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ShareScore release 0.9.0
Dataset results
756 results for “Plankton”
Differential gene expression profiling and functional network analysis of Mycobacterium tuberculosis bioflims and planktonic populations
GEO Series GSE70718. Mycobacterium tuberculosis H37Rv. 4 samples. Type: Expression profiling by high throughput sequencing.
Transcriptome analysis of planktonic, initial pellicle, and mature pellicle of Pseudoalteromonas sp. SCSIO 11900
GEO Series GSE97322. Pseudoalteromonas sp. SCSIO_11900. 3 samples. Type: Expression profiling by high throughput sequencing.
FIGURE 1 in Planktonic Ctenophora of the Madeira Archipelago (Northeastern Atlantic)
FIGURE 1. Madeira Archipelago including locations of ctenophore observations and collections (*)
model output used for Paper "Simulating ecosystem dynamics and marine biogeochemical cycles with multiple plankton functional types"
<p>This dataset contains the model output from CESM2.2-8p4z, used in Yu et al., 2024 in Journal of Advances in Modeling Earth Systems (JAMES). </p>
Time resolved transcriptome of isolated biofilm cells compared with planktonic cells
GEO Series GSE115528. Bacillus cereus ATCC 14579. 14 samples. Type: Expression profiling by high throughput sequencing.
Escherichia coli biofilms vs planktonic culture
GEO Series GSE24914. Escherichia coli K-12; Escherichia coli. 4 samples. Type: Expression profiling by array.
Transcriptome profiling of planktonic stage to biofilm stage of deep sea bacterium Pseudoalteromonas sp. SM9913
GEO Series GSE74569. Pseudoalteromonas sp. SM9913. 3 samples. Type: Expression profiling by high throughput sequencing.
Expression data from Acinetobacter baumannii planktonic, biofilm, and antibiotic treated biofilm samples
GEO Series GSE186041. Acinetobacter baumannii AB5075. 12 samples. Type: Expression profiling by array.
Identifying genes of Escherichia coli involved in interactions with Stenotrophomonas maltophilia in planktonic cultures and biofilms using transcriptomic analysis
GEO Series GSE24915. Escherichia coli K-12; Escherichia coli. 13 samples. Type: Expression profiling by array.
PA14_mexR vs. wildtype planktonic cells in minimal medium with C-30
GEO Series GSE24262. Pseudomonas aeruginosa; Pseudomonas aeruginosa PA14. 2 samples. Type: Expression profiling by array.
Expression data from S. aureus COL growing under acidic and alkaline conditions in biofilm or planktonic mode
GEO Series GSE138075. Staphylococcus aureus; Staphylococcus aureus subsp. aureus COL. 12 samples. Type: Expression profiling by array.
Example simulation showing spatial and temporal variations in surface carbon biomass of plankton functional groups during a Spring bloom as shown by a 3D hydrodynamic-biogeochemical model (FVCOM-ERSEM), with and without integration of the mixoplankton paradigm.
