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447 results for “Phytoplankton”
May 2004 surface water phytoplankton productivity for 10 Georgia Coastal Ecosystems LTER sampling sites
Water samples were collected by Niskin bottle or by hand from just beneath the surface during low tide surveys at or near 10 GCE-LTER sampling sites in May 2004. The incorporation of radiolabelled bicarbonate in response to varying levels of illumination was measured using a photosynthetron apparatus. Photosynthesis-irradiance (P-I) curves constructed from these measurements will be used in conjunction with algal biomass and PAR versus depth measurements to estimate instantaneous gross primary production in the water at each GCE sampling site. This study was part of the GCE-LTER hydrographic monitoring program, and will be repeated quarterly.
December 2003 surface water phytoplankton productivity for 10 Georgia Coastal Ecosystems LTER sampling sites
Water samples were collected by Niskin bottle or by hand from just beneath the surface during low tide surveys at or near 10 GCE-LTER sampling sites in December 2003. The incorporation of radiolabelled bicarbonate in response to varying levels of illumination was measured using a photosynthetron apparatus. Photosynthesis-irradiance (P-I) curves constructed from these measurements will be used in conjunction with algal biomass and PAR versus depth measurements to estimate instantaneous gross primary production in the water at each GCE sampling site. This study was part of the GCE-LTER hydrographic monitoring program, and will be repeated quarterly.
March 2002 surface water phytoplankton productivity for 10 Georgia Coastal Ecosystems LTER sampling sites
Water samples were collected by Niskin bottle or by hand from just beneath the surface during low tide surveys at or near 10 GCE-LTER sampling sites in March 2002. The incorporation of radiolabelled bicarbonate in response to varying levels of illumination was measured using a photosynthetron apparatus. Photosynthesis-irradiance (P-I) curves constructed from these measurements will be used in conjunction with algal biomass and PAR versus depth measurements to estimate instantaneous gross primary production in the water at each GCE sampling site. 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.
Lake snow removal experiment phytoplankton community data, under ice, 2019-2021
Although it is a historically understudied season, winter is now recognized as a time of biological activity and relevant to the annual cycle of north-temperate lakes. Emerging research points to a future of reduced ice cover duration and changing snow conditions that will impact aquatic ecosystems. The aim of the study was to explore how altered snow and ice conditions, and subsequent changes to under-ice light environment, might impact ecosystem dynamics in a north, temperate bog lake in northern Wisconsin, USA. This dataset resulted from a snow removal experiment that spanned the periods of ice cover on South Sparkling Bog during the winters of 2019, 2020, and 2021. During the winters 2020 and 2021, snow was removed from the surface of South Sparkling Bog using an ARGO ATV with a snow plow attached. The 2019 season served as a reference year, and snow was not removed from the lake. This dataset represents phytoplankton community samples (pooled epilimnion and hypolimnion samples representative of 7 m water column) both under-ice and during some shoulder-season (open water) dates. Samples were collected into amber bottles and preserved with Lugol's solution before they were sent to Phycotech Inc. (St. Joseph MI, USA) for phytoplankton taxonomic identification and quantification.
Plum Island LTER phytoplankton identification using HPLC and Chem Taxonomy along transects in the Plum Island Sound estuary, Massachusetts.
Water column samples are collected along an estuarine salinity gradient as part of our monitoring surveys of the Parker River estuary each spring and late summer (typically high vs low freshwater input). Samples are filtered, and stored frozen for later pigment analyses by HPLC. Pigment data are then analyzed by CHEMTAX, calibrated to a matrix of pigment ratios based on taxonomy and enumeration of selected subsamples by microspcopy. Data are presented in terms of chlorophyll a concentrations partitionaed among the major phytoplankton groups as determined by CHEMTAX. For 2003-2006, sampling stations along the Plum Island Sound-Parker River were at fixed geographic locations at specific "Bends" in the river. In 2008, we began sampling the water column in salinity space rather than at specific geographic locations along the river. This sampling approach was adopted in order to follow particular water masses in this macrotidal estuary. In practical terms, it means that sampling locations, or stations, are not static. Therefore, we have mapped the 11 sampling locations (latitude and longitude are logged at each station) from each transect along the mainstem of the estuary, so each station may be placed along the river (to the nearest 0.5km) as well as in salinity space. We have also used the km marker to assign the sampling locations from each survey to one of four bounding boxes : the Sound (Plum Island Sound; EST-PR-SoundBND) which encompasses approximatly the first 9.5 km or the transect, with Okm at the mouth of the sound; the Lower Parker River (EST-PR-LowerParkerBND) , ~9.5 - 14.5 km; the Middle Parker River (EST-PRMiddleParkerBND), ~14.5 - 18.75 km, and the Upper Parker River (EST-PR-UpperParker BND)., ~18.75 to 24.25 km (the Parker R. Dam).
