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Long-term monitoring of herpetofauna along the Salt and Gila Rivers in and near the greater Phoenix metropolitan area, ongoing since 2012 (Reformatted to a Darwin Core Archive)
This data package is formatted as a Darwin Core Archive (DwC-A, event core). For more information on Darwin Core see https://www.tdwg.org/standards/dwc/. This Level 2 data package was derived from the Level 1 data package found here: https://pasta.lternet.edu/package/metadata/eml/edi/192/5, which was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-cap/627/5. The abstract below was extracted from the Level 0 data package and is included for context:
Water stable isotopes for streams samples in Mary's River Watershed, 2014-2015
Water samples were collected for analysis of water stable isotopes (O18/16 and H2/H1) in the Marys River basin of Benton County, Oregon. Water samples collected approximately monthly between 2014 and 2015 at 24 streams.
Movements of aquatic predators within the Shark River estuary (FCE LTER), Everglades National Park, South Florida, USA, June 2007 - ongoing
In South Florida, the allocation of freshwater resources is a constant source of debate. Stakeholders competing for freshwater include agriculture, rapidly growing urban populations, and the natural environment with its associated ecosystem services. Among these services, one of the most valuable is the provisioning of coastal recreational fisheries, which generates roughly $8 billion annually in angler expenditures in Florida alone. Yet, the interplay between freshwater allocation and the sustainability of these coastal fisheries remains poorly understood. One pathway of influence is through the availability of resources and food. Seasonal rainfall and freshwater management drive pulses of freshwater marsh prey into estuaries, creating short-lived but abundant foraging opportunities. Previous research has shown that these prey pulses occur primarily in the inland reaches of the estuary, providing resources for recreationally and ecologically important consumers such as the Common Snook (Centropomus undecimalis), Florida Largemouth Bass (Micropterus salmoides), Red Drum (Sciaenops ocellatus), Atlantic Tarpon (Megalops atlanticus), Bull Shark (Carcharhinus leucas), and American Alligator (Alligator mississippiensis). However, it is unclear how far these species move to exploit this subsidy, or whether such pulses increase reproductive output and long-term population stability. Further, sea level rise is changing how economically and ecologically important taxa use estuarine environments. To address these questions, we use acoustic telemetry to track the multi-year (2007–present) movements of key estuarine taxa, including Common Snook, Florida Largemouth Bass, American Alligator, and Bull Shark, within the Shark River Estuary of Everglades National Park. This multi-species approach expands our focus from freshwater and estuarine predators to include apex predators that link freshwater, estuarine, and marine ecosystems. From a science perspective, our research provides
Monthly fluorescence parallel factor analysis (PARAFAC) components for Shark River Slough, Taylor Slough, and Florida Bay, Everglades National Park (FCE LTER), Florida, USA, April 2011 - ongoing
Dissolved organic matter plays an important role in biogeochemical processes in aquatic environments such as elemental cycling, microbial loop energetics, and the transport of materials across landscapes. Since most of N (> 90%) and P (around 90%) is in the organic form in the oligotrophic subtropical Florida Coastal Everglades (FCE), study of the source and dynamics of dissolved organic matter (DOM) in the ecosystem is crucial for the better understanding of the biogeochemical cycling of nutrients. FCE are composed of estuaries with distinct regions with different biogeochemical processes. Freshwater marsh primarily receives terrestrial input and local autochthonous vegetation production. Mangrove ecotone, nevertheless, is affected by the tidal contributions from Florida Bay and local mangrove production. Florida Bay (FB) is a wedge-shaped shallow oligotrophic estuary which lays south of the Everglades, the bottom of which is covered with a dense biomass of seagrass. The sources of both freshwater and nutrients in FCE are difficult to quantify, owing to the non-point source nature of runoff from the Everglades and the dendritic cross channels in the mangroves. Furthermore, the combination of multiple DOM sources (freshwater marsh vegetation, mangroves, phytoplankton, seagrass, etc.), and the potential seasonal variability of their relative contribution, along with the history of (photo)chemical and microbial diagenetic processing, and complex advective circulation, makes the study of DOM dynamics in FCE particularly difficult using standard schemes of estuarine ecology. Quantitative information of DOM is very useful to investigate the biogeochemical cycling of DOM to a certain degree, however, qualitative information is necessary to better understand the source and dynamics of DOM. Since fluorescence spectroscopic techniques are very sensitive, quick and simple, they have been applied to investigate the fate of DOM in estuaries. Here, we have quantified a series of
Continuous salinity, temperature and depth measurements from moored hydrographic data loggers deployed at GCE1_Hydro (Sapelo River near Eulonia, Georgia) from 01-Jan-2020 through 31-Dec-2020
Conductivity, temperature and pressure were measured continuously at Georgia Coastal Ecosystems LTER sampling location GCE1_Hydro (Sapelo River near Eulonia, Georgia) from 01-Jan-2020 through 31-Dec-2020. Observations were logged at 30 minute intervals by moored Sea-Bird Electronics MicroCAT 37-SM data loggers and downloaded approximately quarterly. Salinity, depth and sigma-t (density anomaly) were calculated from the measured parameters using standard UNESCO algorithms. This data set was collected as part of the GCE-LTER Project continuous salinity, temperature and water level monitoring program.
