Judith Bayard Cushing

Information technology challenges of biodiversity and ecosystems informatics

Computer scientists, biologists, and natural resource managers recently met to examine the prospects for advancing computer science and information technology research by focusing on the complex and often-unique challenges found in the biodiversity and ecosystem domain. The workshop and its final report reveal that the biodiversity and ecosystem sciences are fundamentally information sciences and often address problems having distinctive attributes of scale and socio-technical complexity. The paper provides an overview of the emerging field of biodiversity and ecosystem informatics and demonstrates how the demands of biodiversity and ecosystem research can advance our understanding and use of information technologies.

Component-based end-user database design for ecologists

To solve today’s ecological problems, scientists need well documented, validated, and coherent data archives. Historically, however, ecologists have collected and stored data idiosyncratically, making data integration even among close collaborators difficult. Further, effective ecology data warehouses and subsequent data mining require that individual databases be accurately described with metadata against which the data themselves have been validated. Using database technology would make documenting data sets for archiving, integration, and data mining easier, but few ecologists have expertise to use database technology and they cannot afford to hire programmers. In this paper, we identify the benefits that would accrue from ecologists’ use of modern information technology and the obstacles that prevent that use. We describe our prototype, the CanopyDataBank, through which we aim to enable individual ecologists in the forest canopy research community to be their own database programmers. The key feature that makes this possible is domain-specific database components, which we call templates. We also show how additional tools that reuse these components, such as for visualization, could provide gains in productivity and motivate the use of new technology. Finally, we suggest ways in which communities might share database components and how components might be used to foster easier data integration to solve new ecological problems.

Database design for ecologists: Composing core entities with observations

Abstract The ecoinformatics community recognizes that ecological synthesis across studies, space, and time will require new informatics tools and infrastructure. Recent advances have been encouraging, but many problems still face ecologists who manage their own datasets, prepare data for archiving, and search data stores for synthetic research. In this paper, we describe how work by the Canopy Database Project (CDP) might enable use of database technology by field ecologists: increasing the quality of database design, improving data validation, and providing structural and semantic metadata — all of which might improve the quality of data archives and thereby help drive ecological synthesis. The CDP has experimented with conceptual components for database design, templates, to address information technology issues facing ecologists. Templates represent forest structures and observational measurements on these structures. Using our software, researchers select templates to represent their study’s data and can generate normalized relational databases. Information hidden in those databases is used by ancillary tools, including data intake forms and simple data validation, data visualization, and metadata export. The primary question we address in this paper is, which templates are the right templates. We argue for defining simple templates (with relatively few attributes) that describe the domains major entities, and for coupling those with focused and flexible observation templates. We present a conceptual model for the observation data type, and show how we have implemented the model as an observation entity in the DataBank database designer and generator. We show how our visualization tool CanopyView exploits metadata made explicit by DataBank to help scientists with analysis and synthesis. We conclude by presenting future plans for tools to conduct statistical calculations common to forest ecology and to enhance data mining with DataBank databases. DataBank could be extended to another domain by replacing our forest–ecology-specific templates with those for the new domain. This work extends the basic computer science idea of abstract data types and user-defined types to ecology-specific database design tools for individual users, and applies to ecoinformatics the software engineering innovations of domain-specific languages, software patterns, components, refactoring, and end-user programming.

Guest Editors' Introduction: Research in the Digital Government Realm

As they focus on the challenges that those who implement digital government face, computer science researchers practice nearly the entire spectrum of their discipline, working in collaboration with scientists from other disciplines in pursuit of answers to questions about information management, policy, and technology in government. Sidebar, p. 27. An IT View of Emergency ManagementJose H. Canos, Technical University of Valencia, SpainMarcos R.S. Borges, Federal University of Rio de Janeiro, Brazil Gustavo Alonso, Swiss Federal Institute of Technology Zurich (ETHZ), Switzerland An emergency plan provides guidelines that government agencies can use for making management decisions promptly and efficiently when a critical emergency occurs. Sidebar, p. 28.Public Safety and Cross-Boundary Data Sharing: Lessons from the CapWin ProjectChristine B. Williams, Janis L. Gogan, and Jane Fedorowicz, Bentley CollegeThe CapWIN project represents one of the first integrated multistate transportation and public safety wireless networks in the US, enabling data interoperability for first responders wherever they are. Sidebar, p. 29.In the Real World of Digital Government: Successes and Challenges of E-RulemakingNeil Eisner, US Department of TransportationThe government currently uses electronic technology in all aspects of the e-rulemaking process, and it is working to develop additional methods that will help the public provide good data to use in making governmental decisions. Sidebar, p. 30.Research Issues in Healthcare InformaticsSylvia J. Spengler, US National Science FoundationAddressing both citizen needs and professional interests, which will be critical to gaining acceptance of a multifaceted approach to healthcare informatics, requires the kind of multifaceted approach that has been a hallmark of the NSF digital government program.