Plumes and Blooms: Curated oceanographic and phytoplankton pigment observations
These data come from the Plumes and Blooms project (PnB), which has conducted approximately monthly 1-day oceanographic cruises since August 1996. The data included here encompass all PnB cruises from August 1996 through December 2018. Data are two data tables: one table includes conductivity-temperature-depth profiles (CTD) and derived physical parameters, the other table includes discrete seawater samples for various biological and biogeochemical parameters. Details are available in Catlett et al., in prep. References: Catlett, D., D. A. Siegel, R. D. Simons, N. Guillocheau, F. Henderikx-Freitas, C. S. Thomas.2021. Diagnosing seasonal to multi-decadal phytoplankton group dynamics in a highly productive coastal ecosystem, Progress in Oceanography. 197. https://doi.org/10.1016/j.pocean.2021.102637.
SBC LTER: Cross-shelf Study 2008-2009: Profiles of CTD, biogeochemistry, primary production, abundance of phytoplankton groups, and abundance and production of bacteria
These data were used by the following papers: Goodman, J., M. A. Brzezinski, E. R. Halewood and C. A. Carlson. 2012. Sources of phytoplankton to the inner continental shelf in the Santa Barbara Channel inferred from cross-shelf gradients in biological, physical and chemical parameters. Continental Shelf Research, 48: 27-39. (DOI: 10.1016/j.csr.2012.08.011) Halewood, E. R., C. A. Carlson, M. A. Brzezinski, D. C. Reed and J. Goodman. 2012. Annual cycle of organic matter partitioning and its availability to bacteria across the Santa Barbara Channel continental shelf. Aquatic Microbial Ecology, 67:189-209. (DOI:10.3354/ame01586) These data were collected on monthly day cruises from January 2008 to April 2009 on the RV Kelp Fish at five stations across the shelf starting at Mohawk Reef in the nearshore area of the Santa Barbara Channel, California, USA. Data were collected with a SBE19-Plus and rosette sampler. Measurements include standard CTD parameters in 1 m bins (e.g. salinity, temperature, density). At selected depths (1, 5, 10, 20 m), rosette bottle samples were collected for nutrients, pigments, particulate and dissolved organic carbon and nitrogen, and bacterial abundance, community structure and productivity. Phytoplankton abundances (to genus) were obtained from the 5 m sample only.
Nutrient, phytoplankton and zooplankton data, 20 year surface biomass from Darwin
<p>Nutrient, phytoplankton and zooplankton data to accompany Sonnewald et al. : Elucidating Ecological Complexity: Unsupervised Learning determines global marine eco-provinces.</p> <p>Note: We discard the areas (gridcells) with biomass <10-4</p> <p> </p>
Supporting data for 'Hourly prediction of phytoplankton biomass and its environmental controls in lowland rivers'
<p>This dataset is used in the manuscript 'Hourly prediction of phytoplankton biomass and its environmental controls in lowland rivers' published in Water Resources Research. The dataset contains hourly observation of water quality in the lower Thames catchment, UK and were made available by the Environment Agency, UK. </p>
Sunburned plankton: Ultraviolet radiation inhibition of phytoplankton photosynthesis in the Community Earth System Model version 2
<p>Climate model output for paper describing CESM2-UVphyto.</p>
LifeWatch observatory data: phytoplankton annotated trainingset by FlowCam imaging in the Belgian Part of the North Sea
<h1>Training dataset </h1> <p>The images were collected in the framework of the Belgian Lifewatch Research Infrastructure. During multidisciplinary campaigns, a number of fixed stations in the Belgian Part of the North Sea (BPNS) are visited on a monthly (onshore stations) or seasonal (offshore stations) basis. Samples are taken using a 55µm mesh size Apstein net and fixed in Lugol's iodine solution. In the lab, the samples are processed using a VS-4 FlowCAM model at 4X magnification targeting a particle size range of 55-300µm. The identification of the image data is done with the use of a CNN and followed by a manual validation step. Since May 2017, this dataset has provided micro- and phytoplankton observations, mainly covering diatoms, dinoflagellates and cilliates, for the Belgian Part of the North Sea (BPNS).