Use Of Shared Data Research Articles

Recommendations for sharing network data and materials

Abstract One of the goals of open science is to promote the transparency and accessibility of research. Sharing data and materials used in network research is critical to these goals. In this paper, we present recommendations for whether, what, when, and where network data and materials should be shared. We recommend that network data and materials should be shared, but access to or use of shared data and materials may be restricted if necessary to avoid harm or comply with regulations. Researchers should share the network data and materials necessary to reproduce reported results via a publicly accessible repository when an associated manuscript is published. To ensure the adoption of these recommendations, network journals should require sharing, and network associations and academic institutions should reward sharing.

African researchers do not think differently about Open Data

A key motivation for Open Science is accessibility. For researchers in resource-poor economies, this translates into access to the methods, data and publications that will foster scientific research and discovery in such communities and environments. Attitudes toward Open Science are in flux, and there is a growing awareness of the roles and responsibilities that researchers have to one another in this regard. This paper explores how African researchers approach issues relating to Open Data by reporting on the State of Open Data Report data. Focusing on the attitudes toward Open Data, this paper reports on how African researchers view (i) data sharing, (ii) the use of shared data, and (iii) the Open Data ecosystem. The findings show that, although the attitudes of African researchers have changed over time, they are not very different from those held by their international counterparts. These findings will aid policymakers, as well as academic and research institutions, in highlighting the areas of future growth for Open Data in Africa.

Status, use and impact of sharing individual participant data from clinical trials: a scoping review

ObjectivesTo explore the impact of data-sharing initiatives on the intent to share data, on actual data sharing, on the use of shared data and on research output and impact of...

National platform for Rare Diseases Data Registry of Japan.

IntroductionIn Japan, there are approximately 300 projects conducting research on rare diseases supported by the Ministry of Health, Labour and Welfare of Japan (MHLW) and the Japan Agency for Medical Research and Development (AMED). Diverse data, including clinical, genomic, and sample‐related data, are generated by these projects. However, at present, such data are managed individually by each project. This makes it difficult for third parties to ascertain the data generated by projects.MethodsAgain this background, at the beginning of 2017, the AMED started the National Platform for Rare Diseases Data Registry of Japan (RADDAR‐J), whose mission is to construct a cross‐sectional data integration platform incorporating projects supported by the AMED and MHLW. RADDAR‐J promotes data sharing by the projects in accordance with the data‐sharing policy established by the AMED, which classifies data sharing into three categories based on the strategies used to protect the rights of researchers while promoting data sharing. RADDAR‐J integrates and analyzes data shared by each project to add value to the resources and promote secondary use by third parties while protecting the rights of the researchers who shared their data. The platform is designed to provide incentives to projects that shared their data by supporting registry construction or genomic analysis to promote data sharing. RADDAR‐J also has the function of data identification to securely integrate data originating from the same person. RADDAR‐J accelerates clinical research by encouraging each project to utilize a central ethics committee.Results/ConclusionThe use of the platform by projects is expected to lead to streamlined data collection, improved quality assurance, improved access to data, and promotion of joint research and the secondary use of shared data. These benefits will accelerate research into diagnosis and treatment technologies and will hopefully lead to improved quality of life for patients with rare diseases.

The Safe and Effective Use of Shared Data Underpinned by Stakeholder Engagement and Evaluation Practice.

