ArticleThe R journal2018
A System for an Accountable Data Analysis Process in R.
Article in The R journal, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- Decreased plasma rifapentine concentrations associated with AADAC single nucleotide polymorphism in adults with tuberculosis.The Journal of antimicrobial chemotherapy · 2021Trial
- Advancing the Taxonomy of the DiatomPlants (Basel, Switzerland) · 2025Article
- Hyperspectral imaging for quantifying Magnaporthe oryzae sporulation on rice genotypes.Plant methods · 2024Article
- Body Mass Index and Total Symptom Burden in Myeloproliferative Neoplasms Discovery of a U-shaped Association.Cancers · 2020Article
- Mobilization-based transplantation of young-donor hematopoietic stem cells extends lifespan in mice.Aging cell · 2020Article
- Growth recovery lines: a specific indicator of child abuse and neglect?Pediatric radiology · 2020Article
- Genetically heterogeneous mice exhibit a female survival advantage that is age- and site-specific: Results from a large multi-site study.Aging cell · 2019Article
- Creating and sharing reproducible research code the workflowr way.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
4 authors.
Funding
Abstract
Efficiently producing transparent analyses may be difficult for beginners or tedious for the experienced. This implies a need for computing systems and environments that can efficiently satisfy reproducibility and accountability standards. To this end, we have developed a system, R package, and R Shiny application called adapr (Accountable Data Analysis Process in R) that is built on the principle of accountable units. An accountable unit is a data file (statistic, table or graphic) that can be associated with a provenance, meaning how it was created, when it was created and who created it, and this is similar to the 'verifiable computational results' (VCR) concept proposed by Gavish and Donoho. Both accountable units and VCRs are version controlled, sharable, and can be incorporated into a collaborative project. However, accountable units use file hashes and do not involve watermarking or public repositories like VCRs. Reproducing collaborative work may be highly complex, requiring repeating computations on multiple systems from multiple authors; however, determining the provenance of each unit is simpler, requiring only a search using file hashes and version control systems.
Identifiers
30505573PMC6261481What 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.