</p> <p> </p> <p>This dataset comprises a trainings datasplit of 337,514 images distributed across 95 classes, with each class containing a minimum of 100 and a maximum of 10,000 images. The goal of this dataset is to be able to facilitate model training, here we have organized the data into a standard split, with 80% allocated for training, 10% for validation, and another 10% for testing purposes. This dataset structure ensures a balanced representation and supports scientific rigor in subsequent analyses.</p> <h1>Technical details </h1> <h2>Data preprocessing </h2> <p>Raw FlowCam output data is fully processed using in-house datapipelines, the VisualSpreadsheet software is only used for data acquisition during the lab run of the sample. Raw images and binary images are never saved during the FlowCam run, we only work on the image collages saved at the end of the run. Single images are cut from these collages using each image coordinates width and height pulled from the .lst file using in-house python code. The background of the images is not removed. These images are then predicted and annotated in-house at VLIZ.</p> <h2>Data splitting </h2> <p>The training dataset is 80% used for training, 10% for validation and 10% for prediction. </p> <h2>Classes, labels and annotations</h2> <p>The dataset comprises 337,514 images distributed across 95 classes, with each class containing a minimum of 100 and a maximum of 10,000 images. Taxonomic coverage of the dataset comprises mainly of diatoms, dinoflagellates and cilliates, but to a lesser extent also zooplankton and other protists.</p> <h2>Parameters </h2> <p>The images are read using cv2.imread and the values are used as parameters.</p> <p><strong>Metadata Parameter Descriptions:</strong></p> <ul> <li> <p><strong>image_path</strong>: The relative path to the image file showing the plankton or particle, usually structured by taxon or project folder.</p> </li> <li> <p><strong>sample_datetime</strong>: The exact date and time (in YYYY-MM-DD HH:MM:SS.sss format) when the sample was acquired using the imaging instrument.</p> </li> <li> <p><strong>flowcam_version</strong>: The software or hardware version of the FlowCAM instrument used to capture the image and process the sample.</p> </li> <li> <p><strong>station</strong>: The sampling location code where the image was taken. These station codes typically refer to predefined geographic or monitoring points in the field.</p> </li> <li> <p><strong>accepted_label</strong>: The final validated taxonomic label (e.g., genus or species) assigned to the organism or particle in the image, often based on expert review.</p> </li> <li> <p><strong>accepted_aphia_id</strong>: The unique identifier (AphiaID) corresponding to the accepted taxonomic label in the <strong>World Register of Marine Species (WoRMS)</strong> database, which ensures standardized taxonomic reference.</p> </li> <li> <p><strong>original_reference_id</strong>: A unique identifier (often a UUID) assigned to the image or sample in the original classification system (e.g., EcoTaxa), useful for traceability and linking back to the source record.</p> </li> </ul> <h2>Data sources </h2> <p>Images are collected during the monthly monitoring of phytoplankton communities in the Belgian Part of the North Sea during the LifeWatch multidisciplinary campaigns by FlowCam VS-4 benchmodel (Fluid Imaging Technologies, Yarmouth, Maine, U.S.A.).</p> <h2>Data quality </h2> <p>All images are predicted and subsequently manually validated to ensure the quality of the trainingset.</p> <h2>Image resolution </h2> <p>The size range imaged is 55-300µm. Images are acquired using a Sony XCD SC90 digital gray-scale camera. Images are during training of CNN resized to 100px by 100px.