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756 results for “Plankton”
An 800-kyr planktonic 𝜹18O stack for the Western Pacific Warm Pool
<p><strong>Our 10 core planktonic </strong>𝜹<strong>18O WPWP stack is available as the "WPWP_planktonic_stack.txt" file, which contains the age, mean </strong>𝜹<strong>18O, and 1 sigma </strong>𝜹<strong>18O alignment uncertainty. The same file is also available under the name "stack.txt" in the Output folder. The stack was produced using alignment software BIGMACS (Lee and Rand et al., 2022).</strong></p><p><strong>The previously published depth and planktonic </strong>𝜹<strong>18O as well as any radiocarbon or additional age constraints for each core used during stack construction can be found in the Inputs folder. This study's BIGMACS produced age models, depth, and planktonic </strong>𝜹<strong>18O data for each core can be found in the Outputs folder under the "results.mat" file or as .txt files in the individual folders named for each core.</strong></p><p><strong>Additional BIGMACS input and output files for planktonic </strong>𝜹<strong>18O from Timor Sea (near the WPWP) core MD01-2378 (Holborn et al., 2005) are provided to demonstrate the alignment and age model differences between using the our new regional planktonic </strong>𝜹<strong>18O WPWP stack and the global benthic </strong>𝜹<strong>18O LR04 stack as alignment targets. </strong> <strong>Differences between the two stacks during MIS 3 and 4 produce a ~17 kyr error in the alignment of the core to the LR04 stack at ~77 kyr ago (depth 8.81 m in core MD01-2378). Because the planktonic </strong>𝛿<strong>18O records near the WPWP share features which differ from those of benthic </strong>𝛿<strong>18O, age model results are expected be more accurate when these planktonic </strong>𝛿<strong>18O records are aligned to the WPWP stack than to a benthic stack.</strong></p><p>Holbourn, A. E., Kuhnt, W., Kawamura, H., Jian, Z. Grootes, P. M., Erlenkeuser, H., and Xu, J.: Stable isotopes on planktic foraminifera of sediment core MD01-2378, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.263757, 2005.</p><p>Lee, T., Rand, D., Lisiecki, L. E., Gebbie, G., and Lawrence, C. E.: Bayesian age models and stacks: Combining age inferences from radiocarbon and benthic 𝜹18O stratigraphic alignment, EGUsphere, 1–29,<a href="https://doi.org/10.5194/egusphere-2022-734"> https://doi.org/10.5194/egusphere-2022-734</a>, 2022.</p><p>Lisiecki, L. E., and Raymo, M. E.: A Pliocene-Pleistocene stack of 57 globally distributed benthic 𝜹18O records, Paleoceanogr., 20,<a href="https://doi.org/10.1029/2004PA001071"> PA1003, https://doi.org/10.1029/2004PA001071</a>, 2005.</p>
Last Glacial Maximum planktonic foraminifera species assemblages
<p>Last Glacial Maximum planktonic foraminifera species assemblages used in Jonkers et al. "Strong temperature gradients in the ice age North Atlantic Ocean revealed by plankton biogeography", Nature Geoscience, 2023, <a href="https://www.nature.com/articles/s41561-023-01328-7">https://www.nature.com/articles/s41561-023-01328-7</a>. The data are split by ocean basin and contain extensive metadata and saved as a list. The data can be opened using the open source software R. This file contains 2,085 assemblages from 647 globally distributed sites.</p><p>An update to this synthesis with more data is available at <a href="https://doi.pangaea.de/10.1594/PANGAEA.962852">PANGAEA</a>.</p>
Data from: Plankton recruitment from coastal sediments under different temperature and light treatments
<p><span>In highly seasonal systems, the emergence of planktonic resting stages from the sediment is a key driver for bloom timing and plankton community composition. The termination of the resting phase is often linked to environmental cues, but the extent to which recruitment of resting stages is affected by climate change remains largely unknown for coastal environments. Here we investigate phyto- and zooplankton recruitment from oxic sediments in the Baltic Sea in a controlled experiment under proposed temperature and light increase during the spring and summer. We find that emergence of resting stage differs between seasons and the abiotic environment. Phytoplankton