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31 results for “Marine Phytoplankton”
Host-derived viral transporter protein for nitrogen uptake in infected marine phytoplankton
<p>Dataset for the article "Host-derived viral transporter protein for nitrogen uptake in infected marine phytoplankton", Monier et al.</p> <p>Data for all phylogenetic tree reconstructions (raw and masked protein sequence alignments in fasta format, tree file in newick format) and placement file (jplace format) of two environmental sequences are available.</p> <p>Data for all assay experiments are available: ammonium and urea assays, Omnilog phenotype screening (Nitrogen substrates).</p>
Predicting global patterns in the biomolecular composition of marine phytoplankton and their stoichiometry
<p>Data to predict the biomolecular composition of marine phytoplankton and their stoichiometry using key environmental properties (temp, nutrients, light, etc.)</p>
Data and scripts for Predictable Ecological Response to Rising CO2 of a Community of Marine Phytoplankton
<p>Rising atmospheric CO<sub>2</sub> and ocean acidification are fundamentally altering conditions for life of all marine organisms, including phytoplankton. Differences in CO<sub>2</sub> related physiology between major phytoplankton taxa lead to differences in their ability to take up and utilise CO<sub>2</sub>. These differences may cause predictable shifts in the composition of marine phytoplankton communities in response to rising atmospheric CO<sub>2</sub>. We report an experiment in which 7 species of marine phytoplankton, belonging to 4 major taxonomic groups (cyanobacteria, chlorophytes, diatoms and coccolithophores) were grown at both ambient (500 µatm) and future (1000 µatm) CO<sub>2</sub> levels. These phytoplankton were grown as individual species, as cultures of pairs of species and as a community assemblage of all seven species in two culture regimes (high-nitrogen batch cultures and lower-nitrogen semi-continuous cultures, though not under nitrogen limitation). All phytoplankton species tested in this study increased their growth rates under elevated CO<sub>2</sub> independent of the culture regime. We also find that, despite species-specific variation in growth response to high CO<sub>2</sub>, the identity of major taxonomic groups provides a good prediction of changes in population growth and competitive ability under high CO<sub>2</sub>. The CO<sub>2</sub>-induced growth response is a good predictor of CO<sub>2</sub>-induced changes in competition (R<sup>2</sup>>0.93) and community composition (R<sup>2</sup>>0.73). This study suggests that it may be possible to infer how marine phytoplankton communities respond to rising CO<sub>2</sub> levels from the knowledge of the physiology of major taxonomic groups, but that these predictions may require further characterisation of these traits across a diversity of growth conditions. These findings must be validated in the context of limitation by other nutrients. Also, in natural communities of phytoplankton, numerous other factors that may all respond to changes in CO2, including nitrogen fixation, grazing and variation in the limiting resource will likely complicate this prediction.</p>
Data and Processing from "Carbon-centric dynamics of Earth's marine phytoplankton"
<div><strong>Brief Summary:</strong></div> <div>This documentation is for associated data and code for: </div> <div>A. Stoer, K. Fennel, Carbon-centric dynamics of Earth's marine phytoplankton. Proceedings of the National Academy of Sciences (2024).</div> <div> </div> <div>To cite this software and data, please use:</div> <div> <div>A. Stoer, K. Fennel, Data and processing from "Carbon-centric dynamics of Earth's marine phytoplankton". Zenodo. <a href="https://doi.org/10.5281/zenodo.10949682" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10949682</a>. Deposited 1 October 2024.</div> </div> <div> </div> <div><strong>List of folders and subfolders and what they contain:</strong></div> <div> <ol> <li>raw data: Contains raw data used in the analysis. This folder does not contain the satellite imagery, which will need to be downloaded from the NASA Ocean Color website (https://oceancolor.gsfc.nasa.gov/). <ol> <li>bgc-argo float data (subfolder): Includes Argo data from its original source or put into a similar Argo format</li> <li>global region data (subfolder): Includes data used to subset the Argo profiles into each 10deg lat region and basin.