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Biogenic silicate concentration in sea water samples, collected from the CTD in the Southern Ocean during the austral summer of 2016/2017, on board the Antarctic Circumnavigation Expedition.
<p><strong>Dataset abstract</strong></p> <p>Biogenic Silicate (Bsi) concentration (µmol/L) in seawater data. Water samples were collected from CTD rosette deployments, filtered on board and then analysed by flow injection following appropriate digestion.</p> <p>This data supports chemical and biological oceanography studies conducted during the Antarctic Circumnavigation Expedition.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ace_biogenic_silicate_concentration_in_seawater_ctd.csv, data file, comma-separated values</li> <li>ace_biogenic_silicate_concentration_in_seawater_ctd_visual_summary.png, metadata, portable network graphics</li> <li>data_file_header.txt, metadata, text format</li> <li>README.md, metadata, text format</li> </ul> <p>All missing values where no data point exists from lack of sample, have been set to NaN.</p> <p><strong>Dataset license</strong></p> <p>This biogenic silica concentration dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Concentration of daily precipitation in the contiguous United States
<p>The contiguous US exhibits a wide variety of precipitation regimes, first, because of the wide range of latitudes and <a href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/altitude">altitudes</a>. The physiographic units with a basic meridional configuration contribute to the differentiation between east and west in the country while generating some large <a href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/continental-interior">interior continental</a> spaces. The frequency distribution of daily precipitation amounts almost anywhere conforms to a negative exponential distribution, reflecting the fact that there are many small daily totals and few large ones. Positive exponential curves, which plot the cumulative percentages of days with precipitation against the cumulative percentage of the rainfall amounts that they contribute, can be evaluated through the Concentration Index. The Concentration Index has been applied to the contiguous United States using a gridded climate dataset of daily precipitation data, at a resolution of 0.25°, provided by CPC/NOAA/OAR/Earth System Research Laboratory, for the period between 1956 and 2006. At the same time, other rainfall indices and variables such as the annual coefficient of variation, seasonal rainfall regimes and the probabilities of a day with precipitation have been presented with a view to explaining spatial CI patterns. The spatial distribution of the CI in the contiguous United States is geographically consistent, reflecting the principal physiographic and climatic units of the country. Likewise, linear correlations have been established between the CI and geographical factors such as latitude, <a href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/longitude">longitude</a> and altitude. In the latter case the Pearson <a href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/correlation-coefficient">correlation coefficient</a> (r) between this factor and the CI is −0.51 (<em>p</em>-value < 0.001). For annual probability of days with precipitation and the CI there is also a significant and negative correlation, <em>r</em> = −0.25 (<em>p</em>-value < 0.001).</p> <p> </p> <p>Fig. 8. Concentration Index values (1956–2006).</p> <p>File: ci_raster_USA.tif (geoTIFF)</p> <p>NOTE: After the publication of the research article we calculate the Concentration Index with the <a href="http://www.prism.oregonstate.edu/">PRISM</a> climate data set, which has a higher resolution with 4km (PRISM Climate Group, Oregon State University). Nevertheless, the temporal coverage is limited to the period from 1981 to 2017.</p> <p>File: CI_PRISM_USA.tif (geoTIFF)</p> <p> </p> <p>Fig. 4. Seasonal rainfall regimes (1956–2006) (P, spring, S, summer, A, autumn, W, winter)</p> <p>File: 1) pulvio_regimes_raster_USA.tif (geoTIFF); 2) pulvio_regimes_id.csv (clasification for regimes)</p> <pre>Map projection details:</pre> <p>EPSG:2163; proj4: "+proj=laea +lat_0=45 +lon_0=-100 +x_0=0 +y_0=0 +a=6370997 +b=6370997 +units=m +no_defs"</p>
Share and spatial concentration of social housing in Dutch urban areas
