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PIE LTER zooplankton surveys using plankton tows along transects in the Plum Island Sound estuary, Massachusetts (Reformatted to the ecocomDP Design Pattern)
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-pie/405/2. The abstract below was extracted from the Level 0 data package and is included for context: Zooplankton were collected in spring and late summer/fall at four stations representing the salinity gradient in the Parker River-Plum Island Sound estuary. Two size classes, greater than 335 micron and greater than 150 micron, were collected by net tows. Conductivity or salinty and temperature were recorded for each sample. Samples were concentrated to less than 250 mls and preserved in 70 percent EtOH. For taxonomy, sample splits were taken such that a minimum of 250 individuals were present, and counted under a dissecting microscope. Individuals were identified to the lowest taxonomic level possible, generally to species. Adult copepods were additionally characterized by sex. The Plum Island Ecosystems (PIE) LTER has, since its inception in 1998, been working towards a predictive understanding of the long-term response of coupled land-estuary-ocean ecosystems to changes in three drivers: climate, sea level, and human activities. The Plum Island Estuary-LTER includes the coupled Parker, Rowley, and Ipswich River watersheds, estuarine areas including a shallow open sound, and extensive tidal marshes. PIE is connected to the Gulf of Maine in the Acadian biogeographic province, which is a cold water, macrotidal environment that is geographically and biologically distinct from coastal ecosystems to the south of Cape Cod, Massachusetts. Over the next four years the LTER will build upon the progress they have made in understanding the importance of spatial patterns and connections across the land-margin ecosystem. The overarching goal is to understand how external drivers, ecosystem
Microbial Planktonic Respiration in Lakes at North Temperate Lakes LTER 2001
Respiration of total plankton passing a 70 micron mesh, and bacteria passing a 1 micron mesh, calculated from loss of oxygen in lake water incubated at in situ temperatures. Oxygen concentration was determined using the Winkler reaction with azide modification. Final product concentration determined via spectrometry or titration with sodium thiosulfate. Titrations that may have overrun the endpoint were not included. The following equation for calculation of dissolved oxygen concentration from titration of Winkler end product with thiosulfate (from Wetzel, R. G. and G. E. Likens. 1991. Limnological Analyses, 2nd ed. Springer-Verlag, New York). Thiosulfate with a molarity of 0.20 N was used for all titrations. mg O2 L-1 = (ml titrant)*(molarity of thiosulfate)*(8000)/((ml of sample titrated)*((ml of bottle -3)/(ml of bottle))). The following equation is for calculation of dissolved oxygen calculation from spectophotometric analysis of Winkler reaction end product (from Roland, F., N. F. Caraco, J. J. Cole. 1999. Rapid and precise determination of dissolved organic oxygen by spectrophotometry: Evaluation of interference from color and turbidity. Limnol. Oceanogr. 44(4):1148-1154). mg O2 L-1 = absorbance at 430 nm (in units of cm-1) * 8.1-0.41 Sampling Frequency: fortnightly during ice-free season - every 6 weeks during ice-covered season Number of sites: 4
PIE LTER zooplankton surveys using plankton tows along transects in the Plum Island Sound estuary, Massachusetts (Reformatted to a Darwin Core Archive)
This data package is formatted as a Darwin Core Archive (DwC-A, event core). For more information on Darwin Core see https://www.tdwg.org/standards/dwc/. This Level 2 data package was derived from the Level 1 data package found here: https://pasta.lternet.edu/package/metadata/eml/edi/337/2, which was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-pie/405/2. The abstract below was extracted from the Level 0 data package and is included for context: Zooplankton were collected in spring and late summer/fall at four stations representing the salinity gradient in the Parker River-Plum Island Sound estuary. Two size classes, greater than 335 micron and greater than 150 micron, were collected by net tows. Conductivity or salinty and temperature were recorded for each sample. Samples were concentrated to less