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9,113 results for “Sampling”
Discrete surface turbidity samples and underway sea surface temperature and sea surface salinity measured in Aarhus Bay during a demonstration of an experimental autonomous surface vehicle
<p>This dataset includes measurements obtained by an autonomous boat that was equipped with a surface water sampling system: the Naval Operating Research Drone Assessing Climate Change (NORDACC). </p> <p>This dataset includes two .csv files</p> <p><br> 2022-10-14_NORDACC_Turbidity.csv<br> This file contains the results of 8 discrete surface water samples that were analyzed for turbidity using a Hach turbidimeter. Surface water samples were acquired by NORDACC on the afternoon of 14 October 2022 in Aarhus Bay. The columns are separated by commas and correspond to: <br> Sample Number, Date (yyyy-mm-dd), UTC time (HH:MM:SS), Longitude (decimal degrees), Latitude (decimal degrees), Sea Surface Temperature (SST; degC), Sea Surface Salinity (SSS)</p> <p><br> 2022-10-14_NORDACC_UnderwayData.csv<br> This file contains 1 Hz data, delimited by commas, that were collected while NORDACC was in operation. The underway data columns correspond to:<br> Date & Time (ISO format yyyy-mm-ddTHH:MM:SS), Operation State (1=initializing, 2=sailing, 3=water sample), Longitude (decimal degrees), Latitude (Latitude), Sea Surface Temperature (SST; degC), Sea Surface Salinity (SSS)</p> <p><br> About NORDACC:</p> <p>The Naval Operating Research Drone Assessing Climate Change (NORDACC) was designed by Serbian Akbulut, Jeppe Fogh Rasmussen, Christian Søndergård Hestbech, and Marius Hjorth Andersen, a group of mechatronics students at Aarhus University. The project was supervised by Prof. Claus Melvad (AU) and received external guidance by Dr. Daniel Carlson (Helmholtz-Zentrum Hereon). The NORDACC project was partially supported by Helmholtz-Zentrum Hereon and the Klaus-Tschira Boost Fund that was administered by the German Scholars Organization.</p> <p>NORDACC designs, software, and BOM are open source and provided via Mendeley Data, doi:10.17632/rpzv35pccr.1 </p> <p>For more information about NORDACC see the accompanying paper in HardwareX. </p>
GSD Sample Data
<p>GSD Open Source Sample Data as found in the Beeline software package that was likely generated using a Boolean Network method described in Ríos, O., Frias, S., Rodríguez, A. <em>et al.</em> (2015). All gene names have been normalized to their NCBI identifiers and any NCBI IDs that contain parentheses such as NCBI:12796(-KTS) or NCBI:12796(+KTS) denoted a sequence variant described by the string within the parentheses.</p>
Figure 1 in A NOTE ON SAMPLING CHIRONOMIDS FOR RNA-BASED STUDIES OF NATURAL POPULATIONS THAT RETAINS CRITICAL MORPHOLOGICAL VOUCHERS Abstract
Figure 1. Exemplar RNA extraction QA/QC assessments from the 42 extractions conducted. a) ten extractions analysed using 2% w/v agarose gel electrophoresis: lane 1 contains 1 µL Hyperladder IV (Bioline), lane 2 was intentionally blank, samples are from lanes 3-12; b) exemplar Agilent 2100 Bioanalyzer plot of fragment lengths (x axis) against fluorescence units (y axis). The software automatically attempts to identify the 18S and 28S peaks (labelled below the peak trace in smaller font on an angle) to calculate a RIN.
