Metadata annotation is key to reproducibility in sequencing experiments. Reproducing this research using the scripts provided will also show the current level of annotation in years since 2015 when the paper was published.
This is perhaps an interesting 'meta' example for ReproHack as in this study we attempted to reproduce analyses reporrted in 25 published articles. So it seems even more important that our own analyses are reproducible! We tried our best to adhere to best practices in this regard, so we would be very keen to know if anyone has problems reproducing our analyses and/or learning how we can make the process easier. A couple of things to note: 1. In addition to the links to the data and analysis scripts provided above, we also have a Code Ocean container for this article (https://doi.org/10.24433/CO.1796004.v3), which should theoretically allow you to reproduce the analyses with the click of a single button (we hope!). 2. In addition to the main research analyses (for which I've provided links above), we also have data, scripts, and Code Ocean containers for each of the reprodubility attempts for the 25 articles we looked at. I don't know if you will also want to look at this level of the analyses, but if you do then take a look at Supplementary Information section E here: https://royalsocietypublishing.org/doi/suppl/10.1098/rsos.201494 For each reproducibility attempt, there is a short 'vignette' describing the outcome, and a link to data/scripts on the OSF and a Code Ocean container.
I suggested a few papers last year. I’m hoping that we’ve improved our reproducibility with this one, this year. We’ve done our best to package it up both in Docker and as an R package. I’d be curious to know what the best way to reproduce it is found to be. Working through vignettes or spinning up a Docker instance. Which is the preferred method?
It is kind of an easy reproducible code. It reads the data, makes few descriptive statistical analysis and plots figures using ggplot2.
Cleaning the databases used for this study was one of the most challenging aspects of it, so making it public is the best way to make the more out of it. We made an effort to document all analyses and data wrangling steps. We are interested to know if it is truly reproducible so that we can follow this same scheme for further projects, or adjust accordingly.
The paper describes pyKNEEr, a python package for open and reproducible research on femoral knee cartilage using Jupyter notebooks as a user interface. I created this paper with the specific intent to make both the workflows it describes and the paper itself open and reproducible, following guidelines from authorities in the field. Therefore, two things in the paper can be reproduced: 1) workflow results: Table 2 contains links to all the Jupyter notebooks used to calculate the results. Computations are long and might require a server, so if you want to run them locally, I recommend using only 2 or 3 images as inputs for the computations. Also, the paper should be sufficient, but if you need further introductory info, there are a documentation website: https://sbonaretti.github.io/pyKNEEr/ and a "how to" video: https://youtu.be/7WPf5KFtYi8 2) paper graphs: In the captions of figures 1, 4, and 5 you can find links to data repository, code (a Jupyter notebook), and the computational environment (binder) to fully reproduce the graph. These computations can be easily run locally and require a few seconds. All Jupyter notebooks automatically download data from Zenodo and provide dependencies, which should make reproducibility easier.
This paper provides a novel approach to identifying oncogenes based on RNA overexpression in subsets of tumor relative to adjacent normal tissue. Showing that this study can be reproduced would aid other researchers who are attempting to identify oncogenes in other cancer types using the same methodology.
It'll a great helpful to independently check the scientific record I've published, so that errors, if there are any, could be corrected. Also, I will learn how to share the data in a more accessible to other if you could give me feedback.
Currently submitted paper on COVID19 on mental health. Unique clinical data (time series during the pandemic onset) & methods, hopefully fun to work on. Possibly too boring / easy to reproduce given my data & code? Not sure.
If all went right, the analysis should be fully reproducible without the need to make any adjustments. The paper aims to find optimal locations for new parkruns, but we were not 100% sure how 'optimal' should be defined. We provide a few examples, but the code was meant to be flexible enough to allow potential decision makers to specify their own, alternative objectives. The spatial data set is also quite interesting and fun to play around with. Cave: The full analysis takes a while to run (~30+ min) and might require >= 8gb ram.
Open data and reproducibility was important in this project.
It is a rare find of full reproducibility in the field of plant disease epidemiology.
The results of the individual studies (4) could be interpreted in support for the hypothesis, but the meta-analysis suggested that implicit identification was not a useful predictor overall. This conclusion is an important goalpost for future work.
The format of the paper is a bit unusual: it is contained, and compiled as, an R package. Although this would seem, on its face, to make it easier to reproduce, it is an open question how obvious it will be. I wonder to what extent people reproducing the results would prefer this to simple R scripts.
The original data took quite a while to produce for a previous paper, but for this paper, all tables and figures should be exactly reproducible by simply running the jupyter notebook.
This is a small dataset with a lot of missing data, so it's quite challenging to produce reliable results. It uses multiple imputation to fill the missing data, so it would be interesting to see whether the results hold up when this is redone. However, since the multiple imputation takes a couple of hours to run (on a decent laptop), the final multiply imputed data is also included. Additionally, multiply imputed data needs a different statistical analysis approach, which you can get familiar with.
Complex analyses over multiple variables. In press, so we can still fix errors ahead of publication!!
I guess it could be a cool learning experience. The paper is written with knitr, uses a seed, is part of the R package it describes, was openly written using version control (SVN, R-Forge) and is available in an open access journal (@up_jors).
The focus of the project is reproducibility. Here we show the differences to access data compared to similar initiatives: https://ropensci.org/blog/2019/05/09/tradestatistics/. Also, similar projects have obscure parts, while our exposes the code from raw data downloading to dashboard creation.
It uses the drake R package that should make reproducibility of R projects much easier (just run make.R and you're done). However, it does depend on very specific package versions, which are provided by the accompanying docker image.