Beyond Big Data

What can we expect to find beyond Big Data? And how can we exploit Big Data to get there?What can we expect to find beyond Big Data? And how can we exploit Big Data to get there?

Eco-informatics for decision makers advancing a research agenda

Resource managers often face significant information technology (IT) problems when integrating ecological or environmental information to make decisions. At a workshop sponsored by the NSF and USGS in December 2004, university researchers, natural resource managers, and information managers met to articulate IT problems facing ecology and environmental decision makers. Decision making IT problems were identified in five areas: 1) policy, 2) data presentation, 3) data gaps, 4) tools, and 5) indicators. To alleviate those problems, workshop participants recommended specific informatics research in modeling and simulation, data quality, information integration and ontologies, and social and human aspects. This paper reports the workshop findings, and briefly compares these with research that traditionally falls under the emerging eco-informatics rubric.

Keeping Pace with Extreme Data

The problems of dealing with very large datasets plague scientists these days, despite emerging new technologies. How can scientists keep up with both their own fields and the dynamic science of data management, applying new technologies to their own work? And how might findings from network science inform this process?

Eco-informatics and decision making managing our natural resources

This panel responds to the December 2004 workshop on Eco-Informatics and Decision Making [1], which addressed how informatics tools can help with better management of natural resources and policy making. The workshop was jointly sponsored by the NSF, NBII, NASA, and EPA. Workshop participants recommended that informatics research in four IT areas be funded: modeling and simulation, data quality, information integration and ontologies, and social and human aspects. Additionally, they recommend that funding agencies provide infrastructure and some changes in funding habits to assure cycles of innovation in the domain were addressed. This panel brings issues raised in that workshop to the attention of digital government researchers.

Metadata and Semantics: A Computational Challenge for Molecular Biology

MOLECULAR BIOLOGY APPLICATIONS, like those of other scientific domains, need to store, mine, and view large amounts of specialized quantitative information. The research challenges for data management are many. A brief informal survey of former collaborators indicates that significant progress has been made in shared domain models; tools for sequence production, curation, publication, and visualization; and UI’s and API’s. That said, many of the research challenges from then, are with us still. Why? Because these are tough problems. To borrow a term from Fred Brooks (who borrowed it from Aristotle), many of the problems we faced 10 years ago were essential to the understanding of the molecular biology and to the wider application of gene and protein sequence data beyond their specific domains. In other words, those problems are problems of semantics. Many of the incidental or accidental problems have been solved, and progress on the essential challenges has been made. If anything, however, today’s challenges could be more complex, since the success of the last decade has bred desire for increased functionality and for using valuable sequence information across many disciplines (cell biology, organism biology, paleobotany, ecology, medical research, medical practice). Thus, molecular biology data promise even deeper understanding of life’s mysteries, and with that promise comes a need for even better semantics, and the increased challenge. Public databases such as GenBank, PDB, EMBL, JIPID, SwissProt, etc., make millions of genetic sequences available to molecular biologists (and others), and industry and university laboratories maintain their own private stores of (many more) sequences. In short, there is lots of information, some of it duplicate for everyone, some of it duplicate for some purposes but not for others, that people use for different purposes. Several specific domain-specific information technology problems come to mind: (1) a need for high-level, domain-specialized common interfaces and query languages to exploit heterogeneous databases, (2) the ability for individuals to filter and annotate the sequences, and to share some of those annotations with close colleagues, or even the pubic at large, (3) ability to integrate and track inputs and results from numerous computational biology programs, (4) the need for molecular biology communities, the T4 phage community, to have a more specific view of T4 sequences than the general molecular biology community; (5) the need for non-molecular biologists to have a view of the data that specializes it for their problem domain, though such views must in some way be tied to the “molecular biology” view so that meaningful collaboration can occur. Regarding this fifth problem, with respect to the scientific domain in which I am now working, tree physiologists collaborate with molecular biologists to identify genetic differences among trees that “breathe” or hold water differently. I suggest as a research challenge that we do not yet know enough of how such “new” wildly interdisciplinary collaborations will use data, and that some resources should be set aside to build small experimental prototypes. A problem not mentioned above, but which is rampant throughout the sciences is how to deal with error (especially as one scales up or down), and missing data. This is not to say that scientists need to work only with data that are “100%” certain and complete, but that they know which data are not so (and by how much). Those working with molecular biology applications need to have a very high degree of trust in, or at least understanding of, the quality and provenance, of the data underlying that information.