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Figure 4 in Vertical distribution and migration of planktonic polychaete larvae in Onagawa Bay, north-eastern Japan
Figure 4. Diel changes in vertical distribution of planktonic polychaete (upper axes) and chlorophyll fluorescence (ppb) (lower axes) larvae at St. 1 in Onagawa Bay from 8:00 a.m. on 20 August to 5:00 a.m. on 21 August, 2012.
Figure 3 in Vertical distribution and migration of planktonic polychaete larvae in Onagawa Bay, north-eastern Japan
Figure 3. Vertical distribution of each species or genus of planktonic spionid larvae at St. 1 in Onagawa Bay from January to December 2012.
Figure 2 in Vertical distribution and migration of planktonic polychaete larvae in Onagawa Bay, north-eastern Japan
Figure 2. Vertical distribution of each family of planktonic polychaete larvae (upper axes) and chlorophyll a concentration (µg L−1) (lower axes) at St. 1 in Onagawa Bay from January to December 2012.
FIG. 2 in Vertical Gradient of Epiphytic Bryophytes in the Amazon: the Rule and its Exception
FIG. 2. — Mean and standard error of the richness of epiphytic bryophytes in the height zones per vegetation type. Lowercase letters are used to indicate differences between height zones and uppercase letters to indicate differences between height zones compared in the different vegetation types in Igapó.
FIG. 4 in Vertical Gradient of Epiphytic Bryophytes in the Amazon: the Rule and its Exception
FIG. 4. — Non-metric multidimensional scaling (NMDS) plot of samples per zone in the vegetation types (stress = 0.1942709) using Sørensen distance. (Z1, base to 1 m; Z2, lower trunk; Z3, upper trunk; Z4, inner canopy; Z5, outer sun-lit twigs/leaves [outer canopy]).
FIG. 3. — A-C in Vertical Gradient of Epiphytic Bryophytes in the Amazon: the Rule and its Exception
FIG. 3. — A-C, Overview of the number of species and shared species per vegetation type. Horizontal bars represent the total richness per zone; vertical bars represent the number of species found per each zone (points) and the number of species shared between zones (points connected by lines); D, mean and standard error of species richness per guild in the zones; E-G, association between zones and guilds based on the absolute frequency of taxa. Abbrevations: Sun, Sun specialist epiphytes; Sha, Shade specialist epiphytes, Gen, Generalist epiphytes; Z, Zone.
FIG. 1 in Vertical Gradient of Epiphytic Bryophytes in the Amazon: the Rule and its Exception
FIG. 1. — Sampling methods and study area. TABLE 2. — Similarity (Sørensen) and dissimilarity (Bray-Curtis) indices between height zones and vegetation types. Species richness and diversity per height zone are highlighted in gray.
Dataset: i3 Verticals, Inc. (IIIV) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Variable vertical land motion for sea level rise projections
<h1><strong>Data for Govorcin et al., 2024: Variable vertical land motion for sea level rise projections [submitted for publication].</strong></h1> <p><strong>Disclaimer:</strong> Data is subject to change due to the review process.</p> <p><strong>Repository Contains:</strong></p> <ul> <li> <p><strong>Vertical Land Motion over California</strong></p> <ul> <li><strong>Reference:</strong> International Terrestrial Reference System, solution 2014 (ITRF2014)</li> <li><strong>Period:</strong> 2015-2023</li> <li><strong>Data Type:</strong> GeoTIFF</li> <li><strong>Unit:</strong> mm/yr</li> </ul> </li> <li> <p><strong>Vertical Land Motion (Propagated) Formal Uncertainties (Rates Std.) over California</strong></p> <ul> <li><strong>Reference:</strong> International Terrestrial Reference System, solution 2014 (ITRF2014)</li> <li><strong>Period:</strong> 2015-2023</li> <li><strong>Data Type:</strong> GeoTIFF</li> <li><strong>Unit:</strong> mm/yr</li> </ul> </li> <li> <p><strong>Vertical Land Motion Temporal Variability over California</strong></p> <ul> <li><strong>Period:</strong> 2015-2023</li> <li><strong>Data Type:</strong> GeoTIFF</li> <li><strong>Unit:</strong> mm/yr</li> </ul> </li> <li> <p><strong>Archive: Output HDF5 (Mintpy format) and GNSS Files</strong></p> <ul> <li>Includes <code>velocity.h5</code>, <code>geometry.h5</code>, <code>gnss_model</code>, <code>calibrated_velocity.h5</code>, <code>CA_3D_rates.h5</code>, and <code>temporal variability</code> per track and merged, projected to vertical. See <strong>README</strong> for more information.