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FIGURE 1. Hierochloe sect. Monoecia micromorphology details. A–C. Leaf transverse section. A. H. pusilla, leaf transverse section open with round ribs. B. H. juncifolia, leaf transverse section closed with polygonal ribs. C. H. redolens var. altissima, leaf transverse section open with quadrangular ribs. D–F. Leaf epidermis. D. H. redolens var. gunckelii, adaxial epidermis with long cells type I3 and stomata. E. H. pusilla, leaf epidermis with long cells type I3 and short cells S5 in A revision of Hierochloe sect. Monoecia (Anthoxanthinae, Pooideae, Poaceae)
FIGURE 1. Hierochloe sect. Monoecia micromorphology details. A–C. Leaf transverse section. A. H. pusilla, leaf transverse section open with round ribs. B. H. juncifolia, leaf transverse section closed with polygonal ribs. C. H. redolens var. altissima, leaf transverse section open with quadrangular ribs. D–F. Leaf epidermis. D. H. redolens var. gunckelii, adaxial epidermis with long cells type I3 and stomata. E. H. pusilla, leaf epidermis with long cells type I3 and short cells S5 (round). F. H. juncifolia, leaf epidermis with long cells type I3 and short cells S2 (ax shaped).
#DeOlhoNosCorais: a polygonal annotated dataset to optimize coral monitoring
<p>The first version of the #DeOlhoNosCorais dataset was built in collaboration with the Laboratory of Marine Ecology (LECOM) at the Federal University of Rio Grande do Norte (UFRN). The dataset contains images extracted from social networks with the hashtag “#DeOlhoNosCorais” and images provided by the LECOM. It contains 1411 images and 21 classes. Each image has two labels, its major class and the segmentation map. The labeling was performed by the LECOM researchers using the Labelme tool. The duplicate images were removed using the FiftyOne tool. </p> <p>One of the objectives of the dataset is to measure the viability to use machine learning to speed up the processing of analyses of the images extracted from the Instagram. So, the extra images taken by divers from the researcher group were used to augmented the train set. The Instagram images were divided into train, validation, and test accordingly to their date. The rules for the division was:</p> <ul> <li>Train set: images dated until 12/31/2019 + extra images from LECOM-UFRN</li> <li>Validation set: images dated between 01/01/2019 to 06/30/2019</li> <li>Test set: images dated between 07/01/2019 to 08/24/2021</li> </ul> <p>The test set includes an interval of 2 years, due to the drop in the number of posts during the Covid-19 pandemic.The full dataset folder contains the raw labeling using the Labelme. The experiments folder contains treated datasets for the following tasks:</p> <ul> <li>Full images classification</li> <li>Sub images classification</li> <li>Binary semantic segmentation</li> <li>Binary object detection</li> </ul> <p>In addition, the experiments folder has an extra data from the Pacific Labeled Corals (PLC). The extra data contains 12 classes of the 20 present in the PLC, and the images patches were extracted centered in the labeled pixels with size of 224 x 224.</p>
IG. 6. Ordination diagram of PCA of the Patagonian bat assemblage for craniodental variables using A) data set not size-corrected; and B) data set size-corrected. Polygons include specimens from each species: H. macrotus (▲), H. magellanicus (), H. montanus (▲), L. varius (■), M. chiloensis (●), and T. brasiliensis (£). Vectors show the strengh of correlation of each variable with the plane of PC1 and PC2. See text for abbreviations in Ecomorphological diversity in the Patagonian assemblage of bats from Argentina
IG. 6. Ordination diagram of PCA of the Patagonian bat assemblage for craniodental variables using A) data set not size-corrected; and B) data set size-corrected. Polygons include specimens from each species: H. macrotus (▲), H. magellanicus (), H. montanus (▲), L. varius (■), M. chiloensis (●), and T. brasiliensis (£). Vectors show the strengh of correlation of each variable with the plane of PC1 and PC2. See text for abbreviations
Nitrous oxide emissions from eroding high-center polygons in an Arctic coastal wetland, 2021.