than 250 mls and preserved in 70 percent EtOH. For taxonomy, sample splits were taken such that a minimum of 250 individuals were present, and counted under a dissecting microscope. Individuals were identified to the lowest taxonomic level possible, generally to species. Adult copepods were additionally characterized by sex. The Plum Island Ecosystems (PIE) LTER has, since its inception in 1998, been working towards a predictive understanding of the long-term response of coupled land-estuary-ocean ecosystems to changes in three drivers: climate, sea level, and human activities. The Plum Island Estuary-LTER includes the coupled Parker, Rowley, and Ipswich River watersheds, estuarine areas including a shallow open sound, and extensive tidal marshes. PIE is connected to the Gulf of Maine in the Acadian biogeographic province, which is a cold water, macrotidal environment that is geographically and biologically distinct from coastal ecosystems to the south of Cape Cod, Massachusetts. Over the next four years the LTER will build upon the progress they have made in understanding the importance of spatial patterns and
Abundance, biovolume, and biomass of Synechococcus and eukaryote pico- and nano- plankton from continuous underway flow cytometry during NES-LTER Transect cruises, ongoing since 2018
These data represent the abundance, biovolume, and biomass of prokaryotic and eukaryotic picoplankton and nanoplankton sampled continuously underway during Northeast U.S. Shelf Long-Term Ecological Research (NES-LTER) Transect cruises, ongoing since 2018. Samples were obtained with an Attune NxT Flow Cytometer sampling at approximately 2-min intervals from the underway science seawater. Cells were identified and enumerated from the flow cytometry data files based on their scattering, phycoerythrin (575 nm) and chlorophyll (680 nm) fluorescence signals.
The FORCIS database: A global census of planktonic Foraminifera from ocean waters
<p>The FORCIS (Foraminifera Response to Climatic Stress) database is a synthesis grouping datasets on living planktonic foraminifera. We assembled foraminiferal diversity and distribution data in the global oceans from 1910 until 2018, curating published and unpublished datasets. This database includes data collected using plankton tows, continuous plankton recorder, sediment traps and plankton pump from the global ocean.</p> <p>The FORCIS database version 01 is composed of 5 files (“.csv” format). All data coming from different sampling devices were put into separate “.csv” files. Only the data of the CPR from the Southern Hemisphere have been separated from the Northern Hemisphere CPR data as the data structure is not the same (species counts resolved vs. binned total counts, respectively). </p> <p>Apart from the file of CPR data from the Northern Hemisphere that contains only metadata and binned total counts, all the remaining four files contain 4 blocks:</p> <ul> <li> <p>Block 1: metadata (from column 1 to 71)</p> </li> <li> <p>Block 2: original counts (from column 72 to 274)</p> </li> <li> <p>Block 3: generated counts based on the validated taxonomy (from column 275 to 331). We added “_VT” to each species name to distinguish it from other taxonomy levels. E.g. “g_bulloides” became “g_bulloides_VT”. The number of species counted per subsample is also reported in the column “number_of_species_counted_VT”</p> </li> <li> <p>Block 4: generated counts based on the lumped taxonomy (from column 332 to 379). In this case, we added “_LT” to each species name. E.g. “n_dutertrei” became “n_dutertrei_VT”. We also calculated the number of species counted per subsample and reported it in the column “number_of_species_counted_LT”</p> </li> </ul> <p>Foraminifera abundance data counts are reported in different categories in the blocks 1,2 and 3 and described in the table below:</p> <table> <tbody> <tr> <td> <p><strong>count_type</strong></p> </td> <td> <p><strong>unit</strong></p> </td> </tr> <tr> <td> <p>Absolute</p> </td> <td> <p>ind/m3</p> </td> </tr> <tr> <td> <p>Relative</p> </td> <td> <p>%</p> </td> </tr> <tr> <td> <p>Raw</p> </td> <td> <p>number of individuals</p> </td> </tr> <tr> <td> <p>Fluxes</p> </td> <td> <p>ind/m2/day</p> </td> </tr> <tr> <td> <p>Bin_Absolute</p> </td> <td> <p>ind/m3</p> </td> </tr> <tr> <td> <p>Bin_Relative</p> </td> <td> <p>%</p> </td> </tr> <tr> <td> <p>Bin_Raw</p> </td> <td> <p>number of individuals</p> </td> </tr> <tr> <td> <p>Bin_Fluxes</p> </td> <td> <p>ind/m2/day</p> </td> </tr> </tbody> </table> <p> </p> <p>For more details about the FORCIS database column description, please check the data descriptor paper <strong>Chaabane et al. (2023) (https://doi.org/10.1038/s41597-023-02264-2).</strong></p> <p>The database is kept open for any new entries and the updated version will be released in csv format. The labels of updated versions of the released “.csv” files will contain the date of their publication and versioning number.</p>
