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Agenda for today’s class:
- 6:30 - 7:30: discussion
- 7:30 - 7:45: break
- 8:00 - 8:45: lecture
- 8:45 - 9:00: break
- 9:00 - 9:20: lab
6.1 Discussion questions
6.2 Lecture
While reproducibility has always been a part of the scientific method, research reproducibilty generally was first stressed in Western literature in the 17th century by the Irish chemist, Robert Boyle. Before research computing, reproducibility generally meant one researcher travelling to anothers’ lab or workspace to try to re-do their work. Since the widespread use of born-digital research materials, pipelines, and processes, the ability to re-do others’ work is just as arduous as trekking across a sea or continent. If you have ever tried to get something to work on Windows that originally worked on Mac, you might understand why that is.
While these definitions vary across disciplines, for this lecture I am using the terms reproducibility and replication like so:
Reproducibility: independent people use the same code and data to verify a claim Download new os for mac wont update.
Replication: independent people use different code and data (collected the same way) to verify a claim
However, as with all things, reproducibility should be defined on a spectrum. The Stodden et al ICERM report (2013) outlines these five tiers for reproducibility that I particularly like:
Reviewable Research: Sufficient detail for peer review & assessment.
Replicable Research: Tools are available to duplicate the author’s results using their data.
Confirmable Research: Main conclusions can be attained independently without author’s software.
Auditable Research: Process & tools archived such that it can be defended later if necessary.
Open/Reproducible Research: Auditable research made openly available
Replicable Research: Tools are available to duplicate the author’s results using their data.
Confirmable Research: Main conclusions can be attained independently without author’s software.
Auditable Research: Process & tools archived such that it can be defended later if necessary.
Open/Reproducible Research: Auditable research made openly available
These can be mapped onto a pyramid, like so:
My idea, made aesthetically pleasing by Andrew Rarig (NYU)
The idea is that we need the whole pyramid for research reproducibility. So the goal becomes to capture the whole pyramid – the code, data, documentation, narrative, and the computational environment. Natural scientists back in the day used to spend a lot of time documenting, with paintings and illustrations, the ecological environment in which they were doing their research. Researchers today use those descriptions to juxtapose the current landscape to the historical one, to draw conclusions about the changing world. Most of the time, researchers can return to these landscapes. However, this becomes much harder with computational environments – operating systems with their various configurations.
We call this computational reproducibility. With so much research today being completely born-digital, it’s just as important to capture these computational environments for posterity – to be able to return to, access, and reproduce the research being done today.
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ICERM Report Definitions | Potential Real-World Examples |
---|---|
Reviewable Research: Sufficient detail for peer review & assessment | the code and data are openly available |
Replicable Research: Tools are available to duplicate the author’s results using their data | the tools (software) used in the analysis are freely available for others to confirm results |
Confirmable Research: Main conclusions can be attained independently without author’s software | other can reach the conclusion using similar tools, not necessarily the same as the author, or on a different operating system |
Auditable Research: Process & tools archived such that it can be defended later if necessary | The tools, environment, data, and code are put into a preservation-ready format |
Open/Reproducible Research: Auditable research made openly available | Everything above is made available in a repository for others to examine and use |
But first – why do we care about reproducibility?
There are many reasons to support reproducibility in research, including:
- Build on top of previous work – after all, research is incremental and we always rely and base ourselves in methods and techniques developed in the past.
- Y’know. That Sir Isaac Newton quote in every reproducibility presentation ever: “If I have seen further, it is by standing on the shoulders of giants.”
- Help newcomers – new students who want to learn the field, post-docs who might need to carry on a project.
- Your reproducible work is their greatest teacher!
- It’s always a struggle to make things work when there’s not enough detail or the right code/data to keep a project going.
- Defeat self-deception, which is in line with verifying the correctness of results.
- This is not a matter of researchers not trusting each other – the issue is that even an honest person is a master of self-deception. Often, we are trying to find reasonable and acceptable outcomes to meet a certain deadline.
- A very common fallacy in research, for instance, is to rigorously check the unexpected results but give expected results a free pass. Inviting others to reproduce your work and be the devil’s advocate is a great way to verify if you are following the right path.
- Others can re-use and extend your work more easily! You can even find interesting collaborations and future research projects out of this.
- YOU can re-use and extend your work more easily! Future you is your greatest collaborator (and past you doesn’t answer emails!)
