His thoughts on stewardship make sense and his methods work. I'm so glad he's finally put his knowledge into a book - it's exactly what the data stewardship community needs! You may have already requested this item. Please select Ok if you would like to proceed with this request anyway.
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The specific requirements or preferences of your reviewing publisher, classroom teacher, institution or organization should be applied. Optimally, effective scientific data stewardship can help establish the credibility of data and information, which is extremely important for scientific findings, as well as help enable informed and traceable decision-making. The health and sustainability of observing networks is critical to the continuous update and extension of data products. Sustainability of observing networks includes continuity and maintenance of existing critical ground-based and remotely sensed observation systems, minimization of data gaps in both spatial and temporal coverage , near-real-time monitoring of instrument performance, and other measures.
These are probably the responsibilities of the scientific community, programs, or agencies rather than those of data centers. However, data centers could play an important role in providing much needed guidance on preservation, stewardship, and services. The processes, procedures, security controls and best practices should be defined and implemented as an integral part of scientific data stewardship. Another growing concern with the increasing capability of web-enabled tools and platforms is that powerful online transformations are producing ephemeral products such as images, maps, graphs, etc.
Use of such products in scientific research threatens the credibility of the results. Properly constructed tools based on, and taking advantage of, a formal holistic stewardship framework could help mitigate those concerns. With more players in the field, the challenge of data accountability becomes critical. Defining roles and responsibilities in every level of stewardship and every stage of the data product lifecycle will help facilitate this challenge. For any of these organizations, long-term health and sustainability, integrity, authenticity, and trustworthiness of data and the institution that hosts the data remain as important challenges for data preservation and data sharing.
Funding agencies need to understand the importance of funding and staff sustainability for successful scientific data stewardship.
- Annals of Improbable Research, Vol. 19, No. 4.
- Dry Run.
- Be Filled: Sermons on the Beatitudes.
- Delphi-Befragungen: Ein Arbeitsbuch (German Edition)?
- Reward Yourself!
Individual data centers need to understand the need for and importance of improving the skillsets of their staff to help them keep up with evolving job requirements. Examples of data sharing plans, principles or procedure directives, and guidelines created by selected U. Nancy Ritchey and Drew Saunders have reviewed Figure 2. Ken Knapp has reviewed Table 3. Richard Kauffold has reviewed Table 4. Walter Glance, and John Faundeen were beneficial. Comments from Data Science Journal anonymous reviewers are beneficial in making the paper more focused.
Peng holds a Ph. She has extensive knowledge of digital data management and experience in working with metadata specialists and software developers. She is an active member of the Federation of Earth Science Information Partners ESIP — a member of its Data Stewardship Committee and co-chair of Information Quality Cluster, where she leads the effort in defining roles and formalizing responsibilities of major product key players and stakeholders for ensuring data quality and improving usability, in collaboration with NCEI. Privette started his career at NASA where he developed methods to measure the land surface using satellites.
He has been developing highly scalable data systems for producing scientific data products from NASA instruments for over 20 years. Tilmes is an accomplished and acknowledged international expert on tracking and representation of scientific provenance.
A Conceptual Enterprise Framework for Managing Scientific Data Stewardship
John Bates technical expertise lies in atmospheric sciences, and his interests include satellite observations of the global water and energy cycle, air-sea interactions, and climate variability. His interest in large volume satellite observations and climate inspired his recent work in archiving and documenting climate data records. He specializes in the development of quantitative methods for the processing and use of satellite remotely-sensed observations to study Earth System processes. Hausman has extensive experience in numerical weather prediction, applied climatology, data management, and data center operations.
He has been managing projects to build tools and capabilities using mostly open source software to provide data and information management capabilities to scientific research teams throughout USGS. Sky strives to develop innovative technology solutions for USGS sciences. Kearns attended the University of Miami B. He has worked for NOAA, the University of Miami, and the National Park Service on satellite products, integrated observing systems, environmental data management, and coastal ecosystem restoration.
Arndt, DS and Brewer, M.
- Unbroken Chain?