IODP Expedition 369 Sample report
<p>Report includes detailed information about samples taken for testing: location/depth, type/form factor, test, request number, and database identifier.</p>
Tree-Ring Measurement File: Lodgepole Pine Samples Collected at Steamboat Geyser, Yellowstone National Park
<p>Decade format (Tucson format) tree-ring measurement file (0.001 mm) for lodgepole pine core samples collected adjacent to Steamboat Geyser in Yellowstone National Park, Wyoming, USA.</p>
Sample datasets for verifying SPREADS installation
<p>This archive contains 6 netCDF files that are used for verifying the installation of SPREADS.</p>
Impacts of taxon-sampling schemes on Bayesian tip dating under the fossilized birth-death process
<p>Evolutionary timescales can be inferred by molecular-clock analyses of genetic data and fossil evidence. Bayesian phylogenetic methods such as tip dating provide a powerful framework for inferring evolutionary timescales, but the most widely used priors for tree topologies and node times often assume that present-day taxa have been sampled randomly or exhaustively. In practice, taxon sampling is often carried out so as to include representatives of major lineages, such as orders or families. We examined the impacts of different densities of diversified sampling on Bayesian tip dating on unresolved fossilized birth-death (FBD) trees, in which fossil taxa are topologically constrained but their exact placements are averaged out. We used synthetic data generated by simulations of nucleotide sequence evolution, fossil occurrences, and diversified taxon sampling. Our analyses under the diversified-sampling FBD process show that increasing taxon-sampling density does not necessarily improve divergence-time estimates. However, when informative priors were specified for the root age or when tree topologies were fixed to those used for simulation, the performance of tip dating on unresolved FBD trees maintains its accuracy and precision or improves with taxon-sampling density. By exploring three situations in which models are mismatched, we find that including all relevant fossils, without pruning off those that are incompatible with the diversified-sampling FBD process, can lead to underestimation of divergence times. Our reanalysis of a eutherian mammal data set confirms some of the findings from our simulation study, and reveals the complexity of diversified taxon sampling in phylogenomic data sets. In highlighting the interplay of taxon-sampling density and other factors, the results of our study have practical implications for using Bayesian tip dating to infer evolutionary timescales across the Tree of Life.</p>
Initial Sample of HYPERNETS Hyperspectral Surface Reflectance Measurements for Satellite Validation from the agricultural land at Demmin, Germany
<p>The HYPERNETS project (www.hypernets.eu) aims to ensure that high-quality in situ measurements are available to support the (VNIR/SWIR) optical Copernicus products. Therefore, it established a new autonomous hyperspectral spectroradiometer (HYPSTAR® - www.hypstar.eu) dedicated to land and water surface reflectance validation with instrument-pointing capabilities. In the prototype phase, the instrument is being deployed at 24 sites covering a range of water and land types and a range of climatic and logistic conditions. This dataset provides the first published data for the HYPERNETS site in Demmin, Germany [53°52'5.80"N,13°16'6.80"E] (DEGE). It is a subset of the complete data record, consisting of the measurements withEthaturements which could be used for satellite validation. </p> <p>The provided NetCDF files are the L2A hypernets products with surface reflectances, their associated uncertainties and error-correlation information. The reflectance in the L2A products is the Hemispherical-directional Reflectance Factor (HDRF) defined as HDRF = π L / E where L is the directional upwelling radiance (with the field o, view of 5 degrees), and E is the (hemispherical) downwelling irradiance (i.e. including both direct solar and diffuse sky irradiance). These reflectances have dimensions of wavelength and series, where each series is a set of measurements for a given geometry (combination of viewing zenith and azimuth angle). In addition to variables for wavelength and bandwidth, the files also contain variables that provide for each series the acquisition time, viewing and solar angles, number of valid scans used, and quality flags (typically, no flags are set in the data provided in this dataset). These NetCDF files also contain further relevant metadata as attributes. See https://hypernets-processor.readthedocs.io/ for further info.