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Data Management Workshop Devil’s Hole Pupfish Program Death Valley National Park

Data Management Workshop Devil’s Hole Pupfish Program Death Valley National Park. Overview, Introductions, Logistics, Workshop format. Introduction Questions What is “data management” to you? What would you rather be doing today?. DEVA Data Management Workshop Devil’s Hole Pupfish Project.

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Data Management Workshop Devil’s Hole Pupfish Program Death Valley National Park

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  1. Data Management WorkshopDevil’s Hole Pupfish ProgramDeath Valley National Park Overview, Introductions, Logistics, Workshop format Introduction Questions What is “data management” to you? What would you rather be doing today?

  2. DEVA Data Management WorkshopDevil’s Hole Pupfish Project Long-term monitoring databases present data management challenges that are unique. The development of an information management system must be carefully planned to determine expectations of the system in terms of use, output and longevity. Data and metadata must be adequate for accurate future analyses. A system must evolve to address an organization's changing needs and take advantage of new technology. Melissa E. Holmes and Geoffrey C. Poole The University of Montana, Flathead Lake Biological Station

  3. What is Data Management? Quality Assurance Data Stewardship Information Management? Reporting and Outreach System Architecture Records Management? Data Mining Infrastructure Project Archiving? Versioning Naming Conventions Analysis and Intrepretation Enterprise Architecture Metadata? Ecosystem Informatics Security and Backup Data Discovery Quality Control? Product Integration Sensitivity and Disclaimers

  4. Been there! Done that! We have all been there, one time or another: • How many times have I been frustrated with finding a file? • How many of us have pushed through a field season, saying I can do the office work off season when I can’t get into the field? • How many of us have just collected data for the sake of collecting data? This is easy to collect too and I may have a need for it later. • I wish I could read what that field tech wrote on this field sheet so I could enter this data. • I’ll remember, I don’t need to write that down, it’s getting ______ (e.g. dark, rainy, etc…).

  5. Rule of thumb: data management is a minimum of 33% (1/3rd) of any project, both in time and funding. Example; 6 weeks of field work = 2 weeks data management, for a total of 8 weeks of field work. • Keep data management integrated; as a part of the warp and weave of a project’s fabric • Try not to just “add on” as a last minute task; sewn into the final products (a quilt) • Do, do it in junction with an activity; one field period with integrated data entry and verification, can remember or revisit if necessary • Software programs, applications and databases don’t fix easily; design and integrate from the very beginning Institutionalize

  6. Information Entropy

  7. Subject Topics – Data Management Plan (DMP) • Chapter 1. Introduction • Chapter 2. Infrastructure and Systems Architecture • Chapter 3. Project Development and Data Management Workflow • Chapter 4. Data Management Roles and Responsibilities • Chapter 5. Databases • Chapter 6. Acquisition, Processing and Reporting • Chapter 7. Quality Assurance and Quality Control (QA/QC) • Chapter 8. Dataset Documentation • Chapter 9. Data Ownership and Sharing • Chapter 10. Data Dissemination • Chapter 11. Records Management and Object Curation • Chapter 12. Project Tracking and Documentation • Chapter 13. Implementation

  8. Subject Topics – Workshop Agenda • Introduction and overview • Project Management overview (Bob) • Roles and Responsibilities (Craig) • Data Life Cycle • Overview and Acquiring Data (Bob) • Data Quality (Craig) • Data Documentation, Storage and Dissemination (Bob) • Information Management (Bob) • Applications and Databases (Craig) • Infrastructure (Bob) • Afternoon Overview • Breakout groups • Discussion • Review and Wrap-up Data Project

  9. Workshop Binders • Presentations • Reference Handouts • General Definitions: • Data • Historic and Legacy • Catalog vs Bibliography • Ecoinformatics • Project Workflow • Diagram/Description • Helpful Templates • Data Life Cycle

  10. Data and Information Science Fiction or is it? Semantics and the semantic web is an evolving extension of the World Wide Web in which web content can be expressed not only in natural language, but also in a format that can be read and used by software agents, thus permitting them to find, share and integrate information more easily. It derives from W3C director Sir Tim Berners-Lee's vision of the Web as a universal medium for data, information, and knowledge exchange. Scientifc Publishing Task Force To develop a general purpose ontology for self-publishing single experiment in RDF format that will facilitate data sharing, discovery and integration. Applications such as web-publishing tool and semantic search engine that are built on top of this ontology will demonstrate the emerging semantic standards and technologies can help developing more interactive scientific communities centered around user-generated scientific contents on the web .

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