Data from Automated plankton image analysis using convolutional neural networks
<p>Datasets and code from Luo et al., "Automated plankton image analysis using convolutional neural networks." Limnology and Oceanography Methods.</p> <p>Data include:</p> <p>1) 42,564 item training library, sorted in 108 classes,</p> <p>2) 42,548 item test set for filtering thresholds, sorted into 38 groups. These images are independent from the training library, and are used for setting the thresholds for post-classification filtering.<br> CSV file: Luo_etal_FT_images_pred.csv contains the image name, predicted class, predicted probability, and validated group. Note that the file class_to_group.csv is needed to match up the class names to the group names.</p> <p>3) 75,000 item fully random, validated set for confusion matrix calculations, sorted into 38 groups. This set is a representation of the full dataset, selected at random after classification. <br> CSV file: Luo_etal_confusionmatrix_images.csv contains the image name, predicted class, predicted probability, and validated group. Note that the file class_to_group.csv is needed to match up the class names to the group names.</p> <p> </p> <p>Scripts and programs:</p> <p>1) Segmentation.zip contains the scripts and executables for the segmentation program.</p> <p>2) Plankton_template.zip contains the archived version of the SparseConvNet program used in manuscript (current version available at: https://github.com/btgraham/SparseConvNet or https://github.com/facebookresearch/SparseConvNet)<br> Note that google-sparsehash is necessary for running SparseConvNet.<br> Also, plankton_epoch-150.cnn are the weights from the training used in the manuscript, and should be placed in the /weights folder if you want to replicate the classifications.</p>
Catalog of GenBank sequence read archive (SRA) entries of 16S and 18S rRNA genes from bacterial and protistan planktonic communities along the Eastern Beaufort Sea coast, North Slope, Alaska, 2011-2013
Microbial communities in the coastal Arctic Ocean experience extreme variability in organic matter and inorganic nutrients driven by seasonal shifts in sea ice extent and freshwater inputs. Lagoons border more than half of the Beaufort Sea coast and provide important habitats for migratory fish and seabirds; yet, little is known about the planktonic food webs supporting these higher trophic levels. To investigate seasonal changes in bacterial and protistan planktonic communities, amplicon sequences of 16S and 18S rRNA genes were generated from samples collected during periods of ice-cover (April), ice break-up (June), and open water (August) from shallow lagoons along the eastern Alaska Beaufort Sea coast from 2011 through 2013. This data package catalogs sequence read archive (SRA) entries available through GenBank BioProject PRJNA530074 at https://www.ncbi.nlm.nih.gov/bioproject/PRJNA530074. This data package is associated with the following publication: Kellogg CTE, McClelland JW, Dunton KH and Crump BC (2019) Strong Seasonality in Arctic Estuarine Microbial Food Webs. Front. Microbiol. 10:2628. doi: 10.3389/fmicb.2019.02628 Environmental variables (physiochemical data from YSI and HOBO data loggers, as well as organic matter analysis and stable isotope data from discrete water samples) associated with this genomic dataset are available from the Arctic Data Center: Kenneth Dunton, Byron Crump, and James McClelland. Physical, chemical, and biological data from lagoons and open coastal waters in the nearshore environment of the eastern Alaska Beaufort Sea, 2011-2013. Arctic Data Center. doi:10.18739/A2DG13. To join the two datasets together, please use the provided site codes (column "site_name" here) and collection dates (column "collection_date" here) in each dataset. Note that the site codes in this package are without hyphens (e.g. JAA) while site codes in the above environmental data package have hyphens (e.g. JA-A). Instead of citing this package which is jus