</p> <p>The HYPSTAR®-XR sensor was installed on 22 July 2021 at the top of a 10m mast on an extended 5 m horizontal boom to minimise interruption of the field of view. The boom faces South at the right angle towards bare soil. The mast is located at 53.868278°N, 13.268556°E. Data are collected every 30 minutes between 9:00 and 17:00 (UTC) from different zenith and azimuth angles.</p> <p>The HYPSTAR®-XR (eXtended Range) instruments deployed at each land HYPERNETS site consist of a VNIR and a SWIR sensor and autonomously collect data between 380-1700 nm at various viewing geometries and send it to a central server for quality control and processing. The VNIR sensor spans 1330 channels between 380 and 1000 nm with an FWHM of 3 nm, and the SWIR sensor has 220 channels between 1000 and 1700 nm with an FWHM of 10 nm. The hypernets_processor (Goyens et al. 2021; De Vis et al. in prep.) automatically processes all this data into various products, including the L2A surface reflectance product provided here. All products have associated uncertainties (divided into random and systematic uncertainties, including error-correlation information) propagated using the CoMet toolkit (www.comet-toolkit.org). </p> <p>To obtain this dataset, we start from the full DEGE data record and omit all the data that do not pass all quality checks performed as part of the hypernets_processor. In addition, an additional screening procedure was also developed to remove outliers and supply the best quality data suitable for satellite validation. To remove the outliers, a sigma-clipping method is used. First, reflectances are extracted in separate 2-hour windows throughout the day (to account for BRDF differences due to different solar positions) for four different wavelengths (500, 900, 1100 and 1600 nm). Outliers in these reflectances are then identified by iteratively calculating the mean reflectance trend with time (by binning the data per maximum of 30 data points), calculating the standard deviation from this trend, and masking any data that is more than three standard deviations away from the trend. This process is repeated on the unmasked data until the standard deviation does not vary by more than 5% between two iterations. The masks for the four different wavelengths are then combined (keeping only measurements for which none of the four wavelengths is an outlier). The reflectances and associated uncertainties for any masked series (i.e. a geometry that is masked either by the sigma-clipping procedure or from the masks of the hypernets_processor) are replaced by NaNs. Any sequence that has more than half of its series masked is removed entirely. </p>
Sampling uncertainty results using gumboot package by Clark et al (2021)
<p>Clark et al 2021 based sampling uncertainty assessment.</p> <p>Clark, M. P., Vogel, R. M., Lamontagne, J. R., Mizukami, N., Knoben, W. J. M., Tang, G., et al. (2021). The Abuse of Popular Performance Metrics in Hydrologic Modeling. Water Resources Research, 57(9). https://doi.org/10.1029/2020WR029001</p>
Fig. 3. Leocrates chinensis Kinberg, 1866. Sample 13 in Redescription of Leocrates chinensis Kinberg, 1866 (Annelida, Hesionidae)
Fig. 3. Leocrates chinensis Kinberg, 1866. Sample 13 (ECOSUR 2904). A-D, H, L: Chaetiger 6; E, J: Chaetiger 16; F: Chaetiger 8; G, K: Chaetiger 3; I: Chaetiger 9; M: Chaetiger 7; N: Chaetiger 15. (A) notochaetae; (B) notochaetal basal region; (C) notochaetal median region; (D) notochaetal tip; (E) notochaetal median region with transverse series of spines; (F) same, under lower magnification; (G-J) subdorsal neurochaetae with longest blades; (K-N) ventral neurochaetae with shortest blades; (O-P) hooded neurochaetae. Scale bars: A-D, K-P = 100 μm; E-F = 3 μm; G-J = 250 μm.
