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There are hundreds of collections of chemical, biological and toxicological data that are widely dispersed, mostly in the open literature, professional research bodies and commercial companies.
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Even routine information and data that is widely available in other countries, notably the US, is too often withheld from the British public.
They rely on proxy data that is widely spaced in time (median sampling interval 120 years) and in many cases may also be subject to significant dating uncertainty.
The discovery that a type of genetic data that is widely shared and often posted online can be traced back to individuals has prompted the U.S. National Institutes of Health and the Wellcome Trust to strip some genetic data from their publicly accessible Web sites and NIH to recommend that other institutions do the same.
In this section, the discussion of several major data sources that are widely applied to predictive toxicology is presented, with particular focus on their data governance aspects.
The AFGROW approach uses crack growth rate data models that are widely available in the open literature and makes no assumption on the cause of the closure phenomenon.
It uses standard data formats that are widely adopted.
Although the study by Prescott et al. used proprietary Episode Treatment Group ETGG) software to calculate some study outcomes [ 10], the present study used only data elements that are widely available in administrative claims databases.
In environmental sciences, GIS can be used in conjunction with Spatial Data Infrastructures (SDIs) that are widely used to share, discover, retrieve and visualize geospatial data through standardized services [e.g., Open Geospatial Consortium services OGC (1994)].
Most data-driven models that are widely applied to fermentation processes are unfit for optimization or provide low precision.
In addition to high quality data, essential analytical tool resources that are widely accepted in the scientific community are also provided in Allergen Atlas.
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