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361 results for “January”
Eddy covariance 30-minute CO2 fluxes with accompanying biophysical variables from the GCE-LTER flux tower site from December 2018 to January 2020
Eddy covariance (EC) CO2 fluxes from December 2018 to January 2020 collected over a Spartina alterniflora marsh located on the western side of Sapelo Island bounded by the Duplin River and Barn Creek. EC fluxes were processed in EddyPro 7. Additional biophysical variables included are air temperature, relative humidity, vapor pressure deficit, soil temperature, and water table height from a nearby tidal creek.
DOM composition in Altamaha River and Sapelo Sound estuaries measured by FT-ICR mass spectrometry in 2017 (April, July, October) and 2018 (January and October)
Extreme events such as hurricanes and tropical storms often result in large fluxes of dissolved organic carbon (DOC) to estuaries. Precipitation associated with tropical storms may be increasing in the southeastern U.S., which can potentially impact dissolved organic matter (DOM) dynamics and cycling in coastal systems. Here, DOM composition at the Altamaha River and Estuary (Georgia, U.S.A.) was investigated over multiple years capturing seasonal variations in river discharge, high precipitation events, and the passage of two hurricanes which resulted in substantial storm surges. Optical measurements of DOM indicate that the terrigenous signature in the estuary is linearly related to freshwater content and is similar after extreme events with or without a storm surge and during peak river flow. Molecular level analysis revealed significant differences, however, with a large increase of highly aromatic compounds after extreme events exceeding what would be expected by freshwater content alone. Although extreme events are often followed by increased DOC biodegradation, the terrigenous material added during those events does not appear to be more labile than the remainder of the DOM pool that was captured by ultrahigh-resolution mass spectrometry analysis. This suggests that the added terrigenous organic matter may be exported to the coastal ocean, while a fraction of the organic matter that co-varied with the terrigenous DOM may contribute to the increased biomineralization in the estuary, with implications to carbon processing in coastal areas.
Listed values of Ibex 35 companies from January 2020 to November 2020
<p>The data included in the dataset are the listed values of the Ibex 35 companies from January 2020 to November 2020. The dataset is a file for each Ibex 35 company.</p>
CTD profiles Peninsula Antarctica-Weddell Sea ECA-59 (January 2023)
<p>CTD profiles Peninsula Antarctica-Weddell Sea ECA-59</p> <p>This dataset includes CTD Castaway deployments at 20 locations along the Peninsula Antarctica-Weddell Sea within the framework of the Antarctic Maritime Campaign ECA-59, conducted by INACH on board the Betanzos Vessel. Data were collected between January 5, 2023 to January 13, 2023. Funded by Programa Areas Marinas Protegidas INACH (2403252)</p> <p>CTD Cast-away serial number (CC2139005)</p> <p>Row1=cruise ECA59</p> <p>Row2_Station type</p> <p>Row3_Date=day/month/year</p> <p>Row_4_Latitude (S)</p> <p>Row_5_Longitude (w)</p> <p>Row_6_ Pressure (Decibar)</p> <p>Row_7_Depth (m)</p> <p>Row_8_Temperature (°C)</p> <p>Row_9_Conductivity (Microsiems per centimeter)</p> <p>Row_10_Salinity (Practical Salinity Units, PSU)</p> <p>Row_11_Sound velocity (Meters per seconds)</p> <p>Row_12_Density (kg per m3)</p>
EXTREMA: Ballistic capture sets at Mars over an Earth–Mars synodic period from January 1, 2030, to February 20, 2032
