Coordinative Entities: Forms of Organizing in Data Intensive Science

Scientific collaboration is a long-standing subject of CSCW scholarship that typically focuses on the development and use of computing systems to facilitate research. The research presented in this article investigates the sociality of science by identifying and describing particular, common forms of organizing that researchers in four different scientific realms employ to conduct work in both local contexts and as part of distributed, global projects. This paper introduces five prototypical forms of organizing we categorize as coordinative entities: the Principal Group, Intermittent Exchange, Sustained Aggregation, Federation, and Facility Organization. Coordinative entities as a categorization help specify, articulate, compare, and trace overlapping and evolving arrangements scientists use to facilitate data intensive research. We use this typology to unpack complexities of data intensive scientific collaboration in four cases, showing how scientists invoke different coordinative entities across three types of research activities: data collection, processing, and analysis. Our contribution scrutinizes the sociality of scientific work to illustrate how these actors engage in relational work within and among diverse, dispersed forms of organizing across project, funding, and disciplinary boundaries

The medical science DMZ: a network design pattern for data-intensive medical science

Abstract: Objective We describe a detailed solution for maintaining high-capacity, data-intensive network flows (eg, 10, 40, 100 Gbps+) in a scientific, medical context while still adhering to security and privacy laws and regulations. Materials and Methods High-end networking, packet-filter firewalls, network intrusion-detection systems. Results We describe a “Medical Science DMZ” concept as an option for secure, high-volume transport of large, sensitive datasets between research institutions over national research networks, and give 3 detailed descriptions of implemented Medical Science DMZs. Discussion The exponentially increasing amounts of “omics” data, high-quality imaging, and other rapidly growing clinical datasets have resulted in the rise of biomedical research “Big Data.” The storage, analysis, and network resources required to process these data and integrate them into patient diagnoses and treatments have grown to scales that strain the capabilities of academic health centers. Some data are not generated locally and cannot be sustained locally, and shared data repositories such as those provided by the National Library of Medicine, the National Cancer Institute, and international partners such as the European Bioinformatics Institute are rapidly growing. The ability to store and compute using these data must therefore be addressed by a combination of local, national, and industry resources that exchange large datasets. Maintaining data-intensive flows that comply with the Health Insurance Portability and Accountability Act (HIPAA) and other regulations presents a new challenge for biomedical research. We describe a strategy that marries performance and security by borrowing from and redefining the concept of a Science DMZ, a framework that is used in physical sciences and engineering research to manage high-capacity data flows. Conclusion By implementing a Medical Science DMZ architecture, biomedical researchers can leverage the scale provided by high-performance computer and cloud storage facilities and national high-speed research networks while preserving privacy and meeting regulatory requirements

Data-Intensive Computing in the 21st Century

The deluge of data that future applications must process—in domains ranging from science to business informatics—creates a compelling argument for substantially increased R&D targeted at discovering scalable hardware and software solutions for data-intensive problems

From open data to data-intensive science through CERIF

OGD (Open Government Data) is provided from government departments for transparency and to stimulate a market in ICT services for industry and citizens. Research datasets from publicly funded research commonly are associated with the open scholarly publications movement. However, the former world commonly is derived from the latter with generalisation and summarisation. There is advantage in a user of OGD being able to ‘drill down’ to the underlying research datasets. OGD encourages cross-domain research because the summarized data from different domains is more easily relatable. Bridging across the two worlds requires rich metadata; CERIF (Common European research Information Format) has proved itself to be ideally suited to this requirement. Utilising the research datasets is data-intensive science, a component of e-Research. Data-intensive science also requires access to an e-infrastructure. Virtualisation of this e-infrastructure optimizes this

From open data to data-intensive science through CERIF

OGD (Open Government Data) is provided from government departments for transparency and to stimulate a market in ICT services for industry and citizens. Research datasets from publicly funded research commonly are associated with the open scholarly publications movement. However, the former world commonly is derived from the latter with generalisation and summarisation. There is advantage in a user of OGD being able to ‘drill down’ to the underlying research datasets. OGD encourages cross-domain research because the summarized data from different domains is more easily relatable. Bridging across the two worlds requires rich metadata; CERIF (Common European research Information Format) has proved itself to be ideally suited to this requirement. Utilising the research datasets is data-intensive science, a component of e-Research. Data-intensive science also requires access to an e-infrastructure. Virtualisation of this e-infrastructure optimizes this

Emerging good practice in managing research data and research information within UK Universities

Sound data intensive science depends upon effective research data and information management. Efficient and interoperable research information systems will be crucial for enabling and exploiting data intensive research however it is equally important that a research ecosystem is cultivated within research-intensive institutions that foster sustainable communication, cooperation and support of a diverse range of research-related staff. Researchers, librarians, administrators, ethics advisors, and IT professionals all have a vital contribution to make in ensuring that research data and related information is available, visible, understandable and usable over the mid to long term. This paper will provide a summary of several ongoing initiatives that the Jisc-funded Digital Curation Centre (DCC) are currently involved with in the UK and internationally to help staff within higher education institutions prepare to meet funding body mandates relating to research data management and sharing and to engage fully in the digital agenda