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Eulerian and Lagrangian diagnostics of the dynamical properties of the water masses sampled during the Tara Pacific Expedition 2016-2018
<p>In order to provide a description of the dynamical properties of the water masses sampled, different Eulerian and Lagrangian diagnostics were calculated. </p> <p>For each of the 246 stations sampled, we proceeded as follows.</p> <p>We identified the water mass sampled at the given station. This was considered as a stadium shape with the two semi-circles centered on the starting and ending points of the transect, respectively. The radius of the stadium semi-circles was considered 0.1°, which is in accordance with previous studies25,29,30. The stadium was filled with virtual particles separated by 0.01°.</p> <p>For each virtual particle inside the stadium shape, we calculated an Eulerian or Lagrangian diagnostic (described above). The Eulerian diagnostics were extracted directly from the velocity field of the day of sampling. Concerning the Lagrangian diagnostics, these were obtained by advecting the virtual particle backward in time for an amount of time 𝞽 from the day of sampling day_S. For the Lagrangian betweenness, the advection was performed between day_S+𝞽/2 and day_S-𝞽/2, so that the advective time window was centered on the sampling day (details in25).</p> <p>For the Lagrangian diagnostics, we used the following advective times 𝞽: 5, 10, 15, 20, 30, and 60 days. The only exception is the retention time, which, by construction, was calculated only with the largest advective time, namely 𝞽=60 days.</p> <p>Once that, a given diagnostic (Eulerian or Lagrangian) was calculated for all the virtual particles filling the stadium shape, we calculated the mean value, and the 25, 50, and 75 percentiles. The percentiles were calculated in order to quantify the spatial variation of the diagnostic inside the stadium shape. Therefore, we associated each station with four values (mean, 25, 50, and 75 percentiles) of a given diagnostic.</p> <p> Furthermore, two different velocity fields were used, which are described as follows. </p> <p>Both the velocity fields were downloaded from E.U. Copernicus Marine Environment Monitoring Service (CMEMS, http://marine.copernicus.eu/). The first velocity field used was MULTIOBS_GLO_PHY_REP_015_004 [GlobEkmanDt]. This was produced by combining the altimetry derived geostrophic velocities and modeled Ekman surface currents. It had a spatial resolution of 0.25° and a temporal resolution of one day. The second velocity field was GLOBAL_REANALYSIS_PHY_001_030 [GloryS12]. It was obtained by a NEMO model assimilating altimetry and other observations. It had a spatial resolution of 1/12° and a temporal resolution of 1 day.</p> <p>The following Eulerian diagnostics were calculated:</p> <ul> <li> <p>Absolute velocity ([Uabs], m s-1): sqrt(u2+v2), where u and v are the zonal and meridional components of the horizontal velocity field used (described below)</p> </li> <li> <p>Kinetic energy ([Ekin], m2 .s-2): 0.5*(u2+v2)</p> </li> <li> <p>Divergence ([EulerDiverg], d-1): du/dx + dv/dy</p> </li> <li> <p>Vorticity ([Vorticity], d-1): dv/dx - du/dy</p> </li> <li> <p>Okubo-Weiss ([OW], d-2): s2-vorticity2, where s2 is (du/dx-dv/dy)2 + (dv/dx+du/dy)2. If negative, it indicates that the station sampled was inside an eddy.</p> </li> </ul> <p>The following Lagrangian diagnostics were calculated:</p> <ul> <li> <p>Finite-Time Lyapunov Exponents ([Ftle], d-1): it indicates the rate of horizontal stirring, and it is a means to quantify the intensity of turbulence in a given region. FTLE are commonly used to identify Lagrangian Coherent Structures, i.e. barriers to transport. In this case, a strong FTLE value indicates a region separating water masses which were far away backward in time.</p> </li> <li> <p>Lagrangian betweenness ([betw], adimensional): this diagnostic draws inspiration from Lagrangian Flow Network Theory26. It can identify regions which act as bottlenecks for the circulation, in that they receive waters coming from different origins, and that are then spread over several different destinations. These can represent possible hotspots driving biodiversity25.