Open Science

Vaclav (Vashek) Petras

NCSU GeoForAll Lab at the Center for Geospatial Analytics
North Carolina State University

GIS 710: Geospatial Analytics for Grand Challenges
October 21, 2019

Learning Objectives

  • Understating motivation for practicing open science
  • Understating complexity of practicing open science
  • General understanding of tools and services involved
  • Practical knowledge of tools for sharing research code and computations
  • Ideas about how to use them in complex geospatial applications
  • Critical thinking about pros, cons, and challenges

Reproducibility of Computational Articles

Stodden et al. (PNAS, March 13, 2018)
204 computational articles from Science in 2011–2012

26% reproducible, 74% irreproducible Stodden, V., Seiler, J., & Ma, Z. (2018). An empirical analysis of journal policy effectiveness for computational reproducibility. In: Proceedings of the National Academy of Sciences 115(11), p. 2584-2589. DOI 10.1073/pnas.1708290115 Discussion questions: Do you know about similar studies? What do they say?

Open Science Beginnings

First journal ever published:
Philosophical Transactions (of the Royal Society)

CC BY Stefan Janusz, Wikipedia

Publishing Goals

  • registration so that scientists get credit
  • archiving so that we preserve knowledge for the future
  • dissemination so that people can use this knowledge
  • peer review so that we know it's worth it

Discussion questions: Do you agree with these publishing goals? How are these different from goals of science? Are these publishing goals fulfilled by journal papers?

Open Science

[Buckheit and Donoho 1995, Peng 2011, Rodríguez-Sánchez et al. 2016, Marwick 2016]

Image credit: CC BY-SA Comtebenoit, Wikimedia (note: file freedom disputed)

Discussion questions: Do you agree with this spectrum? Is it a spectrum? On which side of this spectrum have you published?

Open Science Components

  • 6 pillars [Watson 2015]:
    • open methodology
    • open access
    • open data
    • open source
    • open peer review
    • open education (or educational resources)
  • other components:
    • open hardware
    • open formats
    • open standards
  • related concepts:
    • Open-notebook science
    • Science 2.0 (like Web 2.0)
    • Citizen science
    • Public science
    • Participatory research
    • Open innovation
    • Open organization
    • Crowdsourcing
    • Preprints
    • Inner source

Discussion questions: What would add to the list? Do you see something for the first time?

The “re” Words

No agreement on some of the definitions especially in different fields; definitions are often overlapping or swapped, some don't make any distinction.
  • replicability independent validation of specific findings
  • repeatability same conditions, people, instruments, ... (test–retest reliability)
  • recomputability same results in computational research
  • reproducibility obtain again same results from the raw data
  • reusability use again the same data or methods
For example, Ince et al. (2012) in computational science distinguishes direct reproducibility as rerunning of the code and indirect reproducibility as validate something other than the entire code.

Discussion questions: How do want science to look like? Are there some minimal requirements? What should be possible or easy to do for you when you receive a scientific publication (e.g. for review)?

Internal Reasons for Open Science

Internal or selfish reasons for doing open science
  • in your lab:
    • collaboration work together with your colleagues
    • transfer transfer research between you and your colleague
  • yourself:
    • revisit revisit or return to a project after some time
    • correction correct a mistake in the research
    • extension improve or build on an existing project

Discussion questions: What is your experience with getting back to your own research or continuing research started by someone else? (See PhD Comics: Scratch.)

What Open Means

  • open, free, libre
  • The Open Definition
    • Open means anyone can freely access, use, modify, and share for any purpose (subject, at most, to requirements that preserve provenance and openness). [as officially summed up]
  • Free Cultural Works
  • The Open Source Definition
  • The Free Software Definition
  • the Debian Free Software Guidelines and the Debian Social Contract

Discussion questions: What is the difference between “free as in free beer” and “free as in freedom”? Have you seen “open” being used for something not fulfilling the definition?


CC BY 4.0 Creative Commons

Discussion questions: Do you read “terms and conditions”? Have you ever read any “terms and conditions” or end user license agreement (EULA)? What about an open source software license? (Read license of GDAL right now!)

Computational and Geospatial Research

  • code is a part of method description [Ince et al. 2012, Morin et al. 2012, Nature Methods 2007]
  • use of open source tools is a part of reproducibility [Lees 2012, Alsberg & Hagen 2006]
  • easily reproducible result is a result obtained in 10 minutes [Schwab et al. 2000]
  • geospatial research specifics:
    • some research introduces new code
    • some research requires significant dependencies
    • some research produces user-ready software

Discussion questions: Is spatial special? Is recomputing the results useful for research? Should dependencies be open source as well? How long should it take to recompute results?