Continuous salinity, temperature and depth measurements from moored hydrographic data loggers deployed at GCE7_Hydro (Altamaha River near Carrs Island, Georgia) from 01-Jan-2020 through 31-Dec-2020
Conductivity, temperature and pressure were measured continuously at Georgia Coastal Ecosystems LTER sampling location GCE7_Hydro (Altamaha River near Carrs Island, Georgia) from 01-Jan-2020 through 31-Dec-2020. Observations were logged at 30 minute intervals by moored Sea-Bird Electronics MicroCAT 37-SM data loggers and downloaded approximately quarterly. Salinity, depth and sigma-t (density anomaly) were calculated from the measured parameters using standard UNESCO algorithms. This data set was collected as part of the GCE-LTER Project continuous salinity, temperature and water level monitoring program.
Continuous salinity, temperature and depth measurements from moored hydrographic data loggers deployed at GCE8_Hydro (Altamaha River near Aligator Creek, Georgia) from 01-Jan-2020 through 31-Dec-2020
Conductivity, temperature and pressure were measured continuously at Georgia Coastal Ecosystems LTER sampling location GCE8_Hydro (Altamaha River near Aligator Creek, Georgia) from 01-Jan-2020 through 31-Dec-2020. Observations were logged at 30 minute intervals by moored Sea-Bird Electronics MicroCAT 37-SM data loggers and downloaded approximately quarterly. Salinity, depth and sigma-t (density anomaly) were calculated from the measured parameters using standard UNESCO algorithms. This data set was collected as part of the GCE-LTER Project continuous salinity, temperature and water level monitoring program.
Continuous salinity, temperature and depth measurements from moored hydrographic data loggers deployed at GCE9_Hydro (Altamaha River near Rockdedundy Island, Georgia) from 01-Jan-2020 through 31-Dec-2020
Conductivity, temperature and pressure were measured continuously at Georgia Coastal Ecosystems LTER sampling location GCE9_Hydro (Altamaha River near Rockdedundy Island, Georgia) from 01-Jan-2020 through 31-Dec-2020. Observations were logged at 30 minute intervals by moored Sea-Bird Electronics MicroCAT 37-SM data loggers and downloaded approximately quarterly. Salinity, depth and sigma-t (density anomaly) were calculated from the measured parameters using standard UNESCO algorithms. This data set was collected as part of the GCE-LTER Project continuous salinity, temperature and water level monitoring program.
Continuous salinity, temperature and depth measurements from moored hydrographic data loggers deployed at GCE10_Hydro (Duplin River west of Sapelo Island, Georgia) from 01-Jan-2020 through 31-Dec-2020
Conductivity, temperature and pressure were measured continuously at Georgia Coastal Ecosystems LTER sampling location GCE10_Hydro (Duplin River west of Sapelo Island, Georgia) from 01-Jan-2020 through 31-Dec-2020. Observations were logged at 30 minute intervals by moored Sea-Bird Electronics MicroCAT 37-SM data loggers and downloaded approximately quarterly. Salinity, depth and sigma-t (density anomaly) were calculated from the measured parameters using standard UNESCO algorithms. This data set was collected as part of the GCE-LTER Project continuous salinity, temperature and water level monitoring program.
