Evidence map›Paper›PMID 31719224›Full record

ReviewJournal of biosciences2019

Microbiome data science.

Sudarshan A Shetty, Leo Lahti

Abstract readReview
PubMed
In one paragraph

Review in Journal of biosciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
48citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

48 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Sudarshan A ShettyLaboratory of Microbiology, Wageningen University and Research, Wageningen, The Netherlands.
Leo Lahti

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Best practices from open data science are spreading across research fields, providing new opportunities for research and education. Open data science emphasizes the view that digitalization is enabling new forms of resource sharing, collaboration and outreach. This has the potential to improve the overall transparency and efficiency of research. Microbiome bioinformatics is a rapidly developing area that can greatly benefit from this progress. The concept of microbiome data science refers to the application of best practices from open data science to microbiome bioinformatics. The increasing availability of open data and new opportunities to collaborate online are greatly facilitating the development of this field. A microbiome data science ecosystem combines experimental research data with open data processing and analysis and reproducible tutorials that can also serve as an educational resource. Here, we provide an overview of the current status of microbiome data science from a community developer perspective and propose directions for future development of the field.

Indexed as

Data ScienceMicrobiotaHumans

Identifiers

PMID31719224

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.