TY - GEN A1 - Degkwitz, Andreas A1 - Andermann, Heike T1 - Angebots-, Nutzungs- und Bezugsstrukturen elektronischer Fachinformation in Deutschland N2 - Mit dem Übergang zum digitalen Medium haben sich die Bezugsstrukturen und das Angebot an elektronischer Fachinformation in den Bibliotheken nachhaltig verändert. In den vorliegenden Untersuchungen wird das Angebot elektronischer Zeitschriften und Datenbanken und die Nutzung elektronischer Zeitschriften in fünf ausgewählten Fachgebieten und in unterschiedlichen Bibliothekstypen dargelegt. Darüber hinaus werden die derzeitigen Bezugsstrukturen beschrieben sowie die Ergebnisse einer Befragung der Konsortien zu Zielsetzungen, Vertragsformen und Geschäftsmodellen dargestellt. Chancen und Risiken der konsortialen Bezugsform werden erörtert. N2 - With the transition to the digital medium the structures for purchasing digital information and the offer of scientific information in the libraries changed strongly. In the available examination the offer of electronic journals and databases and the usage of electronic journals in five selected disciplines and in different types of libraries is evaluated. Further more the current purchasing structures and the results of interviews with consortia in regard to objectives, forms of contracts and pricing models are described. Chances and risks of consortia purchasing are discussed. KW - elektronische Zeitschriften KW - Datenbanken KW - Konsortien KW - Bezugsstrukturen KW - electronic journals KW - databases KW - consortia KW - purchasing structures Y1 - 2003 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-672 ER - TY - JOUR A1 - Klie, Sebastian A1 - Nikoloski, Zoran A1 - Selbig, Joachim T1 - Biological cluster evaluation for gene function prediction JF - Journal of computational biology N2 - Recent advances in high-throughput omics techniques render it possible to decode the function of genes by using the "guilt-by-association" principle on biologically meaningful clusters of gene expression data. However, the existing frameworks for biological evaluation of gene clusters are hindered by two bottleneck issues: (1) the choice for the number of clusters, and (2) the external measures which do not take in consideration the structure of the analyzed data and the ontology of the existing biological knowledge. Here, we address the identified bottlenecks by developing a novel framework that allows not only for biological evaluation of gene expression clusters based on existing structured knowledge, but also for prediction of putative gene functions. The proposed framework facilitates propagation of statistical significance at each of the following steps: (1) estimating the number of clusters, (2) evaluating the clusters in terms of novel external structural measures, (3) selecting an optimal clustering algorithm, and (4) predicting gene functions. The framework also includes a method for evaluation of gene clusters based on the structure of the employed ontology. Moreover, our method for obtaining a probabilistic range for the number of clusters is demonstrated valid on synthetic data and available gene expression profiles from Saccharomyces cerevisiae. Finally, we propose a network-based approach for gene function prediction which relies on the clustering of optimal score and the employed ontology. Our approach effectively predicts gene function on the Saccharomyces cerevisiae data set and is also employed to obtain putative gene functions for an Arabidopsis thaliana data set. KW - algorithms KW - biochemical networks KW - combinatorics KW - computational molecular biology KW - databases KW - functional genomics KW - gene expression KW - NP-completeness Y1 - 2014 U6 - https://doi.org/10.1089/cmb.2009.0129 SN - 1066-5277 SN - 1557-8666 VL - 21 IS - 6 SP - 428 EP - 445 PB - Liebert CY - New Rochelle ER - TY - JOUR A1 - Subetto, D. A. A1 - Nazarova, Larisa B. A1 - Pestryakova, Luidmila Agafyevna A1 - Syrykh, Liudmila A1 - Andronikov, A. V. A1 - Biskaborn, Boris A1 - Diekmann, Bernhard A1 - Kuznetsov, D. D. A1 - Sapelko, T. V. A1 - Grekov, I. M. T1 - Paleolimnological studies in Russian northern Eurasia BT - a review JF - Contemporary Problems of Ecology N2 - This article presents a review of the current data on the level of paleolimnological knowledge about lakes in the Russian part of the northern Eurasia. The results of investigation of the northwestern European part of Russia as the best paleolimnologically studied sector of the Russian north is presented in detail. The conditions of lacustrine sedimentation at the boundary between the Late Pleistocene and Holocene and the role of different external factors in formation of their chemical composition, including active volcanic activity and possible large meteorite impacts, are also discussed. The results of major paleoclimatic and paleoecological reconstructions in northern Siberia are presented. Particular attention is given to the databases of abiotic and biotic parameters of lake ecosystems as an important basis for quantitative reconstructions of climatic and ecological changes in the Late Pleistocene and Holocene. Keywords: paleolimnology, lakes, bottom sediments, northern. KW - paleolimnology KW - lakes KW - bottom sediments KW - northern Eurasia KW - Russian Arctic KW - databases Y1 - 2017 U6 - https://doi.org/10.1134/S1995425517040102 SN - 1995-4255 SN - 1995-4263 VL - 10 SP - 327 EP - 335 PB - Pleiades Publ. CY - New York ER - TY - JOUR A1 - Caruccio, Loredana A1 - Deufemia, Vincenzo A1 - Naumann, Felix A1 - Polese, Giuseppe T1 - Discovering relaxed functional dependencies based on multi-attribute dominance JF - IEEE transactions on knowledge and data engineering N2 - With the advent of big data and data lakes, data are often integrated from multiple sources. Such integrated data are often of poor quality, due to inconsistencies, errors, and so forth. One way to check the quality of data is to infer functional