</li> <li>graff et al 2015 data (subfolder): Include the data digitized from Graff et al.'s Fig. 2.</li> </ol> </li> <li>processed data: data processing by this study (Stoer and Fennel, 2024) <ol> <li>processed bgc-argo data (subfolder): A binned processed file is present for each Argo float used in the analysis. Note these files include those describe in Table S1 (these are later processed in "3_stock_bloom_calc.py")</li> <li>processed satellite data (subfolder): includes a 10-deg latitude averaged for each satellite image processed (called "chl_sat_df_merged.csv"). This is later used to calculate a satellite chlorophyll-a climatology in "3_stock_bloom_calc.py".</li> <li>processed chla-irrad data (subfolder): includes the quality-controlled light diffuse attenuation data coupled with the chlorophyll-a fluorescence data to calculate slope factor corrections (the file is called "processed chla-irrad data.csv").</li> <li>processed topography data (subfolder): includes smoothed topography data (file named "ETOPO_2022_v1_60s_N90W180_surface_mod.tiff").</li> </ol> </li> <li>software: <ol> <li>0_ftp_argo_data_download.py: This program downloads the Argo data from the Global Data Assembly Center's FTP. Running this program will provide new Argo float profiles. However, there will be new floats and profiles present if downloaded. This will not match the historical record of Argo floats used in this analysis but could be useful for replicating this analysis when more data becomes available. The historical record of BGC-Argo floats are present in "/raw data/bgc-argo float data/" path. If you wish to downloaded other float data, see Gordon et al. (2020), Hamilton and Leidos (2017) and the data from the misclab website (https://misclab.umeoce.maine.edu/floats/).</li> <li>1_argo_data_processing.py: This program quality-controls and bins the biogeochemical data into a consistent format. This includes corrections and checks, like the spike/noise test or the non-photochemical quenching correction.</li> <li>2_sat_data_processing.py: this program processes the satellite data downloaded from the NASA Ocean Color website.</li> <li>3_stock_bloom_calc.py: this is the main program used to described the results of the study. The program takes the processed Argo data and groups it into regions and calculates slope factors, phytoplankton carbon & chlorophyll-a, global stocks, and bloom metrics.</li> <li>4_stock_calc_longhurst_province.py: This program repeats the global stocks calculations performed in "3_stock_bloom_calc.py" but bases the grouping on Longhurst Biogeochemical Provinces.</li> </ol> </li> </ol> </div> <div><strong>How to Replicate this Analysis:</strong></div> <div>Each program should be run in the order listed above. Path names where the data files have been downloaded will need to be updated in the code.</div> <div> </div> <div>To use the exact same Sprof files as used in the paper, skip running "0_ftp_argo_data_download.py" and start with "1_argo_data_processing.py" instead. Use the float data from the folder "bgc-argo float data". The program "0_ftp_argo_data_download.py" downloads the latest data from Argo database, so it is useful for updating the analysis. The program "1_argo_data_processing.py" may also be skipped to save time and the processed BGC-Argo float data may be used instead (see folder named "processed bgc-argo data"). </div> <div> </div> <div>Similarly, the program "2_sat_data_processing.py" may also be skipped, which otherwise can take multiple hours to process. The raw data is available from the NASA Ocean Color website (<a href="https://oceancolor.gsfc.nasa.gov/">https://oceancolor.gsfc.nasa.gov/</a>). The processed data from "2_sat_data_processing.py" is available so this step may be skipped to save time as well.</div> <div> </div> <div>The program "3_stock_bloom_calc.py" will require running "ocean_toolbox.py" (see below) in another tab. The portion of the program that involves QC for the irradiance profiles has been commented out to save processing time, and the pre-processed data used in the study has been linked instead (see folder "processed light data"). Similarly, pre-processed topography data is present in this repository. The original Earth Topography data can be accessed at <a href="https://www.ncei.noaa.gov/products/etopo-global-relief-model">https://www.ncei.noaa.gov/products/etopo-global-relief-model.