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This technical manual guides the user through the process of creating a data table for the submission of taxonomic and morphological information for plankton and other particles from images to a repository. Guidance is provided to produce documentation that should accompany the submission of plankton and other particle data to a repository, describes data collection and processing techniques, and outlines the creation of a data file. Field names include scientificName that represents the lowest level taxonomic classification (e.g., genus if not certain of species, family if not certain of genus) and scientificNameID, the unique identifier from a reference database such as the World Register of Marine Species or AlgaeBase. The data table described here includes the field names associatedMedia, scientificName/ scientificNameID for both automated and manual identification, biovolume, area_cross_section, length_representation and width_representation. Additional steps that instruct the user on how to format their data for a submission to the Ocean Biodiversity Information System (OBIS) are also included. Examples of documentation and data files are provided for the user to follow. The documentation requirements and data table format are approved by both NASA’s SeaWiFS Bio-optical Archive and Storage System (SeaBASS) and the National Science Foundation’s Biological and Chemical Oceanography Data Management Office (BCO-DMO).

Metabolomics is the study of small molecules, or ‘metabolites’, that are the end products of biological processes. While -omics technologies such as genomics, transcriptomics, and proteomics measure the metabolic potential of organisms, metabolomics provides detailed information on the organic compounds produced during metabolism and found within cells and in the environment. Improvements in analytical techniques have expanded our understanding of metabolomics and developments in computational tools have made metabolomics data accessible to a broad segment of the scientific community. Yet, metabolomics methods have only been applied to a limited number of projects in the marine environment. Here, we review analysis techniques for mass spectrometry data and summarize the current state of metabolomics databases. We then describe a boutique database developed in our laboratory for efficient data analysis and selection of mass spectral targets for metabolite identification. The code to implement the database is freely available on GitHub ( https://github.com/joefutrelle/domdb ). Data organization and analysis are critical, but often under-appreciated, components of metabolomics research. Future advances in environmental metabolomics will take advantage of continued development of new tools that facilitate analysis of large metabolomics datasets.

With the advent of new instrumentation and sensors, more diverse types and increasing amounts of data are becoming available to environmental researchers and practitioners. However, accessing and integrating these data into forms usable for environmental analysis and modeling can be highly time-consuming and challenging, particularly in real time. For example, radar-rainfall data are a valuable resource for hydrologic modeling because of their high resolution and pervasive coverage. However, radar-rainfall data from the Next Generation Radar (NEXRAD) system continue to be underutilized outside of the operational environment because of limitations in access and availability of research-quality data products, especially in real time. This paper addresses these issues through the development of a prototype Web-based virtual sensor system at NCSA that creates real-time customized data streams from raw sensor data. These data streams are supported by metadata, including provenance information. The system uses workflow composition and publishing tools to facilitate creation and publication (as Web services) of user-created virtual sensors. To demonstrate the system, two case studies are presented. In the first case study, a network of point-based virtual precipitation sensors is deployed to analyze the relationship between radar-rainfall measurements, and in the second case study, a network of polygon-based virtual precipitation sensors is deployed to be used as input to urban flooding models. These case studies illustrate how, with the addition of some application-specific information, this general-purpose system can be utilized to provide customized real-time access to significant data resources such as the NEXRAD system. Additionally, the creation of new types of virtual sensors is discussed, using the example of virtual temperature sensors.

The Tupelo semantic content management middleware implements Knowledge Spaces that enable scientists to integrate information into a comprehensive research record as they work with existing tools and domain-specific applications. Knowledge Spaces combine approaches that have demonstrated success in automating parts of this integration activity, including content management systems for domain-neutral management of data, workflow technologies for management of computation and analysis, and semantic web technologies for extensible, portable, citable management of descriptive information and other metadata. Tupelo's ‘Context’ facility and its associated semantic operations both allow existing data representations and tools to be plugged in, and also provide a semantic ‘glue’ of important associative relationships that span the research record, such as provenance, social networks, and annotation. Tupelo has enabled the recent work creating e-Science cyberenvironments to serve distributed, active scientific communities, allowing researchers to develop, coordinate and share datasets, documents, and computational models, while preserving process documentation and other contextual information needed to produce an integrated research record suitable for distribution and archiving. Copyright © 2011 John Wiley & Sons, Ltd.

