10.1007/s00287-021-01392-6 journal-article DE https://www.springernature.com/gp/researchers/text-and-data-mining Springer Science and Business Media LLC 2021-08-26T21:02:39Z

10.5771/9783748940173-119 book-chapter https://creativecommons.org/licenses/by/4.0/ Nomos Verlagsgesellschaft mbH & Co. KG 2024-10-16T11:40:48Z

10.3196/1864295020691230 journal-article EN Vittorio Klostermann GMBH 2022-04-07T07:22:05Z

10.1109/wacv61041.2025.00563 proceedings-article https://doi.org/10.15223/policy-029 IEEE 2025-04-08T17:08:13Z

10.1145/2993318.2993343 proceedings-article http://www.acm.org/publications/policies/copyright_policy#Background ACM 2016-10-11T18:16:29Z

10.1007/978-3-319-73546-7_2 book-chapter http://www.springer.com/tdm Springer International Publishing 2018-02-26T06:06:22Z

10.1145/3184558.3186352 proceedings-article http://www.acm.org/publications/policies/copyright_policy#Background ACM Press 2018-04-18T18:04:25Z

10.1145/3093742.3093916 proceedings-article http://www.acm.org/publications/policies/copyright_policy#Background ACM 2017-06-08T14:42:54Z

10.1007/978-3-030-33220-4 book EN https://creativecommons.org/licenses/by/4.0 Springer International Publishing 2019-11-03T19:02:54Z

10.1007/978-3-030-61244-3_11 book-chapter EN http://www.springer.com/tdm Springer International Publishing 2020-10-26T15:04:46Z

10.1007/3-540-29325-6_19 book-chapter DE Springer Berlin Heidelberg 2007-02-08T18:39:27Z

10.1007/978-3-030-65384-2_3 book-chapter EN http://www.springer.com/tdm Springer International Publishing 2020-12-09T11:16:10Z

10.1007/978-3-319-68204-4_1 book-chapter EN https://www.springer.com/tdm Springer International Publishing 2017-10-04T08:34:49Z

10.1007/978-3-030-33220-4_6 book-chapter EN https://creativecommons.org/licenses/by/4.0 Springer International Publishing 2019-11-03T19:02:54Z

10.1007/978-3-030-00668-6_6 book-chapter EN https://www.springernature.com/gp/researchers/text-and-data-mining Springer International Publishing 2018-09-17T12:04:47Z

10.1007/978-0-387-69900-4 book EN http://www.springer.com/tdm Springer US 2007-10-19T12:21:53Z

10.1007/978-3-030-32327-1_48 book-chapter EN https://www.springernature.com/gp/researchers/text-and-data-mining Springer International Publishing 2019-10-30T15:24:59Z
Displaying top 20 publications out of 201

Alternate Names
Affiliations Karlsruhe Institute of Technology
0000-0002-4522-1099
Alternate Names
Affiliations Brookhaven National Laboratory Columbia University InterDigital Inc New York University Oak Ridge National Laboratory University of Tennessee
0000-0002-7262-2242
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Affiliations Freie Universität Berlin Imperial College London New York University Royal Holloway University of London Royal Holloway, University of London University College London GB University of Fribourg University of Geneva University of Glasgow
0000-0001-6516-4637
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Affiliations Simcoe Muskoka Catholic District School Board York University
0000-0002-6717-8845
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0000-0002-8182-3193
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Affiliations Education Development Center Harvard University Social Emotional Learning Alliance for Massachusetts (SEL4MA) Social Emotional Learning Alliance for the United States (SEL4US) Yale University
0009-0000-6952-3115
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Affiliations
0000-0003-4580-3491
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Affiliations
0009-0002-7598-7674
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Affiliations Binghamton University Icahn School of Medicine at Mount Sinai University of Minnesota
0009-0006-9473-0160
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Affiliations American Institute of Chemical Engineers Indiana University – Purdue University Indianapolis Indiana University-Purdue University Indianapolis International Functional Electrical Stimulation Society Purdue University Society of Petroleum Engineers
0000-0002-2621-5611
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Affiliations Huntington's Disease Society of America
0009-0000-3787-5941
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Affiliations American Society of Clinical Oncology European Society for Medical Oncology Kantonsspital Baselland SAKK Swiss Cancer Research Foundation University of Freiburg
0009-0004-1003-1078
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0000-0003-3291-6059
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Affiliations
0000-0002-9904-9140
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0000-0001-6879-3092
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Affiliations Rhodes University University of Cape Town
0000-0002-2063-5615
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Affiliations Drexel University Glaucoma Research Foundation John Wiley & Sons Inc McGill University National Institutes of Health PEW Charitable Trusts Research to Prevent Blindness The Association for Research in Vision and Ophthalmology Inc University of California San Francisco University of Utah
0000-0001-6262-8504
Alternate Names
Affiliations Goethe University Frankfurt
0000-0002-5977-5535
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Affiliations Martin Luther University Halle-Wittenberg
0000-0002-6642-9585
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Affiliations
0000-0001-9921-3855
Displaying top 20 researchers out of 101

