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278 results for “water samples”
November 2001 surface water bacterial productivity at ten Georgia Coastal Ecosystems LTER sampling sites
Surface water samples were collected during low tide survays near ten Georgia Coastal Ecosystem LTER sampling sites in November, 2001. The incorporation of tritiated leucine in unfiltered samples during one hour incubations was measured using a standard microcentrifuge method to estimate bacterial productivity in each sample. This study was part of the GCE-LTER hydrographic monitoring program, and will be repeated quarterly.
March 2002 surface water bacterial productivity at ten Georgia Coastal Ecosystems LTER sampling sites
Surface water samples were collected during low tide survays near ten Georgia Coastal Ecosystem LTER sampling sites in March, 2002. The incorporation of tritiated leucine in unfiltered samples during one hour incubations was measured using a standard microcentrifuge method to estimate bacterial productivity in each sample. This study was part of the GCE-LTER hydrographic monitoring program, and will be repeated quarterly.
September 2002 surface water bacterial productivity at ten Georgia Coastal Ecosystems LTER sampling sites
Surface water samples were collected during low tide survays near ten Georgia Coastal Ecosystem LTER sampling sites in September, 2002. The incorporation of tritiated leucine in unfiltered samples during one hour incubations was measured using a standard microcentrifuge method to estimate bacterial productivity in each sample. This study was part of the GCE-LTER hydrographic monitoring program, and will be repeated quarterly.
Phytoplankton primary production measurements from discrete water column samples collected from lakes in the McMurdo Dry Valleys, Antarctica (1993-2022, ongoing)
An important part of the McMurdo Long Term Ecological Research (LTER) is monitoring of spatial and temporal patterns, and processes that control phytoplankton production in perennial ice-covered lakes. This dataset addresses this core area of research and quantifies carbon production at specific depths in McMurdo Dry Valley lakes.
Chlorophyll-a concentrations in discrete water column samples collected from lakes in the McMurdo Dry Valleys, Antarctica (1993-2025, ongoing)
An important part of the McMurdo Long Term Ecological Research (LTER) project is monitoring of spatial and temporal patterns, and processes that control primary production in perennial ice-covered lakes. This data package addresses this core area of research by quantifying chlorophyll-a concentrations within specific depths along the water column of several lakes located across the McMurdo Dry Valleys region of Antarctica.
Nitrogen and phosphorus concentrations in discrete water column samples collected from lakes in the McMurdo Dry Valleys, Antarctica (1993-2020, ongoing)
An important part of the McMurdo Long Term Ecological Research (LTER) project is identifying patterns and movements of nutrients in perennial ice-covered lakes. This dataset addresses this core area of research and quantifies macronutrient concentrations (NH4+, NO3-, NO2, SRP) found at specific depths in McMurdo Dry Valley lakes.
Particulate organic carbon (POC) and nitrogen (PON) concentrations in discrete water column samples collected from lakes in the McMurdo Dry Valleys, Antarctica (1993-2024, ongoing)
An important part of the McMurdo Long Term Ecological Research (LTER) is evaluating carbon and nitrogen budgets in perennial ice-covered lakes. This dataset addresses this core area of research and quantifies the particulate organic carbon (POC) and nitrogen (PON) found at specific depths in lakes across the McMurdo Dry Valleys of Antarctica.
Dissolved inorganic carbon (DIC) concentrations in discrete water column samples collected from lakes in the McMurdo Dry Valleys, Antarctica (1993-2023, ongoing)
The McMurdo Long Term Ecological Research (LTER) project monitors patterns of inorganic material transport in perennial ice-capped lakes. This data set addresses this core area of research and quantifies dissolved inorganic carbon concentrations at specific depths in McMurdo Dry Valley lakes. Dissolved inorganic carbon is also necessary for the computation of primary productivity.
