The first issue's "full-length papers" are:

• "DataONE: Facilitating eScience through Collaboration"
• "An Assessment of Needed Competencies to Promote the Data Curation and Management Librarianship of Health Sciences and Science and Technology Librarians in New England"
• "Tiers of Research Data Support Services"

| E-science and Academic Libraries Bibliography | Digital Scholarship |

Here's an excerpt:

The Community Capability Model Framework is a tool developed by UKOLN, University of Bath, and Microsoft Research to assist institutions, research funders and researchers in growing the capability of their communities to perform data-­-intensive research by

• profiling the current readiness or capability of the community,
• indicating priority areas for change and investment, and
• developing roadmaps for achieving a target state of readiness.

The Framework is comprised of eight capability factors representing human, technical and environmental issues. Within each factor are a series of community characteristics that are relevant for determining the capability or readiness of that community to perform data- intensive research.

| E-science and Academic Libraries Bibliography | Digital Scholarship |

| Digital Curation and Preservation Bibliography 2010 | Digital Scholarship |

Here's an excerpt:

To build a respectable cyberinfrastructure, the IT organizations at small/medium research institutions need to use creativity in discovering the needs of their researchers, setting priorities for support, developing support strategies, funding and implementing cyberinfrastructure, and building partnerships to enhance research support. This article presents the viewpoints of four small-to-medium-sized research universities who have struggled with the issue of providing appropriate cyberinfrastructure support for their research enterprises. All four universities have strategic goals for raising the level of research activity and increasing extramural funding for research.

Here's an excerpt:

Scientists today collect, analyze, and generate TeraBytes and PetaBytes of data. These data are often shared and further processed and analyzed among collaborators. In order to facilitate sharing and data interpretations, data need to carry with it metadata about how the data was collected or generated, and provenance information about how the data was processed. This chapter describes metadata and provenance in the context of the data lifecycle. It also gives an overview of the approaches to metadata and provenance management, followed by examples of how applications use metadata and provenance in their scientific processes.

Here's a quick selection of presentations:

• Before and after DRF Initiative: A Cluster of Institutional Repository Programs beyond the Traditional Role of the Library, Hideki Uchijima (Kanazawa University)
• Future Perspectives of Institutional Repositories in Taiwan, Simon C. Lin, Ya-Ning Chen (Academia Sinica)
• Innovation in Scholarly Communication in High-Energy Physics:Repositories and Open Access, Salvatore Mele (CERN)
• Suggestions for Asian/Australasian Regional Cooperation based on a Critical Evaluation of Collaboration and Standardisation across Australian Institutional Repositories, Peter Sefton (University of Southern Queensland)
• A Retrospective of NII Projects on New Perspectives in CSI, Jun Adachi (National Institute of Informatics)

Here's an excerpt:

The FY2010 SDCI solicitation supports the development, deployment, and maintenance of software in the five software focus area listed above, i.e., software for HPC systems, software for digital data management, software for networking, middleware, and cybersecurity, and specifically focuses on cross-cutting issues of CI software sustainability, manageability and power/energy efficiency in each of these software focus areas. . . .

2. Software for Digital Data

The Data focus area addresses software that promotes acquisition, transport, discovery, access, analysis, and preservation of very large-scale digital data in support of large scale applications or data sets transitioning to use by communities other than the ones that originally gathered the data. Examples of such datasets includes climatologic, ecologic, phonologic, observation data, sensor systems, spatial visualizations, multi-dimensional datasets correlated with metadata and so forth.

Specific focus areas in Software for Digital Data for the FY2010 SDCI solicitation include:

• Documentation/Metadata: Tools for automated/facilitated metadata creation/acquisition, including linking data and metadata to assist in curation efforts; tools to enable the creation and application of ontologies, semantic discovery, assessment, comparison, and integration of new composite ontologies.
• Security/Protection: Tools for data authentication, tiered/layered access systems for data confidentiality/privacy protection, replication tools to ensure data protection across varied storage systems/strategies, rules-based data security management tools, and assurance tools to test for digital forgery and privacy violations.
• Data transport/management: Tools to enable acquisition of high data rate high volume data from varied, distributed data sources (including sensors systems and instruments), while addressing stringent space and data quality constraints; tools to assist in improved low-level management of data and transport to take better advantage of limited bandwidth.
• Data analytics and visualization: Tools that operate in (near) real-time, not traditional batch mode, on possible streaming data, in-transit data processing, data integration and fusion.  

