Evidence map›Paper›PMID 36355750›Full record

ArticlePLoS computational biology2022

Reproducibility efforts as a teaching tool: A pilot study.

Nestoras Karathanasis, Daniel Hwang, Vibol Heng, Rimal Abhimannyu, Phillip Slogoff-Sevilla, Gina Buchel, Victoria Frisbie, Peiyao Li, Dafni Kryoneriti, Isidore Rigoutsos

Abstract read
In one paragraph

Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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.

Nestoras KarathanasisComputational Medicine Center, Thomas Jefferson University, Pennsylvania, United States of America.ORCID 0000-0003-4119-0851
Daniel HwangDepartment of Neurology, Thomas Jefferson University, Pennsylvania, United States of America.ORCID 0000-0002-6071-8271
Vibol HengNeuroscience Department, Thomas Jefferson University, Pennsylvania, United States of America.
Rimal AbhimannyuDepartment of Biochemistry and Molecular Biology, Thomas Jefferson University, Pennsylvania, United States of America.ORCID 0000-0002-5973-8048
Phillip Slogoff-SevillaComputational Medicine Center, Thomas Jefferson University, Pennsylvania, United States of America.
Gina BuchelDepartment of Biochemistry and Molecular Biology, Thomas Jefferson University, Pennsylvania, United States of America.ORCID 0000-0001-5266-2477
Victoria FrisbieDepartment of Biochemistry and Molecular Biology, Thomas Jefferson University, Pennsylvania, United States of America.ORCID 0000-0001-7558-9006
Peiyao LiCancer Biology Department, Sidney Kimmel Cancer Center, Thomas Jefferson University, Pennsylvania, United States of America.
Dafni KryoneritiIndependent Researcher, Hamburg, Germany.ORCID 0000-0002-8541-4004
Isidore RigoutsosComputational Medicine Center, Thomas Jefferson University, Pennsylvania, United States of America.ORCID 0000-0003-1529-8631

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The "replication crisis" is a methodological problem in which many scientific research findings have been difficult or impossible to replicate. Because the reproducibility of empirical results is an essential aspect of the scientific method, such failures endanger the credibility of theories based on them and possibly significant portions of scientific knowledge. An instance of the replication crisis, analytic replication, pertains to reproducing published results through computational reanalysis of the authors' original data. However, direct replications are costly, time-consuming, and unrewarded in today's publishing standards. We propose that bioinformatics and computational biology students replicate recent discoveries as part of their curriculum. Considering the above, we performed a pilot study in one of the graduate-level courses we developed and taught at our University. The course is entitled Intro to R Programming and is meant for students in our Master's and PhD programs who have little to no programming skills. As the course emphasized real-world data analysis, we thought it would be an appropriate setting to carry out this study. The primary objective was to expose the students to real biological data analysis problems. These include locating and downloading the needed datasets, understanding any underlying conventions and annotations, understanding the analytical methods, and regenerating multiple graphs from their assigned article. The secondary goal was to determine whether the assigned articles contained sufficient information for a graduate-level student to replicate its figures. Overall, the students successfully reproduced 39% of the figures. The main obstacles were the need for more advanced programming skills and the incomplete documentation of the applied methods. Students were engaged, enthusiastic, and focused throughout the semester. We believe that this teaching approach will allow students to make fundamental scientific contributions under appropriate supervision. It will teach them about the scientific process, the importance of reporting standards, and the importance of openness.

Indexed as

CurriculumEducation, GraduateHumansPilot ProjectsReproducibility of ResultsStudentsTeaching

Identifiers

PMID36355750
PMCPMC9648701

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.