Inventory of High-resolution phylogenetic profiles of the planktonic microbial communities (via 16S and 18S rRNA gene amplicons) from Shark River Slough and Taylor Slough, Everglades National Park (FCE LTER), Florida, USA, 2017 - ongoing
Planktonic microbial communities mediate many vital biogeochemical processes in wetland ecosystems, yet compared to other aquatic ecosystems, like oceans, lakes, rivers, or estuaries, they remain relatively underexplored. Our study site, the Florida Everglades (USA)—a vast iconic wetland consisting of a slow-moving system of shallow rivers connecting freshwater marshes with coastal mangrove forests and seagrass meadows—is a highly threatened model ecosystem for studying salinity and nutrient gradients, as well as the effects of sea level rise and saltwater intrusion. This dataset provides the first high-resolution phylogenetic profiles of planktonic bacterial and eukaryotic microbial communities (using 16S and 18S rRNA gene amplicons) from these environments. The dataset contains 16S and 18S rRNA data from 2017, and contains 16S rRNA data for monthly (2019) and quarterly water samples (2020-ongoing). The 2017 data are published in Laas et al. 2022. A detailed list of sequence data and their accession numbers in GenBank is provided and will be updated as more data are published. This data package is an inventory of sequence read archive (SRA) entries available through GenBank BioProject PRJNA525456 (at https://www.ncbi.nlm.nih.gov/bioproject/PRJNA525456) and BioProject PRJNA1018945 (at https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1018945). This data package is associated with the following publication: Laas, P., Ugarelli, K., Travieso, R., Stumpf, S., Gaiser, E. E., Kominoski, J. S., & Stingl, U. (2022). Water column microbial communities vary along salinity gradients in the Florida Coastal Everglades wetlands. Microorganisms, 10(2), 215. https://doi.org/10.3390/microorganisms10020215 Instead of citing this package, which is an inventory, please cite the original GenBank data or journal article, as appropriate. Citation guidance for the journal article is available on the respective publisher's website.
Soil Lake Inundation Moat Experiment (SLIME): Physical, chemical, and biological measurements from planktonic water columns, McMurdo Dry Valleys, Antarctica (2018-2020)
The Soil Lake Inundation Moat Experiment (SLIME) was developed by the McMurdo Dry Valleys Long Term Ecological Research (MCM LTER) project to investigate the ecological function of lake moats in Antarctica. These moats form during the austral summer when the margins of permanently ice-covered, closed-basin lakes melt, creating open-water zones, or ‘moats,’ between the shoreline and the thick (3-5 m) perennial ice cover. To study these habitats, sampling transects were established on the north and south shores of Lake Fryxell and the East Lobe of Lake Bonney. This data package includes three seasons of physical, chemical, and biological measurements from the planktonic water columns of these lakes, collected from SLIME transects between January 2018 and January 2020. Parameters include water temperature, conductivity, ion and nutrient concentrations, chlorophyll-a concentrations, as well as fluorescence and photochemical efficiencies for major algal classes. NCBI accession numbers are also provided for the microbial sequence data associated with each sample.
PIE LTER zooplankton surveys using plankton tows along transects in the Plum Island Sound estuary, Massachusetts
Zooplankton were collected in spring and late summer/fall at four stations representing the salinity gradient in the Parker River-Plum Island Sound estuary. Two size classes, greater than 335 micron and greater than 150 micron, were collected by net tows. Conductivity or salinty and temperature were recorded for each sample. Samples were concentrated to less than 250 mls and preserved in 70 percent EtOH. For taxonomy, sample splits were taken such that a minimum of 250 individuals were present, and counted under a dissecting microscope. Individuals were identified to the lowest taxonomic level possible, generally to species. Adult copepods were additionally characterized by sex.
