Dataset Information

Towards a ubiquitous user model for profile sharing and reuse.

ABSTRACT: People interact with systems and applications through several devices and are willing to share information about preferences, interests and characteristics. Social networking profiles, data from advanced sensors attached to personal gadgets, and semantic web technologies such as FOAF and microformats are valuable sources of personal information that could provide a fair understanding of the user, but profile information is scattered over different user models. Some researchers in the ubiquitous user modeling community envision the need to share user model's information from heterogeneous sources. In this paper, we address the syntactic and semantic heterogeneity of user models in order to enable user modeling interoperability. We present a dynamic user profile structure based in Simple Knowledge Organization for the Web (SKOS) to provide knowledge representation for ubiquitous user model. We propose a two-tier matching strategy for concept schemas alignment to enable user modeling interoperability. Our proposal is proved in the application scenario of sharing and reusing data in order to deal with overweight and obesity.

SUBMITTER: Martinez-Villasenor Mde L

PROVIDER: S-EPMC3545566 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Towards a ubiquitous user model for profile sharing and reuse.

Martinez-Villaseñor Maria de Lourdes Mde L Gonzalez-Mendoza Miguel M Hernandez-Gress Neil N

Sensors (Basel, Switzerland) 20120928 10

People interact with systems and applications through several devices and are willing to share information about preferences, interests and characteristics. Social networking profiles, data from advanced sensors attached to personal gadgets, and semantic web technologies such as FOAF and microformats are valuable sources of personal information that could provide a fair understanding of the user, but profile information is scattered over different user models. Some researchers in the ubiquitous ...[more]

PMID: 23201995

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Project description:BACKGROUND:With data becoming a centerpiece of modern scientific discovery, data sharing by scientists is now a crucial element of scientific progress. This article aims to provide an in-depth examination of the practices and perceptions of data management, including data storage, data sharing, and data use and reuse by scientists around the world. METHODS:The Usability and Assessment Working Group of DataONE, an NSF-funded environmental cyberinfrastructure project, distributed a survey to a multinational and multidisciplinary sample of scientific researchers in a two-waves approach in 2017-2018. We focused our analysis on examining the differences across age groups, sub-disciplines of science, and sectors of employment. FINDINGS:Most respondents displayed what we describe as high and mediocre risk data practices by storing their data on their personal computer, departmental servers or USB drives. Respondents appeared to be satisfied with short-term storage solutions; however, only half of them are satisfied with available mechanisms for storing data beyond the life of the process. Data sharing and data reuse were viewed positively: over 85% of respondents admitted they would be willing to share their data with others and said they would use data collected by others if it could be easily accessed. A vast majority of respondents felt that the lack of access to data generated by other researchers or institutions was a major impediment to progress in science at large, yet only about a half thought that it restricted their own ability to answer scientific questions. Although attitudes towards data sharing and data use and reuse are mostly positive, practice does not always support data storage, sharing, and future reuse. Assistance through data managers or data librarians, readily available data repositories for both long-term and short-term storage, and educational programs for both awareness and to help engender good data practices are clearly needed.

Project description: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.

Project description:ObjectivesWe examined major issues associated with sharing of individual clinical trial data and developed a consensus document on providing access to individual participant data from clinical trials, using a broad interdisciplinary approach.Design and methodsThis was a consensus-building process among the members of a multistakeholder task force, involving a wide range of experts (researchers, patient representatives, methodologists, information technology experts, and representatives from funders, infrastructures and standards development organisations). An independent facilitator supported the process using the nominal group technique. The consensus was reached in a series of three workshops held over 1 year, supported by exchange of documents and teleconferences within focused subgroups when needed. This work was set within the Horizon 2020-funded project CORBEL (Coordinated Research Infrastructures Building Enduring Life-science Services) and coordinated by the European Clinical Research Infrastructure Network. Thus, the focus was on non-commercial trials and the perspective mainly European.OutcomeWe developed principles and practical recommendations on how to share data from clinical trials.ResultsThe task force reached consensus on 10 principles and 50 recommendations, representing the fundamental requirements of any framework used for the sharing of clinical trials data. The document covers the following main areas: making data sharing a reality (eg, cultural change, academic incentives, funding), consent for data sharing, protection of trial participants (eg, de-identification), data standards, rights, types and management of access (eg, data request and access models), data management and repositories, discoverability, and metadata.ConclusionsThe adoption of the recommendations in this document would help to promote and support data sharing and reuse among researchers, adequately inform trial participants and protect their rights, and provide effective and efficient systems for preparing, storing and accessing data. The recommendations now need to be implemented and tested in practice. Further work needs to be done to integrate these proposals with those from other geographical areas and other academic domains.