</p> </li> <li> <p>Lagrangian Divergence ([LagrDiverg], d-1). This diagnostic was calculated by integrating the Eulerian divergence along the backward trajectories. If positive, it indicates a water mass that, during the previous days, was subjected to a strong divergence, thus to a possible upwelling. If negative, it indicates a strong convergence, thus possible downwelling.</p> </li> <li> <p>Retention Time ([RetentionTime], d). This diagnostic indicates how many days a water mass has spent inside an eddy in the previous period. If the water mass is outside an eddy, then its retention time is set to zero.</p> </li> </ul>
2707 water samples from Vallon de Nant analyzed for stable water isotopes (dD, d17O, d18O) and electric conductivity
<p>Database gathering 2707 water samples from the Vallon de Nant (Westerm Swiss Alps) analyzed for stable isotopes of water (dD, d17O, d18O, d-excess, LC-excess, 17O-excess) and electric conductivity.</p> <p>The "VDN_database.csv" file includes the following columns:<br> - time [dd/mm/yyyy HH:MM, UTC]<br> - latitude [decimal degrees, WGS84]<br> - longitude [decimal degrees, WGS84]<br> - elevation [masl]<br> - point: point name<br> - dD [‰]<br> - d17O [‰]<br> - d18O [‰]<br> - d-excess [‰]<br> - LC-excess [‰]<br> - 17O-excess [per meg]<br> - conductivity [µS/cm]</p> <p>Water is sampled from:<br> - 2 points of main stream (Avançon de Nant) at HyS1 and HyS2<br> - 5 different springs at GRAS, AUBG, ROCK, BRDG and ICEC<br> - 3 piezometers at PZ1, PZ2 and PZ3<br> - 2 weather stations for rain at Auberge and Chalet<br> - various locations (GPS points) for the Snowpack<br> - various locations (GPS points) for the Glacier</p> <p>The points with regular sampling are shown on the map "VDN_sampling_points.png".</p>
MEaSUREs blue band total column water vapor sample data for the Ozone Monitoring Instrument
<p>This dataset contains the MEaSUREs OMI Total Column Water Vapor (TCWV) data and their related data used in the paper titled “Development of the MEaSUREs blue band water vapor algorithm – Towards a long-term data record” by Wang et al. (2023). The unzipped archive contains the following three directories. </p> <ol> <li>OMI-H2O-L2/ contains the MEaSUREs Level 2 data (in molecules/cm2) in netCDF4 format for January and July 2005 and 2006. Selected supporting data are also included in each file.</li> <li>OMI-H2O-L3/ contains MRaSUREs Level 3 data (0.25 degree by 0.25 degree, in molecules/cm2) generated using the standard filtering criteria in netCDF4 format for January and July 2005 and 2006. Selected supporting data are also included.</li> <li>Model3_ncresult/ contains netCDF4 formatted files for the MEaSUREs OMI TCWV data (in mm), the AMSR_E TCWV data sampled onto the corresponding OMI pixel locations, and the LightGBM model 3 predictions for the OMI pixels.</li> </ol> <p>The linux command ‘ncdump -h filename’ can be used to examine the contents of netCDF4 files. Due to the current size limit of Zenodo, only a small subset of the MEaSUREs data is archived here. The full dataset will be released elsewhere, e.g., NASA EARTHDATA GES DISC.</p>
Xylem water oxygen and hydrogen isotopes of Ponderosa Pine trees and soil samples in the southwestern U.S. 2018 and 2021
Across seven sites in southern Utah and northern Arizona, we collected precipitation, xylem water, and soil water isotope samples during two years: 2018 and 2021. All xylem water samples were collected from mature Ponderosa pine forests. At each site, xylem water samples were collected from the same 15 trees during two periods during 2018, and three periods during 2021. Soil pits were dug close to the trees and soil samples were collected at 5, 25, and 45cm depth close to the tree xylem samples. In 2021, we also collected precipitation isotopes using a rain gauge that was topped with mineral oil to prevent evaporation.