<p>This data contains nitrous oxide (N<sub>2</sub>O) and carbon dioxide (CO<sub>2</sub>) fluxes from eroding polygon features located in a coastal wetland near Utqiaġvik, Alaska on the Barrow Environmental Observatory (BEO). Static chamber fluxes were measured with a Gasmet GT5000 Terra Fourier transform infrared (FTIR) greenhouse gas analyzer (GGA) and a clear, cylindrical polycarbonate chamber (50 cm height and 20cm diameter) in a closed system at a 1Hz sampling rate. The FTIR GGA is capable of measuring concentrations by scanning the full infrared spectrum and calculating the concentrations of each gas in the sample based on its absorption.Chamber collars were made of PVC (15 cm height and 20 cm diameter) and installed 3 days prior to greenhouse gas measurements at a depth of 10 cm. Fluxes were measured at 30 locations – 10 vegetated replicates and 20 unvegetated soil replicates – whenever weather permitted during July 2021 for a total of 263 measurements. Ancillary measurements included soil temperature, bulk density, thaw depth, soil water content, stable isotope ratios, and carbon to nitrogen (C:N) ratios. Soil water content, soil surface temperature, and thaw depth were measured at the time of each chamber measurement at the flux collar throughout the study period (n = 263). Soil water content was measured with a Fieldscout 300 TDR soil moisture meter. Soil surface temperature was measured with an infrared thermometer. Depth of thaw was measured with a small diameter metal rod. Bulk density was measured from soil samples from the top 15 cm of the soil column, collected at each collar location at the end of the study period, for a total of 30 samples. Soil samples were dried for 24 hours at 60 °C in a drying oven and results expressed as weighed per unit volume. </p> <p>Soil samples from the top 15 cm of the soil column were removed at both vegetated and unvegetated areas near where fluxes were measured using a handheld soil sampling corer (7 cm diameter, 15 cm height) at the end of the experiment. Soil samples consisted of four profiles with three depths for a total of 24 samples. Samples were then separated into 5 cm depth segments (to check relationship with depth) using a band saw, placed in a drying oven at 65 C for 48 hours, then homogenized with a vibratory ball mill. The abundance of <sup>15</sup>N, <sup>13</sup>C, and C:N ratios were measured using a continuous flow isotope ratio mass spectrometer (IRMS, Delta V Advantage, Thermo Fisher Scientific). A laboratory standard (USGS41, L-glutamic acid) was used as a reference material for the calibration of stable carbon and nitrogen measurements. Isotope values are reported in standard δ notation (‰) relative to Vienna PeeDee Belemnite (δ <sup>13</sup>C) and air-N2 (δ <sup>15</sup>N). </p> <p>Files in this repository:</p> <p>This dataset consists of four files: (1) 'chamber_fluxes.csv' contains measurements for individual flux data; (2) 'collar_means.csv' contains measurements averaged per collar location and include discrete bulk density measurements; (3) 'soil_content.csv' contains measurements of carbon content, nitrogen content, and isotope content; and (4) 'metadata.csv' contains a description of column headers and units.</p>
Polygon Shapefile Outlining Extent of the "NWT" Project Area, Niwot Ridge LTER Project Area, Colorado
This vector shapefile is a polygon shapefile outlining the extent of the "NWT" project area, for the Niwot Ridge Long Term Ecological Research (LTER) project. The shapefile also covers the Green Lakes Valley portion of the Boulder Creek Critical Zone Observatory (CZO). Other datasets available in this series includes orthorectified aerial photograph mosaics (for 1953, 1972, 1985, approximately 1990, 1999, 2000, 2002, 2004, 2006 and 2008), digital elevation models (DEM's), and accessory map layers. Together, the DEM's and imagery will be of interest to students, research scientists, and others for observation and analysis of natural features and ecosystems. NOTE: This EML metadata file does not contain important geospatial data processing information. Before using any NWT LTER geospatial data read the arcgis metadata XML file in either ISO or FGDC compliant format, using ArcGIS software (ArcCatalog > description), or by viewing the .xml file provided with the geospatial dataset.