Continuous salinity, temperature and depth measurements from moored hydrographic data loggers deployed at GCE11_Hydro (Altamaha River near Lewis Creek, Georgia) from 01-Jan-2020 through 31-Dec-2020
Conductivity, temperature and pressure were measured continuously at Georgia Coastal Ecosystems LTER sampling location GCE11_Hydro (Altamaha River near Lewis Creek, Georgia) from 01-Jan-2020 through 31-Dec-2020. Observations were logged at 30 minute intervals by moored Sea-Bird Electronics MicroCAT 37-SM data loggers and downloaded approximately quarterly. Salinity, depth and sigma-t (density anomaly) were calculated from the measured parameters using standard UNESCO algorithms. This data set was collected as part of the GCE-LTER Project continuous salinity, temperature and water level monitoring program.
Mississippi River spatial water chemistry Environmental Research Letters datasets
We mapped surface water chemistry along the entire length of the Upper Mississippi River (UMR) to understand spatial patterns in nitrate sources and processing. We used a sensor-based and boat-mounted sensing platform to continuously measure underway water chemistry. Measurements were linked with global positioning systems (GPS) to create maps of surface water chemistry. Here, we archive data associated with an Environmental Research Letters publication (Loken et al. 2018). Data include a single spatial survey of the entire length of the UMR (Minneapolis, Minnesota to Cairo, Illinois) in August 2015 and repeat surveys in Navigation Pool 8 (located near La Crosse, WI). Data have been provided in three formats (raw, hydraulic-corrected, and tau-corrected). Additionally, we archive laboratory chemistry data from water samples collected during the project. Sites include a range of main channel, backwaters, and tributaries. Water chemistry samples were analyzed at the North Temperate Lakes - Long Term Ecological Research facility and linked with underway sensor measurements.
Columbia River spatial water chemistry
We mapped surface water chemistry along a ~600 km segment of the Columbia River. We used a sensor-based and boat-mounted sensing platform to continuously measure underway water chemistry. Measurements were linked with global positioning systems (GPS) to create maps of surface water chemistry. Data were collected over 6 days in July 2018 and include a single longitudinal survey of the Columbia River. Data have been provided in three formats (raw, hydraulic-corrected, and tau-corrected). Additionally, we archive laboratory chemistry data from water samples collected during the project. Sites include a range of main channel, backwaters, and tributaries. Water chemistry samples were analyzed at the North Temperate Lakes - Long Term Ecological Research facility and linked with underway sensor measurements.
PIE LTER water-column nutrient and particulate transects along the Parker River Estuary, Massachusetts, 1994 - 2019.
Water chemistry including nutrient concentrations for various forms of N, P, C, as well as suspended sediments and light extinction coefficients, was determined from bi-annual nutrient transects along the Plum Island Sound estuary from the Parker River Dam to the mouth of the sound. Grab samples were taken at 11 sites along a 24 km transect from the mouth of the estuary to as near the dam at the head of the estuary as poosible. These samples have generally been collected in Spring and Fall and are done in conjunction with metabolism transects measuring dissolved oxygen levels. The Spring and Fall transects correspond to the high-flow, pre-growth season and the low-flow, post-growth season, respectively.
