DataCite as a novel bibliometric source: Coverage, strengths and limitations

This paper explores the characteristics of DataCite to determine its possibilities and potential as a new bibliometric data source to analyze the scholarly production of open data. Open science and the increasing data sharing requirements from governments, funding bodies, institutions and scientific journals has led to a pressing demand for the development of data metrics. As a very first step towards reliable data metrics, we need to better comprehend the limitations and caveats of the information provided by sources of open data. In this paper, we critically examine records downloaded from the DataCite's OAI API and elaborate a series of recommendations regarding the use of this source for bibliometric analyses of open data. We highlight issues related to metadata incompleteness, lack of standardization, and ambiguous definitions of several fields. Despite these limitations, we emphasize DataCite's value and potential to become one of the main sources for data metrics development.Comment: Paper accepted for publication in Journal of Informetric

Building a Data Ecosystem: A New Data Stewardship Paradigm for the Multi-Mission Algorithm and Analysis Platform (MAAP)

New adaptive approaches to Earth observation data stewardship need to be adopted in order to allow for higher data volumes, heterogeneous data and constantly evolving technologies. The data ecosystem approach to stewardship offers a viable solution to this need by placing an emphasis on the relationships between data, technologies and people. In this paper, we present the Joint ESA-NASA Multi-Mission Algorithm and Analysis Platforms (MAAP) creation of a data ecosystem to support global aboveground terrestrial carbon dynamics research. We present the components needed to support the MAAP data ecosystem along with two data stewardship workflows used in the MAAP and the development of extended metadata for MAAP

Understanding climate data through commentary metadata: the CHARMe project

We describe the CHARMe project, which aims to link climate datasets with publications, user feedback and other items of "commentary metadata". The system will help users learn from previous community experience and select datasets that best suit their needs, as well as providing direct traceability between conclusions and the data that supported them. The project applies the principles of Linked Data and adopts the Open Annotation standard to record and publish commentary information. CHARMe contributes to the emerging landscape of "climate services", which will provide climate data and information to influence policy and decision-making. Although the project focuses on climate science, the technologies and concepts are very general and could be applied to other fields

Open Data, Grey Data, and Stewardship: Universities at the Privacy Frontier

As universities recognize the inherent value in the data they collect and hold, they encounter unforeseen challenges in stewarding those data in ways that balance accountability, transparency, and protection of privacy, academic freedom, and intellectual property. Two parallel developments in academic data collection are converging: (1) open access requirements, whereby researchers must provide access to their data as a condition of obtaining grant funding or publishing results in journals; and (2) the vast accumulation of 'grey data' about individuals in their daily activities of research, teaching, learning, services, and administration. The boundaries between research and grey data are blurring, making it more difficult to assess the risks and responsibilities associated with any data collection. Many sets of data, both research and grey, fall outside privacy regulations such as HIPAA, FERPA, and PII. Universities are exploiting these data for research, learning analytics, faculty evaluation, strategic decisions, and other sensitive matters. Commercial entities are besieging universities with requests for access to data or for partnerships to mine them. The privacy frontier facing research universities spans open access practices, uses and misuses of data, public records requests, cyber risk, and curating data for privacy protection. This paper explores the competing values inherent in data stewardship and makes recommendations for practice, drawing on the pioneering work of the University of California in privacy and information security, data governance, and cyber risk.Comment: Final published version, Sept 30, 201

Open Research Data and Innovative Scholarly Writing: OPERAS highlights

Pre-print of the article to be puslihed in OA on http://www.ressi.ch/ We present here highlights from an enquiry on the innovations in scholarly writing in the Humanities and Social Sciences in the H2020 project OPERAS-P. This article explores the theme of Open Research Data and its role in the emergence of new models of scholarly writing. We examine more closely the obstacles and fostering conditions to the publication of research data, both from a social and a technical perspective

Report of the Research Coordination Network RCN : OceanObsNetwork, facilitating open exchange of data and information

The OceanObsNetwork goals and objectives are to foster a broad, multi-disciplinary dialogue, enabling more effective use of sustained ocean observatories and observing systems. To achieve these, the activities for the RCN include a working group titled “Facilitating Open Exchange of Data and Information.” Within this area 3 task teams were created dealing with elements that impact on open exchange of data and information. This report examines the foundation of Open Data and its importance to the international community, science, innovation and jobs. While the goal may be similar, the paths to Open Data are varied and drawing together a pervasive approach will take time. There are however, near term steps, technical and social, that could have significant impacts. Stimulating interdisciplinary collaboration occurs through adoption of common standards for data exchange, creation of information brokering for improved discovery and access and working toward common or defined vocabularies. Simply finding other scientists’ data has been noted as a major barrier for research. Open Data impinges on existing reward systems and social interactions. Areas that need to be addressed are the academic reward system (in terms of promotion and resources), the peer review panels and grant selection processes (in terms of acknowledging the importance and challenge of data collection) and the needs for acceptable citation mechanisms. Intellectual property should not be abandoned in an Open Data environment and managing IPR is necessary. A sustainable Open Data Policy is essential and sustainability is a matter for all parties, government, private sector, academia and non-profit organizations. As full implementation of Open Data will involve a change in practices in a number of research and publication activities, an end-to-end perspective and strategy would most likely allow a long-term sustainable path to be pursued. Various business models are discussed in the paper that would not have been considered a decade ago. These range from cloud storage to publication of data with Digital Object Identifiers. These set a possible foundation for the future.National Science Foundation through Grant Award No. OCE-1143683

Two-Stream Model: Toward Data Production for Sharing Field Science Data

Scientific data play a central role in the production of knowledge reported in scientific publications. Today, data sharing policies together with technological capacity are fueling visions of data as open and accessible where data appear to stand-alone as products of the research process. Yet, guidelines and outputs are constantly being produced that impact subsequent work with the data, particularly in field-oriented, data-rich earth science research. We propose a model that focuses on two distinct yet intertwined data streams: internal-use data and public-reuse data. Internal-use data often involves a complex mix of processing, analysis and integration strategies creating data in forms leading to the publication of papers. Public-reuse data is prepared with a more standardized set of procedures creating data packages in the form of well-described, parameter-based datasets for release to a data repository and for reuse by others. While scientific researchers are familiar with collecting and analyzing data for publication in the scientific literature, the second data stream helps to identify tasks relating to the preparation of data for future, unanticipated reuse. The second stream represents an expansion in conceptualization of data management for the majority of natural scientists from a publication metaphor to recognition of a release metaphor. A combined dual-function model brings attention to some of the less recognized barriers that impede preparation of data for reuse. Digital data analysis spawns a multitude of files often assessed while ‘in use’ so for reuse of data, scientists must first identify what data files to share. They must also create robust data processes that frequently involve establishing new distributions of labor. The two-stream approach creates a visual representation for data generators who now must think about what data are most likely to have value not only for their work but also for the work of others. Development of this approach is part of a collaborative project studying site-based data curation in geobiology for geologists, geochemists, and microbiologists at Yellowstone National Park.Ope