Ecology as an Emerging Discipline Nadkarni 2001. Enhancement of forest canopy research, education, and conservation in the new millennium. Plant Ecology. 153: 361-367.
“Because informatics activity ultimately reflects the science, we concluded that a database cannot become an effective integrative tool until the science itself is integrated. Paradoxically, the science cannot easily become integrated without the use of database tools.” Ecology as Emerging Discipline Nadkarni 2001. Enhancement of forest canopy research, education, and conservation in the new millennium. Plant Ecology. 153: 361-367.
Ecology as Emerging Discipline “Our reviews of tools applicable to canopy science discovered a wealth of software tools used in other disciplines for displaying information about complex structures, processes, and datasets, but the best of these were not easily portable to other disciplines.” Nadkarni 2001. Enhancement of forest canopy research, education, and conservation in the new millennium. Plant Ecology. 153: 361-367.
Topic is highly technical Habitat condition Population sampling Survival and growth Genetics
BiOp Workflows In Development Cartoon from UC RTT Analysis workshop The cartoon shows a blackboard with the equations that describe Einstein's grand unifying theory. The caption was modified to say “RME Simplified”.
Professional Norms Ecology is exploratory and independent –Analysis is highly iterative –Trained as independent researchers –Rewarded for innovation Database developers design, then build –Trained within engineering programs –Has worked well for business applications –Rewarded for meeting requirements on time
Approaches Dissect into components –Monitoring type –Integration of Monitoring –Historic / future –Requirements / design solutions / implementation Manage for uncertainty –Broaden scope of information to be managed –Metadata-driven –Standardize data formats –Separate storage from analysis Adaptive software development
Monitoring Type –Status and Trend –Implementation –Effectiveness Site specific Watershed scale Process oriented or mechanistic Dissect Into Components
Integration –High-level discuss –Should not impede progress on other components Dissect Into Components
Historic –Summary or reporting metrics –Evaluate cost/benefit ratio for field-level observations (Tetra Tech, 2008) Future –Field-level observations –Standardized format –Full metadata Dissect Into Components
Requirements –Scientists, managers, data stewards Design solutions –Developers, programmers, and data stewards –Feedback from scientists and managers Implementation –all Dissect Into Components
Manage for Uncertainty Broaden scope of information –Resource management questions –Monitoring program design and evaluation Metadata-driven Applications –Smart tools (lessons from social networking) Standardize data formats Separate data storage and analysis utilities
Broaden Scope of Information Resource Management Questions Monitoring Program Design and Evaluation Metadata Field Observations Who When Where How Why
>0 and <=1500>60 and <=250>3 and <=90>3 and <=250>60 and <=1000 Metadata-driven Applications
Adaptive Software Development Waterfall approach –Requirements driven –Well defined workflows –Learned in training programs Adaptive approach –Mission focused, risk driven, feature based –Adaptation to emergent state of the project –Deliverable specifications defined broadly –Normal state of affairs
Adaptive Software Development http://en.wikipedia.org/wiki/Adaptive_Software_Development http://en.wikipedia.org/wiki/Agile_software_development http://en.wikipedia.org/wiki/Scrum_(development)
Take home message Metadata Standardize data formats Separate data storage and analysis utilities Adaptive software development