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FAIR, CARE, licences materials
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Expand Up @@ -228,133 +228,119 @@ Open Data are a critical aspect of open science. There are three key attributes

??? Tip "FAIR & CARE Principles"

[Wilkinson et al. (2016)](https://doi.org/10.1038/sdata.2016.18){target=_blank} established the guidelines to improve the Findability, Accessibility, Interoperability, and Reuse (FAIR) of digital assets for research.
**FAIR Principles**

[Go-FAIR website](https://www.go-fair.org/fair-principles/){target=_blank}
In 2016, the [FAIR Guiding Principles](https://www.nature.com/articles/sdata201618) for scientific data management and stewardship were
published in Scientific Data. Read it.

[Carroll et al. (2020)](http://doi.org/10.5334/dsj-2020-043){target=_blank} established the CARE Principles for Indigenous Data Governance. [full document :fontawesome-solid-file-pdf:](https://static1.squarespace.com/static/5d3799de845604000199cd24/t/5da9f4479ecab221ce848fb2/1571419335217/CARE+Principles_One+Pagers+FINAL_Oct_17_2019.pdf){target=_blank}

[Indigenous Data Sovereignty Networks](https://indigenousdatalab.org/networks/){target=_blank}
---

## FAIR Data

!!! Info "Learning Objectives"
- Recall the meaning of FAIR
- Understand why FAIR is a collection of principles (rather than rules)
- Use self-assessments to evaluate the FAIRness of your data

### FAIR Principles

In 2016, the [FAIR Guiding Principles](https://www.nature.com/articles/sdata201618) for scientific data management and stewardship were
published in Scientific Data. Read it.

**Findable**
*Findable*

- F1. (meta)data are assigned a globally unique and persistent identifier
- F2. data are described with rich metadata (defined by R1 below)
- F3. metadata clearly and explicitly include the identifier of the data it describes
- F4. (meta)data are registered or indexed in a searchable resource
- F1. (meta)data are assigned a globally unique and persistent identifier
- F2. data are described with rich metadata (defined by R1 below)
- F3. metadata clearly and explicitly include the identifier of the data it describes
- F4. (meta)data are registered or indexed in a searchable resource

**Accessible**
*Accessible*

- A1. (meta)data are retrievable by their identifier using a
standardized communications protocol
- A1.1 the protocol is open, free, and universally implementable
- A1.2 the protocol allows for an authentication and authorization
procedure, where necessary
- A2. metadata are accessible, even when the data are no longer
available
- A1. (meta)data are retrievable by their identifier using a
standardized communications protocol
- A1.1 the protocol is open, free, and universally implementable
- A1.2 the protocol allows for an authentication and authorization
procedure, where necessary
- A2. metadata are accessible, even when the data are no longer
available

**Interoperable**
*Interoperable*

- I1. (meta)data use a formal, accessible, shared, and broadly
applicable language for knowledge representation.
- I2. (meta)data use vocabularies that follow FAIR principles
- I3. (meta)data include qualified references to other (meta)data
- I1. (meta)data use a formal, accessible, shared, and broadly
applicable language for knowledge representation.
- I2. (meta)data use vocabularies that follow FAIR principles
- I3. (meta)data include qualified references to other (meta)data

**Reusable**
*Reusable*

- R1. meta(data) are richly described with a plurality of accurate
and relevant attributes
- R1.1. (meta)data are released with a clear and accessible data
usage license
- R1.2. (meta)data are associated with detailed provenance
- R1.3. (meta)data meet domain-relevant community standard
- R1. meta(data) are richly described with a plurality of accurate
and relevant attributes
- R1.1. (meta)data are released with a clear and accessible data
usage license
- R1.2. (meta)data are associated with detailed provenance
- R1.3. (meta)data meet domain-relevant community standard

!!! Tip "Open vs. Public vs. FAIR"
!!! Tip "Open vs. Public vs. FAIR"

FAIR does not demand that data be open: See one definition of open:
http://opendefinition.org/
FAIR does not demand that data be open: See one definition of open:
http://opendefinition.org/

!!! Question "Why Principles?"
!!! Question "Why Principles?"