MultiScent-20 Data - Binary responses (correct vs. incorrect answers) data from full sample, for each odour
<p>Data used for IRT analysis in the article</p> <p>"The Multiscent-20: A Digital Odour Identification Test Developed with Item<br> Response Theory", with the following authors:</p> <p>Marcio Nakanishi 1, *, Pedro Renato de Paula Brandão 2, *, Gustavo Subtil Magalhães<br> Freire 1 , Luis Gustavo do Amaral Vinha 3 , Marco Aurélio Fornazieri 4 , Wilma Terezinha<br> Anselmo Lima 5 , Claudia Galvão 6 , Thomas Hummel 7</p> <p> </p> <p>1 Department of Otorhinolaryngology, University Hospital of Brasília, University of<br> Brasília, Brasília, DF, Brazil<br> 2 Neurology Department, Hospital Sírio-Libanês, Brasília, DF, Brazil<br> 3 Statistics Department, University of Brasília, DF, Brazil<br> 4 State University of Londrina, Pontifical Catholic University of Paraná<br> 5 Department of Ophthalmology and Otorhinolaryngology, Ribeirão Preto Medical<br> School-University of São Paulo, Brazil<br> 6 NOAR Brasil Ltda, São Paulo-SP, Brazil<br> 7 Smell and Taste Clinic, Department of Otorhinolaryngology, Technical University of<br> Dresden, Dresden, Germany</p> <p> </p> <p> </p> <p>MultiScent-20 descrition:</p> <p>The Multiscent-20 is a portable tablet computer with an integrated hardware<br> system consisting of a central processing unit, touchscreen, Wi-Fi antenna, USB dock,<br> power supply, and rechargeable battery. Additionally, it contains an odour system<br> composed of 20 microcartridges, an air filter, a system that generates a dry air stream,<br> and an odour-dispensing opening. The device is capable of presenting 20 different<br> odours from individual odour capsules, which store the olfactory stimuli by incorporating<br> the odours into an oil-resistant polymer. The capsules are loaded through an insertion<br> port on the back of the device.<br> A software-controlled processing unit governs the flow of dry air, generating a<br> constant air stream that passes through the capsule, releasing individual odours<br> through a small opening at the upper front of the device. In this study, each scent was<br> presented for 5 seconds, with an interval of at least 6 seconds between odour<br> presentations. Each capsule contained 35 µL of oil-based odour solution, allowing the<br> device to maintain consistent odour intensity and identifiability for up to 100 activations,<br> as per the manufacturer's instructions. The Givaudan Flavours and Fragrance<br> Corporation (São Paulo, Brazil) prepared the odours used in the capsules.<br> A digital application (software) was developed to present odours and record<br> olfactory function test results. The device delivers odours through a dry air system,<br> leaving no residue in the environment or on users. Users can access the test using an<br> application on the device (screen version) or mobile phone. The results are accessible<br> through the integrated software, allowing for storage and analysis of the data.</p>
Data from the manuscript 'Accurate detection of shared genetic architecture from GWAS summary statistics in the small-sample context'
<p>Data sets from the manuscript 'Accurate detection of shared genetic architecture from GWAS summary statistics in the small-sample context'. These include the test statistics from analyses of real and simulated data, and the data used to generate the figures relating to the goodness-of-fit of the generalised extreme value distribution to the GPS test statistics under the null. Please see the enclosed README for more details.</p>
HoloFood Data Portal Samples
<p>HoloFood data portal table with full list of samples contained within the portal</p>
Vegetation sampling around Balma del Gai site (Moià, Barcelona, Spain); Spring 2023