SWOT River Database (SWORD)
<p><strong>VERSION NOTES:</strong></p> <p><strong>v17 versus v17b</strong></p> <ul> <li>"Type" change for 1662 reaches and associated nodes globally. Please reference the Product Description Document for the "Type" identifier definition. </li> <li>Updates to reach and node lengths and distance-from-outlet variable to correct a bug in the node length calculation in select reaches (<2% of reaches were impacted globally).</li> <li>SWORD v17b is the official version for SWOT <strong>Version D</strong> <a href="https://podaac.jpl.nasa.gov/SWOT?tab=datasets-information&sections=about"><strong>RiverSP Vector Products</strong></a>.</li> </ul> <p>The project and public versions of SWORD were kept separate while algorithms were being developed in preparation for SWOT's launch in 2022. Now that the SWOT mission is here, the project version of SWORD is published as the public version which is why the version numbers jump after v2. The primary difference between the project and public versions of SWORD are extra "filler" variables in the NetCDF format that will be used for calculating discharge. For details on the filler variables please reference the Product Description Document provided with the downloads. </p> <p>If you use the SWORD Database in your work, please cite: Altenau et al., (2021) The Surface Water and Ocean Topography (SWOT) Mission River Database (SWORD): A Global River Network for Satellite Data Products. <em>Water Resources Research</em>. <a href="https://doi.org/10.1029/2021WR030054">https://doi.org/10.1029/2021WR030054</a></p> <p>You can also visit <a href="http://www.swordexplorer.com"><strong>www.swordexplorer.com</strong></a> to explore the current version of SWORD before downloading. </p> <p><strong>1. Summary:</strong></p> <p>The Surface Water and Ocean Topography (SWOT) satellite mission vastly expands observations of river water surface elevation (WSE), width, and slope. In order to facilitate a wide range of new analyses with flexibility, the SWOT mission provides a range of relevant data products. One product the SWOT mission provides are river vector products stored in shapefile format for each SWOT overpass (JPL Internal Document, 2020b). The <strong>SWO</strong>t <strong>R</strong>iver <strong>D</strong>atabase (<strong>SWORD</strong>) combines multiple global river- and satellite-related datasets to define the nodes and reaches that constitute SWOT river vector data products. SWORD provides high-resolution river nodes (200 m) and reaches (~10 km) in shapefile and netCDF formats with attached hydrologic variables (WSE, width, slope, etc.) as well as a consistent topological system for global rivers 30 m wide and greater.</p> <p><strong>2. Data Formats:</strong></p> <p>The SWORD database is provided in netCDF, geopackage, and shapefile formats. All files start with a two-digit continent identifier ("af" – Africa, "as" – Asia / Siberia, "eu" – Europe / Middle East, "na" – North America, "oc" – Oceania, "sa" – South America). File syntax denotes the regional information for each file and varies slightly between netCDF and shapefile formats.</p> <p>NetCDF files are structured in 3 groups: centerlines, nodes, and reaches. The centerline group contains location information and associated reach and node ids along the original GRWL 30 m centerlines (Allen and Pavelsky, 2018). Node and reach groups contain hydrologic attributes at the ~200 m node and ~10 km reach locations (see description of attributes below). NetCDFs are distributed at continental scales with a filename convention as follows: [continent]_sword_v17.nc (<em>i.e. na_sword_v17.nc</em>).</p> <p>SWORD shapefiles consist of four main files (.dbf, .prj, .shp, .shx). There are separate shapefiles for nodes and reaches, where nodes are represented as ~200 m spaced points and reaches are represented as polylines. All shapefiles are in geographic (latitude/longitude) projection, referenced to datum WGS84. Shapefiles are split into HydroBASINS (Lehner and Grill, 2013) Pfafstetter level 2 basins (hbXX) for each continent with a naming convention as follows: [continent]_sword_[nodes/reaches]_hb[XX]_v17.shp (<em>i.e. na_sword_nodes_hb74_v17.shp; na_sword_reaches_hb74_v17.shp</em>).</p> <p>SWORD geopackage files are split into two files for nodes and reaches per continental region, where nodes are represented as 200 m spaced points and reaches are represented as polylines. All geopackage files are in geographic (latitude/longitude) projection, referenced to datum WGS84. Geopackage file names are distributed at continental scales and are defined by a two-digit identifier (Table 2): [continent]_sword_[nodes/reaches]_v17.gpkg (i.e. na_sword_nodes_v17.gpkg; na_sword_reaches_v17.gpkg).