Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
378
datasets available to search
ShareScore release 0.9.0
Dataset results
378 results for “chlorophyll a”
Seagrass shoot density and benthic chlorophyll density from the Seagrass Recovery Experiment, South Bay, VA 2020-2022
To understand intra-meadow stability, the Seagrass Recovery Experiment was designed to ask 1) is recovery faster at sites with less thermal stress owing to greater exchange with cooler oceanic water at the meadow edge? 2) what is the shape of recovery? and 3) what are the recovery mechanisms? To conduct this experiment, aboveground seagrass biomass was removed from 28.3 m2 plots within the interior and along an edge of a restored seagrass meadow in South Bay, VA. Sites 1-3 correspond to the meadow interior while sites 4-6 correspond to the northern edge. Each site was comprised of a control (i.e., C) where no seagrass was disturbed and a treatment (i.e., T) where seagrass was removed (n = 12 sites total, e.g., 1C, 1T, 2C...). This dataset includes monthly seagrass shoot counts between June-October 2020, May-October 2021, and April-October 2022. To further characterize differences between the meadow interior and edge, benthic chlorophyll samples were also collected.
Discrete water temperature, flow, solar radiation, chlorophyll-a and inundation, Sacramento-San Joaquin Delta, CA, 1999-2019
The objective of our study is to better understand the factors affecting chlorophyll-a production within a floodplain and its transport downstream to determine how lateral connectivity influences longitudinal connectivity. The Yolo Bypass is an engineered floodplain of the Sacramento River that inundates during periods of high outflow via overtopping weirs. Water traveling through the Yolo Bypass flows parallel to the Sacramento River and re-connects to the mainstem at the southern extent of the floodplain. Several monitoring programs in the Sacramento San-Joaquin Delta and Yolo Bypass collect discrete and continuous water quality data, including chlorophyll measurements. For this study, we synthesized available flow, water temperature, chlorophyll and inundation data between March 1999 to December 2019 and modeled the effects of environmental variables and inundation on chlorophyll-a production in the floodplain, the mainstem, and downstream of the floodplain/mainstem.
High-frequency measurements of chlorophyll fluorescence, characterizing F.I.Z. bias.
This dataset consists of high-frequency measurements of chlorophyll a fluorescence (Fchl), collected as part of manipulative experiments conducted in Lake George, NY. Experiments were set up to test for the potential of phototactic zooplankton to interfere with Fchl measurements. To test for any bias associated with fluorometer interference by zooplankton (FIZ), fluorometers were placed in the shallows of Lake George, and collected data under various treatment conditions. It was found that excitation light from fluorometers triggered a positive phototactic response during nighttime hours, biasing Fchl data by as much as 31x. Full results gleaned from this dataset can be found in: Moriarty, V.W., Lucius, M.A., Johnston, K.E., Borrelli, J.J., Mattes, B.M., Pezzuoli, A.R., Watson, C.D., Eichler, L.W. and Relyea, R.A. (2021), Fluorometer optical path interference via zooplankton phototaxis: Implications for high‐frequency data collection. Limnol Oceanogr Methods. https://doi.org/10.1002/lom3.10411
Chlorophyll a and primary productivity data for various lakes near Toolik Research Station, Arctic LTER. Summer 1983 to 1989.
Decadal file describing the chlorophyll a and primary production in various lakes near Toolik Research Station (68 38'N, 149 36'W) during summers from 1983 to 1989. Sample site descriptors include an assigned number (sortchem), site, date of analysis (incubation), time, depth and rates of primary production. The amount of chlorophyll a and pheophytin were also measured.