Open Science Publication: Use Case

Petras et al. 2017

Petras, V., Newcomb, D. J., & Mitasova, H. (2017). Generalized 3D fragmentation index derived from lidar point clouds. In: Open Geospatial Data, Software and Standards 2(1), 9. DOI 10.1186/s40965-017-0021-8

Open Science Publication: Components

Publication Component in the Petras et al. 2017 use case
Text background, methods, results, discussion, conclusions, … (OA)
Data input data (formats readable by open source software)
Reusable Code methods as GRASS GIS modules (C & Python)
Publication-specific Code scripts to generate results (Bash & Python)
Computational Environment details about all dependencies and the code (Docker, Dockerfile*)
Versions repository with current and previous versions* (Git, GitHub)

* Version associated with the publication included also as a supplemental file.

Petras, V. (2018). Geospatial analytics for point clouds in an open science framework. Doctoral dissertation. URI

Discussion questions: What are other technologies which are good fit for these components? Are there other components or categories?

Open Science Publication: In a Single Package Online

  • Components other than Text and Versions for Petras et al. 2017 are now also available at Code Ocean as a capsule.
Code Ocean capsule content in a web browser
DOI 10.24433/CO.3986355.v2

Discussion questions: How do we even call this? What is the long-term sustainability of something like Code Ocean?

Open Science Publication: Software Platform

  • Preprocessing, visualization, and interfaces (GUI, CLI, API)
  • Data inputs and outputs, memory management
  • Integration with existing analytical tools
  • Preservation of the reusable code component (long-term maintenance)
  • Dependency which would be hard to change for something else
  • Example: FUTURES model implemented as a set of GRASS GIS modules (r.futures.pga, r.futures.demand, r.futures.parallelpga, ...)

Discussion questions: What software can play this role? What are the different levels of integration with a piece of software?


  • Alsberg, Bjørn K., and Ole Jacob Hagen. “How Octave Can Replace Matlab in Chemometrics.” Chemometrics and Intelligent Laboratory Systems, Selected papers presented at the 9th Scandinavian Symposium on Chemometrics Reykjavik, Iceland 21–25 August 2005, 84, no. 1 (December 1, 2006): 195–200. doi:10.1016/j.chemolab.2006.04.025.
  • Buckheit, Jonathan B., and David L. Donoho. “WaveLab and Reproducible Research.” In Wavelets and Statistics, edited by Anestis Antoniadis and Georges Oppenheim, 103:55–81. Lecture Notes in Statistics. New York, NY: Springer New York, 1995. doi:10.1007/978-1-4612-2544-7_5.
  • Ince, Darrel C., Leslie Hatton, and John Graham-Cumming. “The case for open computer programs”. In: Nature 482.7386 (2012), pp. 485–488. doi:10.1038/nature10836
  • Lees, Jonathan M. “Open and free: Software and scientific reproducibility”. In: Seismological Research Letters 83.5 (2012), pp. 751–752. doi:10.1007/s10816-015-9272-9
  • Marwick, Ben. “Computational reproducibility in archaeological research: basic principles and a case study of their implementation”. In: Journal of Archaeological Method and Theory 24.2 (2017), pp. 424–450. doi:10.1007/s10816-015-9272-9
  • Morin, A et al. “Shining light into black boxes”. In: Science 336.6078 (2012), pp. 159–160. doi:10.1126/science.1218263
  • Nature Publishing Group. “Social Software.” Nature Methods 4, no. 3 (March 2007): 189. doi:10.1038/nmeth0307-189.
  • Peng, Roger D. “Reproducible Research in Computational Science.” Science (New York, N.Y.) 334, no. 6060 (December 2, 2011): 1226–27. doi:10.1126/science.1213847
  • Petras, Vaclav. “Geospatial analytics for point clouds in an open science framework.” Doctoral dissertation. 2018.
  • Petras, Vaclav, Douglas J. Newcomb, and Helena Mitasova. “Generalized 3D Fragmentation Index Derived from Lidar Point Clouds.” Open Geospatial Data, Software and Standards 2, no. 1 (April 2017): 9. doi:10.1186/s40965-017-0021-8.
  • Rocchini, Duccio and Markus Neteler. “Let the four freedoms paradigm apply to ecology”. In: Trends in Ecology and Evolution (2012). doi:10.1016/j.tree.2012.03.009
  • Rodriguez-Sanchez, Francisco, Antonio Jesús Pérez-Luque, Ignasi Bartomeus, and Sara Varela. “Ciencia Reproducible: Qué, Por Qué, Cómo.” Revista Ecosistemas 25, no. 2 (2016): 83–92.
  • Schwab, Matthias, Martin Karrenbach, and Jon Claerbout. “Making Scientific Computations Reproducible.” Computing in Science & Engineering 2, no. 6 (2000): 61–67. doi:10.1109/5992.881708.
  • Stodden, V., Seiler, J., & Ma, Z. (2018). “An empirical analysis of journal policy effectiveness for computational reproducibility.” In: Proceedings of the National Academy of Sciences 115(11), p. 2584-2589. doi:10.1073/pnas.1708290115
  • Watson, M. (2015). When will ‘open science’ become simply ‘science’?. Genome biology, 16(1), 101. doi:10.1186/s13059-015-0669-2