</li> </ul> </li> </ul> <h2>Citation:</h2> <p>If you use this data in your work, research or publication, please cite the following article:</p> <p>Govorcin, M. Bekaert, D., Hamlington, B., Sangha, S., Sweet, W. (2024). Variable Vertical Land Motion for Sea Level Rise Projections, 01 August 2024, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-4676043/v1]</p> <h2>Acknowledgment</h2> <p>The research was conducted at the Jet Propulsion Laboratory, California Institute of Technology. This research was supported by the Observational Products for End-Users from Remote Sensing Analysis (OPERA) project (<a href="https://www.jpl.nasa.gov/go/opera" target="_blank" rel="noopener">https://www.jpl.nasa.gov/go/opera</a>), managed by the Jet Propulsion Laboratory and funded by the Satellite Needs Working Group, that is creating remote sensing products to address Earth observation needs across U.S. civilian federal agencies.</p>
FIGURE 21. Cheiracanthus grandispinus scale histology. 1, NMS G.2019.9.3.9 from Gamrie, vertical longitudinal section through posterior protuberance. 2, 9, 12, NMS G.2019.9.8 in A redescription of the three longest-known species of the acanthodian Cheiracanthus from the Middle Devonian of Scotland
FIGURE 21. Cheiracanthus grandispinus scale histology. 1, NMS G.2019.9.3.9 from Gamrie, vertical longitudinal section through posterior protuberance. 2, 9, 12, NMS G.2019.9.8 from Jessie Port, Ross and Cromarty: 2, NMS G.2019.9.8.14, anterior crown vertical transverse section; 9, NMS G.2019.9.8.10, crown vertical transverse section; 12, NMS G.2019.9.8.13, crown horizontal section. 3, NMS G.2019.9.35.6, from Achanarras, midscale vertical transverse section. 4, 5, 10, 11, from Achanarras: 4, NMS G.2019.9.2.4, posterior half of scale, vertical transverse section; 5, NMS G.2019.9.2.2, vertical longitudinal section; 10, NMS G.1893.107.9.2, horizontal section through crown base; 11, NMS G.1893.107.9.2, horizontal section through midcrown. 6-8, NMS G.2019.9.10 from Cromarty: 6, NMS G.2019.9.10.3, off-centre vertical longitudinal section; 7, NMS G.2019.9.10.5, vertical transverse section; 8, NMS G.2019.9.10.9, oblique vertical section through side of scale. Scale bars equal 0.1 mm. Arrows indicate anterior.
Рис. 5. Общая схема Δинамики эпизоотии в приамурской попуΛяции коΛьчатого шеΛкопряΔа. ВертикаΛьно: коΛичество погибших гусениц (% от чисΛа собранных за весь периоΔ иссΛеΔований в 2019 г. гусениц). ГоризонтаΛьно: Δата сбора гусениц на территории УПН. — гибеΛь от вируса яΔерного поΛиэΔроза; — гибеΛь от бактериоза Fig. 5. General scheme of the Lackey moth epizootic dynamics for the Cisamurian population. Vertical: number of the deaths, (percentage from the total number of caterpillars collected in 2019 (578 caterpillars)); horizontal: dates of laboratory controls. — death from the NPV; — death from the bacteriosis in Lackey Moth (Malacosoma Neustria L., Lasiocampidae, Lepidoptera) Population During The Eruptive Phase
Рис. 5. Общая схема Δинамики эпизоотии в приамурской попуΛяции коΛьчатого шеΛкопряΔа. ВертикаΛьно: коΛичество погибших гусениц (% от чисΛа собранных за весь периоΔ иссΛеΔований в 2019 г. гусениц). ГоризонтаΛьно: Δата сбора гусениц на территории УПН. — гибеΛь от вируса яΔерного поΛиэΔроза; — гибеΛь от бактериоза Fig. 5. General scheme of the Lackey moth epizootic dynamics for the Cisamurian population. Vertical: number of the deaths, (percentage from the total number of caterpillars collected in 2019 (578 caterpillars)); horizontal: dates of laboratory controls. — death from the NPV; — death from the bacteriosis