Ok, so we care about reproducibility. It’s so hard, though!
Yes, achieving that 100% reproducibility mark is very hard (and some say impossible). This is in part due to three major challenges:
- Workload & Time Challenges: it is a time commitment to get data and code ready to share, and to share it
- the Incentive Problem: reproducibility takes time, and is not always valued by the academic reward structure
“Insufficient time is the main reason why scientists do not make their data and experiment available and reproducible.” - Carol Tenopir, Beyond the PDF2 Conference
“77% claim that they do not have time to document and clean up the code.” – Victoria Stodden, Survey of the Machine Learning Community – NIPS 2010
- Technical Obsolescence: technology changes affect the reproducibility
- the Pipeline Problem: reproducibility requires skills that are often not included in most curricula!
“It would require huge amount of effort to make our code work with the latest versions of these tools.” – Collberg et al., Repeatability and Benefaction in Computer Systems Research, University of Arizona TR 14-04
- Normative Dissonance: espoused values do not always match behavior, for a number of reasons.
“Scientists’ views of their fields as cooperative or competitive were associated with their normative perspectives, with competitive fields showing more counternormative behavior.” – Melissa S. Anderson, Brian C. Martinson, and Raymond De Vries, “Normative Dissonance in Science: Results from a National Survey of U.s. Scientists”
Some workflow-related barriers to reproducibility
We talked in week 4 about the importance of using open file formats and tools for data management and reproducibility, so I won’t rehash that here, but the file format/tool problem in research (the problem being, too many tools and formats are closed and proprietary and expensive) is a high barrier to reproducible research. So keep it in the back of your head! You probably noticed that the Dekker & Lackie article was largely going over best practices in data management…
I want to highlight something related to workflows & reproducibility: the idea that clicking is not reproducible but learning to program is a high barrier. Dekker & Lackie highlight this in their Databrarianship chapter: “Novice researchers rarely have a chance to systematically learn about the essentials […] before they are faced with applying the necessary principles in their own research”. And it often is – where in methodology sections do you see things like “played with settings in Adobe until my figure was exactly the way I want it” or “clicked into seven sub-menus in SPSS to get to this one specific feature essential for my stats”.
The Yenni et. al. article from last week (it was optional) highlights these reasons for automating their workflow:
We do this by leveraging existing tools to: 1) perform quality assurance and control; 2) import, restructure, version, and archive data; 3) rapidly publish new data in ways that ensure appropriate credit to all contributors; and 4) automate most steps in the data pipeline to reduce the time and effort required by researchers. The workflow uses two tools from software development, version control and continuous integration, to create a modern data management system that automates the pipeline.
While the authors discuss efficiency, the automation of their workflow (and thus, the reduction of click around…) is a really important step in the pursuit of reproducibility. Human error is greatly reduced (it’s hard to keep track of you click in the heat of the moment, and even harder to remember after the fact), and reporting out is a lot simpler – one markdown file, one jupyter notebook, instead of a laundry list of menus to click!
Computational reproducibility
An article about computational results is advertising, not scholarship. The actual scholarship is the full software environment, code and data, that produced the result. – Johnathan Buckheit and David Donoho, Stanford University
I always posit that good research data management enables reproducibility but does not guarantee it. Open file formats, good documentation, using open tools, and backing up your data is amazing and necessary, but even that isn’t enough to ensure that I can rerun your work. That’s computational reproducibility is about. What works on my Linux machine should give the same results when run on your macOS laptop. This phenomenon has been explored in publications such as:
We investigated the effects of data processing variables such as FreeSurfer version (v4.3.1, v4.5.0, and v5.0.0), workstation (Macintosh and Hewlett-Packard), and Macintosh operating system version (OSX 10.5 and OSX 10.6). Significant differences were revealed between FreeSurfer version v5.0.0 and the two earlier versions. […] About a factor two smaller differences were detected between Macintosh and Hewlett-Packard workstations and between OSX 10.5 and OSX 10.6.
The challenge: environments are hard to capture
Gap: tools that can automatically capture all the dependencies in the original environment in a research compendium and automatically set them up in another environment. There are a few tools that try to address this gap in slightly different ways:
Containers: lightweight virtual operating systems you can send around to other people.