</p> <h2>Spatial coverage </h2> <p>The data comes from a number of fixed stations in the Belgian Part of the North Sea (BPNS). </p> <p>Nine stations onshore are visited monthly:</p> <table> <tbody> <tr> <td><strong>Station</strong></td> <td><strong>Longitude</strong></td> <td><strong>Latitude</strong></td> </tr> <tr> <td>130</td> <td>2.90535</td> <td>51.27055</td> </tr> <tr> <td>780</td> <td>3.057283</td> <td>51.471367</td> </tr> <tr> <td>330</td> <td>2.809083</td> <td>51.434117</td> </tr> <tr> <td>230</td> <td>2.85035</td> <td>51.308683</td> </tr> <tr> <td>710</td> <td>3.138283</td> <td>51.441217</td> </tr> <tr> <td>215</td> <td>2.61075</td> <td>51.274867</td> </tr> <tr> <td>ZG02</td> <td>2.500717</td> <td>51.33515</td> </tr> <tr> <td>120</td> <td>2.702483</td> <td>51.186083</td> </tr> <tr> <td>700</td> <td>3.221017</td> <td>51.377</td> </tr> </tbody> </table> <p>Eight additional offshore stations are visited seasonally:</p> <table> <tbody> <tr> <td><strong>Station</strong></td> <td><strong>Longitude</strong></td> <td><strong>Latitude</strong></td> </tr> <tr> <td>LW01</td> <td>2.256</td> <td>51.568667</td> </tr> <tr> <td>LW02</td> <td>2.556</td> <td>51.8</td> </tr> <tr> <td>435</td> <td>2.790333</td> <td>51.580667</td> </tr> <tr> <td>W07bis</td> <td>3.012517</td> <td>51.588033</td> </tr> <tr> <td>W08</td> <td>2.35</td> <td>51.458333</td> </tr> <tr> <td>W09</td> <td>2.7</td> <td>51.75</td> </tr> <tr> <td>W10</td> <td>2.416667</td> <td>51.683333</td> </tr> <tr> <td>421</td> <td>2.45</td> <td>51.4805</td> </tr> </tbody> </table> <h2> </h2> <h2>Temporal coverage </h2> <p>The monitoring was initiated in May 2017 and has been running continuously every month.</p> <h2>Contact information </h2> <p>For technical questions about training dataset, you can contact wout.decrop@vliz.be. </p> <p> </p> <p> </p> <p> </p> <p> </p>
OLCI_300m_WCP_phytoplankton
<p>Daily full resolution (300m) phytoplankton type product derived from combined Sentinel 3A and 3B Ocean and Land Colour Imager (OLCI) reflectance using the algorithm of Smith and Bernard 2020 (https://doi.org/10.3389/fmars.2020.00061), but with the addition of a <em>Mesodinium rubrum</em> type. The region covered by this product is 31 to 35 degrees South, and 17 to 20 degrees East, for the period of 2016-04-26 to 2025-06-30. Within this dataset the following values represent the following phytoplankton types:</p> <p>0 = no bloom </p> <p>1 = Low biomass mixed assemblage</p> <p>2 = High biomass mixed assemblage</p> <p>3 = high likelihood of Pseudo nitzschia </p> <p>4 = Mesodinium rubrum </p> <p>5 = Low biomass Dinoflagellate conditions </p> <p>6 = High biomass Dinoflagellates conditions (classic "red tide")</p> <p>NaN = cloud / land / high sunglint / no satellite data</p>
A data repository for: Changing phytoplankton phenology in the marginal ice zone west of the Antarctic Peninsula
<p>This data repository is a permanent archive of the results presented in the associated publication (Turner et al. 2024, Marine Ecology Progress Series, <a href="https://doi.org/10.3354/meps14567">https://doi.org/10.3354/meps14567</a>). The objective of this study was to investigate phytoplankton phenology patterns west of the Antarctic Peninsula using satellite ocean color remote sensing data. This dataset extends from 80<sup>o</sup>W to 55<sup>o</sup>W longitude and from 70<sup>o</sup>S to 60<sup>o</sup>S latitude. The data span the time period September 1997 through August 2022. This dataset includes the data and code used to create the figures in the publication. The data in this repository include chlorophyll-a concentration (Chl-a), dates of phytoplankton bloom start date and phytoplankton bloom peak date, photosynthetically active radiation (PAR), sea surface temperature (SST), wind speed, and dates of sea ice retreat and advance. Downloaded spatially-subsetted data files are included as netCDF files (extension .nc) compressed into .zip archives. Additional files used to perform the analyses and make the figures are included as MATLAB scripts and MATLAB data files (extensions .m and .mat, respectively). </p> <p>Recommended citation:</p> <p>Turner, Jessica S., (2024) A data repository for: Changing phytoplankton phenology in the marginal ice zone west of the Antarctic Peninsula. Zenodo. https://doi.org/10.5281/zenodo.10790613</p>
LifeWatch observatory data: phytoplankton annotated image library by FlowCam imaging for the Belgian part of the North Sea.
