Digital Library Research in Action: Supporting Information Retrieval in Sowiport

Hienert, Daniel; Sawitzki, Frank; Mayr, Philipp

doi:10.1045/march2015-hienert

Cited by 36 publications

(44 citation statements)

References 15 publications

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“…The integrated search system consists of the following technical components: 1) the web application, 2) the search engine and 3) the link database (Link-DB) shown in Figure 1. The web application is a JavaScript client software based on FacetView from the Open Knowledge Foundation 12 . It communicates with the search engine through a web interface (API).…”

Section: Technical Architecturementioning

confidence: 99%

A Digital Library for Research Data and Related Information in the Social Sciences

Hienert

Kern

Boland

et al. 2019

2019 ACM/IEEE Joint Conference on Digital Libraries (JCDL)

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In the social sciences, researchers search for information on the Web, but this is most often distributed on different websites, search portals, digital libraries, data archives, and databases. In this work, we present an integrated search system for social science information that allows finding information around research data in a single digital library. Users can search for research data sets, publications, survey variables, questions from questionnaires, survey instruments, and tools. Information items are linked to each other so that users can see, for example, which publications contain data citations to research data. The integration and linking of different kinds of information increase their visibility so that it is easier for researchers to find information for re-use. In a log-based usage study, we found that users search across different information types, that search sessions contain a high rate of positive signals and that link information is often explored. CCS CONCEPTS• Information systems~Digital libraries and archives

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Section: Technical Architecturementioning

confidence: 99%

A Digital Library for Research Data and Related Information in the Social Sciences

Hienert

Kern

Boland

et al. 2019

2019 ACM/IEEE Joint Conference on Digital Libraries (JCDL)

Self Cite

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“…As stated earlier, there are many other contributing factors, too, but a variety of user intelligence‐gathering techniques, such as transaction log analysis, can help us build better user profiles, and this can lead to more sustainable digital information services. Hienert, Sawitzki, and Mayr () discuss a tool that can analyze user sessions, and the data can be used to answer specific questions such as “How has the search process evolved for a certain topic?” “Which documents have been finally viewed?” “How has a search process evolved over several sessions?” The authors recommend that such analyses can help us build a set of value‐added services allowing personalization, recommendation, and awareness. For example, term suggestions can be generated based on the personal history of a user, or recommendations can be made based on an analysis of the documents viewed by other users who used the same search query (Hienert et al., ).…”

Section: User Behavior and Interactionsmentioning

confidence: 99%

How to improve the sustainability of digital libraries and information Services?

Chowdhury

2015

Asso for Info Science & Tech

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Arguing that environmental sustainability is a growing concern for digital information systems and services, this paper proposes a simple methodology for estimation of the energy and environmental costs of digital libraries and information services. It is shown that a number of factors contribute to the overall energy and environmental costs of ICT in general, and digital information systems and services in particular. It is also shown that end-user energy costs play a key role in the overall environmental costs of a digital library or information service. It is argued that appropriate user research, transaction log analysis, user modelling, and better design and delivery of services can significantly reduce the user interaction time, and thus the environmental costs of digital information systems and services making them more sustainable.

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“…Afterwards, the assessor attempted to discover at least one correct match in da|ra for each detected reference, resulting in a list of correct datasets per article. These lists were used as a gold standard 7 to compare with the results of our algorithm to examine differences and similarities.…”

Section: Discussionmentioning

confidence: 99%

A semi-automatic approach for detecting dataset references in social science texts

Ghavimi

Mayr

Lange

et al. 2017

ISU

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Abstract. Today, full-texts of scientific articles are often stored in different locations than the used datasets. Dataset registries aim at a closer integration by making datasets citable but authors typically refer to datasets using inconsistent abbreviations and heterogeneous metadata (e.g. title, publication year). It is thus hard to reproduce research results, to access datasets for further analysis, and to determine the impact of a dataset. Manually detecting references to datasets in scientific articles is time-consuming and requires expert knowledge in the underlying research domain. We propose and evaluate a semi-automatic three-step approach for finding explicit references to datasets in social sciences articles. We first extract pre-defined special features from dataset titles in the da|ra registry, then detect references to datasets using the extracted features, and finally match the references found with corresponding dataset titles. The approach does not require a corpus of articles (avoiding the cold start problem) and performs well on a test corpus. We achieved an F-measure of 0.84 for detecting references in full-texts and an F-measure of 0.83 for finding correct matches of detected references in the da|ra dataset registry.

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Digital Library Research in Action: Supporting Information Retrieval in Sowiport

Cited by 36 publications

References 15 publications

A Digital Library for Research Data and Related Information in the Social Sciences

A Digital Library for Research Data and Related Information in the Social Sciences

How to improve the sustainability of digital libraries and information Services?

A semi-automatic approach for detecting dataset references in social science texts

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