- Singularity (made for & popular in high performance computing)
- Starting a Singularity container “swaps” out the host operating system environment for one the user controls – instantly virtualize the operating system, without having root access, and allow you to run that application in its native environment!
- Docker
- Docker was made to “pack, ship and run any application as a lightweight container.” – idea is to provide a comprehensive abstraction layer that allows developers to “containerize” or “package” any application and have it run on any infrastructure (doesn’t really work on HPC though…).
The research community has been increasingly using and sharing containers (especially Docker) to try to mitigate this problem. However there are a few problems with containers:
- No idea of provenance. If I got a container and some code/data, I’d still need to know what to run first, which data is input/output, etc.
- Not trivial for new users to make or to use; they have a steep learning curve.
- Not sustainable; I can only use a Dockerfile with Docker, and it’s not always backwards compatible. This is a big problem thinking long-term.
Packaging Systems: auto-capture of dependencies & source code used at time of running.
- ReproZip (I work on this!)
- Open source tool that automatically captures provenance of research and packs all the necessary files, library dependencies, and variables to reproduce the results. Anyone can then unpack and reproduce the research without having to install any additional software!
- o2r
- Give them a R workspace with an RMarkdown file, get a re-runnable paper in-browser. Uses docker to do this in the backend.
A few systems like Yale University Library’s Emulation as a Service or Carnegie Mellon University’s Olive Archive offer legacy base operating system access to users that could in time start to address computational reproducibility as analysis software is added to their collections, or they integrate with existing tools like Whole Tale or ReproZip.
This recent report discusses fair use in regards to software preservation (helpful for capturing proprietary environments for the scholarly record and sets us up for the licensing talk next week!): https://www.softwarepreservationnetwork.org/bp-fair-use/
The goal is: at the end of a project, take all the great data and project management skills and make what’s called a research compendium or a reproducible package of all your work! This is a package that contains all of the things necessary to reproduce your work, taking even the computational environment into account.
“Research compendia are an increasingly used form of publication, which packages not only the research paper’s text and figures, but also all data and software for better reproducibility.” - Nuest, Bottinger, & Marwick, How to read a research compendium
ReproZip, the tool I work on, was made in order to facilitate the creation of these self-contained, distributable bundles of pipelines! I thought I’d give a demo on packing and unpacking work with ReproZip to give you a better idea of how the workflow and resulting file might look like. Let’s do that now! I’m using our demo virtual machine and example use cases from: https://examples.reprozip.org.
To recap:
- Good data management is necessary for reproducibility, but doesn’t guarantee it
- You can work reproducibly in many ways – your data cleaning work can be somewhat reproducible, the analysis fully reproducible, or data collection not at all reproducible. It’s a spectrum
- Introduce reproducible workflows in small bits, get comfortable, and expand.
- Open formats & open tools enable more reproducibility. Use them!
6.3 In-class
Arduino 1.8.3 download for mac. We’re going to explore reproducibility in the cloud with myBinder!
They also have a neat add-on: https://addons.mozilla.org/en-US/firefox/addon/open-with-binder/
6.4 Outside class
Please submit your homework here: . The password will be given out in class. If you have more than one file to submit, please to it as a
.zip
file.Reading/Writing
Please read and respond to the following two articles:
Write 700-1,000 words about the articles, giving your reaction/thoughts and also the questions outlined below:
- What do you think about the idea of a research compendium? Especially in the context of publishing supplementary materials?
- Why do you think there is a high barrier to computational reproducibility? What can librarians do to help that?
- How would you offer services in reproducibility to patrons? Which aspects of computational reproducibility would you focus on, if any at all?
Hands-on
Download and install Anaconda 3.6 on your computer. This includes Jupyter Notebooks and a number of relevant python packages.
- Download this repository: https://github.com/arokem/visual-white-matter
- Try to rerun the jupyter notebooks locally on your own computer in the right order, to get the same results.
- Launch the myBinder instance of that repository: https://mybinder.org/v2/gh/arokem/white-matter-matters/master
- Try to rerun the jupyter notebooks in your browser via myBinder in the right order, to get the same results.
Write 750 words maximum about the experience. Were you ever able to get the notebooks to run on your local computer? If you were reviewing Dr. Rokem’s paper, what would your preference be – receiving the github link or the binder link? What are the pros/cons of trying to configure things locally vs. in-browser? Don’t spend more than ~1 hour trying to get this to work locally if you are having trouble.
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