</p> <p><strong>3. Attribute Description:</strong></p> <p>This list contains the primary attributes contained in the SWORD database.</p> <ul> <li><strong>x:</strong> Longitude of the node or reach ranging from 180°E to 180°W (units: decimal degrees).</li> <li><strong>y:</strong> Latitude of the node or reach ranging from 90°S to 90°N (units: decimal degrees).</li> <li><strong>node_id:</strong> ID of each node. The format of the id is as follows: CBBBBBRRRRNNNT where C = Continent (the first number of the Pfafstetter basin code), B = Remaining Pfafstetter basin code up to level 6, R = Reach number (assigned sequentially within a level 6 basin starting at the downstream end working upstream), N = Node number (assigned sequentially within a reach starting at the downstream end working upstream), T = Type (1 – river, 3 – lake on river, 4 – dam or waterfall, 5 – unreliable topology, 6 – ghost node).</li> <li><strong>node_length </strong><em>(node files only</em>): Node length measured along the GRWL centerline points (units: meters).</li> <li><strong>reach_id:</strong> ID of each reach. The format of the id is as follows: CBBBBBRRRRT where C = Continent (the first number of the Pfafstetter basin code), B = Remaining Pfafstetter basin codes up to level 6, R = Reach number (assigned sequentially within a level 6 basin starting at the downstream end working upstream, T = Type (1 – river, 3 – lake on river, 4 – dam or waterfall, 5 – unreliable topology, 6 – ghost reach).</li> <li><strong>reach_length </strong>(<em>reach files only</em>): Reach length measured along the GRWL centerline points (units: meters).</li> <li><strong>wse:</strong> Average water surface elevation (WSE) value for a node or reach. WSEs are extracted from the MERIT Hydro dataset (Yamazaki et al., 2019) and referenced to the EGM96 geoid (units: meters).</li> <li><strong>wse_var:</strong> WSE variance along the GRWL centerline points used to calculate the average WSE for each node or reach (units: square meters).</li> <li><strong>width:</strong> Average width for a node or reach (units: meters).</li> <li><strong>width_var:</strong> Width variance along the GRWL centerline points used to calculate the average width for each node or reach (units: square meters).</li> <li><strong>max_width: </strong>Maximum width value across the channel for each node or reach that includes island and bar areas (units: meters).</li> <li><strong>facc: </strong>Maximum flow accumulation value for a node or reach. Flow accumulation values are extracted from the MERIT Hydro dataset (Yamazaki et al., 2019) (units: square kilometers).</li> <li><strong>n_chan_max:</strong> Maximum number of channels for each node or reach.</li> <li><strong>n_chan_mod:</strong> Mode of the number of channels for each node or reach.</li> <li><strong>obstr_type: </strong>Type of obstruction for each node or reach based on the Globale Obstruction Database (GROD, Whittemore et al., 2020) and HydroFALLS data (http://wp.geog.mcgill.ca/hydrolab/hydrofalls). Obstr_type values: 0 - No Dam, 1 - Dam, 2 - Channel Dam, 3 - Lock, 4 - Low Permeable Dam, 5 - Waterfall.</li> <li><strong>grod_id:</strong> The unique GROD ID for each node or reach with obstr_type values 1-4.</li> <li><strong>hfalls_id:</strong> The unique HydroFALLS ID for each node or reach with obstr_type value 5.</li> <li><strong>dist_out:</strong> Distance from the river outlet for each node or reach (units: meters).</li> <li><strong>type:</strong> Type identifier for a node or reach: 1 – river, 2 – lake off river, 3 – lake on river, 4 – dam or waterfall, 5 – unreliable topology, 6 – ghost reach/node.</li> <li><strong>lakeflag</strong>: GRWL water body identifier for each reach: 0 – river, 1 – lake/reservoir, 2 – canal, 3 – tidally influenced river.</li> <li><strong>manual_add </strong>(<em>node files only</em>): Binary flag indicating whether the node was manually added to the public GRWL centerlines (Allen and Pavelsky, 2018). These nodes were originally given a width = 1, but have since been updated to have the reach width values.</li> <li><strong>meand_len </strong>(<em>node files only</em>): Length of the meander that a node belongs to, measured from beginning of the meander to its end in meters. For nodes longer than one meander, the meander length will represent the average length of all meanders belonging to the node (units: meters).</li> <li><strong>sinuosity </strong>(<em>node files only</em>): The total reach length the node belongs to divided by the Euclidean distance between the reach end points.