Hydrogen ion concentrations (pH) in discrete water column samples collected from lakes in the McMurdo Dry Valleys, Antarctica (1993-2024, ongoing)
As part of the McMurdo Long Term Ecological Research (LTER) project in the Dry Valleys of Antarctica, hydrogen ion concentrations were monitored in various lakes of the region. An Orion portable pH meter was used to record hydrogen ion concentrations at depth specific intervals in perennial ice-covered lakes.
Particulate phosphorus concentrations in discrete water column samples collected from lakes in the McMurdo Dry Valleys, Antarctica (2006-2019)
An key component of the McMurdo Dry Valleys Long Term Ecological Research (LTER) project involves the long-term monitoring of nutrient cycles. This data package contributes to this core area of research by quantifying particulate phosphorus concentrations found at specific depths in several perennially ice-covered lakes in the McMurdo Dry Valleys region of Antarctica.
Dissolved oxygen concentrations in discrete water column samples collected from lakes in the McMurdo Dry Valleys, Antarctica (2010-2018, ongoing)
As part of the McMurdo Dry Valleys Long Term Ecological Research (LTER) project, dissolved oxygen (DO) concentrations have been monitored in several perennially ice-covered lakes in the McMurdo Dry Valleys of Antarctica, including Lakes Fryxell, Hoare, Bonney, and Miers. DO concentrations at varying depths within the water column were measured using mini-Winkler titrations, providing valuable insights into DO dynamics in these extreme polar environments.
16S rRNA gene sequence accessions from discrete water column samples collected from lakes in the McMurdo Dry Valleys, Antarctica (2013-2023, ongoing)
An important component of the McMurdo Dry Valleys Long Term Ecological Research (MCM LTER) project is monitoring spatial and temporal patterns in the biological composition of perennially ice-covered lakes in Antarctica’s McMurdo Dry Valleys. This data package contributes to this core research area by providing a curated table linking 16S rRNA gene sequence accession numbers archived in NCBI to MCM LTER limnological sampling campaigns conducted at specific depths along the water column of Lakes Fryxell, Hoare, Bonney, and Miers. These data enable integration of microbial community data with co-collected biological, chemical, and physical measurements.
Spectral algal concentrations measured with a bbe Moldaenke FluoroProbe in discrete water column samples from lakes in the McMurdo Dry Valleys, Antarctica (2004–2023, ongoing)
An important component of the McMurdo Dry Valleys Long Term Ecological Research (MCM LTER) project is monitoring the spatial and temporal patterns and processes that regulate primary production in perennial ice-covered lakes. Phytoplankton are the dominant primary producers in the photic zones of these lakes and play a central role in carbon cycling. This data package quantifies spectral algal concentrations measured with a bbe Moldaenke FluoroProbe at discrete depths throughout the water column of multiple lakes across the McMurdo Dry Valleys of Antarctica, providing long-term observations that support investigations into primary production dynamics and ecosystem responses to environmental change.
Dissolved inorganic nutrients from the Martha's Vineyard Coastal Observatory (MVCO), including 4 macro-nutrients from water column bottle samples, ongoing since 2003 (NES-LTER since 2017)
Dissolved inorganic nutrients including nitrate + nitrite, ammonium, silicate, and phosphate are measured from water column bottle and bucket samples taken on NES-LTER day cruises in the vicinity of the Martha's Vineyard Coastal Observatory (MVCO). Sampling frequency near MVCO is approximately monthly, ongoing since 2003. Samples were filtered, frozen, then processed at the Woods Hole Oceanographic Institution's Nutrient Analytical Facility. These macro-nutrients are analyzed in seawater using a colorimetric assay in which light absorbance is measured versus known standards, and final concentrations are calculated (in micromole per liter). Each sample may have up to 3 replicates.
Chlorophyll and phaeopigments from water column samples, collected at selected depths at Palmer Station Antarctica, during the Palmer LTER field seasons, 1991-2025.