The Hubbard Brook Stream Ecology Record: Algal Biomass (Chlorophyll-a), 2018 - ongoing
The Hubbard Brook Stream Ecology record is a companion dataset to the Hubbard Brook Watershed Stream and Precipitation Chemistry record. The Stream Ecology record started in 2018 and HBWatER collects ecological samples from seven gauged watersheds: Watersheds 1 through 6 and Watershed 9. HBWatER measures algal biomass, aquatic invertebrate emergence, and stream decomposition by measuring (1) chlorophyll-a on tiles and artificial moss, which approximate algal biomass growth on bare rock and bryophyte mats, (2) preserved algal biomass on artificial moss substrates in Lugol’s Iodine solution, (3) aquatic invertebrate emergence on replicate sticky traps placed above the stream, and (4) stream decomposition through leaf litter pack and cotton strip decay. To complement these ecological records, HBWaTER installed light sensors and field cameras to obtain better information about the light and stream environment daily. Three replicate light sensors that take sub-daily measurements of light level intensity are placed at each watershed at the weir pond (full-sun), and two under the canopy (partial shade). Field cameras take daily photos at noon of the stream canopy and the stream channel. While many studies at Hubbard Brook have measured algal biomass, aquatic invertebrates, and stream decomposition, they are scattered in locations across the valley, were performed at non-continuous times, and use various semi-comparable methods. The HBWatER Stream Ecology record was created to address this gap and systematically measure any long-term changes in the organisms living in the stream. The collection of HBWatER samples is currently sustained by Tammy Wooster (Cary IES) and analyses of these samples has been performed by Heather Malcom (Cary IES), Audrey Thellman (Duke), Steve Anderson (Duke), Geoff Wilson (Cary IES), and Adam Rok (Duke). The dataset is curated and maintained by a team of researchers: Chris Solomon (Cary IES), Emma Rosi (Cary IES), and Emily Bernhardt (Duke). Curr
Size-fractionated chlorophyll from water column bottle samples collected during NES-LTER Transect cruises, ongoing since 2017.
Size-fractionated chlorophyll a and phaeopigments are measured from discrete bottle samples collected during CTD-rosette casts on Northeast U.S. Shelf Long-Term Ecological Research (NES-LTER) Transect cruises (ongoing since 2017), as a proxy for phytoplankton biomass. Sampling frequency is approximately seasonal. Samples were processed by three lab groups using different methods. Size fractions in addition to whole seawater (>0.7 micron) include >5 , <10, >10, and >20 microns with some samples pre-filtered <200 microns. Pigments were analyzed using fluorometers in which fluorescence was measured versus a blank and a standard, and final concentrations were calculated in micrograms per liter (or mg per cubic meter). Some of the data are from cruises in collaboration with the Ocean Observatories Initiative (OOI).
Landscape Position Project at North Temperate Lakes LTER: Chlorophyll 1998 - 2000
Parameters characterizing the chemical limnology and spatial attributes of 49 lakes were surveyed as part of the Landscape Position Project. Most parameters are measured at or close to the deepest part of the lake. Chlorophyll is measured by collecting separate integrated samples from the epilimnion, metalimnion, and hypolimnion Sampling Frequency: generally monthly for one summer; for some lakes, one or two samples in one summer Number of sites: 51
Benthic algae chlorophyll measurements for Rowley River tidal creeks associated with long term fertilization experiments, Rowley and Ipswich, MA.
Benthic algae chlorophyll measurements for Rowley River tidal creeks associated with long term fertilization experiments, Rowley and Ipswich, MA.
Advective nitrate fluxes, sea surface chlorophyll concentrations and other physical metrics in the Santa Barbara Channel (2012-2019)
