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Open Science for Physicists, Lecture 4 , Open and Collaborative Innovation

  • Last updated: 30 08 2024
  • Lecturer: e.g. @SanliFaez

Contributors:

  • Tanja Hinderer
  • Marc Schneiter

Development goal

Open science is making a big impact on how knowledge created at universities can be transferred. After this lecture students become familiar with different routes of knowledge transfer and the role of creative commons licensing in fair access to scientific research.

Motivation:

Open hardware is an essential pillar of open science, because there is little chance of reproducing an experiment without having thorough access to the necessary methods and equipment. But making hardware blueprints accessible is an area of contention because of the historical path of the knowledge transfer practices and conflicts that are still unresolved.

Beyond academia, open hardware has the potential to radically transform science, education, and society by facilitating collaborative innovation and democratizing access to technology. It can massively accelerate the transition of an invention into a useful product, and simultaneously reduce costs and promote sustainable practices.

The collaborative skills needed for open projects are very close to those needed for thriving in high tech companies. These lessons are thus also useful for those who are choosing a conventional path in research and development.

Vision of outcome

  • Based on the lecture, students are informed about open innovation as a method of knowledge transfer. They will also ellaborate on the potential use of their own research and scholarly work outside their discipline.
  • In the guest lecture, we will learn more about the organization of big collaborations in a high-tech company and the importance of regular communication and engagement with stakeholders for the successful implementation of new creative ideas.
  • During the assignment, students will create a measurement project of their own and document it with the goal of perfect reproducibility. These projects will be used as a resource for the reproducibility exercise on week 7 of the course.

Embedding/community

  • The community of stakeholders for open innovation includes researchers (inside or outside of academia), science policymakers, and people involved in entrepreneurial activities.
  • The measurement project is aimed at being fully reproducible by fellow students.

Exercises

Assignment Executions

  1. Form a team of 3 or 4 members and think of a measurement project that satisfies the requirements below.
  2. One person from each team should create an issue on the course repository and specify: a- your measurement goal, b- your team members
  3. Clone the course repository on your computer and create your project folder structure in your local repo, inside the ../Projects/ subfolder, by copying _template_ProjectName_Team_MAKEaCOPY folder from The resources directory and adjusting the name.
    • NOTE: Do NOT edit the _template folder. It is important to put all projects in the mentioned subfolder to maintain the structure of the course repository and avoid merge conflicts.
  4. Perform your measurement and document it with instructions for replication in your subfolder under Projects. Respect the required name convention.
  5. Submit your assignment as a merge request to the course repository.
  6. After your merge request is accepted, add a hyperlink to your project folder to the main text of your project issue and label it as "Ready for replication".

Project minimal requirements

  • Use an electronic sensor to measure a physical quantity or to perform a demonstration experiment. You can use any type of open hardware controller boards or a smartphone using the phyphox app
  • The measurements for your experiment should be recorded electronically and digitally.
    • you must perform at least one measurement to prove the functioning of your experiment and report the result.
    • The measurements are done via a microcontroller, computer hardware, or digitally-logged sensors.
    • At least one plot is created based on the measured data and used for the goal of the measurements. The preferred coding language is Python.
    • The plot data is analyzed with a computer script.
    • The measurements are safe for other students and can be executed in maximum 15 minutes.
    • The measurements results and conclusions can be independently verified by a different team without face to face interaction with the project team.
    • The TA or coordinator approves the project.
    • The analysis code should use the measured data to create your anticipated result, and preferably a plot of your data.
    • a third person should be able to reproduce your measurement(s) based on your documentation.
  • Document the measurements you have done for reproducibility and prepare the instructions on how to:
    1. safely operate your setup
    2. replicate your results based on new measurements
    3. reproduce your results based on the raw data that you have obtained

Additional recommendations

  • Check these peer review instructions before finalizing your documentation. Other teams will be asked to follow these instructiosn for reviewing and replicating your work.
  • Invite people who are not your teammates to check your documentation for clarity and sufficiency. You can offer them the same favor in exchange.
  • Separate the instructions for reproducing your results from those for replicating the measurement

Evaluation

During week 7, another student team will try to reproduce your measurement and will write a feedback report based on the peer-evaluation template

Information Sources / Bibliography