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>
Harmonized data and code for "Plankton response to global warming is characterized by non-uniform shifts in assemblage composition since the last ice age"
<p>Harmonized data and R code for "Plankton response to global warming is characterized by non-uniform shifts in assemblage composition since the last ice age" by Tonke Strack, Lukas Jonkers, Marina C. Rillo, Helmut Hillebrand and Michal Kucera (in <em>Nature Ecology & Evolution</em>, 2022, https://doi.org/10.1038/s41559-022-01888-8).</p> <p>Analyse planktonic foraminifera species assemblages from the North Atlantic Ocean over the past 24,000 years.</p> <p>Scripts written by Tonke Strack</p> <p>DATA SOURCES<br>* WOA18: Locarnini, R. A. et al. World Ocean Atlas 2018, Volume 1: Temperature. A. Mishonov, Technical Editor. NOAA Atlas NESDIS 81, 52 (2019).<br>* LGMR: Osman, M. B. et al. Globally resolved surface temperatures since the Last Glacial Maximum. Nature 599, 239-244, doi:10.1038/s41586-021-03984-4 (2021).<br>* MARGO: Kucera, M., Rosell-Melé, A., Schneider, R., Waelbroeck, C. & Weinelt, M. Multiproxy approach for the reconstruction of the glacial ocean surface (MARGO). Quat. Sci. Rev. 24, 813-819, doi:10.1016/j.quascirev.2004.07.017 (2005). Kucera, M. et al. Reconstruction of sea-surface temperatures from assemblages of planktonic foraminifera: multi-technique approach based on geographically constrained calibration data sets and its application to glacial Atlantic and Pacific Oceans. Quat. Sci. Rev. 24, 951-998, doi:10.1016/j.quascirev.2004.07.014 (2005).<br>* planktonic foraminifera assemblage data: individual citations provided in CoreList_PlanktonicForaminifera.csv</p> <p>DATA<br>1. Harmonized assemblage data*: FullDataTable_PF_harmonized.txt<br>2. Core list with additional information to time series: CoreList_PlanktonicForaminifera.csv<br>3. Reference list for PF names: ReferenceList_PlanktonicForaminifera.csv</p> <p>CODE<br>1. 01_DataAnalysis_PCA.R: principal component analysis on assemblage data of individual time series as well as on whole dissimilarity matrix (results shown in Fig. 1 and 2)<br>2. 02_DataAnalysis_LocalBiodiversityChange.R: local biodiversity change analysis of individual time series (results shown in Fig. 3 and Extended Data Fig. 1); also recalculates resolution of time-series<br>3. 03_DataAnalysis_NoAnalogueAssemblages.R: calculates compositional dissimilarity to the nearest LGM sample to analyse existence of no-analogues (results shown in Fig. 4, as well as Extended Data Fig. 3 and 4)<br>4. 04_DataAnalysis_LDG_LGMresiduals.R: visualises latitudinal diversity gradient through time and the difference between richness and Shannon diversity to their respective LGM mean values (results shown in Fig. 5)</p> <p>*Assemblage data of individual time series were manually downloaded, checked and harmonized following the taxonomy of Siccha and Kucera (2017) and combined into one data file. Species not reported in the time series data were assumed to be absent (i.e., zero abundance). We merged <em>Globigerinoides ruber ruber</em> and <em>Globigerinoides ruber albus</em>, because some studies only reported them together as <em>Globigerinoides ruber</em>. Also, P/D intergrades (an informal category of morphological intermediates between <em>Neogloboquadrina incompta</em> and <em>Neogloboquadrina dutertrei</em>) were merged with <em>Neogloboquadrina incompta</em>. In total, 41 species of planktonic foraminifera were included in this study.</p> <p>Siccha, M. & Kucera, M. ForCenS, a curated database of planktonic foraminifera census counts in marine surface sediment samples. <em>Sci. Data</em> 4, 170109, doi:10.1038/sdata.2017.109 (2017).</p>