This data package includes 6 files: (1 & 2) In-situ nitrate concentrations at the surface and mixed layer depth, and collocated remotely-sensed and reanalysis quantities of satellite sea surface temperature, 15-day cumulative wind stress, satellite sea surface chlorophyll with a 5-day lag, index of offshore position of the California Current, indices for along-channel and across-channel distance, and index for day of the year. (3) An R script for generating generalized additive models (GAMs) to predict nitrate concentrations at the surface and at the mixed layer depth using the collocated data in files 1 & 2. (4) Daily maps of satellite sea surface chlorophyll concentrations (SSChl), High-frequency radar (HFR) surface currents, weather research and forecasting (WRF) model wind-derived vertical velocities, estimated nitrate concentrations at the surface and mixed layer depth, horizontal advective nitrate fluxes at the surface and vertical advective nitrate fluxes. (5) Daily time series of spatial mean SSChl, principal component amplitude of the first mode of variability in surface currents estimated using complex empirical orthogonal function (EOF) analysis, alongshore pressure gradient, wind stress, spatial mean horizontal velocities at the western and eastern Santa Barbara Channel boundaries, spatial mean vertical velocities, spatial mean surface nitrate concentrations at the channel boundaries and across the entire channel, spatial mean mixed layer depth nitrate concentrations across the entire channel, spatial mean horizontal advective nitrate fluxes at the channel boundaries, and spatial mean vertical advective nitrate fluxes. (6) A MATLAB script for plotting examples of the daily maps and time series in files 4 & 5. These data were processed in order to investigate the impact of local nutrient delivery mechanisms on phytoplankton blooms in the Santa Barbara Channel, California, details of which are available in the study: Brokaw, R.J., D.A. Siegel, L. Washburn,
Enrichment index related to seamounts and islands in the South West Indian Ocean from chlorophyll-a satellite remote sensing data
<p>This data set is the result of the calculation of an original “enrichment index” (EI) from chlorophyll-a (chl-a) remote sensing data (MODIS-Aqua sensor) and initially dedicated to highlight localized chl-a enrichments associated to isolated seamounts and islands in the South West Indian Ocean, in order to estimate their contribution in increasing the local primary productivity. Details and results are described in the DSR-II paper entitled “Satellite observations of phytoplankton enrichments around seamounts in the South West Indian Ocean with a special focus on the Walters Shoal” from Demarcq et al. 2020.<br> 1. Initial data used<br> We used daily L3 data chl-a and sea surface temperature (SST) collected by the MODIS (Moderate-resolution Imaging Spectroradiometer) sensor on board the Aqua platform (downloaded from https://oceancolor.gsfc.nasa.gov/) from January 2003 to December 2018. This has a spatial resolution of 1/24° (ca. 4.5–5 km). The data covers the region (45°S – 10°S / 25°W – 80°W).<br> 2. The calculation method<br> The calculations were done at the pixel level. The EI is the difference (expressed in %) between the value of each ‘candidate pixel’ and its medium range surrounding, defined as the average value of all chl-a values around the candidate pixel between a fix range of distance between 30 and 90 km, the R1 and R2 terms of the equation enclosed.<br> 3. Data sets<br> The data set contains two files:<br> - the monthly climatology (12 frames) of the EI from January to December (2003 to 2018 average), in an internally compressed netCDF-4 format (NC-compliant or almost)<br> - the yearly average of the EI (period 01/2003 - 12/2018)<br> <br> Two images are joined with this data set:<br> - a "technical view" of the yearly average of the index for the full region sub-region (45°S – 10°S / 25°W – 80°W)<br> (file: indsw4_modis_p100_4km_16y_20030101_20181231.R2018.0.enrichment-index.dist-30-90km.png).</p> <p> - a slightly improved view of the yearly average of the index for the sub-region (40°S – 10°S / 30°W – 70°W).<br> (file: Figure-enrichment-index.pdf)<br> <br> An improved version of this index will be available in a near future.</p>
Multiple years of Seaglider observations of hydrography, dissolved oxygen, chlorophyll a, and optical backscatter at Station ALOHA