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- A Conceptual Enterprise Framework for Managing Scientific Data Stewardship!
- A Twins Journey (The Legends of South Haven Book 1).
Assessing service maturity through end user engagement and climate monitoring. Blunden, J and Arndt, DS. State of the Climate in User engagement and analytics. Version 2 March 13, Soft is harder to deal with than hard — The Ten Commandments. Lecture Notes in Computer Science, Data Management Maturity Model. Out of the Crisis.
The MIT Press edition, Developing criteria to establish trusted digital repositories. Data Science Journal , Content standard for digital geospatial metadata — extension for remote sensing data. Federal Geographic Data Committee. Hu, W and Feng, J. Data and information quality: Computing and Information Systems , 9: Engaging with users of climate information and the co-production of knowledge.
Weather, Climate, and Society. Climate Change Impacts in the United States: The Third National Climate Assessment. Global Change Research Program, Nayab, N and Richter, L. Exploring the users of Plan-Do-Check-Act cycles. Bright Hub Project Management. Environmental data management at NOAA: Archiving, stewardship, and access, The National Academies Press. The art of turning data into product. The state of assessing data stewardship maturity — An overview. Use Case Study and Lessons Learned. ESIP winter meeting, 11—13 January Scientific stewardship in the Open Data and Big Data era — Roles and responsibilities of stewards and other major product stakeholders.
Library of Congress Cataloging-in-Publication Data. Morgan Kaufmann Publishers, Principles of lean thinking. Mattoso, M and Glavic, B eds. Statistical method from the viewpoint of quality control. The Dove edition, A framework for information quality assessment. Journal of the Association for Information Science and Technology , What is best practice? The essential guide to supply chain management best practices. Linking from observations to data to actionable science in the climate data initiative.
Supporting trust with provenance of the findings of the national climate assessment. Global Change Research Program. Global Change Research Program , What data quality means to consumers. Journal of Management Information Systems , 12 4: Wilkinson, MD and 51 others.
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The FAIR guiding principles for scientific data management and stewardship. Scientific Data , 3. Introduction Scientific data stewardship is a fast-evolving area of research. Federally funded digital research data are required to be: Therefore, there is a need for: Definition of Key Terms Generally speaking, data can refer to anything that is collected, observed, or derived and used as a basis for reasoning, discussion, or calculation. Table 1 Examples of NFRs on federally funded digital scientific data and our mapping to information quality dimensions defined by Lee et al.
Enterprise Scientific Data Stewardship Framework In this paper, scientific data stewardship is considered and addressed from two distinct perspectives. Functional areas, controls, and standards necessary for compliance with the requirements are defined and documented.
The "Governance of Data Governance" » TopQuadrant, Inc
They are required changes. They are communicated within the organization across different entities. They are communicated within the organization across different entities to ensure consistency and efficiency. The results are captured and presented in ways suitable to both human and machine end-users. Areas for improvement are identified with a roadmap forward based on where they are and where they need to be. The processes, procedures, and practices of implementations are standardized within the organization once a desired maturity for the requirements is achieved.
Monitoring is in place to trigger a new PDCA improvement cycle if a new requirement or a new area of improvement has been identified. Table 3 Roles, Knowledge, and Capability: Table 4 Maturity Assessment Categories and Descriptions. Category Number Description Category 1 No assessment done. Category 2 Self-assessment—preliminary evaluation carried out by an individual for internal or personal use; abiding to non-disclosure agreement.
Category 3 Internal assessment—complete evaluation carried out by an individual non-certified entity person, group, or institution and reviewed internally with the assessment results ratings and justifications publicly available for transparency. Maturity update frequency is defined and implemented.
Such an approach should include: Defining, creating, continuously reviewing, and updating the following: NFRs set forth by governmental directives and by end user needs, FRs to be compliant with federal directives and aligned with agency guidelines and institutional strategy, processes and procedures, standards and best practices, and roles and high-level responsibilities. Continuously evaluating and assessing organizational process capability and practices applied to individual data products. Cover may not represent actual copy or condition available. Try adding this search to your want list.
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