Search

On Jan 22, 2020, a day after the USA reported its first COVID-19 case, the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE) launched the first global real-time coronavirus surveillance system: the JHU CSSE COVID-19 Dashboard. As of June 1, 2022, the dashboard has served the global audience for more than 30 consecutive months, totalling over 226 billion feature layer requests and 3·6 billion page views. The highest daily record was set on March 29, 2020, with more than 4·6 billion requests and over 69 million views. This Personal View reveals the fundamental technical details of the entire data system underlying the dashboard, including data collection, data fusion logic, data curation and sharing, anomaly detection, data corrections, and the human resources required to support such an effort. The Personal View also covers the challenges, ranging from data visualisation to reporting standardisation. The details presented here help develop a framework for future, large-scale public health-related data collection and reporting.

The DCC Curation Lifecycle Model has played a vital role in the field of data curation for over a decade. During that time, the scale and complexity of data have changed dramatically, along with the contexts of data production and use. This paper reports on a study examining factors impacting data curation practices and presents recommendations for updating the DCC Curation Lifecycle Model. The study was grounded in a review of other lifecycle models and informed by a site visit to the Digital Curation Centre and consultation with expert practitioners and researchers. Framed by contemporary conditions impacting the conduct of research and provision of data services, the analysis and proposed recommendations account for the prominence of machine-actionable data, the importance of machine learning for data processing and analytics, growth of integrated research workflows, and escalating concerns with fairness, accountability, and transparency of data and algorithms.

This chapter looks at two national approaches to research data infrastructure, responding to very different government and research sector environments: Australia and the USA. Australia: a national research data service The Australian National Data Service (ANDS) is working to enable the transformation of research data that are unmanaged, disconnected, invisible and single-use into structured collections that are managed, connected, findable and reusable. This will form a nationally significant resource so that Australian researchers can easily publish, discover, access and use Australian research data. The beginnings of ANDS can be traced back to one of the most significant investments made by Australia in research infrastructure: the National Collaborative Research Infrastructure Strategy (NCRIS). This was an A$542 million, seven year strategy originally spanning from 2004/05 to 2010/11 but with some investments extended to 2012/13 (the Australian financial year, referred to here, runs from 1 July to 30 June). NCRIS provides and supports major research infrastructure, allowing Australia to conduct world-class research. In November 2006, 12 priority areas (capabilities) were announced and projects have since been funded in each of these areas. NCRIS has adopted a strategic and collaborative approach to providing research infrastructure, which can be accessed by researchers across Australia. This type of collaboration is proving effective in delivering wider access to better infrastructure. A key enabler of research collaboration is the high performance computing, data management and electronic communication tools provided by the new information and communication technologies (ICT). The Australian Government continues to support the deployment of these ICT tools so researchers can readily share access to research data and collaborate in analysing the data. The NCRIS investment in this area was collectively called Platforms for Collaboration (PfC) and the Australian National Data Service is one of its major components. Its current status can best be understood through its four sequential stages: establishment, initial funding, additional funding and time extension. Establishment of ANDS During the course of the PfC facilitation process, a number of workshops were held to determine the activities that might be included in the investment plan to assist research data management. Following the approval of the overall PfC investment plan by NCRIS, an implementation workshop with wide representation was held to confirm the proposal to establish an Australian National Data Service. This workshop took place on 29 May 2007.

The Site-Based Data Curation (SBDC) project is developing a framework of guidelines and processes for the curation of research data generated at scientifically significant sites. SBDC will advance the professional work of curation by articulating principles, workflows and inter-institutional relationships essential for site-based curation. Work to date has concentrated on stakeholder analysis, with results informing metadata guidelines for capture of essential site-based specifics and clarifying the value in aggregating data around site factors for both scientist and YNP stakeholder groups.

Among the key services that institutional data management infrastructures must provide are provenance and lineage tracking and the ability to associate data with contextual information needed for understanding and use. These functionalities are critical for addressing a number of key issues faced by data collectors and users, including trust in data, results traceability, data transparency, and data citation support. In this paper, we describe the support for these services within the Data Conservancy Service (DCS) software. The DCS provenance, context, and lineage services cross the four layers in the DCS data curation stack model: storage, archiving, preservation, and curation.