<p><strong>File descriptions:</strong></p> <p>Seaglider missions are identified as GLIDER_MISSION<em> </em>(i.e. sg148_12 is glider 148, mission 12) and each have three files associated. For example:</p> <ol> <li><strong>sg148_12_qc_pass.xlsx</strong> contains only quality controlled (QC flags = 1) core data for an entire mission. Core data may include temperature, conductivity, salinity, potential density anomaly, calibrated dissolved oxygen concentrations, calibrated chlorophyll <em>a</em> concentrations, and the backscattering coefficient due to particles (bbp) at up to three wavelengths (470, either 650 or 660, and 700 nm) and spike flags. Bbp data is corrected with an <em>in situ</em> dark subtraction from near 200 m deep. Associated metadata (datetime, latitude, longitude, depth, dive number, and vertical profile direction) is also included.</li> <li><strong>sg148_12_alldata.nc </strong>contains all data (i.e. all QC flag levels) and associated quality control flags. In addition to core and metadata, factory-only calibrated observations (e.g. dissolved oxygen concentrations, chlorophyll <em>a</em> concentrations, and bbp) are listed. </li> <li><strong>sg148_12_qctests.nc</strong><em> </em>contains all quality control test values (pass: QC = 1, input flag: QC = 2, questionable data QC = 3, bad data: QC = 4). The maximum test QC flag value (e.g. out of range, density inversions, bioflouling, etc.) was passed to the variable QC flag (e.g chla_qcflag or salin_qcflag). . </li> </ol> <p> </p> <p><strong>Dataset description:</strong></p> <p>The SCOPE-ALOHA Seaglider dataset was designed to monitor the spatial and temporal variability of physical and biogeochemical properties around the long term sampling site Station ALOHA (22°45′N, 158°W). Seagliders are autonomous underwater vehicles that take high frequency (up to 0.2 Hz in our dataset), depth-resolved observations over several months and can be used to map large spatial features. The gliders depicted in this study were equipped with sensors to measure temperature, salinity, pressure, dissolved oxygen concentration (O2), chlorophyll a concentration (Chl a) from fluorescence (excitation/emission lambda = 470/695 nm), and the particulate backscattering coefficient (bbp) at three wavelengths (lambda = 470 nm, 700 nm, and either 650 or 660 nm depending upon mission). Vertical profiles down to at least 200 m were collected for all sensors over periods of several months per mission. This dataset comprises 18 missions between 2008 and 2023 centered on Station ALOHA, totaling over 20,000 depth profiles. Chlorophyll <em>a</em> and oxygen concentrations are calibrated with discrete observations. Particulate backscattering coefficients are corrected with an additional dark subtraction. This dataset is an improvement on the raw data files as they are quality controlled, calibrated, and corrected.</p> <p>Raw data files can be found at https://hahana.soest.hawaii.edu/seagliders/index.php.</p> <p>version notes:</p> <p>v1.0 original</p> <p>v1.1 Metaadata tab on xlsx files edited, no change to data</p> <p>v1.2 fixed error: variable qc flags added to alldata.nc files</p> <p>v1.3 Added error estimates and CF_standard_name to alldata.nc files</p> <p><strong>Methods:</strong></p> <p><em><strong>Code for all processing steps is on GitHub </strong></em><strong>(</strong><em><strong>https://github.com/cathygarcia/SeagliderDataprocessing</strong></em><strong>)</strong><em><strong>.</strong> The steps listed here are a brief summary. </em></p> <p><em>Temperature, Conductivity, Salinity, and Potential Density Anomaly</em></p> <ul> <li>Both temperature and conductivity profiles were lag corrected.</li> <li>Practical salinity was calculated using the Gibbs Seawater Toolbox (gsw_SP_from_C.m), and then converted to absolute salinity (gsw_SA_from_SP.m). </li> <li>Potential density anomaly was calculated with respect to a reference water pressure of 0 db using the Gibbs Seawater Toolbox (gsw_sigma0.m).</li> </ul> <p><em>Dissolved oxygen concentrations</em></p> <ul> <li>Raw optode phase values proceeded through a series of corrections to account for the effects of temperature, salinity, pressure, and time response in addition to sensor drift (Bittig et al., 2018, Barone et al., 2019).</li> <li> Optode phase values were converted to dissolved oxygen concentrations, and re-calibrated using discrete Winkler measurements. </li> </ul> <p><em>Chlorophyll</em> <em>a</em></p> <ul> <li>Factory-calibrated chlorophyll <em>a</em> observations were re-calibrated using discrete measurements of either HPLC chlorophyll <em>a </em>(16 missions) or fluorometric chlorophyll <em>a</em> (2 missions).</li> <li>Daytime chlorophyll <em>a</em> values are not quench corrected, and may be lower than actual values. It is recommended to use nighttime profiles near the surface. </li> <li>Additionally, a spike flag is included based on published protocol (Briggs et al., 2011).</li> </ul> <p><em>Backscattering coefficient due to particles (bbp)</em></p> <ul> <li>Factory-calibrated bbp values could have a large offset, that was not expected based on natural variability.</li> <li>A mission-specific deep dark correction (1st percentile of bbp at 190-200 m) was subtracted for each bbp dataset. Both the uncorrected and corrected data are available.</li> <li>Additionally, a spike flag is included based on published protocol (Briggs et al., 2011).</li> </ul> <p> </p>
