Add ref & link to Gundersen paper, not just presentation

Pull from Collab Guide [PR 37](Imageomics/Collaborative-distributed-science-guide#37)

Author: egrace479

docs/wiki-guide/FAIR-Guide.md

@@ -29,5 +29,5 @@ The last topic in this section discusses different methods of [DOI Generation](D
 !!! question "[Questions, Comments, or Concerns?](https://github.com/Imageomics/Imageomics-guide/issues)"
 
 [^1]: While "Reproducible" is not part of the original FAIR principles as defined by the [Go-FAIR Initiative](https://www.go-fair.org/fair-principles/), we include it here to emphasize the importance of computational reproducibility alongside data stewardship. This extension reflects emerging practice in data-intensive science, where code, models, and workflows must be reusable and verifiable to support robust scientific claims. It is not part of the formal FAIR acronym, but aligns with broader community goals for open and transparent research.
-[^2]: Full reproducibility is difficult to achieve; this [presentation](https://drive.google.com/file/d/1BFqZ00zMuyVHaD9A8PvzRDEg7aV0kp3W/view?usp=drive_link) by Odd Erik Gundersen provides a discussion of the varying degrees of reproducibilityand useful references when considering the level of reproducibility achieved by a given project.  
+[^2]: Full reproducibility is difficult to achieve; this [presentation](https://drive.google.com/file/d/1BFqZ00zMuyVHaD9A8PvzRDEg7aV0kp3W/view?usp=drive_link) by Odd Erik Gundersen provides a discussion of the varying degrees of reproducibility and useful references when considering the level of reproducibility achieved by a given project. The presentation is a digestible summary of their [paper](https://doi.org/10.1609/aaai.v39i25.34818): O.E. Gundersen, O. Cappelen, M. Mølnå, and N.G. Nilsen. The Unreasonable Effectiveness of Open Science in AI: A Replication Study, *Proceedings of the AAAI Conference on Artificial Intelligence, 39*(25), 26211-26219. <https://doi.org/10.1609/aaai.v39i25.34818>.
 [^3]: The [Data Archive Options Comparative Overview](../pdfs/Data_Archive-Publication-Options-Comparative-Overview.pdf) was created in May 2023 as part of developing archive recommendations for the Institute, so it does not include information about newer features such as [Hugging Face's dataset viewer](https://huggingface.co/docs/hub/en/datasets-viewer), which greatly simplifies previewing datasets for downstream users.