Astronomy is similar to other scientific disciplines in that scholarly publication relies on the presentation and interpretation of data. But although astronomy now has archives for its primary research telescopes and associated surveys, the highly processed data that is presented in the peer-reviewed journals and is the basis for final analysis and interpretation is generally not archived and has no permanent repository. We have initiated a project whose goal is to implement an end-to-end prototype system which, through a partnership of a professional society, that society’s scholarly publications/publishers, research libraries, and an information technology substrate provided by the Virtual Observatory, will capture high-level digital data as part of the publication process and establish a distributed network of curated, permanent data repositories. The data in this network will be accessible through the research journals, astronomy data centers, and Virtual Observatory data discovery portals.

At Johns Hopkins University, the institutional repository (IR) is being developed as a component of an overall digital library architecture that will emphasize long-term preservation. The IR represents a set of services that will be developed to support the identified needs or requirements of faculty and students. Given the research-intensive environment at Johns Hopkins, one particular area of interest relates to data sets from a diversity of disciplines ranging from the humanities to the sciences. Essentially, the IR is being developed as a "gateway" to the underlying digital archive that will support data curation as part of an evolving cyberinfrastructure featuring open, modular components. In addition to this technological framework, Johns Hopkins is developing new roles and relationships between the library and the academic community, most notably through the development of "data scientists" or "data humanists." These developments reflect the realization that the IR is the first step in a longer journey and that for institutional efforts to be successful, they must be integrated into a larger landscape of repositories that serve a distributed and diverse academic community.

The nature of science and scholarship is being transformed by the ability to collect and integrate vast quantities of information. Some sciences such as ecology and environmental science are inherently integrative, requiring the combination of many types of information from many sources in order to answer more complex questions than has been previously possible. This new information and the information management tools designed to deal with this volume of data will help us make informed decisions that will impact human health and prosperity. To enable this cross-scale, interdisciplinary integration for the coming generations of scholars, data must be managed to facilitate interoperability, preservation, and sharing. This panel will explore best practices in data curation and models of education for new data curation professionals.

This thesis proposes a detailed design layout for the next generation optical metropolitan network to be equipped with Reconfigurable Optical Add/Drop Multiplexer (ROADM) subsystems. It demonstrates the key features of ROADM subsystems including the development of ring-to-ring interconnection in optical domain. Simulation models are developed for the ROADM subsystems that can easily be configured according to the devices' available specification sheet. The developed simulation models have been verified with experimental measurements obtained from a reconfigurable all-optical metro network testbed. Such simulation modules will be valuable in planning and designing the next generation metro optical networks. Combining the outcome of the research results presented in the thesis, a hybrid reconfigurable add/drop module is proposed. The proposed hybrid architecture can achieve lower loss among the existing 3rd generation ROADM modules. The functionality of ROADMs can also be improved by an Intelligent ROADM subsystem proposed for the next generation dynamically provisioned wavelength network.

To reduce loss of life and injury resulting from earthquakes, the relationship between earthquake-induced building failure and injury severity and distribution needs to be clarified. For this purpose, a series of data collection forms were developed to collect pertinent data for post-event analysis and to provide a basis for structural triage in the field shortly following an earthquake for search and rescue purposes. A companion paper described the identification of variables affecting the outcome of an occupant of a damaged building and the classification of these variables into three levels of priority for data collection. This paper continues the development by describing the design of the data collection forms and the application of the forms to damaged buildings from past earthquakes. These forms represent a significant departure from existing forms in that they consider both casualties and building damage jointly, and in a consistent format.

During an earthquake, buildings which are vulnerable to seismic loads will be damaged, resulting in property loss and the potential for casualties. To reduce loss of life and injury, the relationship between earthquake-induced building failure and injury severity and distribution needs to be clarified. To this end, a methodology and a series of data collection forms were developed to collect pertinent data for post-event analysis and to provide a basis for structural triage in the field shortly following an earthquake for search and rescue purposes. The forms were developed in four steps: (1) identifying the variables which affect the outcome of an occupant in a damaged building; (2) classifying the variables into three levels of priority for data collection; (3) designing the forms; and (4) applying the forms to damaged buildings from past earthquakes. These forms represent a significant departure from existing forms in that they consider both casualties and building damage jointly in a consistent format. This paper describes the first two steps of the development process; a companion paper outlines the latter components.