</li> <li><strong>slope </strong>(<em>reach files only</em>): Reach average slope calculated along the GRWL centerline points. Slopes are calculated using a linear regression (units: meters/kilometer).</li> <li><strong>n_nodes</strong> (<em>reach files only</em>): Number of nodes associated with each reach.</li> <li><strong>n_rch_up</strong> (<em>reach files only</em>): Number of upstream reaches for each reach.</li> <li><strong>n_rch_down</strong> (<em>reach files only</em>): Number of downstream reaches for each reach.</li> <li><strong>rch_id_up</strong> (<em>reach files only</em>): Reach IDs of the upstream neighboring reaches.</li> <li><strong>rch_id_dn</strong> (<em>reach files only</em>): Reach IDs of the downstream neighboring reaches.</li> <li><strong>swot_obs </strong>(<em>reach files only</em>): The maximum number of SWOT passes to intersect each reach during the 21 day orbit cycle.</li> <li><strong>swot_orbits </strong>(<em>reach files only</em>): A list of the SWOT orbit tracks that intersect each reach during the 21 day orbit cycle.</li> <li><strong>river_name:</strong> All river names associated with a node or reach. If there are multiple names for a node or reach they are listed in alphabetical order and separated by a semicolon.</li> <li><strong>edit_flag:</strong> Numerical flag indicating the type of update applied to SWORD nodes or reaches from the previous version. Flag descriptions are listed in the Product Description Documentation included with the file downloads.</li> <li><strong>trib_flag: </strong>Binary flag indicating if a large tributary not represented in SWORD is entering a node or reach. 0 - no tributary, 1 - tributary.</li> </ul> <p><strong>4. References:</strong></p> <p>Allen, G. H., & Pavelsky, T. M. (2018). Global extent of rivers and streams. <em>Science</em>, 361(6402), 585-588.</p> <p>Altenau, E. H., Pavelsky, T. M., Durand, M. T., Yang X., Frasson, R. P. d. M., & Bendezu, L. (2021). The Surface Water and Ocean Topography (SWOT) Mission River Database (SWORD): A global river network for satellite data products". Water Resources Research.</p> <p>Biancamaria, S., Lettenmaier, D. P., & Pavelsky, T. M. (2016). The SWOT mission and its capabilities for land hydrology. In Remote Sensing and Water Resources (pp. 117-147). Springer, Cham.</p> <p>JPL Internal Document (2020b). Surface Water and Ocean Topography Mission Level 2 KaRIn high rate river single pass vector product, JPL D-56413, Rev. A, https://podaac-tools.jpl.nasa.gov/drive/files/misc/web/misc/swot_mission_docs/pdd/D-56413_SWOT_Product_Description_L2_HR_RiverSP_20200825a.pdf</p> <p>Lehner, B., Grill G. (2013): Global river hydrography and network routing: baseline data and new approaches to study the world's large river systems. Hydrological Processes, 27(15): 2171–2186. Data is available at www.hydrosheds.org.</p> <p>Tessler, Z. D., Vörösmarty, C. J., Grossberg, M., Gladkova, I., Aizenman, H., Syvitski, J. P. M., & Foufoula-Georgiou, E. (2015). Profiling risk and sustainability in coastal deltas of the world. Science, 349(6248), 638-643.</p> <p>Whittemore, A., Ross, M. R., Dolan, W., Langhorst, T., Yang, X., Pawar, S., Jorissen, M., Lawton, E., Januchowski-Hartley, S., & Pavelsky, T. (2020). A Participatory Science Approach to Expanding Instream Infrastructure Inventories. <em>Earth's Future</em>, <em>8</em>(11), e2020EF001558.</p> <p>Yamazaki, D., Ikeshima, D., Sosa, J., Bates, P. D., Allen, G., & Pavelsky, T. (2019). MERIT Hydro: A high-resolution global hydrography map based on latest topography datasets. Water Resources Research. <a href="https://doi.org/10.1029/2019WR024873">https://doi.org/10.1029/2019WR024873</a>.</p> <p>Yang, X., Pavelsky, T. M., Allen, G. H. (2019). The past and future of global river ice. Nature.</p> <p>SWOT Orbits: https://www.aviso.altimetry.fr/en/missions/future-missions/swot/orbit.html</p> <p>HydroFALLS: <a href="http://wp.geog.mcgill.ca/hydrolab/hydrofalls/">http://wp.geog.mcgill.ca/hydrolab/hydrofalls/</a></p>
St Clair River delta velocities - North, Middle and South channels