OPENAIRE - Products   doi_dedup___::49a477908ef3037779897bbb1dcc36b6

{"references": ["Ngyuen, Anna, et al. (2020). Making Neural Networks FAIR. Knowledge Graphs and Semantic Web Second Iberoamerican Conference and First Indo-American Conference, KGSWC 2020, M\u00e9rida, Mexico, November 26-27, 2020, Proceedings"]}<br/>We present the FAIRnets Knowledge Graph, a large RDF data set with information about neural networks. The data set is based on neural networks published on GitHub using Keras as framework. Furthermore, the data set is implemented in FAIRnets...

10.5281/zenodo.3885249 Datacite DATASET ENG OPEN 2019-04-09

Die Vorlesung vermittelt eine Einführung in die Grundkonzepte der Informatik. Beschreibung: Die Vorlesung bietet eine Einführung in grundlegende Konzepte der Informatik und des Software Engineerings. Wesentliche theoretische Grundlagen und Lösungsansätze, die in allen Bereichen der Informatik von Bedeutung sind, werden vorgestellt sowie anhand von Beispielen erläutert. Literaturhinweise: – H. Balzert. Lehrbuch Grundlagen der Informatik. Spektrum Akademischer Verlag 2004. – U. Schöning....

10.5445/diva/2019-c13 Datacite DATASET UND UNKNOWN 2019-01-01

Data to reproduce the results reported in the paper '''Diefficiency Metrics: Measuring the Continuous Efficiency of Query Processing Approaches'''

10.6084/m9.figshare.5008289.v3 Datacite DATASET UND OPEN 2017-01-01
OPENAIRE - Products   doi_dedup___::2b8aa075baa13a24735d84e5b99a8a81

This is the FAIRnets dataset. It contains Information about publicly available Neural Networks in RDF*. A Search API to query the dataset can be found under https://km.aifb.kit.edu/services/fairnets/

10.5281/zenodo.3228378 Datacite DATASET ENG OPEN 2019-04-09

This dataset contains results of various metric tests performed in the SPARQL query engine nLDE: the network of Linked Data Eddies, in different configurations. The queries themselves are available via the nLDE website and tests are explained in depth within the associated publication.To compute the diefficiency metrics dief@t and dief@k, we need the answer trace produced by the SPARQL query engines when executing queries. Answer traces record the exact point in time when an engine produces...

10.6084/m9.figshare.5008289.v4 Datacite DATASET UND OPEN 2017-01-01

# PhD Thesis---* Title: **[Validation Framework for RDF-based Constraint Languages](http://dx.doi.org/10.5445/IR/1000056458)*** Author: **[Thomas Hartmann](http://www.dr-thomashartmann.de/)*** Examination Date: 08.07.2016* University: [Karlsruhe Institute of Technology (KIT)](https://www.kit.edu/english/)* Chair: [Institute of Applied Informatics and Formal Description Methods](http://www.aifb.kit.edu/web/Hauptseite/en)* Department: [Department of Economics and...

