Merge pull request #576 from bressler1995/main

Upload GitHub version of OS101 curriculum

.github/workflows/check_typos.yml

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-              ignore_words_list: stip,leary,hsi,aas,re-usable
+              ignore_words_list: stip,leary,hsi,aas,re-usable,re-use,criticall,ublished,re-use,hart,ges,automatize,programers

docs/Area2_Capacity_Sharing/Open-Science-101/modules/Module_1/readme.md

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+# Open Science 101: The Ethos of Open Science (October, 2023)
+
+**WELCOME TO OPEN SCIENCE 101: THE ETHOS OF OPEN SCIENCE**
+
+## Module Description
+
+### The Ethos of Open Science
+
+Welcome to this introductory module on open science. Open Science is the principle and practice of making research products and processes available to all, while respecting diverse cultures, maintaining security and privacy, and fostering collaborations, reproducibility, and equity. In this module, you take a closer look at what open science is, the current landscape as well as the benefits and challenges. You then get a glimpse into the practice of open science including a case study. To start your journey with open science, you are presented with actions that you can take starting today, such as exploring communities that you can engage with.
+
+### Target Audience
+
+This module is for anyone who is interested in open science and would like to learn the benefits and ways to get started today.
+
+### Module Learning Objectives
+
+After completing this module, you should be able to:
+
+- Explain what open science is, why it's a good thing to do, and list some of the benefits and challenges of open science adoption.
+- Describe the practice of open science, including considerations when writing a management plan and the tasks in the “Use, Make, Share” framework.
+- Evaluate available options when determining whether research products should or should not be open.
+- List ways to connect with others who are part of the open science community.
+
+## Key terms
+
+These key terms are important topics for this module. Select the term to see the description.
+
+**Open Science** – The principle and practice of making research products and processes available to all, while respecting diverse cultures, maintaining security and privacy, and fostering collaborations, reproducibility and equity.
+
+**Open Data** – Data that can be freely used, re-used and redistributed by anyone. Learn more [here](https://opendatahandbook.org/guide/en/what-is-open-data/).
+
+**Open Source** – Computer programs in which the source code is available to the general public for use or modification from its original design. Learn more [here.](https://en.wikipedia.org/wiki/Open_source)
+
+**Open Access** – Free access to information and unrestricted use of electronic resources for everyone. Learn more [here.](https://en.unesco.org/open-access/what-open-access)
+
+**Interdisciplinary** – Combining multiple [academic disciplines](https://en.wikipedia.org/wiki/Academic_discipline) into one activity, such as a research project. Learn more [here.](https://en.wikipedia.org/wiki/Interdisciplinarity)
+
+**Equitable** – Indicates the absence of unfair, avoidable or remediable differences among groups of people, whether those groups are defined socially, economically, demographically, or geographically or by other dimensions of inequality (e.g. sex, gender, ethnicity, disability, or sexual orientation). Learn more [here.](https://www.who.int/health-topics/health-equity)
+
+**Citizen Science or Community Science** – The practice of public participation and collaboration in scientific research to increase scientific knowledge. Learn more [here.](https://education.nationalgeographic.org/resource/citizen-science/)
+
+**Open Research** – How research is performed and how knowledge is shared based on the principle that research should be as open as possible. Learn more [here.](https://www.ukri.org/what-we-do/supporting-healthy-research-and-innovation-culture/open-research/)
+
+**Open Scholarship** – An expansive term meant to encompass the rapid and widespread sharing of a range of scholarly activities and outputs across multiple disciplines. Learn more [here.](https://www.heliosopen.org/about)
+
+**Reproducibility and Replicability** – Reproducibility is defined as obtaining consistent results using the same data and code as an original study (synonymous with computational reproducibility). Replicability means obtaining consistent results across studies aimed at answering the same scientific question using new data or other new computational methods. Learn more [here.](https://www.nationalacademies.org/news/2019/09/reproducibility-and-replicability-in-research)
