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On this page
  • Reproducible Research
  • Better methods can’t make up for mediocre theory
  • A push for reproducibility in biomedical research
  • Statcheck
  • Coursera: Reproducible Templates for Analysis and Dissemination
  • Reproducibility Articles
  • Document Conversion
  • Other Resources
  • Reproducible Research
  • Statcheck
  • Coursera: Reproducible Templates for Analysis and Dissemination
  • Reproducibility Articles
  • Document Conversion
  • Other Resources
  • Reproducibility
  • Reproducible Research
  • Statcheck
  • Coursera: Reproducible Templates for Analysis and Dissemination
  • Reproducibility Articles
  • Document Conversion
  • Other Resources
  • Reproducible Reports

Was this helpful?

  1. Writing Journal Articles

Reproducibility

PreviousReviewNextOpen Access

Last updated 2 years ago

Was this helpful?

Reproducible Research

A push for reproducibility in biomedical research

Statcheck

  • Controversial software is proving surprisingly accurate at spotting errors in psychology papers

  • We need a similar program for #pathology articles. Though most pathology articles do not report #statistics in APA style. #statcheck

  • Stat-checking software stirs up psychology

Coursera: Reproducible Templates for Analysis and Dissemination

Reproducibility Articles

  • by Thomas Herndon, Michael Ash, Robert Pollin.

  • An example of an article given the highest designation for a fully reproducible article:

    published in 2009 by Duncan Lee, Claire Ferguson, and Richard Mitchell. To see the article's marking, download the PDF and look for the marking letter in a bold box at the top right.

  • by Geir Kjetil Sandve, Anton Nekrutenko, James Taylor, Eivind Hovig.

Document Conversion

    • A universal document converter

Other Resources

  • There is an R package called “rrrpkg,” which was created to “facilitate reproducible research.” Their focus is on creating a Research Compendium. Here are some illustrations of

    they use that may be helpful to you.

Reproducible Research

  • A push for reproducibility in biomedical research

Statcheck

  • Controversial software is proving surprisingly accurate at spotting errors in psychology papers

  • We need a similar program for #pathology articles. Though most pathology articles do not report #statistics in APA style. #statcheck

  • Stat-checking software stirs up psychology

Coursera: Reproducible Templates for Analysis and Dissemination

Reproducibility Articles

  • by Thomas Herndon, Michael Ash, Robert Pollin.

  • An example of an article given the highest designation for a fully reproducible article:

    published in 2009 by Duncan Lee, Claire Ferguson, and Richard Mitchell. To see the article's marking, download the PDF and look for the marking letter in a bold box at the top right.

  • by Geir Kjetil Sandve, Anton Nekrutenko, James Taylor, Eivind Hovig.

Document Conversion

    • A universal document converter

Other Resources

  • There is an R package called “rrrpkg,” which was created to “facilitate reproducible research.” Their focus is on creating a Research Compendium. Here are some illustrations of

    they use that may be helpful to you.

Reproducibility

Reproducible Research

  • A push for reproducibility in biomedical research

Statcheck

  • Controversial software is proving surprisingly accurate at spotting errors in psychology papers

  • We need a similar program for #pathology articles. Though most pathology articles do not report #statistics in APA style. #statcheck

  • Stat-checking software stirs up psychology

Coursera: Reproducible Templates for Analysis and Dissemination

Reproducibility Articles

  • by Thomas Herndon, Michael Ash, Robert Pollin.

  • An example of an article given the highest designation for a fully reproducible article:

    published in 2009 by Duncan Lee, Claire Ferguson, and Richard Mitchell. To see the article's marking, download the PDF and look for the marking letter in a bold box at the top right.

  • by Geir Kjetil Sandve, Anton Nekrutenko, James Taylor, Eivind Hovig.

Document Conversion

    • A universal document converter

Other Resources

  • There is an R package called “rrrpkg,” which was created to “facilitate reproducible research.” Their focus is on creating a Research Compendium. Here are some illustrations of

    they use that may be helpful to you.