Phytoplankton chlorophyll sampling was led by Smith from the 1991-1992 season through the 2001-2002 season, and then by Vernet from the 2002-2003 season through the 2006-2007 season. Schofield is the third, and current lead, beginning in the 2008-2009 season. Methods have been kept consistent as much as possible over the full time series and different Principal Investigators. Chlorophyll a (Chl a) is the principal photosynthetic pigment of phytoplankton, and is used as a proxy measurement for estimating phytoplankton biomass in water samples. Chl a concentrations reflect the distribution of active phytoplankton spatially and with depth in the water column and their changes over time. Phaeopigments are non-photosynthetic pigments that are degradation products of phytoplankton chlorophylls which form during and after phytoplankton blooms. Water samples are collected throughout the water column at stations within the Palmer LTER region (primarily B and E, to 50m and 65m respectively). Beginning in the 2020-2021 season, Station B is no longer sampled. Chlorophyll and phaeopigment concentrations are determined by filtration, extraction, and fluorometric detection of samples. The primary source of error for phaeopigment measurement is Chlorophyll b. If high amounts of Chlorophyll b are present in the sample, phaeopigments may be overestimated. There was no field season in 2021-2022.
Photosynthetic pigments of water column samples analyzed using High Performance Liquid Chromatography (HPLC), sampled during the Palmer LTER field seasons at Palmer Station, Antarctica, 1991 – 2023.
Phytoplankton pigment sampling was led by Prezelin from the 1991-1992 season through the 1993-1994 season, and then by Vernet from the 1994-1995 season through the 2006-2007 season. Schofield is the third, and current lead, beginning in the 2008-2009 season. Methods have been kept consistent as much as possible over the full time series and different Principal Investigators. Phytoplankton have a suite of accessory pigments in addition to Chlorophyll a, including other Chlorophyll’s (e.g. Chlorophyll b), Xanthophylls, and Carotenes. These accessory pigments can be used as chemotaxonomic markers to assess the composition and distribution of the phytoplankton community. For example, Fucoxanthin is a marker pigment of Diatoms, whereas Alloxanthin is a marker pigment of Cryptophytes. Accessory pigments also assist in photoacclimation and photoprotective processes. Water samples are collected throughout the water column at stations within the Palmer LTER region (primarily B and E, to 50m and 65m respectively). Water samples are filtered onto GF/F filters, and filters kept frozen at -80C until analysis. HPLC analysis is completed following Wright et al (1991). Following the guidelines set by NASA SeaHARRE, we use an internal standard and replicate injects on the HPLC to track recovery and replicability of the pigment extraction methods. Data is unavailable for the Palmer 2009-2010 season due to instrumentation problems and for the Palmer 2011-2012 season due to a freezer failure which resulted in the loss of samples. There is a temporary data gap for the Palmer 2015-2016, Palmer 2016-2017, Palmer 2019-2020, Palmer 2020-2021, and Palmer 2023-2024 seasons because those samples have not been analyzed yet.
Chlorophyll and phaeopigments from water column samples, collected at selected depths aboard Palmer LTER annual cruises off the coast of the Western Antarctic Peninsula, 1991-2024.
Phytoplankton chlorophyll sampling was led by Smith from 1991-2002, and then by Vernet from 2003-2008. Schofield is the third, and current lead, beginning in 2009. Methods have been kept consistent as much as possible over the full time series and different Principal Investigators. Chlorophyll a (Chl a) is the principal photosynthetic pigment of phytoplankton, and is used as a proxy measurement for estimating phytoplankton biomass in water samples. Chl a concentrations reflect the distribution of active phytoplankton spatially and with depth in the water column and their changes over time. Phaeopigments are non-photosynthetic pigments that are degradation products of phytoplankton chlorophylls which form during and after phytoplankton blooms. Water samples are collected throughout the water column along the Western Antarctic Peninsula at regular LTER grid stations where CTD casts are preformed and in surface waters at underway stations, where CTD casts are not done, using the ship's flow-through seawater system. Chlorophyll and phaeopigment concentrations are determined by filtration, extraction, and fluorometric detection of samples. The primary source of error for phaeopigment measurement is Chlorophyll b. If high amounts of Chlorophyll b are present in the sample, phaeopigments may be overestimated.