Water chemistry data including nitrate stable isotopes sampled from zero-tension lysimeters in an Iowa corn-soybean field in 2017 and 2018
These data were used in the manuscript titled "Mechanisms underlying episodic nitrate and phosphorus leaching from poorly drained agricultural soils" published in the Journal of Environmental Quality. We measured nitrate, ammonium, and phosphate concentrations in zero-tension lysimeters installed along a topographic gradient in a corn and soybean field in north-central Iowa, USA, during 2017 and 2018. We measured nitrate stable isotope compositions in a subset of lysimeter samples. Concentrations of nitrate, ammonium, and ferrous and ferric iron were measured in periodic soil extractions co-located with the lysimeters.
Total nitrogen and total phosphorus concentrations from surface water samples collected by the Citizen-Led Environmental Observatory (CLEO) from multiple nearshore sites in Lake Lillinonah, Connecticut, USA, 2011-current
Included in this data package are water quality data from the Citizen-Led Environmental Observatory (CLEO), a volunteer water quality monitoring program run by Friends of the Lake (FOTL, friendsofthelake.org) and Fairfield University at Lake Lillinonah, Connecticut, USA. The program has been operational since 2008 (data available 2011-current). Trained volunteer monitors collect surface water samples from multiple nearshore sites twice a month from Memorial Day through Labor Day. These samples are analyzed for total nitrogen and total phosphorus concentrations. Water samples are also analyzed for levels of the toxin microcystin. Additionally, CLEO volunteers collect data on water temperature, Secchi disk depth, water color, presence of floating woody debris, recreation potential, trash, particle type and surface scum every 1-3 days during the same period. These additional data are available in EDI packages EDI567 (general water quality) and EDI569 (toxins).
November 2001 to March 2003 water column chlorophyll and phaeopigment concentrations for Georgia Coastal Ecosystems LTER sampling sites
Water samples were collected from the surface and the bottom of the water column at ten GCE-LTER sampling sites and from the surface of the water column during a low water transect along the Altamaha River in November 2001, March 2002, June 2002, September 2002, December 2002, and March 2003 . Samples were taken at various times of day and under various tidal conditions. The particulate matter was separated by filtration and analyzed for chlorophyll and phaeopigment content by flourometric analysis. This study was part of the GCE-LTER hydrographic monitoring program, and is repeated quarterly.
Archived plant, soil, water and microbial samples at the Kellogg Biological Station, Hickory Corners, MI (1988 to 2016)
Dataset AbstractPlants, soil, water and microbial samples are archived. Small quantities are available for research by contacting: Plant and Soil Samples — Stacey Vanderwulp Water Samples — Steve Hamilton Microbial Samples — Tom Schmidt original data source http://lter.kbs.msu.edu/datasets/55
Dissolved organic carbon (DOC) concentrations in discrete water column samples collected from lakes in the McMurdo Dry Valleys, Antarctica (1993-2022, ongoing)
The McMurdo Long Term Ecological Research (LTER) project monitors patterns of organic material transport in perennial ice-capped lakes. This data set addresses this core area of research and quantifies dissolved organic carbon concentrations at specific depths in McMurdo Dry Valley lakes.
Ion concentrations in discrete water column samples collected from lakes in the McMurdo Dry Valleys, Antarctica (1991-2019, ongoing)
As part of the Long Term Ecological Research (LTER) project in the McMurdo Dry Valleys of Antarctica, a systematic aqueous geochemical sampling program has been undertaken. A series of terrestrial water samples have been collected and analyzed for major ion chemistry by ion chromatography. The concentrations of ions cover a wide range of total dissolved solids from fresh to hypersaline lake waters. This dataset shows concentrations of lithium, sodium, potassium, magnesium, calcium, chloride, bromide, silicon, fluoride, SO4, found in various depths of Taylor Valley lakes.