+
+**Peer Review** – The evaluation of work by one or more people with similar competencies as the producers of the work - that is, the authors’ peers. Learn more [here.](https://en.wikipedia.org/wiki/Peer_review)
+
+**FAIR principles** – Principles to improve the Findability, Accessibility, Interoperability, and Reuse of digital assets. The principles emphasize machine- actionability (i.e., the capacity of computational systems to find, access, interoperate, and reuse data with none or minimal human intervention) because humans increasingly rely on computational support to deal with data as a result of the increase in volume, complexity, and creation speed of data.
+
+**Metrics (in context of scientific merit)** – Quantitative tools used to help assess the quality and impact of research outputs (eg. scientific articles, researchers, and more). Learn more [here](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8397294/) and [here.](https://editorresources.taylorandfrancis.com/understanding-research-metrics/)
+
+**Altmetrics** – Alternative tools to assess the impact of a scientific article that do not involve journal-level usage information like impact factors.
+Learn more [here.](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3792863/)
+
+**Openness** – A concept or personality trait that involves transparency, collaboration, honesty, and receptivity to new ideas and experiences.
+
+**Transparency** – The quality of being easy to perceive or detect.
+
+**Rigor** – Widely used by educators to describe instruction, schoolwork, learning experience, and educational expectations that are academically, intellectually, and personally challenging.
+
+**Computational Provenance** - Seeks to develop systematic, computationally-based processes and standards for capturing, and making available for us, information about who created an object, when it was created or modified, and the process or procedure that modified the object.
+
+## Navigation
+
+### [Lesson 1: What is Open Science?](./Lesson_1)
+
+* [Motivation for Open Science](./Lesson_1#motivation-for-open-science)
+* [What is Open Science?](./Lesson_1#what-is-open-science)
+* [Who Does Open Science?](./Lesson_1#who-does-open-science)
+* [Lesson 1: Summary](./Lesson_1#lesson-1-summary)
+* [Lesson 1: Knowledge Check](./Lesson_1#lesson-1-knowledge-check)
+
+### [Lesson 2: Why is Open Science Important?](./Lesson_2)
+
+* [Open Science Breaks Down Stovepipes and Increases Innovation](./Lesson_2#open-science-breaks-down-stovepipes-and-increases-innovation)
+* [Benefits to You](./Lesson_2#benefits-to-you)
+* [Benefits to Science](./Lesson_2#benefits-to-science)
+* [Benefits to Society](./Lesson_2#benefits-to-society)
+* [Lesson 2: Summary](./Lesson_2#lesson-2-summary)
+* [Lesson 2: Knowledge Check](./Lesson_2#lesson-2-knowledge-check)
+
+### [Lesson 3: How to do Open Science](./Lesson_3)
+
+* [Maintaining Security and Protecting Privacy](./Lesson_3#maintaining-security-and-protecting-privacy)
+* [Intellectual Property](./Lesson_3#intellectual-property)
+* [Policies and Practices around Open Science](./Lesson_3#policies-and-practices-around-open-science)
+* [Lesson 3: Summary](./Lesson_3#lesson-3-summary)
+* [Lesson 3: Knowledge Check](./Lesson_3#lesson-3-knowledge-check)
+
+### [Lesson 4: When Not to be Open](./Lesson_4)
+
+* [Common Fears Around Openness](./Lesson_4#common-fears-around-openness)
+* [Misaligned Incentives](./Lesson_4#misaligned-incentives)
+* [Social Barriers](./Lesson_4#social-barriers)
+* [Institutional and Infrastructure Barriers](./Lesson_4#institutional-and-infrastructure-barriers)
+* [Lesson 4: Summary](./Lesson_4#lesson-4-summary)
+* [Lesson 4: Knowledge Check](./Lesson_4#lesson-4-knowledge-check)
+
+### [Lesson 5: Planning for Open Science: From Theory to Practice](./Lesson_5)
+
+* [Planning for Open Science](./Lesson_5#planning-for-open-science)
+* [Designing for Openness](./Lesson_5#designing-for-openness)
+* [Case Study: The Outcomes of an Open Plan](./Lesson_5#case-study-the-outcomes-of-an-open-plan)
+* [Steps to Continue Your Open Science Journey](./Lesson_5#steps-to-continue-your-open-science-journey)
+* [Lesson 5: Summary](./Lesson_5#lesson-5-summary)
+* [Lesson 5: Knowledge Check](./Lesson_5#lesson-5-knowledge-check)
+* [The Ethos of the Open Science Summary](./Lesson_5#the-ethos-of-the-open-science-summary)