Summary Objectives: The paper draws attention to: i) key considerations involving the confidentiality, privacy, and security of shared data; and ii) the requirements needed to build collaborative arrangements encompassing all stakeholders with the goal of ensuring safe, secure, and quality use of shared data. Method: A narrative review of existing research and policy approaches along with expert perspectives drawn from the International Medical Informatics Association (IMIA) Working Group on Technology Assessment and Quality Development in Health Care and the European Federation for Medical Informatics (EFMI) Working Group for Assessment of Health Information Systems. Results: The technological ability to merge, link, re-use, and exchange data has outpaced the establishment of policies, procedures, and processes to monitor the ethics and legality of shared use of data. Questions remain about how to guarantee the security of shared data, and how to establish and maintain public trust across large-scale shared data enterprises. This paper identifies the importance of data governance frameworks (incorporating engagement with all stakeholders) to underpin the management of the ethics and legality of shared data use. The paper also provides some key considerations for the establishment of national approaches and measures to monitor compliance with best practice. Conclusion: Data sharing endeavours can help to underpin new collaborative models of health care which provide shared information, engagement, and accountability amongst all stakeholders. We believe that commitment to rigorous evaluation and stakeholder engagement will be critical to delivering health data benefits and the establishment of collaborative models of health care into the future.

Availability and Use of Shared Data From Cardiometabolic Clinical Trials.

Sharing of patient-level clinical trial data has been widely endorsed. Little is known about how extensively these data have been used for cardiometabolic diseases. We sought to evaluate the availability and use of shared data from cardiometabolic clinical trials. We extracted data from ClinicalStudyDataRequest.com, a large, multisponsor data-sharing platform hosting individual patient-level data from completed studies sponsored by 13 pharmaceutical companies. From January 2013 to May 2017, the platform had data from 3374 clinical trials, of which 537 (16%) evaluated cardiometabolic therapeutics (phase 1, 36%; phase 2, 17%; phase 2/3, 1%; phase 3, 42%; phase 4, 4%). They covered 74 therapies and 398 925 patients. Diabetes mellitus (60%) and hypertension (15%) were the most common study topics. Median time from study completion to data availability was 79 months. As of May 2017, ClinicalStudyDataRequest.com had received 318 submitted proposals, of which 163 had signed data-sharing agreements. Thirty of these proposals were related to cardiometabolic therapies and requested data from 79 unique studies (15% of all trials, 29% of phase 3/4 trials). Most (96%) data requesters of cardiometabolic clinical trial data were from academic centers in North America and Western Europe, and half the proposals were unfunded. Most proposals were for secondary hypothesis-generating questions, with only 1 proposed reanalysis of the original study primary hypothesis. To date, 3 peer-reviewed articles have been published after a median of 19 months (9-32 months) from the data-sharing agreement. Despite availability of data from >500 cardiometabolic trials in a multisponsor data-sharing platform, only 15% of these trials and 29% of phase 3/4 trials have been accessed by investigators thus far, and a negligible minority of analyses have reached publication.

EVALITA Goes Social: Tasks, Data, and Community at the 2016 Edition

EVALITA, the evaluation campaign of Natural Language Processing and Speech Tools for the Italian language, was organised for the fifth time in 2016. Six tasks, covering both re-reruns as well as completely new tasks, and an IBM-sponsored challenge, attracted a total of 34 submissions. An innovative aspect at this edition was the focus on social media data, especially Twitter, and the use of shared data across tasks, yielding a test set with layers of annotation concerning PoS tags, sentiment information, named entities and linking, and factuality information. Differently from the previous edition(s), many systems relied on a neural architecture, and achieved best results when used. From the experience and success of this edition, also in terms of dissemination of information and data, and in terms of collaboration between organisers of different tasks, we collected some reflections and suggestions that prospective EVALITA chairs might be willing to take into account for future editions.

CHARMed PyMca, Part I: A Protocol for Improved Inter‐laboratory Reproducibility in the Quantitative ED‐XRF Analysis of Copper Alloys