10.5445/ir/1000067357 Datacite DATASET UND OPEN 2016-01-01

We present the FAIRnets Knowledge Graph, a large RDF data set with information about neural networks. The data set is based on neural networks published on GitHub using Keras as framework. Furthermore, the data set is implemented in FAIRnets Search (https://km.aifb.kit.edu/services/fairnets/), an online search system by which users can explore and analyze neural networks. More information can be found at https://km.aifb.kit.edu/services/fairnets/ and in the paper Making Neural Networks...

10.5281/zenodo.3885249 DATASET ENG cc-by-4.0 2019-04-09

Here we release the code of Vetter. We also provide our collected web page snapshots (snapshot.zip) and SKPaint API invocation logs (skpdata.tar.gz) of the Alexa top and bottom 2,500 websites, as well as our user study results (results_of_user_study.zip) in Googl Drive. The main Github repo for Vetter is at https://github.com/Web-Distortion/Vetter.

10.5281/zenodo.7601984 SOFTWARE other-open 2023-02-03

-- Documentation --Name: VettermusiXplora-ID: v0646musiXplora-URI: https://musixplora.de/mxp/v0646Gender: mFirst Mentioned: 1840Last Mentioned: 1860Sectors: HolzblasinstrumentenbauProfessions (Musical): Holzblasinstrumentenbauer, KlarinettenbauerOther Places of Activity: DresdenPortfolio:GroupRoleNamemXp-IDSortimenteSortimentKlarinette2001466Titel/Medien:RoleSigelTitlemXp-IDRelatedNew Langwill Index 1993The New Langwill Index. A Dictionary of Musical Wind-Instrument Makers and Inventors....

10.5281/zenodo.11605868 DATASET cc-by-4.0 2024-06-12

This is the original release of EDI-Vetter.

10.5281/zenodo.3585940 SOFTWARE other-open 2019-12-19

-- Documentation --Name: Johann Christoph VettermusiXplora-ID: v0508musiXplora-URI: https://musixplora.de/mxp/v0508Gender: mDate of Birth: 1693Place of Birth: UndefinedDate of Death: 03 March 1761Place of Death: UndefinedFirst Mentioned: 1722Sectors: InstrumentenbauProfessions (Musical): Geigenbauer, LautenmacherOther Places of Activity: StraßburgTitel/Medien:RoleSigelTitlemXp-IDRelatedLütgendorff 1922Die Geigen- und Lautenmacher vom Mittelalter bis zur Gegenwart. 2 Bände....

10.5281/zenodo.11605738 DATASET cc-by-4.0 2024-06-12

-- Documentation --Name: Johann Paul VettermusiXplora-ID: v0056musiXplora-URI: https://musixplora.de/mxp/v0056Gender: mDate of Birth: 1675Place of Birth: AnsbachFirst Mentioned: 1730Sectors: InstrumentenbauProfessions (Musical): Harfenbauer, Harfenist, ZupfinstrumentenbauerOther Places of Activity: NürnbergPortfolio:GroupRoleNamemXp-IDSortimenteSortimentHarfe2001474Titel/Medien:RoleSigelTitlemXp-IDRelatedZingel 1977Lexikon der Harfe. Ein biographisches, bibliographisches, geographisches und...

10.5281/zenodo.11605551 DATASET cc-by-4.0 2024-06-12

Historical questionnaire/s 1924/1948 and index cards, partly selected enclosures regarding the history of aGerman pharmacy, catalogued via Kalliope portal (Historischer Fragebogen 1924/1948 und Karteikarten, ggf.gemeinfreie Anlagen zur Apothekengeschichte; als Katalog dient das Nachlassportal Kalliope):https://kalliope-verbund.info/DE-611-BF-70963[Funktion: Im Findbuch anzeigen] Please note: The Kalliope catalogue entry might indicate related material in the archival folder which cannotbe...