<p>Vegetation sampling raw data conducted during May 2023, aimed to assess the diversity and abundance of vegetation in the vicinity of the Balma del Gai site, located in the Moià plateau, Spain. Various transects were conducted within the study area to systematically record and analyze the presence and frequency of different plant taxa.</p>
Light intensity reflected from PAAO containing samples (AoI 45 deg., 60 deg., two polarisations)
<p>The file contains raw intensity data measured in reflection mode for various material combinations: porous anodized aluminium oxide (PAAO), gold nanoparticles (Au NPs), diamond-like carbon with silver nanoparticles composite (DLC:Ag), quartz, and silicon.</p> <p>List of investigated samples:</p> <table> <thead> <tr> <th scope="col">Sample name</th> <th scope="col">Sample description</th> </tr> </thead> <tbody> <tr> <td>AJ1</td> <td>Aluminium substrate, ~241 nm thickness PAAO (anodized in 0.3 mol/L oxalic acid at 40 V for 3 min 16 s), 60 nm diameter Au NPs (dip-coated at a speed of 1 μm/s)</td> </tr> <tr> <td>AJ2</td> <td>Aluminium substrate, ~259 nm thickness PAAO (anodized in 0.3 mol/L oxalic acid at 40 V for 3 min 37 s), 60 nm diameter Au NPs (dip-coated at a speed of 1 μm/s)</td> </tr> <tr> <td>AJ3</td> <td>Aluminium substrate, ~293 nm thickness PAAO (anodized in 0.3 mol/L oxalic acid at 40 V for 4 min 8 s), 60 nm diameter Au NPs (dip-coated at a speed of 1 μm/s)</td> </tr> <tr> <td>AJ4</td> <td>Aluminium substrate, ~317 nm thickness PAAO (anodized in 0.3 mol/L oxalic acid at 40 V for 4 min 30 s), 60 nm diameter Au NPs (dip-coated at a speed of 1 μm/s)</td> </tr> <tr> <td>AJ5</td> <td>Aluminium substrate, ~345 nm thickness PAAO (anodized in 0.3 mol/L oxalic acid at 40 V for 4 min 57 s), 60 nm diameter Au NPs (dip-coated at a speed of 1 μm/s)</td> </tr> <tr> <td>AJ6</td> <td>Aluminium substrate, ~276 nm thickness PAAO (anodized in 0.3 mol/L oxalic acid at 40 V for 3 min 42 s), ~55 nm thickness, 20 V.% Ag concentration DLC:Ag (magnetron sputtered using silver target, 80 sccm argon gas flow, 5.4 sccm C<sub>2</sub>H<sub>2</sub> gas flow, 405 V voltage, 0.09-0.10 A current, 7·10<sup>-3</sup> mbar work pressure, 2 minutes 32 seconds process duration)</td> </tr> <tr> <td>AJ7</td> <td>Aluminium substrate, ~316 nm thickness PAAO (anodized in 0.3 mol/L oxalic acid at 40 V for 4 min 25 s), ~55 nm thickness, 20 V.% Ag concentration DLC:Ag (magnetron sputtered using silver target, 80 sccm argon gas flow, 5.4 sccm C<sub>2</sub>H<sub>2</sub> gas flow, 405 V voltage, 0.09-0.10 A current, 7·10<sup>-3</sup> mbar work pressure, 2 minutes 32 seconds process duration)</td> </tr> <tr> <td>AJ8</td> <td>Aluminium substrate, ~345 nm thickness PAAO (anodized in 0.3 mol/L oxalic acid at 40 V for 5 min 4 s), ~55 nm thickness, 20 V.% Ag concentration DLC:Ag (magnetron sputtered using silver target, 80 sccm argon gas flow, 5.4 sccm C<sub>2</sub>H<sub>2</sub> gas flow, 405 V voltage, 0.09-0.10 A current, 7·10<sup>-3</sup> mbar work pressure, 2 minutes 32 seconds process duration)</td> </tr> <tr> <td>Q140</td> <td>Quartz substrate, ~55 nm thickness, 20 V.% Ag concentration DLC:Ag (magnetron sputtered using silver target, 80 sccm argon gas flow, 5.4 sccm C<sub>2</sub>H<sub>2</sub> gas flow, 405 V voltage, 0.09-0.10 A current, 7·10<sup>-3</sup> mbar work pressure, 2 minutes 32 seconds process duration)</td> </tr> <tr> <td>Si140</td> <td>Silicon substrate, ~55 nm thickness, 20 V.% Ag concentration DLC:Ag (magnetron sputtered using silver target, 80 sccm argon gas flow, 5.4 sccm C<sub>2</sub>H<sub>2</sub> gas flow, 405 V voltage, 0.09-0.10 A current, 7·10<sup>-3</sup> mbar work pressure, 2 minutes 32 seconds process duration)</td> </tr> </tbody> </table> <p>The file also contains data for lamp and dark spectra. Images are for illustrative purposes and contain measured sample intensity minus dark intensity.</p> <p>The spectra were collected by a custom-made computer-controlled motorized goniometer set-up with an incandescent lamp light source and an AvaSpec-2048 (Avantes) spectrometer covering a wavelength range of 360–860 nm with 1.2 nm resolution.</p> <p>Notations inside the file: AoI - angle of incidence (45 deg. or 60 deg.), AoD - angle of detection (90 deg. is mirror reflection from 45 deg. incidence, while 120 deg. is the same for 60 deg.), pol - angle of the polarizing prism (0 deg. or 90 deg., light intensity was lower for the 0 deg.).</p>