The ECHO DEPository project is a digital preservation research and development project funded by the National Digital Information Infrastructure and Preservation Program (NDIIPP) and administered by the Library of Congress. A key goal of this project is to investigate both practical solutions for supporting digital preservation activities today, and the more fundamental research questions underlying the development of the next generation of digital preservation systems. To support on-the-ground preservation efforts in existing technical and organizational environments, we have developed tools to help curators collect and manage Web-based digital resources, such as the Web Archives Workbench (Kaczmarek et al., 2008), and to enhance existing repositories’ support for interoperability and emerging preservation standards, such as the Hub and Spoke Tool Suite (Habing et al., 2008). In the longer term, however, we recognize that successful digital preservation activities will require a more precise and complete account of the meaning of relationships within and among digital objects. This article describes project efforts to identify the core underlying semantic issues affecting long-term digital preservation, and to model how semantic inference may help next-generation archives head off long-term preservation risks.

We review the efforts of the Open Grid Computing Environments collaboration. By adopting a general three-tiered architecture based on common standards for portlets and Grid Web services, we can deliver numerous capabilities to science gateways from our diverse constituent efforts. In this paper, we discuss our support for standards-based Grid portlets using the Velocity development environment. Our Grid portlets are based on abstraction layers provided by the Java CoG kit, which hide the differences of different Grid toolkits. Sophisticated services are decoupled from the portal container using Web service strategies. We describe advance information, semantic data, collaboration, and science application services developed by our consortium. Copyright © 2006 John Wiley & Sons, Ltd.

Music Information Retrieval (MIR) is an interdisciplinary research area that has grown out of the need to manage burgeoning collections of music in digital form. Its diverse disciplinary communities, exemplified by the recently established ISMIR conference series, have yet to articulate a common research agenda or agree on methodological principles and metrics of success. In order for MIR to succeed, researchers need to work with real user communities and develop research resources such as reference music collections, so that the wide variety of techniques being developed in MIR can be meaningfully compared with one another. Out of these efforts, a common MIR practice can emerge.

The Open Provenance Model is a model of provenance that is designed to meet the following requirements: (1) Allow provenance information to be exchanged between systems, by means of a compatibility layer based on a shared provenance model. (2) Allow developers to build and share tools that operate on such a provenance model. (3) Define provenance in a precise, technology-agnostic manner. (4) Support a digital representation of provenance for any "thing", whether produced by computer systems or not. (5) Allow multiple levels of description to coexist. (6) Define a core set of rules that identify the valid inferences that can be made on provenance representation. This document contains the specification of the Open Provenance Model (v1.1) resulting from a community effort to achieve inter-operability in the Provenance Challenge series. © 2011 Elsevier B.V. All rights reserved.

Sensor web applications such as real-time environmental decision support systems require the use of sensors from multiple heterogeneous sources for purposes beyond the scope of the original sensor design and deployment. In such cyberenvironments, provenance plays a critical role as it enables users to understand, verify, reproduce, and ascertain the quality of derived data products. Such capabilities are yet to be developed in many sensor web enablement (SWE) applications. This paper develops a provenance-aware “Virtual Sensor” system, where a new persistent live “virtual” sensor is re-published in realtime after some model-based computational transformations of the raw sensor data streams. We describe the underlying OPM (Open Provenance Model) API's (Application Programming Interfaces), architecture for provenance capture, creation of the provenance graph and publishing of the provenance-aware virtual sensor where the new virtual sensor time-series data is augmented with OPM-compliant provenance information. A case study on creating real-time provenance-aware virtual rainfall sensors is illustrated. Such a provenance-aware virtual sensor system allows digital preservation and verification of the new virtual sensors.

Every modern portal ships with some form of internal document library portlet tools that can be used to enable groups to share files. Unfortunately, certain limitations -- that the portlet can only view data managed directly by the portal and that document and data files are the only resource that can be browsed -- make these tools less valuable in many real-world collaborations. This paper describes a semantically-enhanced scientific resource library portlet that extends the traditional document library to enable interaction with multiple distributed repositories in the cloud and to broaden the set of resources that can be viewed beyond simple hierarchical document folder-files structure to include people, sensors, data streams and other complex digital entities and their relationships. Our technology is based on semantic content abstraction and context aggregation functionality supported by Tupelo, a semantic content middleware and is implemented as a portlet plugin to the Liferay-based CyberCollaboratory portal. We describe the architecture components and the browsing features currently implemented and present a water science use case in which users are able to share documents, raw sensor data streams, and derived virtual sensor data (rainfall) from distributed sources within the same semantic resource library.