1 Abstract Digital research data can only be managed and preserved over time through a sustained institutional commitment. Research data curation is a multi-faceted issue, requiring technologies, organizational structures, and human knowledge and skills to come together in complementary ways. This article provides a high-level description of the Data Conservancy Instance, an implementation of infrastructure and organizational services for data collection, storage, preservation, archiving, curation, and sharing. While comparable to institutional repository systems and disciplinary data repositories in some aspects, the DC Instance is distinguished by featuring a data-centric architecture, discipline-agnostic data model, and a data feature extraction framework that facilitates data integration and cross-disciplinary queries. The Data Conservancy Instance is intended to support, and be supported by, a skilled data curation staff, and to facilitate technical, financial, and human sustainability of organizational data curation services. The Johns Hopkins University Data Management Services (JHU DMS) are described as an example of how the Data Conservancy Instance can be deployed.

This paper addresses issues in digital curation, which is the management of digital assets throughout their life cycle for maximum interoperability, discovery, preservation and re-use. The experiences of two institutions—Johns Hopkins University’s Sheridan Libraries and Penn State University Libraries—are presented as examples of how libraries can collaborate successfully with publishers to provide preservation and other back-end services to support scholarly publishing. One example (Johns Hopkins) relates to scientific data, while the other (Penn State) relates to the humanities. The paper also discusses the potential of digital curation to expand and enhance library services broadly and describes the investment the Institute of Museum and Library Services has made in digital curation research, education, and practice.

As initiatives such as Google Book Search (http://books.google.com/) and the Open Content Alliance (http://www.opencontentalliance.org/) advance efforts to digitize millions of books, there is great potential to make available vast amounts of information. To truly unlock this knowledge, however, it will be necessary to process the resulting digital page images to recognize important content, including both the semantic and structural aspects. Given the vast diversity of fonts, symbols, tables, languages and a host of other elements, it will be necessary to create flexible, modular, scalable document recognition systems. Document recognition involves extracting features from the images and even transcriptions of other documents in order to group diverse content.

The Agile All-photonic Backbone Network (AAPN) architecture has been proposed by the telecommunication industry as a potential candidate for the ultra high speed Next Generation Optical Network (NGON) architecture. AAPN network structure is composed of adaptive optical core switches and edge routers in an overlaid star physical topology. The AAPN employs fast packet switching architecture for the network traffic, and the packet scheduling is the main part of the AAPN. The objective is to forward the packets to their destination with the lowest drop rate and delay, the bandwidth allocation can be either located at the core node or the edge switch. Two types of scheduling are considered in the AAPN architecture, namely the centralized and the distributed schemes. In the centralized scheme all decisions are made at the core node while in the distributed scheme, they are made at the edge nodes. In this paper, we want to compare both scheduling schemes. We would also like to propose a promising integrated TDM architecture that combines the good attributes of both centralized and distributed scheduling schemes. We shall characterize such architecture by various measures such as delay and loss probabilities.

This article provides an overview of evaluation studies for libraries, a brief introduction to the CAPM Project, a description of the theoretical background for the CAPM methodology and, finally, a discussion of the implementation of the methodology for the CAPM Project.

The CAPM Project features the development and evaluation of an automat ed, robotic on-demand scanning system for materials at remote locations. To date, we have developed a book retrieval robot and a valuation analysis framework for evaluating CAPM. We intend to augment CAPM by exploring approaches for automated page turning and improved valuation. These extensions will results in a more fully automated CAPM system and a valuation framework that will not only be useful for assessing CAPM specifically, but also for library services and functions generally.

[TODO] Add abstract here.

This paper describes the development of a set of workflow management tools (WMS) that will reduce the manual input necessary to manage the workflow of large-scale digitization projects. The WMS will also support the path from physical object and/or digitized material into a digital library repository by providing effective tools for perusing multimedia elements. The Lester S. Levy Collection of Sheet Music Project at the Milton S. Eisenhower Library at The Johns Hopkins University provides an ideal testbed for the development and evaluation of the WMS. Building upon the previous effort to digitize the entire Collection of over 29,000 pieces of sheet music, optical music recognition (OMR) software will create sound files and full-text lyrics. The combination of image, text and sound files provide a comprehensive multimedia environment. The functionality of the Collection will be enhanced by the incorporation of metadata, the implementation of a disk-based search engine for lyrics, and the development of toolkits for searching sound files.