Instant EPA’s Pesticide Facts, published in 1994, provides information from EPA at a fraction of the cost of obtaining this data from EPA’s on-line database. It contains information on trade names, use patterns, formulations, chemical and physical properties, toxicological acute and chronic effects, physiological, biochemical, and ecological effects and EPA contacts for over 200 pesticides (both chemical and biological types). We’ve added hyperlinked glosssaries of acronyns, abbreviations and technical terms to make it easier to use this comprehensive technical database. Hot keys link data from chemical names and EPA Methods in this publication to all of the others in the Professional PC References / Windows tm Series.
Using high end data collect methods the EPA gathered resources from a number of manufacturers to find what kind of pesticides were being used. These products were then cross checked using a series of other points of data to find the optimal resources. Data collection methods used statistical regression to see how much each country used in there crops.
In the world of big data it’s becoming easier for scientists to get access to safe crop data and know what is being used where. In the past such methods were difficult to find with thousand of not millions of data points being used to find statistically relevant results. Now with more powerful algorithms and CPUS it becomes gradually easier to find out exactly what kind of trends are out there.
Data gathered from using multi-sig authentication by online servers helps find exactly the amount of toxic ingredients out there in the atmosphere and the soil. This is extremely important when considering what to do and what are the best practices.
Models of analytics and predictive forecasting can be found using a series of what could be considered unrelated variables borrowing from various industries and companies. Data aggregation methods like those used to test web speed can be instrumental in finding and quantifying the exact amount of data going out.
Combining these with real world applications we can see that something previously unrelated like web speed algorithms can be used to determine with great accuracy the amount of data passing into a system and exactly how it can be used in the future.
The important takeaway here is that data can be gathered from many differnet sources and the methods used to gather those forms of data can be replicated and used again in what would seem like a potentially unrelated subset of data. This is the beautiful thing about data models it allows for 100% fluidity of movement and expression.