Distribution of Honckenya peploides ssp. diffusa on Surtsey in 1980 and 1990 (as polygons)
<p>The distribution of <em>Honckenya peploides</em> subsp. <em>difussa</em> in 1980 and 1990 digitised as polygons. The data is available in ESRI shapefile: Honckenya pepl 1980_1990 polygons.shp (ESRI shape file, CRS: EPSG:3057 - ISN93/Lambert 1993). This dataset is a part of a paper: Wasowicz,P.,<strong> </strong>Thorsteinsson, S., Magnússon, B., Einarsson, E., Bjarnason, V., Bjarnason,Á.H., Guðmundsson, J., Richter, S.H., Jónasson, R., Sveinbjörnsson B., Magnússon, S.Þ. 2020. Vascular plant colonization of Surtsey island (1965-1990) - a dataset. <em>Biodiversity Data Journal 8: e54812. https://doi.org/10.3897/BDJ.8.e54812</em></p>
Raster-based imagery and terrain variables with wetland/deepwater polygons for select areas in the Great Lakes region
<p>Data was obtained from public sources to produce the aerial imagery, DEM, DEM-derived indices, and other raster variables. Please see that attached "DATA SOURCES & PREP METHODS.pdf" for more information.</p> <p>The shapefiles are comprised of polygons that represent wetland and deep-water classes drawn and attributed by experienced photo-interpreters and are similar to a National Wetlands Inventory (NWI; fws.gov/program/national-wetlands-inventory) product, though these have not been obtained from nor approved by the NWI Program, nor do they exist in the official NWI database.</p> <p>These data can be used for training and validating machine learning and other predictive models for wetland classification.</p>
FIGURES 3–4 in Tiny diatoms Minidiscus vodyanitskiyi sp. nov. (Bacillariophyta) from the Sea of Azov and consideration of polygonal areolae pattern
FIGURES 3–4. Construction of cingulum of M. vodyanitskiyi. SEM.
Figure 14. Our gait data for a in Support polygons and symmetrical gaits in mammals
Figure 14. Our gait data for a single long-legged dog, displayed (A) on the standard Hildebrand diagram and (B) on the modified diagram, in which the relevant duty factor specified by the revised support-pattern model – Sf for L-S, D-C walks, Sh for other gaits – is plotted on the horizontal axis.
Figure 9 in Support polygons and symmetrical gaits in mammals
Figure 9. Diagonality (D) plotted against the duty-factor index. The 100% line, along which Sf = Sh, represents the plane of the Hildebrand diagram, here seen edge-on. The diagonal black line is the bivariate least-squares regression line for all primate data (circles), excluding Callithrix (crosses). Black diamonds represent nonprimate data.
Figure 5 in Support polygons and symmetrical gaits in mammals
Figure 5. Curves of unilateral bipedality (Bu) and diagonal bipedality (Bd) against diagonality (D) for the case where the duty factors of the hind- and forelimbs are equal (Sh = Sf = S). K = S − 50. All variables (Bu, Bd, D, K, S) represent durations expressed as percentages of the stride period (time between two successive falls of the same foot).
Figure 4 in Support polygons and symmetrical gaits in mammals
Figure 4. Support patterns for two gait cycles with similar duty factors but different diagonalities. In each diagram, the horizontal axis represents time, expressed as a percentage of stride period. Horizontal black bars represent stance phase (period of ground contact) for each foot (LH, left hind; LF, left fore; RF, right fore; RH, right hind). Duty factors are 64 for hind feet and 54 for fore feet. Since the gait is symmetrical, LH and RH footfalls differ by exactly 50% of the cycle, as do LF and RF footfalls, and left and right duty factors are the same. Each cycle starts with the LH footfall. Diagonality (percentage of cycle by which LF footfall trails LH footfall) equals 26 in cycle A and 56 in cycle B. Hatched bars below each diagram show periods of different support patterns (by 2 feet on the same side, 2 diagonally opposite feet, 3 feet, and 4 feet) during each cycle. The lengths and timing of these periods can be inferred from the timing of the liftoff and touchdown of each foot (vertical dashed lines), which can in turn be inferred from duty factors and diagonality.