Photosynthetic pigments of water column samples and analyzed with High Performance Liquid Chromatography (HPLC), collected aboard Palmer LTER annual cruises off the coast of the Western Antarctica Peninsula, 1991-2024.
Phytoplankton pigment sampling was led by Prezelin from 1991-1994, and then by Vernet from 1995-2008. Schofield is the third, and current lead, beginning in 2009. Methods have been kept consistent as much as possible over the full time series and different Principal Investigators. Phytoplankton have a suite of accessory pigments in addition to Chlorophyll a, including other Chlorophyll's (e.g. Chlorophyll b), Xanthophylls, and Carotenes. These accessory pigments can be used as chemotaxonomic markers to assess the composition and distribution of the phytoplankton community. For example, Fucoxanthin is a marker pigment of Diatoms, whereas Alloxanthin is a marker pigment of Cryptophytes. Accessory pigments also assist in photoacclimation and photoprotective processes. Water samples are collected throughout the water column along the Western Antarctic Peninsula at regular LTER grid stations where CTD casts are preformed and in surface waters at underway stations, where CTD casts are not done, using the ship's flow-through seawater system. Water samples are filtered onto GF/F filters, and filters kept frozen at -80C until analysis. HPLC analysis is completed following Wright et al (1991). Following the guidelines set by NASA SeaHARRE, we use an internal standard and replicate injects on the HPLC to track recovery and replicability of the pigment extraction methods and the HPLC. Data is unavailable for the LMG10-01 cruise due to instrumentation problems and for the LMG12-01 cruise due to a freezer failure which resulted in the loss of samples. There is no data for 2021 because there was no LTER cruise.
Dissolved inorganic carbon and alkalinity of discrete water column samples, collected aboard Palmer LTER annual cruises of the Western Antarctic Peninsula, 1993 - 2019.
Dissolved inorganic carbon (DIC or total CO2 – TCO2) and total alkalinity (TALK) are two of the four parameters defining the carbonate system in seawater. DIC is composed of dissolved CO2 gas, which dissociates into carbonate, 〖CO〗_3^(2-), and bicarbonate, 〖HCO〗_3^- in seawater. About 90% of the DIC is in the form of bicarbonate, ~10% is carbonate, and ~1% is CO2. The dissociation of CO2 dissolved in seawater into carbonate and bicarbonate gives seawater its great capacity to absorb CO2 from the atmosphere. Alkalinity (also known as “buffer capacity”) is a measure of the capacity of water to neutralize acids. Alkalinity is a complex product of the concentrations of (in decreasing order of importance) the DIC components, borate, hydroxide, phosphate, silicate and dissolved ammonium. Ocean biology regulates the alkalinity through the uptake and release of the DIC and the macronutrients N, P and Si. We measure surface DIC and ALK to understand the exchange of CO2 across the air-sea interface in our study area. With DIC and dissolved CO2, we can also derive estimates of ocean pH and thus monitor the extent and evolution of ocean acidification. Analytical methods and QC are presented under the Methods and Protocols tab.
