ArticleGigaScience2019
Software engineering for scientific big data analysis.
Article in GigaScience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- A multi-omics data analysis workflow packaged as a FAIR Digital Object.GigaScience · 2024Article
- iCPET Calculator: A Web-Based Application to Standardize the Calculation of Alpha Distensibility in Patients With Pulmonary Arterial Hypertension.Journal of the American Heart Association · 2023Article
- DivBrowse-interactive visualization and exploratory data analysis of variant call matrices.GigaScience · 2022Article
- Ten simple rules for making a software tool workflow-ready.PLoS computational biology · 2022Article
- Principles for data analysis workflows.PLoS computational biology · 2021Article
- Drug2ways: Reasoning over causal paths in biological networks for drug discovery.PLoS computational biology · 2020Article
- The Impact of Pathway Database Choice on Statistical Enrichment Analysis and Predictive Modeling.Frontiers in genetics · 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The increasing complexity of data and analysis methods has created an environment where scientists, who may not have formal training, are finding themselves playing the impromptu role of software engineer. While several resources are available for introducing scientists to the basics of programming, researchers have been left with little guidance on approaches needed to advance to the next level for the development of robust, large-scale data analysis tools that are amenable to integration into workflow management systems, tools, and frameworks. The integration into such workflow systems necessitates additional requirements on computational tools, such as adherence to standard conventions for robustness, data input, output, logging, and flow control. Here we provide a set of 10 guidelines to steer the creation of command-line computational tools that are usable, reliable, extensible, and in line with standards of modern coding practices.
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What OpenQuestion holds
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.