<p>EXTREMA (short for Engineering Extremely Rare Events in Astrodynamics for Deep-Space Missions in Autonomy) enables self-driving spacecraft, challenging the current paradigm under which spacecraft are piloted in the interplanetary space. Deep-space guidance, navigation, and control applied in a complex scenario is the subject of EXTREMA, which wants to engineer ballistic capture in a totally autonomous fashion. EXTREMA is erected on three pillars. Pillar 1 is on autonomous navigation. Pillar 2 involves autonomous guidance and control. Pillar 3 deals with autonomous ballistic capture, the focus of this work. The project has been awarded a European Research Council (ERC) Consolidator Grant in 2019.</p> <p>In Pillar 3 it is investigated how a spacecraft can attain ballistic capture in autonomy. Ballistic capture is an event that occurs in extremely-rare occasions, and requires acquiring a proper state (position, velocity) far away from the target planet [1]. Massive numerical simulations are required to find the specific conditions that support capture [2]. On average, 1 out of 10,000 conditions explored by the algorithm grants capture [3]. The union of these points defines the capture set, which in turn is used to find the capture corridors: these are streams of orbits that can be targeted far away from the planet and that guarantee ballistic capture.</p> <p>The data set contains the initial conditions of weakly-stable, unstable, crash, moon-crash, and capture sets at Mars with initial epochs uniformly distributed from 01 JAN 2030 12:00:00.000 (UTC) to 20 FEB 2032 10:32:39.144 (UTC), covering a complete Earth–Mars synodic period of approximately 780 days. The grid of initial conditions is built to maximize the capture ratio for Mars (see Figure 10 in [3]). Initial conditions are propagated in high-fidelity. The equations of motion of the restricted n-body problem are considered. The gravitational attractions of the Sun, Mercury, Venus, Earth (B*), Mars (central body), Jupiter (B), Saturn (B), Uranus (B), and Neptune (B) are taken into account. Additionally, solar radiation pressure, Mars’ non-spherical gravity, and relativistic corrections [4] (Schwarzschild solution, geodesic precession, and Lense-Thirring precession) are also included in the model.</p> <p>For additional information about the EXTREMA project visit the page <a href="http://extrema.polimi.it">extrema.polimi.it</a>.</p> <p><strong>References</strong><br> [1] F. Topputo and E. Belbruno,'Earth–Mars transfers with ballistic capture', Celestial Mechanics and Dynamical Astronomy, Vol. 121, No. 4, 2015, pp. 329–346. DOI: <a href="http://doi.org/10.1007/s10569-015-9605-8">10.1007/s10569-015-9605-8</a>.<br> [2] F. Topputo and E. Belbruno, 'Computation of weak stability boundaries: Sun–Jupiter system', Celestial Mechanics and Dynamical Astronomy, Vol. 105, No. 1-3, 2009, pp. 3–17. DOI: <a href="http://doi.org/10.1007/s10569-009-9222-5">10.1007/s10569-009-9222-5</a><br> [3] Z.-F. Luo and F. Topputo, 'Analysis of ballistic capture in Sun–planet models', Advances in Space Research, Vol. 56, No. 6, 2015, pp. 1030–1041. DOI: <a href="http://doi.org/10.1016/j.asr.2015.05.042">10.1016/j.asr.2015.05.042</a><br> [4] C. Huang, J. C. Ries, B. D. Tapley, and M. M.Watkins, 'Relativistic effects for near-earth satellite orbit determination', Celestial Mechanics and Dynamical Astronomy, Vol. 48, No. 2, 1990, pp. 167–185. DOI: <a href="http://doi.org/10.1007/BF00049512">10.1007/BF00049512</a></p> <p>* Here B stands for barycenter.</p>
Data on 'Gelatinous macrozooplankton diversity and distribution in the North Sea and Skagerrak/Kattegat during January-February 2021'
<p>This dataset includes raw and analysed data from '<strong>Gelatinous macrozooplankton diversity and distribution in the North Sea and Skagerrak/Kattegat during January-February 2021</strong>'</p> <p><strong>Louise G. Køhler<sup>1</sup>, Bastian Huwer<sup>2</sup>, José Martín Pujolar<sup>1</sup>, Malin Werner<sup>3</sup>, Karolina Wikström<sup>3</sup>, Anders Wernbo<sup>3</sup>, Maria Ovegård<sup>3</sup>, Cornelia Jaspers<sup>1*</sup></strong></p> <p> </p> <p><sup>1</sup>Centre for Gelatinous Zooplankton Ecology and Evolution, National Institute of Aquatic Resources, Technical University of Denmark, Kemitorvet 202, 2800 Kgs. Lyngby, Denmark</p> <p><sup>2</sup>National Institute of Aquatic Resources, Technical University of Denmark, Kemitorvet 201, 2800 Kgs. Lyngby, Denmark</p> <p><sup>3</sup>Institute