Hoekstra, R.

Idrissou, A.K.

Khalili, A.

van den Besselaar, P.

English

Here, we describe the RISIS-SMS metadata system, developed to support the use of heterogeneous datasets in the field of Science, Technology and Innovation Studies (STIS). These data are partly within the RISIS infrastructure, but often elsewhere. The system has three aims: (i) to help researchers to search for and understand data that will help to answer specific research questions, without having to access or download the data. As datasets often have restricted access, browsing metadata is a key feature of the system: researchers need help identifying the relevant data from different sources for their research, and for which data it is worthwhile asking for access; (ii) to support the enrichment of data By linking the metadata system to the Linked Open Data environment (LOD); (iii) to facilitate application-driven data integration

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UvA-DARE is a service provided by the library of the University of Amsterdam (https://dare.uva.nl)UvA-DARE (Digital Academic Repository)Managing metadata for science, technology and innovation studies: The RISIScaseIdrissou, A.K.; Khalili, A.; Hoekstra, R.; van den Besselaar, P.Publication date2016Document VersionFinal published versionPublished inWHiSe 2016 : Humanities in the Semantic WebLink to publicationCitation for published version (APA):Idrissou, A. K., Khalili, A., Hoekstra, R., & van den Besselaar, P. (2016). Managing metadatafor science, technology and innovation studies: The RISIS case. In A. Adamou, E. Daga, & L.Isaksen (Eds.), WHiSe 2016 : Humanities in the Semantic Web: Proceedings of the 1stWorkshop on Humanities in the Semantic Web, co-located with 13th ESWC Conference 2016(ESWC 2016) : Anissaras, Greece, May 29th, 2016 (pp. 15-20). [3] (CEUR WorkshopProceedings; Vol. 1608). 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Youwill be contacted as soon as possible.Download date:10 Mar 2023Managing metadata for science, technology andinnovation studies: The RISIS caseAl Koudous Idrissou1,3, Ali Khalili1, Rinke Hoekstra1,2, and Peter van denBesselaar31 Department of Computer Science, Vrije Universiteit Amsterdam, NL{o.a.k.idrissou,a.khalili,rinke.hoekstra}@vu.nl2 Faculty of Law, University of Amsterdam, NL3 Department of Organization Sciences, Vrije Universiteit Amsterdm, NLp.a.a.vanden.besselaar@vu.nlAbstract. Here, we describe the RISIS-SMS metadata system, devel-oped to support the use of heterogeneous datasets in the field of Science,Technology and Innovation Studies (STIS). These data are partly withinthe RISIS infrastructure, but often elsewhere. The system has three aims:(i) to help researchers to search for and understand data that will help toanswer specific research questions, without having to access or downloadthe data. As datasets often have restricted access, browsing metadata isa key feature of the system: researchers need help identifying the rele-vant data from different sources for their research, and for which datait is worthwhile asking for access; (ii) to support the enrichment of dataBy linking the metadata system to the Linked Open Data environment(LOD); (iii) to facilitate application-driven data integration.Keywords: metadata, Linked Data, Science & Technology Studies, Re-search Infrastructures, digital humanities1 IntroductionThe field of Science, Technology and Innovation Studies (STIS) is an interdisci-plinary field between the social sciences and the humanities. It covers many fieldsfrom the economics of science and innovation up to the history and philosophy ofscience [5]. It relies on the availability of a large volume of highly heterogeneousdata: structured and unstructured, qualitative and quantitative. STIS studies thedynamics of scientific ideas by analysing the content of scientific publicationsand project descriptions. For example, to help understand the selection processestaking place in the scientific community or to better understand life histories ofscientists and research organizations.Based on requirements extracted from interviews we conducted, we iden-tify the need for researchers to search across datasets and for data providersto attract researchers while keeping restricted datasets access. To address theseproblems, we describe in this paper a rich RDF metadata vocabulary to overcomeaccess limitations and facilitate data discovery, integration and use by humans16 A. Adamou, E. Daga and L. Isaksen (eds.)and machines [3]. The vocabulary is used by the Semantically-Mapping Science4infrastructure (SMS) to provide metadata services besides Geo services, Integra-tion services and Category services. It enables the integration of qualitative andquantitative