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396 results for “Surface water”
Estimating surface water availability in high mountain rock slopes using a numerical energy balance model
<p>Model output, forcing data and physical parameters used to estimate water and energy balance. The model was calibrated with field measurements from a study site in the Mont-Blanc massif, at 3842 m a.s.l, at a slope of 55 deegrees and aspect azimut of 150 degrees (south-east). The different ModelOutput files are from simulations at different elevastions (from 4800 m to 2700 m at steps of 300 m). We used the CryoGrid community model (version 1.0) toolbox (Westermann et al., 2022) to simulate the 1D ground thermal regime and ice/water balance, and estimate the availability of surface water and its potential for infiltration in rock fractures. The S2M-SAFRAN dataset combines output from a numerical weather prediction model and <em>in situ</em> observations, and was originally developed for operational needs to estimate avalanche hazard in mountainous areas (Durand et al., 1993). The S2M-SAFRAN dataset that we used is available for various mountain areas, at elevation steps of 300 m, and with an hourly resolution between the years 1958 to 2021 (Vernay et al., 2022). It includes most parameters that are required for modeling with CryoGrid: Relative humidity, air T, incoming long wavelength radiation, incoming short wavelength solar radiation, and wind speed. To complete the forcing data we used top of the atmosphere incident solar radiation from ERA5 global reanalysis dataset (Hersbach et al., 2020).</p>
Seasonal Carbonate Chemistry Variability in Marine Surface Waters of the Pacific Northwest. Data Archive.
<p>This archive includes two .nc files (NetCDF format) containing observational data (discrete and mooring) from marine surface waters of the Pacific Northwest that have not yet been submitted to a long-term data repository. These data contributed to the development of seasonal cycle data products described in the manuscript by Fassbender et al. A metadata file is provided for the discrete data subset (upper 10 m of discrete observational data); however, the complete cruise datasets and metadata will be submitted for archival in the National Centers for Environmental Information’s (NCEI) Ocean Carbon and Acidification Data repository (<a href="https://www.nodc.noaa.gov/oceanacidification/">https://www.nodc.noaa.gov/oceanacidification/</a>). Data subsets are provided here for accelerated public access. Data users are encouraged to download the complete datasets from NCEI once they are available (<a href="https://www.nodc.noaa.gov/oceanacidification/stewardship/data_portal.html">https://www.nodc.noaa.gov/oceanacidification/stewardship/data_portal.html</a>). Metadata for the University of Washington Oceanic Remote Chemical/Optical Analyzer (ORCA) mooring observations used by Fassbender et al., including the temperature and salinity data from the Dabob Bay and Twanoh moorings, are not provided here. Quality control protocols applied to the ORCA mooring data are outlined in the Quality Assurance Project Plan (<a href="http://nwem.ocean.washington.edu/ORCA_QAPP.pdf">http://nwem.ocean.washington.edu/ORCA_QAPP.pdf</a>; Newton and Devol, 2012).</p>
LakeSST: Lake Skin Surface Temperatures in French inland water bodies
<p>The data set LakeSST contains skin surface temperature data for 442 French water bodies for the period 1999-2016 obtained from archives of Landsat 5 and Landsat 7 thermal infrared images. The overall accuracy of the satellite-derived temperature measurements is about 1.2 ºC, similar to other applications of satellite images to estimate freshwater surface temperatures. The spatial and temporal coverage of the data set makes it an ideal resource for studies on the temporal evolution of lake surface temperatures and for geographical studies of temperature patterns.</p>
Global river density, seasonal and surface water occurrence and upstream area at 250 m in the Goode Homolosine projection
