scholarly journals Data Communities: Empowering Researcher-Driven Data Sharing in the Sciences

1970 ◽  
Vol 15 (1) ◽  
pp. 7
Author(s):  
Rebecca Springer ◽  
Danielle Cooper
Keyword(s):  
Data Sharing ◽  
Large Scale ◽  
Data Repository ◽  
Disciplinary Boundaries ◽  
Success Stories ◽  
Scholarly Communications ◽  
Information Technologists ◽  
Share Data ◽  
Technological Intervention ◽  
Informal Groups

There is a growing perception that science can progress more quickly, more innovatively, and more rigorously when researchers share data with each other. However many scientists are not engaging in data sharing and remain skeptical of its relevance to their work. As organizations and initiatives designed to promote STEM data sharing multiply – within, across, and outside academic institutions – there is a pressing need to decide strategically on the best ways to move forward. In this paper, we propose a new mechanism for conceptualizing and supporting STEM research data sharing.. Successful data sharing happens within data communities, formal or informal groups of scholars who share a certain type of data with each other, regardless of disciplinary boundaries. Drawing on the findings of four large-scale qualitative studies of research practices conducted by Ithaka S+R, as well as the scholarly literature, we identify what constitutes a data community and outline its most important features by studying three success stories, investigating the circumstances under which intensive data sharing is already happening. We contend that stakeholders who wish to promote data sharing – librarians, information technologists, scholarly communications professionals, and research funders, to name a few – should work to identify and empower emergent data communities. These are groups of scholars for whom a relatively straightforward technological intervention, usually the establishment of a data repository, could kickstart the growth of a more active data sharing culture. We conclude by offering recommendations for ways forward.

scholarly journals Data sharing practices and data availability upon request differ across scientific disciplines

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Leho Tedersoo ◽  
Rainer Küngas ◽  
Ester Oras ◽  
Kajar Köster ◽  
Helen Eenmaa ◽  
...  
Keyword(s):  
Data Sharing ◽  
Large Scale ◽  
Modern Science ◽  
Research Data ◽  
Data Availability ◽  
Research Articles ◽  
Scientific Disciplines ◽  
Funding Agencies ◽  
Share Data ◽  
Bonus Points

AbstractData sharing is one of the cornerstones of modern science that enables large-scale analyses and reproducibility. We evaluated data availability in research articles across nine disciplines in Nature and Science magazines and recorded corresponding authors’ concerns, requests and reasons for declining data sharing. Although data sharing has improved in the last decade and particularly in recent years, data availability and willingness to share data still differ greatly among disciplines. We observed that statements of data availability upon (reasonable) request are inefficient and should not be allowed by journals. To improve data sharing at the time of manuscript acceptance, researchers should be better motivated to release their data with real benefits such as recognition, or bonus points in grant and job applications. We recommend that data management costs should be covered by funding agencies; publicly available research data ought to be included in the evaluation of applications; and surveillance of data sharing should be enforced by both academic publishers and funders. These cross-discipline survey data are available from the plutoF repository.

scholarly journals Raising Awareness of Data Sharing Consent Through Knowledge Graph Visualisation

2021 ◽  
Author(s):  
Christof Bless ◽  
Lukas Dötlinger ◽  
Michael Kaltschmid ◽  
Markus Reiter ◽  
Anelia Kurteva ◽  
...  
Keyword(s):  
Data Sharing ◽  
Large Scale ◽  
User Study ◽  
Smart Cities ◽  
Sensor Data ◽  
Knowledge Graph ◽  
Raising Awareness ◽  
Share Data ◽  
Knowledge Graphs ◽  
Machine Readable

Knowledge graphs facilitate systematic large-scale data analysis by providing both human and machine-readable structures, which can be shared across different domains and platforms. Nowadays, knowledge graphs can be used to standardise the collection and sharing of user information in many different sectors such as transport, insurance, smart cities and internet of things. Regulations such as the GDPR make sure that users are not taken advantage of when they share data. From a legal standpoint it is necessary to have the user’s consent to collect information. This consent is only valid if the user is aware about the information collected at all times. To increase this awareness, we present a knowledge graph visualisation approach, which informs users about the activities linked to their data sharing agreements, especially after they have already given their consent. To visualise the graph, we introduce a user-centred application which showcases sensor data collection and distribution to different data processors. Finally, we present the results of a user study conducted to find out whether this visualisation leads to more legal awareness and trust. We show that with our visualisation tool data sharing consent rates increase from 48% to 81.5%.