10.5281/zenodo.10623142 DATASET cc-by-4.0 2024-02-06

Historical questionnaire/s 1924/1948 and index cards, partly selected enclosures regarding the history of a German pharmacy, catalogued via Kalliope portal (Historischer Fragebogen 1924/1948 und Karteikarten, ggf. gemeinfreie Anlagen zur Apothekengeschichte; als Katalog dient das Nachlassportal Kalliope): https://kalliope-verbund.info/DE-611-BF-70963[Funktion: Im Findbuch anzeigen]Please note: The Kalliope catalogue entry might indicate related material in the archival folder which cannot be...

10.5281/zenodo.12748433 DATASET cc-by-4.0 2024-07-16

Surface rupturing data from the historical earthquakes is used for obtaining empirical regression parameters for fault displacement hazard assessment. This is the second version of the SUrface Ruptures due to Earthquake (SURE) database. This new release contains slip measurements and mapped surface rupture traces of 50 surface rupturing earthquakes of reverse, normal, and strike-slip kinematics. As a novelty, a ranking scheme of the rupture features is applied to all the traces and slip...

10.5281/zenodo.7020265 DATASET cc-by-4.0 2022-04-15

The SURE-KG RDF dataset provides a knowledge graph built from a real dataset to represent Real Estate and Uncertain Spatial Data from Advertisements. It relies on natural language processing and machine learning methods for information extraction, and semantic Web frameworks for representation and integration. It describes more than 100K real estate ads and 6K place-names extracted from French Real Estate advertisements from various online advertiser and located in the French Riviera. It can...

10.5281/zenodo.7885757 DATASET cc-by-nc-sa-4.0 2023-05-02

This archive contains the docked TYK2 structures used in the paper "Optimizing active learning for free energy calculations" (https://doi.org/10.1016/j.ailsci.2022.100050).  AM1-BCC charges are stored in the field "AM1Cache" in the SD file.  The charges can be extracted using the code sample below.    from rdkit import Chem import base64 import pickle suppl = Chem.SDMolSupplier("10k_most_similar_tyk2_charged.sdf", removeHs=False) for mol in suppl: am1 = mol.GetProp("AM1Cache") ...

10.5281/zenodo.13759490 DATASET ENG cc-by-4.0 2024-09-13

Fictional datasets, for testing and learning purposes, composed of basic/measurable and computed variables of the Munich Chronotype Questionnaire (MCTQ) standard, shift, and micro versions. The *.csv files represent the raw data and *.rda files the processed data. This data was created by randomized sampling and by manual insertions of special cases. You can find it in the mctq R package. Its purpose is to demonstrate common cases and data issues that researchers may find in their MCTQ data,...

10.5281/zenodo.4904423 DATASET ENG cc-by-4.0 2021-06-06

An improvement to scaled-gradient search that resists overfitting noise.

10.5281/zenodo.10278919 SOFTWARE cc-by-4.0 2023-12-06

Uncovering relationships between neural activity and behavior represents a critical challenge, one that would benefit from facile tools that can capture complex structures within large datasets. Here we demonstrate a generalizable strategy for capturing such structures across diverse behaviors: Time-REsolved BehavioraL Embedding (TREBLE). Using data from synthetic trajectories, adult and larval Drosophila, and mice we show how TREBLE captures both continuous and discrete behavioral dynamics,...

10.5061/dryad.ksn02v743 DATASET cc-zero 2021-04-12
Displaying top 20 resources out of 23

01 | 0:00:00 Start 0:00:05 Start 0:08:11 Lehrstuhl von Prof. Dr. York Sure-Vetter 0:16:14 Warum ist Informatik wichtig? 0:22:33 Informatik - Definition 0:29:06 Grundlegende Begriffe -Information 0:39:56 Systeme - Entwicklungsprozesse 0:43:03 Teilgebiete der Informatik 0:51:17 ""Berühmte"" Prognosen 0:55:20 Zur Entwicklung der Informatik 1:17:39 Übersicht