Şöyle birşey düşünün, Pankreas patolojisi ile ilgileniyorsunuz. "Bizim pankreas serisi ne durumda" diye merak ettiniz. Yaptığınız şey birkaç düğmeye basmak, ve o zamana kadar bölümünüzde rapor edilen pankreas vakalarının yaş, cinsiyet, tümör çapı, tümör tipi, evre, derece, lenf nodu durumu vesair bilgileri sağ kalım grafikleri ile word dökümanı olarak oluşturuluveriyor. Bu hayal değil. Yapılabilir. Makul bir bilgi işlem çalışanı, CAP ve AJCC'ye uygun doldurulması zorunlu yapılandırılmış patoloji raporları, ana veri tablosuna erişim, biraz SQL, biraz R, biraz da R Markdown kullanarak bunu yapmak işten bile değil.

# https://github.com/spgarbet/tangram
# http://htmlpreview.github.io/?https://github.com/spgarbet/tg/blob/master/vignettes/example.html

library(tangram)
library(Hmisc)
getHdata(pbc)
# View(pbc)
table <- tangram(drug ~ bili + albumin + stage + protime + sex + age + spiders, data = pbc)

table
html5(table)
latex(table)
index(table)

write(
html5(tangram("drug ~ bili[2] + albumin + stage::Categorical + protime + sex + age + spiders", pbc, msd=TRUE, quant=seq(0, 1, 0.25)),
      fragment=TRUE, inline="hmisc.css", caption = "HTML5 Table Hmisc Style", id="tbl2"),
"tangram1.html")

write(
html5(tangram("drug ~ bili[2] + albumin + stage::Categorical + protime + sex + age + spiders", pbc),
      fragment=TRUE, inline="nejm.css", caption = "HTML5 Table NEJM Style", id="tbl3"),
"tangram_nejm.html")


tbl <- tangram("drug ~ bili[2] + albumin + stage::Categorical[1] + protime + sex[1] + age + spiders[1]", 
               data=pbc,
               pformat = 5)
write(html5(tbl,
      fragment=TRUE,
      inline="lancet.css",
      caption = "HTML5 Table Lancet Style", id="tbl4"
),
"tangram_lancet.html")

index(tangram("drug ~ bili + albumin + stage::Categorical + protime + sex + age + spiders", pbc))[1:20,]


library(readxl)
MDL307_Data <- read_excel("MDL307 - Data.xlsx")

MDL307_Data <- as.data.frame(MDL307_Data)

names(MDL307_Data)

View(MDL307_Data)

MDL307_Data$biyokimyasalrekurrens <- as.factor(MDL307_Data$biyokimyasalrekurrens)
levels(MDL307_Data$biyokimyasalrekurrens)[1] <- "yok"
levels(MDL307_Data$biyokimyasalrekurrens)[2] <- "var"

collist <- c("gleasonskor",
                 "tersiyer",
                 "kribriform",
                 "cerrahisinir",
                 "ekstaprostatik",
                 "lenfnodu",
                 "seminalvezikul"
                 )


MDL307_Data[collist] <- lapply(MDL307_Data[collist], as.factor)


table <- tangram(biyokimyasalrekurrens ~ yas +
                 gleasonskor +
                 tersiyer +
                 kribriform +
                 kribriformyuzde +
                 cerrahisinir +
                 ekstaprostatik +
                 lenfnodu +
                 seminalvezikul +
                 biyokimyasalrekurrens,
                 data = MDL307_Data)
table
  • Export R output to a file

out <- capture.output(summary(my_very_time_consuming_regression))

cat("My title", out, file="summary_of_my_very_time_consuming_regression.txt", sep="n", append=TRUE)

Şöyle birşey düşünün, Pankreas patolojisi ile ilgileniyorsunuz. "Bizim pankreas serisi ne durumda" diye merak ettiniz. Yaptığınız şey birkaç düğmeye basmak, ve o zamana kadar bölümünüzde rapor edilen pankreas vakalarının yaş, cinsiyet, tümör çapı, tümör tipi, evre, derece, lenf nodu durumu vesair bilgileri sağ kalım grafikleri ile word dökümanı olarak oluşturuluveriyor. Bu hayal değil. Yapılabilir. Makul bir bilgi işlem çalışanı, CAP ve AJCC'ye uygun doldurulması zorunlu yapılandırılmış patoloji raporları, ana veri tablosuna erişim, biraz SQL, biraz R, biraz da R Markdown kullanarak bunu yapmak işten bile değil.

