Normal People

ReadWhile lemme howdt has been visiting the Independent Country blog (cool links) and commenting on lost causes, he cracked a joke about not being a normal person. Taking this in a more serious vein, what is normal? It certainly is not the values and mechanisms that I grew up with. They are no longer normal.
ThisIn science, normalizing a function is setting it equal to 1. No matter what scale you work on, grand or small (see powers of ten blog below), science always tries to normalize things back to our real world reality of one. Or at least we did in the past. Physics things like quantum mechanical tunneling have no real meaning to the general public at all - they barely have meaning to literate educated members of the non-physics scientific community.
ReadInformation can either be freely shared, or kept totally proprietary. But when government and the university system use their power and bulk to administrate proprietary systems to handle information management in rural communities and compete with the private sector in our communities for the limited grant funding available using the proprietary bells and whistles as advantage - how can we develop a rural economy independent of the imposed land grant system. And how can we be assured that the information will be kept available, or that our rural data remains under local control? Public grant money cannot develop proprietary models for public institutes!
ThisI also have a gripe concerning general acceptibility of data. The QA/QC process for rejecting outlyers means that irregular phenomena observed require a highly unlikely repeat experience to be acceptible. But every situation is different in the detail and no one size fits all. If we constantly reject data that doesn't make immediate sense in the field and keep it out of the data base, how can we ever recognize trends when these one time occurrances repeat themselves in different temporal time and place. Big science perpetuates itself by stomping on competition by the requirement of artificial field rigor. The honest collector of data should never be the sole analyst of the data and certainly should never reject any collected data for any reason before the entire analytical set is evaluated and normalized! Then statistical rejection of the data can take place. Having data rejection done in the field by the data collector just invalidates the real science value of the data set.
Now Raw data was always provided before science became proprietary. Students: To properly reject mistaken field data, draw a single line through the data in error. Never erase from a notebook or obliterate and always look for meta-data.