<p>Several layers describing density of surface water / streams projected to the <a href="https://en.wikipedia.org/wiki/Goode_homolosine_projection">Good Homolosine projection</a>. List of layers included:</p> <ul> <li>hyd_log1p.upstream.area_merit.hydro_m = Upstream Drainage Area based on the <a href="http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_Hydro">MERIT Hydro</a>,</li> <li>hyd_river.density_gloric_p = rasterized <a href="https://www.hydrosheds.org/page/gloric">Global River Classification (GLORIC)</a> DB,</li> <li>lcv_water.occurance_jrc.surfacewater_p = Surface Water based on the JRC's <a href="https://global-surface-water.appspot.com/">Global Surface Water</a>,</li> <li>lcv_water.seasonal_probav.glc.lc100_p = Seasonal Inland Water probability based on the <a href="https://lcviewer.vito.be/">Copernicus LC100 map</a>,</li> <li>lcv_wetlands.cw_upmc.wtd_c = composite wetland (CW) map based on <a href="https://doi.org/10.1594/PANGAEA.892657">Tootchi et al. (2019)</a>,</li> <li>Goode_Homolosine_domain_250m.tif = map domain prepared by <a href="https://doi.org/10.5281/zenodo.1475152">Luís de Sousa</a>,</li> <li>tiles_GH_100km_land.gpkg = 100 km x 100 km tiling system covering the land mass,</li> </ul> <p>Important notes: Processing steps are described in detail <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/input_layers/WaterDensity">here</a></strong>. Antartica is not included. Reprojecting maps to Goode Homolosine projection can be cumbersome and small amount of artifacts at the edges of the map can be anticipated.</p> <p>These maps were develop in connection to the <a href="http://www.OpenLandMap.org">OpenLandMap.org</a> initiative.</p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>hyd = theme: hydrology and water dynamics,</li> <li>log1p.upstream.area = variable: log(X+1)*10 of the upstream area,</li> <li>merit.hydro = determination method: MERIT Hydro,</li> <li>m = mean value,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b0..0cm = vertical reference: surface,</li> <li>2017 = time reference: period 2017,</li> <li>v0.1 = version number: 0.1,</li> </ul>
Surface water loss hotspots and areas of human pressure in Italy
<p>In Italy, surface water bodies are the main source of water withdrawals. However, growing human pressures are significantly changing surface water availability, gradually reducing its extent.</p> <p>We analyze the influence of human activities on surface water losses occurred in Italy between 1984 and 2021. To do so, we identify three areas of human pressure, i.e., regions of human activities that heavily rely on the use of surface water:</p> <ol> <li>Irrigated area (IRR);</li> <li>Built-up area (BUP), indicating areas of human settlements (urban and industrial areas);</li> <li>Anthropogenic area (ANT), indicating areas of either irrigation practices or human settlements.</li> </ol> <p>Here, we provide the datasets describing the spatial distribution of surface water loss (SWL), irrigated areas, built-up areas, and anthropogenic areas, and the land cover classification for 2021 across Italy (LC). Such datasets have been derived from remotely-sensed products. In particular, the location of SWL is determined using the Transitions layer of the Global Surface Water dataset (Pekel et al., 2016), whereas the maps of irrigated and built-up areas are obtained from the Corine Land Cover (CLC) 2018 dataset (EEA, 2018). Finally, the land cover map is extracted from the ESA WorldCover map (version 2) for the year 2021 (Zanaga et al., 2022).</p> <p>In the map of SWL, irrigated areas, built-up areas, and anthropogenic areas the value 1 indicates the presence of SWL or irrigated area or built-up area or anthropogenic area, respectively. The 2021 land cover map follows the classification system of the ESA WorldCover map (11 classes).</p> <p>References:</p> <p><em>Pekel, JF.; Cottam, A.; Gorelick, N.; Belward, A.S. (2016). High-resolution mapping of global surface water and its long-term changes. Nature, 540, 418–422.</em></p> <p><em>European Union, Copernicus Land Monitoring Service 2018, European Environment Agency (EEA).</em></p> <p><em>Zanaga, D.; Van De Kerchove, R.; Daems, D.; De Keersmaecker, W.; Brockmann, C.; Kirches, G.; Wevers, J.; Cartus, O.; Santoro, M.; Fritz, S.; Lesiv, M.; Herold, M.; Tsendbazar, N.E.; Xu, P.; Ramoino, F.; Arino, O. ESA WorldCover 10 m 2021 v200, 2022.</em></p>
Measuring water quality parameters to estimate Nitrate concentration in surface water in Bonet catchment, Sligo, Ireland