Discrete Flow Cytometry of Underway Samples from Gradients 5 (2023) Using a BD Influx Cell Sorter
<p>The dataset consists of BD Influx-based analysis of phytoplankton populations from discrete flow cytometry data collected underway during the Gradients 2023 (Gradients 5/TN412) oceanographic research cruise in the equatorial Pacific Ocean. The data consists of cell abundance, cell size (equivalent spherical diameter), carbon quota, and carbon biomass for picophytoplankton populations, namely the cyanobacteria Prochlorococcus and Synechococcus, and small eukaryotic phytoplankton (<5 µm ESD). Time is in UTC format, latitude and longitude are in decimal degrees, and depth is in meters. Further information can be found here: https://github.com/fribalet/FCSplankton</p>
Model-based analysis of sample index hopping reveals its widespread artifacts in multiplexed single-cell RNA-sequencing
<p>Supplementary data that are needed to rerun the reproducible notebooks from the first steps using Alevin output and configuration files.</p> <p>Intermediate R data object that can be used to rerun the reproducible notebooks after the filtering steps.</p> <p>Validation data for inferring the sample index hopping rate. The <em>hiseq4000_joined_datatable_plexed_nonplexed.zip file contains read counts for four samples (two non-multiplexed and two multiplexed) joined by a cell-barcode, UMI, and gene-ID (CUG) key combination. The hiseq4000_inner_joined_with_labels.zip file contains only those CUGs that are observed in both the non-multiplexed and multiplexed samples.</em><em> </em></p>
Рис. 26. Сравнение среΑних значений частоты изменчивости эΛементов рисунка и инΑекса пигментации Mesobuthus eupeus в посеΛениях из разнотипных (А) и оΑнотипных (В) биотопов в кажΑой из выборок: I – Северо-ЗапаΑный Гобустан; II – Северо-Восточный Гобустан; III – ЦентраΛьный Гобустан; IV – Юго-Восточный Гобустан; V – Юго-Восточный Ширван. ИнΑекс корреΛяции r в преΑеΛах 0.744–0.989; p <0.05. Fig. 26. Comparison of the average values of the frequency of variability of pattern elements and the pigmentation index of Mesobuthus eupeus in conglomerations from habitats of different types (A) and of the same type (B) in each sample: I – northwestern Gobustan; II – northeastern Gobustan; III – central Gobustan; IV – southeastern Gobustan; V – southeastern Shirvan. Correlation index r = 0.744–0.989; p <0.05. in Materials on the colour pattern variability of Mesobuthus eupeus (C.L. Koch, 1839) (Arachnida: Scorpiones) in southeastern Shirvan and Gobustan (Eastern Azerbaijan)
Рис. 26. Сравнение среΑних значений частоты изменчивости эΛементов рисунка и инΑекса пигментации Mesobuthus eupeus в посеΛениях из разнотипных (А) и оΑнотипных (В) биотопов в кажΑой из выборок: I – Северо-ЗапаΑный Гобустан; II – Северо-Восточный Гобустан; III – ЦентраΛьный Гобустан; IV – Юго-Восточный Гобустан; V – Юго-Восточный Ширван. ИнΑекс корреΛяции r в преΑеΛах 0.744–0.989; p <0.05. Fig. 26. Comparison of the average values of the frequency of variability of pattern elements and the pigmentation index of Mesobuthus eupeus in conglomerations from habitats of different types (A) and of the same type (B) in each sample: I – northwestern Gobustan; II – northeastern Gobustan; III – central Gobustan; IV – southeastern Gobustan; V – southeastern Shirvan. Correlation index r = 0.744–0.989; p <0.05.