</a></div> <p> </p> <p>A version of "3_stock_bloom_calc.py" using Longhurst provinces is available for exploring alternative groupings and their effects on stock calculations. See the program named "4_stock_calc_longhurst_province.py". You will need to download the Longhurst biogeochemical provinces from <a href="https://www.marineregions.org/">https://www.marineregions.org/</a>.</p> <p>To explore the effects of different slope factors, averaging methods, bbp spectral slopes, etc, the user will likely want to make changes to "3_stock_bloom_calc.py". Please do not hesitate to contact the correponding author (Adam Stoer) for guidance or questions.</p> <p><strong>ocean_toolbox.py:</strong></p> <p>import statsmodels.formula.api as smf<br>import os<br>import matplotlib.pyplot as plt<br>import numpy as np<br>from uncertainties import unumpy as unp<br>from scipy import stats</p> <p>def file_grab(root,find,start): #grabs files by file extensions and location<br> filelst = []<br> for subdir, dirs, files in os.walk(root):<br> for file in files:<br> filepath = subdir + os.sep + file<br> if filepath.endswith(find):<br> if filepath.startswith(start):<br> filelst.append(filepath)<br> return filelst</p> <p>def sep_bbp(data, name_z, name_chla, name_bbp):<br> <br> '''<br> data: Pandas Dataframe containing the profile data<br> name_z: name of the depth variable in data<br> name_chla: name of the chlorophyll-a variable in data<br> name_bbp: name of the particle backscattering variable in data <br> <br> returns: the data variable with particle backscattering partitioned into <br> phytoplankton (bbpphy) and non-algal particle components (bbpnap).<br> '''<br> #name_chla = 'chla'<br> #name_z = 'depth'<br> #name_bbp = 'bbp470'<br> dcm = data[data.loc[:,name_chla]==data.loc[:,name_chla].max()][name_z].values[0] # Find depth of deep chla maximum<br> part_prof = data[(data.loc[:,name_bbp]<np.median(data.loc[:,name_bbp]))] # find median bbp of profile<br> <br> mod = smf.quantreg('bbp470 ~ ' + str(name_z), <br> part_prof).fit(q=0.01) # Find model to 1 percentile<br> y_pred = mod.predict(part_prof.loc[:,name_z]) # Create predicted bbp_nap<br> <br> part_prof.loc[:,'bbp_back'] = y_pred.values # Predicted bbp NAP from linear trend<br> z_lim = part_prof.loc[(part_prof.loc[:,'bbp_back'].div(part_prof.loc[:,name_bbp])>=1), name_z].min() <br> <br> # Find depth where bbp NAP and bbp intersect<br> data.loc[data[name_z]>=z_lim, 'bbp_back'] = data.loc[data[name_z]>=z_lim, name_bbp].tolist()<br> data.loc[data[name_z]<z_lim,'bbp_back'] = data.loc[data[name_z]==z_lim, name_bbp].values[0] #data.loc[data[name_z]<z_lim, name_z].mul(lr.slope).add(lr.intercept)<br> <br> <br> data.loc[:,'bbpphy'] = data.loc[:, name_bbp].sub(data.loc[:,'bbp_back']) # Subtract bbp NAP from bbp for bbp from phytoplankton<br> data.loc[(data['bbpphy']<0)|(data['depth']>z_lim),'bbpphy'] = 0 # Subtract bbp NAP from bbp for bbp from phytoplankton</p> <p> return data['bbpphy'], z_lim</p> <p>def bbp_to_cphy(bbp_data, sf):<br> <br> '''<br> data: Pandas Dataframe containing the profile data<br> name_bbp: name of the particulate backscattering variable in data<br> name_bbp_err: name of particulate backscattering error variable in data<br> <br> returns: the data variable with particle backscattering [/m] converted into<br> phytoplankton carbon [mg/m^3].<br> '''<br> <br> cphy_data = bbp_data.mul(sf) </p> <p> return cphy_data</p>
Annotation of genes encoding enzymes across marine phytoplankton genomes
<p>Phytoplankton cells span a large size range, from picoplankton (<2µm), nanoplankton (2 to 20µm), microplankton (20 to 200µm) to macroplankton (200 to <2000µm). Cell size interacts with multiple selective pressures, including cellular metabolic rate, light absorption, nutrient uptake, cell nutrient quotas, trophic interactions and diffusional exchanges with the environment. Beyond simple size, cells of different shapes differ in surface area to volume ratio. For example, more elongated cells, such as pennate diatoms, have a larger surface area to volume ratio compared to more rounded cells, such as centric diatoms, of equivalent biovolume, which can in turn influence diffusional exchanges between cells and their environment. We assembled metadata on diverse marine phytoplankters, in parallel with genomic or transcriptomic data annotations to identify genes encoding enzymes, to facilitate analyses of genomic patterns of encoded enzymes across diverse taxa, sizes, growth forms and origins of strains.</p>