# https://github.com/spgarbet/tangram
# http://htmlpreview.github.io/?https://github.com/spgarbet/tg/blob/master/vignettes/example.html

library(tangram)
library(Hmisc)
getHdata(pbc)
# View(pbc)
table <- tangram(drug ~ bili + albumin + stage + protime + sex + age + spiders, data = pbc)

table
html5(table)
latex(table)
index(table)

write(
html5(tangram("drug ~ bili[2] + albumin + stage::Categorical + protime + sex + age + spiders", pbc, msd=TRUE, quant=seq(0, 1, 0.25)),
      fragment=TRUE, inline="hmisc.css", caption = "HTML5 Table Hmisc Style", id="tbl2"),
"tangram1.html")

write(
html5(tangram("drug ~ bili[2] + albumin + stage::Categorical + protime + sex + age + spiders", pbc),
      fragment=TRUE, inline="nejm.css", caption = "HTML5 Table NEJM Style", id="tbl3"),
"tangram_nejm.html")


tbl <- tangram("drug ~ bili[2] + albumin + stage::Categorical[1] + protime + sex[1] + age + spiders[1]", 
               data=pbc,
               pformat = 5)
write(html5(tbl,
      fragment=TRUE,
      inline="lancet.css",
      caption = "HTML5 Table Lancet Style", id="tbl4"
),
"tangram_lancet.html")

index(tangram("drug ~ bili + albumin + stage::Categorical + protime + sex + age + spiders", pbc))[1:20,]


library(readxl)
MDL307_Data <- read_excel("MDL307 - Data.xlsx")

MDL307_Data <- as.data.frame(MDL307_Data)

names(MDL307_Data)

View(MDL307_Data)

MDL307_Data$biyokimyasalrekurrens <- as.factor(MDL307_Data$biyokimyasalrekurrens)
levels(MDL307_Data$biyokimyasalrekurrens)[1] <- "yok"
levels(MDL307_Data$biyokimyasalrekurrens)[2] <- "var"

collist <- c("gleasonskor",
                 "tersiyer",
                 "kribriform",
                 "cerrahisinir",
                 "ekstaprostatik",
                 "lenfnodu",
                 "seminalvezikul"
                 )


MDL307_Data[collist] <- lapply(MDL307_Data[collist], as.factor)


table <- tangram(biyokimyasalrekurrens ~ yas +
                 gleasonskor +
                 tersiyer +
                 kribriform +
                 kribriformyuzde +
                 cerrahisinir +
                 ekstaprostatik +
                 lenfnodu +
                 seminalvezikul +
                 biyokimyasalrekurrens,
                 data = MDL307_Data)
table
  • Export R output to a file

out <- capture.output(summary(my_very_time_consuming_regression))

cat("My title", out, file="summary_of_my_very_time_consuming_regression.txt", sep="n", append=TRUE)

Reproducible Reports

Şöyle birşey düşünün, Pankreas patolojisi ile ilgileniyorsunuz. "Bizim pankreas serisi ne durumda" diye merak ettiniz. Yaptığınız şey birkaç düğmeye basmak, ve o zamana kadar bölümünüzde rapor edilen pankreas vakalarının yaş, cinsiyet, tümör çapı, tümör tipi, evre, derece, lenf nodu durumu vesair bilgileri sağ kalım grafikleri ile word dökümanı olarak oluşturuluveriyor. Bu hayal değil. Yapılabilir. Makul bir bilgi işlem çalışanı, CAP ve AJCC'ye uygun doldurulması zorunlu yapılandırılmış patoloji raporları, ana veri tablosuna erişim, biraz SQL, biraz R, biraz da R Markdown kullanarak bunu yapmak işten bile değil.