3S-GEOPROF-COMB: A Global Gridded Dataset for Cloud Vertical Structure from combined CloudSat and CALIPSO observations
<p>Global cloud dataset from combined spaceborne radar and lidar.</p> <p>This repository contains the 3S-GEOPROF-COMB product, a globally-gridded dataset for cloud vertical structure retrieved from hybrid active remote sensing (CloudSat radar and CALIPSO lidar) reported at 240 m vertical resolution. Science variables include vertical cloud fraction and vertically-integrated cloud cover for various geometrical criteria (i.e. high, middle, low, and thick clouds, along with with unique high, middle, and low cloud cover variants).</p> <p>A Python notebook showing how to work with the dataset is available <a href="https://github.com/bertrandclim/3S-GEOPROF-COMB/blob/main/notebooks/brief_intro.ipynb">on GitHub</a>, as is the source code used to produce the data product.</p> <p>Our product is calculated from the latest release (R05) of per-orbit (level 2) combined cloud mask profiles in 2B-GEOPROF-LIDAR with additional data from 2B-GEOPROF. Validation and a complete description of the data product is given in the paper <a href="https://doi.org/10.5194/essd-16-1301-2024">"A Global Gridded Dataset for Cloud Vertical Structure from Combined CloudSat and CALIPSO Observations"</a> (Earth System Science Data).</p> <p>Please cite "Bertrand, L., Kay, J. E., Haynes, J., and de Boer, G.: A global gridded dataset for cloud vertical structure from combined CloudSat and CALIPSO observations, Earth Syst. Sci. Data, 16, 1301–1316, https://doi.org/10.5194/essd-16-1301-2024, 2024."</p> <p>The files contained in each folder are given via the following format:</p> <p><strong> instruments_frequency_resolution.zip</strong></p> <ul> <li><strong>instruments:</strong> <ul> <li><strong>radarlidar:</strong> the standard product, computed from merged geometrical profiles of hydrometeor occurrence</li> <li><strong>radaronly:</strong> computed solely from CloudSat radar profiles, otherwise processing is identical. For when users need to determine which instrument is responsible for observations of interest.</li> <li><strong>lidaronly: </strong>computed solely from CALIPSO lidar profiles, otherwise processing is identical. For when users need to determine which instrument is responsible for observations of interest.</li> </ul> </li> <li><strong>frequency:</strong> <ul> <li><strong>monthly:</strong> data files report fields aggregated over a 1-month period</li> <li><strong>seasonal:</strong> data files report fields aggregated over a 3-month period (DJF, MAM, JJA, SON)</li> </ul> </li> <li><strong>resolution:</strong> <ul> <li><strong>2.5x2.5: </strong>each grid box spans 2.5 degrees latitude and 2.5 degrees longitude</li> <li><strong>5x5:</strong> each grid box spans 5 degrees latitude and 5 degrees longitude</li> <li><strong>10x10:</strong> each grid box spans 10 degrees latitude and 10 degrees longitude</li> </ul> </li> </ul> <p>Each folder contains a netCDF data file and a cloud cover quicklook plot image file for each time period over the 2006-2019 data record. Individual files are named according to the following format:</p> <p><strong> timeperiod_instruments_datastream_version.nc (or .png)</strong></p> <ul> <li><strong>timeperiod: </strong>the time step at the given frequency, either e.g. 2006-08 (August 2006) or 2012-DJF (December 2012 to February 2013).</li> <li><strong>instruments</strong><strong>:</strong> the instruments used in the data product as a whole, always CSCAL (CloudSat and CALIPSO).