10.5445/diva/2019-270 Datacite OTHER UND UNKNOWN 2019-01-01

10 | 0:00:00 Start 0:01:41 Einführung 0:03:33 Verschiedene Sotieralgorithmen visualisiert 0:08:16 Ziesetzung 0:12:25 Sortieren durch direktes Einfügen 0:22:01 Quicksort 0:47:48 Heapsort

10.5445/diva/2019-613 Datacite OTHER UND UNKNOWN 2019-01-01

08 | 0:00:00 Start 0:00:10 heutiges Thema: Datenstrukturen: Graphen 0:02:06 Graphen: Begriffe 0:33:44 Graphen: Tiefen und Breitensuche 0:45:03 Tiefen- und Breitensuche: Bäume 1:04:39 Tiefen- und Breitensuche: Binäre Bäume

10.5445/diva/2019-538 Datacite OTHER UND UNKNOWN 2019-01-01

Interview with York Sure-Vetter, Director of NFDI, Germany and Professor at Karlsruhe Institute of Technology; Eva Maria Méndez, PhD in Library and Information Science; Joaquín Tintoré, Professor of Physical Oceanography; Michael Arentoft, Head of Unit, Open Science, DG R&I, European Commission; and Iryna Kuchma, Open Access Programme Manager for EIFL on the necessity of funding Open Science infrastructure and Open Science in general in order to accelerate the shift towards more openness and...

10.5281/zenodo.10564841 Datacite OTHER ENG OPEN 2024-01-24

An interview on the need of data professionals and Open Science skills with York Sure-Vetter, Director of NFDI, Germany and Professor at Karlsruhe Institute of Technology; Jessica Lindvall, Head of Training at SciLifeLab Training Hub; Anne Sophie Fink, Head of Data Management at DeiC (Denmark); and Sally Chambers, Director at DARIAH-EU. Modern research and technology can require not only large amounts of data, but also good data quality. Ensuring good data quality requires specialized...

10.5281/zenodo.10564860 Datacite OTHER ENG OPEN 2024-01-24

05 | 0:00:00 Start 0:00:31 Heutiges Thema: Algorithmen 0:02:24 Übersicht 0:09:13 Eigenschaften von Algorithmen 0:10:13 Eigenschaft: Endlichkeit 0:23:40 Eigenschaft: Eindeutigkeit 0:33:58 Eigenschaft: Rekursion 0:51:29 Beispiel Rekursion: Türme von Hanoi 1:10:38 Eigenschaft: Rekursion - Universalität

10.5445/diva/2019-420 Datacite OTHER UND UNKNOWN 2019-01-01

07 | 0:00:00 Start 0:02:15 Einführung 0:06:34 Beispiele für Datenstrukturen 0:11:01 Warum dynamische Datenstrukturen ? 0:19:10 Abstrakter Datentyp (ADT) 0:37:27 Der Abstrakte Datentyp Liste 0:41:53 Verkettete Liste 0:47:56 Verkettete Listen: Implementierung (Einfügen/Löschen) 0:56:04 Doppelt verkettete Listen Implementierung 1:01:58 Keller: Einführung 1:14:17 Schlangen: Einführung

10.5445/diva/2019-519 Datacite OTHER UND UNKNOWN 2019-01-01

11 | 0:00:00 Start 0:00:05 5.4 Heapsort: Konstruktion eines neuen Heaps 0:02:38 Komplexität von Heapsort 0:04:49 Aufwand für das Neuorganisieren des Heaps 0:05:36 6 Komplexität 0:08:11 Beispiel 0:13:10 Idealisierenden Annahmen 0:14:07 Fragestellungen 0:16:01 6 Elementaroperationen und Schleifen 0:20:17 6. Komplexität 0:22:19 Bemerkungen 0:30:10 Beispiel