# https://github.com/spgarbet/tangram
# http://htmlpreview.github.io/?https://github.com/spgarbet/tg/blob/master/vignettes/example.html

library(tangram)
library(Hmisc)
getHdata(pbc)
# View(pbc)
table <- tangram(drug ~ bili + albumin + stage + protime + sex + age + spiders, data = pbc)

table
html5(table)
latex(table)
index(table)

write(
html5(tangram("drug ~ bili[2] + albumin + stage::Categorical + protime + sex + age + spiders", pbc, msd=TRUE, quant=seq(0, 1, 0.25)),
      fragment=TRUE, inline="hmisc.css", caption = "HTML5 Table Hmisc Style", id="tbl2"),
"tangram1.html")

write(
html5(tangram("drug ~ bili[2] + albumin + stage::Categorical + protime + sex + age + spiders", pbc),
      fragment=TRUE, inline="nejm.css", caption = "HTML5 Table NEJM Style", id="tbl3"),
"tangram_nejm.html")


tbl <- tangram("drug ~ bili[2] + albumin + stage::Categorical[1] + protime + sex[1] + age + spiders[1]", 
               data=pbc,
               pformat = 5)
write(html5(tbl,
      fragment=TRUE,
      inline="lancet.css",
      caption = "HTML5 Table Lancet Style", id="tbl4"
),
"tangram_lancet.html")

index(tangram("drug ~ bili + albumin + stage::Categorical + protime + sex + age + spiders", pbc))[1:20,]


library(readxl)
MDL307_Data <- read_excel("MDL307 - Data.xlsx")

MDL307_Data <- as.data.frame(MDL307_Data)

names(MDL307_Data)

View(MDL307_Data)

MDL307_Data$biyokimyasalrekurrens <- as.factor(MDL307_Data$biyokimyasalrekurrens)
levels(MDL307_Data$biyokimyasalrekurrens)[1] <- "yok"
levels(MDL307_Data$biyokimyasalrekurrens)[2] <- "var"

collist <- c("gleasonskor",
                 "tersiyer",
                 "kribriform",
                 "cerrahisinir",
                 "ekstaprostatik",
                 "lenfnodu",
                 "seminalvezikul"
                 )


MDL307_Data[collist] <- lapply(MDL307_Data[collist], as.factor)


table <- tangram(biyokimyasalrekurrens ~ yas +
                 gleasonskor +
                 tersiyer +
                 kribriform +
                 kribriformyuzde +
                 cerrahisinir +
                 ekstaprostatik +
                 lenfnodu +
                 seminalvezikul +
                 biyokimyasalrekurrens,
                 data = MDL307_Data)
table
  • Export R output to a file

out <- capture.output(summary(my_very_time_consuming_regression))

cat("My title", out, file="summary_of_my_very_time_consuming_regression.txt", sep="n", append=TRUE)

by John P. A. Ioannidis

by Alawi A. Alsheikh-Ali , Waqas Qureshi, Mouaz H. Al-Mallah, John P. A. Ioannidis.

by Mark Ziemann, Yotam Eren, and Assam El-Osta.

by Keith A. Baggerly and Kevin R. Coombes.

(multiple authors). The associated and .

by John P. A. Ioannidis

by Alawi A. Alsheikh-Ali , Waqas Qureshi, Mouaz H. Al-Mallah, John P. A. Ioannidis.

by Mark Ziemann, Yotam Eren, and Assam El-Osta.

by Keith A. Baggerly and Kevin R. Coombes.

(multiple authors). The associated and .

by John P. A. Ioannidis

by Alawi A. Alsheikh-Ali , Waqas Qureshi, Mouaz H. Al-Mallah, John P. A. Ioannidis.

by Mark Ziemann, Yotam Eren, and Assam El-Osta.

by Keith A. Baggerly and Kevin R. Coombes.

(multiple authors). The associated and .

