Jillian C. Wallis

Little science confronts the data deluge: habitat ecology, embedded sensor networks, and digital libraries

Abstracte-Science promises to increase the pace of science via fast, distributed access to computational resources, analytical tools, and digital libraries. “Big science” fields such as physics and astronomy that collaborate around expensive instrumentation have constructed shared digital libraries to manage their data and documents, while “little science” research areas that gather data through hand-crafted fieldwork continue to manage their data locally. As habitat ecology researchers begin to deploy embedded sensor networks, they are confronting an array of challenges in capturing, organizing, and managing large amounts of data. The scientists and their partners in computer science and engineering make use of common datasets but interpret the data differently. Studies of this field in transition offer insights into the role of digital libraries in e-Science, how data practices evolve as science becomes more instrumented, and how scientists, computer scientists, and engineers collaborate around data. Among the lessons learned are that data on the same variables are gathered by multiple means, that data exist in many states and in many places, and that publication practices often drive data collection practices. Data sharing is embraced in principle but little sharing actually occurs, due to interrelated factors such as lack of demand, lack of standards, and concerns about publication, ownership, data quality, and ethics. We explore the implications of these findings for data policy and digital library architecture. Research reported here is affiliated with the Center for Embedded Networked Sensing.

If we share data, will anyone use them? Data sharing and reuse in the long tail of science and technology.

Research on practices to share and reuse data will inform the design of infrastructure to support data collection, management, and discovery in the long tail of science and technology. These are research domains in which data tend to be local in character, minimally structured, and minimally documented. We report on a ten-year study of the Center for Embedded Network Sensing (CENS), a National Science Foundation Science and Technology Center. We found that CENS researchers are willing to share their data, but few are asked to do so, and in only a few domain areas do their funders or journals require them to deposit data. Few repositories exist to accept data in CENS research areas.. Data sharing tends to occur only through interpersonal exchanges. CENS researchers obtain data from repositories, and occasionally from registries and individuals, to provide context, calibration, or other forms of background for their studies. Neither CENS researchers nor those who request access to CENS data appear to use external data for primary research questions or for replication of studies. CENS researchers are willing to share data if they receive credit and retain first rights to publish their results. Practices of releasing, sharing, and reusing of data in CENS reaffirm the gift culture of scholarship, in which goods are bartered between trusted colleagues rather than treated as commodities.

Know thy sensor: trust, data quality, and data integrity in scientific digital libraries

For users to trust and interpret the data in scientific digital libraries, they must be able to assess the integrity of those data. Criteria for data integrity vary by context, by scientific problem, by individual, and a variety of other factors. This paper compares technical approaches to data integrity with scientific practices, as a case study in the Center for Embedded Networked Sensing (CENS) in the use of wireless, in-situ sensing for the collection of large scientific data sets. The goal of this research is to identify functional requirements for digital libraries of scientific data that will serve to bridge the gap between current technical approaches to data integrity and existing scientific practices.

Building digital libraries for scientific data: an exploratory study of data practices in habitat ecology

As data become scientific capital, digital libraries of data become more valuable. To build good tools and services, it is necessary to understand scientists’ data practices. We report on an exploratory study of habitat ecologists and other participants in the Center for Embedded Networked Sensing. These scientists are more willing to share data already published than data that they plan to publish, and are more willing to share data from instruments than hand-collected data. Policy issues include responsibility to provide clean and reliable data, concerns for liability and misappropriation of data, ways to handle sensitive data about human subjects arising from technical studies, control of data, and rights of authorship. We address the implications of these findings for tools and architecture in support of digital data libraries.

Who’s Got the Data? Interdependencies in Science and Technology Collaborations

Science and technology always have been interdependent, but never more so than with today’s highly instrumented data collection practices. We report on a long-term study of collaboration between environmental scientists (biology, ecology, marine sciences), computer scientists, and engineering research teams as part of a five-university distributed science and technology research center devoted to embedded networked sensing. The science and technology teams go into the field with mutual interests in gathering scientific data. “Data” are constituted very differently between the research teams. What are data to the science teams may be context to the technology teams, and vice versa. Interdependencies between the teams determine the ability to collect, use, and manage data in both the short and long terms. Four types of data were identified, which are managed separately, limiting both reusability of data and replication of research. Decisions on what data to curate, for whom, for what purposes, and for how long, should consider the interdependencies between scientific and technical processes, the complexities of data collection, and the disposition of the resulting data.