recruitment from resting stages were high in spring with significantly higher emergence rates at increased temperature and light levels for dinoflagellate and cyanobacteria than for diatoms, which had highest emergence under cold and dark conditions. In comparison, copepod hatchlings were most abundant in summer and emergence was not affected by increased temperature and light levels. These results show that activation of plankton resting stages are affected to different degrees by increasing temperature and light levels, indicating that climate change affects plankton dynamics through processes related to resting stage termination with potential consequences for bloom timing, community composition and trophic mismatch.</span></p>
Size normalizing planktonic Foraminifera abundance in the water column
<p>Data and R code for the paper <strong>Size normalizing planktonic Foraminifera abundance in the water column</strong> (<a href="https://doi.org/10.1002/lom3.10637">https://doi.org/10.1002/lom3.10637</a>) by Sonia Chaabane, Thibault de Garidel-Thoron, Xavier Giraud, Julie Meilland, Geert-Jan A. Brummer, Lukas Jonkers, P. Graham Mortyn, Mattia Greco, Nicolas Casajus, Olivier Sulpis, Michal Kucera, Azumi Kuroyanagi, Hélène Howa, Gregory Beaugrand, Ralf Schiebel</p> <p> </p> <p>The codes serve to generate a new normalization approach for estimating the abundance of planktonic Foraminifera (ind/m³) within the specified collection size fraction range. Data utilized in this study are sourced from the FORCIS database, containing records collected from the global ocean at various depths spanning the past century. A cumulative distribution across size fractions is identified and modeled using a Michaelis-Menten function. This modeling results in multiplication factors enabling the normalization of one fraction to any other size fraction equal to or larger than 100 µm. The resultant size normalization model is then tested across various depths and compared against a previous size normalization solution.</p> <p>Scripts written by Sonia Chaabane.</p> <p>DATA SOURCES</p> <ul> <li>FORCIS database: <a href="https://doi.org/10.5281/zenodo.7390791" target="_new">https://doi.org/10.5281/zenodo.7390791</a></li> </ul> <p>DATA</p> <ol> <li>data_raw_from_excel.RDS</li> </ol> <p>CODES</p> <ol> <li>Code 1_Prepare the data.R: Reads the data and prepares it for analysis.</li> <li>Code 2_Data-model_training.R: Analyzes the data and builds the model.</li> <li>Code 2_MM_confidence interval_all oceans_depths_seasons.R: Analyzes the data and computes confidence intervals across all oceans, depths, and seasons.</li> <li>Code 3_Validation.R: Compares actual vs. estimated number concentrations.</li> <li>Code 4_Test with berger scheme.R: Compares actual vs. estimated number concentrations using Berger 1969 correction scheme.</li> <li>Code 5_Cross validation_Retailleau et al.R: Applies the FORCIS number concentration-size correction scheme on an independent dataset.</li> <li>Code 6_Retailleau et al. using berger approach.R: Compares actual vs. estimated number concentrations using Berger 1969 correction scheme from an independent dataset.</li> <li>function.R: Additional functions used in the analysis.</li> </ol> <p> </p>
Changes in phytoplankton size-structure alter trophic-transfer in a temperate, coastal planktonic food-web
<p>Datasets, Metadata, Figures and Matlab codes used to produce the figures used in "Changes in phytoplankton size-structure alter trophic-transfer in a temperate, coastal planktonic food-web"</p>
Data to support plankton model construction
<p>This work provides a database of references supportive of modelling targeted at developing plankton digital twins.</p> <p>The database, containing over 120 entries, can be explored using multi-level sort functions. </p> <p>Additional contributions are welcome, and the published database will be re-published to make such additions available on open access.</p>
Figure 72 in Diversity and distribution of species of the planktonic dinoflagellate genus Alexandrium (Dinophyta) from the tropical and subtropical Mexican Pacific Ocean
Figure 72: Distribution map of Alexandrium species from this study in the Mexican Pacific.