approaches that have been strongly diverging over time [1].2 RequirementsProblem Summary. At its core, SMS comprises a collection of proprietary andpublic databases relevant to the field of STIS. For most RISIS datasets, accessis restricted to users with authorization only granted after an explicit requestis submitted to the data owner. Other data can only be accessed on site at aphysical location. This gets in the way of a good understanding of the contentand coverage of a dataset: if data is not reachable, how can one decide if a closeddataset is relevant enough for addressing her research question before requestingfor access, or travel to visit a dataset owner? This access limitation does notonly hinder findability and relevance assessment ex ante, but also hinders the expost integration of heterogeneous datasets after they have been identified.Methodology. To address this problem we designed a metadata vocabulary guidedby informal interviews conducted with STIS researchers and data-owners.Thishelped to identify and categorize (Figure 1) the information the metadata shouldcover. After the first version was developed, owners of some 12 datasets used thesystem. We visited all the data-owners, and discussed the user experiences aswell as the benefits and problems. This was used to improve the vocabularydesign. Finally exchanging email with users helped in fine tuning the metadata.Interviews Outcome & Solutions. Protect proprietary datasets - To protectdatasets that contain private and sensitive data or data for which a specificpermission or subscription is required, we categorised RISIS datasets as: confi-dential data, publicly accessible data, and all other relevant public data on theWeb. Overcome access limitation - Because data access is limited, we pro-vide users with means for browsing dataset metadata rather than inspecting thedata itself. The metadata should meet six requirements: R1 - Facilitate infor-mation displaying at a user interface level. R2 - Provide information guiding theuse of data. R3 - Provide detailed information about the datasets available onRISIS-SMS. R4 - Support users to get an in-depth understanding of the dataat hand, in such a way that they easily identify how the data should be inter-preted, used, or linked to other data. R5 - Facilitate trust by providing detailsabout the quality of the underlying data. R6 - Facilitate simple and advancedsearch for relevant data. The latter is considered to be a crucial task for datadiscovery and link discovery across datasets. Trigger research opportunities- Use LOD to organize and integrate databases far beyond the internal RISISdatasets to create new opportunities for research. For example a link to a cityor a university described in DBpedia or GeoNames could be exploited to infernew knowledge such as the entity location or geographical boundaries.4 more details are available at http://sms.risis.eu1st Workshop on Humanities in the Semantic Web (WHiSe 2016) 17Metadata Operationalization. From the interviews, we concluded that our meta-data should cover a broad range of different aspects which we categorised intothe following nine metadata types: dataset details {overview, temporal aspects,content, structure, technical aspects, legal aspects, access, visit and data qual-ity/used methodologies} and person details. Detailed description about eachof the aforementioned aspects is beyond the scope of this paper. However, weshortly describe here how the categories are mapped to the requirements. Techni-cal aspects, access-visit which provides information on the conditions for visitingone or more data-owners and legal aspects, this satisfies R2 . Data overview,description of the content, temporal aspects and structure of the data, this sup-ports R3 and R4 . The provenance which helps to know the origin, creator,when and how of the data, this satisfies R5-Trust . The methodologies followedfor addressing some dimensions of data quality such as records de-duplication,resource disambiguation and, data consistency and correctness, this again satis-fies R5-Trust . All aspects of the metadata could be used for simple search. Forcomplex search, only attributes that link to external knowledge are exploitableR6 . R1 does not follow the above mapping. Instead, it is covered by the creationof a User-friendly Interface5 that addresses Categorization of metadata types,use of non technical words and text hint.3 Reused VocabulariesSince the RISIS metadata covers a broad range of aspects, a platform or a vo-cabulary that is all inclusive does not exist. So, we selected a set of nine domain-specific dataset metadata vocabularies designed for one or more aspects dis-cussed in the requirements. The main vocabularies