Planktonic Mg/Ca-derived IPWP upper ocean temperature, heat content and sea water δ18O over the last 360 ka
<p>This dataset contains planktonic foraminifera Mg/Ca-derived temperature estimates, age control points and sea water δ18O (δ18Osw) of cores ODP807, KX21-2, MD10-3340, SO18480-3 and MD98-2162 from the Indo-Pacific Warm Pool (IPWP) over the last 360 ka. It also includes reconstructed IPWP stacks of SST, TWT, upper OHC and δ18Osw, and numerical simulated upper OHC, δ18Osw (sea water) and δ18Op (rainfall) from the CESM model and GISS-ModelE2-R model.</p>
Data, scripts and model output to perform spatiotemporal analysis of plankton drivers in the Belgian part of the North Sea
<p>This archive contains the input data, R scripts and final results of a mechanistic model that uses near real-time data from the Belgian Part of the North Sea (2011-2017) to quantify the relative contributions of the bottom-up and top-down drivers in phytoplankton dynamics. Input data are zooplankton and phytoplankton abundances, nutrients, Sea Surface Temperature (SST), photosynthetically active radiation (PAR); from the LifeWatch data and infrastructure, funded by Research Foundation - Flanders (FWO). Water temperature data for one of the locations was obtained from Flemish Banks Monitoring Network at https://meetnetvlaamsebanken.be/. The R scripts are presented in a R Markdown file that can be executed in the Blue-Cloud Zoo and Phytoplankton EOV products Vlab at https://blue-cloud.d4science.org/web/zoo-phytoplankton_eov, operated by D4Science.org, www.d4science.org (Assante et al., 2019). </p>
Supplemental material for the manuscript "Extreme genome scrambling in marine planktonic Oikopleura dioica cryptic species".
<p><strong>Supplementary material for the manuscript “Extreme genome scrambling in marine planktonic <em>Oikopleura dioica</em> cryptic species”.<br></strong></p> <p><strong><em>BreakpointsData.tar.xz contains:</em></strong></p> <ul> <li>Pairwise genome alignment files for <em>Oikopleura</em>, <em>Ciona</em>, <em>Caenorhabditis</em>, insects and muntjaks in GFF format in `inst/extdata/`.</li> <li>dN / dS computation results in `inst/extdata/dNdS/`.</li> <li>Annotations of gene models and repeat elements in GFF format in `inst/extdata/Annotations/`.</li> <li>OrthoGroups in `inst/extdata/OrthoFinder/`, where N19 represents the _O. dioica_ clade,</li> <li>N3 the tunicates and N20 the _Ciona_ clade.</li> <li>`BreakpointsData_3.11.0.tar.gz`, a R package installing the above files in the R environments where we ran our computations.</li> <li>The files needed to build the `BreakpointsData` package.</li> </ul> <p><em><strong>Oidioi_pairwise_v3.tar.gz contains:</strong></em></p> <ul> <li>The pairwise alignment files between genomes, in MAF format.</li> <li>A copy of the Nextflow pipeline used to generate them.</li> </ul> <p><em><strong>oist-assembler.tar.gz contains:</strong></em></p> <ul> <li>A Singularity image and its definition file for flye version 2.8.3-b1763` Flye-flye.2.8.3-b1763.sif` and `Flye-flye.def`.</li> <li>A copy of the Nextflow pipeline used to assemble the Bar2_p4 genome in `oist-assembler-Bar2_p4`.</li> <li>A copy of the Nextflow pipeline used to assemble the other genome in `oist-assembler-other_genomes`.</li> </ul> <p><em>Please note that these files are provided for reproducibility only and probably can not be used easily for other purposes.</em></p> <p><em><strong>Oidioi_genomes.tar.gz contains:</strong></em></p> <ul> <li>For each genome, one file (`<genome>.fa`) containing the whole genome sequence and one directory (`<genome>`) containing each chromosome, scaffold or contig of the genome as a separate file.