Microplankton counts from discrete water column samples using flow imaging microscopy (FlowCam) from Lakes Fryxell and Hoare, McMurdo Dry Valleys, Antarctica (2007-2011)
This data package consists of microplankton counts from discrete water column samples collected at various depths in Lake Fryxell and Lake Hoare in the McMurdo Dry Valleys region of Antarctica. Samples were collected and preserved during the austral summer-autumn transition in 2007-2008 and in addition to routine McMurdo Dry Valley Long Term Ecological Research (LTER) core limnological sampling in November and December of 2008, 2010, and 2011. Data were imaged using flow cytometry (FlowCam VS-IV) and classified with statistical image-based software (Visual Spreadsheet, v4.17.14). These data include haptorian ciliates, filamentous cyanobacteria, and coccoidal algae particle counts per milliliter that were automatically classified from user-built libraries and statistical filters based on the morphological characterizations of the plankton.
Haptorian ciliate measurements from discrete water column samples using flow imaging microscopy (FlowCam) from Lakes Fryxell and Hoare, McMurdo Dry Valleys, Antarctica (2007-2020)
This data package consists of particle diameter and biovolume data for haptorian ciliates classified from discrete water column samples collected at various depths in Lake Fryxell and Lake Hoare in the McMurdo Dry Valleys region of Antarctica. Samples were collected and preserved between November 2007 and January 2020 in Lake Fryxell and between November 2007 and March 2008 in Lake Hoare. Samples were collected and analyzed as part of the McMurdo Dry Valleys Long Term Ecological Research (LTER) core limnological sampling. Data were imaged using flow cytometry (FlowCam VS-IV) and imaged particles were classified with statistical image-based software (Visual Spreadsheet, v4.17.14). Diameter and biovolume was generated for each particle using FlowCam’s area-based diameter (ABD) algorithm.
Surface water sample measurements of dissolved carbon dioxide in 10 New Hampshire and Massachusetts streams, 2015-2020.
Dissolved carbon dioxide concentration from surface water samples in stream water from 10 stream and river locations in New Hampshire and northeast Massachusetts, from 2015 to 2020. Five sites were part of the New Hampshire EPSCoR High Intensity Aquatic Network, three sites are part of the Plum Island Ecosystems LTER, and two sites are part of long-term monitoring projects of the Oyster River watershed near Durham, NH.
Fig.ç1.C ollection sites of pelagic caligids including 3 stations in Japanese waters (St. 2–4, 2010) and 1 station in the Gulf of ffiailand (St. 1, 2006). in Occurrence of Caligid Copepods (Crustacea) in Plankton Samples Collected from Japan and Ŋailand, with the Description of a New Species
Fig.ç1.C ollection sites of pelagic caligids including 3 stations in Japanese waters (St. 2–4, 2010) and 1 station in the Gulf of ffiailand (St. 1, 2006).
Fig.ç1.M aps and photograph showing the sampling locality for Echinoderes ohtsukai sp. nov. A, Map of eastern Asia; B, enlargement of the rectangle in A; C, enlargement of the area indicated by the black circle in B; D, photograph of the sampling locality; white arrow indicates the Kamogawa River and dotted circle indicates the sampling site. in A New Brackish-water Species of Echinoderes (Kinorhyncha: Cyclorhagida) from the Seto Inland Sea, Japan
Fig.ç1.M aps and photograph showing the sampling locality for Echinoderes ohtsukai sp. nov. A, Map of eastern Asia; B, enlargement of the rectangle in A; C, enlargement of the area indicated by the black circle in B; D, photograph of the sampling locality; white arrow indicates the Kamogawa River and dotted circle indicates the sampling site.
Caudal fin area: body length ratio (A:L 2; mean..) FIGURE 5 CF s S E measured from photographs of Salmo trutta parr at 20 and 32 weeks after exercise treatment initiation. A:L 2 values between the two CF s groups were significantly different (Welch's two sample t- test p <0.05) in Body shape and robustness response to water flow during development of brown trout Salmo trutta parr
Caudal fin area: body length ratio (A:L 2; mean..) FIGURE 5 CF s S E measured from photographs of Salmo trutta parr at 20 and 32 weeks after exercise treatment initiation. A:L 2 values between the two CF s groups were significantly different (Welch's two sample t- test p <0.05)
Water sample analysis and satellite imagery of a thermo-erosion gully and its surroundings in Adventdalen, Svalbard.
