Decentralized provenance-aware publishing with nanopublications

Author:

Kuhn Tobias1,Chichester Christine2,Krauthammer Michael34,Queralt-Rosinach Núria5,Verborgh Ruben6,Giannakopoulos George78,Ngonga Ngomo Axel-Cyrille9,Viglianti Raffaele10,Dumontier Michel11

Affiliation:

1. Department of Computer Science, VU University Amsterdam, Amsterdam, Netherlands

2. Nestle Institute of Health Sciences, Lausanne, Switzerland

3. Yale University School of Medicine, Yale University, New Haven, CT, United States

4. Yale Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, United States

5. Research Programme on Biomedical Informatics, Hospital del Mar Medical Research Institute, Universitat Pompeu Fabra, Barcelona, Spain

6. Data Science Lab, Ghent University, Ghent, Belgium

7. Institute of Informatics and Telecommunications, NCSR Demokritos, Athens, Greece

8. SciFY Private Not-for-profit Company, Athens, Greece

9. AKSW Research Group, University of Leipzig, Leipzig, Germany

10. Maryland Institute for Technology in the Humanities, University of Maryland, College Park, MD, United States

11. Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, United States

Abstract

Publication and archival of scientific results is still commonly considered the responsability of classical publishing companies. Classical forms of publishing, however, which center around printed narrative articles, no longer seem well-suited in the digital age. In particular, there exist currently no efficient, reliable, and agreed-upon methods for publishing scientific datasets, which have become increasingly important for science. In this article, we propose to design scientific data publishing as a web-based bottom-up process, without top-down control of central authorities such as publishing companies. Based on a novel combination of existing concepts and technologies, we present a server network to decentrally store and archive data in the form of nanopublications, an RDF-based format to represent scientific data. We show how this approach allows researchers to publish, retrieve, verify, and recombine datasets of nanopublications in a reliable and trustworthy manner, and we argue that this architecture could be used as a low-level data publication layer to serve the Semantic Web in general. Our evaluation of the current network shows that this system is efficient and reliable.

Funder

Research Foundation–Flanders (FWO)

Publisher

PeerJ

Subject

General Computer Science

Reference50 articles.

1. Provenance-centered dataset of drug-drug interactions;Banda,2015

2. Workflow-centric research objects: first class citizens in scholarly discourse;Belhajjame,2012

3. Linked data—design issues;Berners-Lee,2006

4. Documents and data: modelling materials for humanities research in XML and relational databases;Bradley;Literary and Linguistic Computing,2005

5. SPARQL web-querying infrastructure: ready for action?;Buil-Aranda,2013

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3