Add chapter on FAIR principles

.gitignore

@@ -5,3 +5,4 @@
 /.quarto/
 /images/
 /_book/
+*.html

_quarto.yml

@@ -27,6 +27,7 @@ book:
     - index.qmd
     - contents.qmd
     - chapters/intro.qmd
+    - chapters/fair-principles.qmd
     - summary.qmd
     - references.qmd
     - acknowledgements.qmd

_variables.yml

@@ -29,3 +29,4 @@ links:
   funding-info: "https://stiftung-hochschullehre.de/projekt/ddlitlab/"
   plausible: "https://plausible.io/"
   quarto: "https://quarto.org/"
+  STP: "https://teachpsych.org/index.php"

references.bib

@@ -15,3 +15,66 @@ @article{knuth84
   pages = {97–111},
   numpages = {15}
 }
+
+
+@article{wilkinson2016,
+	title = {The {FAIR} Guiding Principles for scientific data management and stewardship},
+	volume = {3},
+	rights = {2016 The Author(s)},
+	issn = {2052-4463},
+	url = {https://www.nature.com/articles/sdata201618},
+	doi = {10.1038/sdata.2016.18},
+	abstract = {There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the {FAIR} Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the {FAIR} Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the {FAIR} Principles, and includes the rationale behind them, and some exemplar implementations in the community.},
+	pages = {160018},
+	number = {1},
+	journaltitle = {Scientific Data},
+	shortjournal = {Sci Data},
+	author = {Wilkinson, Mark D. and Dumontier, Michel and Aalbersberg, {IJsbrand} Jan and Appleton, Gabrielle and Axton, Myles and Baak, Arie and Blomberg, Niklas and Boiten, Jan-Willem and da Silva Santos, Luiz Bonino and Bourne, Philip E. and Bouwman, Jildau and Brookes, Anthony J. and Clark, Tim and Crosas, Mercè and Dillo, Ingrid and Dumon, Olivier and Edmunds, Scott and Evelo, Chris T. and Finkers, Richard and Gonzalez-Beltran, Alejandra and Gray, Alasdair J. G. and Groth, Paul and Goble, Carole and Grethe, Jeffrey S. and Heringa, Jaap and ’t Hoen, Peter A. C. and Hooft, Rob and Kuhn, Tobias and Kok, Ruben and Kok, Joost and Lusher, Scott J. and Martone, Maryann E. and Mons, Albert and Packer, Abel L. and Persson, Bengt and Rocca-Serra, Philippe and Roos, Marco and van Schaik, Rene and Sansone, Susanna-Assunta and Schultes, Erik and Sengstag, Thierry and Slater, Ted and Strawn, George and Swertz, Morris A. and Thompson, Mark and van der Lei, Johan and van Mulligen, Erik and Velterop, Jan and Waagmeester, Andra and Wittenburg, Peter and Wolstencroft, Katherine and Zhao, Jun and Mons, Barend},
+	urldate = {2024-11-21},
+	date = {2016-03-15},
+	langid = {english},
+	note = {Publisher: Nature Publishing Group},
+	keywords = {Publication characteristics, Research data},
+	file = {Full Text PDF:C\:\\Users\\User\\Zotero\\storage\\I8DGAPWX\\Wilkinson et al. - 2016 - The FAIR Guiding Principles for scientific data management and stewardship.pdf:application/pdf},
+}
+
+
+@article{dogucu2022,
+	title = {Tools and Recommendations for Reproducible Teaching},
+	volume = {30},
+	issn = {null},
+	url = {https://doi.org/10.1080/26939169.2022.2138645},
+	doi = {10.1080/26939169.2022.2138645},
+	abstract = {It is recommended that teacher-scholars of data science adopt reproducible workflows in their research as scholars and teach reproducible workflows to their students. In this article, we propose a third dimension to reproducibility practices and recommend that regardless of whether they teach reproducibility in their courses or not, data science instructors adopt reproducible workflows for their own teaching. We consider computational reproducibility, documentation, and openness as three pillars of reproducible teaching framework. We share tools, examples, and recommendations for the three pillars.},
+	pages = {251--260},
+	number = {3},
+	journaltitle = {Journal of Statistics and Data Science Education},
+	author = {Dogucu, Mine and Çetinkaya-Rundel, Mine},
+	urldate = {2024-11-26},
+	date = {2022-11-18},
+	note = {Publisher: Taylor \& Francis
+\_eprint: https://doi.org/10.1080/26939169.2022.2138645},
+	keywords = {Computational reproducibility, Data science education, Open education, Teaching materials, Workflows},
+	file = {Full Text PDF:C\:\\Users\\User\\Zotero\\storage\\QXUGJCXP\\Dogucu and Çetinkaya-Rundel - 2022 - Tools and Recommendations for Reproducible Teaching.pdf:application/pdf},
+}
+
+@article{garcia2020,
+	title = {Ten simple rules for making training materials {FAIR}},
+	volume = {16},
+	issn = {1553-7358},
+	url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007854},
+	doi = {10.1371/journal.pcbi.1007854},
+	abstract = {Author summary Everything we do today is becoming more and more reliant on the use of computers. The field of biology is no exception; but most biologists receive little or no formal preparation for the increasingly computational aspects of their discipline. In consequence, informal training courses are often needed to plug the gaps; and the demand for such training is growing worldwide. To meet this demand, some training programs are being expanded, and new ones are being developed. Key to both scenarios is the creation of new course materials. Rather than starting from scratch, however, it’s sometimes possible to repurpose materials that already exist. Yet finding suitable materials online can be difficult: They’re often widely scattered across the internet or hidden in their home institutions, with no systematic way to find them. This is a common problem for all digital objects. The scientific community has attempted to address this issue by developing a set of rules (which have been called the Findable, Accessible, Interoperable and Reusable [{FAIR}] principles) to make such objects more findable and reusable. Here, we show how to apply these rules to help make training materials easier to find, (re)use, and adapt, for the benefit of all.},
+	pages = {e1007854},
+	number = {5},
+	journaltitle = {{PLOS} Computational Biology},
+	shortjournal = {{PLOS} Computational Biology},
+	author = {Garcia, Leyla and Batut, Bérénice and Burke, Melissa L. and Kuzak, Mateusz and Psomopoulos, Fotis and Arcila, Ricardo and Attwood, Teresa K. and Beard, Niall and Carvalho-Silva, Denise and Dimopoulos, Alexandros C. and Angel, Victoria Dominguez del and Dumontier, Michel and Gurwitz, Kim T. and Krause, Roland and {McQuilton}, Peter and Pera, Loredana Le and Morgan, Sarah L. and Rauste, Päivi and Via, Allegra and Kahlem, Pascal and Rustici, Gabriella and Gelder, Celia W. G. van and Palagi, Patricia M.},
+	urldate = {2024-11-26},
+	date = {2020-05-21},
+	langid = {english},
+	note = {Publisher: Public Library of Science},
+	keywords = {Bioinformatics, Computational biology, Computer software, Internet, Metadata, Ontologies, Open source software, Trainees},
+	file = {Full Text PDF:C\:\\Users\\User\\Zotero\\storage\\5GMQQFJK\\Garcia et al. - 2020 - Ten simple rules for making training materials FAIR.pdf:application/pdf},
+}
+