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Dataset . 2017
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2017
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2017
License: CC BY
Data sources: ZENODO
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Dataset: Quantifying Cell Densities And Biovolumes Of Phytoplankton Communities And Functional Groups Using Scanning Flow Cytometry, Machine Learning And Unsupervised Clustering

Authors: Thomas, Mridul K.; Fontana, Simone; Reyes, Marta; Pomati, Francesco;

Dataset: Quantifying Cell Densities And Biovolumes Of Phytoplankton Communities And Functional Groups Using Scanning Flow Cytometry, Machine Learning And Unsupervised Clustering

Abstract

This dataset contains all relevant data for the manuscript (in submission) "Quantifying cell densities and biovolumes of phytoplankton communities and functional groups using scanning flow cytometry, machine learning and unsupervised clustering". Code written to analyse this dataset (which may be adapted for other flow cytometry datasets) is found at https://zenodo.org/record/999747 -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Naming convention for raw flow cytometry data files (located in /Script 3. Generating raw data subset/input/): [Allparameters] _ [Year] - [Month] - [Date] [Hour] [u] [Minute] _ [Depth] e.g: Allparameters_2014-07-31 08u08_1.0m The date, time and depth indicate the location and time at which the measurement was taken.

We are presently using this dataset for multiple publications that are in preparation. To avoid duplicated efforts, we request that you contact the authors if you are interested in pursuing a research project with this data. Contact information: Mridul K. Thomas - mrit {at} dtu {dot} dk Francesco Pomati - francesco {dot} pomati {at} eawag {dot} ch

Keywords

phytoplankton, flow cytometry, automated monitoring, traits

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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