<p>This dataset contains the amount of social housing units of the Netherlands per urban area, as well as the intensity of their spatial autocorrelation and its proportion compared to the total housing stock, for the year 2023. <a href="https://www.cbs.nl/nl-nl/dossier/nederland-regionaal/geografische-data/kaart-van-100-meter-bij-100-meter-met-statistieken">Original data</a> comes from Statistics Netherlands (<em>Centraal Bureau voor de Statistiek</em>) released for 100 m x 100 m grid cells covering a large share of the Dutch territory. Grid cells with missing values were excluded from the analysis. The spatial autocorrelation of social housing was calculated with urban area-level and U-style computations of Global Moran's I based on the share of social housing units compared to the total housing stock of every grid cell. Limits and definition of urban areas are extracted from <a href="https://www.oecd.org/en/data/datasets/oecd-definition-of-cities-and-functional-urban-areas.html">the OECD</a>. Data show considerable variation in the levels of social housing and its spatial concentration among Dutch urban areas.</p>
Particulate organic carbon (POC) concentration in meltwater runoff of Leverett Glacier, Russell Glacier, and Isunnguata Sermia, southwest Greenland (2009-2018)
<p>This dataset describes particulate organic carbon (POC) and particulate carbon (PC) concentrations of suspended sediments in the proglacial rivers of 3 land-terminating glaciers in the Kangerlussuaq area, Southwest Greenland: Leverett Glacier (LG), Leverett River; Russell Glacier (RG), Akuliarusiarsuup Kuua; and Isunnguata Sermia (IS), Isortoq River. Both the Leverett River and Akuliarusiarsuup Kuua are tributaries of the Qinnguata Kuussua (also known as Watson River). The data have already been part of 3 different publications (Lawson et al. 2014, Kohler et al. 2017, and Vrbická et al. 2022) but are archived here for the first time.</p> <p>POC data was collected for LG during the 2009 and 2010 melt seasons (Lawson et al. 2014) as well as 2015 (Kohler et al. 2017). For the 2018 melt season, only total carbon concentrations of suspended sediments (PC) is archived as opposed to POC (see Vrbická et al. 2022).</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>
Paired field measurements of suspended-sediment concentration, turbidity, acoustic backscatter, and particle size compiled from various estuaries in the United States and Australia
<p>Field measurements of suspended-sediment concentration, turbidity, acoustic backscatter, and particle size are compiled from various estuaries in the United States and Australia to investigate the utility of combining optical and acoustic backscatter measurements for the estimation of suspended-sediment concentration under changes in floc particle size and density. </p> <p>Theory, analysis, and interpretation of the data is available in Livsey et al (2023). Data collected from the Chesapeake Bay, US were compiled from Fall et al (2022). Data collected on the Brisbane River were collected by Livsey et al (2022). Data collected for all other locations were compiled from Livsey et al (2022). </p> <p>Data collected by Fall et al (2022) utilized a LISST 100x. Data collected by Livsey et al (2022, 2023) utilized a LISST 200x. Data files for each instrument are provided. </p> <p>Funding for this research was provided by an Advance Queensland Industry Research Fellowship, Queensland University of Technology, and Queensland Department of Environment and Science.</p> <p>References</p> <p>Fall, Kelsey A., Massey, Grace M., and Friedrichs, Carl T., (2020). The importance of organic content to fractal floc properties in estuarine surface waters, insights from video, LISST, and pump sampling: Supporting data. Data. William & Mary. https://doi.org/10.25773/7gbc-794 6739</p> <p>Livsey, D., Turner, R., Grace, P., and Crosswell, & Andy Steven. (2022). Field and laboratory measurements of suspended-sediment particle size and concentration from nine rivers draining to the Great Barrier Reef (1.0). Data. Zenodo. https://doi.org/10.5281/zenodo.6788303</p> <p>Livsey, D., Turner, R., and Grace, P. (2023). Combining optical and acoustic backscatter measurements for monitoring of fine suspended-sediment concentration under changes in particle size and density. Water Resources Research. <a href="https://doi.org/10.1029/2022WR033982">https://doi.org/10.1029/2022WR033982</a></p> <p> </p>
NO2, O3, PM10 and PM2.5 concentrations - Daily geographical aggregates at NUTS3 level from CAMS European Air Quality Re-analyses.
