| Lines 24-29 were replaced by lines 24-29 |
| - # __high-level ecological ontologies__: develop detailed ontologies for Biodiversity & Productivity-- in terms of relevant concepts and relationships from the very general theoretical level, and drilling all the way down to the operational level (algorithms and measurements used in quantifying biodiversity and productivity). |
| - # __ontologies of data sets and analyses__: develop detailed ontologies of the data sets and analyses needed for some "past" Biodiversity/Productivity research, including clarifying the semantics and data transformations carried out to merge/integrate/summarize data, as well as "describing" the analytical components in ways that sufficiently expose their Inputs/Outputs and "functions" in ways that will facilitate discover and re-use of these components in alternative scientific workflows. |
| - # __ontologies of ecological methodologies__: develop detailed ontologies that capture the essential features and differentiating nuances of the field and other methodologies employed in capturing the data to be dealt with in these investigations (overlaps with task 2 above). (We must __not__ lose sight of the need for "spatiotemporal ontologies", and the specific capabilities these will provide us with regards to 1-3 here) |
| - # __scientific workflows__: develop detailed scientific workflows for some "past" Biodiversity/Productivity research, to formally capture at a fine grain the steps and operations, with sufficient semantic annotation to facilitate their transparency and reusability by other researchers. |
| - # __taxonomic capabilities__: identify the specific types of services that "ecologists" might need on the select use case to improve the ability to deal with taxonomic names, especially in the context of historical, long-term and globally distributed biodiversity information |
| - # __ecological research challenge__: conceive of a "Challenging" Biodiversity/Productivity analyses, which could be enabled via a scientific workflow managment (Kepler), and that demonstrates the power of accessing distributed data and computing resources, and would otherwise be highly inefficient or intractable to a "typical" individual researcher. |
| + # __High-level ecological ontologies__: develop detailed ontologies for Biodiversity & Productivity-- in terms of relevant concepts and relationships from the very general theoretical level, and drilling all the way down to the operational level (algorithms and measurements used in quantifying biodiversity and productivity). |
| + # __Ontologies of data sets and analyses__: develop detailed ontologies of the data sets and analyses needed for some "past" Biodiversity/Productivity research, including clarifying the semantics and data transformations carried out to merge/integrate/summarize data, as well as "describing" the analytical components in ways that sufficiently expose their Inputs/Outputs and "functions" in ways that will facilitate discover and re-use of these components in alternative scientific workflows. |
| + # __Ontologies of ecological methodologies__: develop detailed ontologies that capture the essential features and differentiating nuances of the field and other methodologies employed in capturing the data to be dealt with in these investigations (overlaps with task 2 above). (We must __not__ lose sight of the need for "spatiotemporal ontologies", and the specific capabilities these will provide us with regards to 1-3 here) |
| + # __Scientific workflows__: develop detailed scientific workflows for some "past" Biodiversity/Productivity research, to formally capture at a fine grain the steps and operations, with sufficient semantic annotation to facilitate their transparency and reusability by other researchers. |
| + # __Taxonomic capabilities__: identify the specific types of services that "ecologists" might need on the select use case to improve the ability to deal with taxonomic names, especially in the context of historical, long-term and globally distributed biodiversity information |
| + # __Ecological research challenge__: conceive of a "Challenging" Biodiversity/Productivity analyses, which could be enabled via a scientific workflow managment (Kepler), and that demonstrates the power of accessing distributed data and computing resources, and would otherwise be highly inefficient or intractable to a "typical" individual researcher. |
