2021, Navarrete, Evelyn, Hoppe, Anett, Ewerth, Ralph, Cong, Gao, Ramanath, Maya

Human learning shifts stronger than ever towards online settings, and especially towards video platforms. There is an abundance of tutorials and lectures covering diverse topics, from fixing a bike to particle physics. While it is advantageous that learning resources are freely available on the Web, the quality of the resources varies a lot. Given the number of available videos, users need algorithmic support in finding helpful and entertaining learning resources. In this paper, we present a review of the recent research literature (2020-2021) on video-based learning. We focus on publications that examine the characteristics of video content, analyze frequently used features and technologies, and, finally, derive conclusions on trends and possible future research directions.

2019, Thakkar, Harsh, Auer, Sören, Vidal, Maria-Esther, Samavi, Reza, Consens, Mariano P., Khatchadourian, Shahan, Nguyen, Vinh, Sheth, Amit, Giménez-García, José M., Thakkar, Harsh

Graph data management (also called NoSQL) has revealed beneficial characteristics in terms of flexibility and scalability by differ-ently balancing between query expressivity and schema flexibility. This peculiar advantage has resulted into an unforeseen race of developing new task-specific graph systems, query languages and data models, such as property graphs, key-value, wide column, resource description framework (RDF), etc. Present-day graph query languages are focused towards flex-ible graph pattern matching (aka sub-graph matching), whereas graph computing frameworks aim towards providing fast parallel (distributed) execution of instructions. The consequence of this rapid growth in the variety of graph-based data management systems has resulted in a lack of standardization. Gremlin, a graph traversal language, and machine provide a common platform for supporting any graph computing sys-tem (such as an OLTP graph database or OLAP graph processors). In this extended report, we present a formalization of graph pattern match-ing for Gremlin queries. We also study, discuss and consolidate various existing graph algebra operators into an integrated graph algebra.

2018, Struß, J.M.

3D models, objects and data are being used in an increasing number of scientific disciplines throughout different points in the research life cycle. In addition to architecture, civil engineering and mechanical engineering – disciplines that traditionally plan and construct in three-dimensional space – they are also used in the fields of electrical engineering and information technology, physics and astronomy as well as in the conservation of cultural heritage. The types of models used differ considerably in the different disciplines. In addition to CAD models, there are for example formats such as point clouds resulting from laser scans, which are used to capture buildings or landscapes. In addition, there are simulations in which the temporal dimension also plays a role. Furthermore printable 3D models that allow the direct generation of a physical object are increasingly being created. The study presented here takes a closer look at the diversity of 3D artefacts, the point of their creation in the research lifecycle as well as the purpose these artefacts serve.

2017, Hauschke, Christian

Vortrag mit den Highlights der VIVO-Conference 2017, einem Überblick über VIVO 1.10 und einem Ausblick auf die VIVO-Aktivitäten der TIB.

2021-11-09, Taller, Nelli, Dreyer, Britta, Burger, Felix, Hagemann-Wilholt, Stephanie

Folien für den virtuellen Workshop "Herbst TIB DOI Konsortium online Workshops - Metadaten Best Practice".

2023, Benz, Martina, Kirchner, Andreas, Mikautsch, Emilia, Strauß, Helene

Das Poster "open-access.network. Wie gestalten wir die Zukunft?" wurde im Rahmen des BMBF-geförderten Projekts open.access-network erstellt und für die Open-Access-Tage 2023 angenommen. Das Poster skizziert den Weg zur Verstetigung des Kompetenz- und Vernetzungsportals open-access.network. Mit Blick auf das Ziel eines Community-basierten Organisations- und Finanzierungsmodells, wird aktiv zur Interaktion mit dem Poster sowie zur Ideen- und Meinungsäußerung zur Verstetigung eingeladen.