<p><span>Time series data of surface water quality (temperature, pH, dissolved oxygen, oxidation-reduction potential and electrical conductivity) collected from May 2024 to September 2024 at 1m intervals. The file contains tabular data with the following columns: Date and time, Battery (%), temperature (ºC), fix Quality in fix code (Fix), Latitude (in deg), Longitude (in deg), pH,<span> </span>electrical conductivity (µS/cm), TDS (in ppm),<span> </span>Salinity in PSU(ppt), Specific Gravity (in SG), Dissolved Oxygen (in mg/L), Oxygen Saturation (in %), ORP (in mV), Altitude (in meters), Ground Speed (in m/s), Horizontal dilution, Satellites in number.</span></p>
A framework for estimating global river discharge from the Surface Water and Ocean Topography satellite mission example data
<p>These files contain the Confluence pipeline outputs, prior information (SOS) and Simulated SWOT shape files from the example in the "A framework for estimating global river discharge from the Surface Water and Ocean Topography satellite mission" manuscript. </p>
Revisiting Interior Water Mass Responses to Surface Forcing Changes and the Subsequent Effects on Overturning in the Southern Ocean
<p>This dataset contains processed model data used in</p> <p>Tesdal, J.-E., A. MacGilchrist, G., Beadling, R. L., Griffies, S. M., Krasting, J. P., & Durack, P. J. (2023). Revisiting interior water mass responses to surface forcing changes and the subsequent effects on overturning in the Southern Ocean. Journal of Geophysical Research: Oceans, 128, e2022JC019105. <a href="https://doi.org/10.1029/2022JC019105">https://doi.org/10.1029/2022JC019105</a>.</p> <p>The above publication uses two coupled climate models (AOGCMs), GFDL-CM4 and GFDL-ESM4, to assess the impact of perturbations in wind stress and Antarctic ice sheet melting on the Southern Ocean meridional overturning circulation (SO MOC) and associated water mass transformations (WMT).</p> <p>The attached archive includes netCDF files to recreate all figures and tables in <a href="https://doi.org/10.1029/2022JC019105">Tesdal et al. (2023)</a>, including overturning streamfunction (moc), volume storage change (dVdt), surface water mass transformation (swmt), meridional volume transports (mvt) zonal mean potential density referenced to 2000 dbar (sigma2) and mixed layer depth (mld). These variables are derived from preindustrial control (piControl) and idealized perturbation runs of Antarctic melting (Antwater), wind stress (Stress), as well as the combination (Antwater-Stress) using the Flux-Anomaly-Forced Model Intercomparison Project (FAFMIP) protocol.</p> <p>The FAFMIP protocol (<a href="https://doi.org/10.5194/gmd-9-3993-2016">Gregory et al., 2016</a>) involves adding perturbations to the surface fluxes that are computed within the atmosphere-ocean general circulation model (AOGCM) from the state of the system (<a href="https://doi.org/10.1029/2005JC003421">Lowe and Gregory, 2006</a>; <a href="https://doi.org/10.1088/1748-9326/9/3/034004">Bouttes and Gregory, 2014</a>). The perturbations in this dataset were technically added as a flux adjustment similar to that formerly used in AOGCMs (<a href="https://doi.org/10.1007/BF01053472">Sausen et al., 1988</a>).</p> <p>The data files contain processed model output and do not include any raw model output. Model data from the piControl runs of CM4 and ESM4 are available at the Earth System Grid Federation archive (<a href="https://esgf-node.llnl.gov/projects/cmip6">https://esgf-node.llnl.gov/projects/cmip6</a>). The forcing fields (perturbations) used in the perturbation experiments can be found at <a href="https://github.com/becki-beadling/Beadling_et_al_2022_JGROceans">https://github.com/becki-beadling/Beadling_et_al_2022_JGROceans</a>. Python scripts and Jupyter notebooks to reproduce the tables and figures can be accessed at <a href="https://github.com/jetesdal/Tesdal_et_al_2023_JGROceans">https://github.com/jetesdal/Tesdal_et_al_2023_JGROceans</a>.</p> <p><strong>Contents</strong>:</p> <ul> <li>Overturning streamfunction (moc)</li> <li>Volume storage change (dVdt)</li> <li>Surface water mass transformation (swmt)</li> <li>Meridional volume transports (mvt) </li> <li>Zonal-mean potential density referenced to 2000 dbar (sigma2)</li> <li>Mixed layer depth (mld) </li> <li>Antarctic shelf mask</li> <li>Static grid files</li> </ul> <p><strong>Models</strong>:</p> <ul> <li>GFDL-CM4</li> <li>GFDL-ESM4</li> </ul> <p><strong>Simulations</strong>:</p> <ul> <li>Preindustrial control (piControl)</li> <li>Experiment with a 0.1 Sv freshwater perturbation entering at the Antarctic coast (Antwater)</li> <li>Experiment with zonal and meridional wind stress perturbations (Stress)</li> <li>Experiment with combined perturbation of both Antarctic melting and wind stress (Antwater-Stress)</li> </ul> <p><strong>NetCDF file name structure</strong>:<br> <model>_<simulation>_<member_id>_<domain>_<time_period>_<variable>.nc</p> <ul> <li>model: CM4, ESM4</li> <li>simulation: control, antwater, stress, antwaterstress</li> <li>member_id (only for antwater, stress, antwaterstress): 251, 290, 332 (CM4), 101, 151, 201 (ESM4)</li> <li>domain: global, so</li> <li>time_period: yyyy-yyyy (first year to last year)</li> <li>variable: e.g., moc_rho2_online_lores, dVdt_rho2_online_lores, swmt_sigma2_005, sigma2_jmd95_zmean</li> </ul>