identified to share many termswithin RISIS metadata concepts are respectively DCMI,6 PROV-O,7 VoID8 andFOAF.9 Although provenance is not shown in Figure 1, we use it extensivelybehind the scene for describing data manipulations. Other reused vocabulariesthat involved less coverage of the RISIS requirements include DCAT,10 DISCO,11WAIVER,12 PAV [2] and SKOS.13 Figure 1 illustrates the mapping between re-quirements and existing shared vocabularies where, the table header expressesa domain, the namespace prefix indicates the vocabulary and the suffix (localname) indicates the term mapped to a requirement term. Yet, reusing these vo-cabularies did not entirely satisfy RISIS’s view on describing a dataset. As aresult, we created new terms such as risis:usecase (see Figure 1) for conceptsthat were not covered by any of the selected vocabularies.5 User-friendly Interface designed for the matter http://datasets.risis.eu/6 Dublin Core Metadata Initiative: http://dublincore.org/documents/dcmi-terms/7 PROV Ontology: https://www.w3.org/TR/2013/REC-prov-o-20130430/8 Vocabulary of Interlinked Datasets: https://www.w3.org/TR/void/9 FOAF Vocabulary: http://xmlns.com/foaf/spec/10 Data Catalog Vocabulary: https://www.w3.org/TR/vocab-dcat/11 Disco Vocabulary: http://rdf-vocabulary.ddialliance.org/discovery.html12 Waivers of rights vocabulary: http://vocab.org/waiver/terms13 SKOS Vocabulary: https://www.w3.org/TR/swbp-skos-core-spec18 A. Adamou, E. Daga and L. Isaksen (eds.)The work done so far is a pilot that needs a more exhaustive investigation of sixother potential vocabularies (see footnote) before finalizing the RISIS ontology.A better understanding of these vocabularies is expected lead to (1) in depthunderstanding for better data integration at the schema level;14 (1,2) facilitatepublishing mapping between a tabular dataset and its RDF converted version;15(3) offer providers and consumers means to assess the quality of datasets;16 (4,5)using the right concept describing statistical information relevant for RISIS and,for publishing RISIS multi-dimensional statistical data;17,18 and (6) to applyingthe concept of “One ontology to bind them all” [7] to the RISIS problem andbetter coverage of legal aspects such as licensing.19Fig. 1. The RISIS Ontology. A view over mapped vocabularies reused. The table headerexpresses a domain, the namespace prefix indicates the vocabulary and the suffix (localname) indicates the term mapped to a requirement term.4 Related Work and DiscussionRelated work. Projects - Open PHACTS20 gathers pharmacological resources inan integrated and interoperable infrastructure to connect for example, informa-tion about chemistry to biological information such that users can determine thepotential impact of a chemical on a biological system [4]. CLARIAH21 also gath-ers large collections of data and software from different humanities disciplines.14 Vocabulary for Tabular Data: https://www.w3.org/TR/tabular-metadata/15 D2RQ Mapping Language: http://d2rq.org/d2rq-language16 Data Quality Vocabulary: https://www.w3.org/TR/vocab-dqv/17 SDMX vocabulary: http://lov.okfn.org/dataset/lov/vocabs/sdmx18 Data Cube vocabulary: https://www.w3.org/TR/vocab-data-cube/19 Meta-Share OWL meta model: http://purl.org/ms-lod/MetaShare.ttl20 The Open PHACTS project: https://www.openphacts.org/21 CLARIAH: http://www.clariah.nl1st Workshop on Humanities in the Semantic Web (WHiSe 2016) 19CEDAR22 inter-links Dutch census data with other data hubs to create a seman-tic data-web of historical information. Such construct allows researchers to bridgeinformation diversity [8] for historical research to ask complex questions thatinvolves historical, socio-economic, demographic data and more domains. TheCenter for Expanded Data Annotation and Retrieval also known as CEDAR23provides a unified framework for researchers in all scientific disciplines who needto create consistent and easily searchable metadata. Platforms - Datahub.ioprovides a public registry for datasets and metadata-based data discovery. Only,its metadata coverage is not adequate for, the language, provenance and licenseof a resource are not properly represented. http://lodlaundromat.org/ gives freeaccess to LOD collected on the web. Although it produces valuable informationfor data comparison, analytics and more, it fails to provide sufficient descriptionsatisfying R1-6. Vocabularies - DCAT does not provide format specific infor-mation, it does not provide information on the content of a dataset nor does itdescribe the provenance of data and, its coverage of legal aspects of a dataset islimited. Open PHACTS uses VoID, a data specific vocabulary24. Through us-age, the specifications defined in there turned out to be so strict that, creatinga