Digital libraries for scientific data discovery and reuse: from vision to practical reality

Science and technology research is becoming not only more distributed and collaborative, but more highly instrumented. Digital libraries provide a means to capture, manage, and access the data deluge that results from these research enterprises. We have conducted research on data practices and participated in developing data management services for the Center for Embedded Networked Sensing since its founding in 2002 as a National Science Foundation Science and Technology Center. Over the course of eight years, our digital library strategy has shifted dramatically in response to changing technologies, practices, and policies. We report on the development of several DL systems and on the lessons learned, which include the difficulty of anticipating data requirements from nascent technologies, building systems for highly diverse work practices and data types, the need to bind together multiple single-purpose systems, the lack of incentives to manage and share data, the complementary nature of research and development in understanding practices, and sustainability.

Unearthing the Infrastructure: Humans and Sensors in Field-Based Scientific Research

Distributed sensing systems for studying scientific phenomena are critical applications of information technologies. By embedding computational intelligence in the environment of study, sensing systems allow researchers to study phenomena at spatial and temporal scales that were previously impossible to achieve. We present an ethnographic study of field research practices among researchers in the Center for Embedded Networked Sensing (CENS), a National Science Foundation Science & Technology Center devoted to developing wireless sensing systems for scientific and social applications. Using the concepts of boundary objects and trading zones, we trace the processes of collaborative research around sensor technology development and adoption within CENS. Over the 10-year lifespan of CENS, sensor technologies, sensor data, field research methods, and statistical expertise each emerged as boundary objects that were understood differently by the science and technology partners. We illustrate how sensing technologies were incompatible with field-based environmental research until researchers “unearthed” their infrastructures, explicitly reintroducing human skill and expertise into the data collection process and developing new collaborative languages that emphasized building dynamic sensing systems that addressed human needs. In collaborating around a dynamic sensing model, the sensing systems became embedded not in the environment of study, but in the practices of the scientists.

Data, data use, and scientific inquiry: two case studies of data practices

Data are proliferating far faster than they can be captured, managed, or stored. What types of data are most likely to be used and reused, by whom, and for what purposes? Answers to these questions will inform information policy and the design of digital libraries. We report findings from semi-structured interviews and field observations to investigate characteristics of data use and reuse and how those characteristics vary within and between scientific communities. The two communities studied are researchers at the Center for Embedded Network Sensing (CENS) and users of the Sloan Digital Sky Survey (SDSS) data. The data practices of CENS and SDSS researchers have implications for data curation, system evaluation, and policy. Some data that are important to the conduct of research are not viewed as sufficiently valuable to keep. Other data of great value may not be mentioned or cited, because those data serve only as background to a given investigation. Metrics to assess the value of documents do not map well to data.

Towards a virtual organization for data cyberinfrastructure

We report on the exploratory stages of multi-university, multi-research-site, multi-year effort to investigate and compare data practices in multiple cyberinfrastructure projects and their emerging virtual organizations. Our long-term goal is to understand the data practices and data management requirements of virtual organizations and their implications for the design and development of data digital libraries. We have constructed our own virtual organization as a participant-observer approach to the research. Results to date suggest that collaborative technologies are emergent and that defining and scoping the data products of collaborations continues to be problematic.

The Special Case of Scientific Data Sharing with Education

The seemingly simple task of reusing data for science education relies on the presence of scientific data, scientists willing to share, infrastructure to provide access, and mechanisms to share between the two disparate communities of scientists and science students. What makes sharing between scientists and science students a special case of data sharing, is that all of the implicit knowledge attending the data must pass along this same vector. Our work at the Center for Embedded Networked Sensing studying aspects of this data reuse problem has shown us a rough outline of how the future of this data sharing will look. Our approach is to start from the prospective of the scientists, looking for opportunities to support scientific research, and then leveraging the data for reuse by education. The investment needed to capture high quality scientific data necessitates the consideration of reuse by the general population as well as other interested scientific parties.