Surface and bottom hourly water temperature from the San Francisco Estuary, 2012-2019
Projected temperature increases due to global climate change are likely to have localized impacts on the San Francisco Estuary (SFE). Increased water temperature in the SFE will lead to challenges for managing water resources. Many native species, such as salmon and smelt, rely on cooler water, and will be further stressed by increased water temperature, which may cause them to seek microrefugia. While several state and federal agencies in the SFE collect real-time water temperature data, most of the water temperature collection sites are at a fixed location or floating near the surface of the water column. This dataset includes four real-time water quality stations that provide water temperature data for both the surface and bottom positions in the water column. We obtained surface water temperature data from an integrated hourly water temperature dataset (https://doi.org/10.6073/pasta/7385985f68b02c0deb2a9e425a9f3ad8). This dataset included data downloaded from the California Data Exchange Center (CDEC; https://cdec.water.ca.gov/) and cleaned with a series of quality control (QC) checks (see integrated dataset metadata). We obtained bottom temperature data from the California Department of Water Resources (DWR) internal database Water Quality Portal (WQP). Data were integrated and standardized to hourly water temperature data in degrees Celsius, and the same series of quality control (QC) checks from the surface dataset were applied in a consistent manner to all stations. Bottom temperatures were selected from surface temperatures to provide measures of temperature difference. Datasets included in this package include source hourly surface and bottom data, both obtained from DWR’s WQP, as well as an integrated dataset of cleaned hourly surface and bottom data, with calculated surface-bottom temperature differences. Both datasets are filtered to the timeframe used in an analysis of surface-bottom temperature differences. Additionally, information regarding current
Petit-lac-Saint-François surface water quality monitoring data
Water Quality samples were collected at Lake Inlet, Outlet, and In-Lake sites between October 2009 and September 2020. Water Samples were collected on a weekly, same-day-of-the-week basis, between 10 a.m. and noon, year-round. Field sampling was conducted by the same technician during the entire period to ensure method consistency and sampling frequency was maintained throughout, except for the winter of 2017, during which sampling was suspended. Epilimnion samples were collected using a swing sampler with a wide neck, polyethylene bottle (Nasco Sampling, Madison, WI, USA), and composited in an acid-washed, opaque, 4-L polyethylene bottle pre-conditioned with lake water. Four grab samples were taken to account for spatial heterogeneity that can be substantial within distances of a few meters, particularly when surface phytoplankton blooms are present. The grab samples were collected by tilting the swing sampler bottle approximately 45 degrees and allowing it to fill as it was submerged 8 to 15 cm below the surface. During periods of ice cover, access holes were drilled or cut through the ice to collect samples. For dissolved parameters, samples were filtered on the same day as collection. Quality control measures, including field, transport, and laboratory blanks, consisting of HPLC grade water and preservative where required, as well as split samples for interlaboratory comparisons, were included quarterly in the sampling program along with regular samples. These were used to establish practicable detection limits and to monitor for levels of contaminants to which field samples might be exposed.