MeanDRS River Width Sampling: Data products corresponding to "Intrinsic spatial scales of river stores and fluxes and their relative contributions to the global water cycle"
<p><strong>Corresponding peer-reviewed publication</strong></p> <p>This dataset corresponds to all input and output files that were used in the study reported in:</p> <ul> <li>Wade, J., David, C.H., Collins, E.L., Denbina, M., Cerbelaud, A., Tom, M., Reager, J.T., Frasson, R.P.M., Famiglietti, J.S., Lee, T., Gierach, M.M. (In Review), Intrinsic spatial scales of river stores and fluxes and their relative contributions to the global water cycle.</li> </ul> <p>When making use of any of the files in this dataset, please cite both the aforementioned article and the dataset herein.</p> <p><strong>Summary</strong></p> <p>The Earth’s rivers vary in size across several orders of magnitude. Yet, the relative significance of small upstream reaches compared to large downstream rivers in the global water cycle remains unclear, challenging the determination of adequate spatial resolution for observations. Using monthly simulations of river stores and fluxes from the MeanDRS river routing dataset, we sample global rivers by a range of estimated river width thresholds to investigate the intrinsic spatial scales of the global river water cycle. We frame these scale-dependent river dynamics in terms of observational capabilities, assessing how the size of rivers that can be resolved influences our ability to capture key global hydrologic stores and fluxes.</p> <p>We aim to answer two questions:</p> <p>1. What is the intrinsic spatial resolution of global river dynamics?</p> <p>2. How can the spatial scale of river processes be used to inform efficient monitoring and modeling strategies of global river stores and fluxes?</p> <p><strong>Data sources</strong></p> <p>The following sources were used to produce files in this dataset:</p> <ul> <li>Mean Discharge Runoff and Storage (MeanDRS) dataset (version v0.4) available under a CC BY-NC-SA 4.0 license. <a href="../records/10013744">https://zenodo.org/records/10013744</a>. DOI: 10.5281/zenodo.10013744; 10.1038/s41561-024-01421-5</li> <li>MERIT-Basins (version 1.0) derived from MERIT-Hydro (version 0.7) available under a CC BY-NC-SA 4.0 license. <a href="https://www.reachhydro.org/home/params/merit-basins">https://www.reachhydro.org/home/params/merit-basins</a></li> </ul> <p><strong>Software</strong></p> <p>The software that was used to produce files in this dataset are available at https://github.com/jswade/meandrs-width-sampling.</p> <p><strong>Data Products</strong></p> <p>The following files represent the primary outputs of the analysis. Each file class generally has 61 files, corresponding to the 61 global hydrologic regions (region ii).</p> <p><strong>Riv_coast.zip</strong> contains shapefiles of corrected and uncorrected MeanDRS river reaches that intersect with the global coast and are inferred to drain to the ocean.</p> <p><strong>· </strong><strong>riv_coast.zip</strong></p> <p><strong> o </strong><strong>cor:</strong> riv_coast_pfaf_ii_COR.shp</p> <p><strong> o </strong><strong>uncor: </strong>riv_coast_pfaf_ii_UNCOR.shp</p> <p><strong> </strong></p> <p><strong>Qout_rivwidth.zip </strong>contains csv files of the aggregate river discharge to the ocean (km<sup>3</sup>/yr) of under each tested river width sampling scenario for each of the 61 global hydrologic regions.</p> <p><strong>· </strong><strong>Qout_rivwidth.zip: </strong>Qout_pfaf_ii_rivwidth.csv</p> <p><strong> </strong></p> <p><strong>V_rivwidth_low.zip</strong> contains csv files of the aggregate river storage (km<sup>3</sup>) for the low residence time scenario under each tested river width sampling scenario for each of the 61 global hydrologic regions.