Evidence map›Paper›PMID 34151028›Full record

ArticleSynthetic biology (Oxford, England)2021

Aquarium: open-source laboratory software for design, execution and data management.

Justin Vrana, Orlando de Lange, Yaoyu Yang, Garrett Newman, Ayesha Saleem, Abraham Miller, Cameron Cordray, Samer Halabiya, Michelle Parks, Eriberto Lopez and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Synthetic biology (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–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

13 citing papers in PubMed.

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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

14 authors.

Justin VranaDepartment of Bioengineering, University of Washington, Seattle, WA, USA.ORCID https://orcid.org/0000-0002-0862-0824
Orlando de LangeDepartment of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA.ORCID https://orcid.org/0000-0002-7264-4484
Yaoyu YangDepartment of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA.ORCID https://orcid.org/0000-0002-5745-3970
Garrett NewmanDepartment of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA.
Ayesha SaleemDepartment of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA.
Abraham MillerDepartment of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA.
Cameron CordrayDepartment of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA.
Samer HalabiyaDepartment of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA.
Michelle ParksDepartment of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA.
Eriberto LopezDepartment of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA.
Sarah GoldbergDepartment of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA.ORCID https://orcid.org/0000-0003-1062-2084
Benjamin KellerDepartment of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA.ORCID https://orcid.org/0000-0001-7377-6051
Devin StricklandDepartment of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA.ORCID https://orcid.org/0000-0002-6468-0928
Eric KlavinsDepartment of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Automation has been shown to improve the replicability and scalability of biomedical and bioindustrial research. Although the work performed in many labs is repetitive and can be standardized, few academic labs can afford the time and money required to automate their workflows with robotics. We propose that human-in-the-loop automation can fill this critical gap. To this end, we present Aquarium, an open-source, web-based software application that integrates experimental design, inventory management, protocol execution and data capture. We provide a high-level view of how researchers can install Aquarium and use it in their own labs. We discuss the impacts of the Aquarium on working practices, use in biofoundries and opportunities it affords for collaboration and education in life science laboratory research and manufacture.

Indexed as

automationLIMSreplicabilitysoftware

Identifiers

PMID34151028
PMCPMC8209617

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.