In situ plankton footage for testing image processing routines
<p>This dataset contains 12 video files collected from the deep-focus plankton imager (DPI) version of the in situ ictyoplankton imaging system (ISIIS). These files are intended to be used for testing, validating, and comparing computational pipelines. These videos were collected in the Northern Gulf of Alaska as part of an NSF-sponsored field campaign (NGA LTER).</p>
Unravelling Plankton Adaptation in Global Oceans through the Analysis of Lipidomes
<p>This file contains all the data relevant to the manuscript titled “Unravelling Plankton Adaptation in Global Oceans through the Analysis of Lipidomes,” as well as all the code used to generate the data and the figures presented in the manuscript.</p>
Stoichiometric mismatch causes a warming-induced regime shift in experimental plankton communities
<p>Many plant and algal communities respond to warming with shifts towards more carbon-rich species and growth forms, thus diluting essential elements in their biomass and intensifying the stoichiometric mismatch with herbivore nutrient requirements. The dataset is from a 95-day mesocosm experiment on the spring succession of an assembled plankton community in which we manipulated temperature (ambient vs. +3.6°C) and presence vs. absence of two types of grazers (ciliates and <i>Daphnia</i>) in a 2x2x2 factorial design with 3 replicates of each treatment (= 24 mesocosms in total). All mesocosms were initially stocked with low amounts of 6 phytoplankton taxa and were spontaneously colonized by an additional 6 taxa over the course of the experiment.</p> <p>At ambient temperatures, a typical spring succession developed, where a moderate bloom of nutritionally adequate phytoplankton was grazed down to a clear-water phase by a developing <i>Daphnia</i> population.</p> <p>Warming accelerated initial <i>Daphnia</i> population growth but speeded up algal growth rates even more, triggering a massive phytoplankton bloom of poor food quality (i.e. high carbon to phosphorus ratio of phytoplankton biomass). Consistent with the predictions of a stoichiometric producer-grazer model, accelerated phytoplankton growth promoted the emergence of an alternative system attractor, where extremely low phosphorus content of abundant algal food eventually drove <i>Daphnia</i> to extinction. Where present, ciliates slowed down the phytoplankton bloom and the deterioration of its nutritional value, but this only delayed the regime shift. Eventually, phytoplankton grew out of grazer control also in presence of ciliates, and the <i>Daphnia</i> population crashed. The results support the notion that warming can exacerbate the stoichiometric mismatch at the plant-herbivore interface and limit energy transfer to higher trophic levels.</p> <p>One replicate of the 'ambient temperature, <em>Daphnia </em>present, ciliates absent' treatment failed. The dataset therefore consists of data from 23 mesocosms including measurements of the following variables: water temperature, chlorophyll a concentration (a proxy for total phytoplankton biomass), abundances of ciliates and <em>Daphnia </em>(no. of individuals per volume), the concentrations of soluble reactive phosphorus (SRP) and total phosphorus (TP), the carbon to phosphorus ratio (C_P) of seston, and the proportional contribution of different phytoplankton taxa to total phytoplankton biovolume.</p> <p>The zenodo folder (see link https://doi.org/10.5281/zenodo.4715500 below) contains the Matlab and excel files that were used to run a dynamical mathematical model of the study system that were used to generate the model output shown in Figs. 2, 6 and 7 of the publication.</p>
Linking variation in planktonic primary production to coral reef fish growth and condition
