Permaculture Database

[Bear Kaufmann <bear@ursine-design.com>]

This is pretty interesting and may well prove to be an important feature of the database,

the ability of the software to display abstractions, speculative concepts, quasi scientific

observations. Proven guilds could be listed and user-browsed with complete information about

each entry displayed on demand. Can't you have a table field(?) for guilds that would be unique in that it would 

describe why a certain collection of organisms qualifies as a guild -

of course the system needs a good definition for guild for users unfamiliar with the concept.

This gets deep.

A really comprehensive, broad spectrum dataset might acquire a huge amount of useful information

about fauna, flora, microorganisms, climate, soil conditions, drainage, cultural likes and dislikes (calciphobe, water 

tolerant, aquatic, tolerates dry conditions, parasitic, symbiosis, nitrogen fixation, ability to make plant-available 

nutrients (mycorrhizal fungi). All these individual bits, properly tagged could become source data for the DB to use 

when searching for possible guild components, as per user query.

There is some interesting commentary from Kevin Kelly on new scientific methods for the future, which seems relevant to the discussion.

I'll pull a few tidbits from http://www.edge.org/3rd_culture/kelly06/kelly06_index.html :

Pattern Augmentation – Pattern-seeking software which recognizes a pattern in noisy results. In large bodies of information with many variables, algorithmic discovery of patterns will become necessary and common. These exist in specialized niches of knowledge (such particle smashing) but more general rules and general-purpose pattern engines will enable pattern-seeking tools to become part of all data treatment.

Compiled Negative Results — Negative results are saved, shared, compiled and analyzed, instead of being dumped. Positive results may increase their credibility when linked to negative results. We already have hints of this in the recent decision of biochemical journals to require investigators to register early phase 1 clinical trials. Usually phase 1 trials of a drug end in failure and their negative results are not reported. As a public heath measure, these negative results should be shared. Major journals have pledged not to publish the findings of phase 3 trials if their earlier phase 1 results had not been reported, whether negative or not.

Wiki-Science – The average number of authors per paper continues to rise. With massive collaborations, the numbers will boom. Experiments involving thousands of investigators collaborating on a "paper" will commonplace. The paper is ongoing, and never finished. It becomes a trail of edits and experiments posted in real time — an ever evolving "document." Contributions are not assigned. Tools for tracking credit and contributions will be vital. Responsibilities for errors will be hard to pin down. Wiki-science will often be the first word on a new area. Some researchers will specialize in refining ideas first proposed by wiki-science.

Hyper-analysis Mapping – Just as meta-analysis gathered diverse experiments on one subject and integrated their (sometimes contradictory) results into a large meta-view, hyper-analysis creates an extremely large-scale view by pulling together meta-analysis. The cross-links of references, assumptions, evidence and results are unraveled by computation, and then reviewed at a larger scale which may include data and studies adjacent but not core to the subject. Hyper-mapping tallies not only what is known in a particular wide field, but also emphasizes unknowns and contradictions based on what is known outside that field. It is used to integrate a meta-analysis with other meta-results, and to spotlight "white spaces" where additional research would be most productive.

Cheers,

Bear