of Marine Research, Department of Aquatic Resources (SLU Aqua), Swedish University of Agricultural Sciences, Turistgatan 5, S- 453 30 Lysekil, Sweden</p> <p>* Corresponding author: <a href="mailto:coja@aqua.dtu.dk">coja@aqua.dtu.dk</a></p> <p>This dataset includes data on the qualitative and quantitative description of the gelatinous macrozooplankton community of the North Sea during January-February 2021. Sampling was conducted during the 1<sup>st</sup> quarter International Bottom Trawl Survey (IBTS) on board the Danish R/V DANA (DTU Aqua Denmark) and the Swedish R/V Svea (SLU Sweden), as part of the ichthyoplankton investigation during night-time. A total of 147 stations were investigated in the western, central and eastern North Sea as well as the Skagerrak and Kattegat. Sampling was conducted with a 13 m long Midwater Ring Net (MIK net, Ø 2 m, mesh size 1.6 mm, cod end with smaller mesh size of 500 µm), equipped with a flow meter. The MIK net was deployed in double oblique hauls from the surface to c. 5 m above the sea floor. Samples were visually analysed unpreserved on a light table and/or with a stereomicroscope or magnifying lamp within 2 hours after catch. A total of 13,610 individuals were counted/sized. Twelve gelatinous macrozooplankton species or genera were encountered, namely the hydrozoan <em>Aequorea vitrina</em>, <em>Aglantha digitale</em>, <em>Clytia</em> spp., <em>Leuckartiara octona,</em> <em>Tima bairdii, Muggiaea atlantica</em>; the scyphozoans <em>Cyanea</em> <em>capillata and Cyanea lamarckii</em> and the ctenophores <em>Beroe</em> spp., <em>Bolinopsis infundibulum</em>, <em>Mnemiopsis leidyi</em>, <em>Pleurobrachia pileus</em>. Abundance data are presented on a volume specific (m<sup>-3</sup>) and area specific (m<sup>-2</sup>) basis. Size data have been used to estimate wet weights based on published length-weight regressions (see reference column in the dataset). This dataset contributes baseline information about the gelatinous macrozooplankton diversity and its specific distribution pattern in the extended North Sea area during winter (January-February) 2021. These data can be an important contribution to address global change impacts on marine systems, especially considering gelatinous macrozooplankton abundance changes in relation to anthropogenic stressors.</p>
High-level Data Products for NEID Solar Observations (January 1, 2021 - June 30, 2024)
<p>High-level data products derived from Sun-as-a-star observations by the NEID Solar Telescope at WIYN Observatory, spanning January 1, 2021 through June 30, 2024. We identify 117,060 observations which are unlikely to be significantly affected by weather, hardware or major calibration issues. This repository provides several high-level data products to the community to aid in the interpretation and inter comparisons of NEID solar observations. These include individual radial velocities (RV) measurements from the Penn State Research Pipeline, RVs binned over solar oscillation timescales, and daily-binned RVs, activity indicators, and cross correlation functions, as well as estimated corrections for differential extinction and the apparent solar rotation velocity. For additional information, see the manscript, "Earths within Reach: Evaluation of Strategies for Mitigating Solar Variability using 3.5 years of NEID Sun-as-a-Star Observations". </p>
ECCO Iter22 Global Ocean State Estimate - 1 January 2004 to 30 April 2005