</li> <li>For each genome, one R package, its source directory, and the vignette to create it, providing the genome information as a `BSgenome` object.</li> </ul> <p><em><strong>OrthoFinderRun.tar.xz contains:</strong></em></p> <ul> <li>A full copy of the OrthoFinder2 run that we used to compute hierarchical orthogroups.</li> </ul> <p><em><strong>Supplemental_Code.tar.gz contains:</strong></em></p> <ul> <li>A copy of <https://github.com/oist/LuscombeU_OikScrambling>, where the `.git` and `doc` directories were removed to save space.</li> </ul> <p><em><strong>AugustusAnnotation.tar.gz (added July 26th 2024) contains:</strong></em></p> <ul> <li>AUGUSTUS runs to produce the annotations that were input to OrthoFinder2. We provide them for reproducibility, with no guarantee that they are suitable for other purposes. The annotations used in the manuscript are AOM-5-5f.sm.OSKA-CDS, Bar2_p4_Flye.sm, Bsty_SCLE01.1.sm.abi.cionamodel, Fbor_SDII01.1.sm.abi, KUM-M3-7f.sm.OKI-CDS, Mery_SCLF01.1.sm.abi.cionamodel, Oalb_SCLG01.1.sm.abi.cionamodel, OKI2018_I69_annotv2.sm, Olon_SCLD01.1.sm.abi, OSKA2016v1.9.sm and Ovan_SCLH01.1.sm.abi.cionamodel.</li> </ul>
Plankton Temperature Measurements - Data Management - University of Tennessee - Mock Data
<p><strong>Comparison of conochilus unicornis (CONI) and conochilus hippocrepus (CHIP) depth and colony size over time at three different locations. </strong></p> <p>This contains colony size, depth, and density measurements from Name Pond and the data on which these data were gathered wereTHESE DATES.</p> <p><a href="https://zenodo.org/api/files/984109f0-dde8-46b7-bf8c-5556ac8b76b2/SEH_PlanktonNamePond_v7.27.2012.csv">SEH_PlanktonNamePond_v7.27.2012.csv </a></p> <p>This contains time, temperature, colony size, depth, and density measurements from Name Pond and the data on which these data were gathered wereTHESE DATES.</p> <p><a href="https://zenodo.org/api/files/984109f0-dde8-46b7-bf8c-5556ac8b76b2/SEH_PlanktonTempB_v.7.27.2012.csv">SEH_PlanktonTempA_v.7.27.2012.csv </a></p> <p>This contains time, temperature, colony size, depth, and density measurements from site A and the data on which these data were gathered wereTHESE DATES.</p> <p><a href="https://zenodo.org/api/files/984109f0-dde8-46b7-bf8c-5556ac8b76b2/SEH_PlanktonTempB_v.7.27.2012.csv">SEH_PlanktonTempB_v.7.27.2012.csv </a></p> <p>This contains time, temperature, colony size, depth, and density measurements from Site B and the data on which these data were gathered wereTHESE DATES.</p> <p><strong>Metadata</strong></p> <p>Date: Day that samples were collected</p> <p>Time_Day_Night: Gives time that the sample was gathered in the day</p> <p>Temp_C: Temperature of the water containing the plankton.</p> <p>CONI: Conochilus unicornis - species of plankton</p> <p>CHIP: Conochilus hippocrepis - plankton</p> <p>XXXX_ColonySize_mm: Average diameter (mm) of 5 randomly chose plankton colonies in the sample </p> <p>Temp: TemperatureYSI probe taken once at each depth</p> <p>ChlorophyllA_Units: Chlorophyll A values</p>
Gulf of Mexico planktonic foraminifera and stable isotope data from core EN-032-18PC spanning MIS 9 to MIS 5
<p><em>Database of the accepted manuscript</em>: Arellano-Torres et al., 2023. The Loop Current circulation over the MIS 9 to MIS 5 based on planktonic foraminifera assemblages from the Gulf of Mexico. Paleoceanography and Paleoclimatology, DOI: 10.1029/2022PA004568</p> <p>In the sediment Core EN-032-18PC collected below the influence of the Loop Current in the eastern Gulf of Mexico, we studied mixed layer conditions and the intensity of the surface and subsurface waters flowing from the Caribbean to the gulf. This database includes analyses of 136 samples in three Supplementary Tables. (Table S1) Bulk sediment and sand fraction (>62 μm) weight (g), the absolute abundance (tests per sample) of 33 species of planktonic foraminifera. (Table S2) Relative abundance (%) of planktonic foraminifera and factor loadings of two factors (Q-mode factor analysis). (Table S3) Stable isotopes (δ<sup>18</sup>O-PDB and δ<sup>13</sup>C-PDB) (‰) of <em>Globigerinoides ruber</em> (white) and the loess-smoothing of the series with polynomial regression.</p>