scholarly journals Beyond trust: Amplifying unheard voices on concerns about harm resulting from health data-sharing

2021 ◽  
Vol 5 ◽  
pp. 239920262110484
Author(s):  
Stephanie Mulrine ◽  
Mwenza Blell ◽  
Madeleine Murtagh
Keyword(s):  
Data Sharing ◽  
Large Scale ◽  
Point Of Care ◽  
Health Data ◽  
Data Generation ◽  
Health Records ◽  
North East ◽  
Marginalized Groups ◽  
Health And Social Care ◽  
Share Data

Background: The point of care in many health systems is increasingly a point of health data generation, data which may be shared and used in a variety of ways by a range of different actors. Aim: We set out to gather data about the perspectives on health data-sharing of people living in North East England who have been underrepresented within other public engagement activities and who are marginalized in society. Methods: Multi-site ethnographic fieldwork was carried out in the Teesside region of England over a 6-month period in 2019 as part of a large-scale health data innovation program called Connected Health Cities. Organizations working with marginalized groups were contacted to recruit staff, volunteers, and beneficiaries for participation in qualitative research. The data gathered were analyzed thematically and vignettes constructed to illustrate findings. Results: Previous encounters with health and social care professionals and the broader socio-political contexts of people’s lives shape the perspectives of people from marginalized groups about sharing of data from their health records. While many would welcome improved care, the risks to people with socially produced vulnerabilities must be appreciated by those advocating systems that share data for personalized medicine or other forms of data-driven care. Conclusion: Forms of innovation in medicine which rely on greater data-sharing may present risks to groups and individuals with existing vulnerabilities, and advocates of these innovations should address the lack of trustworthiness of those receiving data before asking that people trust new systems to provide health benefits.

scholarly journals CHORD: Distributed Data-sharing via Hybrid ROS 1 and 2 for Multi-robot Exploration of Large-scale Complex Environments

2021 ◽  
pp. 1-1
Author(s):  
Muhammad Fadhil Ginting ◽  
Kyohei Otsu ◽  
Jeffrey Edlund ◽  
Jay Gao ◽  
Ali-akbar Agha-mohammadi
Keyword(s):  
Data Sharing ◽  
Large Scale ◽  
Distributed Data ◽  
Complex Environments ◽  
Multi Robot

Regulatory frameworks for clinical trial data sharing during the Pandemic and beyond: A scoping review (Preprint)

2021 ◽  
Author(s):  
Nachiket Gudi ◽  
Prashanthi Kamath ◽  
Trishnika Chakraborty ◽  
Anil G. Jacob ◽  
Shradha Parsekar ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Data Sharing ◽  
Scoping Review ◽  
Data Extraction ◽  
Grey Literature ◽  
Clinical Trial Data ◽  
Trial Data ◽  
Policy Documents ◽  
Regulatory Documents ◽  
Share Data

BACKGROUND Data sharing from clinical trials is well recognized and has widely gained recognition amid the COVID-19 pandemic. The competing interests of powerful stakeholders expressed through data exclusivity practices make clinical trial data sharing a complex phenomenon. The wider acceptance of data sharing practices in the absence of mandated policy creates uncertainty among trial investigators to count for risks vs benefit from sharing trial data. Data sharing becomes further complex as the trial data sharing is governed by the regional policies. This drew our attention to explore policies for informed data sharing. OBJECTIVE This scoping review aimed to map the existing literature around the regulatory documents that guide trial investigators to share clinical trial data. METHODS We followed a Joanna Briggs Institute scoping review approach and have reported the article according to the PRISMA extension for Scoping reviews (PRISMA-ScR). In addition to the use of the electronic databases, a targeted website search was performed to access relevant grey literature. The articles were screened at the title-abstract and the full text stages based on the selection criteria. All the included articles for data extraction were in English language. Data extraction was done independently using a pre-tested data extraction sheet. Included literature focused on clinical trial data sharing policies, guidelines, or SOPs. A narrative synthesis approach was used to summarize the findings. RESULTS This scoping review identified four articles and 13 policy documents from the grey literature. A majority of the clinical trial agencies require an agreement for data sharing between the data requestor/organization and trial agency. None of the policy documents mandates informed consent for data sharing. The time interval to share data underlying results, varies from six to 18 months from the time of trial publication. Depending upon trial data, policies follow both controlled and open access models. Regulatory documents identified in both scientific and grey literature emphasized on good research principles of protection of privacy of participant data and data anonymization through data sharing agreement between the data requester and trial agency. Need for an informed consent and cost of data sharing, timeline to share data, incentives, or reward to promote data sharing and capacity building for data sharing have remained grey areas in these policy documents. CONCLUSIONS This paper acknowledges the vital role of clinical data sharing from a public health perspective. We found that given the challenges around clinical trial data sharing, developing a feasible mechanism for data sharing is important. We suggest that standardizing data sharing processes by framing a concise policy with key elements of data sharing mechanisms could be easier to practice rather than a rigid and comprehensive data sharing policy. CLINICALTRIAL This scoping review protocol has not been registered and published.