<p>Time series of global ocean temperature, salinity, and sound speed derived from the “Estimating the Circulation and Climate of the Ocean" (ECCO) program Iter22 state estimates. The sound speed fields were computed for simulation of acoustic propagation over basin scales or longer in a realistic oceanic environment. These estimates were computed in 2010 by the JPL-MIT-SIO ECCO program. <br>Original link: http://ecco2.jpl.nasa.gov/data9/cube/iter22/lat_lon/quart_80S_80N/THETA/ , now defunct.</p> <p>The solution is mesoscale permitting. The solution was obtained on a cube sphere grid between 80S and 80N with 18-km horizontal grid spacing and 50 vertical levels (Menemenlis et al., 2005, NASA supercomputer improves prospects for ocean <br>climate research, Eos Trans., AGU 86, 89, 95–96.). State estimates were averaged over a 3-day interval. File 003 is averaged over 2004/1/1 -- 2004/1/3. Three-day-mean temperature and salinity profiles from the iter22 solution were provided on 1/4 degree <br>grid for the period 1 January 2004 to 30 April 2005. There are 162 snapshots at 3-day intervals. </p> <p>Depths were decimated to the standard 33 depths of the World Ocean Atlas to 5500 m. YearDay 1 is 1 January 1992. The number of the filename indicates the yearday in 2004. In situ temperature was computed from model potential temperature. Sound speed was computed using the Del Grosso sound speed equation. Original model profiles descended only to the model sea floor. Temperature, salinity and sound speed were filled in on a uniform grid using nearest neighbor to 5500 m depth. Values on a regular grid make life easier. Product documented in Dushaw and Menemenlis, 2014, Antipodal acoustic thermometry: 1960, 2004, Deep Sea Research Part I: Oceanographic Research Papers, 86, 1–20, https://doi.org/10.1016/j.dsr.824 2013.12.008.</p> <p>Each snapshot is stored as a netcdf 4 file. Latitude, Longitude, Depth, and YearDay variables given separately in sspgrid.nc . <br>N.B.: Values in the files are stored as 32-bit or 16-bit integers to save disk space:</p> <p>Sound Speed: saved as "round( (c-1000)*1000 )", so to get actual c: c=1000. + double(c)/1000. <br>Sound speed is stored to 3 decimal places as a 32-bit integer.</p> <p>Temperature: saved as "round( (T-10)*1000 )", so to get actual T: T=10. + double(T)/1000. <br>Temperature is stored to 3 decimal places as a 16-bit integer. Note that abyssal temperature can sometimes be negative.</p> <p>Salinity: saved as "round( (S-10)*1000 )", so to get actual S: S=10. + double(S)/1000. <br>Salinity is stored to 3 decimal places as a 16-bit integer.</p> <p>Data directory also has two matlab routines: get_section.m and dist.m. get_section.m shows how to load the files, compute the physical variable from the stored value, and compute a section of ssp, T, or S. dist.m is a utility for computing geodesics; it relies on R. Pawlowitz's m_map package which can be downloaded freely from his University of Vancouver web page.</p> <p>$ md5sum *tgz <br>53ca3621f422b89c599c92fbab71d2fc S_ecco_iter22.tgz (2.99 GB)<br>a9e9109b6dae7355bf2fa0926d436d9a ssp_ecco_iter22.tgz (6.09 GB)<br>0cb8d1c2cdee4c7377353dff722ece34 T_ecco_iter22.tgz (4.33 GB)</p>
Seefeld Cold-Air Pool Experiment (SEECAP): WRF Simulation Output with snow cover January 12 2020 0000 UTC to January 13 2020 1200 UTC
<p>The Seefeld Cold-Air Pool Experiment (SEECAP) focused on the cross-country skiing area Olympiaregion Seefeld and in particular the topographic setting in the Nordic ski arena which favors the formation of cold-air pools and took place between December 2019 and March 2020. The measurement data are described in Rudolph (2022) and Rauchöcker et al. (2024d) and meteorological measurement data associated with SEECAP are published in Rauchöcker et al. (2024c). This upload contains WRF simulation output data for the night between January 12 and January 13 2020 with snow cover. The night between January 12 and January 13 2020 featured an undisturbed cold-air pool for almost the entire night. This case was considered to feature in Rauchöcker et al. (2024d), but a different case was chosen because some measurement data was not available during this period. Also available in a different dataset are data from simulations of the night between January 16 and January 17 2020, which initially featured ideal condition for cold-air pool formation followed by a disturbance around midnight, with snow cover (Rauchöcker et al., 2024a) and also without snow cover (Rauchöcker et al., 2024b).