Effects of freshwater salinization on a salt-naïve planktonic eukaryote community
Salinization of freshwater ecosystems is a widespread issue, but evidence of ecological effects on aquatic eukaryote communities remains scarce. We experimentally exposed naive planktonic communities of a north-temperate, freshwater lake to a gradient of chloride (Cl-) concentration (0.27-1400 mg Cl-.L-1) with in-situ mesocosms. Following six weeks of exposure, we measured changes in the diversity, composition, and abundance of eukaryotic 18S rRNA gene. Total phytoplankton biomass remained unchanged, but we observed a shift in dominant phytoplankton groups with elevated salt concentration, from Cryptophyta and Chlorophyta that dominated in lower chloride concentrations (<185 mg Cl-.L-1) to Ochrophyta that dominated at higher conductivity (>185 mg Cl-.L-1). Most zooplankton and rotifer taxa were sensitive to the salinity and disappeared at low chloride concentrations (<40 mg Cl-.L-1). While ciliates thrived at low chloride concentrations (<185 mg Cl-.L-1), fungal groups dominated at intermediate chloride concentrations (185 mg Cl-.L-1 to 640 mg Cl-.L-1), and only phytoplankton remained at the highest chloride concentrations (> 640 mg Cl-.L-1).
High-throughput in-situ plankton imaging from the East China Sea: raw images and acantharian ROIs
<p>Vertical imaging profiles were performed at four stations (3, 10, 15, 17; closed circles on the map) during the Japan Agency for Marine-Earth Science and Technology (JAMSTEC) MR17-03C cruise from May 29 to June 13, 2017 with an ISIIS small-imager (<a href="https://www.planktonimaging.com/smaller-imagers">https://www.planktonimaging.com/smaller-imagers</a>) attached to the JAMSTEC DEEP TOW 6KCTD (<a href="https://www.jamstec.go.jp/e/about/equipment/ships/deeptow.html">https://www.jamstec.go.jp/e/about/equipment/ships/deeptow.html</a>). The ISIIS camera was programmed to take 1 photo per second coinciding with an LED flash. Each photo imaged 0.39 L (st. 3 and 10) or 0.35 L (st. 15 and 17) parcels of water in 2448 x 2050 pixel resolution, with each pixel being 22.5 µm. A Sea-Bird SBE 9 CTD was deployed with the DEEP TOW and the ISIIS internal clock was calibrated to match the CTD’s so that CTD data could be used to determine the depth at which each image was taken. Raw images are labeled with the time stamp. Acantharian ROIs are labeled with the timestamp for the raw image from which they were cropped. If more than one acantharian ROI was found in a single raw image, a letter was appended to the ROI file name. </p> <p>Accompanying data (CTD, sequencing) and analyses are available from the GitHub repository: <a href="https://github.com/maggimars/Acanth_ImageSeq">https://github.com/maggimars/Acanth_ImageSeq</a>.</p> <p> </p>
Figure 2 in Zoeal stages of Hiplyra variegata (Rüppell, 1830) (Crustacea: Brachyura: Leucosiidae) reared in the laboratory and collected from plankton at Al-Kharrar creek, central Red Sea
Figure 2. Hiplyra variegata (Rüppell, 1830), maxillule: (a) zoea I; (c) zoea II; (e) zoea III. Maxilla: (b) zoea I; (d) zoea II; (f) zoea III.
ScienceDex guides
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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.