The Fourth Paradigm 10 Years On

1. The National Oceanic and Atmospheric Administration OAA Data Discovery Portal. data.noaa.gov, last access: 15.10.2019

2. The big ocean button challenge. https://www.herox.com/bigoceanbutton/entries, last access: 15.10.2019

3. The Dryad Digital Repository. www.datadryad.org/, last access: 15.10.2019

4. The Zendo repository. https://zenodo.org/, last access: 15.10.2019

5. Alfred P Sloan foundation funding. http://blog.zenodo.org/2019/07/17/2019-07-17-dryad-partnership/, last access: 15.10.2019

6. Argelaguet R, Velten B, Arnol D, Dietrich S, Zenz T, Marioni JC, Buettner F, Huber W, Stegle O (2018) Multi-omics Factor Analysis – a framework for unsupervised integration of multi-omics data sets. Molecular Systems Biology, https://doi.org/10.15252/msb.20178124, last access: 15.10.2019

7. Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J, Bellemare MG, Graves A, Riedmiller M, Fidjeland AK, Ostrovski G, Petersen S, Beattie C, Sadik A, Antonoglou I, King H, Kumaran D, Wierstra D, Legg S, Hassabis D (2015) Human-level control through deep reinforcement learning. Nature 518:529–533, https://www.nature.com/articles/nature14236, last access: 15.10.2019

   Article  Google Scholar 

8. Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC, Fei-Fei L (2015) ImageNet large scale visual recognition challenge. Int J Comput Vision 115:211–252, https://link.springer.com/article/10.1007/s11263-015-0816-y

   Article  MathSciNet  Google Scholar 

9. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature 542:115–118, https://www.nature.com/articles/nature21056

   Article  Google Scholar 

10. Fogel AL, Kvedar JC (2018) Artificial intelligence powers digital medicine. npj Digital Medicine, https://doi.org/10.1038/s41746-017-0012-2, last access: 15.10.2019

11. Hinton G (2018) Deep learning – a technology with the potential to transform health care. JAMA 320(11):1101–1102, https://doi.org/10.1001/jama.2018.11100, last access: 15.10.2019

    Article  Google Scholar 

12. Foster I, Gannon DB (2017) Cloud Computing for Science and Engineering. MIT Press, Cambridge London, https://mitpress.mit.edu/books/cloud-computing-science-and-engineering, last access: 15.10.2019

    Google Scholar 

13. The National Infrastructure Commission. https://www.nic.org.uk, last access: 10/2019

14. The National Infrastructure Commission Data As Infrastructure Report. https://www.nic.org.uk/wp-content/uploads/Data-As-Infrastructure.pdf, last access: 15.10.2019

15. The National Infrastructure Commission Data for the Public Good Report. https://www.nic.org.uk/wp-content/uploads/Data-for-the-Public-Good-NIC-Report.pdf, last access: 15.10.2019

16. DAFNI: Data & Analytics Facility for National Infrastructure. https://www.dafni.ac.uk/, last access: 15.10.2019

17. Atkinson M, Gesing S, Montagnat J, Taylor I (2017) Scientific workflows: past, present and future. Future Gener Comp Sy 75:216–227

    Article  Google Scholar 

18. The Common Workflow Language. https://www.commonwl.org/, last access: 15.10.2019

19. The ELIXIR project. https://elixir-europe.org/, last access: 15.10.2019

20. The Oxford Research Archive. https://www.bodleian.ox.ac.uk/bdlss/digital-services/data-archiving, last access: 15.10.2019

21. Research Data at Springer Nature. https://researchdata.springernature.com/, last access: 15.10.2019

22. Nature Scientific data. https://www.nature.com/sdata/, last access: 15.10.2019

23. OECD Open Science. http://www.oecd.org/sti/inno/open-science.htm, last access: 15.10.2019

24. Fostering Open Science Project. https://www.fosteropenscience.eu/, last access: 15.10.2019

25. Schema.org. https://schema.org/, last access: 15.10.2019

26. Bioschemas. https://bioschemas.org/, last access: 15.10.2019

27. JSON for Linking Data. https://json-ld.org/, last access: 15.10.2019

28. Turing award winners. https://amturing.acm.org/, last access: 15.10.2019

29. Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L (2009) ImageNet: A Large-Scale Hierarchical Image Database. In: CVPR09, http://image-net.org/papers/imagenet_cvpr09.pdf, last access: 15.10.2019