<p class="MsoNormal"><span>Within low nutrient tropical oceans, islands and atolls with higher primary production support higher reef fish biomass and reef organism abundance. External energy subsidies can be delivered onto reefs via a range of physical mechanisms. However, the influence of spatial variation in primary production on reef fish growth and condition is largely unknown. It is not yet clear how variability in food delivery onto a reef interacts with reef depth and slope, and affects reef fish productivity. </span><span>Here we test the hypothesis that with increased proximity to deep-water oceanic allochthonous nutrient sources, or at sites where transportation of these water bodies onto reefs is facilitated by shallower reef slopes, parameters of fish growth and condition will be higher, and this pattern will be further emphasised in areas naturally higher in primary production. Contrary to expectations, we found no association between fish growth rate and sites with higher mean chlorophyll values. There were no differences in </span><span>δ</span><sup><span>15</span></sup><span>N or</span><span> δ</span><sup><span>13</span></sup><span>C values in fish collected at greater depths across reefs, suggesting a homogeneous primary production resource. However, the relationship between fish condition and primary production was influenced by depth of collection, driven by higher fish condition at shallow depths within a study site which is a 'hotspot' of primary production. Carbon </span><span>δ</span><sup><span>13</span></sup><span>C values were depleted at sites with increasing primary production, and this trend was reversed by an interactive effect with shallower reef slopes. </span><span>Our results indicate that deep-water ocean nutrient influences did not </span><span>translate into observable increases in overall population growth in </span><span>planktivorous </span><em><span>Chromis fieldi </span></em><span>with</span><span>in the </span><span>10–17.5 m</span><span> </span><span>depth range, but show the importance of site specific variation in hydrodynamics and reef physical characteristics influencing fish carbon isotopic composition and condition. </span></p>
Prey naiveté alters the balance of consumptive and non-consumptive predator effects and shapes trophic cascades in freshwater plankton
<p><span>Predators drive trophic cascades by reducing prey biomass and altering prey traits, selecting for prey that exhibit constitutive and induced anti-predator defenses that decrease susceptibility to consumption. These defense traits are often costly, generating a tradeoff between consumptive (CEs) and non-consumptive predator effects (NCEs). The ecological and evolutionary experience that prey share with a given predator may determine their position along this tradeoff curve, affecting the nature and strength of top-down control of ecosystems. Conceptual models predict that predator-experienced prey suffer greater NCEs than predator-naive prey, which suffer stronger CEs and total predator effects (CEs + NCEs), but this has not been tested in diverse prey communities. We tested these predictions by comparing the effects of predation (CEs + NCEs) and predation risk (NCEs only) of planktivorous fish on food web structure in pond mesocosms with diverse natural communities of either predator-naive or predator-experienced zooplankton. Contrary to expectations, top-down control of zooplankton and phytoplankton biomass was strengthened by prey community experience: in systems with experienced relative to naive zooplankton communities both predation risk (NCEs only) and predation (CEs + NCEs) had stronger effects on zooplankton prey biomass and trophic cascades were twice as strong. These results show that the ecological and evolutionary experience of diverse prey communities alters the balance of consumptive and non-consumptive predator effects and influences trophic cascade strength.</span></p>
Plankton Planet Pilot Project rDNA 18S V9 OTU tables
