3 datasets found

  1. Annotated timeseries from yeast cell lifespans - Training and Test sets -...

    • zenodo.org
    bin
    Updated Feb 14, 2022
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    ASPERT Théo; ASPERT Théo (2022). Annotated timeseries from yeast cell lifespans - Training and Test sets - DetecDiv (id03) [Dataset]. http://doi.org/10.5281/zenodo.6075691
    Explore at:
    binAvailable download formats
    Unique identifier
    https://doi.org/10.5281/zenodo.6075691
    Dataset updated
    Feb 14, 2022
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    ASPERT Théo; ASPERT Théo
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This dataset represents timeseries lables of cell divisions, to train & test a classifier to detect cell-cycle slowdown.
    It has been generated by manual annotation from yeast cells lifespans using the DetecDiv software (see below).

    It is made of 1 file containing :

    • Groundtruth Input data (Xdata): 250 timeseries of classes "1. unbudded", "2. small", "3. large", "4. dead", "5. empty", "6. clog" which are outputs from the doi.org/10.5281/zenodo.5553862 network
    • Groundtruth outputdata (Ydata): 250 timeseries of classes "1. pre-slowdown", "2. post-slowdown", which have been annotated manually.

    The indexes from the Xdata correspond to that of the Ydata. Timeseries from 1->200 were used as training while 201->250 were used as validation.

    It is related to the trained network doi.org/10.5281/zenodo.5553829 from the software DetecDiv: github.com/gcharvin/DetecDiv

    biorxiv.org/content/10.1101/2021.10.05.463175v1

    Data type: Vector timeseries (.mat) (250xF with F the number of frames, between 700 and 1000).

    File format: .mat

    Author(s): Théo, ASPERT

    Contact email: theo.aspert@gmail.com

    Affiliation: IGBMC, Université de Strasbourg

    Funding bodies: This work was supported by the Agence Nationale pour la Recherche, the grant ANR-10-LABX-0030-INRT, a French State fund managed by the Agence Nationale de la Recherche under the frame program Investissements d'Avenir ANR-10-IDEX-0002-02.

  2. e

    ΥΔΑΤΑ ΚΟΛΥΜΒΗΣΗΣ ΝΟΤΙΟΥ ΑΙΓΑΙΟΥ - ΣΗΜΕΙΑ ΔΕΙΓΜΑΤΟΛΗΨΙΑΣ

    • data.europa.eu
    • cloud.csiss.gmu.edu
    • +1more
    csv, excel xls
    Updated May 2, 2021
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    ΔΙΕΥΘΥΝΣΗ ΥΔΑΤΩΝ ΝΟΤΙΟΥ ΑΙΓΑΙΟΥ (2021). ΥΔΑΤΑ ΚΟΛΥΜΒΗΣΗΣ ΝΟΤΙΟΥ ΑΙΓΑΙΟΥ - ΣΗΜΕΙΑ ΔΕΙΓΜΑΤΟΛΗΨΙΑΣ [Dataset]. https://data.europa.eu/data/datasets/ydata-kolymbhshs-notioy-aigaioy-shmeia-deigmatolhpsias?locale=ga
    Explore at:
    csv, excel xlsAvailable download formats
    Dataset updated
    May 2, 2021
    Dataset authored and provided by
    ΔΙΕΥΘΥΝΣΗ ΥΔΑΤΩΝ ΝΟΤΙΟΥ ΑΙΓΑΙΟΥ
    Description

    Περιλαμβάνει τα σημεία δειγματοληψίας των υδάτων κολύμβησης της Αποκεντρωμένης Διοίκησης Αιγαίου - τμήμα Νοτίου Αιγαίου

  3. t

    Synthetic Data Generation Market Demand, Size and Competitive Analysis |...

    • techsciresearch.com
    Updated Oct 15, 2024
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    TechSci Research (2024). Synthetic Data Generation Market Demand, Size and Competitive Analysis | TechSci Research [Dataset]. https://www.techsciresearch.com/report/synthetic-data-generation-market/18984.html
    Explore at:
    Dataset updated
    Oct 15, 2024
    Dataset authored and provided by
    TechSci Research
    License

    https://www.techsciresearch.com/privacy-policy.aspxhttps://www.techsciresearch.com/privacy-policy.aspx

    Description

    Global Synthetic Data Generation Market was valued at USD 310 Million in 2023 and is anticipated to project robust growth in the forecast period with a CAGR of 30.4% through 2029F.

    Pages180
    Market Size2023: USD 310 Million
    Forecast Market Size2029: USD 1537.87 Million
    CAGR2024-2029: 30.4%
    Fastest Growing SegmentHybrid Synthetic Data
    Largest MarketNorth America
    Key Players1. Datagen Inc. 2. MOSTLY AI Solutions MP GmbH 3. Tonic AI, Inc. 4. Synthesis AI , Inc. 5. GenRocket, Inc. 6. Gretel Labs, Inc. 7. K2view Ltd. 8. Hazy Limited. 9. Replica Analytics Ltd. 10. YData Labs Inc.

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Click to copy link
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ASPERT Théo; ASPERT Théo (2022). Annotated timeseries from yeast cell lifespans - Training and Test sets - DetecDiv (id03) [Dataset]. http://doi.org/10.5281/zenodo.6075691
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Annotated timeseries from yeast cell lifespans - Training and Test sets - DetecDiv (id03)

Related Article
Explore at:
binAvailable download formats
Unique identifier
https://doi.org/10.5281/zenodo.6075691
Dataset updated
Feb 14, 2022
Dataset provided by
Zenodohttp://zenodo.org/
Authors
ASPERT Théo; ASPERT Théo
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Description

This dataset represents timeseries lables of cell divisions, to train & test a classifier to detect cell-cycle slowdown.
It has been generated by manual annotation from yeast cells lifespans using the DetecDiv software (see below).

It is made of 1 file containing :

  • Groundtruth Input data (Xdata): 250 timeseries of classes "1. unbudded", "2. small", "3. large", "4. dead", "5. empty", "6. clog" which are outputs from the doi.org/10.5281/zenodo.5553862 network
  • Groundtruth outputdata (Ydata): 250 timeseries of classes "1. pre-slowdown", "2. post-slowdown", which have been annotated manually.

The indexes from the Xdata correspond to that of the Ydata. Timeseries from 1->200 were used as training while 201->250 were used as validation.

It is related to the trained network doi.org/10.5281/zenodo.5553829 from the software DetecDiv: github.com/gcharvin/DetecDiv

biorxiv.org/content/10.1101/2021.10.05.463175v1

Data type: Vector timeseries (.mat) (250xF with F the number of frames, between 700 and 1000).

File format: .mat

Author(s): Théo, ASPERT

Contact email: theo.aspert@gmail.com

Affiliation: IGBMC, Université de Strasbourg

Funding bodies: This work was supported by the Agence Nationale pour la Recherche, the grant ANR-10-LABX-0030-INRT, a French State fund managed by the Agence Nationale de la Recherche under the frame program Investissements d'Avenir ANR-10-IDEX-0002-02.

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