Figure 1 in Support polygons and symmetrical gaits in mammals
Figure 1. Quadrupedal gaits modelled as though being executed by two people in a horse costume. A, walking pace. The two bipeds are walking exactly in phase (diagonality = 0). B, running pace, shown during aerial phase (with all feet off the ground). The two bipeds are running in phase (diagonality = 0). C, running trot, shown during aerial phase. The two bipeds are running 180 ° out of phase; that is, the hind biped's cycle is running 50% of the cycle period ahead of the front biped's cycle (diagonality = 50). In a pace (A,B), both left feet (or both right feet) strike down together; in a trot (C), diagonally opposite feet strike down together. D, lateral-sequence (L-S) walk. The hind biped's cycle is running 25% of the cycle period ahead of the front biped's cycle (diagonality = 25%). E, diagonal-sequence (D-S) walk. The hind biped's cycle is running 75% of the cycle period ahead of the front biped's cycle (diagonality = 75%). In an L-S walk (D), the left hind footfall is followed by that of the left forefoot; in a D-S walk (E), it is followed by that of the right forefoot.
Figure 13 in Support polygons and symmetrical gaits in mammals
Figure 13. Standard Hildebrand diagram, comparing Hildebrand's (1968) dog data with our data for 41 gait cycles of a single long-legged dog. Diagonal lines represent the relationships between duty factor and diagonality predicted by the revised support-polygon model (using hind-limb duty factor throughout to accommodate Hildebrand's data).
Pontocaspian habitat polygon shapefiles from: Decline of unique Pontocaspian biodiversity in the Black Sea Basin: a review
<p>The unique aquatic Pontocaspian (PC) biota of the Black Sea Basin (BSB) is in decline. The lack of detailed knowledge on the status and trends of species, populations and communities hampers a thorough risk assessment and precludes effective conservation. This paper reviews PC biodiversity trends in the BSB ( Bulgaria, Romania, Moldova, Ukraine and Russia) using endemic molluscs as a model group. We aim to assess changes in PC habitats, community structure and species distribution over the past century and to identify direct anthropogenic threats. Presence/absence data of target mollusc species was assembled from literature, reports and personal observations. Pontocaspian biodiversity trends in the north-western BSB coastal regions were established by comparing 20<sup>th</sup> and 21<sup>st</sup> century occurrences. Direct drivers of habitat and biodiversity change were identified and documented. A pronounced decline of PC species and communities is driven by a) damming of rivers, b) habitat modifications that disturbed previous natural salinity gradients and settings in the studied area c) pollution and eutrophication, d) invasive alien species and e) climate change. Four out of the 10 studied regions, namely, the Danube Delta – Razim Lake system, Dniester Liman, Dnieper-Bug Estuary and Taganrog Bay-Don Delta contain the entire spectrum of ecological conditions to support PC communities and still host threatened endemic PC mollusc species. Distribution data is incomplete, but the scale of deterioration of PC species and communities is evident from the assembled data, as are major direct threats. Pontocaspian biodiversity in the BSB is profoundly affected by human activities. Standardised observation and collection data as well as precise definition of PC biota and habitats are necessary for targeted conservation actions. This study will help to set the research and policy agenda required to improve data collection to accommodate effective conservation of the unique PC biota.</p>
Pontocaspian habitat polygon shapefiles from: Decline of unique Pontocaspian biodiversity in the Black Sea Basin: a review
Open the record for dataset details and reuse information.