FAIR is a collection of principles. Ultimately, different
communities within different scientific disciplines must work to
interpret and implement these principles. Because technologies
change quickly, focusing on the desired end result allows FAIR to be
applied to a variety of situations now and in the foreseeable
future.
FAIR is a collection of principles. Ultimately, different
communities within different scientific disciplines must work to
interpret and implement these principles. Because technologies
change quickly, focusing on the desired end result allows FAIR to be
applied to a variety of situations now and in the foreseeable
future.

### CARE Principles
**CARE Principles**

The [CARE Principles](https://www.gida-global.org/care) for Indigenous Data Governance were drafted at the International Data Week and Research Data Alliance Plenary co-hosted event "Indigenous Data Sovereignty Principles for the Governance of Indigenous Data Workshop," 8 November 2018, Gaborone, Botswana.
The [CARE Principles](https://www.gida-global.org/care) for Indigenous Data Governance were drafted at the International Data Week and Research Data Alliance Plenary co-hosted event "Indigenous Data Sovereignty Principles for the Governance of Indigenous Data Workshop," 8 November 2018, Gaborone, Botswana.

**Collective Benefit**
*Collective Benefit*

- C1. For inclusive development and innovation
- C2. For improved governance and citizen engagement
- C3. For equitable outcomes
- C1. For inclusive development and innovation
- C2. For improved governance and citizen engagement
- C3. For equitable outcomes

**Authority to Control**
**Authority to Control*

- A1. Recognizing rights and interests
- A2. Data for governance
- A3. Governance of data
- A1. Recognizing rights and interests
- A2. Data for governance
- A3. Governance of data

**Responsibility**
*Responsibility*

- R1. For positive relationships
- R2. For expanding capability and capacity
- R3. For Indigenous languages and worldviews
- R1. For positive relationships
- R2. For expanding capability and capacity
- R3. For Indigenous languages and worldviews

**Ethics**
*Ethics*

- E1. For minimizing harm and maximizing benefit
- E2. For justice
- E3. For future use
- E1. For minimizing harm and maximizing benefit
- E2. For justice
- E3. For future use

### FAIR - TLC
**FAIR - TLC**

**Traceable, Licensed, and Connected**
*Traceable, Licensed, and Connected*

- The need for metrics: https://zenodo.org/record/203295#.XkrzTxNKjzI
- The need for metrics: https://zenodo.org/record/203295#.XkrzTxNKjzI

### How to get to FAIR?
**How to get to FAIR?**

This is a question that only you can answer, that is because it depends
on (among other things)
This is a question that only you can answer, that is because it depends
on (among other things)

1. Your scientific discipline: Your datatypes and existing standards
for what constitutes acceptable data management will vary.
2. The extent to which your scientific community has implemented
FAIR: Some disciplines have significant guidelines on FAIR, while
others have not addressed the subject in any concerted way.
3. Your level of technical skills: Some approaches to implementing
FAIR may require technical skills you may not yet feel comfortable
with.
1. Your scientific discipline: Your datatypes and existing standards
for what constitutes acceptable data management will vary.
2. The extent to which your scientific community has implemented
FAIR: Some disciplines have significant guidelines on FAIR, while
others have not addressed the subject in any concerted way.
3. Your level of technical skills: Some approaches to implementing
FAIR may require technical skills you may not yet feel comfortable
with.

While a lot is up to you, the first step is to evaluate how FAIR you
think your data are:
While a lot is up to you, the first step is to evaluate how FAIR you
think your data are:

??? Question "Exercise"
??? Question "Exercise"
Thinking about a dataset you work with, complete the ARDC [FAIR assessment](https://ardc.edu.au/resource/fair-data-self-assessment-tool/).