30. Krizhevsky A, Sutskever I, Hinton GE (2017) ImageNet classification with deep convolutional neural networks. Commun ACM 60(6):84–90, https://doi.org/10.1145/3065386, last access: 15.10.2019

    Article  Google Scholar 

31. Microsoft Research 2015 win. https://blogs.microsoft.com/ai/microsoft-researchers-win-imagenet-computer-vision-challenge/, last access: 15.10.2019

32. AlphaFold: Using AI for scientific discovery: https://deepmind.com/blog/alphafold/, last access: 15.10.2019

33. Evans R, Jumper J, Kirkpatrick J, Sifre L, Green TFG, Qin C, Zidek A, Nelson A, Bridgland A, Penedones H, Petersen S, Simonyan K, Crossan S, Jones DT, Silver D, Kavukcuoglu K, Hassabis D, Senior AW (2018) De novo structure prediction with deep-learning based scoring. In: Thirteenth Critical Assessment of Techniques for Protein Structure Prediction (Abstracts), 1–4 December 2018. Retrieved from: https://kstatic.googleusercontent.com/files/b4d715e8f8b6514cbfdc28a9ad83e14b6a8f86c34ea3b3cc844af8e76767d21ac3df5b0a9177d5e3f6a40b74caf7281a386af0fab8ca62f687599abaf8c8810f, last access: 15.10.2019

34. Geoffrey Hinton as quoted by Jeremy Howard in 2014. https://www.ted.com/talks/jeremy_howard_the_wonderful_and_terrifying_implications_of_computers_that_can_learn, last access: 15.10.2019

35. OSTP Memorandum. https://obamawhitehouse.archives.gov/sites/default/files/microsites/ostp/ostp_public_access_memo_2013.pdf, last access: 15.10.2019

36. The Global Research Council statement on Open Access. https://www.globalresearchcouncil.org/fileadmin/documents/GRC_Publications/grc_action_plan_open_access_FINAL.pdf, last access: 15.10.2019

37. G8 Open Data Charter. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/207772/Open_Data_Charter.pdf, last access: 15.10.2019

38. Plan–S. https://www.coalition-s.org/, last access: 15.10.2019

39. Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, Blomberg N, Boiten J-W, da Silva Santos LB, Bourne PE, Bouwman J, Brookes AJ, Clark T, Crosas M, Dillo I, Dumon O, Edmunds S, Evelo CT, Finkers R, Gonzalez-Beltran A, Gray AJG, Groth P, Goble C, Grethe JS, Heringa J, ’t Hoen PAC, Hooft R, Kuhn T, Kok R, Kok J, Lusher SJ, Martone ME, Mons A, Packer AL, Persson B, Rocca-Serra P, Roos M, van Schaik R, Sansone S-A, Schultes E, Sengstag T, Slater T, Strawn G, Swertz MA, Thompson M, van der Lei J, van Mulligen E, Velterop J, Waagmeester A, Wittenburg P, Wolstencroft K, Zhao J, Mons B (2016) The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3:160018, https://doi.org/10.1038/sdata.2016.18, last access: 15.10.2019

    Article  Google Scholar 

40. Sansone S-A, McQuilton P, Rocca-Serra P, Gonzalez-Beltran A, Izzo M, Lister AL, Thurston M, the FAIRsharing Community (2019) FAIRsharing as a community approach to standards, repositories and policies. Nat Biotechnol 7:358–367, https://doi.org/10.1038/s41587-019-0080-8, last access: 15.10.2019

    Article  Google Scholar 

41. FAIRsharing standards and policy site. https://fairsharing.org/, last access: 15.10.2019

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