This list is a formal description of a functional view for DDI4. Functional views are a list of elements and restrictions on their properties and relationships. Functional views can be used to create XML schemas and/or RDF vocabularies. This includes (in general terms) the following stage in the GSBPM:
See GLBPM work by Jon and Barry: https://docs.google.com/spreadsheet/ccc?key=0AmjuyyBzwvcodE9Qam5HbFhJRTV6U19rbnY5WUVZZEE&usp=drive_web#gid=1
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This small group developed some XML for their content, created an XSLT spreadsheet, and then showed the output of what they are calling Study Inception (given below)
This document describes all the elements that might be used to record information on the beginning phases of a research study, such as an analysis plan, sampling method, weighting strategies, funding information, and infrastructure requirements, which can be combined into a Data Management Plan. This list is a formal description of a functional view for DDI4. Functional views are a list of elements and restrictions on their properties and relationships. Functional views can be used to create XML schemas and/or RDF vocabularies.
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Points to research or theory.
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We need to look at existing stuff in 3.2.
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This would include data sets, publications, and visualizations
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Might be tabulated data - this is what you might produce.
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Look at existing objects; this may even include references to specific variables and their roles in known or proposed data sets.
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Identifies organizations, infrastructure needs, expertise, budget, capacity, etc.
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Would include specific fundedr requirements, requirements of target publications - relationship to some organization where data is deposited
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Groups Conceptual Variables or RepresentedVariables
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Groups Conceptual Variables or RepresentedVariables
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Groups Conceptual Variables or RepresentedVariables
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Links to a Process description - would need a proicess step status field
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Groups a set of qualified individuals.
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Organizations needed to grant access to the population of the study or sources of data such as administrative records.
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Legal and financial requirements to use or modify instruments (proprietary scales, etc.)
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Sub-populations which will have special sampling applied to them. May not be needed if normal Universe is hierarchical.
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An identification of the individuals who will act as respondents to study the identified population. Would have a reference to an instrument.
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Rules for conducting the questionnaire, and checks made during administration of the questionnaire.
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Includes all the possiblke ways of assessing the goodness of an Instrument - prior use, modifications to questions or addition of administrative sources, etc.
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Groups RepresentedVariables which are created through computatiuon.
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Take from 3.2 - associated with each derived variable.
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Query: Is this just a type of derivation?
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The rules for data cleaning and processing/editing.
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This is whatever set of process steps are used to transform the raw data into the data set used for analysis.
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Includes requirements like computing infrastructure
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Describes the process for meeting ethics requirements
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The project budget.
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Overall project duration.
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The creation date of a data management plan. This may just be a property of other objects.
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The target audience for a DMP.
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Why do you need to collect new data.
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Existing comparable/harmonized data sets - includes description of methodology.
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How new data be integrated with existing data.
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How new data will ehance the overall research picture beyond existing data (coverage, detail, etc.)
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Describes needed documentation of the research context.
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Standard or format of metadata.
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Excuses why you didn't documebnt squat.
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The plan for licensing data for use - can include details about release timing, etc. (May use existing embargo object from 3.2).
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For multi-partner research.
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What are the expectations for data reuse as regards new technology, metadata, formats, etc.
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Includes data transfer.
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Includes data transfer
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Timeline property may need to be its own object.
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Plan for data curation, archiving, and preservation. Includes a description of what data will preserved.
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How long after the end of the project will the data be available and relevant, and details about data disposal.
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Points to a process. Includes details about data cleaning and anonymization.
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How will data be documented for long-term use.
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How and who will metadata/documentation be captured?
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DOIs? Etc.
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Staff and organization for implementing the data management plan.
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What is budget for long-term data management.
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Includes information about reviews, etc.
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