Chlorophyll a concentration, particulate organique carbon, and particle mean size index [gamma; 0.2 - 20 µm] measured using an hyperspectral spectrophotometer [ACS, Wetlabs] during the Tara Pacific Expedition 2016-2018
<p>The Tara Pacific expedition (2016-2018) sampled coral ecosystems around 32 islands in the Pacific Ocean, and sampled the surface of oceanic waters at 249 locations, resulting in the collection of nearly 58,000 samples (Gorsky et al. 2019, Planes et al. 2019, Flores et al. 2020). The expedition was designed to systematically study corals, fish, plankton, and seawater, and included the collection of samples for advanced biogeochemical, molecular, and imaging analysis. Here we provide the continuous dataset originating from the hyperspectral and multispectral spectrophotometers [ACS] instruments acquiring continuously during the full course of the campaign. Surface seawater was pumped continuously through a hull inlet located 1.5 m under the waterline using a membrane pump (10 LPM; Shurflo), circulated through a vortex debubbler, a flow meter, and distributed to a number of flow-through instruments. An [ACS] spectrophotometer (WETLabs) measured hyper-spectral (4 nm resolution) attenuation and absorption in the visible and near infrared except between Panama and Tahiti where an AC-9 multispectral spectrophotometer (WETLabs) was used instead. The flow was automatically directed through a 0.2 µm filter for 10 minutes every hour before being circulated through the spectrophotometer to eliminate the impact of biofouling and instrument drift and estimate particulate absorption [ap] and attenuation [cp] (Slade et al. 2010). Chlorophyll a content was estimated from particulate absorption line height at 676 nm (Boss et al. 2001). The particulate organic carbon concentration [poc] was estimated using an empirical relation (Gardner et al. 2006) between measured [poc] and measured [cp]. An indicator for size distribution of particles between 0.2 and ~20 µm [gamma] was calculated from [cp] (Boss et al 2001). The data was processed with custom software for underway optical data (InLineAnalysis software available on GitHub). The detailed information regarding the data processing is given in the processing report attached with the data and in Lombard et al. (In prep.). These results are preliminary: no matchup with in-situ chlorophyll from HPLC or [poc] measurements were performed.</p>
OCNET global daily Chlorophyll-a products
<p>We constructed the Ocean Chl-a Reconstruction Neural Ensemble Network (OCNET) model to generate global daily chlorophyll-a (Chl-a) concentration data. The model utilized the climatological data from the Ocean Color Climate Change Initiative (OCCCI) version 6 as the background field and the NOAA MSL12 spatiotemporally continuous daily-scale data as the target dataset. Sea surface temperature (SST), salinity (SAL), photosynthetically available radiation (PAR), and sea surface pressure (SSP) were selected as the primary environmental factors influencing phytoplankton growth and distribution for data reconstruction. Ultimately, we developed a daily-scale global ocean surface Chl-a concentration dataset for the period 2001–2023, with a spatial resolution of 0.25°. This dataset is spatiotemporally continuous, spans a long time period, and shows high consistency with satellite data products in regions with available data. It also addresses the issue of severe data gaps in satellite daily-scale products, providing critical information for long-term and large-scale studies of marine phytoplankton. Compared with traditional interpolation methods, the OCNET model fully leverages the environmental information provided by ERA5 reanalysis data and MODIS satellite data to reconstruct Chl-a concentration data. Moreover, the model is not limited by the size of the marine area or the temporal coverage of the original Chl-a concentration dataset. Provided that reliable environmental variable data are available, the model can serve as an important reference for reconstructing historical Chl-a data and predicting future changes in Chl-a concentration.