10.5445/diva/2019-644 Datacite OTHER UND UNKNOWN 2019-01-01

06 | 0:00:00 Start 0:00:19 Vorbemerkung 0:09:30 Entwurfsprinzipien 0:10:04 Schrittweise Verfeinerung 0:19:43 Modularisierung 0:28:55 Backtracking 0:45:26 Divide and Conquer 0:54:36 Problemtransformation 0:58:52 Testmethoden für Algorithmen 1:01:30 Verifikation 1:10:49 Einteilung der Eingabedaten in Klassen 1:14:56 Black-Box-Test 1:15:41 White-Box-Test 1:19:56 Robustheit von Algorithmen

10.5445/diva/2019-492 Datacite OTHER UND UNKNOWN 2019-01-01

09 | 0:00:00 Start 0:00:37 Binäre Suchbäume 0:15:15 Vergleich: Position eines Knotens 0:18:14 Vergleich: Laufzeiten der Grundoperationen 0:20:13 Pfadsuchprobleme 0:24:29 Eulerscher Zyklus 0:26:32 Rundreiseproblem 0:30:48 Kürzeste Pfade 0:33:49 Dijkstras Algorithmus 0:39:32 Kantenfolge des kürzesten Pfades 0:40:48 Aufwand 0:43:18 Bellman-Ford Algorithmus 0:53:04 Minimaler Spannbaum 0:54:26 Anwendungsbeispiel 0:57:07 Algorithmus von Kruskal

10.5445/diva/2019-584 Datacite OTHER UND UNKNOWN 2019-01-01

02 | 0:00:00 Start 0:00:05 Start 0:03:23 Logische Ausdrücke im Business Process Engineering 0:05:23 Logische Ausdrücke im Datenbankbereich 0:07:50 Logische Ausdrücke im Software Engineering 0:13:32 Aussagenlogik: Atomare Aussagen 0:15:51 Aussagenlogik: Aussagezeichen 0:19:12 Aussagenlogik: Syntax 0:24:41 Aussagenlogik: Wahrheitsgehalt von atomaren Aussagen 0:30:19 Aussagenlogik: Beispiel 0:34:46 Aussagenlogik: Beispiel Wahrheitstafel 0:38:12 Aussagenlogik: Definition 0:41:33 Beispiel...

10.5445/diva/2019-293 Datacite OTHER UND UNKNOWN 2019-01-01
Simple-ML: Towards a Framework for Semantic Data Analytics Workflows

In this paper we present the Simple-ML framework that we develop to support efficient configuration, robustness and reusability of data analytics workflows through the adoption of semantic technologies. We present semantic data models that lay the foundation for the framework development and discuss the data analytics workflows based on these models. Furthermore, we present an example instantiation of the Simple-ML data models for a real-world use case in the mobility domain. © 2019, The Author(s).

Institutionelles Repositorium der Leibniz Universität Hannover OTHER ENG CC BY 4.0 Unported 2019-01-01

12 | 0:00:00 Start 0:00:25 Übersicht 0:00:45 Klassendiagramm 0:01:37 Unified Modelling Language (UML) 0:05:47 Rolle von Modellierung 0:15:42 Strukturdiagramme 0:17:21 Verhaltensdiagramme 0:20:44 Schrittweise Verfeinerung der Modellierung 0:21:23 Anwendungsfalldiagramme 0:29:57 Modellierungstipps 0:31:27 Klassendiagramme 0:47:02 Allgemeine Entwurfsprinzipien 0:50:02 Objektdiagramme 0:52:52 Sequenzdiagramme 1:01:20 Operatoren: Verzweigungen und Schleifen 1:02:58 Nebenläufigkeiten und Ordnung...

10.5445/diva/2019-674 Datacite OTHER UND UNKNOWN 2019-01-01

03 | 0:00:00 Start 0:00:05 Logik 1-3 Aussagenlogik 0:00:30 Apple - das wertvollste Unternehmen im Vergleich 0:03:06 Übersicht 0:03:28 2.1 Aussagenlogik: Satz 0:05:06 Beispiel 0:07:45 Algorithmus II: Wahrheitstafeln 0:08:45 Bestimmung einer KNF 0:08:58 Beispiel 0:13:09 2.1 Aussagenlogik 0:22:33 Resolutionslemma 0:27:47 Resolution 0:30:28 2.2 Prädikatenlogik 0:30:30 2.2 Grenzen der Aussagenlogik 0:32:44 2.2 Vokabular der Prädikatenlogik 0:34:09 Syntax der Prädikatenlogik 0:34:21 2.2 Bedeutung...

