| Lines 166-167 were replaced by line 166 |
| - *** Participants: Steve Cox, David Chalcraft, Shawn Bowers, Bertram Ludaescher, Mark Schildhauer, Chad Berkley, |
| - Dan Higgins, Jianting Zhang ([jzhang@lternet.edu|mailto:jzhang@lternet.edu]) |
| + *** Participants: Steve Cox, David Chalcraft, Shawn Bowers, Bertram Ludaescher, Mark Schildhauer, Chad Berkley, Dan Higgins, Jianting Zhang |
| Removed line 174 |
| - |
| Removed line 209 |
| - |
| At line 232 added 38 lines. |
| + ** __Traits of Interactions__ |
| + *** Competitive ability |
| + **** Measure how an individual suppresses the growth of a neighbor |
| + *** Interaction strength |
| + *** Effect on environment (ability to reduce resources) (Tilman) |
| + |
| + ** __Experimental Methods__ |
| + *** Experiment |
| + **** Field Experiment |
| + ***** Observational/Empirical Experiment |
| + ***** Manipulation |
| + *** All field experiments have |
| + **** Where (site, plots etc) |
| + **** When (sampling regime) |
| + **** What (properties of organism/population/community/system) |
| + *** (An empirical experiment is a field experiment with no manipulation) |
| + *** Manipulations have one or more Treatments |
| + *** Treatment has |
| + **** What was treated? |
| + **** Strength (amount), can be positive (addition) or negative (exclusion) |
| + **** Temporal extent |
| + *** When defining a treatment, a scientist might describe a substance (nutrient, presence of an organism) as being manipulated, or describe the manipulation of a process. |
| + *** Sampling Regime |
| + **** Random |
| + **** Stratified |
| + **** Stratified random |
| + **** Nested |
| + **** Regular (uniform) |
| + **** Haphazard |
| + **** Random haphazard |
| + *** Note that the choice of a sampling regime (and of plot layout?) constrains the possible statistical analysis techniques that can be applied. |
| + *** Traits of Experiments |
| + **** Balanced or unbalanced sampling |
| + **** Replication |
| + *** Traits of Treatment Regime |
| + **** Factorial (all possible combinations of treatments) or not |
| + **** Random factors (treatments along a natural gradient) |
| + |
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| - |
| - |
| + ** [http://jornada-www.nmsu.edu/studies/lter/datasets/plants/nppqdbio/data/nppqdbio.htm] |
| + ** General steps outlined: |
| + *** Data Request |
| + *** Quality Control and Assurance (if from different sites) |
| + *** Data Integration |
| + *** Quality Control and Assurance (of the integration) |
| + *** Analysis |
| + *** Capture result of analysis … |
| + ** Workflow we examined: |
| + [http://cvs.ecoinformatics.org/cvs/cvsweb.cgi/~checkout~/seek/projects/kr-sms/docs/beam_kr_sms_meeting_sept_04_workflow.png] |
| + ** Useful Actors |
| + *** List Summarizer |
| + **** A set of values in a data column |
| + *** List Comparator |
| + **** Given two sets (lists), do they match? |
| + **** Which ones in the first list aren’t in the second |
| + **** Assign first list values to new values |
| + *** Nested Transpose |
| + **** (site, taxon, count) |
| + **** {(A, x, 3), (A, y, 1), (B, y, 4), (C, z, 2)} |
| + **** Transpose to: |
| + ***** (site, x, y, z) |
| + ***** {(A, 3, 1, 0), (B, 0, 4, 0), (C, 0, 0, 2)} |
| + **** Notes about this from Bertram and Shawn after meeting: |
| + ***** Given an annotated schema S, denoted S*. And a white-box actor q s.t. q(S*) -> S’. We want to “push through” the annotations to obtain S’*. |
| + ***** The “nested” transpose is basically a combination of various lower-level algebraic operators, such as (theoretical) group-by, matrix transpose, projection, etc. So, given q as such a plan of operators, can we reason over the plan (white box-actor) q to obtain S*’? Using symbolic manipulation? Using the chase, e.g., for similar problems in integrity constraints? |
| + ** Often-found pattern of computation |
| + *** Can Kepler/Ptolemy efficiently and conveniently support the following pattern? |
| + *** Given a data set, construct a scatter plot for pairs of variables, allow user to select a subset of the plots -or- pairs of variables of interest, return data subsets based on chosen pairs (with no extraneous variables) |
| + *** Similarly, given data sets, an actor computes a set of regressions, the user is shown the results, the user selects the regressions of interest, and the workflow then proceeds using only those selected regressions |
| + *** These "patterns" can be supported now (with lots of plumbing) using the browser actor. Can we also add functionality to better support/model these patterns? |
