Standardization Initiatives in the (eco)toxicogenomics Domain: A Review

The purpose of this document is to provide readers with a resource of different ongoing standardization efforts within the ‘omics’ (genomic, proteomics, metabolomics) and related communities, with particular focus on toxicological and environmental applications. The review includes initiatives within the research community as well as in the regulatory arena. It addresses data management issues (format and reporting structures for the exchange of information) and database interoperability, highlighting key objectives, target audience and participants. A considerable amount of work still needs to be done and, ideally, collaboration should be optimized and duplication and incompatibility should be avoided where possible. The consequence of failing to deliver data standards is an escalation in the burden and cost of data management tasks.

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Framework for Biosignal Interpretation in Intensive Care and Anesthesia

Methods of Information in Medicine ◽

10.1055/s-0038-1636865 ◽

1997 ◽

Vol 36 (04/05) ◽

pp. 340-344 ◽

Cited By ~ 3

Author(s):

I. Korhonen ◽

M. van Gils ◽

A. Kari ◽

N. Saranummi

Keyword(s):

Critical Care ◽

Intensive Care ◽

Data Management ◽

Clinical Evaluation ◽

Patient Monitoring ◽

Research Community ◽

Clinical Routine ◽

The Poor ◽

Outcomes Of Care

Abstract:Improved monitoring improves outcomes of care. As critical care is “critical”, everything that can be done to detect and prevent complications as early as possible benefits the patients. In spite of major efforts by the research community to develop and apply sophisticated biosignal interpretation methods (BSI), the uptake of the results by industry has been poor. Consequently, the BSI methods used in clinical routine are fairly simple. This paper postulates that the main reason for the poor uptake is the insufficient bridging between the actors (i.e., clinicians, industry and research). This makes it difficult for the BSI developers to understand what can be implemented into commercial systems and what will be accepted by clinicians as routine tools. A framework is suggested that enables improved interaction and cooperation between the actors. This framework is based on the emerging commercial patient monitoring and data management platforms which can be shared and utilized by all concerned, from research to development and finally to clinical evaluation.

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NFDI4Chem - Towards a National Research Data Infrastructure for Chemistry in Germany

Research Ideas and Outcomes ◽

10.3897/rio.6.e55852 ◽

2020 ◽

Vol 6 ◽

Cited By ~ 3

Author(s):

Christoph Steinbeck ◽

Oliver Koepler ◽

Felix Bach ◽

Sonja Herres-Pawlis ◽

Nicole Jung ◽

...

Keyword(s):

Data Management ◽

Data Science ◽

Open Data ◽

Research Data ◽

Data Standards ◽

Data Repositories ◽

Data Infrastructure ◽

Research Data Management ◽

Wide Range ◽

Chemistry Community

The vision of NFDI4Chem is the digitalisation of all key steps in chemical research to support scientists in their efforts to collect, store, process, analyse, disclose and re-use research data. Measures to promote Open Science and Research Data Management (RDM) in agreement with the FAIR data principles are fundamental aims of NFDI4Chem to serve the chemistry community with a holistic concept for access to research data. To this end, the overarching objective is the development and maintenance of a national research data infrastructure for the research domain of chemistry in Germany, and to enable innovative and easy to use services and novel scientific approaches based on re-use of research data. NFDI4Chem intends to represent all disciplines of chemistry in academia. We aim to collaborate closely with thematically related consortia. In the initial phase, NFDI4Chem focuses on data related to molecules and reactions including data for their experimental and theoretical characterisation. This overarching goal is achieved by working towards a number of key objectives: Key Objective 1: Establish a virtual environment of federated repositories for storing, disclosing, searching and re-using research data across distributed data sources. Connect existing data repositories and, based on a requirements analysis, establish domain-specific research data repositories for the national research community, and link them to international repositories. Key Objective 2: Initiate international community processes to establish minimum information (MI) standards for data and machine-readable metadata as well as open data standards in key areas of chemistry. Identify and recommend open data standards in key areas of chemistry, in order to support the FAIR principles for research data. Finally, develop standards, if there is a lack. Key Objective 3: Foster cultural and digital change towards Smart Laboratory Environments by promoting the use of digital tools in all stages of research and promote subsequent Research Data Management (RDM) at all levels of academia, beginning in undergraduate studies curricula. Key Objective 4: Engage with the chemistry community in Germany through a wide range of measures to create awareness for and foster the adoption of FAIR data management. Initiate processes to integrate RDM and data science into curricula. Offer a wide range of training opportunities for researchers. Key Objective 5: Explore synergies with other consortia and promote cross-cutting development within the NFDI. Key Objective 6: Provide a legally reliable framework of policies and guidelines for FAIR and open RDM.

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Data Management Issues in Information Systems

Journal of Database Management ◽

10.4018/jdm.1995100101 ◽

1995 ◽

Vol 6 (4) ◽

pp. 3-13

Author(s):

Carl Stephen Guynes ◽

Michael T. Vanecek

Keyword(s):

Information Systems ◽

Data Management ◽

Management Issues

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Healthcare data management issues and the eCert solution

International Conference on Information Society (i-Society 2011) ◽

10.1109/i-society18435.2011.5978520 ◽

2011 ◽

Author(s):

Lisha Chen-Wilson ◽

Xin Wang ◽

Gary B Wills ◽

David Argles ◽

Charles Shoniregun

Keyword(s):

Data Management ◽

Healthcare Data ◽

Management Issues

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Connecting Chemistry with Global Challenges through Data Standards

Chemistry International ◽

10.1515/ci-2017-0303 ◽

2017 ◽

Vol 39 (3) ◽

Cited By ~ 1

Author(s):

Ian Bruno ◽

Jeremy G. Frey

Keyword(s):

Wicked Problems ◽

Research Community ◽

Research Data ◽

Free Flow ◽

Valuable Resource ◽

Data Standards ◽

Global Challenges ◽

Resource Needs ◽

Transparency And Accountability ◽

New Millennium

AbstractThe new millennium, now almost 20 years old, has been characterised by a recognition within the research community of the importance of the free flow of research data; not simply in the ability to access the data, but also in the understanding that this valuable resource needs to be reused and built upon. We believe there have been at least two main drivers for this. First, those who pay for the research want to know it is leading to useful outcomes with impact–the transparency and accountability agenda. Second is an appreciation that the major global concerns (food, health, climate, economy) are extraordinarily complex (‘wicked’) problems, [

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Clustering Web Information Services

Web Data Management Practices ◽

10.4018/978-1-59904-228-2.ch002 ◽

2007 ◽

pp. 34-55 ◽

Cited By ~ 1

Author(s):

Athena Vakali ◽

George Pallis ◽

Lefteris Angelis

Keyword(s):

Data Mining ◽

Data Management ◽

Information Sources ◽

Theoretical Background ◽

Information Services ◽

Information Availability ◽

Web Information ◽

Information Circulation ◽

The Web ◽

Management Issues

The explosive growth of the Web scale has drastically increased information circulation and dissemination rates. As the number of both Web users and Web sources grows significantly everyday, crucial data management issues, such as clustering on the Web, should be addressed and analyzed. Clustering has been proposed towards improving both the information availability and the Web users’ personalization. Clusters on the Web are either users’ sessions or Web information sources, which are managed in a variation of applications and implementations testbeds. This chapter focuses on the topic of clustering information over the Web, in an effort to overview and survey on the theoretical background and the adopted practices of most popular emerging and challenging clustering research efforts. An up-to-date survey of the existing clustering schemes is given, to be of use for both researchers and practitioners interested in the area of Web data mining.

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