SolutionsUseCase CoastsOcean Arctic 1a

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Contents

Plain Language Description

Short Definition

This use case was developed during a small workshop that brought together scientists from diverse physical, biological, and social science disciplines to address how they would search for and assess interdisciplinary data to address important Arctic coastal science questions. Three facilitated groups each discussed the use case and then the full workshop discussed and agreed on a general flow. We used specific science questions to provide a tangible scenario for the use case.


Purpose

Problem statements:

  • It’s difficult to locate data needed to improve our understanding of Arctic coastal processes.
  • Once data are located, lack of interoperability between distributed data streams and systems makes it difficult to acquire desired data and difficult to analyze/compare data from different data sets.

Describe a scenario of expected use

A Data Seeker needs to identify and assess a diverse set of data to address a specific interdisciplinary science question related to Arctic Coastal processes. Two questions include:

  1. Examine the relationship between sea ice retreat and coastal changes (exposure to storminess, wave erosion, and coastal retreat) and assess the vulnerability of coastal settlements to projected reductions in sea ice.
  2. Examine the kinetics of UV effects on photosynthesis in kelp (Laminaria saccharina and L. solidungula), which are distributed throughout the circumpolar Arctic. Use existing measurements of surface UVB and UVB penetration to arctic kelp beds to establish differences in sensitivity to UV radiation in plants exposed to different natural light environments during the nine-month ice covered period.

The Seeker searches a portal that unites collection and granule level metadata from multiple data centers supporting the International Polar Year. The Seeker may browse a hierarchical presentation of relevant holdings or search specifically on space, time, and keywords to identify potentially relevant data sets. Data sets are presented in a table that shows major attributes of each data set (format, temproal and spatial coverage, title, etc.) and allows the user to sort and filter the list by these attributes. There are links to detailed explanations and means to contact domain experts for each data set.

Ultimately, the Data Seeker has data and associated metadata in preferred format and on local media.

Definition of Success

Prompt access to a data with a useful description. (One group said they wanted to get from an initial query to actual data within 8 clicks)

Formal Use Case Description

Use Case Identification

Use Case Designation
CoastsOcean.Arctic.1a
Use Case Name
Search, Identify, Assess, and Acquire Interdisciplinary Data Related to Arctic Coastal Processes

Revision Information

Prepared by:
Mark A. Parsons and Julia Collins
National Snow and Ice Data Center
23 January 2007
Version 0.2 (can be labeled draft if X=0)
Modified by:
<Modifier Name/Affil>, <Date/time>, <Brief Description>

Definition

Through this use case, the Data Seeker locates and identifies data sets (collections of related granules) for use or processing. This process results in the User having access to a subset of the data sets in the portal that meet the requirements of the User. Individual data sets, or specific constituent granules, may then be identified for further action or processing (e.g. visualization, analysis, download).

Successful Outcomes

  1. Operation succeeds and Seeker obtains relevant data and metadata.

Failure Outcomes

  1. Operation fails to return any relevant data or links to relevant expertise.
  2. Seeker does not find a relevant expert

General Diagrams

Schematic of Use case

Search flow.jpg

Use Case Elaboration

Actors

  • Data Creator
  • Data Seeker
  • Data Expert
  • Data Visualizer
  • Data Modeler
  • Metadata Archiver
  • Data Archiver: Manages both data and metadata

See also the "Stakeholders and User Types":http://ipydata.projectpath.com/W359651.


Primary Actors

  • Data Seeker


Preconditions and Assumptions

  • Definition of _Arctic coastal_ : Use definition from the International Conference on Arctic Research Planning II (ICARP II) "Working Group on Arctic Coastal Processes":http://www.icarp.dk/WGreports/WG3_Final.PDF. This is an adaptive definition, and for practical purposes we will include data From 45° to 90° N. this will include all Arctic river basins.
  • Data Seeker has not previously focused on this research question and is looking for data they don't already have or know about.
  • Data will be downloaded to a local system for in-depth analysis.
  • Metadata have been entered into the system

Postconditions

Data Seeker has data and associated metadata in preferred format and on local media.

Normal Flow (Process Model)

Begin

The use case begins when the Data Seeker establishes a general research question. The nature of the question determines the spatial and temporal domain of the search and the types of data to be sought.

Find expert

Data Seeker needs to locate expert knowledge in the subject area. The general implication is that the source of expertise is a human who can provide advice regarding data sources and data quality. Expert knowledge is very important for data sets which are getting more complex (e.g. satellite data). In many cases data can only be obtained from the person(s) who originally conducted the research. This is especially true for data "published" before larger data archives were established. There is a concern that as scientists retire, data will be lost. Ways of finding experts:

  • Google scholar search to find scientists as well as abstracts and/or articles.
  • ACDC site
  • NSIDC site (or other known site) to look for data
  • AGU and AMS meeting abstracts to find people who work on related projects and then go to their web site.
  • ARCUS for database of people and projects
  • look at scientific literature, grey literature

Outcome:

  • Names of data sets.
  • Knowledge of data archive locations
  • Expert(s) who may be consulted in the future.

