ArticleF1000Research2019
Introduction to Genomic Analysis Workshop: A catalyst for engaging life-science researchers in high throughput analysis.
Article in F1000Research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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.
Who cites it
2 citing papers in PubMed.
- Educating and engaging new communities of practice with high performance computing through the integration of teaching and research.Interface focus · 2020Article
- Introduction to Genomic Analysis Workshop: A catalyst for engaging life-science researchers in high throughput analysis.F1000Research · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Researchers in the life sciences are increasingly faced with the task of obtaining compute resources and training to analyze large, high-throughput technology generated datasets. As demand for compute resources has grown, high performance computing (HPC) systems have been implemented by research organizations and international consortiums to support academic researchers. However, life science researchers lack effective time-of-need training resources for utilization of these systems. Current training options have drawbacks that inhibit the effective training of researchers without experience in computational analysis. We identified the need for flexible, centrally-organized, easily accessible, interactive, and compute resource specific training for academic HPC use. In our delivery of a modular workshop series, we provided foundational training to a group of researchers in a coordinated manner, allowing them to further pursue additional training and analysis on compute resources available to them. Efficacy measures indicate that the material was effectively delivered to a broad audience in a short time period, including both virtual and on-site students. The practical approach to catalyze academic HPC use is amenable to diverse systems worldwide.
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Registered trials
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.