Agile Scientific Paper Development

Agile scientific paper development describes a set of values and principles derived from software development under which requirements and solutions evolve through the collaborative effort of self-organizing cross-functional teams. The value-driven approach to business intelligence and data warehousing is transfered to scientific paper development by advocating adaptive planning, evolutionary development, early delivery, and continuous improvement. Scientific paper developement (like in WikiJournal of Medicine) encourages a Green Open Access publication strategy with rapid and flexible response to change similar to Agile Software Development These transfered principles support the definition and continuing evolution of Green Open Access papers by providing rationals for the key development steps of the paper.

Learning Tasks

• (Comparison Software and Paper Development) Explore the key principles for Agile Software Development to scientific paper development and apply criticism,challenges and benefits from a neutral point of view in this learning resource.
• (Open Access and Open Licensing) Explore the concept of Green Open Access and explain how open access publication in conjunction with Agile Scientific Paper Development (ASPD) contribute to the support of the evolutionary process of paper!
• (Scientific Hackathon) Explore the concept of a Scientific Hackathon and identify options to use a scientific hackathon in the context of Agil Scientific Paper Developement. Regard the Scientific Hackathon as a prototyping environment for the scientific paper. Identify objectives, that must be accomplished at end of the Scientific Hackathon
• (Wiki Journal) Explore the concept of Wiki Journal which allows to see "who contributed when what" to the paper. Agile Scientific Paper Development (ASPD) creates new version by
  • adaption of code in KnitR update input data for the analysis or
  • integration of new scientific results into an updated version of a scientific paper,

Explain how version control of code chunks in a paper and version of paper itself contribute to the transparency of the evolutionary process!

• by a peer-reviewing of scientist that updated or improved parts of the paper.

• (Deep Learning) Artificial Intelligence and in general adaptive design of algorithms, that learn from input data and perform pattern recognition is linked to Big Data analysis. Scientific papers are often build on

• data collection and data aggregation (Big Data analysis),
• data analysis (Deep Learning) and
• interpretation of the results.

Assume that you apply the agile software development principle on Deep Learning and an ongoing input stream of "Big Data" (e.g. satellite images and pattern recognition of environmental data with data analysis in social media). What are the challenges to combine those data resource to answer scientific questions like ("How do local changes environmental parameters X affect the social interaction in media Y?". This is a dynamic process and the analysis of the data defined by the algorithms might require an iterative approach to update the analytic instrument. Instead of writing a static paper in a journal the dynamic paper provides an up-to-date analysis of the current data and the any updates in the algorithms (e.g. optimization of the pattern recognition) are documented in new version of the paper in an agile paper development.

Analyse the concepts of Big Data analysis, machine Learning and Artificial Intelligence and identify the requirements and constraint for the integration in Agile Scientific Paper Development!

See also

• Agile Software Development
• Deep Learning
• Green Open Access
• WikiJournal of Medicine
• WikiJournal of Science
• Reader2Author
• Science2Education
• WikiNews2Learning
• Scientific Hackathon
• KnitR - Integration of
  • paper document,
  • algorithmic description of methodology in R-Code and
  • the data loading

into a single R-Markdown document for support of Reproducible Science