This paper describes a protocol for quantification of heritage copper alloys by energy‐dispersive X‐ray fluorescence spectroscopy (ED‐XRF). The protocol, nicknamed CHARMed PyMca, is designed for users who wish to maximize inter‐laboratory reproducibility of quantitative ED‐XRF results for the wide range of copper alloys found in heritage materials. By maximizing reproducibility, this protocol should facilitate collaboration and allow the rigorous use of shared data and databases. The protocol uses free, open‐source, fundamental parameters software called PyMca. PyMca allows for a consistent and transparent application of the fundamental parameters approach independent of the ED‐XRF instrumentation used. The proposed protocol calls for calibration of standardless PyMca results against a set of certified reference materials designed specifically for use with heritage copper alloys, the so‐called copper CHARM set. Finally, this protocol calls for the calibration‐to‐standards to be carried out following a consistent strategy, including error modelling and the incorporation of a validation procedure. A reproducibility study was conducted using CHARMed PyMca and eight different ED‐XRF instruments of six different types. In comparison to a 2010 study conducted according to the same method, CHARMed PyMca showed a dramatic improvement in reproducibility and method sensitivity.

VARIABILITY IN CLINICAL RESEARCH DATA MANAGEMENT PRACTICES: LESSONS FROM THE MALARIA COMMUNITY

BackgroundAppropriate data management (DM) is critical to produce valuable research data, especially with growing prospects for long-term archiving, sharing and individual patient data meta-analysis. The experience of the WorldWide Antimalarial...

The Virtuous Cycle of a Data Ecosystem.

Digital data of all types are being created at an ever-increasing rate, doubling approximately every two years. Annual data creation rates are estimated to reach 44 trillion gigabytes by 2020 [1]. Similarly, the rate at which primary scientific data are being collected is accelerating [2]. This astounding growth in scientific data creation has led to the contemporary discussion of scientific data sharing policies. Many of the criticisms levied against data sharing have focused on practical issues such as the economics and logistics of data storage, technical challenges for doing so, or appropriate attribution of credit [2–9]. In contrast, the arguments in favor of data sharing have focused largely on scientific replication, reproducibility [10], facilitation of collaborative research, and increased citations for publications that share data [11]. This is largely an ethical argument wherein there is an obligation to share data collected using public funds [3–6,12,13]. Rather than focusing on the much-discussed arguments against data sharing—cost, infrastructure, curation, privacy, and attribution/credit concerns—in this Perspective, I outline the overlooked benefits of data sharing: novel remixing and combining as well as bias minimization and meta-analysis. I argue that we must consider the weight of the costs against the true value of the possible benefits. If the decision for any individual researcher, university, or funding agency to implement data sharing policies comes down to a cost—benefit analysis based solely on replication versus storage, the cost—benefit analysis may be artificially tipped in favor of not sharing data caused by overlooking more subtle—but critical—benefits. These hidden benefits of data remixing cannot be appreciated when considering each individual dataset as an independent entity, and thus a richer consideration of those benefits is warranted. Although there is some evidence that, on the local scale, research groups may not make use of shared data [14], in this Perspective, I outline the ways in which research groups are beginning to take advantage of open data in novel, and sometimes surprising, ways. Rather than arguing for a centralized, large-scale data repository, I am advocating for a more organic development wherein we, institutionally, encourage the growth of a data ecosystem. This can be done via multiple venues, such as the general scientific data sharing sites figshare (https://figshare.com/) or the Dryad Digital Repository (http://datadryad.org/), each of which, in addition to Nature Publishing Group’s recently launched peer-reviewed data sharing journal, Scientific Data [15], provides citable Digital Object Identifiers for the data themselves. Such developments are addressing concerns regarding credit and help motivate data curation and contextualization. A data sharing ecosystem provides space for multiple diverse datasets to intermingle to encourage new, multidisciplinary discoveries for current and future scientists.

Exploring the determinants of scientific data sharing: Understanding the motivation to publish research data

The research community is working to create new capabilities to share data and to deal with issues of data quality, standards, and protection, and ethical and responsible use of shared data. These issues have been found to influence the willingness of researchers to publish data created during the course of their research. We use the results of a survey conducted by the working groups of the DataONE project to present a new understanding of challenges to the development of global data collections and preservation by systematically examining the determinants of the researchers' likelihood to openly publish research data. This study found two key determinants affecting researchers' willingness to publish their data. First is data management in terms of data management skills and organization support. Second is the acknowledgement of the data set's originator in terms of appreciation and legal and policy requirements. This study also found that the impact of the significant determinants is contingent on the amount of data to be published. Finally, this study calls for further investigation to ascertain the relationship of data management and data quality, and systematic investigation on the roles and responsibility of government within these global data preservations.