2016, Hauck, Reingis, Kaps, Reiko, Krojanski, Hans Georg, Meyer, Anneke, Neumann, Janna, Soßna, Volker

Im Kontext des Projekts „Entwicklung eines institutionellen Konzepts zum Forschungsdatenmanagement an der Leibniz Universität Hannover“ wurden 2015/16 eine universitätsinterne Online-Umfrage und ergänzende Interviews durchgeführt. Deren Ergebnisse ermöglichen einen Einblick in den derzeitigen Umgang mit Forschungsdaten und eine Abschätzung des Bedarfs an Beratung, Schulung und technischer Infrastruktur seitens des wissenschaftlichen Personals. In diesem Bericht werden die Daten präsentiert und ausgewertet.

2018, Neumann, Janna

Das Thema Forschungsdatenmanagement (FDM) wurde an der Leibniz Universität Hannover im Sommer 2014 offiziell durch das Präsidium der Universität mit dem Auftrag einer Konzeptentwicklung zum Umgang mit Forschungsdaten in Angriff genommen. Der Entwurf wurde von einem einrichtungsübergreifendem Team, bestehend aus Vertretern des Dezernates für Forschung, des Rechenzentrums und der Technischen Informationsbibliothek entwickelt. Er enthält Bausteine zur Erarbeitung einer Richtlinie und einer technischen Infrastruktur und beschreibt zudem den Aufbau von Beratungsund Schulungsangeboten für Forschende der Leibniz Universität Hannover. Seit Ende 2016 befindet sich die erarbeitete und vom Präsidium verabschiedete Gesamtstrategie in der Umsetzung.

2016, Müller, Eric, Otto, Christian, Ewerth, Ralph

Some recent approaches for character identification in movies and TV broadcasts are realized in a semi-supervised manner by assigning transcripts and/or subtitles to the speakers. However, the labels obtained in this way achieve only an accuracy of 80% - 90% and the number of training examples for the different actors is unevenly distributed. In this paper, we propose a novel approach for person identification in video by correcting and extending the training data with reliable predictions to reduce the number of annotation errors. Furthermore, the intra-class diversity of rarely speaking characters is enhanced. To address the imbalance of training data per person, we suggest two complementary prediction scores. These scores are also used to recognize whether or not a face track belongs to a (supporting) character whose identity does not appear in the transcript etc. Experimental results demonstrate the feasibility of the proposed approach, outperforming the current state of the art.

2021, Peng, Ge, Downs, Robert R., Lacagnina, Carlo, Ramapriyan, Hampapuram, Ivánová, Ivana, Moroni, David, Wei, Yaxing, Larnicol, Gilles, Wyborn, Lesley, Goldberg, Mitch, Schulz, Jörg, Bastrakova, Irina, Ganske, Anette, Bastin, Lucy, Khalsa, Siri Jodha S., Wu, Mingfang, Shie, Chung-Lin, Ritchey, Nancy, Jones, Dave, Habermann, Ted, Lief, Christina, Maggio, Iolanda, Albani, Mirko, Stall, Shelley, Zhou, Lihang, Drévillon, Marie, Champion, Sarah, Hou, C. Sophie, Doblas-Reyes, Francisco, Lehnert, Kerstin, Robinson, Erin, Bugbee, Kaylin

Knowledge about the quality of data and metadata is important to support informed decisions on the (re)use of individual datasets and is an essential part of the ecosystem that supports open science. Quality assessments reflect the reliability and usability of data. They need to be consistently curated, fully traceable, and adequately documented, as these are crucial for sound decision- and policy-making efforts that rely on data. Quality assessments also need to be consistently represented and readily integrated across systems and tools to allow for improved sharing of information on quality at the dataset level for individual quality attribute or dimension. Although the need for assessing the quality of data and associated information is well recognized, methodologies for an evaluation framework and presentation of resultant quality information to end users may not have been comprehensively addressed within and across disciplines. Global interdisciplinary domain experts have come together to systematically explore needs, challenges and impacts of consistently curating and representing quality information through the entire lifecycle of a dataset. This paper describes the findings of that effort, argues the importance of sharing dataset quality information, calls for community action to develop practical guidelines, and outlines community recommendations for developing such guidelines. Practical guidelines will allow for global access to and harmonization of quality information at the level of individual Earth science datasets, which in turn will support open science.