Re:produce
Workshop 5 - Open Data, Materials and Code
Check Out the Source Code! Learn more about the Re:produce workshop
Workshop 5 - Open Data, Materials and Code
Check Out the Source Code! Learn more about the Re:produce workshop
This workshop introduces how you can make your research data, materials and code open and available. We intend to introduce the concept of a research compendium as a means to ensure that these these goals are met and why you would want to share your materials openly. To help familiriase participants, information on FAIR compendia (Findable, Accessible, Interoperable, Reusable), using tools such as Jupyter notebooks and R Markdown for organising your materials and GitHub, GitLab, Bitbucket, Figshare, Zenodo, OSF and others for sharing them openly will be presented.
The main presentation will be delivered by Associate Professor Emerson M. Del Ponte, @edelponte, from Universidade Federal de Viçosa (UFV) Brazil on his experiences using research compendia and pre-prints for making data, materials and code open and available.
A panel discussion will include Dr. Francis Gacenga, A/Prof Adam H. Sparks, A/Prof Emerson M. Del Ponte and Ms Belinda Weaver (Griffith University), @cloudaus.
Attendees will then have the opportunity to create a research compendium using OSF using either their own materials, or a set of materials provided by us for this exercise.
By the end of this workshop users will have learned what tools are available to make their research data, materials and code open and available. To aid in this, we have put together a website with more information from the workshop with further suggested reading on the topic of research compendia and other resources that can be useful for participants to refer to, https://re-produce-opendatamaterialscode.netlify.com/.
We have created this website using these tools: blogdown, RStudio, GitHub and Netlify and released it under a Creative Commons Attribution-NonCommercial 4.0 International License, these materials are available through OSF, DOI 10.17605/OSF.IO/8CSZW.
All of the code and materials are available from GitHub, https://github.com/adamhsparks/ReProduce_Open_Data_Materials_and_Code.
In this hands-on activity we will go through setting up an OSF account (if you do not already have one), upload materials to OSF, documenting them with metadata and create a very brief research compendium that describes the data set to upload to OSF to create a complete research compendium and repository for sharing.
The downloadable file, reproduce_session5.zip, contains five files. These files will be used in a hands-on exercise where you create your own research compendium using these files, or your own files if you wish. The downloadable zip file includes the following files that can be used if you do not have your own:
example-manuscript.docx - An example MS Word document manuscript
example-manuscript.Rmd - An example RMarkdown file to generate an MS Word document
example-script.R - An example R script that imports data, munges it and creates a graph
pop_density_change.png - An example figure used in the example manuscript
population_density_per_square_km.csv - Population density data, Source: UN World Population Prospects through www.gapminder.org
Australia wide, local and university resources to help you with Open Data, Open Materials and Open Code.
Australia and New Zealand Resources
Local Resources
University Resources
Research Compendia Exemplars
We present a set of computing tools and techniques that every researcher can and should consider adopting. These recommendations synthesize inspiration from our own work, from the experiences of the thousands of people who have taken part in Software Carpentry and Data Carpentry workshops over the past 6 years, and from a variety of other guides. Our recommendations are aimed specifically at people who are new to research computing.
Researchers spend a great deal of time reading research papers. Keshav (2012) provides a three-pass method to researchers to improve their reading skills. This article extends Keshav’s method for reading a research compendium.
Computers are a central tool in the research process, enabling complex and large-scale data analysis. As computer-based research has increased in complexity, so have the challenges of ensuring that this research is reproducible. To address this challenge, we review the concept of the research compendium as a solution for providing a standard and easily recognizable way for organizing the digital materials of a research project to enable other researchers to inspect, reproduce, and extend the research. We investigate how the structure and tooling of software packages of the R programming language are being used to produce research compendia in a variety of disciplines. We also describe how software engineering tools and services are being used by researchers to streamline working with research compendia. Using real-world examples, we show how researchers can improve the reproducibility of their work using research compendia based on R packages and related tools.
It is increasingly necessary for researchers in all fields to write computer code, and in order to reproduce research results, it is important that this code is published. We present Jupyter notebooks, a document format for publishing code, results and explanations in a form that is both readable and executable. We discuss various tools and use cases for notebook documents.
We here present ten simple rules for reproducibility of computational research. These rules can be at your disposal for whenever you want to make your research more accessible—be it for peers or for your future self.