Sounder SIPS: Suomi-NPP ATMS Level 1 Daily Polygon Granule Map at GES DISC
The Advanced Technology Microwave Sounder (ATMS) Level 1B data files contain brightness temperature measurements along with ancillary spacecraft, instrument, and geolocation data of the ATMS instrument on the Suomi National Polar-orbiting Partnership Project (S-NPP).The ATMS instrument is a cross-track scanner with 22 microwave channels in the range 23.8-183.31 Gigahertz (GHz). The beam width is 1.1 degrees for the channels in the 160-183 GHz range, 2.2 degrees for the 80 GHz and 50-60 GHz channels, and 5.2 degrees for the 23.8 and 31.4 GHz channels. Since the SNPP satellite is orbiting at an altitude of about 830 km, the instantaneous spatial resolution on the ground at nadir is about 16 km, 32 km, or 75 km depending upon the channel. The brightness temperature data are contained in an array with 135 rows in the along-track direction, 96 columns in the cross-track direction, and a 3rd dimension for each of the 22 channels. The ATMS cross-track scan interval is 0.018 seconds and the along-track scan period is 8.3 seconds. Data products are constructed on six minute boundaries. The Granule Map Product consists of daily images of granule coverage in PDF format.
MASTER: Flight Line Geospatial Polygons and Contextual Data
This dataset provides resources for identifying flight lines of interest for the MODIS/ASTER Airborne Simulator (MASTER) instrument based on spatial and temporal criteria. MASTER first flew in 1998 and has ongoing deployments as a Facility Instrument in the NASA Airborne Science Program (ASP). MASTER is a joint project involving the Airborne Sensor Facility (ASF) at the Ames Research Center, the Jet Propulsion Laboratory (JPL), and the Earth Resources Observation and Science Center (EROS). The primary goal of these airborne campaigns is to demonstrate important science and applications research that is uniquely enabled by the full suite of MASTER thermal infrared bands as well as the contiguous spectroscopic measurements of the AVIRIS (also flown in similar campaigns), or combinations of measurements from both instruments. This dataset includes a table of flight lines with dates, bounding coordinates, site names, investigators involved, flight attributes, and associated campaigns for the MASTER Facility Instrument Collection. A shapefile containing flights for all years, a GeoJSON version of the shapefile, and separate KMZ files for all years allow users to visualize flight line locations using GIS software.
Sounder SIPS: JPSS-1 ATMS Level 1 Daily Polygon Granule Map at GES DISC
The Advanced Technology Microwave Sounder (ATMS) Level 1B data files contain brightness temperature measurements along with ancillary spacecraft, instrument, and geolocation data of the ATMS instrument on the Joint Polar Satellite System-1 (JPSS-1) platform. This platform is also known as NOAA-20 (National Oceanic and Atmospheric Administration). The ATMS is a 22-channel mm-wave radiometer. The ATMS will measure upwelling radiances in six frequency bands centered at 23 GHz, 31 GHz, 50-58 GHz, 89 GHz, 66 GHz, and 183 GHz. The ATMS is a total power radiometer, with "through-the-antenna" radiometric calibration. Radiometric data is collected by a pair of antenna apertures, scanned by rotating flat plate reflectors. Scanning is performed cross-track to the satellite motion from sun to anti-sun, using the "integrate-while-scan" type data collection. The scan period is 8/3 second, synchronized to the Cross-track Infrared Sounder (CrIS) using a spacecraft provided scan synchronization pulse.Since the JPSS-1 satellite is orbiting at an altitude of about 830 km, the instantaneous spatial resolution on the ground at nadir is about 16 km, 32 km, or 75 km depending upon the channel. The brightness temperature data are contained in an array with 135 rows in the along-track direction, 96 columns in the cross-track direction, and a 3rd dimension for each of the 22 channels. The ATMS cross-track scan interval is 0.018 seconds and the along-track scan period is 8/3 seconds.. Data products are constructed on six minute boundaries. The Granule Map Product consists of daily images of granule coverage in PDF format.
Achilleion Statue - Ultra Low Polygons - Fantasy
Software: Poser 2.0 - Model Obj + Textures Esplora in Hyria 3d World: http://www.massimogreco.xyz/3d/index.htm Source: Objaverse 1.0 / Sketchfab
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.