Annotation of genes encoding enzymes across marine phytoplankton genomes
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Data from: Everything is not everywhere: marine compartments shape phytoplankton assemblages
The idea that "everything is everywhere, but the environment selects" has been seminal in microbial biogeography and marine phytoplankton is one of the prototypical groups used to illustrate this. The argument has typically been that phytoplankton is ubiquitous, but that distinct assemblages form under environmental selection. It is well established that phytoplankton assemblages vary considerably between coastal ecosystems. However, the relative role of compartmentalisation of regional seas and site-specific environmental conditions in shaping assemblage structures, has not been specifically examined. We collected data from coastal embayments falling within two different water compartments within the same regional sea and also characterised by highly localised environmental pressures. We used PCNM and AEM models to partition the effects that spatial structures, environmental conditions and their overlap had on the variation in assemblage composition. Our model explained a high percentage of variation in assemblage composition (59-65%) and showed that spatial structure consistent with marine compartmentalisation played a more important role than local environmental conditions. At least during the study period, surface currents connecting sites within the two compartments failed to generate sufficient dispersal to offset the impact of differences due to compartmentalisation. In other words, our findings suggest that, even for a prototypical cosmopolitan group, everything is not everywhere.
Data from: Everything is not everywhere: marine compartments shape phytoplankton assemblages
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Data from: Everything is not everywhere: marine compartments shape phytoplankton assemblages
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Physiological control on carbon isotope fractionation in marine phytoplankton
<p><span>One of the great challenges in biogeochemical research over the past half a century has been to quantify and understand the mechanisms underlying stable carbon isotope fractionation (εp) in phytoplankton in response to changing CO2 concentrations. Partly, this interest is grounded in the use of fossil photosynthetic organism remains as a proxy for past atmospheric CO2 levels. Phytoplankton organic carbon is depleted in 13C compared to its source because of kinetic fractionation by the enzyme RubisCO during photosynthetic carbon fixation, as well as through physiological pathways upstream of RubisCO. Moreover, other factors such as nutrient limitation, variations in light regime as well as phytoplankton culturing systems and inorganic carbon manipulation approaches may confound the influence of aquatic CO2 concentration ([CO2]) on εp. Here, based on experimental data compiled from the literature, we assess which underlying physiological processes cause the observed differences in εp for various phytoplankton groups in response to C-demand/C-supply (i.e., POC production/[CO2]) and test potential confounding factors. Culturing approaches and methods of carbonate chemistry manipulation were found to best explain the differences in εp between studies, although daylength was an important predictor for εp in haptophytes. Extrapolating results from culturing experiments to natural environments and for proxy applications therefore requires caution, and it should be carefully considered whether culture methods and experimental conditions are representative of natural environments.</span></p>
Data for contribution of marine phytoplankton and bacteria to ocean alkalinity
<p>The contributions of phytoplankton and bacteria cells to alkalinity (A<sub>T</sub>) were measured in seawater samples obtained from 205 locations including the East Sea, the North Pacific Ocean, the Bering Sea, the Chukchi Sea, and the Arctic Ocean. We attributed the differences in A<sub>T</sub> values measured for unfiltered versus filtered samples to A<sub>T</sub> components contributed by phytoplankton (retained on a 0.7 µm filter) and by phytoplankton and bacteria combined (A<sub>T</sub><sub>-BIO</sub>; retained on a 0.45 µm filter). The A<sub>T</sub><sub>-BIO </sub>values reached 10-19 μmol kg<sup>-1</sup> in the East Sea and the North Pacific Ocean, and progressively decreased to a level of 1 μmol kg<sup>-1 </sup>with distance<sup> </sup>towards the Arctic Ocean. The study shows that the A<sub>T</sub><sub>-BIO </sub>values are non-negligible in coastal and open ocean environments and need to be considered when assessing the accuracy of carbon parameters calculated using the thermodynamic models that use measured A<sub>T</sub> as an input parameter.</p>
