-point optical manipulation reveals mechanosensitive remodeling of cell-cell contacts in vivo)
<p>Data used in the paper (Two-point optical manipulation reveals mechanosensitive remodeling of cell-cell contacts in vivo)</p>
Monthly nitrogen point-source loading dataset for the Long Island Sound Watershed
<p>The attached dataset and code accompanies a Data in Brief article, "Monthly nitrogen point-source loading dataset for the Long Island Sound Watershed."</p> <p>Abstract: Quantifying point source nitrogen loads to the Long Island Sound watershed is important because they contribute to the annual occurrence of hypoxia in Long Island Sound. However, the data are not easily accessible and are not available at a central location, making it difficult to characterize the magnitude of loading, seasonal patterns and long-term trends. For the period between April 1989 and September 2021, we gathered all available monthly nitrogen data for wastewater treatment plants within the Long Island Sound watershed from the U.S. Environmental Protection Agency Integrated Compliance Information System-National Pollutant Discharge Elimination System (ICIS-NPDES) and Permit Compliance System, and Connecticut Department of Energy and Environmental Protection databases. Data were checked for quality assurance and unreasonable outliers were imputed with means of other observations within the same year and season. Estimates were also imputed for missing observations. We publish both raw data with missing observations and a curated dataset with imputed estimates for missing observations. Monthly point source nitrogen load data can be used for water quality modeling and to infer, by subtraction, the relative contribution of nonpoint sources to gauged watershed loads. Additionally, the scripts used to reformat data from the ICIS could be repurposed to collect similar data in other regions.</p>
Data from: Time series of bird abundances, land cover and temperature from standardized breeding bird monitoring schemes (line transects and point count routes) from Norway, Sweden and Finland, for 1975-2016
<p><span>These data on bird species abundance and environmental variables were used in testing and comparing two different species distribution model validation methods that are applied to models which are used to predict the effects of climate change on species' distributions. The aim of the study was to investigate whether different validation methods give different results of the model's predictive performance and to demonstrate that validation methods based on measuring and validating a "static" pattern in distribution can assess model performance over-optimistically compared to methods based on measuring and validating a "change" in the distribution, which can assess the predictive performance more critically. </span></p>
Point sample database with spatial holdbacks for global forest edge model training
<p>Global point dataset sampling 50 biophysical parameters to train the global forest carbon edge model at https://github.com/springinnovate/carbon_edge_model/releases/tag/1.2.0</p> <p>Field schema:</p> <p>accessibility_to_cities_2015_30sec_compressed (Integer64)<br> altitude_10sec_compressed (Integer64)<br> baccini_carbon_data_2014_compressed (Integer64)<br> bio_01_30sec_compressed (Real)<br> bio_02_30sec_compressed (Real)<br> bio_03_30sec_compressed (Real)<br> bio_04_30sec_compressed (Real)<br> bio_05_30sec_compressed (Real)<br> bio_06_30sec_compressed (Real)<br> bio_07_30sec_compressed (Real)<br> bio_08_30sec_compressed (Real)<br> bio_09_30sec_compressed (Real)<br> bio_10_30sec_compressed (Real)<br> bio_11_30sec_compressed (Real)<br> bio_12_30sec_compressed (Real)<br> bio_13_30sec_compressed (Real)<br> bio_14_30sec_compressed (Real)<br> bio_15_30sec_compressed (Real)<br> bio_16_30sec_compressed (Real)<br> bio_17_30sec_compressed (Real)<br> bio_18_30sec_compressed (Real)<br> bio_19_30sec_compressed (Real)<br> cec_0-5cm_mean_compressed (Integer64)<br> cec_5-15cm_mean_compressed (Integer64)<br> cfvo_0-5cm_mean_compressed (Integer64)<br> cfvo_5-15cm_mean_compressed (Integer64)<br> clay_0-5cm_mean_compressed (Integer64)<br> clay_5-15cm_mean_compressed (Integer64)<br> fc_stack_hansen_forest_cover2014_compressed (Integer64)<br> gf_0.4_masked_forest_ESACCI-LC-L4-LCCS-Map-300m-P1Y-2014-v2.0.7 (Real)<br> gf_1.45_masked_forest_ESACCI-LC-L4-LCCS-Map-300m-P1Y-2014-v2.0.7 (Real)<br> gf_5.0_fc_stack_hansen_forest_cover2014_compressed (Real)<br> gf_5.0_masked_forest_ESACCI-LC-L4-LCCS-Map-300m-P1Y-2014-v2.0.7 (Real)<br> hillshade_10sec_compressed (Integer64)<br> masked_forest_ESACCI-LC-L4-LCCS-Map-300m-P1Y-2014-v2.0.7 (Integer64)<br> night_lights_10sec_compressed (Real)<br> night_lights_5min_compressed (Real)<br> nitrogen_0-5cm_mean_compressed (Integer64)<br> nitrogen_10sec_compressed (Integer64)<br> nitrogen_5-15cm_mean_compressed (Integer64)<br> phh2o_0-5cm_mean_compressed (Integer64)<br> phh2o_5-15cm_mean_compressed (Integer64)<br> sand_0-5cm_mean_compressed (Integer64)<br> silt_0-5cm_mean_compressed (Integer64)<br> silt_5-15cm_mean_compressed (Integer64)<br> slope_10sec_compressed (Real)<br> soc_0-5cm_mean_compressed (Integer64)<br> soc_5-15cm_mean_compressed (Integer64)<br> tri_10sec_compressed (Real)<br> wind_speed_10sec_compressed (Real)</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.