NEON Biorepository Surface Water Microbe Collection (Sterivex Filters) (repackaging of occurrences published by the NEON Biorepository Data Portal)
This collection contains surface water microbe samples collected on 67 mm long, 1.7 cm diameter, 0.22 um Sterivex capsule filters (NEON sample class: amc_fieldCellCounts_in.archiveID). Surface water microbe samples are collected at the same time and location as surface water cell count samples and surface water chemistry samples once per month in wadeable streams (12 times per year) and every-other month in lakes and rivers (6 times per year). Details on sampling locations and timing are provided in the NEON document titled Surface Water Chemistry Sampling in Aquatic Habitats (https://data.neonscience.org/documents). In wadeable streams, surface water microbe samples are collected near the downstream S2 sensor location. In lakes, microbial samples are collected near the the 'buoy', 'littoral 1', and 'littoral 2' sensors, and sampling depth(s) is dependent on lake stratification. In rivers, microbial samples are collected near the buoy sensor. Water samples are filtered on 0.22 um Sterivex capsule filters, capped and flash-frozen in the field. Sterivex filters are archived at the NEON Biorepository at -80 degrees Celsius. See related links below for protocol.
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).
Marcell Experimental Forest biweekly surface water and monthly porewater chemistry at Bog Lake Peatland, 2007 - ongoing
This data set reports the chemistry of surface and porewater water from the Bog Lake peatland in the Marcell Experimental Forest (MEF) in Itasca County, Minnesota, which is operated and maintained by the USDA Forest Service, Northern Research Station. Surface water has been collected about every other week since 2007 from a pool of water and sampling is ongoing. Once covered with ice, water was typically sampled once a month. Porewaters at five depths (0 to 2 m depths) have been collected about monthly from three different nest of piezometers since 2013, though never when samplers were frozen. Samples are measured for pH, specific conductivity, anions (chloride, sulfate), cations (calcium, magnesium, potassium, sodium, aluminum, iron, manganese, strontium), silicon, nutrients (ammonium, nitrate, soluble reactive phosphorus, total nitrogen, total phosphorus), and total organic carbon.
Lagrangian Water Age trajectories initiated from the coastal 500m isobath and derived from surface velocities obtained from satellite observations
We conduct a Lagrangian particle trajectory analysis of surface velocities. We define an “offshore water age” as the time taken by a water parcel to be advected backward in time from its current position along its trajectory until it crosses the 500 m isobath. The rationale of this diagnostic is to detect filaments of coastal water advected offshore by horizontal transport and to estimate the time for water parcels in the filament o leave the coastal area. For example, a value of “20 days” assigned to a pixel means that the water parcel in that area was in the coastal area approximately 20 days before, where it was likely enriched in nutrients.