Supplemental Tables for Heal et al: Marine community metabolomes carry fingerprints of phytoplankton community composition
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Data for contribution of marine phytoplankton and bacteria to ocean alkalinity
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Data from: Dynamic sinking behaviour in marine phytoplankton: rapid changes in buoyancy may aid in nutrient uptake
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Physiological control on carbon isotope fractionation in marine phytoplankton
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Raw data for: "Abrupt declines marine phytoplankton production driven by warming and biodiversity loss in a microcosm experiment"
<p><strong>Raw data for the article:</strong> Bestion, E, Barton, S, García, FC, Warfield, R, Yvon-Durocher, G (2020). Abrupt declines in marine phytoplankton production driven by warming and biodiversity loss in a microcosm experiment. Ecology Letters. 2020.</p> <p><br> <strong>This data should be cited as:</strong> Bestion, E, Barton, S, García, FC, Warfield, R, Yvon-Durocher, G (2020). Raw data for: "Abrupt declines marine phytoplankton production driven by warming and biodiversity loss in a microcosm experiment" [Data set]. Bestion et al 2020 Ecology Letters. Zenodo. http://doi.org/10.5281/zenodo.3555223<br> -----------------------------</p> <p><strong>The data is composed of two datasets:</strong><br> -----------------------------------------<br> - Biodiversity_ecosystem_function_data.csv<br> - Cell_traits_data.csv</p> <p> </p> <p><strong>Composition of the Biodiversity ecosystem function dataset</strong><br> ------------------------------------------------------------<br> The dataset contains 27 columns<br> - Temperature: the temperature treatment, either 15, 25or 30°C<br> - R: the community richness (1, 2, 4, 8 or 16 species)<br> - log2_R: the log2-scaled richness<br> - M: the community identity (e.g. ABCD is a community composed of 4 species, species A, B, C and D)<br> - P: the partition id (5 independent partitions of the species pool were drawn, following Bell et al 2009)<br> - Q: the partitioned species pool id (following Bell et al 2009)<br> - R: the replicate id (3 replicates per community within a partition, named 1 to 3)<br> - SA to SP: the species presence-absence status for each of the 16 species (species A to species P), with 1: species present within the community, 0: species absent<br> - Abundance: number of cells per ml at the end of the experiment<br> - ln_Abundance: log-transformed Abundance<br> - Chl_a : chlorophyll a content at the end of the experiment in pg ml-1<br> - ln_Chl_a : log-transformed chlorophyll a</p> <p><br> <strong>Composition of the Cell traits dataset</strong><br> -----------------------------------------<br> The dataset contains 9 columns<br> - Species_alpha: the alphanumeric id of the species used in the Biodiversity ecosystem function dataset<br> - Species_name: species identity<br> - Phylum: the phylum<br> - ln.c: the ln transformed metabolic rate b(Tc) at the reference temperature Tc = 293.15°K from the Sharpe-Schoolfield equation (eq. 4 in the article) in µgO2 cell-1 hour-1<br> - Ea: the activation energy (eV) from the Sharpe-Schoolfield equation<br> - Eh: the deactivation energy (eV) from the Sharpe-Schoolfield equation<br> - Th: the temperature at which half of the enzyme have become non functional (°K) from the Scharpe-Schoolfield equation<br> - Topt: the optimum temperature from differentiating the Sharpe-Schoolfield equation. It is presented in °C to be easier to link to the temperature treatments in the experiment<br> - cell_volume: the cell volume, in µm3</p>
Supporting Data for Tanioka and Matsumoto (2020), A meta-analysis on environmental drivers of marine phytoplankton C:N:P, Biogeosciences