</p> <p><strong>· </strong><strong>V_rivwidth_low.zip:</strong> V_pfaf_ii_rivwidth_low.csv</p> <p><strong> </strong></p> <p><strong>V_rivwidth_nrm.zip </strong>contains csv files of the aggregate river storage (km<sup>3</sup>) for the normal (medium) residence time scenario under each tested river width sampling scenario for each of the 61 global hydrologic regions.</p> <p><strong>· </strong><strong>V_rivwidth_nrm.zip: </strong>V_pfaf_ii_rivwidth_nrm.csv</p> <p><strong> </strong></p> <p><strong>V_rivwidth_hig.zip </strong>contains csv files of the aggregate river storage (km<sup>3</sup>) for the high residence time scenario under each tested river width sampling scenario for each of the 61 global hydrologic regions.</p> <p><strong>· </strong><strong>V_rivwidth_hig.zip: </strong>V_pfaf_ii_rivwidth_hig.csv</p> <p><strong> </strong></p> <p><strong>Largest_rivs.zip </strong>contains files related to our analysis of the relative contributions of discharge to the ocean from the 10 largest global river basins.</p> <p><strong>· </strong><strong>largest_rivs.zip</strong></p> <p><strong> o </strong><strong>cat: </strong>cat_dis_top10_nxx.shp – dissolved catchments of reaches draining from the 10 largest basins</p> <p><strong> o </strong><strong>csv:</strong> Q_df_top10.csv – total discharge contributed by each basin</p> <p><strong> o </strong><strong>riv:</strong> riv_top10_nxx.shp – river reaches that drain the 10 largest basins</p> <p><strong> </strong></p> <p><strong>Smallest_rivs.zip </strong>contains files related to our analysis of the relative contributions of discharge to the ocean from global rivers narrower than 100 m.</p> <p><strong>· </strong><strong>smallest_rivs.zip</strong></p> <p><strong> o </strong><strong>cat: </strong>cat_pfaf_pfaf_ii_small_100m.shp – dissolved catchments of narrow reaches draining to the ocean for each region ii</p> <p><strong> o </strong><strong>csv:</strong> Q_df_top10.csv – total discharge to the ocean from each narrow river reach</p> <p><strong> o </strong><strong>riv: </strong>riv_pfaf_ii_small_100m.shp – river reaches narrower than 100 m that drain to the ocean for each region ii</p> <p><strong> </strong></p> <p><strong>Global_summary.zip </strong>contains files related to the global aggregation of our region-specific river width sampling estimates for discharge to the ocean and river storage.</p> <p><strong>· </strong><strong>global_summary.zip</strong></p> <p><strong> o </strong><strong>Qout_rivwidth: </strong>global summary files for discharge to the ocean (km<sup>3</sup>/yr) under river width sampling</p> <p><strong> o </strong><strong>V_rivwidth_low:</strong> global summary files for total river storage (km<sup>3</sup>) for the low residence time scenario under river width sampling</p> <p><strong> o </strong><strong>V_rivwidth_nrm:</strong> global summary files for total river storage (km<sup>3</sup>) for the normal (medium) residence time scenario under river width sampling</p> <p><strong> o </strong><strong>V_rivwidth_hig: </strong>global summary files for total river storage (km<sup>3</sup>) for the hig residence time scenario under river width sampling</p> <p><strong> o </strong><strong>cat_small_gl: </strong>cat_dis_global_small_100m.shp – global dissolved catchments contributing to all rivers narrower than 100 m that drain to the ocean</p> <p><strong> </strong></p> <p><strong>Rivwidth_sens.zip </strong>contains files related to our supplemental analysis of the sensitivity of our width estimation approach to choice of input discharge dataset. Here, we compute estimated river widths using 3 versions of MeanDRS discharge outputs (VIC, CLSM, NOAH) and compare the results of river width sampling from those runs to that of the primary analysis. The file formats and explanations follow those presented above, with added information for the land surface model used to generate those discharge simulations.