scholarly journals Data management aspects of public engagement with biodiversity documentation

2015 ◽  
Author(s):  
Alvaro Ortiz-Troncoso
Keyword(s):  
Quality Control ◽  
Data Management ◽  
Public Engagement ◽  
General Public ◽  
Museum Specimens ◽  
Data Governance ◽  
Technological Developments ◽  
Data Policy ◽  
Quality Awareness ◽  
Environmental Monitoring Data

Technological developments open up new opportunities for collaboration between biodiversity researchers and the general public. Three exemplary use cases were examined: digitizing museum specimens, text-mining archived expedition journals and handling environmental monitoring data. Data management principles were applied to refine and map the ensuing requirements to specific deliverables: data policy, standards and procedures; workflows, integration architectures and data products; data quality awareness and improvement methods. Implications for data governance and quality control are discussed.

scholarly journals Data management aspects of public engagement with biodiversity documentation

2015 ◽  
Author(s):  
Alvaro Ortiz-Troncoso
Keyword(s):  
Quality Control ◽  
Text Mining ◽  
Environmental Monitoring ◽  
Data Management ◽  
Public Engagement ◽  
General Public ◽  
Museum Specimens ◽  
Data Governance ◽  
Technological Developments ◽  
Environmental Monitoring Data

Technological developments open up new opportunities for collaboration between biodiversity researchers and the general public. Three exemplary case studies were reviewed from literature: digitizing museum specimens, text-mining archived expedition journals and handling environmental monitoring data. Data management principles were applied to refine the ensuing requirements. Specific requirements were found to exist in three areas: collecting data, sharing data and improving data quality. Implications for data governance and quality control are discussed.

scholarly journals Data management aspects of public engagement with biodiversity documentation

2015 ◽  
Author(s):  
Alvaro Ortiz-Troncoso
Keyword(s):  
Quality Control ◽  
Text Mining ◽  
Environmental Monitoring ◽  
Data Management ◽  
Public Engagement ◽  
General Public ◽  
Museum Specimens ◽  
Data Governance ◽  
Technological Developments ◽  
Environmental Monitoring Data

Technological developments open up new opportunities for collaboration between biodiversity researchers and the general public. Three exemplary case studies were reviewed from literature: digitizing museum specimens, text-mining archived expedition journals and handling environmental monitoring data. Data management principles were applied to refine the ensuing requirements. Specific requirements were found to exist in three areas: collecting data, sharing data and improving data quality. Implications for data governance and quality control are discussed.

scholarly journals Automation in urinalysis: sample and data management, and quality control

1986 ◽  
Vol 8 (3) ◽  
pp. 142-146 ◽  
Author(s):  
Giuliano Barbaresi ◽  
Maria Luisa Gozzo ◽  
Cecilia Zuppi
Keyword(s):  
Quality Control ◽  
Data Management

scholarly journals US Federal Data Policy: An Update on The Federal Data Strategy and The Evidence Act

2020 ◽  
Vol 5 (5) ◽  
Author(s):  
Amy O’Hara
Keyword(s):  
Action Plan ◽  
Population Data ◽  
Federal Agencies ◽  
Evidence Based ◽  
Data Governance ◽  
The Us ◽  
Data Policy ◽  
The Government ◽  
Data Linkages ◽  
Federal Data

IntroductionThe US federal data landscape is evolving through the implementation of the Foundations for Evidence-Based Policymaking Act of 2018 and the 2020 Action Plan of the Federal Data Strategy (FDS). The Act and Plan seek better data governance; making data accessible and useful for the American public, businesses, and researchers; and improving how the government uses data to make decisions and for program oversight. Objectives and ApproachThis paper provides a brief overview of the Evidence Act, describing what has already been implemented and what is forthcoming and how it involves population data linkages. We will also describe the FDS, using the Five Safes framework to categorize its priorities for federal agencies. ResultsWe explain how the Evidence Act established new roles for Chief Data, Evaluation, and Statistical Officials. We describe efforts to set learning agendas and data inventories in agencies. We point to some successes, such as new repositories for tools and metadata, and progress on forming an advisory committee to explore how the US could build a National Secure Data Service. We tie the FDS action plan to these Evidence Act efforts, showing how agencies and communities of practice are expected to develop over time. We focus on the ten actions that involve shared solutions across government that focus on ethics, privacy, tools and standards. Conclusion / ImplicationsThis paper shares updates on US federal data policy that started with the 2016 Commission for Evidence-based Policymaking, up through the current administration’s efforts to leverage data as a strategic asset. We highlight accomplishments, opportunities, and challenges for federal policy, noting how political will and funding ultimately affect progress.