<p>This repository contains the rDNA 18S V9 OTU table, its rarefied version, and the related contextual data from Plankton Planet Pilot Project.</p> <p>In the file <strong>P2_TO_18SV9_otu_table.tsv.gz</strong>, each OTU, one per row, is described by the following fields: <strong>amplicon</strong> = identifier of the representative (most abundant) sequence of the swarm; <strong>total</strong> = total number of reads in the entire dataset; <strong>cloud</strong> = number of unique sequences constituting the OTU; <strong>length</strong> = length of the representative sequence; <strong>spread</strong> = number of samples in which the OTU has been found; <strong>quality</strong> = minimum expected error observed for the representative sequence, divided by sequence length; <strong>sequence</strong> = nucleic acid sequence of the representative sequence; <strong>identity</strong> = percentage of identity of the representative sequence to the closest reference sequence from PR2_V9 (https://doi.org/10.5281/zenodo.3768951); <strong>references</strong> = best hit reference sequence(s); <strong>taxonomy</strong> = taxonomic path assigned to the representative sequence; <strong>taxogroup</strong> = high-taxonomic level assignation of the representative barcode; <strong>chloroplast</strong> = <em>yes</em>: presence of permanent chloroplast / <em>no</em>: absence of permanent chloroplast / <em>NA</em>: undetermined; <strong>symb_small</strong> = <em>parasite</em>: the species is a parasite / <em>commensal</em>: the species is a commensal / <em>mutualist</em>: the species is a mutualist symbiont, most often a microalgal taxa involved in photosymbiosis / <em>no</em>: the species is not involved in a symbiosis as small partner / <em>NA</em>: undetermined; <strong>symbiont_host</strong> = <em>photo</em>: the host species relies on a mutualistic microalgal photosymbiont to survive (obligatory photosymbiosis) / <em>photo_falc</em>: same as photo, but facultative relationship / <em>photo_klep</em>: the host species maintains chloroplasts from microalgal prey(s) to survive / <em>photo_klep_falc</em>: same as <em>photo_klep</em>, but facultative / <em>Nfix</em> = the host species must interact with a mutualistic symbiont providing N2 fixation to survive / <em>Nfix_falc</em> = same as Nfix, but facultative / <em>no</em>: the species is not involved in any mutualistic symbioses; <em>NA</em>: undetermined; <strong>silicification</strong> = <em>yes</em>: the species has a silicified skeleton / <em>no</em>: it does not / <em>NA</em>: undetermined; <strong>calcification</strong> = <em>yes</em>: the species has a calcified skeleton / <em>no</em>: it does not / <em>NA</em>: undetermined; <strong>strontification</strong> = <em>yes</em>: the species has a skeleton made of strontium / <em>no</em>: it does not / <em>NA</em>: undetermined; <strong>PPXXX</strong> = number of reads in each of the 214 Plankton Planet samples; <strong>TARA_XXXXXXXXXX</strong> = number of reads in each of the 386 <em>Tara</em> Oceans samples.</p> <p>The file <strong>P2_TO_18SV9_otu_table_raref_313539.tsv.gz</strong> contains the same fields but with number of reads (total and per sample) obtained after random subsampling (313,539 reads per sample).</p> <p>In the file <strong>P2_TO_18SV9_context.tsv.gz</strong>, each sample is described by the following fields: <strong>sample</strong> = identifier of the sample; <strong>lower_size_fraction</strong> = lower limit of the size fraction in µm; <strong>upper_size_fraction</strong> = lower limit of the size fraction in µm; <strong>event_date</strong> = date (year-month-day); <strong>event_latitude</strong> = geographic position (latitude in DD); <strong>event_longitude</strong> = geographic position (longitude in DD); <strong>depth</strong> = depth in meters; <strong>temperature</strong> = sea water temperature in °C</p>
In the right place, at the right time: the integration of bacteria into the Plankton Ecology Group model