<p>This dataset offers daily aggregated measurements of air pollutants – NO2, O3, PM10, and PM2.5 – across distinct NUTS3 regions in continetal Europe. The temporal coverage spans from January 1, 2013, to December 31, 2022, providing a comprehensive temporal context for analyzing long-term air quality dynamics.</p> <p>Each daily entry comprises key statistical descriptors, encompassing mean, maximum, minimum, and standard deviation values of pollutant concentrations specific to each NUTS3 area. Additionally, for O3, the dataset includes an eight-hour rolling mean daily maximum.</p> <p>Spatial reference is established via shapefiles (EPSG:4326) sourced from Eurostat's official repository (<a href="https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units/nuts">https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units/nuts</a>). These shapefiles link the air quality data to precise NUTS3 regions through unique identifiers.</p> <p>The concentration data spanning from 2018 to 2022 originate from the European Air Quality Reanalyses dataset of the Atmosphere Data Store (ADS), an initiative by the Copernicus Atmosphere Monitoring Service (CAMS). Accessible via <a href="https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-europe-air-quality-reanalyses?tab=doc">https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-europe-air-quality-reanalyses?tab=doc</a>, this dataset offers a robust foundation for assessing air quality. For the years 2013 to 2017, data were previously obtained from a former download platform for the same dataset. Important: in future all data will be migrated to the Atmosphere Data Store (ADS) platform.</p> <p>The native resolution of the CAMS data is 0.1° x 0.1° spatially and hourly temporally. To enhance spatial accuracy, the spatial resolution was virtually increased by a factor of 5 using bilinear interpolation, resulting in a refined grid. The daily mean concentrations were subsequently computed for this augmented grid.</p> <p>Aggregated statistics were derived for each NUTS3 polygon, employing all grid cells intersecting with the polygons. The computation was based on the proportion of cell area included within the respective polygons.</p> <p>This dataset constitutes a valuable resource for conducting ecologically designed epidemiological studies, as it facilitates the exploration of potential associations between air quality and health trends across broad geographical areas.</p>
Cyanobacteria abundance, cyanotoxin concentration, and water quality data for the upper San Francisco Estuary, California, USA: 2014-2019
The goal of these measurements was to quantify Microcystis abundance and microcystin concentration and associated water quality conditions during summer blooms in the upper San Francisco Estuary in California, USA. Blooms of harmful algae are a major ecological concern in the area because harmful algae produce toxins and other metabolites, which deteriorate water quality and negatively impact the aquatic ecosystem. Our research team collected biological, physical, and chemical data at 2-week to 4-week intervals during the summer and fall from 2014 through 2019. Data included surface measurements of Microcystis volume (area-based diameter) by microscopy (flowCAM digital imaging flow cytometry) and subsurface (1 m depth) measurements of Microcystis, Aphanizomenon and Dolichospermum cell abundance measured by quantitative PCR, cyanotoxin concentration (total microcystins, anatoxin a and saxitoxin) measured by protein phosphatase inhibition assay or enzyme linked immunosorbent assay, and a suite of water quality parameters (water temperature, dissolved oxygen, nutrient concentration, water transparency, specific conductance, turbidity, pH, and chlorophyll a concentration). Details for the field sampling and analytical methods are available in Lehman et al. (2017). We also performed shotgun metagenomic analyses to investigate biodiversity of cyanobacteria and other aquatic microorganisms and all the DNA sequencing data are publicity available (www.ncbi.nlm.nih. gov/; BioProject ID: PRJNA434758, Kurobe et al. 2018, Lehman et al. 2021). During the study, we experienced critically dry (2014 and 2015), below normal (2016 and 2018), and wet years (2017 and 2019), therefore data obtained in this study provided a unique opportunity to assess impacts of extreme conditions on the aquatic ecosystem (Kurobe et al. 2018, Lehman et al. 2020).
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
Exposure to sublethal concentrations of a pesticide or predator cues induces changes in brain architecture in larval amphibians, 2013.
Naturally occurring environmental factors shape developmental trajectories to produce variable phenotypes. Such developmental phenotypic plasticity can have important effects on fitness, and has been demonstrated for numerous behavioral and morphological traits. However, surprisingly few studies have examined developmental plasticity of the nervous system in response to naturally occurring environmental variation, despite accumulating evidence for neuroplasticity in a variety of organisms. Here, we asked whether the brain is developmentally plastic by exposing larval amphibians to natural and anthropogenic factors. Leopard frog tadpoles were exposed to predator cues, reduced food availability, or sublethal concentrations of the pesticide chlorpyrifos in semi-natural enclosures. Mass, growth, survival, activity, larval period, external morphology, brain mass, and brain morphology were measured in tadpoles and after metamorphosis. Tadpoles in the experimental treatments had lower masses than controls, although developmental rates and survival were similar. Tadpoles exposed to predator cues or a high dose of chlorpyrifos had altered body shapes compared to controls. In addition, brains from tadpoles exposed to predator cues or a low dose of chlorpyrifos were narrower and shorter in several dimensions compared to control tadpoles and tadpoles with low food availability. Interestingly, the changes in brain morphology present at the tadpole stage did not persist in the metamorphs. Our results show that brain morphology is a developmentally plastic trait that is responsive to ecologically relevant natural and anthropogenic factors. Whether these effects on brain morphology are linked to performance or fitness is unknown.