docs/Area2_Capacity_Sharing/Open-Science-101/modules/Module_2/Lesson_1/readme.md

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+# Lesson 1: Introduction to the Process of Open Science
+
+## Navigation
+* [Definition of Open Science and Research Products](#definition-of-open-science-and-research-products)
+* [Using Tools for Open Science in Practice](#using-tools-for-open-science-in-practice)
+* [Lesson 1: Summary](#lesson-1-summary)
+* [Lesson 1: Knowledge Check](#lesson-1-knowledge-check)
+
+## Overview
+
+In this lesson you review the definition of open science and several other common terms including research products, data, software, and results. In addition, you will read examples that demonstrate how these open science tools are used in practice. The lesson wraps up with an example of how one group openly shared their data, results, software, and paper.
+
+## Learning Objectives
+
+After completing this lesson, you should be able to:
+
+- Define common types of research products including data, software, and results.
+- List common ways to share data, code, and results while practicing open science.
+
+
+
+## Definition of Open Science and Research Products
+
+### What is Open Science?
+
+![](../images/media/image3.png)
+
+---
+
+"Open Science is the principle and practice of making research products and processes available to all, while respecting diverse cultures, maintaining security and privacy, and fostering collaborations, reproducibility, and equity."
+
+\- The White House Office of Science and Technology Policy (OSTP) and the National Science and Technology Council (NSTC)
+
+---
+
+**Open Research Products**
+
+Scientific knowledge, or research products, take the form of:
+
+ <img src="../images/media/image5.png" style="width:350px;height:auto;" />
+
+### What is Data?
+
+<img src="../images/media/image6.png" style="width:350px;height:auto;" />
+
+In general, data are pieces of information about a subject, including theoretical truths, raw measurements, or highly processed values.
+
+There can even be data about data, called metadata. In our lessons, when we talk about data we are referring to scientifically or technically relevant information that can be stored digitally and accessed electronically such as:
+
+- Information produced by missions and experiments, including calibrations, coefficients, and documentation
+- Information needed to validate scientific conclusions of peer-reviewed publications
+- Open data can have many characteristics, including rich and robust metadata and be made available in a range of formats. These characteristics are detailed more later in this module, and even further in the module on Open Data.
+
+### What is Code?
+
+Many scientists write source code to produce software to analyze data or model observations. Code is a language that humans can type and understand. Software is often a collection of programs, data, and other information that a computer system uses to perform specific tasks. Scientists write and use many different types of software as part of their research.
+
+**General Purpose Software** – Software produced for widespread use, not specialized scientific purposes. This encompasses both commercial software and open-source software.
+
+**Operational and Infrastructure Software** – Software used by data centers and large information technology facilities to provide data services.
+
+**Libraries** – No creative process is truly complete until it manifests a tangible reality. Whether your idea is an action or a physical creation, bringing it to life will likely involve the hard work of iteration, testing, and refinement.
+
+Just be wary of perfectionism. Push yourself to share your creations with others. By maintaining an open stance, you’ll be able to learn from their feedback. Consider their responses new material that you can draw from the next time you’re embarking on a creative endeavor.
+
+**Modeling and Simulation Software** – Software that either implements solutions to mathematical equations given input data and boundary conditions, or infers models from data.
+
+**Analysis Software** – Software developed to manipulate measurements or model results to visualize or gain understanding.
+
+**Single-use Software** – Software written for use in unique instances, such as making a plot for a paper, or manipulating data in a specific way.
+
+
+Some of the tools that you can use to develop software are introduced in Lesson 4. Understanding how to find and use others' code, create your own, and share it are an important part of advancing science and covered in the module on Open Code.
+
+### What are Results?
+
+Results capture the different research outputs of the scientific process. Publications are the most common type of results, but this can include a number of other types of products. Both data and software can be considered a type of result, but when we discuss results, we will focus on other types of results. Results can include the following:
+
+- Peer-reviewed publications
+- Computational notebooks
+- Blog posts
+- Videos and podcasts
+- Social media posts
+- Conference abstracts and presentations
+- Forum discussions
+
+ You may already be familiar with the research life cycle, but still unfamiliar with the types of results that can be shared openly throughout this process. When sharing results, we strive to be as open as possible, with the goal of increasing reproducibility, accessibility, and inclusion of our science.
+