Project description:Open research data practices are a relatively new, thus still evolving part of scientific work, and their usage varies strongly within different scientific domains. In the literature, the investigation of open research data practices covers the whole range of big empirical studies covering multiple scientific domains to smaller, in depth studies analysing a single field of research. Despite the richness of literature on this topic, there is still a lack of knowledge on the (open) research data awareness and practices in materials science and engineering. While most current studies focus only on some aspects of open research data practices, we aim for a comprehensive understanding of all practices with respect to the considered scientific domain. Hence this study aims at 1) drawing the whole picture of search, reuse and sharing of research data 2) while focusing on materials science and engineering. The chosen approach allows to explore the connections between different aspects of open research data practices, e.g. between data sharing and data search. In depth interviews with 13 researchers in this field were conducted, transcribed verbatim, coded and analysed using content analysis. The main findings characterised research data in materials science and engineering as extremely diverse, often generated for a very specific research focus and needing a precise description of the data and the complete generation process for possible reuse. Results on research data search and reuse showed that the interviewees intended to reuse data but were mostly unfamiliar with (yet interested in) modern methods as dataset search engines, data journals or searching public repositories. Current research data sharing is not open, but bilaterally and usually encouraged by supervisors or employers. Project funding does affect data sharing in two ways: some researchers argue to share their data openly due to their funding agency's policy, while others face legal restrictions for sharing as their projects are partly funded by industry. The time needed for a precise description of the data and their generation process is named as biggest obstacle for data sharing. From these findings, a precise set of actions is derived suitable to support Open Data, involving training for researchers and introducing rewards for data sharing on the level of universities and funding bodies.

Project description:Signal detection and management is a key activity in pharmacovigilance (PV). When a new PV signal is identified, the respective information is publicly communicated in the form of periodic newsletters or reports by organizations that monitor and investigate PV-related information (such as the World Health Organization and national PV centers). However, this type of communication does not allow for systematic access, discovery and explicit data interlinking and, therefore, does not facilitate automated data sharing and reuse. In this paper, we present OpenPVSignal, a novel ontology aiming to support the semantic enrichment and rigorous communication of PV signal information in a systematic way, focusing on two key aspects: (a) publishing signal information according to the FAIR (Findable, Accessible, Interoperable, and Re-usable) data principles, and (b) exploiting automatic reasoning capabilities upon the interlinked PV signal report data. OpenPVSignal is developed as a reusable, extendable and machine-understandable model based on Semantic Web standards/recommendations. In particular, it can be used to model PV signal report data focusing on: (a) heterogeneous data interlinking, (b) semantic and syntactic interoperability, (c) provenance tracking and (d) knowledge expressiveness. OpenPVSignal is built upon widely-accepted semantic models, namely, the provenance ontology (PROV-O), the Micropublications semantic model, the Web Annotation Data Model (WADM), the Ontology of Adverse Events (OAE) and the Time ontology. To this end, we describe the design of OpenPVSignal and demonstrate its applicability as well as the reasoning capabilities enabled by its use. We also provide an evaluation of the model against the FAIR data principles. The applicability of OpenPVSignal is demonstrated by using PV signal information published in: (a) the World Health Organization's Pharmaceuticals Newsletter, (b) the Netherlands Pharmacovigilance Centre Lareb Web site and (c) the U.S. Food and Drug Administration (FDA) Drug Safety Communications, also available on the FDA Web site.

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Towards a ubiquitous user model for profile sharing and reuse.

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Towards a ubiquitous user model for profile sharing and reuse.

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