Nitrogen and carbon concentrations and stable isotope ratios (δ¹⁵N and δ¹³C) in European moss samples, 2005-2006
This dataset contains nitrogen (N) and carbon (C) concentrations and stable isotope ratios (δ¹⁵N and δ¹³C) measured in moss samples collected across Europe within the framework of the ICP Vegetation programme (International Cooperative Programme on Effects of Air Pollution on Natural Vegetation and Crops, UNECE LRTAP Convention). Moss surveys are conducted every five years and the data presented here correspond specifically to the first sampling campaign, carried out in 2005/2006. During the 2005/2006 European moss survey, approximately 3,000 moss samples were collected at non-urban and semi-natural sites across 16 European countries following a standardized biomonitoring protocol. The dataset used in this study comprises a subset of 1,022 moss samples (approximately 35 % of the total survey), provided by 12 European countries, which were selected for the determination of nitrogen and carbon concentrations and their corresponding stable isotope signatures (δ¹⁵N and δ¹³C). Moss samples collected by each participating country were sent to the Integrated Environmental Quality Laboratory (LICA), Institute for Biodiversity and Environment (BIOMA - University of Navarra), where all chemical and isotopic analyses were subsequently performed under uniform analytical conditions. In addition, this dataset incorporates moss data from Sweden, Croatia and Macedonia for the same sampling year, which were not included in the official ICP Vegetation 2005/2006 dataset. The European moss biomonitoring network was established to provide a complementary, high spatial resolution and time-integrated measure of atmospheric deposition of nitrogen and other pollutants within terrestrial ecosystems. The approach is based on the ability of ectohydric mosses to accumulate nutrients and trace elements directly from wet and dry atmospheric deposition, enabling dense spatial sampling across large geographical areas. This biomonitoring framework supports the assessment of spatial patterns of atmos
Greenhouse gas fluxes and concentrations and associated habitat data in western Dane County, Wisconsin, USA, streams during the 2018 growing season
Streams are often sources of carbon dioxide (CO2) and methane (CH4), particularly in agricultural regions where sediment and organic matter inputs can be substantial. Floods are occurring more often and more intensely in southern Wisconsin, one such agricultural region, due to climate change and few studies have investigated how floods impact stream CO2 and CH4 fluxes and concentrations. I compared concentrations and fluxes of CO2 and CH4 with greater than 30 variables representing in-stream and watershed attributes at 10 sites in mixed agricultural and suburban locations in southern Wisconsin. Sampling was conducted 10 times at each site during the growing season (May-November) in 2018
Summer high frequency measurements of dissolved O2 and CO2 concentrations and water temperature at the surface of 11 northern lakes
This dataset includes high frequency paired measurements of dissolved O2 and CO2 concentrations at the surface (0.5 to 2 m depth) of 11 lakes in the Northern Hemisphere. Measurements were taken every 2 hours in summer (July and August) of various years depending on lakes (between 2011 and 2014). The dataset is used to test a conceptual framework on the controls of coupling and decoupling of these two gases. In all lakes dissolved CO2 was measured with infrared analyzer coupled with a diffusion membrane and dissolved O2 with optodes. All gas measurements are paired with water temperature provided by one of the gas probe (usually from the O2 probe).
Lake chloride concentrations and model predictions for 49,432 lakes in the Midwest and Northeast United States.
Lakes in the Midwest and Northeast United States are at risk of anthropogenic chloride contamination, but we have little knowledge of the prevalence and spatial distribution of the problem. The majority of salt pollution in north temperate regions stems from road salt application but other chloride sources include water softeners, synthetic fertilizers, and livestock excretion. Although chloride contamination of lakes is well documented, it is unknown how many lakes are at risk of long-term salinization. We used a quantile regression forest to leverage information from 2,773 lakes to predict the chloride concentration of all 49,432 lakes greater than 4 ha in a 17-state area. The QRF used 22 predictor variables, which included lake morphometry characteristics, watershed land use, and distance to the nearest interstate and road. Model predictions had an r2 of 0.94 for all chloride observations, and 0.87 for predictions of the mean chloride concentration observed at each lake.