<p><strong><span>Background</span></strong></p> <p><span>Planktonic microbial communities have critical impacts on the pelagic food web and water quality status in freshwater ecosystems, yet no general model of bacterial community assembly linked to higher trophic levels and hydrodynamics has been assessed. In this study, we utilized a two-year survey of planktonic communities from bacteria to zooplankton on three freshwater reservoirs to investigate their spatiotemporal dynamics.</span></p> <p><strong><span>Result</span></strong></p> <p><span>We observed the site-specific presence and microdiversification of bacteria in lacustrine and riverine environments, as well as in deep hypolimnia. Moreover, we determined recurrent bacterial seasonal patterns driven by both biotic and abiotic conditions, which could be integrated into the well-known Phytoplankton Ecology Group (PEG) model describing primarily the seasonalities of larger plankton groups. Importantly, bacteria with different ecological potentials showed finely coordinated successions affiliated with four seasonal phases, including the spring bloom dominated by fast-growing opportunists, the clear-water phase associated with oligotrophic ultramicrobacteria, the summer phase characterized by phytoplankton bloom-associated bacteria, and the fall/winter phase driven by decay-specialists. </span><span> </span></p> <p><strong><span>Conclusion</span></strong></p> <p><span>Our findings elucidate the principles driving the spatiotemporal microbial community distribution in freshwater ecosystems. We suggest an extension to the original PEG model by integrating recurrent bacterial seasonal trends.</span></p>
Marine and freshwater planktonic ciliates differ in their thermal performance
<p><span>Predicting the performance of aquatic organisms in a future warmer climate depends critically on understanding how current temperature regimes affect the organisms' growth rates. Using a meta-analysis for published experimental data, we calculated the activation energy (E<sub>a</sub>) to parameterize the thermal sensitivity of marine and freshwater ciliates, major players in marine and freshwater food webs. We hypothesized that their growth rates increase with temperature but that ciliates dwelling in the immense, thermally stable ocean are closely adapted to their ambient temperature and have lower E<sub>a</sub> than ciliates living in smaller, thermally more variable freshwater environments. The E<sub>a</sub> was in the range known from other taxa but significantly lower for marine ciliates (</span><span>0.390 ± 0.105 eV) than for freshwater ciliates (0.633 ± 0.060 eV), supporting our hypothesis. Accordingly, models aiming to predict the ciliate response to increasing water temperature should apply the environment-specific activation energies provided in this study. </span></p>
Supplementary materials for "Effects of mesozooplankton growth and reproduction on plankton and organic carbon dynamics in a marine biogeochemical model"
<h2>Overview</h2> <p>This folder contains supplementary materials corresponding to the analysis conducted for "Effects of mesozooplankton growth and reproduction on plankton and organic carbon dynamics in a marine biogeochemical model". The folder is structured into two .zip files. <a href="../api/records/10720907/draft/files/ZENODO_PISCES_MLC.zip/content" target="_blank" rel="noopener noreferrer">ZENODO_PISCES_MLC.zip</a> contains the analysis presented in the paper. BDM-MAREDAT-ZENODO.zip contains the outputs from the Biomass Distribution Models pipeline developped by Nielja Knecht (<a href="../doi/10.5281/zenodo.7888451">10.5281/zenodo.7888451</a>) applied to the MAREDAT mesozooplankton product. </p> <h2>ZENODO_PISCES_MLC Folder Structure</h2> <h3>BDM</h3> <ul> <li><strong>MAREDAT_TUNED_SDM.csv</strong>: This file contains the BDM mesozooplankton biomass monthly climatology from MAREDAT data.</li> </ul> <h3>CODE</h3> <p>This directory contains Jupyter Notebook files (<code>.ipynb</code>) and related Python scripts used for data analysis and visualization. Below is a list of the files:</p> <ul> <li><strong>Code_Fig3_FigA8_FigA17.ipynb</strong>: Jupyter Notebook for generating figures 3, A8, and A17.</li> <li><strong>Code_Fig4.ipynb</strong>: Jupyter Notebook for generating figure 4.</li> <li><strong>Code_Fig5_FigA12_FigA13.ipynb</strong>: Jupyter Notebook for generating figures 5, A12, and A13.</li> <li><strong>Code_Fig6.ipynb</strong>: Jupyter Notebook for generating figure 6.</li> <li><strong>Code_Fig7.ipynb</strong>: Jupyter Notebook for generating figure 7.</li> <li><strong>Code_FigA10.ipynb</strong>: Jupyter Notebook for generating figure A10.</li> <li><strong>Code_FigA11.ipynb</strong>: Jupyter Notebook for generating figure A11.