Groundwater and surface water phosphorus concentrations, Everglades National Park (FCE), South Florida for June, July, August and November 2003
Seawater intrusion into a coastal aquifer mixes with the discharging fresh water to form a zone of mixed composition. This mixing zone is considered to be geochemical important in a carbonate aquifer as an area of enhanced carbonate mineral dissolution and,or precipitation. Ion exchange reactions are also common within the mixing zone as the dominant cation in seawater, sodium, replaces other ions adsorbed to the aquifer matrix. Phosphorus has a strong affinity for adsorption to calcium carbonate minerals. Both the dissolution of calcium carbonate minerals as well as ion exchange reactions have the potential to release phosphorus from the aquifer matrix to the groundwater. Discharge of this phosphorus-laden groundwater, as induced by the fresh/saline water interface, may then be an additional source of phosphorus to the overlying coastal environments. Both surface water and groundwater were collected across the seawater intrusion zone of the coastal Everglades in south Florida during the summer of 2003. Hydrogen sulfide was released from the groundwater wells during sampling, indicating the groundwater was most likely anoxic. In order to reduce the potential exposure of the groundwater to oxygen during sampling, groundwater samples were collected in the following manner. Groundwater wells were first purged of at least three well volumes prior to sampling. Water samples were then collected using a submersible pump with a 16-gauge needle fitted at the end of the discharge hose. The needle was pushed through a rubber stopper covering an acid-washed vacutainer that was first flushed with nitrogen gas to remove and oxygen, then evacuated with a vacuum pump to establish a negative pressure within the vacutainer. Water samples were stored on ice and transported to the laboratory. Samples were processed for total phosphorus using colorimetery following dry-oxidation/acid hydroloysis methods within 1 to 5 days following sample collection. Salinity of the groundwater and surf
The photooxidation of dissolved organic matter in surface waters analyzed by Fourier-transform ion cyclotron resonance mass spectrometry.
Dissolved organic matter (DOM) plays an important role in carbon cycling in natural waters. The processing of DOM in these waters can occur via photooxidation, or interaction with sunlight. This processing can lead to the production of CO2, and also the alteration of organic compounds that make up DOM. It is likely that the extent of photooxidation is at least partially determined by the chemical composition of DOM. Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS) was used to characterize the dissolved organic matter at the molecular level for all water samples, both before and after light exposure to better understand the photooxidation of DOM. Chemical formulas were assigned to mass to generated mass to charge ratios using a custom script in R, resulting in a list of chemical formula assignments for each DOM sample, at multiple light exposure time points.
PIE LTER chlorophyll concentrations in the surface waters and sediments of six high marsh ponds, Rowley, MA, during the summer of 2016.
We measured chlorophyll concentrations in the surface waters and sediments of six ponds in three regions of the PIE-LTER marshes during summer 2016. The goal was to assess whether pond microalgal community abundances varied predictably with pond dimensions (e.g., surface area, volume) or geographic attributes (e.g., elevation, distance from upland, marsh region). Samples were collected from several locations in each pond in order to capture spatial heterogeneity.
PIE LTER 15-minute surface water dissolved oxygen, temperature, and salinity of six high marsh ponds, Rowley, MA, during the summer of 2016.
We estimated the oxygen metabolism of six ponds in three regions of the PIE-LTER marshes during summer 2016. The goal was to assess whether pond metaoblism rates varied predictably with pond dimensions (e.g., surface area, volume) or geographic attributes (e.g., elevation, distance from upland, marsh region). Sensors recording dissolved oxygen (DO), temperature, and salinity were deployed at mid-depth and rotated between the six ponds through the June - August study period. Metaoblism rates were calcluated based on a free-water diel oxygen approach.
PIE LTER Methane isotopes (13C and D) for methane in sediments and dissolved in surface water from four headwater streams in Massachusetts and New Hampshire.
Gas samples for methane isotopes were collected from four headwater streams. Benthic gas samples were collected by physcially distrubing the sediment and collecting ebullated gas. Dissolved gas samples were extracted from surface water. 13C and deuterium isotopes were analyzed. Relevant publications: A.L. Robison (2021) Carbon emissions from streams and river: Integrating methane emission pathways and storm carbon dioxide emissions into stream and river carbon balances. Doctoral Dissertation. University of New Hampshire. A.L. Robison, W.M. Wollheim, C.R. Perryman, A. Cotter, J.E. Mackay, R.K. Varner, P. Clarizia, and J.G. Ernakovich (in review). Dominance of diffusive methane emissions from lowland headwater streams promotes oxidation and isotopic enrichment. Frontiers in Environmental Science.
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.
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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.