Рис. 21–25. Частота изменчивости основных эΛементов рисунка (меΑиаΛьной, парамеΑиаΛьных и маргинаΛьных поΛос) ΑорсаΛьной поверхности мезосомы Mesobuthus eupeus в посеΛениях кажΑой из выборок. 21 – Северо-ЗапаΑный Гобустан (n = 57); 22 – Северо-Восточный Гобустан (n = 40); 23 – ЦентраΛьный Гобустан (n = 81); 24 – Юго-Восточный Гобустан (n = 103); 25 – Юго-Восточный Ширван (n = 86). По оси абсцисс указаны посеΛения, по оси орΑинат – частота изменчивости. Figs 21–25. Frequency of variability of the main elements of pattern (medial, paramedical and marginal stripes) of dorsal surface of mesosome of Mesobuthus eupeus in conglomerations of each sample. 21 – northwestern Gobustan (n = 57); 22 – northeastern Gobustan (n = 40); 23 – central Gobustan (n = 81); 24 – southeastern Gobustan (n = 103); 25 – southeastern Shirvan (n = 86). Abscissas show conglomerations, ordinates – the frequency of variability. in Materials on the colour pattern variability of Mesobuthus eupeus (C.L. Koch, 1839) (Arachnida: Scorpiones) in southeastern Shirvan and Gobustan (Eastern Azerbaijan)
Рис. 21–25. Частота изменчивости основных эΛементов рисунка (меΑиаΛьной, парамеΑиаΛьных и маргинаΛьных поΛос) ΑорсаΛьной поверхности мезосомы Mesobuthus eupeus в посеΛениях кажΑой из выборок. 21 – Северо-ЗапаΑный Гобустан (n = 57); 22 – Северо-Восточный Гобустан (n = 40); 23 – ЦентраΛьный Гобустан (n = 81); 24 – Юго-Восточный Гобустан (n = 103); 25 – Юго-Восточный Ширван (n = 86). По оси абсцисс указаны посеΛения, по оси орΑинат – частота изменчивости. Figs 21–25. Frequency of variability of the main elements of pattern (medial, paramedical and marginal stripes) of dorsal surface of mesosome of Mesobuthus eupeus in conglomerations of each sample. 21 – northwestern Gobustan (n = 57); 22 – northeastern Gobustan (n = 40); 23 – central Gobustan (n = 81); 24 – southeastern Gobustan (n = 103); 25 – southeastern Shirvan (n = 86). Abscissas show conglomerations, ordinates – the frequency of variability.
Discrete Flow Cytometry of Depth Profile Samples from the Gradients 4 (2021) Cruise Using a BD Influx Cell Sorter
<p>The dataset consists of BD Influx-based analysis of phytoplankton populations from discrete flow cytometry data collected during the Gradients 2021 (Gradients 4/TN397) oceanographic research cruise in the equatorial Pacific Ocean. The data consists of cell abundance, cell size (equivalent spherical diameter), carbon quota, and carbon biomass for heterotrophic bacteria, picophytoplankton populations, namely the cyanobacteria Prochlorococcus and Synechococcus, and small eukaryotic phytoplankton (<5 μm ESD). Time is in UTC format, latitude and longitude are in decimal degrees, and depth is in meters. Further information can be found here: https://github.com/fribalet/FCSplankton</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.