Sensor and nutrient data associated with the article Harrison et al. 2020. Prediction of stream nitrogen and phosphorus concentrations from high-frequency sensors using Random Forests Regression
This document describes a dataset used to produce Random Forests Regression models of stream nitrogen and phosphorus concentrations from high-frequency sensor data, as reported in: Harrison, J.W., Lucius, M.A., Farrell, J.L., Eichler, L.W., and Relyea, R.A. 2020. Prediction of stream nitrogen and phosphorus concentrations from high-frequency sensors using Random Forests Regression. Science of the Total Environment: https://doi.org/10.1016/j.scitotenv.2020.143005. The dataset consists of paired values of stream nitrogen and phosphorus concentrations and various high-frequency sensor parameters (water temperature, specific conductance, pH, fluorescent dissolved organic matter, turbidity, hydrostatic pressure, soil moisture) collected during baseflow and storm events from 2018 to 2019 as part of routine monitoring of eleven tributaries of Lake George, New York. This dataset does not include raw data; two levels of processing were performed: (1) erroneous values (extreme or otherwise outlying values with no apparent environmental cause) were removed from the sensor data as part of the routine QA/QC process of the Jefferson Project, and (2) one-hour rolling medians of the raw sensor data were calculated at a 1-minute timestep to maximize pairing of sensor data with nutrient concentrations. The resultant dataset was used to train and test the models presented in Harrison et al. 2020.
Stream chemistry concentrations and fluxes using proportional sampling in the Andrews Experimental Forest, 1968 to present
Stream chemistry sampling and analysis was initiated at the H.J. Andrews Experimental Forest in 1968 in two small watersheds (Watersheds 9,10). Sampling has expanded as additional paired watershed studies (Watersheds 1, 2, 6, 7, 8) and monitoring (Mack, Lookout Cr) were initiated over time. Water samples are collected proportionally to streamflow as a function of stage height and composited at each stream gauging site. Composite sample periods are generally three weeks and 3 one-week samples are commonly composited in the lab before analysis. Water samples are analyzed at the Cooperative Chemical Analytical Lab (CCAL) (http://www.ccal.oregonstate.edu/ ). Concentrations of analytes include dissolved and particulate nitrogen, phosphorus, carbon, as well pH, conductivity, suspended sediment, and full suite of cations and anions. Monthly and annual mean concentrations are calculated by weighting 3-week periods by streamflow. Fluxes are calculated using concentrations and flow. The original objective was to examine the nutrient budgets for small watersheds and to evaluate changes in average concentrations and fluxes following timber harvest in comparison with unharvested reference watersheds. This study is conducted in conjunction with Andrew's precipitation chemistry (CP002) and the U.S. National Atmospheric Deposition Program (NADP).
Precipitation and dry deposition chemistry concentrations and fluxes, Andrews Experimental Forest, 1969 to present
Collection and analyses of precipitation chemistry were initiated in 1969 at the low-elevation Primary Met site, and in 1973 at a mid-elevation Hi-15 site. Rain collection samples accumulate from one week to three weeks in bulk and NADP type collectors and then are transported to Cooperative Chemical Analytical Laboratory (CCAL) for analysis. Analytes include nitrogen, phosphorus, carbon, and cations and anions as well as pH, conductivity, alkalinity and particulate sediment. Concentration and volume of precipitation are combined for inflow. Dry deposition chemistry concentrations began in 1989 and are analyzed 2-4 times per year at one site. The original objectives were to evaluate precipitation chemistry inputs versus chemistry outputs in streamflow from forested watersheds. The study has evolved into a general monitoring effort for precipitation chemistry that is among the least contaminated of any within the USA. This study is conducted in conjunction with Andrews streamflow chemistry (CF002) and the U.S. National Atmospheric Deposition Program (NADP).
Methane concentrations in dissolved organic carbon (DOC) leachates from permafrost soils collected from the North Slope of Alaska in the summers of 2018 and 2019
Methane (CH4) concentrations were measured in dissolved organic carbon (DOC) leachates of permafrost soils collected from the frozen permafrost layer at five sites underlying tussock tundra or wet sedge vegetation on the North Slope of Alaska during the summers of 2018 and 2019.
Concentration of dissolved inorganic carbon (DIC) and del 13C isotope value for lakes and rivers on North Slope from Brooks Range to Prudhoe Bay, Arctic LTER 1988 to 1989.
Concentration of dissolved inorganic carbon (DIC) and del 13C isotope value for lakes and rivers on North Slope from Brooks Range to Prudhoe Bay, Arctic LTER 1988 to 1989.
Stream nitrate concentrations and discharge, stream nitrate uptake, and results of stream network nitrate model to determine lateral nitrate load from land to stream in Oak Creek, Arizona, USA
Data package associated with Handler et al. (2024) "Nitrate loads from land to stream are balanced by in-stream nitrate uptake across season in a dryland stream". The study describes the nitrate dynamics in Oak Creek watershed. Data include measurements from four seasonal synoptic sampling campaigns, nine seasonal stream nitrate uptake experiments on the main stem and tributaries, and the results of a network model that estimates the lateral load of nitrate from surrounding landscape to the stream network as well as network-level stream nitrate uptake and retention.
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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.