</p> <p><strong>· </strong><strong>Rivwidth_sens.zip</strong></p> <p><strong> o </strong><strong>riv_coast</strong></p> <p><strong> o </strong><strong>Qout_rivwidth_VIC</strong></p> <p><strong> o </strong><strong>Qout_rivwidth_CLSM</strong></p> <p><strong> o </strong><strong>Qout_rivwidth_NOAH</strong></p> <p><strong> o </strong><strong>V_rivwidth_low_VIC</strong></p> <p><strong> o </strong><strong>V_rivwidth_nrm_VIC</strong></p> <p><strong> o </strong><strong>V_rivwidth_hig_VIC</strong></p> <p><strong> o </strong><strong>V_rivwidth_low_CLSM</strong></p> <p><strong> o </strong><strong>V_rivwidth_nrm_CLSM</strong></p> <p><strong> o </strong><strong>V_rivwidth_hig_CLSM</strong></p> <p><strong> o </strong><strong>V_rivwidth_low_NOAH</strong></p> <p><strong> o </strong><strong>V_rivwidth_nrm_NOAH</strong></p> <p><strong> o </strong><strong>V_rivwidth_hig_NOAH</strong></p> <p><strong> o </strong><strong>global_summary_VIC</strong></p> <p><strong> o </strong><strong>global_summary_CLSM</strong></p> <p><strong> o </strong><strong>global_summary_NOAH</strong></p> <p><strong> </strong></p> <p><strong>Cor_sens.zip </strong>contains files related to our supplemental analysis of the sensitivity use of corrected ensemble MeanDRS discharge and volume simulations as opposed to uncorrected ensemble simulations. Here, we repeat our primary analysis using only uncorrected simulations throughout, rather than performing river width sampling using corrected simulations. The file formats and explanations follow those presented above, with the files using uncorrected ensemble (ENS) discharge and storage values in contrast to the primary analysis.</p> <p><strong>· </strong><strong>Cor_sens.zip</strong></p> <p><strong> o </strong><strong>Qout_rivwidth_ENS</strong></p> <p><strong> o </strong><strong>V_rivwidth_low_ENS</strong></p> <p><strong> o </strong><strong>V_rivwidth_nrm_ENS</strong></p> <p><strong> o </strong><strong>V_rivwidth_hig_ENS</strong></p> <p><strong> o </strong><strong>global_summary_ENS</strong></p> <p><strong> </strong></p> <p><strong>Width_val.zip </strong>contains files related to our supplemental validation of river widths estimated from MeanDRS discharge simulations through comparison with optical measurements of widths from the Global River Widths from Landsat (GRWL) Databse (Allen & Pavelsky, 2018).</p> <p><strong>· Width_val.zip: </strong>width_validation_pfaf_ii.csv</p> <p> </p> <p><strong>Known bugs in this dataset or the associated manuscript</strong></p> <p>No bugs have been identified at this time.</p> <p> </p> <p><strong>References</strong></p> <p>Allen, G. H., & Pavelsky, T. M. (2018). Global extent of rivers and streams. <em>Science</em>, <em>361</em>(6402), 585-588. https://doi.org/10.1126/science.aat0636</p> <p>Collins, E. L., David, C. H., Riggs, R., Allen, G. H., Pavelsky, T. M., Lin, P., Pan, M., Yamazaki, D., Meentemeyer, R. K., & Sanchez, G. M. (2024). Global patterns in river water storage dependent on residence time. <em>Nature Geoscience</em>, 1–7. https://doi.org/10.1038/s41561-024-01421-5</p> <p>Lin, P., Pan, M., Beck, H. E., Yang, Y., Yamazaki, D., Frasson, R., David, C. H., Durand, M., Pavelsky, T. M., Allen, G. H., Gleason, C. J., & Wood, E. F. (2019). Global Reconstruction of Naturalized River Flows at 2.94 Million Reaches. <em>Water Resources Research</em>, <em>55</em>(8), 6499–6516. https://doi.org/10.1029/2019WR025287</p> <p>Yang, Y., Pan, M., Lin, P., Beck, H. E., Zeng, Z., Yamazaki, D., David, C. H., Lu, H., Yang, K., Hong, Y., & Wood, E. F. (2021). Global Reach-Level 3-Hourly River Flood Reanalysis (1980–2019). <em>Bulletin of the American Meteorological Society</em>, <em>102</em>(11), E2086–E2105. https://doi.org/10.1175/BAMS-D-20-0057.1</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.