</p> <h3><strong>WRF Simulation Output</strong></h3> <h3><strong> </strong></h3> <p>This Dataset includes data generated with WRFlux v1.4.1 (Göbel et al., 2022), a fork of the Weather Research and Forecasting model WRF (Skamarock et al. 2021). WRFlux allows to calculate the contribution of different processes to the potential temperature tendency at each grid point. The data published here is from the innermost simulation domain with 40m horizontal resolution and 10m vertical resolution close to the surface. The simulations were initialized at 00:00 UTC January 12 2020 and run until 12:00 UTC January 13 2020 and results for the same night but a coarser grid spacing are described in Rauchöcker (2022). Compared to the simulation with 200m grid spacing presented there, this simulation offers a significantly improved resolution. As input, we used ERA5 reanalysis data, 1-arc second SRTM terrain data and Corine 2018 land cover classification. The simulation was performed with modified snow cover as described in Rauchöcker (2022) and the MYNN 2.5-order PBL parameterization. A detailed description of the model setup can be found in Rauchöcker et al (2024d) and in the file <em>namelist.input</em> that was used to generate the simulation results.</p> <p>Standard WRF output can be found in <em>wrfout_40m_jan12</em>. The mean wind speed components, which were necessary to rotate the tendencies in a coordinate system that is aligned with the valley orientation, are contained in <em>windout_40m_jan12</em>. These variables were contained in the unprocessed output files produced by WRFlux; the full files were unfortunately too large to be included here. The postprocessed tendencies are stored in <em>tend_40m_jan12</em>.</p>
Underwater sounds, including killer whale and humpback whale vocalizations, recorded in northern Norway in January 2023
<p>Dataset of underwater acoustic recordings obtained during the expedition “Orcalize” that took place in Skjervøy in northern Norway from 29<sup>th</sup> December 2022 till 6<sup>th</sup> January 2023. The data contains vocalizations from killer whales and songs from humpback whales which gather in the local fjords during the winter months to feed on herring. We recorded in the band of 20 Hz – 60 kHz with calibrated hydrophones arranged in a compact tetrahedral array that we deployed over board of a motorboat. In total we provide 16 files of continuous recordings with duration from several minutes to over one hour. The total dataset is about 7 hours 37 minutes long and the memory size is 62.8 GB. See the file info.pdf for more information.</p>
Digital Elevation Models of Hunga Volcano; pre- and post- 15 January 2022 eruption
<p>This dataset contains digital elevation models (DEM) of the Hunga Volcano complex. The first is a pre-2022 eruption elevation model. The second is a post-2022 eruption elevation model.</p><p>Hunga Volcano is a volcanic complex near the island of Tongatapu in the Kingdom of Tonga. The volcano rises from ~2,500 m depth, a caldera at its summit, and two islands, Hunga Tonga and Hunga-Ha'apai, at the on the rim of the caldera. An eruption during December 2014-January 2015 was centered between the islands and combined them into one larger structure named Hunga Tonga – Hunga Ha'apai (HTHH). HTHH erupted violently on 15th January 2022, sending large clouds of ash into the atmosphere, triggering a tsunami, and reducing the size of the islands of Hunga Tonga and Hunga Ha'apai.</p><p>As a result of this event, the NIWA-Nippon Foundation Tonga Eruption Seabed Mapping Project (TESMaP) is a multidisciplinary research plan involving geological, oceanographic and biological studies that centered around three objectives:</p><ol><li>To determine the impacts of volcanic ash on ocean productivity, species composition, and biogeochemical cycling in the water column.</li><li>To determine the immediate nature and extent of the impact of ash fall/turbidity flows on deep-sea sediments and benthic ecosystems.</li><li>To determine the recovery potential of the deep-sea ecosystem.</li></ol><p>This project involved two survey voyages of the volcano and its surrounding waters. The first was carried out from <i>RV Tangaroa</i> in April and May 2022 (Mackay et al., 2022) and the second was carried out by the <i>USV Maxlimer</i> in August 2022.</p><p>TESMaP was funded from a combination of sources including The Nippon Foundation, Japan; the Natural Environmental Research Council, UK, Japan Agency for Marine Earth Science and Technology, the Tangaroa Reference Group (TRG) for ship time and the NIWA Oceans Centre. Support was given by The Nippon Foundation Seabed 2030 project and by GEBCO Alumni.</p>