??? Note "Resources"

### References and Resources

<https://www.nature.com/articles/sdata201618>

- [The FAIR Guiding Principles for scientific data management and stewardship](<https://www.nature.com/articles/sdata201618>)
- [Wilkinson et al. (2016)](https://doi.org/10.1038/sdata.2016.18){target=_blank} established the guidelines to improve the Findability, Accessibility, Interoperability, and Reuse (FAIR) of digital assets for research.
- [Go-FAIR website](https://www.go-fair.org/fair-principles/){target=_blank}
- [Carroll et al. (2020)](http://doi.org/10.5334/dsj-2020-043){target=_blank} established the CARE Principles for Indigenous Data Governance. [full document :fontawesome-solid-file-pdf:](https://static1.squarespace.com/static/5d3799de845604000199cd24/t/5da9f4479ecab221ce848fb2/1571419335217/CARE+Principles_One+Pagers+FINAL_Oct_17_2019.pdf){target=_blank}
- [Indigenous Data Sovereignty Networks](https://indigenousdatalab.org/networks/){target=_blank}

??? Tip "LOD Cloud"

Expand Down Expand Up @@ -481,7 +467,7 @@ The use of version control systems like [GitHub](https://github.com/search?q=ope

#### :material-pillar: Open Source Software

[![](https://upload.wikimedia.org/wikipedia/commons/4/42/Opensource.svg){width=240}](https://opensource.org/){target=_blank}
[![oss](https://upload.wikimedia.org/wikipedia/commons/thumb/e/eb/Open_Source_Initiative.svg/1024px-Open_Source_Initiative.svg.png){width=240}](https://opensource.org/){target=_blank}

??? Quote "Definitions"

Expand All @@ -493,6 +479,16 @@ The use of version control systems like [GitHub](https://github.com/search?q=ope

[Awesome list](https://tyson-swetnam.github.io/awesome-open-science/software/){target=_blank}

??? Tip "Liceses"

??? Quote "Definitions"
Licensing is a crucial aspect of Open Science, as it helps define the terms under which research outputs, such as data, software, publications, and other digital resources, can be accessed, used, shared, and reused by others. Licensing enables the dissemination of knowledge, fosters collaboration, and ensures that research outputs are properly attributed and protected.

In the context of Open Science, the most commonly used licese models are the [:material-creative-commons: Creative Commons Licences](https://creativecommons.org/licenses/) and the [:material-open-source-initiative: Open Source Initiative Licenses](https://opensource.org/licenses/). In summary:

- **Creative Commons** licenses allow content creators to retain certain rights while granting others the freedom to use and distribute their work under specific conditions, including work outside of science.
- **Open Science Initiative** licenses specifically focus on licensing scientific research outputs, including data, software, publications, and related materials. It is tailored to meet the unique requirements and challenges of the scientific community, taking into account FAIR and CARE priciples.

### *WHY* do Open Science?

There are many reasons to do Open Science, and presumably one or more of them brought you to this workshop.
Expand All @@ -511,7 +507,6 @@ A paper from [Bartling & Friesike (2014)](https://doi.org/10.1007/978-3-319-0002

5. **Measurement school**: primarily concerned with the existing focus on journal publications as a means of measuring scholarly output, and focused on developing alternative measurements of scientific impact


<figure markdown>
<a href="https://library.oapen.org/bitstream/handle/20.500.12657/28008/1001989.pdf" target="blank" rel="fecher_friesike">![fecher_friesike](../assets/five_schools.png){ width="700" } </a>
<figcaption> In [Bartling & Friesike (2014)](https://doi.org/10.1007/978-3-319-00026-8){target=_blank} Open Science: One Term, Five Schools of Thought </figcaption>
Expand Down Expand Up @@ -991,7 +986,7 @@ A big first step on the road to reproducibility is **repeatability**. In the con

In many ways, this is the biggest hurdle to reproducibility, as it often requires the biggest leap in skills. You can think of repeatability in a few ways.

#### Software Management
### Software Management

Have you ever tried to run a script, only to realize you had updated a package without knowing, and now the script doesn't work?

Expand Down

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