Papers by Claus-Peter Klas
Zenodo (CERN European Organization for Nuclear Research), Sep 25, 2023
The FAIR Data Principles are widely applied to research data. These principles have been broadly ... more The FAIR Data Principles are widely applied to research data. These principles have been broadly adopted by scientific and scholarly institutions to guide research data infrastructures and services, ensuring data is findable, accessible, interoperable, and reusable. However, due to their interpretation scope, it is still challenging to assess the extent to which a data infrastructure addresses the FAIR principles. The Research Data Alliance (RDA) set up the FAIR Data Maturity Model Working Group to specify the required indicators for institutions to assess their levels of FAIR compliance, producing the FAIR Data Maturity Model (RDA-FDMM) . The RDA-FDMM defines 41 FAIR indicators, organized into three classes (Essential, Important, and Useful), and five levels . We applied the RDA-FDMM to the PID service [3] of KonsortSWD ii , which aims to assign PIDs to data elements below study level (such as survey variables). Furthermore, we discuss automatic assessment using the F-UJI Tool iii , which employs RDA-FDMM and FAIRsFAIR Metrics [4] in a machine-readable fashion. At the PID service, which is based on the data registration agency da|ra [5], we manually assessed some elements at the PID service level and others at the da|ra level, using the pass-or-fail method ('yes' or 'no') questions. For the automatic assessment we adopted the F-UJI tool, a web service to automatically assess FAIRness of research data objects based on FAIR object assessment metrics . We applied the F-UJI tool to GESIS Search iv in the context of KonsortSWD, motivated by the European landscape study , which also relies on F-UJI tool and led us to improve our metadata [8]. The manual assessment results show that the PID service meets all the indicators classified as essential and most of the indicators from the classes important and useful (see Table ).
This graph database is created by the Research Data Alliance DDRI working group as part of the Re... more This graph database is created by the Research Data Alliance DDRI working group as part of the Research Data Switchboard project. The group initiated a collaborative software project that connects researchers, publications, datasets and grants across data repositories and registries such as Dryad, ORCID, and Research data Australia. The goal is to find conceptually related datasets based on the co-authorship or jointly funded grants. The best metaphor us a SEE ALSO service that recommends related datasets, grants and publications. This database captures the footprint of the research life cycle so that peer researchers can trace and follow the path to understanding the research work. The outcome enables researchers and data providers to query related datasets to avoid duplicating efforts in creating research data, as well as stimulating novel ideas when joining the dots.
2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), 2021
In this demo paper, we present ConSTR, a novel Contextual Search Term Recommender that utilises t... more In this demo paper, we present ConSTR, a novel Contextual Search Term Recommender that utilises the user's interaction context for search term recommendation and literature retrieval. ConSTR integrates a two-layered recommendation interface: the first layer suggests terms with respect to a user's current search term, and the second layer suggests terms based on the users' previous search activities (interaction context). For the demonstration, ConSTR is built on the arXiv, an academic repository consisting of 1.8 million documents.
Scientific Data, 2018
This paper describes the open access graph dataset that shows the connections between Dryad, CERN... more This paper describes the open access graph dataset that shows the connections between Dryad, CERN, ANDS and other international data repositories to publications and grants across multiple research data infrastructures. The graph dataset was created using the Research Graph data model and the Research Data Switchboard (RD-Switchboard), a collaborative project by the Research Data Alliance DDRI Working Group (DDRI WG) with the aim to discover and connect the related research datasets based on publication co-Authorship or jointly funded grants. The graph dataset allows researchers to trace and follow the paths to understanding a body of work. By mapping the links between research datasets and related resources, the graph dataset improves both their discovery and visibility, while avoiding duplicate efforts in data creation. Ultimately, the linked datasets may spur novel ideas, facilitate reproducibility and re-use in new applications, stimulate combinatorial creativity, and foster col...
Proceedings of the 1st …, 2007
User Studies for Digital Library Development
An expert is a person who has made all the mistakes that can be made in a very narrow field. (Nie... more An expert is a person who has made all the mistakes that can be made in a very narrow field. (Niels Bohr) Introduction Comprehensive, generalizable evaluations of digital libraries (DLs) are rare. Where evaluation does occur, it is generally minimal. Saracevic (2004) analysed around 80 evaluations to conclude that, both in scientific research and in practice, thorough evaluations of DLs are rather the exception than the rule. The complexity of DL systems can be identified as one reason for this. Examining them in their entirety is not straightforward. Even when attempting to do so, we lack scientifically accepted concepts, approaches and models. Another reason is the allocated funding in DL projects. Evaluation is always a ‘must have’ stated by the funder, but is rarely supported by adequate resources. These reasons are, unfortunately, still valid at the time of writing. In this chapter the method of expert evaluation is presented and shown to be one possible way of addressing such problems. Expert evaluations are heuristic or qualitative in nature, as opposed to quantitative evaluations, which aim to provide statistically significant results. Heuristic evaluations (Nielsen, 1994) are common in usability engineering, where user interfaces are evaluated by a small group of experts on the basis of their conformity to certain usability principles (heuristics). Expert evaluations differ from other types of heuristic evaluation in that they lack predefined heuristics. The experts are free to provide any comment, on the assumption that their views will be informed ones. Some examples of the 10 general heuristics defined by Nielsen are visibility of the system status, error recognition, error prevention, user control (support undo and redo), aesthetics and minimalistic design. The advantages of such an expert evaluation are fast and cost-effective results, in contrast to the more expensive types of qualitative user study, which require a larger number of evaluators in order to reflect a representative result. We can distinguish further between two types of expert evaluation: in the first case, the experts themselves are the evaluators, conducting the evaluation and providing the results. In the second, the experts are monitored by evaluators, who lead the evaluation and assess the results.