Physical oceanography and meteorological data from the W1M3A observatory, Ligurian Sea (North Western Mediterranean) January 2014 - December 2014
<p>Time series data of physical oceanography (salinity, temperature) and meteorology (atmospheric pressure, wind speed and direction, air temperature and humidity, shortwave radiation, longwave radiation and rain) collected from January 2014 to December 2014 by observatory W1M3A at 1h interval. The file contains tabular data (tab delimited) with the following columns: Day; Month; Year; UTC Hour; Minute ; Longitude [deg]; Latitude [deg]; Atmospheric Pressure [hPa]; Wind speed [m/s]; Wind direction [deg]; Air Temperature [°C]; Relative air humidity [%]; Short wave Radiation [W/m2]; Long wave radiation [W/m2]; Rainfall [mm/h]; Sea temperature @ 20 m [°C]; Salinity @ 20 m [psu].</p>
NOAA Daily Surface Meteorologic Data at NCDC Flamingo Ranger Station (ID-083020) (FCE), South Florida, USA, January 1951 - January 2021
The National Climatic Data Center's (NOAA) daily mean, maximum, and minimum air temperatures and daily precipitation collected at Flamingo Station (Coop ID- 083020). This site was terminated in January 2021.
NOAA Daily Surface Meteorologic Data at NCDC Miami International Airport Station (ID-085663), South Florida, USA, January 1948 - ongoing
The National Climatic Data Center's (NOAA) daily mean, maximum, and minimum air temperatures and daily precipitation collected at Miami International Airport Station (Coop ID- 085663).
Monthly monitoring of Fluorescence, UV, Humic and non-Humic Carbon, Carbohydrates, and DOC for Shark River Slough, Taylor Slough, and Florida Bay, Everglades National Park (FCE LTER) for January 2002 to August 2004
A better understanding of the biogeochemical cycling of nutrients in the Florida Coastal Everglades is a key issue regarding the restoration of the Everglades, which is expected to change the water quality throughout South Florida. In addition to rain, the main freshwater supply to Florida Bay will be derived from Taylor Slough and the C-111 Basin in the north-east section of the Bay. While it is known that these areas deliver significant amounts of nitrogen to the Bay, a significant portion of this nitrogen is in its dissolved organic form (DON). The sources, environmental fate and bioavailability to microorganisms of this DON are however, not known. Preliminary data suggest that although proteins have been detected in canal samples, labile dissolved organic matter (DOM) components were found to increase in abundance in the freshwater marshes compared to their levels in the adjacent canal waters. Leaching experiments of biomass showed the presence of such labile DOM. However, this DOM was found to be susceptible to both biodegradation and photodecomposition. In this study we will focus on the determination of the molecular characteristics of both DOM and DON and assess the bioavailability of these materials in transects ranging from the C-111 canal and Taylor Slough to the central part of Florida Bay. Relevant water quality and spectroscopic parameters will be monitored at 11 sites on a monthly basis, while six of these sites will be sampled biannually for DOM and DON chemical characterization and bioavailability studies. Advanced analytical techniques such as pyrolysis-GC/MS, FTIR, 13C- and 15N-NMR, gel electrophoresis and LC/MS will be used in the molecular characterization effort. We envisage that this study will allow for a better assessment of the sources of DON and its bioavailability in this system.