<h2><strong>Data description</strong></h2> <p>This dataset is part of the supplemental information to the paper "Rapid Ice-Wedge Collapse and Permafrost Carbon Loss Triggered by Increased Snow Depth and Surface Runoff" by Parmentier et al. (2024). It includes the analysis of water quality in and around a thermo-erosion gully on the high-Arctic archipelago of Svalbard, and three satellite images that give an overview of the wider area around this gully in the context of a snow fence experiment (Cooper et al. 2011). More details are provided in Parmentier et al. (2024).</p> <h2><strong>Background</strong></h2> <p>Thicker snow cover in permafrost areas causes deeper active layers and thaw subsidence, which alter local hydrology and may amplify the loss of soil carbon. However, the potential for changes in snow cover and surface runoff to mobilize permafrost carbon remains poorly quantified. The data presented here is part of a study that showed that a snow fence experiment on High-Arctic Svalbard inadvertently led to surface subsidence through warming, and extensive downstream erosion due to increased surface runoff. Within a decade of artificially-raised snow depths, several ice wedges collapsed, forming a 50 m long and 1.5 m deep thermo-erosion gully in the landscape. We estimate that 1.1 to 3.3 tons C may have eroded, and that the gully is a hotspot for processing of mobilised aquatic carbon. Our study show that interactions among snow, runoff and permafrost thaw form an important driver of soil carbon loss.</p> <h2><strong>Water samples</strong></h2> <p>The following datafile includes the analysis of several water samples taken in and near a thermo-erosion gully on Svalbard on August 5<sup>th</sup> and 6<sup>th</sup>, 2017. These were analyzed for dissolved organic carbon (DOC), particulate organic carbon (POC), particulate nitrogen (PN) content, and stable carbon isotope ratios δ<sup>13</sup>C-DOC and δ<sup>13</sup>C-POC. In addition, temperature, pH, oxygen, and electrical conductivity were measured in the field on the day of sampling. This data is provided in the following Excel file that also includes the latitude and longitude for each sample point: </p> <ul> <li>Parmentier et al - 2024 - Water Sample Analysis.xlsx</li> </ul> <h3><strong> </strong><strong>Sample analysis</strong></h3> <p>A full description of the analysis is repeated here from the supplemental information in the accompanying publication (Parmentier et al. 2024). The water samples were filtered on the day of collection through a pre-combusted glass fiber filter with pore size of 0.7 µm (Whatman, Grade GF/F). After filtration, the filters were packed in aluminum foil and frozen for later analysis of the collected particulate matter. From the filtrate, three samples of ~50 ml were taken and immediately frozen for transport.</p> <p>The filtered water samples were analyzed for their dissolved organic carbon (DOC) content and their stable carbon isotope ratio δ<sup>13</sup>C-DOC. This combined analysis was carried out at the labs of UCLouvain, Belgium with an Aurora 1030W TOC Carbon Analyzer, from OI Analytical, coupled to an IRMS (Thermo delta V Advantage). In the Aurora 1030W, the water samples were purged with H<sub>3</sub>PO<sub>4</sub>(phosphoric acid) to remove any dissolved inorganic carbon (DIC). Afterwards, Na<sub>2</sub>S<sub>2</sub>O<sub>8</sub> (sodium persulfate) was added to the heated sample (97 °C) to oxidize any DOC to CO<sub>2</sub>. With N<sub>2</sub> as the carrier gas, the CO<sub>2</sub> was transferred to the analyzing units where the total concentration and δ<sup>13</sup>C-DOC of the CO<sub>2</sub> were detected. The δ<sup>13</sup>C-DOC samples were calibrated against the certified standard IAEA-CH-6 (-10.449 ± 0.033 ‰VPDB) and an internal sucrose standard (-26.99 +/- 0.04 ‰). The DOC measurements were calibrated against a concentration range (n=8) of the same standards (Morana et al., 2015).