<p>This dataset contains files used to make figures for Tanioka, T., & Matsumoto, K. (2020). A meta-analysis on environmental drivers of marine phytoplankton C:N:P. <em>Biogeosciences</em>, <em>17</em>(11), 2939–2954. <a href="https://doi.org/10.5194/bg-17-2939-2020">https://doi.org/10.5194/bg-17-2939-2020</a></p> <p><strong>Files uploaded:</strong></p> <p><strong>Bibliography</strong></p> <ol> <li>AppendixS1.pdf : list of 104 papers used in the main meta-analysis.</li> </ol> <p><strong>Excel spreadsheets</strong>:</p> <ol> <li>New_data_190211a.xlsx: C:N and C:P dataset</li> <li>res_all_190211a_P.csv: Effect sizes calculated for each P experiment</li> <li>res_all_190211a_N.csv: Effect sizes calculated for each N experiment</li> <li>res_all_190211a_Fe.csv: Effect sizes calculated for each Fe experiment</li> <li>res_all_190211a_I.csv: Effect sizes calculated for each I experiment</li> <li>res_all_190211a_I.csv: Effect sizes calculated for each T experiment</li> <li>META_BIiblio_articles1st_k4899.xlsx : list of 4899 in the first round of data collection/screening (see Fig. 1 in the main text)</li> <li>META_Biblio_articles2nd_k948.xlsx : list of 948 papers in the second round of data collection/screening (see Fig. 1 in the main text)</li> <li>META_Biblio_articles3rd_k196.xlsx : list of 196 papers in the third round of data collection/screening (see Fig. 1 in the main text)</li> </ol> <p><strong>R scripts:</strong></p> <ol> <li>analysis_190211a_test_P.R: script to conduct meta-analysis on P experiments</li> <li>analysis_190211a_test_N.R: script to conduct meta-analysis on N experiments</li> <li>analysis_190211a_test_F.R: script to conduct meta-analysis on Fe experiments</li> <li>analysis_190211a_test_I.R: script to conduct meta-analysis on I experiments</li> <li>analysis_190211a_test_T.R: script to conduct meta-analysis on T experiments</li> <li>functions_eff_logrr.R: function file to calculate ln(RR) (used in “analysis_190211a_test_X.R”)</li> <li>functions_scalc.R function file to calculate s-factor (used in “analysis_190211a_test_X.R”)</li> </ol>
phytoplankton dataset: Marine diatoms in a warming world: phospholipidome model experiments and long-term field analysis
<p>Phytoplankton data set for Marine diatoms in a warming world: phospholipidome model experiments and long-term field analysis. The data set includes abundances of total phytoplankton and of Chaetoceros pseudocurvisetus.</p>
Data from: Metaecosystem dynamics of marine phytoplankton alters resource use efficiency along stoichiometric gradients
Metaecosystem theory addresses the link between local (within habitats) and regional (between habitats) dynamics by simultaneously analyzing spatial community ecology and abiotic matter flow. Here, we experimentally address how spatial resource gradients and connectivity affect resource use efficiency (RUE) and stoichiometry in marine phytoplankton at local and regional scales. We created gradostat metaecosystems consisting of five linearly interconnected patches, which either were arranged in countercurrent gradients of nitrogen (N) and phosphorus (P) supply or with a uniform spatial distribution of nutrients, and which had either low or high connectivity. Gradient metaecosystems were characterized by higher remaining N and P concentrations (and N:P ratios) than uniform ones, a difference reduced by higher connectivity. The position of the patch in the gradient strongly constrained elemental stoichiometry, local biovolume production and RUE. Expectedly, algal C:N, biovolume and N-specific RUE decreased towards the N-rich end of gradient metaecosystem, whereas the opposite was observed for most of the gradient for C:P, N:P and P-specific RUE. However, at highest N:P supply, unexpectedly low C:P, N:P, and P-specific RUE values were found, indicating that the low availability of P inhibited efficient use of N and biovolume production. Consequently, gradient metaecosystems had lower overall biovolume at the regional scale, but higher dissimilarity in species composition. Thus, the performance of phytoplankton in metaecosystems strongly depended on the stoichiometry of resource supply and spatial connectivity between patches.
Supplementary material 9 from: Pearman JK, Casas L, Michell C, Aldanondo N, Mojib N, Holtermann K, Georgakakis I, Curdia J, Carvalho S, Gusti A, Irigoien X (2022) Comparative metagenomics of phytoplankton blooms after nutrient enrichment of oligotrophic marine waters. Metabarcoding and Metagenomics 6: e79208. https://doi.org/10.3897/mbmg.6.79208
Table S4
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.