</li> <li><strong>datastream:</strong> either 3S-GEOPROF-COMB (COMBined radar and lidar), 3S-GEOPROF-COMB-RO (the auxiliary Radar Only variant of the product), or 3S-GEOPROF-COMB-LO (the auxiliary Lidar Only variant of the product)</li> <li><strong>version:</strong> current release is v8.4</li> </ul> <p>The product handles the 2011 CloudSat battery anomaly, after which the satellite only collects data in the sunlit portion of its orbit, by allowing users to subsample the pre-anomaly period to mimic the post-anomaly collection patterns. This allows users to estimate the effect of the reduced sampling on their analyses or apply a consistent sampling mode to the entire dataset. This option is provided to users via the "<strong>doop</strong>" dimension. Dimension coordinate value "All cases" reports variables computed using all observations, while "DO-OP observable" reports variables using only input data that either were or would have been collected in DO-OP mode (i.e. the pre-DO-OP period is subsampled to DO-OP collection patterns).</p>
Data on Crop Yield, Nutrient Content of Barley (Hordeum vulgare L.) and Weather of a Vertical Agrivoltaic System in Sweden
<p>The dataset location is latitude 59.55° N and longitude 16.76° E in Kärrbo Prästgård, Sweden. </p> <p>Crop data:</p> <p>Raw data of barley related to yield kernels and straws (kg DM/ha), nitrogen content in kernels (%), crude protein in kernels (%), kernels yield (kg DM/ha), straws yield (kg DM/ha), starch content in kernels (%), and thousand kernel weight (%) from the harvest on September 12<sup>th</sup>, 2023, at the agrivoltaics research site in Kärrbo Prästgård, Sweden. Fifty squared samples (each 0.25 m<sup>2</sup>) distributed in 5 groups (A, B, C, D, E) were collected according to the layout presented in the corresponding publication. Groups A, B and C are based on the spatial location in the crop area between the three vertical rows of PV modules: west side (A), center side (B) and east side (C). Group R corresponds to the reference control plot conditions. Group D represent the crops that are growing in the space between the rows of the conventional ground-mounted PV system with 30° tilt. </p> <p> </p> <p>Weather data:</p> <p>1-hour timeseries averaged data measurements at local time, raw data, not quality controlled from the barley growing season at Kärrbo Prästgård, Sweden from May 7<sup>th</sup> to September 12<sup>th</sup>,2023.</p> <p>Temperature of air (°C), relative humidity (%), relative air pressure (hPa), wind speed (m/s), and precipitation (mm/h) are measured with a Lufft WS600-UMB Smart Weather Sensor located on-site on a 5 m height mast.</p> <p>Global and diffuse horizontal irradiance (W/m<sup>2</sup>) are measured with a Delta-T SPN1 Sunshine Pyranometer.</p> <p>Photosynthetically active radiation (µmol/m<sup>2</sup>/s) is measured with an Apogee PAR Quantum sensor SQ-500.</p>
Turkish Straits System - Vertical Velocity
<p>Vertical Velocity daily mean estimates from a six-year simulation of Turkish Straits System (TSS) using high-resolution unstructured triangular mesh ocean model FESOM between 2008-2013. Other variables are provided separately.</p> <p>The mesh files are appended to the dataset for processing purposes.</p> <p>Aydogdu, A., Pinardi, N., Ozsoy, E., Danabasoglu, G., Gurses, O., and Karspeck, A.: Circulation of the Turkish Straits System under interannual atmospheric forcing, Ocean Sci., 14, 999-1019, doi:10.5194/os-14-999-2018, 2018.'</p>
Fig. 3 in Seasonal and vertical distribution of Dalbulus maidis (Hemiptera: Cicadellidae) in Brazilian corn fields
Fig. 3. Abundance of Dalbulus maidis from yellow sticky card and yellow pan traps positioned at 2 heights in corn grown during (a, c) the rainy season and (b, d) the dry season.
Fig. 1 in Seasonal and vertical distribution of Dalbulus maidis (Hemiptera: Cicadellidae) in Brazilian corn fields
Fig. 1. Trends in capture of Dalbulus maidis, and weather variables, during the study at Teresina, Piauí, Brazil, in 2013.
Fig. 2 in Seasonal and vertical distribution of Dalbulus maidis (Hemiptera: Cicadellidae) in Brazilian corn fields
Fig. 2. Quality of fit of generalized linear mixed models used to assess the effect of trap type on capture of D. maidis at 2 heights, expressed as the ratio of values observed to values predicted by the models. Type 1 = yellow sticky card and type 2 = yellow water pan. (a) Rainy season. (b) Dry season.