</p> <p> The particulate matter retained on the filters was analyzed for particulate organic carbon (POC) and particulate nitrogen (PN) concentrations, as well as δ<sup>13</sup>C-POC. The glass fiber filters were subsampled and repeatedly acidified with HCl (1.5 M) in pre-combusted Ag capsules to remove carbonates. Analyses were performed at the Stable Isotope Facility of the University of California in Davis using an Elementar Vario EL Cube (Elementar Analysensysteme GmbH, Hanau, Germany) connected to a PDZ Europa 20-20 isotope ratio mass spectrometer (Sercon Ltd., Cheshire, UK). Isotope ratios of δ<sup>13</sup>C are reported relative to the international standard VPDB (Vienna PeeDee Belemnite).</p> <h2><strong>Satellite imagery</strong></h2> <p>To show the development of the thermo-erosion gully over time, we provide three high resolution satellite images from the Digital Globe constellation of satellites. The areal extent of these images covers the entire snow fence experiment in the valley of Adventdalen on Svalbard. They were acquired on August 5<sup>th</sup>, 2011, August 30<sup>th</sup>, 2013, and July 9<sup>th</sup>, 2015 by the WorldView-2, GeoEye-1 and WorldView-3 satellites, respectively. These images are provided as GeoTiffs – projected in the UTM 33X coordinate system:</p> <ul> <li>SnoEco_2011AUG05_WV2_MUL_Pansharpened_bco_rcs_dobj.tif</li> <li>SnoEco_2013AUG30_GE1_MUL_Pansharpened_bco_rcs_dobj.tif</li> <li>SnoEco_2015JUL09_WV3_MUL_Pansharpened_bco_rcs_dobj.tif</li> </ul> <p>Each of these files includes the following color bands: </p> <ul> <li>Band 1: Blue</li> <li>Band 2: Green</li> <li>Band 3: Red</li> <li>Band 4: Near Infrared</li> </ul> <p>In addition, the images are clipped to the following coordinate bounds (in UTM 33X):</p> <ul> <li> <p><span>x<sub>min</sub>, x<sub>max</sub></span><span>: 523740, 524825</span></p> </li> <li> <p><span>y<sub>min</sub>, y<sub>max</sub></span><span>: 8677150, 8678100</span></p> </li> </ul> <p>For full details on these satellite products, we refer to DigitalGlobe/Maxar.<strong> </strong></p> <h3><strong>Image processing</strong></h3> <p>The satellite imagery was processed according to DigitalGlobe guidelines and calibration coefficient adjustment factors. The radiometrically corrected source images were first converted to top-of-the-atmosphere spectral radiance, and thereafter to top-of-the-atmosphere reflectance. Following this processing, each color band of the image was pansharpened (using Bicubic interpolation) with the RCS algorithm in the Orfeo ToolBox of QGIS 2.18 to increase the horizontal resolution to ~50 cm. To reduce haze effects, the images were further corrected through a dark object subtraction (bottom 1 percentile of the blue band) which was applied to each band separately. Subsequent negative values were set to zero.<strong> </strong></p> <h2><strong>Acknowledgments</strong></h2> <p>This research was funded by the Research Council of Norway (RCN; grant agreement 230970), and the FRAM - Terrestrial flagship (362255 and 642018). F.J.W.P. and S.W. received additional funding from the RCN (grant agreement 323945). The high-resolution satellite imagery comes courtesy of the DigitalGlobe Foundation. We thank UCLouvain and the University of California, Davis for assisting in the sample analysis.