Suchergebnisse

The vast majority of research output is grey; white (peer reviewed scholarly publications) forms a minor proportion. Historically, grey material was generated and used within an organisation. However, in recent years some research- relevant grey material is being made openly available. Among grey outputs, research datasets are the largest proportion by volume and increasingly these are being made openly available. It is necessary for users of grey material to have some indication of reliability (quality, context, availability) so that they can judge whether the grey material is appropriate to their requirements. Rich metadata provides a mechanism for such evaluation. CERIF (Common European Research Information Format) provides such a rich metadata environment. Furthermore, CERIF allows generation of discovery level metadata (such as DC (Dublin Core), DCAT (Data Catalog Vocabulary), CKAN (Comprehensive Knowledge Archive Network) for simple retrieval or browsing and provides navigation to more detailed and specific metadata about the grey object. CERIF provides a bridge over research datasets and open government data. CERIF thus forms the lowest common level of metadata across grey (and white) objects. ; publishedVersion

OGD (Open Government Data) is provided from government departments for transparency and to stimulate a market in ICT services for industry and citizens. Research datasets from publicly funded research commonly are associated with the open scholarly publications movement. However, the former world commonly is derived from the latter with generalisation and summarisation. There is advantage in a user of OGD being able to 'drill down' to the underlying research datasets. OGD encourages cross-domain research because the summarized data from different domains is more easily relatable. Bridging across the two worlds requires rich metadata; CERIF (Common European research Information Format) has proved itself to be ideally suited to this requirement. Utilising the research datasets is data-intensive science, a component of e-Research. Data-intensive science also requires access to an e-infrastructure. Virtualisation of this e-infrastructure optimizes this. ; publishedVersion

This article analyses the operationalization of One Health in the context of data-intensive science in response to the COVID-19 outbreak. Building on ethnographic field research and revisiting the lives of a knowledge infrastructure of interdisciplinary collaboration set up online in the early phase of the COVID-19 health emergency, the article develops the notion of "data as environment." This environment is a contact structure that entangles knowledge systems, subjects, processing tools, and mediated bio-socialities in processes of data-intensive knowledge co-production. Claims for new collaborative approaches between the biomedical, environmental, and social sciences are increasingly marked by the emergence of digital knowledge-making infrastructure that leverages data, knowledge, and expertise from different disciplines and sectors to increase scientific productivity via data-sharing technologies. Yet, digital knowledge-making infrastructures appear self-evident when they are in place, while data are often conceived as inert and disembodied information units separated from social relations of research. The argument that data are an environment expands anthropological thinking on data and digital knowledge-making infrastructures by enlightening political-ethical questions that are at stake in the emerging technoscientific worlds of the Anthropocene.

As a lead contributor to the OECD's Building Digital Workforce Capacity and Skills for Data-Intensive Science (2020) report, Dr Michelle Barker outlines in this presentation the goal of the report, i.e. to make recommendations to policy makers on how to facilitate the digital workforce capacity needed for data-intensive science, based on analysis of best practice. The presentation highlights: - Digital workforce capacity and COVID19: the importance of digital skills, the need for shared access to open data, software and code, and the shortfall in skills to enable a comprehensive response to such emergencies - The ongoing need for a digital skilled workforce for data-intensive science - Five focus areas in the report include: 1. Enablers for digital workforce capacity development 2. Defining needs: digital skills, frameworks and roles 3. Provision of training 4. Community development 5. Career paths and reward structures - Recommendations for actors incl. universities, national or regional governments

IntroductionResearch using linked data sets can lead to new insights and discoveries that positively impact society. However, the use of linked data raises concerns relating to illegitimate use, privacy, and security (e.g., identity theft, marginalization of some groups). It is increasingly recognized that the public needs to be consulted to develop data access systems that consider both the potential benefits and risks of research. Indeed, there are examples of data sharing projects being derailed because of backlash in the absence of adequate consultation. (e.g., care.data in the UK).
Objectives and methodsThis talk will describe the results of public deliberations held in Vancouver, British Columbia in April 2018 and the fall of 2019. The purpose of these events was to develop informed and civic-minded public advice regarding the use and the sharing of linked data for research in the context of rapidly evolving data availability and researcher aspirations.
ResultsIn the first deliberation, participants developed and voted on 19 policy-relevant statements. Taken together, these statements provide a broad view of public support and concerns regarding the use of linked data sets for research and offer guidance on measures that can be taken to improve the trustworthiness of policies and process around data sharing and use. The second deliberation will focus on the interplay between public and private sources of data, and role of individual and collective or community consent I the future.
ConclusionGenerally, participants were supportive of research using linked data because of the value such uses can provide to society. Participants expressed a desire to see the data access request process made more efficient to facilitate more research, as long as there are adequate protections in place around security and privacy of the data. These protections include both physical and process-related safeguards as well as a high degree of transparency.