Locate Data

Find the resources which allow the Data Seeker to specify data criteria and determine whether desired data exist. (There's some overlap here with *Find Expert*). Two challenges:

  1. Locate the "address" of the data source (a data center, informal data archive, research scientist, a data portal).
  2. Once at the correct address, need to sort out the desired data from all of the other holdings which may be at that location.

In addition to the actions listed in *Find Expert*, Data Seeker may:

  • Look at list of research projects and their abstracts with contact info (similar to armap)
  • Contact government agencies and their websites
  • Look at published reports with tables and maps
  • Research local communities and information on their website
  • Contact the person(s) who compiled data.

Once at the data source (e.g., an archive web site), the Data Seeker looks for a familiar search interface (tries to find consistent "search" mechanisms regardless of who provides/archives data).

  • Data seeker identifies space in which the data need to exist by:
    • drawing a box on a map OR
    • supplying a center point and radius to define a circle over a map OR
    • providing coordinates in UTM meters or decimal degrees or dd:mm:ss (or other formats that may exist)
  • Data Seeker enters a single time or time range over which the data should exist.
  • Data Seeker enters a word or text phrase describing measured variable or other keyword(s) meaningful to him.
  • Data Seeker asks the System to return data which match the indicated criteria.

If data are found, Data Seeker System looks at results which are grouped into general categories (e.g., by discipline? see clusty.com for an example). Grouping helps avoid being overwhelmed by results.

If no data found:

  • System provides a list of places the Data Seeker has already been, so that they don't waste time repeating unsuccessful searches.
  • System provides referrals to likely sources of data, if not found in current search location.

Alternate to time/space search specification: Data Seeker drills down through hierarchy of data information provided by the System, working from general disciplines to scientific (measured) variables. Data Seeker may alternate between the drill-down process and a temporal/spatial search.

Alternate to time/space search specification: Experienced Data Seeker knows what data sets they need and go directly to *Get Data* for those data sets.

Filter Results

Refine and narrow down search results.

  • Data seeker may sort results by:
    • Data set title
    • Data type
    • Temporal coverage and resolution
    • Spatial coverage and resolution
    • etc.
  • Data Seeker refines search parameters (spatial, temporal coordinates) and performs *Locate Data* again to see a new list of results.
  • Data Seeker browses through results and examines expert overviews or summaries associated with each result in order to get a feel for which data sets are most relevant to his needs.
  • Provide some sort of way of saving search specification/results, but don't want to have to log in again to use those. Save anonymously (Allow searchers to save searches in a public library of sorts with notes about what the search is designed to find? - JAC)
  • Data Seeker identifies data sets of interest and proceeds to *Assess Data* for those data sets.

Assess Data

  • Data Seeker reviews history of data, including originating or true data source and subsequent contributors. They evaluate the data source or authority and assess uncertainty in or quality of the data. The Data Seeker reviews the algorithms or mechanisms used to create data.
  • Data Seeker previews data from a particular data set by examining an image of the data reflecting the Data Seeker's desired spatial and temporal ranges. data sets without adequate coverage are rejected.
  • The Data Seeker identifies two or more data sets to compare over the specified spatial/temporal range and examines the resulting visual output.

The Data Seeker identifies those data sets they wish to retrieve. They "bookmark" the data (possibly the link to the data themselves, possibly the documentation) in case they want to return for additional data from this set.

Consult Expert

  • Clarify uncertainties regarding data.

Get Data

  • Data Seeker specifies format, projection, and spatial/temporal boundaries and resolution for data.
  • Data Seeker reviews the file number and sizes that will be produced by their request, and customizes the min/max file size and/or number of files preferred for the data request download. Data Seeker may request that delivered data files divide the data according to specified spatial or temporal limits.
  • If data not available in desired format, projection, etc., Data Seeker may enlist a "data facilitator" to help with reformatting, regridding, reprojecting, geolocating, digitizing, etc.

Use Data

  • Do we need to worry about this?
  • Reformatting/conversion tools (or links to them) to move data between formats and projections.
  • Need support system to help people working with data from different disciplines
  • Provide some way to get info about algorithm updates, errors discovered in the data, reprocessing campaigns, etc.

Alternative Flows

Special Functional Requirements

None

Extension Points

  • <Cluster>.<SubArea>.<number>.<letter+1> something added or a variant.

E.g. AQ.Smoke.1.b something added or a variant

  • <Cluster>.<SubArea>.<number>.<letter+2> something added or a variant
  • <Cluster>.<SubArea>.<number>.<letter+3> something added or a variant

Diagrams

Use Case Diagram

State Diagram

Activity Diagram

Other Diagrams

Non-Functional Requirements

Performance

Reliability

Scalability

Usability

Security

Other Non-functional Requirements

Selected Technology

Overall Technical Approach

Architecture

Technology A

Description

Benefits

Limitations

Technology B

Description

Benefits

Limitations

References

None

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