Periphyton Accumulation Rates from Shark River Slough, Taylor Slough and Florida Bay, Everglades National Park (FCE LTER), South Florida, USA, January 2001 - ongoing
Periphyton accumulation rates were measured quarterly at FCE LTER sites in SRS and TS/Ph marshes (SRS 1-3, TS/Ph 1-3) and the seagrass meadows of Florida Bay (TS/Ph 9-11). Glass slides were used as artificial substrates for periphyton growth. Twenty slides were incubated for 2 months in triplicate periphytometers (flow-through plastic boxes) at the surface (marsh sites) and bottom (seagrass sites) at each site. In addition, at seagrass sites, twelve artificial seagrass blades were incubated for two months in triplicate at each site. Blades were made from strips of transparency film anchored to the bottom and floating upward toward the surface by use of a styrofoam bead on the top of the blade. Data are presented in terms of grams dry and ash-free dry mass accumulated per area per time, as well as the chlorophyll a content of the dry mass and its rate of accumulation. Total phosphorus content of accumulated periphyton is also provided. This is part of continuous data collection to test the hypothesis that phosphorus availability and hydroperiod influence periphyton production in the Everglades.
Periphyton Biomass Accumulation from the Shark River and Taylor Sloughs, Everglades National Park (FCE LTER), South Florida, USA, January 2003 - ongoing
Periphyton biomass was measured quarterly at three replicate locations (1, 2, and 3) at FCE LTER marsh sites (SRS 1-3; TS/Ph 1-3). A 1-m2 plot was placed in the marsh and percent cover of periphyton was visually estimated, including cover on the water surface (floating), plant stems (epiphytic), and on the bottom (benthic). Periphyton was then harvested from the quadrats into a 2000 ml perforated graduated cylinder and the volume measured. A 120-ml subsample was removed and taken back to the laboratory for analysis. Data provided include percent cover by substrate, dry, ash, and ash-free dry mass per area, chlorophyll a per gram ash-free dry mass and per area, and the total phosphorus, nitrogen and carbon concentration on a dry weight basis. This is part of a continuous data collection to test the hypothesis that phosphorus and hydrology interact to influence periphyton abundance in Everglades marshes.
Mangrove Forest Growth from the Shark River Slough, Everglades National Park (FCE), South Florida, USA, January 1995 - ongoing
All mangrove trees having a diameter at breast height (DBH) greater than 2.5 cm were tagged in two 20 x 20 m plot in stations SRS4-7 and TS/Ph-8. Measurements in Plot Num1 began in 1995; measurements in Plot Num 2 began in 2001. Plot Num1 in TS/Ph-8 was established in 2001. Measurements at SRS-7 began in 2022. DBH has been measured in the period 1995-2023. Mangrove species include Rhizophora mangle, Laguncularia racemosa, Avicennia germinans, Conocarpus erectus.
Water flow velocity data, Shark River Slough (SRS) near Chekika tree island, Everglades National Park (FCE LTER) from January 2006 to March 2021
Water velocity data measured every 5 or 15 minutes in Shark River Slough beside Chekika tree island, Everglades National Park, using Sontek Agronaut water flow sampler. Data collection is complete.
Capture data for sharks caught in standardized drumline fishing in Shark Bay, Western Australia, with accompanying abiotic data, from January 2012 to April 2014.
This file provides data on standardized drumline fishing effort (gear deployment data) targeting large elasmobranchs in Shark Bay, Western Australia between 2012 and 2014
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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.