As scientific work becomes more complex, contextual information is critical to enable its use. We have developed the concept of Knowledge Spaces that manage such information and scope the space of data, metadata, and algorithms relevant to the work of a Virtual Organization. This enables scientists to produce and manage descriptive metadata, process description, data provenance, as well as VO-specific quality assertions, vocabulary mappings, inferences, computation, approximations, and security policies. We have developed middleware and cooperating applications that demonstrate the potential of Knowledge Spaces in the production and dissemination of scientific data.

This paper presents a semantic data, document and event management environment, implemented as extensions to the open-source Liferay portal which provides enhanced support for storage and retrieval of data, metadata, user interaction and computational provenance and opens the door to new modes of search and navigation based on geospatial, social, provenance, and thematic contexts. This also enables discovery of relations among data, events, workflow and people. This environment, packaged as the CyberCollaboratory, provides a much richer interaction environment for groups and communities.

In this demonstration paper, we describe the technologies and implementations that allow near real-time creation of new virtual precipitation sensors using NEXRAD Level II streaming data at user-specified point locations and time intervals in an integrated digital watershed with a Google Map-based web interface. The spatiotemporal and thematic transformation steps to produce such new time series data stream are implemented as a set of scientific workflows. A streaming data ontology is developed to handle temporal proximity concepts such as "previous" and "next" for irregular temporal data streams. Data and metadata management is provided by a semantic content management middleware. The new point-based virtual sensor can lower the barriers of using NEXRAD data for many hydrological applications.

The management of scientific data and information is becoming increasingly important as the data volume grows rapidly. Scientific data management is affected by many factors in policy, legal, and technical areas. What is involved in scientific data management? What is the status of research and development in this area? How are libraries and information institutions responding to this new area of collections and services? This panel brings experts in this area to address some of these questions.

NCSA's Environmental Cyberinfrastructure Demonstration (ECID) project is developing a cyberenvironment to support environmental science and hydrology research. The ECID Cyberenvironment is based on a set of coordinating technologies that together provide unique capabilities for integrating local and remote work and for capturing and exploiting data and interaction provenance. The CyberCollaboratory portal provides collaboration tools to discover, share, analyze, and discuss data and information. The CyberIntegrator is a meta-workflow engine combining heterogeneous (local and remote) tools into workflows to support complex scientific analyses and simulations. Both of these tools record rich provenance into the RDF-based Tupelo 2 Toolkit where it can be graphically browsed or used through CI-KNOW, social network analysis software, to provide context-specific recommendations within the CyberCollaboratory and CyberIntegrator. These capabilities, and the ability to dynamically connect new tools into group spaces, workflows, and provenance trails, will be critical to the next-generation of community-scale, persistent cyberinfrastructure efforts.

A project supported by the PEER-Lifelines Program is currently finalizing a pilot system architecture supporting web-based interfaces capable of providing access to publicly available geotechnical data sets. This paper describes the pilot system architecture design developed by the project's Virtual Data Center Work Group (VDCWG). The design is based on the results of a geotechnical data use and user scenario survey and a data dictionary developed by the project's User Scenario Work Group (USWG) and Data Dictionary Work Group (DDWG) respectively. The design allows multiple data providers to make their data available through a uniform web interface, while each retains possession and control of their data. A user is able to view and download data from multiple organizations in a uniform file format. Over the long-term, the main objective is to extend the pilot system and link multiple databases of government agencies, universities and private companies. A workshop planned for June 21-23, 2004 will review the system to obtain input and consensus from the geotechnical community. The workshop proceedings will serve as a future implementation plan for expansion into a large-scale web portal.

The demonstration will illustrate digital library component technologies joined together to provide solutions to common data mining, parsing, and archiving problems.

In this poster, we describe how components that implement emerging standards have been used to produce custom solutions to metadata archive problems.

In this paper, we describe the NBDL (National Biology Digital Library) project, one of the six CIS (Core Integration System) projects of the NSF NSDL (National SMETE Digital Library) Program.

In this paper we describe our efforts to bring scientific data into the digital library. This has required extension of the standard WWW, and also the extension of metadata standards far beyond the Dublin Core. Our system demonstrates this technology for real scientific data from astronomy.

Provenance is well understood in the context of art or digital libaries, where it respectively refers to the documented history of an art object, or the documentation of processes in a digital object's life cycle. Interest for provenance in the "e-science community" [12] is also growing, since provenance is perceived as a crucial component of workflow systems that can help scientists ensure reproducibility of their scientific analyses and processes [2,4].