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-<p>TODO: see also https://data.research.cornell.edu/content/preparing-fair-data-reuse-and-reproducibility</p>
 <p>FAIR stands for Findable, Accessible, Interoperable and Reusable. Implementing the FAIR principles for data can be a challenge though. Let’s talk about how to do it in practice.</p>
 <p>The FAIR principles are at the core of many current initiatives in research and beyond. For example the German National Research Data Infrastructure (NFDI) consortia are working on making research data (and sometimes software) FAIR. There is even a special FAIR Data Spaces project. But what does FAIR mean for you?</p>
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     "title": "2  FAIR principles for reproducible research",
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-    "text": "TODO: see also https://data.research.cornell.edu/content/preparing-fair-data-reuse-and-reproducibility\nFAIR stands for Findable, Accessible, Interoperable and Reusable. Implementing the FAIR principles for data can be a challenge though. Let’s talk about how to do it in practice.\nThe FAIR principles are at the core of many current initiatives in research and beyond. For example the German National Research Data Infrastructure (NFDI) consortia are working on making research data (and sometimes software) FAIR. There is even a special FAIR Data Spaces project. But what does FAIR mean for you?\n\n\n\n\nFAIR prinicples\n\n\n\nThis chapter is based on ressources from the University of Mannheim, that I recommend to check out: https://github.com/UB-Mannheim/FAIR-Data-Week. In the following I will use these and my own experience to give you a bit of a guidance on how to get started.\n\nGenerally for all FAIR principles\nThe FAIR principles try to help you answer the most common questions people have about data.\n\n\n\n\nFAIR questions\n\n\n\nBy the way, “data” in this context can mean many different things. Of course things like regular tabular data sets, but also images and other research materials.\n\nHow to get started\nStore your data somewhere that makes sense. If you can make your data openly available regular data platforms such as Zenodo or the Open Science Framework (OSF) will do. Of course also field specific or institutional platforms/repositories are good options.\n\n\n\nView the images of this chapter on Zenodo\n\n\nIf you cannot make them openly available, you can usually still make the metadata available. Metadata is information about your data such as the author(s), how to cite it, what the data set contains, and so on.\nMaking your data known in the community increases not only your chance of creating an impact with your work but also your work’s FAIRness. You can do so by publishing a data paper or otherwise sharing more info with the community (social media, podcasts, conferences, …).\nAnd then additionally…\n\n\nF for Findable\n(Three ingredients: data, metadata and infrastructure)\n\nAttach a DOI to your data. Many data platforms (e.g. Zenodo) make that really easy for you.\nProvide rich machine-readable metadata. If you upload your data to a good data platform, the most relevant metadata will be asked from you anyhow. So it’s easy to do things right.\n\n\n\nA for Accessible\n(FAIR is not the same as Open 👉 the point is to provide the exact conditions of accessibility)\n\nExplain how someone can access your data. May that be via accessing it through a data platform or through an application that is evaluated by a data-use-and-access committee.\n\n\n\nI for Interoperable\n\nUse common data formats. For tabular data that could for example be csv, for images jpeg. What’s best in your community might be decided through a community standard.\nUse words that others will understand or define them. For example if the column names in your table are not self explanatory, explain them.\nProvide context for your data. Is it connected with other data or papers? You can also add your metadata to public knowledge graphs, e.g. Wikidata.\n\n\n\nR for Reusable\n\nInclude rich machine-readable metadata according to the community standards.\nAttach a license to your data (license is part of the metadata) that makes it clear, what others can do with your data. You can for example use the Creative Commons license CC-BY (more on how to choose a license)."
+    "text": "FAIR stands for Findable, Accessible, Interoperable and Reusable. Implementing the FAIR principles for data can be a challenge though. Let’s talk about how to do it in practice.\nThe FAIR principles are at the core of many current initiatives in research and beyond. For example the German National Research Data Infrastructure (NFDI) consortia are working on making research data (and sometimes software) FAIR. There is even a special FAIR Data Spaces project. But what does FAIR mean for you?\n\n\n\n\nFAIR prinicples\n\n\n\nThis chapter is based on ressources from the University of Mannheim, that I recommend to check out: https://github.com/UB-Mannheim/FAIR-Data-Week. In the following I will use these and my own experience to give you a bit of a guidance on how to get started.\n\nGenerally for all FAIR principles\nThe FAIR principles try to help you answer the most common questions people have about data.\n\n\n\n\nFAIR questions\n\n\n\nBy the way, “data” in this context can mean many different things. Of course things like regular tabular data sets, but also images and other research materials.\n\nHow to get started\nStore your data somewhere that makes sense. If you can make your data openly available regular data platforms such as Zenodo or the Open Science Framework (OSF) will do. Of course also field specific or institutional platforms/repositories are good options.\n\n\n\nView the images of this chapter on Zenodo\n\n\nIf you cannot make them openly available, you can usually still make the metadata available. Metadata is information about your data such as the author(s), how to cite it, what the data set contains, and so on.\nMaking your data known in the community increases not only your chance of creating an impact with your work but also your work’s FAIRness. You can do so by publishing a data paper or otherwise sharing more info with the community (social media, podcasts, conferences, …).\nAnd then additionally…\n\n\nF for Findable\n(Three ingredients: data, metadata and infrastructure)\n\nAttach a DOI to your data. Many data platforms (e.g. Zenodo) make that really easy for you.\nProvide rich machine-readable metadata. If you upload your data to a good data platform, the most relevant metadata will be asked from you anyhow. So it’s easy to do things right.\n\n\n\nA for Accessible\n(FAIR is not the same as Open 👉 the point is to provide the exact conditions of accessibility)\n\nExplain how someone can access your data. May that be via accessing it through a data platform or through an application that is evaluated by a data-use-and-access committee.\n\n\n\nI for Interoperable\n\nUse common data formats. For tabular data that could for example be csv, for images jpeg. What’s best in your community might be decided through a community standard.\nUse words that others will understand or define them. For example if the column names in your table are not self explanatory, explain them.\nProvide context for your data. Is it connected with other data or papers? You can also add your metadata to public knowledge graphs, e.g. Wikidata.\n\n\n\nR for Reusable\n\nInclude rich machine-readable metadata according to the community standards.\nAttach a license to your data (license is part of the metadata) that makes it clear, what others can do with your data. You can for example use the Creative Commons license CC-BY (more on how to choose a license)."
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