This survey is part of an effort to capture community feedback on potential competencies for data advisors (e.g. data curators - individuals that provide guidance to researchers in the selection, management, sharing, and preservation of their research data), and data service providers (e.g. data librarians - individuals that provide training, infrastructure support, preservation services, and other services to researchers related to their data management needs).  The information provided by your response will be integrated into a "Data Advisor" and "Data Services Provider" Career Compass for the American Geosciences Institute, and as input into the process of developing learning assessment capabilities for a future version of ESIP's Data Management Training Clearinghouse. Thank you for taking a few minutes to answer the following questions about the importance of different competencies for these two activities related to research data management. 

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* 1. What is the required level of competence for each of the following competencies for data advisors (e.g. curators). 

Data Advisors:
- Plan for the stewardship and sharing of FAIR outputs
- Use or develop FAIR research tools or services
- Prepare and document data/code to make outputs FAIR
- Publish FAIR outputs on recommended repositories

  Novice Competent Fluent N/A
Context (i.e.) research lifecycles, 2) data lifecycles, 3) data management standards, & 4) roles involved in research & data management.
Activities to support data access and reuse.
Data and metadata best practices (i.e. 1) data structures, types, formats, vocabularies, 2) metadata, and 3) ontologies).
Function of data management plans and tools]
Scholarly publication requirements & role of open access.
Data sharing options including IPR & licensing options.
Discipline specific funders’ policies & requirements.
Domain / discipline / institution specific data repositories’ requirements.
Data collection evaluation & assessment for retention as well as immediate and ongoing data management needs.
Data linking/integration/discovery techniques & tools.
Applications/adaptions of standards, including metadata schemas, data formats, domain ontologies, identifiers, data citation, and data licenses.
Data repository and storage platforms, database design types and structures.
Data wrangling techniques and tools for tasks related to data visualization, programming (such as R, Python, Javascript, etc.), website development and maintenance, application of statistical software (SAS, MATLAB, SPSS, etc.), and GIS software.
Consultation and training development, including online tutorials, course materials or instructional guides.
Integration of educational & training into full courses, and with course, instructional program and service evaluation.
Collaborative relationship development with other research and data service team members.
Effective communication through a variety of techniques including scholarly & more informal publication, oral presentations and small group discussion, and social media.

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* 2. What is the required level of competence for each of the following competencies for data service providers (e.g. data librarians).

Data Service Providers:
- Use or develop FAIR research tools or services 
- Apply policies to comply with legal, ethical and FAIR principles
- Prepare and document data/code to make outputs FAIR
- Develop open research strategy and vision

  Novice Competent Fluent N/A
Context (i.e. 1) research lifecycles, 2) data lifecycles, 3) data management standards, & 4) roles involved in research & data management).
Activities to support data access and reuse.
Data and metadata best practices (i.e.) data structures, types, formats, vocabularies), 2) metadata, and 3) ontologies).
Function of data management plans and tools
Scholarly publication requirements & role of open access.
Data sharing options including IPR & licensing options.
Discipline specific funders’ policies & requirements.
Domain / discipline / institution specific data repositories’ requirements.
Data collection evaluation & assessment for retention as well as immediate and ongoing data management needs.
Data linking/integration/discovery techniques & tools.
Applications/adaptions of standards, including metadata schemas, data formats, domain ontologies, identifiers, data citation, and data licenses.
Data repository and storage platforms, database design types and structures.
Data wrangling techniques and tools for tasks related to data visualization, programming (such as R, Python, Javascript, etc.), website development and maintenance, application of statistical software (SAS, MATLAB, SPSS, etc.), and GIS software.
Consultation and training development, including online tutorials, course materials or instructional guides.
Integration of educational & training into full courses, and with course, instructional program and service evaluation.
Collaborative relationship development with other research and data service team members.
Effective communication through a variety of techniques including scholarly & more informal publication, oral presentations and small group discussion, and social media.
Adapted from Whyte, A., et al, "Annex B: Skills Tables" of "D7.5 Strategy for Sustainable Development of Skills and Capabilities", EOSC pilot, v1.1, April 29, 2019. https://eoscpilot.eu/content/d75-strategy-sustainable-development-skills-and-capabilities. Last viewed September 17, 2019.

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* 3. What is the perspective from which you are responding to this survey

If you would like to engage with this effort and/or share materials like job descriptions or skill/competencies assessments you have encountered please provide your contact information and links to any materials you can share.

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* 4. Email Address

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* 5. Shared material information

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* 6. Any additional comments

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