<p>The outputs are from simulations from using the FVCOM hydrodynamic model coupled to two different versions of ERSEM – (i) ERSEM and (ii) ERSEM-PB (the latter includes the implementation of the mixoplankton paradigm through integration of the 'Perfect Beast' PB model; Flynn and Mitra 2009 <em>Journal of Plankton Research</em>).</p> <p>The FVCOM domain was configured to represent Lyme Bay: a protected bay on the South Coast of England. This region is an important area for shellfish aquaculture. The domain was configured at 350 m – 5 km high-resolution, resolving sub-km scale dynamics in the area. A nested modelling approach of increasing model resolution was set up using two model domains. For the coupled hydrodynamic-biogeochemical model, a parent domain of 1.5 km – 10 km resolution was used to drive Lyme Bay model domain. The atmospheric forcing was provided by a 3-step downscaling of GFS global datasets to reach the 3 km of the final model domain using the Weather Research Forecast (WRF) model. Hydrodynamic boundary conditions are extracted from the European Copernicus Marine System North West European Shelf Forecast system. River flows were extracted from a National scale hydrology model run by the Center for Hydrology and Ecology in the UK. Simulations were initialised at Jan 1<sup>st</sup> 2005, and spun up for 3 months prior to the output of the data visualised in these videos. </p> <p>The 6 videos portray spatial and temporal variation of daily averaged surface carbon biomass (μgC L<sup>-1</sup>) during the month of April 2005 for the different plankton functional types (FTs) as follows:</p> <ul> <li>Video 1: all phytoplankton FTs in standard ERSEM grouped together. These thus include diatoms, nano-, pico- and micro- plankton; i.e., these simulations do not discriminate between phytoplankton and constitutive mixoplankton (CM).</li> <li>Video 2: phytoplankton FT in ERSEM-PB now considering only diatoms and picoplankton (i.e., cyanobacteria) only; CM are now included in Video 3 outputs.</li> <li>Video 3: all mixoplankton FTs grouped together in ERSEM-PB. These outputs thus include biomasses of micro-CM, nano-CM and NCM.</li> <li>Video 4: all zooplankton FTs grouped together in standard ERSEM. Thus, these include nanoflagellates, meso- and micro- zooplankton and thus includes the primary producing non-constitutive mixoplankton</li> <li>Video 5: zooplankton FT representing only the heterotrophic nano- and micro- zooplankton in ERSEM-PB.</li> <li>Video 6: spatio-temporal variability between the constitutive and non-constitutive mixoplankton functional groupings within FVCOM-ERSEM-PB. </li> </ul> <p>For further information about the mixoplankton paradigm, please see the following open access publications and references there in:</p> <p>Mitra A, Caron DA, Faure E, Flynn KJ, Leles SG, Hansen PJ, McManus GB, Not F, Gomes HR, Santoferrara L, Stoecker DK, Tillmann U (2023) <strong>The Mixoplankton Database – diversity of photo-phago-trophic plankton in form, function and distribution across the global ocean</strong>. <em>Journal of Eukaryotic Microbiology</em>, e12972. <a href="https://doi.org/10.1111/jeu.12972">https://doi.org/10.1111/jeu.12972</a></p> <p>Glibert PM, Mitra A (2022) <strong>From webs, loops, shunts, and pumps to microbial multitasking: evolving concepts of marine microbial ecology, the mixoplankton paradigm, and implications for a future ocean</strong>. <em>Limnology and Oceanography</em> 67: 585-597 <a href="https://doi.org.10.1002/lno.12018">https://doi.org.10.1002/lno.12018</a> </p> <p>Mitra A, Irigoien X (2022) <strong>Mixoplankton – Marine Organisms that break the rules</strong>. EU Researcher. <a href="https://issuu.com/euresearcher/docs/mixitin_eur28_h_res">https://issuu.com/euresearcher/docs/mixitin_eur28_h_res</a> </p> <p>Flynn KJ, Mitra A, Anestis K, Anschütz AA, Calbet A, et al. (2019) <strong>Mixotrophic protists and a new paradigm for marine ecology: where does plankton research go now?</strong> <em>Journal of Plankton Research</em> 41: 375-391 <a href="https://doi.org/10.1093/plankt/fbz026">https://doi.org/10.1093/plankt/fbz026</a></p>
Escherichia coli planktonic cultures: mono-species culture vs. mixed-species culture
GEO Series GSE24913. Escherichia coli; Escherichia coli K-12. 4 samples. Type: Expression profiling by array.
Expression data of Pseudomonas aeruginosa cells in planktonic or biofilm mode of growth
GEO Series GSE30021. Pseudomonas aeruginosa; Pseudomonas aeruginosa PAO1. 9 samples. Type: Expression profiling by array.
Phenotype and expression profile analysis of Staphylococcus aureus biofilms and planktonic cells in response to Licochalcone A
GEO Series GSE58938. Staphylococcus aureus. 8 samples. Type: Expression profiling by array.
Dredging impacts on the natural phenology of coastal plankton assemblages in Sepetiba Bay, Rio de Janeiro, Brazil
<p>Raw dataset of biotic and abiotic variables estimated during the monitoring program</p>
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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