<p>In the framework of the Lifewatch marine observatory a number of fixed stations in the Belgian Part of the North Sea (BPNS) are sampled for phytoplankton monitoring. Samples are processed using a VS-4 FlowCAM model at 4X magnification, size range imaged is 55-300µm. The identification of the image data is done with the use of a classifier and followed by a manual validation step. These dataset comprises the full annotated image dataset which can be sampled for training of convolutional neural networks.</p>
Proportions of Phytoplankton Functional Groups (PFT) retrieved using a Self-Organizing Map in the North Atlantic
<p>Concentrations of diagnostic pigments are retrieved from satellite data (Chl-a + Rrs at 4 wavelengths + SST) using a SOM trained on a global in-situ HPLC dataset (see El Hourany et al. 2019). Pigments are converted to PFTs using empirical coefficients. Three sets of coefficients are used and the results are averaged (see El Hourany et al. 2024). The PFT data is expressed as the proportion of each group in the total community abundance:<br><code>Proportion_i = (alpha_i * Pig_i) / sum_j(alpha_j * Pig_j)</code><br>There are seven PFT groups (diatoms, dinoflagellates, haptophytes, green algae, cryptophytes, pelagophytes, prokaryotes).</p> <p>The satellite input Chl-a data is included as well. It was retrieved from the Globcolour portal (<a href="https://hermes.acri.fr/">https://hermes.acri.fr/</a>) in 2022. The CHL-1 product for case 1 waters is used. It uses the AVW merging method: single-sensor level-2 Chl-a data is merged from multiple sensors (SeaWiFS, MERIS, MODIS-Aqua, VIIRS-NPP/JPSS1, OLCI-A/B).</p> <p>The data spans from 2002 to 2020, at a daily resolution. It is available on a regular latitude/longitude grid, at a resolution of 1/24° (approximately 4 km) in a window of bounds 15°N−55°N ; 82°W−40°W.</p> <h2><strong>Storage</strong></h2> <p>All variables are stored in the same daily file: <code>PFT_SOM-DAP_GLOB_4km_daily/[year]/[month]/PFT_[year][month][day]_GLOB_4.nc</code>. The PFT proportions are stored as percentages (ranging between 0 and 100). Files are NetCDF4, and the metadata follow CF conventions.</p> <p>The PFT variables are stored using linear packing (see <a href="https://nco.sourceforge.net/nco.html#Linear-Packing">https://nco.sourceforge.net/nco.html#Linear-Packing</a>) as 16-bits unsigned integers (<code>NC_USHORT</code>), with a scale factor of 1.54e-3. This means values are discretized between 0 and ~100.92 (<code>=(2**16 - 2) * 1.54e-3</code>), with a discretization step of 1.54e-3.<br>As for the PFT variables, these values are in percent points.</p> <h2><strong>References</strong></h2> <ul> <li>El Hourany, R., Abboud-Abi Saab, M., Faour, G., Aumont, O., Crépon, M., Thiria, S.<br>“Estimation of secondary phytoplankton pigments from satellite observations using Self-Organizing Maps (SOMs)”,<br><em>J. Geophys. Res. Oceans</em> 124, 1357–1378, <a href="https://doi.org/10.1029/2018jc014450">https://doi.org/10.1029/2018jc014450</a>, <strong>2019</strong></li> <li> <div>El Hourany, R, Pierella Karlusich J., Zinger L., Loisel H., Levy M., and Bowler C.<br>“Linking Satellites to Genes with Machine Learning to Estimate Phytoplankton Community Structure from Space”<em>,<br>Ocean Science</em> 20 no. 1, 217−39. <a href="https://doi.org/10.5194/os-20-217-2024">https://doi.org/10.5194/os-20-217-2024</a>, <strong>2024</strong></div> </li> </ul> <h2><strong>Changelog</strong></h2> <h3>v1.2</h3> <ul> <li>[2024-01-24] Finished re-arranging data and checked validity. 553 missing days / source files (about 6% of total data).</li> <li>[2023-12-01] Fix SST projection at PFT generation. Data fully re-generated.</li> </ul> <h3>v1.1</h3> <ul> <li>[2023-08-30] Store PFT data using linear packing. </li> </ul> <h3>v1.0</h3> <ul> <li>[2023-08-03] those data are reorganised, and metadata is added, using the script <a href="https://gitlab.in2p3.fr/clementhaeck/submeso-color/-/blob/develop/Compute/arrange_pft_data.py?ref_type=heads">https://gitlab.in2p3.fr/clementhaeck/submeso-color/-/blob/develop/Compute/arrange_pft_data.py?ref_type=heads</a></li> <li>[2023-07] data generated with the SOM were stored on `spirit:/data/lollier/PFT`</li> </ul>
Data for creating figures to the paper "Assessing Net Growth of Phytoplankton Biomass on Hourly to Annual Timescales Using the Geostationary Ocean Color Instrument."