+Throughout the research lifecycle, there are multiple opportunities to openly share different results that can lead to new collaborations and lines of inquiry. Additional details on the scope of open results are shared in Module 5 – Open Results.
+
+
+## Using Tools for Open Science in Practice
+
+The following lessons in this module explore different tools and resources available to researchers for using, making, and sharing open science. As mentioned, it is important to think about how to integrate open science principles across all stages of the research process. Here is an overview of one way the various pieces might work together.
+
+### The Components of Open Science
+
+<img src="../images/media/image7.png" style="width:100%;height:auto;" />
+
+The four principal components of open science can be organized in a pyramid of openly-shared research products.
+
+The research paper, closely tied to the results, sits at the top of the pyramid and summarizes how you’ve combined your software and your data to produce your results.
+
+The practice of sharing these components can occur at varying degrees of completeness. For the following guidance on how to share components of open science, we simplify the range of completeness to "good", "better", and "best." This range reflects one’s commitment to sharing open science at all steps in the research process and to all of its products.
+
+### Sharing Open Data
+
+Data can be easily shared through many different services - the best way for scientific data to be shared is often through a long term data repository that will both preserve your data and make it discoverable. The image provides some of the considerations when sharing the data through [Zenodo](https://zenodo.org/), a generalist data repository. These considerations would be similar for other data repositories. See Module 3 - Open Data for more details on sharing open data.
+
+<img src="../images/media/image8.png" style="width:100%;height:auto;" />
+
+### Sharing Open Code
+
+When sharing open code, it is often through an online version controlled platform that allows others to contribute to the software and provides a history of changes to the software. For example, many researchers choose to post code files on  [GitHub](https://github.com/) with a BSD 3-Clause license. This permits others to contribute and reuse the software. Steps to preserve code and make it discoverable are discussed in Module 4 - Open Code.
+
+ <img src="../images/media/image9.png" style="width:100%;height:auto;" />
+
+### Sharing an Open Paper
+
+Researchers can choose to publish in a journal with an open access license. Researchers can search for open access journals through the Directory of Open Access Journals (DOAJ). (See Module 5 - Open Results)
+
+### Sharing Open Results
+
+When sharing results, include your methodology that was used to produce results (i.e. the “provenance”) directly with your software. Software tends to evolve with time while the outputs of the software itself can retain some consistency. Therefore, sharing your methodology helps others to reproduce your aging results with newer software, even if the methodology to produce them can vary as the software evolves.
+
+### An Open Science Project Example
+
+Here is an example of how one group openly shared their data, results, software, and paper; all with their own unique identifiers. Note that data and software can each have multiple identifiers, enabling others to cite all versions or one unique version.
+
+<img src="../images/media/image10.jpeg" style="width:100%;height:auto;" />
+
+ Here, you can review the separate elements of the image above. Select "\<" and "\>" buttons to navigate.
+
+<img src="../images/media/image11.jpeg" style="width:100%;height:auto;" />
+
+ **Data**
+
+ This version:  [https://doi.org/10.5281/zenodo.3688691](https://doi.org/10.5281/zenodo.3688691)
+
+All versions:  [https://doi.org/10.5281/zenodo.3688690](https://doi.org/10.5281/zenodo.3688690)
+
+<img src="../images/media/image12.jpeg" style="width:100%;height:auto;" />
+
+**Results**
+
+  [https://doi.org/10.1175/JHM-D-19-0084.1](https://doi.org/10.1175/JHM-D-19-0084.1)
+
+ <img src="../images/media/image13.jpeg" style="width:100%;height:auto;" />
+
+**Software**
+
+This version:  [https://github.com/c-h-david/rapid](https://github.com/c-h-david/rapid) 
+
+All versions:  [https://doi.org/10.5281/zenod](https://doi.org/10.5281/zenod)
+
+
+## Lesson 1: Summary
+
+In this lesson, you learned:
+
+- Scientific knowledge, or research products, take the form of: data, software, and results.
+- In general, data are pieces of information about a subject, including theoretical truths, raw measurements, or highly processed values.
+
+
+## Lesson 1: Knowledge Check
+
+Answer the following questions to test what you have learned so far.
+
+*Question*
+
+**01/03** 
+
+Read the statement below and decide whether it's true or false:
+
+Open Science is the principle and practice of making research products and processes available to all, while respecting diverse cultures, maintaining security and privacy, and fostering collaborations, reproducibility, and equity.
+
+- True
+- False
+
+*Question*
+
+**02/03** 
+
+What are the four principal outputs of open science? Select all that apply.
+
+- Budgets
+- Results
+- Inclusion
+- Data
+- Code
+- Paper
+
+*Question*
+
+**02/03** 
+
+Which of the following is a type of software? Select all that apply.
+
+- General purpose
+- Operational and Infrastructure
+- Modeling and Simulation
+- Analysis
+- Material