Here's an excerpt:

Data from high-energy physics (HEP) experiments are collected with significant financial and human effort and are mostly unique. At the same time, HEP has no coherent strategy for data preservation and re-use. An inter-experimental Study Group on HEP data preservation and long-term analysis was convened at the end of 2008 and held two workshops, at DESY (January 2009) and SLAC (May 2009). This document is an intermediate report to the International Committee for Future Accelerators (ICFA) of the reflections of this Study Group.

Here's an excerpt from the press release:

Researchers at UNM have partnered with dozens of other universities and agencies to create DataONE, a global data access and preservation network for earth and environmental scientists that will support breakthroughs in environmental research.

DataONE is designed to provide universal access to data about life on Earth and the environment that sustains it. The underlying technologies will provide open, persistent, robust, and secure access to well-described and easily discovered Earth observational data.

Expected users include scientists, educators, librarians, resource managers, and the public. By providing easy and open access to a broad range of science data, as well as tools for managing, analyzing, and visualizing data, DataONE will be transformative in the speed with which researchers will be able to assemble and analyze data sets and in the types of problems they will be able to address. . . .

DataONE is one of two $20 million awards made this year as part of the National Science Foundation's (NSF) DataNet program. The collaboration of universities and government agencies coalesced to address the mounting need for organizing and serving up vast amounts of highly diverse and inter-related but often-incompatible scientific data. Resulting studies will range from research that illuminates fundamental environmental processes to identifying environmental problems and potential solutions. . . .

The DataONE team will study how a vast digital data network can provide secure and permanent access into the future, and also encourage scientists to share their information. The team will help determine data citation standards, as well as create the tools for organizing, managing, and publishing data.

The resulting computing and processing "cyberinfrastructure" will be made permanently available for use by the broader national and international science communities. DataONE is led by the University of New Mexico, and includes additional partner organizations across the United States as well as from Europe, Africa, South America, Asia, and Australia.

This grant is important nationally, and locally especially for our research community. University Libraries Dean Martha Bedard said, "The University Libraries are key partners in UNM research initiatives, and are excited and committed to supporting the emerging area of data curation, which this grant seeks to support in sophisticated ways."

DataONE will build a set of geographically distributed Coordinating Nodes that play an important role in facilitating all of the activities of the global network, as well as a network of Member Nodes that host relevant data and tools. The initial three Coordinating Nodes will be at the University of New Mexico, UC Santa Barbara (housed at the Davidson Library), and at the University of Tennessee/Oak Ridge National Laboratory. Member Nodes will be located in association with universities, libraries, research networks, and agencies worldwide.

Here's an excerpt from the press release:

The Association of Research Libraries (ARL) E-Science Working Group surveyed ARL member libraries in the fall of 2009 to gather data on the state of engagement with e-science issues. An overview of initial survey findings was presented by E-Science Working Group Chair Wendy Lougee, University Librarian, McKnight Presidential Professor, University of Minnesota Libraries, at the October ARL Membership Meeting. Lougee's briefing explored contrasting approaches among research institutions, particularly in regard to data management. The briefing also summarized survey findings on topics such as library services, organizational structures, staffing patterns and staff development, and involvement in research grants, along with perspectives on pressure points for service development. To better explicate the findings, Lougee reviewed specific cases of activities at six research institutions. . . .

A full report of the survey findings is being prepared and will be published in 2010 by ARL through its Occasional Papers series.

Here's an excerpt:

This Report has attempted to draw together and synthesise evidence and opinion from a wide range of sources. Examples of data intensive science at extremes of scale and complexity which enable forecasting and predictive assertions, have been described together with compelling exemplars where an open and participative culture is transforming science practice. It is perhaps worth noting that the pace of change in this area is such, that it has been a challenging piece to compose and at best, it can only serve as a subjective snapshot of a very dynamic data space. . . .

