2017, Henning, Christian, Ewerth, Ralph

Photos, drawings, figures, etc. supplement textual information in various kinds of media, for example, in web news or scientific pub- lications. In this respect, the intended effect of an image can be quite different, e.g., providing additional information, focusing on certain details of surrounding text, or simply being a general il- lustration of a topic. As a consequence, the semantic correlation between information of different modalities can vary noticeably, too. Moreover, cross-modal interrelations are often hard to describe in a precise way. The variety of possible interrelations of textual and graphical information and the question, how they can be de- scribed and automatically estimated have not been addressed yet by previous work. In this paper, we present several contributions to close this gap. First, we introduce two measures to describe cross- modal interrelations: cross-modal mutual information (CMI) and semantic correlation (SC). Second, a novel approach relying on deep learning is suggested to estimate CMI and SC of textual and visual information. Third, three diverse datasets are leveraged to learn an appropriate deep neural network model for the demanding task. The system has been evaluated on a challenging test set and the experimental results demonstrate the feasibility of the approach.

2017, Meyer, Anneke, Neumann, Janna

Die Leibniz Universität Hannover hat den bedarfsgerechten Auf- und Ausbau des Unterstützungsangebots zum Umgang mit Forschungsdaten als strategisches Ziel definiert, um den eigenen Forschungsstandort zu stärken. Fachpersonal aus dem Dezernat Forschung, den Leibniz Universität IT Services (LUIS) und der Technischen Informationsbibliothek (TIB) haben dazu ein institutionelles Konzept entworfen, das seit Dezember 2016 umgesetzt wird. Ausgangspunkt des Konzepts bildete eine Umfrage zum Umgang mit Forschungsdaten an der Leibniz Universität Hannover, die durch qualitative Interviews ergänzt wurde. Das institutionelle Konzept umfasst folgende Elemente: Etablierung einer Policy zum Umgang mit Forschungsdaten für die gesamte Universität, Beratung und Schulung für Wissenschaftlerinnen und Wissenschaftler und die Service-Einrichtungen, Auf- und Ausbau eines institutionellen Datenrepositoriums und Entwicklung von Schnittstellen zum Forschungsinformationssystem und zum Volltextrepositorium, Universitätsübergreifende Kooperation & Vernetzung. Die vier Elemente befinden sich in einem unterschiedlichen Umsetzungsstand. Bereits seit 2014 führen die beteiligten Institutionen gemeinsam Beratungen und Schulungen durch und nutzen dafür zur Qualitätssicherung und gegenseitigen Information gemeinsame Dokumentationssysteme. In diesem Bereich konnten in den letzten zwei Jahre Erfahrungen gesammelt werden und Prozesse entsprechend optimiert werden. Die Herausforderung des Ansatzes an der Leibniz Universität besteht darin, ein einrichtungsübergreifendes Service-Angebot vorzuhalten und kollaborativ weiter zu entwickeln. Dadurch ist gewährleistet, dass Kompetenzen effektiv gebündelt werden und sich keine Parallelstrukturen an einzelnen Einrichtungen bilden. Durch die gemeinsam entwickelten Services werden Wissenschaftlerinnen und Wissenschaftler mit einer Stimme und auf mehreren Ebenen zum aktiven und bewussten Umgang mit Forschungsdaten angeregt. In diesem Artikel werden die ersten Erfahrungen in der Umsetzung der einzelnen Elemente des institutionellen Konzepts sowie in der Zusammenarbeit beleuchtet. Außerdem wird ein Ausblick auf die zukünftig angestrebte Entwicklung gegeben.

2017, Müller, E., Springstein, M., Ewerth, R.

The problem of automatically estimating the creation date of photos has been addressed rarely in the past. In this paper, we introduce a novel dataset Date Estimation in the Wild for the task of predicting the acquisition year of images captured in the period from 1930 to 1999. In contrast to previous work, the dataset is neither restricted to color photography nor to specific visual concepts. The dataset consists of more than one million images crawled from Flickr and contains a large number of different motives. In addition, we propose two baseline approaches for regression and classification, respectively, relying on state-of-the-art deep convolutional neural networks. Experimental results demonstrate that these baselines are already superior to annotations of untrained humans.

2020, Stocker, Markus, Darroch, Louise, Krahl, Rolf, Habermann, Ted, Devaraju, Anusuriya, Schwardmann, Ulrich, D'Onofrio, Claudio, Häggström, Ingemar

Instruments play an essential role in creating research data. Given the importance of instruments and associated metadata to the assessment of data quality and data reuse, globally unique, persistent and resolvable identification of instruments is crucial. The Research Data Alliance Working Group Persistent Identification of Instruments (PIDINST) developed a community-driven solution for persistent identification of instruments which we present and discuss in this paper. Based on an analysis of 10 use cases, PIDINST developed a metadata schema and prototyped schema implementation with DataCite and ePIC as representative persistent identifier infrastructures and with HZB (Helmholtz-Zentrum Berlin für Materialien und Energie) and BODC (British Oceanographic Data Centre) as representative institutional instrument providers. These implementations demonstrate the viability of the proposed solution in practice. Moving forward, PIDINST will further catalyse adoption and consolidate the schema by addressing new stakeholder requirements.