scholarly journals Unified Ciphertext-Policy Weighted Attribute-Based Encryption for Sharing Data in Cloud Computing

2018 ◽  
Vol 8 (12) ◽  
pp. 2519
Author(s):  
Wei Li ◽  
Wei Ni ◽  
Dongxi Liu ◽  
Ren Liu ◽  
Shoushan Luo
Keyword(s):  
Cloud Computing ◽  
Mutual Information ◽  
Data Sharing ◽  
Rapid Development ◽  
Sharing Scheme ◽  
Attribute Based Encryption ◽  
Share Data ◽  
Efficient Data ◽  
Ciphertext Policy ◽  
Access Policies

With the rapid development of cloud computing, it is playing an increasingly important role in data sharing. Meanwhile, attribute-based encryption (ABE) has been an effective way to share data securely in cloud computing. In real circumstances, there is often a mutual access sub-policy in different providers’ access policies, and the significance of each attribute is usual diverse. In this paper, a secure and efficient data-sharing scheme in cloud computing, which is called unified ciphertext-policy weighted attribute-based encryption (UCP-WABE), is proposed. The weighted attribute authority assigns weights to attributes depending on their importance. The mutual information extractor extracts the mutual access sub-policy and generates the mutual information. Thus, UCP-WABE lowers the total encryption time cost of multiple providers. We prove that UCP-WABE is selectively secure on the basis of the security of ciphertext-policy weighted attribute-based encryption (CP-WABE). Additionally, the results of the implementation shows that UCP-WABE is efficient in terms of time.

scholarly journals PsychData – Experiences from 12 Years of Research Data Archiving

2015 ◽  
Author(s):  
Peter Weiland ◽  
Ina Dehnhard
Keyword(s):  
Data Sharing ◽  
Large Scale ◽  
Research Data ◽  
Data Reuse ◽  
German Research Foundation ◽  
Cross Sectional ◽  
Data Archiving ◽  
Wide Range ◽  
Domain Specific Knowledge ◽  
Meta Analyses

See video of the presentation.The benefits of making research data permanently accessible through data archives is widely recognized: costs can be reduced by reusing existing data, research results can be compared and validated with results from archived studies, fraud can be more easily detected, and meta-analyses can be conducted. Apart from that, authors may gain recognition and reputation for producing the datasets. Since 2003, the accredited research data center PsychData (part of the Leibniz Institute for Psychology Information in Trier, Germany) documents and archives research data from all areas of psychology and related fields. In the beginning, the main focus was on datasets that provide a high potential for reuse, e.g. longitudinal studies, large-scale cross sectional studies, or studies that were conducted during historically unique conditions. Presently, more and more journal publishers and project funding agencies require researchers to archive their data and make them accessible for the scientific community. Therefore, PsychData also has to serve this need.In this presentation we report on our experiences in operating a discipline-specific research data archive in a domain where data sharing is met with considerable resistance. We will focus on the challenges for data sharing and data reuse in psychology, e.g.large amount of domain-specific knowledge necessary for data curationhigh costs for documenting the data because of a wide range on non-standardized measuressmall teams and little established infrastructures compared with the "big data" disciplinesstudies in psychology not designed for reuse (in contrast to the social sciences)data protectionresistance to sharing dataAt the end of the presentation, we will provide a brief outlook on DataWiz, a new project funded by the German Research Foundation (DFG). In this project, tools will be developed to support researchers in documenting their data during the research phase.

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