<strong> </strong></p> <h2><strong>References</strong></h2> <p>Cooper, E. J., Dullinger, S., & Semenchuk, P. (2011). Late snowmelt delays plant development and results in lower reproductive success in the High Arctic. <em>Plant Science</em>, 180(1), 157–167. https://doi.org/10.1016/j.plantsci.2010.09.005</p> <p>Morana, C., Darchambeau, F., Roland, F. A. E., Borges, A. V., Muvundja, F., Kelemen, Z., et al. (2015). Biogeochemistry of a large and deep tropical lake (Lake Kivu, East Africa: insights from a stable isotope study covering an annual cycle. <em>Biogeosciences</em>, 12(16), 4953–4963. https://doi.org/10.5194/bg-12-4953-2015</p> <p>Parmentier, F. J. W., Nilsen, L, Tømmervik, H., Meisel, O. H., Bröder, L., Vonk, J. E., Westermann, S., Semenchuk, P. R., Cooper, E. J., Rapid Ice-Wedge Collapse and Permafrost Carbon Loss Triggered by Increased Snow Depth and Surface Runoff, <em>Geophysical Research Letters</em>, In press</p>
Рис. 1. РаспоΛожение стационаров вбΛизи насеΛенных пунктов, в окрестностях которых собираΛись воΑные и почвенные пробы: 1 —ЗакатаΛа (41.755469 N, 46.658248 E; 2 — ИсмаиΛΛы (40.971043 N, 48.133806 E; 3 — ПиргуΛи (40.868328 N, 48.599481 E); 4 — АΛтыагач (40.942707 N, 49.027354 E); 5 — Шемаха (40.742370 N, 48.639842 E); 6 — Куба (41.424798 N, 48.487536 E) Fig 1. Location of Permanent Sampling Points near the settlements in the vicinity of which water and soil samples were collected: 1 — Zagatala (41.755469 N, 46.658248 E; 2 — Ismayilli (40.971043 N, 48.133806 E; 3 — Pirguli (40.868328 N, 48.599481 E); 4 — Altiagach (40.942707 N, 49.027354 E); 5 — Shemakha (40.742370 N, 48.639842 E); 6 — Сuba (41.424798 N, 48.487536 E) in Ciliates of fresh waters and soils of the Greater Caucasus (within Azerbaijan)
Рис. 1. РаспоΛожение стационаров вбΛизи насеΛенных пунктов, в окрестностях которых собираΛись воΑные и почвенные пробы: 1 —ЗакатаΛа (41.755469 N, 46.658248 E; 2 — ИсмаиΛΛы (40.971043 N, 48.133806 E; 3 — ПиргуΛи (40.868328 N, 48.599481 E); 4 — АΛтыагач (40.942707 N, 49.027354 E); 5 — Шемаха (40.742370 N, 48.639842 E); 6 — Куба (41.424798 N, 48.487536 E) Fig 1. Location of Permanent Sampling Points near the settlements in the vicinity of which water and soil samples were collected: 1 — Zagatala (41.755469 N, 46.658248 E; 2 — Ismayilli (40.971043 N, 48.133806 E; 3 — Pirguli (40.868328 N, 48.599481 E); 4 — Altiagach (40.942707 N, 49.027354 E); 5 — Shemakha (40.742370 N, 48.639842 E); 6 — Сuba (41.424798 N, 48.487536 E)
FIG. 26 in Triphoridae (Gastropoda) from Martinique sampled by the MADIBENTHOS expedition, with notes on shallow-water species from Guadeloupe
FIG. 26. — Proportion of triphorid species sampled by MADIBENTHOS, according to depth zones and total number of shells/specimens (n).
FIG. 23. — A in Triphoridae (Gastropoda) from Martinique sampled by the MADIBENTHOS expedition, with notes on shallow-water species from Guadeloupe
FIG. 23. — A, Geographic range of triphorids from Martinique, whether endemic vs present in other sites in Lesser Antilles or beyond in the Northwest or West Atlantic; B, total shallow-water triphorids recorded in each well-sampled locality, including the number of shared species with Martinique; references from Brazil are those of M. Fernandes and colleagues, from Cuba those of E. Rolán (or J. Espinosa) and colleagues, from Aruba, Bonaire and Curaçao (ABC Is.) are De Jong & Coomans (1988), Moolenbeek & Faber (1989), Faber & Moolenbeek (1991) and Faber (2010), and Redfern (2013) from Abaco, Bahamas; C, number of triphorid species per geographic zone; D, number of triphorid species per Caribbean or Atlantic side in Martinique; E, F, nMDS of geographic and bathymetric zones regarding triphorid species, based on Bray-Curtis (E) and Jaccard (F) indexes: dark green, Nord Atlantique; light green, Sud Atlantique; dark blue, Nord Caraibe; light blue, Sud Caraibe; red, Baie de Fort-de-France; dots, 0-10 m; squares, 11-20 m; triangles, 21-30 m; X, 31-60 m; + - 61-85 m.
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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.