<p>Processed data to generate figures for the paper "Assessing Net Growth of Phytoplankton Biomass on Hourly to Annual Timescales Using the Geostationary Ocean Color Instrument."</p> <p>The rate at which microscopic ocean plants, or phytoplankton, consume carbon dioxide represents a gap in scientific knowledge that needs to be filled in order to better model the earth system. To aid in this understanding we use a novel technique that allows us to track the growth behavior of phytoplankton in the Yellow Sea and the East Sea-Japan Sea. This is enabled by using satellite data from the Geostationary Ocean Color Imager, which has the unprecedented ability to collect quality biological information from the ocean surface each daylight hour. We find that the results, while in agreement with local observations and other satellite studies, also contain information about how phytoplankton change over daily to annual cycles and how native communities adapt in response to the annual solar cycle. This information is useful to the ocean modeling community, that seeks to understand various ways in which phytoplankton communities affect the cycling of Earth’s carbon.</p>
CCE LTER P1908 Upwelling Filament Ocean Acidification Phytoplankton Iron Incubation Experiments
<p>Metatranscriptome assembly, read counts, and annotations from four sets of trace metal clean ocean acidification experimeints in the California Current Ecosystem. Experiments were conducted during August 2019 as part of the CCE LTER program. Samples were collected at the initial time point (T0), and three pCO<sub>2</sub> treatments (400, 800, and 1200 ppm) with two time points for each experiment. </p> <p>Poly-A selected mRNA was sequenced on an Illumina NovaSeq 6000 and then assembled with Trinity for each separate experiment. Proteins from the assembly were then predicted with Genemark S-T. Taxonomic annotation was performed with DIAMOND BLASTP searches against PhyloDB v1.076 and based on the Lineage Proability Index from the top hits. Functional annotation was similarly performed using KEGG with KEGG Orthology annotation based on KofamKOALA results. Read quantification was conducted with Bowtie2. </p> <p>Predicted proteins from each assembly is provided in fasta format. The read counts and annotations for each experiment are in tab-delimited files. </p>
Cascading effects augment the direct impact of CO2 on phytoplankton growth in a biogeochemical model, links to model results
<p>This dataset provides the output of eight model simulations with the global ocean biogeochemical model FESOM-REcoM necessary to reproduce the findings of Seifert et al. (2022). In addition to information on the mesh, the dataset contains 1) 5-year means of global phytoplankton biomass, chlorophyll, net primary production, growth rates, limitations, calcification, grazing rates, calcite concentrations, zooplankton biomass, export fluxes as well as CO<sub>2(aq)</sub>, HCO<sub>3</sub><sup>-</sup> and nutrient concentrations, and 2) a time series of global and North Atlantic coccolithophore biomass, temperature, and CO<sub>2(aq)</sub> concentrations from 1958 to 2018.</p> <p>File names refer to the Figures and Tables in the paper where the respective data are used. See “readme” for detailed information on the dataset and separate files.</p>
Surface inherent optical properties and phytoplankton pigment concentrations from the Atlantic Meridional Transect (2009 - 2019): NetCDF format
<p>This dataset is a compilation of particulate inherent optical properties (IOPs) and co-incident high performance liquid chromatography (HPLC) phytoplankton pigment concentrations measured underway on nine Atlantic Meridional Transect (AMT) cruises. The time period of data collection is 2009 - 2019, between Sep-Nov within each year, with measurements collected between approximately 50 degrees South to 50 degrees North. A separate netCDF file is provided for each cruise (AMT 19, and AMT 22-29), including particulate IOPs (absorption, scattering, beam attenuation), pigment concentrations, and associated metadata.</p> <p>A manuscript containing a full description of the dataset, including associated code, will soon be submitted to Earth System Science Data. A Jupytper notebook illustrating data access is provided at: https://github.com/tjor/AMT_ACSpaperplots/blob/main/AMT_DataAccess.ipynb.</p> <p>The data are also released in SeaBASS format: https://seabass.gsfc.nasa.gov/archive/PML/AMT</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.