"Scientific data"의 두 판 사이의 차이

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* ID :  [https://www.wikidata.org/wiki/Q20081319 Q20081319]

2020년 12월 26일 (토) 05:13 판

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  1. IF of Scientific data is decreased by a factor of 0.09 compared to 2017.[1]
  2. Scientific Data is open to submissions from a broad range of natural science disciplines, including, but not limited to, data from the life, biomedical and environmental science communities.[2]
  3. Scientific Data is an open-access, peer-reviewed publication for descriptions of scientifically valuable datasets.[3]
  4. So why don't study and implement something similar for scientific data?[4]
  5. Below I listed common problem and opportunities in scientific data access.[4]
  6. But the increasing of scientific data collections size brings not only problems, but also a lot of opportunities.[4]
  7. An important note: the biopharmaceutical industry attacks a more specific meaning to Scientific Data Management.[4]
  8. Data collected in a lab experiment done under controlled conditions is an example of scientific data.[5]
  9. Scientific Data primarily publishes Data Descriptors, a new type of publication that focuses on helping others reuse data, and crediting those who share.[6]
  10. Scientific Data publishes content from all research disciplines, including descriptions of big or small datasets, from major consortiums to single research groups.[6]
  11. While it has inherited some of their methods and thinking, it also seeks to blend them, refocus them, and develop them to address the context and needs of modern scientific data analysis.[7]
  12. The contested nature of core concepts in data practices has limited progress toward improving the dissemination and reuse of scientific data.[8]
  13. In 2016, the ‘FAIR Guiding Principles for scientific data management and stewardship’ were published in Scientific Data.[9]
  14. In addition to knowing what scientific data is and how it is represented, it is important to consider how it is stored (and hopefully annotated).[10]
  15. Detailed below is an initial attempt to create a framework upon which to organize scientific data and its metadata.[10]
  16. Terms have been grouped into classes (see Fig. 10): metadata, context, dataset, methodology, scientific data, and unit of measure.[10]
  17. To illustrate the use of JSON-LD to represent scientific data and what it means consider the JSON text below for a ‘parameter’ (Fig. 12).[10]
  18. Such ‘Big Scientific Data’ comes from the Diamond Light Source and Electron Microscopy Facilities, the ISIS Neutron and Muon Facility and the UK's Central Laser Facility.[11]
  19. Finally, we discuss the importance of developing some realistic machine learning benchmarks using Big Scientific Data coming from several different scientific domains.[11]
  20. In this paper, we make some initial explorations into the application of such Deep Learning approaches to scientific data.[11]
  21. The Rutherford Appleton Laboratory (RAL), at Harwell near Oxford, hosts several large-scale experimental facilities that now generate large volumes of increasingly complex scientific data.[11]

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