The perspective of openness as a continuum is helpful in positioning the range of behaviours and practices observed in different disciplines and contexts. By separating the twin aspects of openness (access and participation), we can begin to understand the full scope and potential of the open science vision. Whilst a listing of the perceived values and benefits of open science is given, further work is required to provide substantive and tangible evidence to justify and support these assertions. Available evidence suggests that transparent data sharing and data re-use are far from commonplace. The peer production approaches to data curation which have been described, are really in their infancy but offer considerable promise as scaleable models which could be migrated to other disciplines. The more radical open notebook science methodologies are currently on the "fringe" and it is not clear whether uptake and adoption will grow in other disciplines and contexts.

Here's an excerpt from the press release:

An initiative that will determine how Triangle area universities access, manage, and share ever-growing stores of digital data launched this fall with funding from the Triangle Universities Center for Advanced Studies, Inc. (TUCASI).

The two-year TUCASI data-Infrastructure Project (TIP) will deploy a federated data cyberinfrastructure—or data cloud—that will manage and store digital data for Duke University, NC State University, UNC Chapel Hill, and the Renaissance Computing Institute (RENCI) and allow the campuses to more seamlessly share data with each other, with national research projects, and private sector partners in Research Triangle Park and beyond.

RENCI and the Data Intensive Cyber Environments (DICE) Center at UNC Chapel Hill manage the $2.7 million TIP. The provosts, heads of libraries and chief information officers at the three campuses signed off on the project just before the start of the fall semester.

"The TIP focuses on federation, sharing and reuse of information across departments and campuses without having to worry about where the data is physically stored or what kind of computer hardware or software is used to access it," said Richard Marciano, TIP project director, and also professor at UNC's School of Information and Library Science (SILS), executive director of the DICE Center, and a chief scientist at RENCI. "Creating infrastructure to support future Triangle collaboratives will be very powerful."

The TIP includes three components—classroom capture, storage, and future data and policy, which will be implemented in three phases. In phase one, each campus and RENCI will upgrade their storage capabilities and a platform-independent system for capturing and sharing classroom lectures and activities will be developed. . . .

In phase two, the TIP team will develop policies and practices for short- and long-term data storage and access. Once developed, the policies and practices will guide the research team as it creates a flexible, sustainable digital archive, which will connect to national repositories and national data research efforts. Phase three will establish policies for adding new collections to the TIP data cloud and for securely sharing research data, a process that often requires various restrictions. "Implementation of a robust technical and policy infrastructure for data archiving and sharing will be key to maintaining the Triangle universities' position as leaders in data-intensive, collaborative research," said Kristin Antelman, lead researcher for the future data and policy working group and associate director for the Digital Library at NC State.

The tasks of the TIP research team will include designing a model for capturing, storing and accessing course content, determining best practices for search and retrieval, and developing mechanisms for sharing archived content among the TIP partners, across the Triangle area and with national research initiatives. Campus approved social media tools, such as YouTube and iTunesU, will be integrated into the system.

Of particular interest is the "Scholarly Communication" chapter.

Here are some selections from that chapter:

• "Jim Gray’s Fourth Paradigm and the Construction of the Scientific Record," Clifford Lynch
• "Text in a Data-Centric World," Paul Ginsparg
• "All Aboard: Toward a Machine-Friendly Scholarly Communication System," Herbert Van de Sompel and Carl Lagoze
• "I Have Seen the Paradigm Shift, and It Is Us," John Wilbanks

Here's a selection from the "Open Access and Preservation" session:

• Open Access And Preservation: How Can Knowledge Sharing Be Improved in ERA? (presentation slides), Alma Swan
• "Optimizing Research Sharing in the European Research Area: Cyberinfrastructure, Quality, and Open Access," Jean-Claude Guédon

Here's the abstract:

Cloud computing is the delivery of scalable IT resources over the Internet, as opposed to hosting and operating those resources locally, such as on a college or university network. Those resources can include applications and services, as well as the infrastructure on which they operate. By deploying IT infrastructure and services over the network, an organization can purchase these resources on an as-needed basis and avoid the capital costs of software and hardware. With cloud computing, IT capacity can be adjusted quickly and easily to accommodate changes in demand. Cloud computing also allows IT providers to make IT costs transparent and thus match consumption of IT services to those who pay for such services. Operating in a cloud environment requires IT leaders and staff to develop different skills, such as managing contracts, overseeing integration between in-house and outsourced services, and mastering a different model of IT budgets.