Better methods can’t make up for mediocre theory
https://www.nature.com/articles/d41586-019-03350-5
http://www.emoryhealthsciblog.com/a-push-for-reproducibility-in-biomedical-research/?utm_source=feedburner&utm_medium=twitter&utm_campaign=Feed%3A+EmoryHealthNowBlog+(Lab+Land)
http://www.sciencemag.org/news/2017/11/controversial-software-proving-surprisingly-accurate-spotting-errors-psychology-papers
http://statcheck.io/index.php
http://statcheck.io/
http://www.nature.com/news/stat-checking-software-stirs-up-psychology-1.21049
https://www.coursera.org/learn/reproducible-templates-analysis/supplement/Pw4r9/articles-resources-and-file-organization-examples
“Why Most Published Research Findings Are False”
“Public Availability of Published Research Data in High-Impact Journals”
“Gene name errors are widespread in the scientific literature”
“Deriving chemosensitivity from cell lines: Forensic bioinformatics and reproducible research in high-throughput biology”
“Does high public debt consistently stifle economic growth? A critique of Reinhart and Rogoff”
“Air pollution and health in Scotland: a multicity study”
“Ten Simple Rules for Reproducible Computational Research”
“Open-Source Genomic Analysis of Shiga-Toxin–Producing E. coli O104:H4”
Dataset GigaScience
GitHub Wiki
Pandoc
directory structure and file organization
Center for Open Science
Gitbook
Fivethirtyeight.com
http://www.emoryhealthsciblog.com/a-push-for-reproducibility-in-biomedical-research/?utm_source=feedburner&utm_medium=twitter&utm_campaign=Feed%3A+EmoryHealthNowBlog+(Lab+Land)
http://www.sciencemag.org/news/2017/11/controversial-software-proving-surprisingly-accurate-spotting-errors-psychology-papers
http://statcheck.io/index.php
http://statcheck.io/
http://www.nature.com/news/stat-checking-software-stirs-up-psychology-1.21049
https://www.coursera.org/learn/reproducible-templates-analysis/supplement/Pw4r9/articles-resources-and-file-organization-examples
“Why Most Published Research Findings Are False”
“Public Availability of Published Research Data in High-Impact Journals”
“Gene name errors are widespread in the scientific literature”
“Deriving chemosensitivity from cell lines: Forensic bioinformatics and reproducible research in high-throughput biology”
“Does high public debt consistently stifle economic growth? A critique of Reinhart and Rogoff”
“Air pollution and health in Scotland: a multicity study”
“Ten Simple Rules for Reproducible Computational Research”
“Open-Source Genomic Analysis of Shiga-Toxin–Producing E. coli O104:H4”
Dataset GigaScience
GitHub Wiki
Pandoc
directory structure and file organization
Center for Open Science
Gitbook
Fivethirtyeight.com
http://www.emoryhealthsciblog.com/a-push-for-reproducibility-in-biomedical-research/?utm_source=feedburner&utm_medium=twitter&utm_campaign=Feed%3A+EmoryHealthNowBlog+(Lab+Land)
http://www.sciencemag.org/news/2017/11/controversial-software-proving-surprisingly-accurate-spotting-errors-psychology-papers
http://statcheck.io/index.php
http://statcheck.io/
http://www.nature.com/news/stat-checking-software-stirs-up-psychology-1.21049
https://www.coursera.org/learn/reproducible-templates-analysis/supplement/Pw4r9/articles-resources-and-file-organization-examples
“Why Most Published Research Findings Are False”
“Public Availability of Published Research Data in High-Impact Journals”
“Gene name errors are widespread in the scientific literature”
“Deriving chemosensitivity from cell lines: Forensic bioinformatics and reproducible research in high-throughput biology”
“Does high public debt consistently stifle economic growth? A critique of Reinhart and Rogoff”
“Air pollution and health in Scotland: a multicity study”
“Ten Simple Rules for Reproducible Computational Research”
“Open-Source Genomic Analysis of Shiga-Toxin–Producing E. coli O104:H4”
Dataset GigaScience
GitHub Wiki
Pandoc
directory structure and file organization
Center for Open Science
Gitbook
Fivethirtyeight.com
https://www.serdarbalci.com/2018/05/tekrarlanabilir-ve-otomatik-raporlar.html
https://www.r-bloggers.com/export-r-output-to-a-file/
https://www.serdarbalci.com/2018/05/tekrarlanabilir-ve-otomatik-raporlar.html
https://www.r-bloggers.com/export-r-output-to-a-file/
https://www.serdarbalci.com/2018/05/tekrarlanabilir-ve-otomatik-raporlar.html
https://www.r-bloggers.com/export-r-output-to-a-file/