Evidence map›Paper›PMID 41332714›Full record

ArticlebioRxiv : the preprint server for biology2025

Insights into the Datasets, Tools, and Training Needs of the AnVIL Community: 2024.

Kathryn J Isaac, Katherine E L Cox, Kai Yin Ho, Elizabeth M Humphries, Natalie Kucher, Jeffrey T Leek, Stephen Mosher, Michael C Schatz, Frederick J Tan, Ava M Hoffman

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Kathryn J IsaacFred Hutch Cancer Center, Biostatistics Program, Seattle, WA, USA.ORCID 0000-0001-8701-4197
Katherine E L CoxJohns Hopkins University, Department of Biology, Baltimore, MD, USA.ORCID 0000-0003-3058-6121
Kai Yin HoVanderbilt University Medical Center, Nashville, TN, USA.ORCID 0000-0003-0887-3845
Elizabeth M HumphriesFred Hutch Cancer Center, Biostatistics Program, Seattle, WA, USA.ORCID 0000-0002-2276-4066
Natalie KucherJohns Hopkins University, Department of Biology, Baltimore, MD, USA.ORCID 0000-0002-3404-4795
Jeffrey T LeekFred Hutch Cancer Center, Biostatistics Program, Seattle, WA, USA.ORCID 0000-0002-2873-2671
Stephen MosherJohns Hopkins University, Department of Biology, Baltimore, MD, USA.ORCID 0000-0002-3752-1606
Michael C SchatzJohns Hopkins University, Department of Biology, Baltimore, MD, USA.ORCID 0000-0002-4118-4446
Frederick J TanJohns Hopkins University, Department of Biology, Baltimore, MD, USA.
Ava M HoffmanFred Hutch Cancer Center, Biostatistics Program, Seattle, WA, USA.ORCID 0000-0002-1833-4397

Funding

The AnVIL Data Ecosystem DACReS SupplementU24HG010262 · NHGRI · BROAD INSTITUTE, INC. · PI Robert J Carroll, Jonathan Lawson · 2018 to 2026
$40.5M
Implementing the Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL)U24HG010263 · NHGRI · JOHNS HOPKINS UNIVERSITY · PI Enis Afgan, VINCENT JAMES CAREY · 2018 to 2026
$23.8M
NHGRI NIH HHS U24 HG010262NHGRI NIH HHS U24 HG010263
6 · The paper itself

Abstract

The NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL) provides a secure cloud-based environment where research and education communities can analyze genomic and biomedical data. The platform supports a wide range of data analysis as well as the ability to safely store and access data in compliance with NIH policies. Work on the AnVIL platform can be easily shared to promote reproducible science and collaboration. The purpose of this study is to better understand the current user base of the AnVIL platform. The AnVIL Community Poll aimed to collect baseline information, identify development opportunities, guide the prioritization of user support strategies, and succinctly but comprehensively describe the current AnVIL Community. The AnVIL Team disseminated the inaugural AnVIL Community Poll by sharing it broadly on social media and through AnVIL and related consortia mailing lists. We categorized respondents as either returning or potential users of the AnVIL platform (based on their provided usage description) and examined user experiences: specifically user backgrounds, technological comfort, research interests, computational needs, and preferences for training and support. Our sample of the AnVIL community found opportunities for platform adoption beyond the current user base and identified areas where training should be enhanced, training preferences, and user computational needs. Specifically, while most respondents were involved in human genomics research, there may be potential for growth in adoption of the platform by prioritizing materials to support clinical researchers. All respondents felt availability of specific tools or datasets was a key feature of the platform. The broader community may also benefit from further development or showcasing of resources to facilitate cost management, finding and incorporating analysis tools, and data import. Our sample greatly preferred virtual training opportunities and returning users of the platform foresaw needing large amounts of storage. This poll provided an insightful snapshot of the current state of the AnVIL and demonstrated areas where the AnVIL Team can take specific steps to address barriers related to platform adoption and further support the existing and varied AnVIL Community. This work can be built upon through user interviews, community discussion, and coordinating a recurring poll.

Identifiers

PMID41332714
PMCPMC12667874

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.