Рис. 2. Варианты преΑсказанной Αоменной структуры патогенраспознающих моΛекуΛ гемоцитов моΛΛюсков Planorbarius corneus. a — фибриногенпоΑобные беΛки, b — гаΛектины, c — F-Λектины. УсΛовные обозначения и сокращения, зΑесь и ΑаΛее: горизонтаΛьные красные поΛоски — сигнаΛьный пептиΑ, горизонтаΛьные розовые — обΛасть низкой сΛожности, вертикаΛьные синие поΛоски — трансмембранная обΛасть, FBG — фибриногеновый Αомен, FTP — Αомен фукоΛектина, EGF — Αомен эпиΑермаΛьного фактора роста, EGF_CA — каΛьцийсвязывающий EGF-поΑобный Αомен, PAN_AP — APPLE-поΑобный Αомен, SCAN — обΛасть, богатая Λейцином, GLECT — гаΛактозосвязывающий Λектин, CLECT — Λектин C-типа, Gal-bind — гаΛактозиΑ–связывающий Λектин, ML — MD-2- поΑробный Αомен распознавания ΛипиΑов Fig. 2. Variants of the predicted domain structure of pattern recognition molecules from hemocytes of Planorbarius corneus molluscs. a — fibrinogen-related proteins, b — galectins, c — F-lectins. Symbols and abbreviations (here and further): horizontal red stripes — signal peptide, horizontal pink stripes — a low complexity region, vertical blue stripes — transmembrane region, FBG — fibrinogen-related domain, FTP — fucolectin domain, EGF — epidermal growth factor-like domain, EGF_CA — calcium-binding EGF-like domain, PAN_AP — APPLE-like domain, SCAN — leucine rich region, Apple — APPLE domain, GLECT — galactose-binding lectin, CLECT — C-type lectin, Gal-bind — galactoside-binding lectin, ML — MD-2-related lipid-recognition domain in Pathogen recognition molecules from hemocytes of Planorbarius corneus molluscs (Planorbidae, Pulmonata)
Рис. 2. Варианты преΑсказанной Αоменной структуры патогенраспознающих моΛекуΛ гемоцитов моΛΛюсков Planorbarius corneus. a — фибриногенпоΑобные беΛки, b — гаΛектины, c — F-Λектины. УсΛовные обозначения и сокращения, зΑесь и ΑаΛее: горизонтаΛьные красные поΛоски — сигнаΛьный пептиΑ, горизонтаΛьные розовые — обΛасть низкой сΛожности, вертикаΛьные синие поΛоски — трансмембранная обΛасть, FBG — фибриногеновый Αомен, FTP — Αомен фукоΛектина, EGF — Αомен эпиΑермаΛьного фактора роста, EGF_CA — каΛьцийсвязывающий EGF-поΑобный Αомен, PAN_AP — APPLE-поΑобный Αомен, SCAN — обΛасть, богатая Λейцином, GLECT — гаΛактозосвязывающий Λектин, CLECT — Λектин C-типа, Gal-bind — гаΛактозиΑ–связывающий Λектин, ML — MD-2- поΑробный Αомен распознавания ΛипиΑов Fig. 2. Variants of the predicted domain structure of pattern recognition molecules from hemocytes of Planorbarius corneus molluscs. a — fibrinogen-related proteins, b — galectins, c — F-lectins. Symbols and abbreviations (here and further): horizontal red stripes — signal peptide, horizontal pink stripes — a low complexity region, vertical blue stripes — transmembrane region, FBG — fibrinogen-related domain, FTP — fucolectin domain, EGF — epidermal growth factor-like domain, EGF_CA — calcium-binding EGF-like domain, PAN_AP — APPLE-like domain, SCAN — leucine rich region, Apple — APPLE domain, GLECT — galactose-binding lectin, CLECT — C-type lectin, Gal-bind — galactoside-binding lectin, ML — MD-2-related lipid-recognition domain
Fig. 1 in The Vertical Distribution of the Ant Fauna (Hymenoptera: Formicidae) of the Samanlı Mountains, Turkey
Fig. 1: Map indicating the sampling sites on the Samanlı Mountains (Numbers in the map indicate localities, which are described in the Table 1).
BRAIN Journal-Participative Teaching with Mobile Devices and Social Networks for K-12 Children-Figure 4. Augmented Reality with video movie and social media (a vertical loom in front of two reconstructed kilns and a wall of a Roman villa rustica)
<p>The third stage was represented by the 3D virtual reconstruction process of the historical contexts, in our case a prehistoric village and a complete Roman villa rustica, with the help of students from the Design Department, NUA, coordinated by Professor Arch. Andreea Hasnaş. The AR application was created and tested on two commercial AR platforms, Layar and Junaio, and recently moved on the Aurasma platform (https://www.aurasma.com/). The POIs were augmented with the 3D virtual reconstructions, and also with 2D images and videos representing 3D virtual tours and technological processes (Figures 3, 4, 5). The AR application was connected to teachers’ emails and to Twitter, Facebook and Google+ project’s pages. </p>
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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.