Mike Frame

Data Sharing by Scientists: Practices and Perceptions

Background Scientific research in the 21st century is more data intensive and collaborative than in the past. It is important to study the data practices of researchers – data accessibility, discovery, re-use, preservation and, particularly, data sharing. Data sharing is a valuable part of the scientific method allowing for verification of results and extending research from prior results. Methodology/Principal Findings A total of 1329 scientists participated in this survey exploring current data sharing practices and perceptions of the barriers and enablers of data sharing. Scientists do not make their data electronically available to others for various reasons, including insufficient time and lack of funding. Most respondents are satisfied with their current processes for the initial and short-term parts of the data or research lifecycle (collecting their research data; searching for, describing or cataloging, analyzing, and short-term storage of their data) but are not satisfied with long-term data preservation. Many organizations do not provide support to their researchers for data management both in the short- and long-term. If certain conditions are met (such as formal citation and sharing reprints) respondents agree they are willing to share their data. There are also significant differences and approaches in data management practices based on primary funding agency, subject discipline, age, work focus, and world region. Conclusions/Significance Barriers to effective data sharing and preservation are deeply rooted in the practices and culture of the research process as well as the researchers themselves. New mandates for data management plans from NSF and other federal agencies and world-wide attention to the need to share and preserve data could lead to changes. Large scale programs, such as the NSF-sponsored DataNET (including projects like DataONE) will both bring attention and resources to the issue and make it easier for scientists to apply sound data management principles.

Participatory design of DataONE—Enabling cyberinfrastructure for the biological and environmental sciences

Abstract The scope and nature of biological and environmental research are evolving in response to environmental challenges such as global climate change, invasive species and emergent diseases. In particular, scientific studies are increasingly focusing on long-term, broad-scale, and complex questions that require massive amounts of diverse data collected by remote sensing platforms and embedded environmental sensor networks; collaborative, interdisciplinary science teams; and new approaches for managing, preserving, analyzing, and sharing data. Here, we describe the design of DataONE (Data Observation Network for Earth)—a cyberinfrastructure platform developed to support rapid data discovery and access across diverse data centers distributed worldwide and designed to provide scientists with an integrated set of familiar tools that support all elements of the data life cycle (e.g., from planning and acquisition through data integration, analysis and visualization). Ongoing evolution of the DataONE architecture is based on participatory, user-centered design processes including: (1) identification and prioritization of stakeholder communities; (2) developing an understanding of their perceptions, attitudes and user requirements; (3) usability analysis and assessment; and (4) engaging science teams in grand challenge exemplars such as understanding the broad-scale dynamics of bird migration. In combination, the four approaches engage the broad community in providing guidance on infrastructure design and implementation.

Changes in Data Sharing and Data Reuse Practices and Perceptions among Scientists Worldwide

The incorporation of data sharing into the research lifecycle is an important part of modern scholarly debate. In this study, the DataONE Usability and Assessment working group addresses two primary goals: To examine the current state of data sharing and reuse perceptions and practices among research scientists as they compare to the 2009/2010 baseline study, and to examine differences in practices and perceptions across age groups, geographic regions, and subject disciplines. We distributed surveys to a multinational sample of scientific researchers at two different time periods (October 2009 to July 2010 and October 2013 to March 2014) to observe current states of data sharing and to see what, if any, changes have occurred in the past 3–4 years. We also looked at differences across age, geographic, and discipline-based groups as they currently exist in the 2013/2014 survey. Results point to increased acceptance of and willingness to engage in data sharing, as well as an increase in actual data sharing behaviors. However, there is also increased perceived risk associated with data sharing, and specific barriers to data sharing persist. There are also differences across age groups, with younger respondents feeling more favorably toward data sharing and reuse, yet making less of their data available than older respondents. Geographic differences exist as well, which can in part be understood in terms of collectivist and individualist cultural differences. An examination of subject disciplines shows that the constraints and enablers of data sharing and reuse manifest differently across disciplines. Implications of these findings include the continued need to build infrastructure that promotes data sharing while recognizing the needs of different research communities. Moving into the future, organizations such as DataONE will continue to assess, monitor, educate, and provide the infrastructure necessary to support such complex grand science challenges.

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.

Managing scientific data as public assets: Data sharing practices and policies among full-time government employees

This paper examines how scientists working in government agencies in the U.S. are reacting to the “ethos of sharing” government‐generated data. For scientists to leverage the value of existing government data sets, critical data sets must be identified and made as widely available as possible. However, government data sets can only be leveraged when policy makers first assess the value of data, in much the same way they decide the value of grants for research outside government. We argue that legislators should also remove structural barriers to interoperability by funding technical infrastructure according to issue clusters rather than administrative programs. As developers attempt to make government data more accessible through portals, they should consider a range of other nontechnical constraints attached to the data. We find that agencies react to the large number of constraints by mostly posting their data on their own websites only rather than in data portals that can facilitate sharing. Despite the nontechnical constraints, we find that scientists working in government agencies exercise some autonomy in data decisions, such as data documentation, which determine whether or not the data can be widely shared. Fortunately, scientists indicate a willingness to share the data they collect or maintain. However, we argue further that a complete measure of access should also consider the normative decisions to collect (or not) particular data.

Integrated science for environmental decision-making: the challenge for biodiversity and ecosystem informatics

Information concerning biodiversity and ecosystems is critical to a wide range of scientific, educational, and government uses; however, much of this information is not easily accessible. This paper presents the core concepts underlying the National Biological Information Infrastructure (NBII) , a Web-based system coordinated by the U.S. Geological Survey that provides data and information on U.S. biological resources and, through a variety of partnerships, biological resources in many other nations. This paper will highlight NBII development, implementation, technological innovation, and successful user applications at two regional nodes: the NBII Southern Appalachian Information Node and the NBII Central Southwest/Gulf Coast Node.

The National Biological Information Infrastructure: Coming of Age.

Coordinated by the US Geological Survey, the National Biological Information Infrastructure (NBII) is a Web‐based system that provides increased access to data and information on the nation’s biological resources. The NBII can be viewed from a variety of perspectives. This article – an individual case study and not a broad survey with extensive references to the literature – addresses the structure of the NBII related to thematic sections, infrastructure sections and place‐based sections, and other topics such as the Integrated Taxonomic Information System (one of our more innovative tools) and the development of our controlled vocabulary.

Academic Libraries Follow-Up dataset

This is the data set to the Libraries Follow-Up only. This is the planned 1st follow-up for DataONE Libraries stakeholders

OME: Tool for generating and managing metadata to handle BigData

The next-generation On-line Metadata Editor (OME) is an easy-to-use tool to help document scientific data in a well-structured popular metadata format. In this paper, we discuss the newest tool that Oak Ridge National Laboratory has developed to input, edit, and manage metadata and how it is helping data intensive science centers across many federal agencies to prepare metadata and to make their BigData discoverable.