2017, Tobschall, Esther

Auch nach 25 Jahren ist der E-Print-Server arXiv noch immer eine bedeutende Plattform für die schnelle Veröffentlichung von Forschungsergebnissen und we-sentliche Informationsquelle für seine Fachgebiete. arXiv ist zentrales Fachrepo-sitorium und gilt als Prototyp des Open-Access-Publizierens. Dennoch hat Erfolg auch immer seinen Preis: Dieser Beitrag stellt die Informationsplattform arXiv vor und beschreibt die Erfahrungen mit einem Geschäftsmodell, das über Mit-gliedsbeiträge eine nachhaltige Finanzierung erreichen will.

2017, Ewerth, Ralph, Springstein, Matthias, Phan-Vogtmann, Lo An, Schütze, Juliane

“Do machines perform better than humans in visual recognition tasks?” Not so long ago, this question would have been considered even somewhat provoking and the answer would have been clear: “No”. In this paper, we present a comparison of human and machine performance with respect to annotation for multimedia retrieval tasks. Going beyond recent crowdsourcing studies in this respect, we also report results of two extensive user studies. In total, 23 participants were asked to annotate more than 1000 images of a benchmark dataset, which is the most comprehensive study in the field so far. Krippendorff’s α is used to measure inter-coder agreement among several coders and the results are compared with the best machine results. The study is preceded by a summary of studies which compared human and machine performance in different visual and auditory recognition tasks. We discuss the results and derive a methodology in order to compare machine performance in multimedia annotation tasks at human level. This allows us to formally answer the question whether a recognition problem can be considered as solved. Finally, we are going to answer the initial question.

2018, Musyaffa, Fathoni A., Engels, Christiane, Vidal, Maria-Esther, Orlandi, Fabrizio, Auer, Sören

A pre-print paper detailing recommendation for publishing fiscal data, including assessment framework for fiscal datasets. This paper has been accepted at ACM Journal of Data and Information Quality (JDIQ) in 2018.

2017, Heck, Tamara, Blümel, Ina, Heller, Lambert, Mazarakis, Athanasios, Peters, Isabella, Scherp, Ansgar, Weisel, Luzian

Based on a checklist that was developed during a workshop at OER Camp 2016 and presented as a Science 2.0 conference 2016 poster [1], we conducted an online survey among university teachers representing a sufficient variety of subjects. The survey was online from Feb 6th to March 3rd 2017. We got 360 responses, whereof 210 were completes, see raw data [2]. The poster is presented at Open Science Conference, 21.-22.3.2017, Berlin.

2020, Stocker, M., Darroch, L., Krahl, R., Habermann, T., Devaraju, A., Schwardmann, U., D’onofrio, C., Häggström, I.

Instruments play an essential role in creating research data. Given the importance of instruments and associated metadata to the assessment of data quality and data reuse, globally unique, persistent and resolvable identification of instruments is crucial. The Research Data Alliance Working Group Persistent Identification of Instruments (PIDINST) developed a community-driven solution for persistent identification of instruments which we present and discuss in this paper. Based on an analysis of 10 use cases, PIDINST developed a metadata schema and prototyped schema implementation with DataCite and ePIC as representative persistent identifier infrastructures and with HZB (Helmholtz-Zentrum Berlin für Materialien und Energie) and BODC (British Oceanographic Data Centre) as representative institutional instrument providers. These implementations demonstrate the viability of the proposed solution in practice. Moving forward, PIDINST will further catalyse adoption and consolidate the schema by addressing new stakeholder requirements.

2020, Otto, C., Springstein, M., Anand, A., Ewerth, R.

The beneficial, complementary nature of visual and textual information to convey information is widely known, for example, in entertainment, news, advertisements, science, or education. While the complex interplay of image and text to form semantic meaning has been thoroughly studied in linguistics and communication sciences for several decades, computer vision and multimedia research remained on the surface of the problem more or less. An exception is previous work that introduced the two metrics Cross-Modal Mutual Information and Semantic Correlation in order to model complex image-text relations. In this paper, we motivate the necessity of an additional metric called Status in order to cover complex image-text relations more completely. This set of metrics enables us to derive a novel categorization of eight semantic image-text classes based on three dimensions. In addition, we demonstrate how to automatically gather and augment a dataset for these classes from the Web. Further, we present a deep learning system to automatically predict either of the three metrics, as well as a system to directly predict the eight image-text classes. Experimental results show the feasibility of the approach, whereby the predict-all approach outperforms the cascaded approach of the metric classifiers.