<p>Velocity data collected from the Middle Channel of the St. Clair River Delta. These data were collected using a vertically mounted ADCP, Teledyne RDI Sentinel V, 1000MHz.</p><p>The data are velocity magnitude and direction beginning 0.99m above the riverbed and a value reported every 0.5 meters of depth to within approximately 1.5 meters of the surface. </p><p> </p><p>-The instrument was set up to ping every 1 second for 120 seconds with a new collection of vertical bins collected beginning every 600 seconds. </p><p>-Setup provides a two minute average, in each bin, every 10 minutes</p><p>'Range to Boundary' set by pressure</p><p>removed the 'side lobe interference'</p><p> </p><p>Instruments were deployed on different days but generally have data for the following period</p><p>Start Date Dec 2018 10:10 am Eastern Standard Time</p><p>End Date: April 2019 12:20 pm Eastern Standard Time</p><p> </p><p>The instruments were placed at the following coordinates:</p><p>North Channel: lat: N42.61720 Long: W82.57020 </p><p>Middle Channel: lat: N42.59983 long: W82.60316</p><p>South Channel: lat N42.58007 long: W82.56192</p>
Historical and future water demand for households and industry for the STARS4Water river basins
<pre>This repository contains the data related to the deliverable D2.5 "Data sets on scenario narratives" prepared within the STARS4Water project ("Supporting STakeholders for Adaptive, Resilient and Sustainable Water Management").</pre> <p>The data spans historical years (2000-2020) and projections under different Shared Socioeconomic Pathways (SSP1-5) scenarios for the years 2020-2050.</p> <p>The repository contains historical and future water demand for households and industry for the STARS4Water river basins divided into two items packed in zip file:<br>1. STARS4Water_Domestic_and_Industrial_Water_Demands_historical.zip for years 2000-2020<br>2. STARS4Water_Domestic_and_Industrial_Water_Demands_projections.zip for years 2020-2050 (SSP1-SSP5)<br><br>The data in the repository was prepared based on Python scripts developed by Stephanie E. Lips and described in <em>Towards a global high </em><em>resolution water demand dataset. Effect of data quality and downscaling techniques - the case for Europe</em>, Utrecht University, 2020 as well as open source databases of WorldPop, WorldBank, UNCTADstat, EIA, Eurostat, Aquastat, UNEP an others. </p>
Bulk, biomarker and mineralogy data of grain size fractions along a land-sea transect offshore the Atchafalaya river, northern Gulf of Mexico
<p>This dataset comprises the bulk, biomarker and mineralogy data of partitioned surface sediments along a land-sea transect offshore the Atchafalaya River, northern Gulf of Mexico. It includes the total concentrations of the biomarkers and proxies as presented in the accompanied publication, as well as concentrations of single isomers. Supplement to: Yedema et al., (2024); Influence of Organo-mineral Associations on Terrestrial Particulate Organic Matter Dispersal in the northern Gulf of Mexico (doi.)</p> <p> </p> <p><strong>This research has been supported by the Netherlands Earth System Science Centre (grant no. 024.002.001)</strong></p> <p> </p>
Methane concentrations and oxidation rates in land-terminating glacial runoff: measurements from three glacial rivers and a paraglacial lake in Iceland and a literature review
<div> <p>This dataset contains methane measurements from Icelandic lakes and rivers during the summer of 2018 and 2019. This includes data from net methane oxidation assays with sediment and overlying water from one paraglacial lake and one glacial river, and surface methane concentration data from grab samples in 3 glacial streams and 15 Icelandic lakes (1 of which is paraglacial). The dataset also contains methane concentration data from a synthesis of relevant aquatic ecosystems, used to compare against the original measurements collected. </p> </div> <div> <p>Data and Literature Review Synthesis is supplement to Strock et al. 2024 <em>Oxidation is a potentially significant methane sink in land-terminating glacial runoff</em> published in Nature Scientific Reports. </p> <div> <p>This study was funded by: National Geographic Society Changing Polar Systems grant (CP4-162R-18); In-kind support from the U.S. Geological Survey; Dickinson College Research and Development; Churchill Exploration Fund at Dickinson College </p> </div> </div>
LTER-Italy site Saldur River Catchment figure
<p>Geographical representation of the LTER-Italy site Saldur River Catchment (LTER_EU_IT_099) - DEIMS-ID <a href="https://deims.org/97ff6180-e5d1-45f2-a559-8a7872eb26b1">https://deims.org/97ff6180-e5d1-45f2-a559-8a7872eb26b1</a></p>
LTER-Italy site Saldur river figure
<p>Geographical representation of the LTER-Italy site Saldur river (LTER_EU_IT_100) - DEIMS-ID <a href="https://deims.org/7f479263-8f0b-447e-a33d-e08723c86184">https://deims.org/7f479263-8f0b-447e-a33d-e08723c86184</a></p>
ScienceDex guides
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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.