</p> <p>The latitude and longitude of the left bottom corner, the number of rows and columns, and grid cells information are all included in each netCDF file. In addition to the Chl-a data generated by the OCNET model, we also provide the OCNET model algorithm (based on Matlab version 2022b), example datasets, and the detection results of Marine High Chlorophyll-a (Chl-a) Events. </p>
HYPSTAR hyperspectral water reflectance and derived water quality products (suspended particulate matter and chlorophyll-a concentration) at the Blankaart surface water reservoir (BE)
<p><strong>Hyperspectral Water Leaving Reflectance spectra (2988)</strong> measured between 2021-02-03 and 2022-08-03 at the Blankaart Surface Water Reservoir (Belgium, 50.98857N, 2.835213E) with the <strong>HYPSTAR®</strong> (ID: HYPSTAR_12120241). Detailed description of the data collection, processing and analysis can be found in <strong>Goyens et al.- Remote Sens. 2022</strong> - 14(21)- 5607; https://doi.org/10.3390/rs14215607.</p> <ol> <li>HYPSTAR_W_BSBE_L2A_REFL_20210203_20220803_v1.csv</li> </ol> <p><strong>Chlorophyll-a (Chl-a) concentration and Suspended Particulate Matter (SPM) </strong>were derived from the above dataset of hyperspectral water reflectance and estimated according to different algorithms found in the litterature, i.e.,</p> <ol> <li>HYPSTAR_W_BSBE_CHLA_SIMIS_20210203_20220803_v1.csv<strong>:</strong> <strong>Chlorophyll-a concentration</strong> estimated from the <strong>HYPSTAR®</strong> reflectance measurements and following the algorithm suggested by Simis et al. (2005; https://doi.org/10.4319/lo.2005.50.1.0237) with a variable absorption coefficient of phytoplankton per unit of Chl-a concentration as described in Goyens et al. (2022)</li> <li>HYPSTAR_W_BSBE_CHLA_CRAT_20210203_20220803_v1.csv: <strong>Chlorophyll-a concentration</strong> estimated with the <strong>HYPSTAR®</strong> reflectance measurements and following the algorithm suggested by Ruddick et al. (2001; https://doi.org/10.1364/AO.40.003575.) with a variable absorption coefficient of phytoplankton per unit of Chl-a concentration as described in Goyens et al. (2022)</li> <li>HYPSTAR_W_BSBE_SPM_20210203_20220803_v1.csv: <strong>Suspended particulate matter </strong>estimated with the <strong>HYPSTAR®</strong> reflectance measurements and following the algorithm suggested by Nechad et al. (2010) at 700 nm</li> </ol>
Phytoplankton chlorophyll in Iowa state recreational lakes 2018-2020
Chlorophyll a dataset is curated by Phyto-PAM, a multi-wavelength fluorometer that rapidly infers the abundance and composition of phytoplankton communities based on the fluorescence excitation of chlorophyll a and accessory pigments. This dataset categorizes phytoplankton into four taxa: 1) cyanobacteria; 2) ‘Green algae’ (euglenophytes and chlorophytes; 3) ‘Brown algae’ (ochrophytes, dinophytes, haptophytes); and 4) ‘Red organisms’ (phycoerythrin-rich phytoplankton). Sub-surface water samples (n > 450) collected in eutrophic lakes for three summer seasons (2018-2020; 15 weeks per season; from May through September). DNA sequencing data for bacteria and eukaryotes are available at NCBI (16s rRNA: PRJNA764241, PRJNA763651, and 18s rRNA: PRJNA787030).
Seagrass morphometry and chlorophyll content at nine stations along a eutrophication gradient in West Falmouth Harbor, 2019
West Falmouth Harbor (West Falmouth, MA, USA) has been experiencing a dramatic increase in nitrogen loading from an upgradient municipal wastewater treatment facility since the early 2000’s. As part of a long-term study into the effects of this nitrogen enrichment, in 2019 we sampled seagrass (Zostera marina) to assess the relationship between sediment biogeochemistry and seagrass ecosystem parameters. This dataset contains information on aboveground and belowground biomass ratios, seagrass density, epiphyte biomass, and chlorophyll content from sites across the eelgrass bed in West Falmouth Harbor that receive varying inputs of nitrogen. The data supports findings reported in Haviland et al., 2022 (https://doi.org/10.1002/lno.12025).