10.5445/diva/2019-320 Datacite OTHER UND UNKNOWN 2019-01-01
Interaction Network Analysis Using Semantic Similarity Based on Translation Embeddings

Biomedical knowledge graphs such as STITCH, SIDER, and Drugbank provide the basis for the discovery of associations between biomedical entities, e.g., interactions between drugs and targets. Link prediction is a paramount task and represents a building block for supporting knowledge discovery. Although several approaches have been proposed for effectively predicting links, the role of semantics has not been studied in depth. In this work, we tackle the problem of discovering interactions...

Institutionelles Repositorium der Leibniz Universität Hannover OTHER ENG CC BY 4.0 Unported 2019-01-01

The FAIR-IMPACT FAIR National Roadshow series visited Germany on the 24th of January 2024. The event was hosted by NFDI, the German National Research Data Infrastructure. The event focused on how Germany, and NFDI in particular, is moving forward with the definition and setting up of FAIR Data spaces, as common, cloud-based data spaces for industry and research in compliance with the FAIR Principles, i.e., to share data in a findable, accessible, interoperable, reusable way. T he agenda saw...

10.5281/zenodo.10568500 ZENODO OTHER ENG OPEN 2024-01-24

04 | 0:00:00 Start 0:00:05 Logik 2-3 Prädikatenlogik 0:04:31 Übersicht 0:04:43 2.2 Grenzen der Aussagenlogik 0:05:44 Resolution in der Prädikatenlogik 0:06:36 2.2 Prädikatenlogik 0:08:42 Definition: Skolemisierung 0:12:18 Algorithmus Skolemisierung 0:14:29 Beispiel: Umformung in Skolemform 0:17:17 Grundresolution: Umwandlung in KNF 0:20:47 Definition: Grundinstanzen 0:23:23 Beispiel Grundinstanz 0:26:07 Definition: Grundresolutionsalgorithmus 0:28:34 Grundresolutionssatz 0:28:59...

10.5445/diva/2019-355 Datacite OTHER UND UNKNOWN 2019-01-01

Abstract ; Der Wegweiser „Auffinden – Zitieren – Dokumentieren: Forschungsdaten in den Sozialwissenschaften“ greift einige grundsätzliche Aspekte im Umgang mit den Forschungsdaten auf und orientiert sich dabei an den Regeln guter wissenschaftlicher Praxis (DFG, 2013). Der Wegweiser soll nicht nur Nachwuchswissenschaftler beim Umgang mit Forschungsdaten unterstützen, sondern er ist auch für etablierte Wissenschaftler hilfreich. Sicher kann dieser Wegweiser nicht alle Fragen beantworten, die...

10.5281/zenodo.50959 Datacite OTHER UND UNKNOWN 2015-01-01

This is a coffee lecture about NFDI.

10.5281/zenodo.4700504 PRESENTATION cc-by-4.0 2021-04-19

Hier wird am 21.04.2021 die Ergebnissicherung der CL mit Herrn Prof. Dr. York Sure Vetter zur Verfügung gestellt und ÖFFENTLICH gemacht (Antworten der Fragen aus der Diskussion). Thema: NFDI - Nationale Forschungsdateninfrastruktur: Daten, Kekse ... und mehr Referenten*innen: Prof. Dr. York Sure-Vetter Info: Die Universität Hildesheim informiert in einer neuen Veranstaltungsreihe im Sommersemester 2021 über das Forschungsdatenmanagement. Annette Strauch ist an der Universität Hildesheim für...

10.5281/zenodo.4699937 PRESENTATION 2021-04-19
Displaying top 20 digital objects out of 59

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