proper VoID document was too hard to manage by developers; something RI-SIS planed to avoid with its metadata. Summary - Projects that gather datafrom various sources and domains exist in disciplines such as Pharmaceutical,Art, Humanities, Socio-economic, Historical, etc. but not in STIS. Platformsthat provide dataset metadata also exist. Only, they are limited or too specific.Although many shared vocabularies exit, they are not rich enough.Discussion. Access limitations and related work described in this paper underpinthe increasing demand for bringing together data from multiple domains to allowfor complex and cross-domain analysis. We argue that this would not be donewithout the creation of metadata for systematic and consistent description ofcollections of datasets within a flexible data model. The need for a User-friendlyInterface25 (UI) arose from choosing RDF as the metadata model for, it facili-tates integration and information sharing on the web. In fact, RISIS data-ownerswho need to generate and maintain metadata about their datasets are not fa-miliar with the Semantic Web technologies and the ways to generate a standardand valid RDF. As a result, following a user-centred approach, the proposed vo-cabulary was implemented as a UI to help data-owners to easily auto-generateand manipulate RDF metadata based on the RISIS metadata vocabulary. TheUI has been in use by RISIS data providers. Exposing the metadata through theRISIS User-friendly Interface [6] stimulated data providers to check the qual-ity of their data before opening it for access/visit. Securing the data quality interms of the standards agreed upon in RISIS was done by satisfying elements ina RISIS defined check-list before the opening of the data. Given the broad scope22 Census data open linked: https://www.cedar-project.nl/23 CEDAR: https://med.stanford.edu/cedar/our-solution.html24 http://www.openphacts.org/specs/2013/WD-datadesc-20130912/25 An example of the RISIS UI http://datasets.risis.eu/metadata/eupro20 A. Adamou, E. Daga and L. Isaksen (eds.)and generic domain of the problem, SMS is intended to be useful not only forSTIS but also for the humanities and social sciences.5 Conclusions and Future WorkThis paper presents an approach for managing metadata in the field of science,technology and innovation studies. The approach was developed and applied inthe context of the RISIS-SMS project with the goal of supporting data integra-tion, discovery and search across datasets, maintaining privacy, and obtaininguser trust while focusing on data that are not directly accessible. A contribu-tion of this work is the requirements elicited by interviewing the stakeholders.The requirement analysis guided the design of a new vocabulary, together withreview of existing metadata vocabularies that helped us filling in part of themetadata needed to accommodate the domain needs. Additionally, to meet therequirements, we designed and implemented a user-friendly interface which al-lows non-expert users to easily author metadata in RDF.As future work, we envisage to extend our vocabulary to cover aspects relatedto the quality and provenance of data. We also plan to conduct a usabilityevaluation with end-users of the system to ensure that our user interface andmetadata specifications fulfil the user needs.References1. Van den Besselaar, P.: The cognitive and the social structure of sts. Scientometrics51(2), 441–460 (2001)2. Ciccarese, P., Soiland-Reyes, S., Belhajjame, K., Gray, A.J., Goble, C., Clark, T.:PAV ontology: Provenance, authoring and versioning. Journal of biomedical seman-tics 4(1), 1–22 (2013)3. Daraio, C., Lenzerini, M., Leporelli, C., Moed, H.F., Naggar, P., Bonaccorsi, A.,Bartolucci, A.: Data integration for research and innovation policy: an ontology-based data management approach. Scientometrics pp. 1–15 (2015)4. Groth, P., Loizou, A., Gray, A.J., Goble, C., Harland, L., Pettifer, S.: API-centriclinked data integration: The Open PHACTS discovery platform case study. WebSemantics: Science, Services and Agents on the World Wide Web 29, 12–18 (2014)5. Hackett, E.J., Amsterdamska, O., Lynch, M., Wajcman, J.: The handbook of scienceand technology studies. The MIT Press (2008)6. Khalili, A., Loizou, A., van Harmelen., F.: Adaptive linked data-driven Web com-ponents: Building flexible and reusable Semantic Web interfaces. Semantic WebConference (ESWC) 2016 (2016), to appear7. 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Managing metadata for science, technology and innovation studies: The RISIS case

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Managing metadata for science, technology and innovation studies: The RISIS case

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