Description
Data Records
The data in this sampling event resource has been published as a Darwin Core Archive (DwC-A), which is a standardized format for sharing biodiversity data as a set of one or more data tables. The core data table contains 17,731 records.
2 extension data tables also exist. An extension record supplies extra information about a core record. The number of records in each extension data table is illustrated below.
This IPT archives the data and thus serves as the data repository. The data and resource metadata are available for download in the downloads section. The versions table lists other versions of the resource that have been made publicly available and allows tracking changes made to the resource over time.
Versions
The table below shows only published versions of the resource that are publicly accessible.
How to cite
Researchers should cite this work as follows:
Swedish Meteorological and Hydrological Institute (2024). SHARK - Phyto- and Microzooplankton Data Collected by Imaging FlowCytobots (IFCB) in Swedish and Adjacent Waters
Rights
Researchers should respect the following rights statement:
The publisher and rights holder of this work is The Swedish Meteorological and Hydrological Institute. To the extent possible under law, the publisher has waived all rights to these data and has dedicated them to the Public Domain (CC0 1.0). Users may copy, modify, distribute and use the work, including for commercial purposes, without restriction.
GBIF Registration
This resource has been registered with GBIF, and assigned the following GBIF UUID: a28ecb21-884c-4cc1-8e95-f0ec703609bd. The Swedish Meteorological and Hydrological Institute publishes this resource, and is itself registered in GBIF as a data publisher endorsed by GBIF Sweden.
Keywords
Samplingevent; Plankton
Contacts
- Metadata Provider ●
- Originator ●
- Point Of Contact
- Data manager
Geographic Coverage
N/A
| Bounding Coordinates | South West [54.833, 5.804], North East [65.166, 23.821] |
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Taxonomic Coverage
No Description available
| Kingdom | Chromista, Plantae, Protozoa, Bacteria |
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| Phylum | Cyanobacteria, Myzozoa, Chlorophyta, Cercozoa, Cryptophyta, Euglenozoa, Bacillariophyta, Ochrophyta, Ciliophora |
| Class | Dinophyceae, Chrysophyceae, Raphidophyceae, Cryptophyceae, Pyramimonadophyceae, Trebouxiophyceae, Oligotrichea, Dictyochophyceae, Euglenophyceae, Cyanophyceae, Chlorophyceae, Litostomatea, Thecofilosea, Ulvophyceae, Bacillariophyceae |
| Order | Gonyaulacales, Ulotrichales, Thalassionematales, Gymnodiniales, Lithodesmiales, Eutreptiales, Coscinodiscales, Fragilariales, Dictyochales, Chaetocerotanae incertae sedis, Thalassiosirales, Cyclotrichiida, Pennales, Thoracosphaerales, Ebriales, Triceratiales, Choreotrichida, Oligotrichida, Chattonellales, Centrales, Dinophysales, Prorocentrales, Chromulinales, Rhizosoleniales, Chlorellales, Bacillariales, Chroococcales, Paraliales, Pyramimonadales, Tovelliales, Leptocylindrales, Pedinellales, Peridiniales, Hemiaulales, Amphidiniales, Cryptomonadales, Nostocales, Sphaeropleales, Licmophorales |
| Family | Paraliaceae, Gymnodiniaceae, Fragilariaceae, Microcystaceae, Stephanodiscaceae, Tontoniidae, Dictyochaceae, Aphanizomenonaceae, Eutreptiaceae, Thalassiosiraceae, Thalassionemataceae, Dinophysaceae, Coscinodiscaceae, Triceratiaceae, Chattonellaceae, Actinomonadaceae, Metacylididae, Cladopyxidaceae, Protoperidiniaceae, Strombidiidae, Dinobryaceae, Chaetocerotaceae, Skeletonemaceae, Thoracosphaeraceae, Lithodesmiaceae, Leptocylindraceae, Pyramimonadaceae, Pyrocystaceae, Selenastraceae, Binucleariaceae, Ebriaceae, Polykrikaceae, Oxytoxaceae, Nodulariaceae, Oocystaceae, Lingulodiniaceae, Ceratiaceae, Prorocentraceae, Rhizosoleniaceae, Peridiniaceae, Licmophoraceae, Mesodiniidae, Gyrodiniaceae, Gonyaulacaceae, Hemiaulaceae, Kareniaceae, Warnowiaceae, Tovelliaceae, Scenedesmaceae, Heterocapsaceae, Amphidiniaceae, Bacillariaceae |
Temporal Coverage
| Start Date / End Date | 2016-08-10 / 2024-12-15 |
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Sampling Methods
Sampling is performed either using shipboard flow through systems (i.e. FerryBox) where the Imaging FlowCytobot (IFCB) is connected as an addon, or at specific locations with the IFCB is submerged in-situ. Other deployment methods are possible. The IFCB uses flow cytometry technology and high-resolution images to detects particles in a water sample. Shapes in images are identified to best possible taxonomical levels using AI-assisted image analysis software. Volumes are estimated from the organism’s two-dimensional boundary.
| Study Extent | Data are collected within the following marine ecoregions: http://marineregions.org/mrgid/2401, http://marineregions.org/mrgid/2374, http://marineregions.org/mrgid/2379, http://marineregions.org/mrgid/2350 |
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Method step description:
- The analysis follows the methods provided - Machine learning: Sosik, H. M. and Olson, R. J. (2007), Automated taxonomic classification of phytoplankton sampled with imaging-in-flow cytometry. Limnol. Oceanogr: Methods 5, 204–216. http://github.com/hsosik/ifcb-analysis - Quality control: Hayashi, K., Walton, J., Lie, A., Smith, J. and Kudela M. Using particle size distribution (PSD) to automate imaging flow cytobot (IFCB) data quality in coastal California, USA. In prep. http://github.com/kudelalab/PSD - Data processing: Anders Torstensson (2024). I 'R' FlowCytobot (iRfcb): Tools for Analyzing and Processing Data from the IFCB. R package version 0.3.10. https://doi.org/10.5281/zenodo.12533225. http://github.com/EuropeanIFCBGroup/iRfcb
Additional Metadata
| Alternative Identifiers | https://www.gbif.se/ipt/resource?r=shark-planktonimaging |
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