Evidence map›Paper›PMID 27412095›Full record

ArticleBioinformatics (Oxford, England)2016

SSBD: a database of quantitative data of spatiotemporal dynamics of biological phenomena.

Yukako Tohsato, Kenneth H L Ho, Koji Kyoda, Shuichi Onami

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

25 citing papers in PubMed.

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  8. Robotic data acquisition with deep learning enables cell image-based prediction of transcriptomic phenotypes.Proceedings of the National Academy of Sciences of the United States of America · 2023
    Article
  9. Systematic analysis of cell morphodynamics inFrontiers in bioinformatics · 2023
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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

4 authors.

Yukako TohsatoLaboratory for Developmental Dynamics, RIKEN Quantitative Biology Center, Kobe 650-0047, Japan.
Kenneth H L HoLaboratory for Developmental Dynamics, RIKEN Quantitative Biology Center, Kobe 650-0047, Japan.
Koji KyodaLaboratory for Developmental Dynamics, RIKEN Quantitative Biology Center, Kobe 650-0047, Japan.
Shuichi OnamiLaboratory for Developmental Dynamics, RIKEN Quantitative Biology Center, Kobe 650-0047, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

motivationRapid advances in live-cell imaging analysis and mathematical modeling have produced a large amount of quantitative data on spatiotemporal dynamics of biological objects ranging from molecules to organisms. There is now a crucial need to bring these large amounts of quantitative biological dynamics data together centrally in a coherent and systematic manner. This will facilitate the reuse of this data for further analysis.

resultsWe have developed the Systems Science of Biological Dynamics database (SSBD) to store and share quantitative biological dynamics data. SSBD currently provides 311 sets of quantitative data for single molecules, nuclei and whole organisms in a wide variety of model organisms from Escherichia coli to Mus musculus The data are provided in Biological Dynamics Markup Language format and also through a REST API. In addition, SSBD provides 188 sets of time-lapse microscopy images from which the quantitative data were obtained and software tools for data visualization and analysis. AVAILABILITY AND IMPLEMENTATION: SSBD is accessible at http://ssbd.qbic.riken.jp CONTACT: sonami@riken.jp.

Indexed as

Biological PhenomenaDatabases, FactualAnimalsHumansMicroscopySoftware

Identifiers

PMID27412095
PMCPMC5181557

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.