IntroductionSocietal and individual benefits of data-intensive science are substantial but raise challenges of balancing individual privacy and public good, while building appropriate governance and socio-technical systems to support data-intensive science. We set out to define a new field of inquiry to move collective interests forward.
Objectives and ApproachOur objectives were: 1. To create a concise definition of the emerging field of Population Data Science; 2. To highlight the characteristics and challenges of Population Data Science; 3. To differentiate Population Data Science from existing fields of data science and informatics; and 4. To discuss the implications and future opportunities for Population Data Science. Objectives 1 and 2 were met largely through International Population Data Linkage Network (IPDLN) member engagement, Objective 3 was evaluated via literature review, and Objective 4 was achieved through iterative and collective work on a peer-reviewed position paper.
ResultsWe define Population Data Science succinctly as the science of data about people. It is related to, but distinct from, the fields of data science and informatics. A broader definition includes four characteristics of: i) data use for positive impact on individuals and populations; ii) bringing together and analyzing data from multiple sources; iii) identifying population-level insights; and iv) developing safe, privacy-sensitive and ethical infrastructure to support research. One implication of these characteristics is that few individuals or organisations possess all of the requisite knowledge and skills comprising Population Data Science, so this is by nature a multi-disciplinary "team science" field. There is a need to advance various aspects of science, such as data linkage technology, various forms of analytics, and methods of public engagement.
Conclusion/ImplicationsThese implications are the beginnings of a research agenda for Population Data Science, which if approached as a collective field, will catalyze significant advances in our understanding of society, health, and human behavior and increase the impact of our research.

We report on a case study which examines the social science community's capability and institutional support for data management. Fourteen researchers were invited for an in-depth qualitative survey between June 2014 and October 2015. We modify and adopt the Community Capability Model Framework (CCMF) profile tool to ask these scholars to self-assess their current data practices and whether their academic environment provides enough supportive infrastructure for data related activities. The exemplar disciplines in this report include anthropology, political sciences, and library and information science. Our findings deepen our understanding of social disciplines and identify capabilities that are well developed and those that are poorly developed. The participants reported that their institutions have made relatively slow progress on economic supports and data science training courses, but acknowledged that they are well informed and trained for participants' privacy protection. The result confirms a prior observation from previous literature that social scientists are concerned with ethical perspectives but lack technical training and support. The results also demonstrate intra- and inter-disciplinary commonalities and differences in researcher perceptions of data-intensive capability, and highlight potential opportunities for the development and delivery of new and impactful research data management support services to social sciences researchers and faculty.

Information is increasingly digital, creating opportunities to respond to pressing issues about human populations in near real time using linked datasets that are large, complex, and diverse. The potential social and individual benefits that can come from data-intensive science are large, but raise challenges of balancing individual privacy and the public good, building appropriate socio-technical systems to support data-intensive science, and determining whether defining a new field of inquiry might help move those collective interests and activities forward. A combination of expert engagement, literature review, and iterative conversations led to our conclusion that defining the field of Population Data Science (challenge 3) will help address the other two challenges as well. We define Population Data Science succinctly as the science of data about people and note that it is related to but distinct from the fields of data science and informatics. A broader definition names four characteristics of: data use for positive impact on citizens and society; bringing together and analyzing data from multiple sources; finding population-level insights; and developing safe, privacy-sensitive and ethical infrastructure to support research. One implication of these characteristics is that few people possess all of the requisite knowledge and skills of Population Data Science, so this is by nature a multi-disciplinary field. Other implications include the need to advance various aspects of science, such as data linkage technology, various forms of analytics, and methods of public engagement. These implications are the beginnings of a research agenda for Population Data Science, which if approached as a collective field, can catalyze significant advances in our understanding of trends in society, health, and human behavior.

A class of longitudinal data has emerged with the use of technological devices for scientific data collection. This class of data is called intensive longitudinal data (ILD). This volume features applied statistical modelling strategies developed by leading statisticians and methodologists working in conjunction with behavioural scientists

Cloud platform is extensively used for data intensive computing. Data intensive computing is a parallel computing that deals with large data sets. Main drawback of cur rent cloud infrastructure is,there is no flexible data mobility which is result of centralization of resources. Restricted data mobility of cloud has made existing cloud infrastructure unsuitable for distributed applications with large datasets. Here we present Nebula - a server which democratizes data intensive computing by incorporating edge computing,and it also avoids replication and provides secure storage by means of data encryption. https://www.ijiert.org/paper-details?paper_id=141273

Cloud platform is extensively used for data intensive computing. Data intensive computing is a parallel computing that deals with large data sets. Main drawback of current cloud infrastructure is, there is noflexible data mobility which is result of centralization of resources.Restricted data mobility of cloud has made existing cloud infrastructure unsuitable for distributed applications with large datasets.Here we present Nebula-a server which democratizes data intensive computing by incorporating edge computing, and it also avoids replication and provides secure storage by means of data encryption.