Lake Tahoe Chlorophyll a concentration for discrete water sample
Chlorophyll a concentrations for discrete water samples taken at Lake Tahoe, CA/NV. There are two sampling stations Index (LTP, 39.0972 -120.155) and Mid-lake (MLTP, 39.1417 -120.0153). See methods for details
Dissolved oxygen, temperature, chlorophyll-a, total phosphorus, total nitrogen, and dissolved organic carbon at multiple depths in 822 lakes from 1921-2022
Rapid changes in climate and land use are having substantial and interacting impacts on lake water quality around the world. Here, we synthesized time-series data for dissolved oxygen, temperature, chlorophyll-a, total phosphorus, total nitrogen, and dissolved organic carbon at multiple depths in 822 lakes to facilitate analyses of these changes. The dataset extends from 1921–2022, with a median data duration of 29 years (range 5-102) and a median of 5 unique sampling dates per year at each lake. Lakes in the dataset have a median depth of 12.5 m (range 1.5–480 m), median surface area of 85.4 ha (range: 0.5–237000 ha) and median elevation of 264 m (range: -215–2804). The lakes are located in 18 countries across 5 continents, with latitudes ranging from -42.6 to 68.3. To facilitate interoperability with other large-scale datasets, each lake is linked to a unique hydroLAKES lake ID when possible (n = 683).
AquaMatch Chlorophyll a Data from Water Quality Portal: ~1970-2024
This dataset, “AquaMatch Chlorophyll a Data from Water Quality Portal ~1970-2024”, is a component of a forthcoming update to AquaSat (Ross et al., 2019), AquaSat version 2 (“v2”). The overarching purpose of AquaSat V2 is to emphasize the individual parts of the AquaSat pipeline that make-up the matchups between satellite and in-situ measurements. As such, we have greatly expanded and improved upon the AquaSat chlorophyll a dataset in two ways: First, we have incorporated additional recent in situ data beyond what was available at the publication of AquaSat. Second, we have created a data quality tiering system to provide end-users with more guidance on data usage. In this schema we have three tiers: restrictive data that are verifiably self-similar across organizations and time-periods and can be considered highly reliable; narrowed data that we have good reason to believe are self-similar, but for which we can not verify full compatibility across data providers; and inclusive data, which are assumed to be reliable and are harmonized to our best ability given the information available from the data provider. We have also added flag columns to help users understand complexities of the available depth and field sampling data. This dataset is a derived data product created using records downloaded from the Water Quality Portal (WQP) spanning January 6, 1970, to June 20, 2024. The WQP is a data warehouse for water-related data measured or observed within the United States and US Territories managed by the Environmental Protection Agency, United States Geological Survey, and the National Water Quality Monitoring Council. The dataset does not contain remote sensing matchups but can be paired with Landsat surface reflectances using the pipeline presented in Ross et al. (2019). Ross, M. R. V., Topp, S. N., Appling, A. P., Yang, X., Kuhn, C., Butman, D. et al. (2019). AquaSat: A data set to enable remote sensing of water quality for inland waters. Water Resources Research, 5
Measurements of water column specific conductivity, salinity, dissolved oxygen, chlorophyll, temperature, and pH by deployed datasondes every 20 minutes for several periods during the summertime in 2017-2020
West Falmouth Harbor (West Falmouth, MA, USA) has been experiencing a dramatic increase in nitrogen loading from an upgradient municipal wastewater treatment facility since the early 2000's. As part of a long-term study into the effects of this nitrogen enrichment, we have measured water column parameters at 20 minute intervals in two locations within West Falmouth Harbor (West Falmouth, MA, USA), one in the well-flushed outer basin and one in the inner basin closer to the dominant groundwater N source. The goal of this dataset is to compare conditions at the two sites, as well as to derive rates of metabolism. Parameters measured include temperature, specific conductivity, salinity, dissolved oxygen, chlorophyll, and pH. YSI Datasondes were deployed during 4 periods ranging from 6 to 11 days in July and August, suspended vertically from a surface buoy. Over all deployments, instruments passed all QA checks, and average differences between the two instruments over all deployments were less than 0.06 degrees C (temperature), 0.3 (salinity), 0.05 (pH), 1.0 µg/L (chlorophyll), 1.5 (%DO Saturation). Data provided here are not corrected for drift, and chlorophyll data are uncorrected as reported by the instruments. Chlorophyll reported is uncorrected from the YSI calculation based on in-situ fluorescence and calibration with a single-point using deionized water. Lab fluorometric analysis checks show that the YSI chlorophyll is over-reporting by at least 20% at low concentrations, and high concentrations were not able to be validated. Methodology details and analysis of earlier data can be found in Howarth et al 2014, "Metabolism of a nitrogen-enriched coastal marine lagoon during the summertime," doi:10.1007/s10533-013-9901-x
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.