Data Publishing and Scientific Journals: The Future of the Scientific Paper in a World of Shared Data

The rapid growth of the internet and related technologieshas already had a tremendous impact on scientific publish-ing. This journal has given attention to open accesspublishing (Ascoli 2005; Bug 2005; Merkel-Sobotta 2005;Velterop 2005), to reforming the review process (DeSchutter 2007; Saper and Maunsell 2009) and to theproblems with getting authors to share their data (Ascoli2006; Kennedy 2006; Teeters et al. 2008; Van Horn andBall 2008) and how to enhance the use of shared data(Gardner et al. 2008; Kennedy 2010).But the impact of the internet and data warehousing onscience will be much larger and there is a growing interestin how these technologies can be leveraged to improve thescientific process (Hey et al. 2009). Let’s travel towards thefuture and imagine that not only the tools and infrastructureare available to share scientific data at any time after it isgenerated, but that it has also become standard practice forthe community to do so. How this can be achieved is notthe focus of this editorial, instead I want to speculate on therelationship between scientific papers and data repositories(Bourne 2005, 2010; Cinkosky et al. 1991) in such anenvironment. It is important for the scientific community todiscuss these issues now because, while these technologiesare expected to radically improve the scientific process,they will also change the way in which our work isevaluated.I propose that we should distinguish data publishingfrom paper publishing (Callaghan et al. 2009; Cinkosky etal. 1991) and, when established for specific scientific fields,promote data publishing as the primary outlet for much ofthe scientific output.A good metaphor for data publishing is to look at howcomplete organism genomic sequences are published inhigh impact journals now (Srivastava et al. 2010; Warren etal. 2010). Such papers serve really two goals: to announcethe availability of the genome sequence in GenBank and todescribe some scientific conclusions based on the analysisof the genome. The perceived importance of the latterdetermines whether a high impact journal will accept thepaper and therefore the authors spend a lot of effort inhyping this part. But are these two components irrevocablyintertwined? Couldn’t one just publish the data, in this caseby depositing the complete sequence in a database, andannounce this fact through a form of publication? Theanalysis can then be published separately at a later time ordistributed over different papers, etc. This is not donebecause at present the publication of the paper in the highimpact journal is considered to be the optimal reward forthe researchers, both for career advancement and forsuccess in obtaining new grants (Bourne 2005). I call datapublication a method where the data providers, who may bedifferent from the people who analyze the data, receivecredit for their work when they deposit the sequence in thedatabase and where subsequent access to the data is trackedand considered equivalent to paper citation.There are a number of advantages to considering datapublication as a separate process. First, credit assignmentbecomes more explicitly defined among the authors.Several journals (like Nature, Science, the PLoS series,etc.) have taken steps towards a more granular creditassignment by asking authors to explicitly list their

Spatiotemporal integration of molecular and anatomical data in virtual reality using semantic mapping

We have developed a computational framework for spatiotemporal integration of molecular and anatomical datasets in a virtual reality environment. Using two case studies involving gene expression data and pharmacokinetic data, respectively, we demonstrate how existing knowledge bases for molecular data can be semantically mapped onto a standardized anatomical context of human body. Our data mapping methodology uses ontological representations of heterogeneous biomedical datasets and an ontology reasoner to create complex semantic descriptions of biomedical processes. This framework provides a means to systematically combine an increasing amount of biomedical imaging and numerical data into spatiotemporally coherent graphical representations. Our work enables medical researchers with different expertise to simulate complex phenomena visually and to develop insights through the use of shared data, thus paving the way for pathological inference, developmental pattern discovery and biomedical hypothesis testing.