scholarly journals IPTEKS INFORMASI BIAYA DALAM PENGGUNAAN PRODUK E-CHANNEL PADA BANK RAKYAT INDONESIA (PERSERO) TBK

2018 ◽  
Vol 2 (02) ◽  
Author(s):  
Jinnyfer J.F Tumbel ◽  
Naomi G.H Pondaag ◽  
Herman Karamoy
Keyword(s):  
Financial Institutions ◽  
General Public ◽  
Rapid Development ◽  
Cost Information ◽  
Technological Developments ◽  
Modern Era ◽  
Keywords Cost

In this modern era, technological developments are experiencing very rapid development. Likewise in financial institutions, namely banks, technology is now a determinant of bank progress and provides benefits in transactions and in carrying out the operations of other banks. And if the bank does not prioritize technology in its operations, banks will find it difficult to move forward and develop to find good profits or profits for the development of the bank. For this reason, banks issue e-channel products that are very useful, and make it easier for people not only to help the community but also to be profitable and bring good profits to the bank, and in the use of e-channel products, of course there are costs truncated which may not be recognized by the general public, but in it is also very beneficial to both parties. Keywords: cost information, e-channel products

Coordinating Enterprise Services and Data

2013 ◽  
pp. 241-274
Author(s):  
Keith R. Worfolk
Keyword(s):  
Data Management ◽  
Portfolio Management ◽  
Best Practice ◽  
Maturity Model ◽  
Data Governance ◽  
Enterprise Information ◽  
Capability Maturity ◽  
Architecture Framework ◽  
Master Data Management ◽  
Enterprise Soa

The critical inter-dependencies between Enterprise Services and Enterprise Data are often not given due consideration. With the advent of Cloud Computing, it is becoming increasingly important for organizations to understand the relationships between them, in order to formulate strategies to jointly manage and coordinate enterprise services and data to improve business value and reduce risk to the enterprise. Enterprise Services encompass Service-driven applications deployed on-premises in the enterprise data centers as well as in the Cloud for the “extended enterprise.” Enterprise Data Management encompasses the cross-application enterprise-level perspective of data in an information-sharing enterprise, and the critical business data that is created, maintained, enriched, and shared outside the traditional enterprise firewall. This chapter discusses and proposes best practice strategies for coordinating the enterprise SOA & EDM approaches for mutual success. Primary coordination aspects discussed include: Service & Data Governance, Master Data Management, Service-driven & EDM Architecture Roadmaps, Service Portfolio Management, Enterprise Information Architecture, and the Enterprise Data Model. It recommends a facilitative Service-driven Data Architecture Framework & Capability Maturity Model to help enterprises evaluate and optimize overall effectiveness of their coordinated Service-driven & EDM strategies.

Challenges of Data Management in Always-On Enterprise Information Systems

2010 ◽  
pp. 443-462
Author(s):  
Mladen Varga
Keyword(s):  
Information Systems ◽  
Data Management ◽  
Data Privacy ◽  
Data Governance ◽  
Enterprise Information ◽  
Enterprise Information Systems ◽  
Degradation Data ◽  
Association Data ◽  
Data Continuity ◽  
Integration Data

Data management in always-on enterprise information systems is an important function that must be governed, that is, planned, supervised, and controlled. According to Data Management Association, data management is the development, execution, and supervision of plans, policies, programs, and practices that control, protect, deliver, and enhance the value of data and information assets. The challenges of successful data management are numerous and vary from technological to conceptual and managerial. The purpose of this chapter is to consider some of the most challenging aspects of data management, whether they are classified as data continuity aspects (e.g., data availability, data protection, data integrity, data security), data improvement aspects (e.g., coping with data overload and data degradation, data integration, data quality, data ownership/stewardship, data privacy, data visualization) or data management aspect (e.g., data governance), and to consider the means of taking care of them.

Challenges of Data Management in Always-On Enterprise Information Systems

2011 ◽  
pp. 1695-1714 ◽  
Author(s):  
Mladen Varga
Keyword(s):  
Information Systems ◽  
Data Management ◽  
Data Privacy ◽  
Data Governance ◽  
Enterprise Information ◽  
Enterprise Information Systems ◽  
Degradation Data ◽  
Association Data ◽  
Data Continuity ◽  
Integration Data

Data management in always-on enterprise information systems is an important function that must be governed, that is, planned, supervised, and controlled. According to Data Management Association, data management is the development, execution, and supervision of plans, policies, programs, and practices that control, protect, deliver, and enhance the value of data and information assets. The challenges of successful data management are numerous and vary from technological to conceptual and managerial. The purpose of this chapter is to consider some of the most challenging aspects of data management, whether they are classified as data continuity aspects (e.g., data availability, data protection, data integrity, data security), data improvement aspects (e.g., coping with data overload and data degradation, data integration, data quality, data ownership/stewardship, data privacy, data visualization) or data management aspect (e.g., data governance), and to consider the means of taking care of them.

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