Difference between revisions of "Usage-based Data Discovery - Episode 2"
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= Notes = | = Notes = | ||
+ | |||
+ | Use Cases: Participating: Bob, Jonathon, Beth, Ed | ||
+ | Ed: interested in interdisciplinary, datasets used together WITHIN a given application | ||
+ | Mark: simple, single dataset use case + complex interdisciplinary use cases | ||
+ | Beth: dataset interconnections circles back to data transformations | ||
+ | Irina: validation requires MANY different datasets being used together, shows up in separate of research papers that focus on the application vs. those that focus on validation | ||
+ | J.B.: discovery / search process: present datasets relevant to their problem, e.g., homeowner types in more colloquial term, interpret what the user's real concern is | ||
+ | Adrienne (Axiom Data Science): how do we interact with users where they know what they are getting back; users expect fancy searches, but they need to be transparent (document how they got there) - show the graph! | ||
+ | B. Downs: combine with location of interest | ||
+ | JB: maybe too big a tech lift at this point? | ||
+ | Mark: location is relevant, sure, but how does it | ||
+ | Chris: dataset used Gulf of Maine, might be useful in Puget Sound | ||
+ | Mark: identify desired outcome | ||
+ | |||
+ | IMPACT Use Case: | ||
+ | In addition to how many, what type of use cases | ||
+ | Beth: Take me to the articles about the data | ||
+ | Irina: Data providers want to know about applications of their data; ask them if they know how their data are being used | ||
+ | Bob: What other data were used in conjunction with the datasets in a given climate study | ||
+ | JB: datasets commonly used together (Mark: closer to Beth's use case variant) | ||
+ | JB: IMPACT might be a roll up of lots of datasets | ||
+ | Mark: may want to contrast similar instrument, different algorithms, (Chris: or different platforms) | ||
+ | Ed: | ||
+ | |||
+ | Agenda: | ||
+ | Irina, Beth more graphs, how to make them | ||
+ | Adrienne: User story, aiming to create at the end | ||
+ | Ed: Using graphs for discovery, esp. related datasets, practical examples | ||
+ | Mark: Take one of these use cases, work a user story end to end to see what the graph looks like, including the tools | ||
+ | Bob: examples of graphs, something about the process we are going through, getting from here to there | ||
+ | Megan: Session proposal, ESIP Collaboration Slide | ||
= New Actions = | = New Actions = |
Revision as of 13:51, March 19, 2020
Participants
- Chris Lynnes
- Joe Lee
- Irina Gerasimov
- Adrienne
- Megan Carter
- Ed Armstrong
- Mark Parsons
- Bob Downs
- Beth Huffer
- Jonathon Blythe
- Doug Newman
- Steve Olding
Current Actions
- Done: Lynnes - write up some text for Megan's newsletter
- Done: Lynnes set up regular meeting with ESIP G2M, some time other than 4th Thursday
- TBD - spin off a subgroup to work on use cases
- Done: Lynnes/Newman - generate a sample export of EOSDIS knowledge graph
- Done: Blythe - provide research_prov_model info on use of W3C Prov model
Agenda
- Use cases for connecting Data with Usage
- Best Practice Use Case: Which rainfall dataset is most often used for flood prediction?
- Impact Use Case: How many climate studies use TRMM 3B42 rainfall?
- <Your use case here>
- Sample graphs
- Any other business?
- Agenda for next telecon…
Notes
Use Cases: Participating: Bob, Jonathon, Beth, Ed Ed: interested in interdisciplinary, datasets used together WITHIN a given application Mark: simple, single dataset use case + complex interdisciplinary use cases Beth: dataset interconnections circles back to data transformations Irina: validation requires MANY different datasets being used together, shows up in separate of research papers that focus on the application vs. those that focus on validation J.B.: discovery / search process: present datasets relevant to their problem, e.g., homeowner types in more colloquial term, interpret what the user's real concern is Adrienne (Axiom Data Science): how do we interact with users where they know what they are getting back; users expect fancy searches, but they need to be transparent (document how they got there) - show the graph! B. Downs: combine with location of interest JB: maybe too big a tech lift at this point? Mark: location is relevant, sure, but how does it Chris: dataset used Gulf of Maine, might be useful in Puget Sound Mark: identify desired outcome
IMPACT Use Case: In addition to how many, what type of use cases Beth: Take me to the articles about the data Irina: Data providers want to know about applications of their data; ask them if they know how their data are being used Bob: What other data were used in conjunction with the datasets in a given climate study JB: datasets commonly used together (Mark: closer to Beth's use case variant) JB: IMPACT might be a roll up of lots of datasets Mark: may want to contrast similar instrument, different algorithms, (Chris: or different platforms) Ed:
Agenda: Irina, Beth more graphs, how to make them Adrienne: User story, aiming to create at the end Ed: Using graphs for discovery, esp. related datasets, practical examples Mark: Take one of these use cases, work a user story end to end to see what the graph looks like, including the tools Bob: examples of graphs, something about the process we are going through, getting from here to there Megan: Session proposal, ESIP Collaboration Slide