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Linked Data for Science and Education Undefined 1 (2009) 1–5 1 IOS Press Linked Data for Science and Education Editor(s): Name Surname, University, Country Solicited review(s): Name Surname, University, Country Open review(s): Name Surname, University, Country Carsten Keßler a, Mathieu d’Aquin b and Stefan Dietze c a Institute for Geoinformatics, University of Münster, Germany E-mail: [email protected] b The Open University, UK E-mail: [email protected] c L3S Research Center, Germany E-mail: [email protected] Abstract. Sharing of resources and metadata is a central principle in scientific and educational contexts. With the emergence of the Linked Data approach as the most recent evolution of the Semantic Web, scientific and educational practitioners have started to adopt those principles. The communities working on Linked Data for science and education have since developed common schemas used for describing scientific or educational resources, substantial collections of structured data, bibliographic collec- tions, domain-specific vocabularies capturing vast amounts of scientific domain knowledge, as well as baseline technologies used to expose and integrate linked datasets. In this paper, we give an overview of the current landscape related to the use of Linked Data in the academic sector. We look at the common challenges, prominent datasets, tools and applications, and conclude on the major directions for research in this area. Keywords: Survey, Linked Science, Linked Learning, Education 1. Introduction and taxonomies have been developed to describe and expose educational resources, and scientific workflows Sharing of resources, resource metadata, and data and data. Due to the prevailing heterogeneity of de- across the Web is a central principle in scientific and ployed approaches and technologies, interoperability educational contexts. Scientific collaboration has long remains an open challenge. been striving for wider reuse and sharing of knowl- At the same time, the Linked Data (LD) [10] ap- edge and data. Likewise, the Open Educational Re- proach has emerged as the most recent evolution of sources community has promoted the widespread ex- the Semantic Web [8] and has widely established it- ploitation of public and reusable educational resources self as the de-facto standard for sharing data on the throughout the last decade. Hence, technologies to en- Web. While the LD approach provides a set of well- able interoperability of shared resources and data have established principles and (W3C) standards, such as long been at the centre of scientific and educational the use of URIs as identifiers, RDF, SPARQL [91], information systems. However, due to the lack of a aiming at Web-scale data interoperability, it has pro- shared technology stack and joint principles, the land- duced an ever growing amount of data sets and scape of developed and utilised standards is very frag- schemas available on the Web. Given the proven ca- mented and covers an increasing variety of heteroge- pabilities of LD technologies towards realising Web neous technologies, such as repositories with propri- scale data sharing and reuse, scientific and educational etary interfaces and query mechanisms. Moreover, a practitioners have started to adopt those principles. Re- broad range of largely incompatible metadata schemas sults of such activities cover joint schemas used for 0000-0000/09/$00.00 c 2009 – IOS Press and the authors. All rights reserved 2 C. Keßler et al. / Linked Data for Science and Education describing scientific or educational resources, vast col- called attention metadata, capturing the perception of lections of structured data about, for instance, cultural learning resources by learners, has become increas- or historic artifacts, bibliographic collections, domain- ingly useful for tailoring learning experiences to par- specific vocabularies capturing extensive amounts of ticular user needs and requirements. To this end, al- scientific domain knowledge – where the life sciences though a vast amount of educational content and data are particularly well represented – as well as base- is shared on the Web in an open way, the integration line technologies used to expose and integrate linked process is still costly as different learning resources are datasets. isolated from each other and based on different imple- In this paper, we give an overview of the current mentation standards [24]. landscape related to the use of LD for science and ed- ucation, looking at the common challenges, prominent 2.2. Scientific data and metadata sharing datasets, tools and applications, to conclude on the ma- jor directions for research in this area. The scientific community has been developing new ways of sharing (meta-)data along with the move to digital research environments. However, similar to the 2. Knowledge and data sharing in Science & field of TEL, the developed approaches were charac- Education: challenges terized by a segregation of the research process into different aspects. Tools such as Taverna4 or MyEx- With technologies evolving, communities of prac- periment5 support researchers in managing workflows titioners in the science and education sectors have for their experiments. Electronic lab notebooks help produced a variety of standards and approaches, es- keeping track of progress. Data repositories and data pecially to facilitate information sharing. These stan- management systems, such as laboratory information dards have however shown limited success in their management systems, provide means to store and ac- concrete adoption, and generally seem not to be suit- cess research data through proprietary APIs. The way able for the Web-scale distribution of information of they handle and enable interaction with data is of- educational and scientific relevance. We discuss here ten domain-specific due to the varying requirements some of their shortcomings, identifying the challenges across disciplines. Finally, once a finding is published, where LD technologies and principles can prove of the publication and its metadata is provided through an valuable use. electronic library catalogue. Even though these tools and systems support and document parts of the re- 2.1. Educational data and metadata sharing search process, there is still a significant lack of in- tegration of different information sources. The docu- Throughout the last decade, research in the field of mentation of a research project is segregated, placing technology-enhanced learning (TEL) has focused fun- each aspect into a different silo, and the entry point to damentally on enabling interoperability and reuse of that documentation – the publication – is decontextu- learning resources and data. That has led to a frag- alized [6]. mented landscape of competing metadata schemas, i.e., general-purpose ones such as Dublin Core [31] 2.3. Challenges or schemas specific to the educational field, like IEEE Learning Object Metadata (LOM) [46] or ADL While there already is a large amount of educational SCORM1, but also interface mechanisms such as OAI- and scientific data available on the Web via proprietary PMH2 or SQI3. These technologies are exploited by and/or competing schemas and interface mechanisms, educational resource repository providers to support the main challenge is to (a) start adopting LD prin- interoperability. In addition, social data as well as so- ciples and vocabularies while (b) leveraging on exist- ing data available on the Web by non-LD compliant 1Advanced Distributed Learning (ADL) SCORM: http:// means [30]. In the following, we list the major research www.adlnet.org challenges which are currently approached by adopt- 2Open Archives Protocol for Metadata Harvest- ing LD-principles within science and education. ing http://www.openarchives.org/OAI/ openarchivesprotocol.html 3Simple Query Interface: http://www.cen-ltso.net/ 4http://www.taverna.org.uk main.aspx?put=859 5http://www.myexperiment.org C. Keßler et al. / Linked Data for Science and Education 3 Data interoperability—infrastructural: Vast amounts RDFS and SKOS facilitate the combination and align- of educational and scientific data have already been ment of different models making it no longer necessary available on the Web, however, due to heterogeneity to subscribe to just one domain model. of existing storage and interface approaches, interop- The wide range of available tools (see Section4) and erability has been limited [94]. LD offers a technology applications (see Section5) which exploit aforemen- stack composed of RDF as representation standard and tioned capabilities of LD demonstrate its applicability SPARQL as query mechanism and standardised infras- to scientific and educational processes. tructural HTTP endpoint which facilitates exposing, integrating and sharing of Web data at the infrastruc- tural level. In addition, a number of tools, for instance, 3. Datasets for storage and integration of data have been provided, often with domain-specific extensions (see Section4). This section gives an overview of existing datasets Data interoperability—semantic and syntactic: In for education (Section 3.1) and science (Section 3.2). addition to infrastructural boundaries, heterogeneity Section 3.3 lists vocabularies specifically developed with respect to data representation also hinders wide for science and education, and provides an analysis of interoperability of data. This includes, for instance, the vocabularies actually in use. The range of datasets the use of heterogeneous schemas, vocabularies and and vocabularies is steadily growing and has already
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