dependencies (fds). However, in many modern applications it might be necessary to extract properties and relationships that are not captured through fds, due to the necessity to admit exceptions, or to consider similarity rather than equality of data values. Relaxed fds (rfds) have been introduced to meet these needs, but their discovery from data adds further complexity to an already complex problem, also due to the necessity of specifying similarity and validity thresholds. We propose Domino, a new discovery algorithm for rfds that exploits the concept of dominance in order to derive similarity thresholds of attribute values while inferring rfds. An experimental evaluation on real datasets demonstrates the discovery performance and the effectiveness of the proposed algorithm. KW - Complexity theory KW - Approximation algorithms KW - Big Data KW - Distributed KW - databases KW - Semantics KW - Lakes KW - Functional dependencies KW - data profiling KW - data cleansing Y1 - 2020 U6 - https://doi.org/10.1109/TKDE.2020.2967722 SN - 1041-4347 SN - 1558-2191 VL - 33 IS - 9 SP - 3212 EP - 3228 PB - Institute of Electrical and Electronics Engineers CY - New York, NY ER - TY - JOUR A1 - Gronau, Norbert A1 - Schaefer, Martin T1 - Why metadata matters for the future of copyright JF - European Intellectual Property Review N2 - In the copyright industries of the 21st century, metadata is the grease required to make the engine of copyright run smoothly and powerfully for the benefit of creators, copyright industries and users alike. However, metadata is difficult to acquire and even more difficult to keep up to date as the rights in content are mostly multi-layered, fragmented, international and volatile. This article explores the idea of a neutral metadata search and enhancement tool that could constitute a buffer to safeguard the interests of the various proprietary database owners and avoid the shortcomings of centralised databases. KW - copyright KW - databases KW - metadata KW - music industry Y1 - 2021 SN - 0142-0461 VL - 43 IS - 8 SP - 488 EP - 494 PB - Sweet & Maxwell CY - London ER - TY - JOUR A1 - Datta, Suparno A1 - Sachs, Jan Philipp A1 - Freitas da Cruz, Harry A1 - Martensen, Tom A1 - Bode, Philipp A1 - Morassi Sasso, Ariane A1 - Glicksberg, Benjamin S. A1 - Böttinger, Erwin T1 - FIBER BT - enabling flexible retrieval of electronic health records data for clinical predictive modeling JF - JAMIA open N2 - Objectives: The development of clinical predictive models hinges upon the availability of comprehensive clinical data. Tapping into such resources requires considerable effort from clinicians, data scientists, and engineers. Specifically, these efforts are focused on data extraction and preprocessing steps required prior to modeling, including complex database queries. A handful of software libraries exist that can reduce this complexity by building upon data standards. However, a gap remains concerning electronic health records (EHRs) stored in star schema clinical data warehouses, an approach often adopted in practice. In this article, we introduce the FlexIBle EHR Retrieval (FIBER) tool: a Python library built on top of a star schema (i2b2) clinical data warehouse that enables flexible generation of modeling-ready cohorts as data frames. Materials and Methods: FIBER was developed on top of a large-scale star schema EHR database which contains data from 8 million patients and over 120 million encounters. To illustrate FIBER's capabilities, we present its application by building a heart surgery patient cohort with subsequent prediction of acute kidney injury (AKI) with various machine learning models. Results: Using FIBER, we were able to build the heart surgery cohort (n = 12 061), identify the patients that developed AKI (n = 1005), and automatically extract relevant features (n = 774). Finally, we trained machine learning models that achieved area under the curve values of up to 0.77 for this exemplary use case. Conclusion: FIBER is an open-source Python library developed for extracting information from star schema clinical data warehouses and reduces time-to-modeling, helping to streamline the clinical modeling process. KW - databases KW - factual KW - electronic health records KW - information storage and KW - retrieval KW - workflow KW - software/instrumentation Y1 - 2021 U6 - https://doi.org/10.1093/jamiaopen/ooab048 SN - 2574-2531 VL - 4 IS - 3 PB - Oxford Univ. Press CY - Oxford ER - TY - JOUR A1 - Rana, Kamal A1 - Öztürk, Ugur A1 - Malik, Nishant T1 - Landslide geometry reveals its trigger JF - Geophysical research letters : GRL / American Geophysical Union N2 - Electronic databases of landslides seldom include the triggering mechanisms, rendering these inventories unusable for landslide hazard modeling. We present a method for classifying the triggering mechanisms of landslides in existing inventories, thus, allowing these inventories to aid in landslide hazard modeling corresponding to the correct event chain. Our method uses various geometric characteristics of landslides as the feature space for the machine-learning classifier random forest, resulting in accurate and robust classifications of landslide triggers. We applied the method to six landslide inventories spread over the Japanese archipelago in several different tests and training configurations to demonstrate the effectiveness of our approach. We achieved mean accuracy ranging from 67% to 92%. We also provide an illustrative example of a real-world usage scenario for our method using an additional inventory with unknown ground truth. Furthermore, our feature importance analysis indicates that landslides having identical trigger mechanisms exhibit similar geometric properties. KW - databases KW - Japan | landslides KW - random forest Y1 - 2021 U6 - https://doi.org/10.1029/2020GL090848 SN - 0094-8276 SN - 1944-8007 VL - 48 IS - 4 PB - American Geophysical Union CY - Washington ER -