A short form of the Dimensional Assessment of Personality Pathology-Basic Questionnaire (DAPP-BQ): The DAPP-SF

A short form of the Dimensional Assessment of Personality Pathology-Basic Questionnaire (DAPP-BQ), called the DAPP-SF, is presented, consisting of 136 of the original 290 items. It was established in a community sample that the factor structure of the DAPP-SF is highly congruent with the structure of the DAPP-BQ. The 18 DAPP-SF scales, which were found to be highly reliable, turned out to correlate substantially with the DAPP-BQ scales, even after applying a necessary correction, as the DAPP-SF was developed in the same sample used to investigate the Dutch DAPP-BQ. The higher-order convergent validity of the DAPP-SF was demonstrated by correlating the DAPP-SF scales and factors with Van Kampen's 5DPT. The same factor structure as found in the community sample of patients with personality disorder, also demonstrating that the agreement between the DAPP-SF and the DAPP-BQ is not dependent on the use of shared data. Finally, following Bagge and Trull [Bagge, C.L., Trull, T.J., 2003. DAPP-BQ: Factor structure and relations to personality disorder symptoms in a non-clinical sample. Journal of Personality Disorders 17, 19–32], specific predictions were tested concerning differences in means on the 18 scales between 10 diagnostic groups in the sample of patients with personality disorder. Results supported the validity of the DAPP-SF with respect to its lower-order structure. Finally, for practical purposes, preliminary norms are presented for females and males from the community in two different age groups.

Ethical and Legal Considerations Regarding Disputed Authorship with the Use of Shared Data

Little guidance is currently available for handling disputes between research mentors and students when working with shared data. This article analyzes how the ethical guidelines from the American Psychological Association (APA), the Office of Research Integrity (ORI), and the American Educational Research Association (AERA) can inform common disputes in this area. Additional insights about the nature of the research relationship are derived from contract and copyright law. Practice guidelines are proposed to safeguard student and faculty welfare in research collaboration, and recommendations are provided to help prevent and resolve disputes between students and faculty.

Challenges, approaches and architecture for distributed process integration in heterogeneous environments

Integration and coordination of distributed processes remains a central challenge of construction information technology research. Extant technologies, while capable, are not yet scalable enough to enable rapid customization and instantiation for specific projects. Specifically, the heterogeneity of existing legacy sources together with firms’ range of approaches to process management makes deployment of integrated information technologies impractical. This paper reports on an architecture for distributed process integration named process connectors that addresses heterogeneity in a scalable manner. The process connectors architecture incorporates two key approaches that address heterogeneity over varying time scales. The SEEK: Scalable Extraction of Enterprise Knowledge toolkit is reviewed as a mechanism to discover semantically heterogeneous source data. The SEEK approach complements existing data integration methods for persistent sharing of information. To make use of shared data on a per project basis, a schedule mapping approach is presented that integrates firms’ diverse individual schedules in a unified representation. The schedule mapping approach allows integration of process views that have different levels of detail, while allowing participants to maintain their own view of the process. Collectively, SEEK and the schedule mapping approach facilitate a broad range of analyses to support coordination of distributed schedules. While this paper focuses primarily on schedule process integration, the process connectors architecture is viewed as providing a broad solution to discovery and integration of firms’ process data.

A survey of cache coherence schemes for multiprocessors

Schemes for cache coherence that exhibit various degrees of hardware complexity, ranging from protocols that maintain coherence in hardware, to software policies that prevent the existence of copies of shared, writable data, are surveyed. Some examples of the use of shared data are examined. These examples help point out a number of performance issues. Hardware protocols are considered. It is seen that consistency can be maintained efficiently, although in some cases with considerable hardware complexity, especially for multiprocessors with many processors. Software schemes are investigated as an alternative capable of reducing the hardware cost. >