The x-science metadata schema represents a list of basic metadata properties selected for identif... more The x-science metadata schema represents a list of basic metadata properties selected for identifying and retrieving digital objects. x-science complies with the DataCite metadata schema, the da|ra metadata schema, and the DDI-CDI metadata standard. Some features specific to social sciences (e.g., factorial surveys) and economics (e.g., game theory experiments) have been included, as well as other research paradigms from economics and the social sciences. All metadata properties include their respective names, definitions, attributes, conditions, and cardinality (maximum occurrence). Some of the properties conform to ISO standards. For some properties, x-science uses the controlled vocabulary of da|ra. In other cases, an x-science-specific controlled vocabulary is added.
Lecture Notes in Computer Science, 2018
This paper discusses the problem of lack of clear licensing and transparency of usage terms and c... more This paper discusses the problem of lack of clear licensing and transparency of usage terms and conditions for research metadata. Making research data connected, discoverable and reusable are the key enablers of the new data revolution in research. We discuss how the lack of transparency hinders discovery of research data and make it disconnected from the publication and other trusted research outcomes. In addition, we discuss the application of Creative Commons licenses for research metadata, and provide some examples of the applicability of this approach to internationally known data infrastructures.
DDI 3.2 presents a solid meta-data model to encode social sciences surveys meta-data, such as stu... more DDI 3.2 presents a solid meta-data model to encode social sciences surveys meta-data, such as studies and variables. However, making this data accessible is another story. In Explore-Data project, our goal is to present a dynamic, multi-facets, and multilingual search portal for both studies and variables. Accordingly, we transform the content of DDI, available via DDI-FlatDB, into ElasticSearch index. We consider two main document types in our index, namely study and variable. Each document type consists of a set of fields in order to keep as much of study/variable structure as possible. To support the multilingual content, we build many copies of each field for each language. During the indexing process, we were obliged to deal with migration of data, missing values, remove duplicated and redundant data, and sometimes to generate new data. Index content can be queried via classical Google-style queries. However, the same content can be also browsed via many pre-defined facets in a...
Scientific Data, 2018
This paper describes a novel search index for social and economic research data, one that enables... more This paper describes a novel search index for social and economic research data, one that enables users to search up-to-date references for data holdings in these disciplines. The index can be used for comparative analysis of publication of datasets in different areas of social science. The core of the index is the da|ra registration agency’s database for social and economic data, which contains high-quality searchable metadata from registered data publishers. Research data’s metadata records are harvested from data providers around the world and included in the index. In this paper, we describe the currently available indices on social science datasets and their shortcomings. Next, we describe the motivation behind and the purpose for the data discovery index as a dedicated and curated platform for finding social science research data and gesisDataSearch, its user interface. Further, we explain the harvesting, filtering and indexing procedure and give usage instructions for the dat...
Scientific data, Jan 29, 2018
This paper describes the open access graph dataset that shows the connections between Dryad, CERN... more This paper describes the open access graph dataset that shows the connections between Dryad, CERN, ANDS and other international data repositories to publications and grants across multiple research data infrastructures. The graph dataset was created using the Research Graph data model and the Research Data Switchboard (RD-Switchboard), a collaborative project by the Research Data Alliance DDRI Working Group (DDRI WG) with the aim to discover and connect the related research datasets based on publication co-authorship or jointly funded grants. The graph dataset allows researchers to trace and follow the paths to understanding a body of work. By mapping the links between research datasets and related resources, the graph dataset improves both their discovery and visibility, while avoiding duplicate efforts in data creation. Ultimately, the linked datasets may spur novel ideas, facilitate reproducibility and re-use in new applications, stimulate combinatorial creativity, and foster col...
DelosDLMS is a prototype of a next-generation Digital Library (DL) management system. It is reali... more DelosDLMS is a prototype of a next-generation Digital Library (DL) management system. It is realized by combining various specialized DL functionalities provided by partners of the DELOS network of excellence. Currently, DelosDLMS combines text and audiovisual searching, offers new information visualization and relevance feedback tools, provides novel interfaces, allows retrieved information to be annotated and processed, integrates and processes sensor data streams, and finally, from a systems engineering point of view, is easily configured and adapted while being reliable and scalable. The prototype is based on the OSIRIS/ISIS platform, a middleware environment developed by ETH Zürich and now being extended at the University of Basel.
International Journal on Digital Libraries, 2012
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Papers by Claus-Peter Klas