</li> <li><strong>Code_FigA14.ipynb</strong>: Jupyter Notebook for generating figure A14.</li> <li><strong>Code_FigA15.ipynb</strong>: Jupyter Notebook for generating figure A15.</li> <li><strong>Code_FigA16.ipynb</strong>: Jupyter Notebook for generating figure A16.</li> <li><strong>Code_FigA1.ipynb</strong>: Jupyter Notebook for generating figure A1.</li> <li><strong>Code_FigA2.ipynb</strong>: Jupyter Notebook for generating figure A2.</li> <li><strong>Code_FigA6_FigA7.ipynb</strong>: Jupyter Notebook for generating figures A6 and A7.</li> <li><strong>Code_FigA9.ipynb</strong>: Jupyter Notebook for generating figure A9.</li> <li><strong>Code_POC_metrics_not_in_the_paper.ipynb</strong>: Jupyter Notebook containing metrics related to particulate organic carbon (POC) not included in the paper.</li> <li><strong>Code_Table3.ipynb</strong>: Jupyter Notebook for generating table 3.</li> <li><strong>Code_Table4.ipynb</strong>: Jupyter Notebook for generating table 4.</li> <li><strong>Code_Table5.ipynb</strong>: Jupyter Notebook for generating table 5.</li> <li><strong>GlobalEstimatesAbstract.ipynb</strong>: Jupyter Notebook containing global estimates abstract.</li> <li><strong>mlctools</strong>: Python package containing utility functions for the analysis.</li> </ul> <h3>OBS</h3> <p>This directory contains observed data used in the analysis:</p> <ul> <li><strong>BATS_zooplankton.csv</strong>: Zooplankton data from the Bermuda Atlantic Time-series Study (BATS).</li> <li><strong>CHL2.nc</strong>: Chlorophyll data in NetCDF format.</li> <li><strong>climatology_n_0_5.nc</strong>: Climatological data in NetCDF format.</li> <li><strong>HOTS_zooplankton.csv</strong>: Zooplankton data from the Hawaii Ocean Time-series (HOTS).</li> </ul> <h3>OUTPUT</h3> <p>This directory contains output files from PISCES simulations (yearly, monthly and 5-day-average outputs). </p> <ul> <li><strong>0class</strong>: Output files for the '0class' classification corresponding to PISCES-v2.</li> <li><strong>0classregrid</strong>: Regridded output files for the '0class' classification corresponding to PISCES-v2.</li> <li><strong>10classes</strong>: Output files for the '10classes' classification corresponding to PISCES-MOG.</li> <li><strong>10classesregrid</strong>: Regridded output files from PISCES-MOG.</li> <li><strong>2classes</strong>: Output files from PISCES-MOG-2LS.</li> <li><strong>2classesregrid</strong>: Regridded output files from PISCES-MOG-2LS.</li> <li><strong>NOALLOregrid</strong>: Regridded output files from PISCES-MOG-NA.</li> </ul> <h3>PLOT</h3> <p>This directory contains plots generated during the analysis:</p> <h3>TEMP</h3> <p>This directory contains temporary files used during the analysis, including data files and matrices.</p> <h2>BDM-MAREDAT-ZENODO Folder </h2> <p>BDM-MAREDAT-ZENODO.zip contains the outputs from the Biomass Distribution Models pipeline developped by Nielja Knecht (<a href="../doi/10.5281/zenodo.7888451">10.5281/zenodo.7888451</a>) applied to the MAREDAT mesozooplankton product. </p> <p>For any inquiries or data access requests, please contact corentin.clerc -at- usys.ethz.ch</p>
Figure 1 in Vertical distribution and migration of planktonic polychaete larvae in Onagawa Bay, north-eastern Japan
Figure 1. Location of the sampling station in Onagawa Bay.
FIGURE 2 in Eocene planktonic foraminifera from the north Eastern Desert, Egypt: Biostratigraphic, paleoenvironmental and sequence stratigraphy implications
FIGURE 2. Lithologic and biostratigraphic units of the studied sections (A, B, C).
FIGURE 5 in Eocene planktonic foraminifera from the north Eastern Desert, Egypt: Biostratigraphic, paleoenvironmental and sequence stratigraphy implications
FIGURE 5. Range chart of the identified planktonic species in section C.
FIGURE 8 in Eocene planktonic foraminifera from the north Eastern Desert, Egypt: Biostratigraphic, paleoenvironmental and sequence stratigraphy implications
FIGURE 8 (caption on next page).
ScienceDex guides
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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.