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Umfragedaten zu "NFDI4Ing - Rückmeldung aus den Forschungscommunities"
No full textDie Online-Umfrage "NFDI4Ing - Rückmeldung aus den Forschungscommunities"
wurde vom Konsortium NFDI4Ing (Nationale Forschungsdateninfrastruktur für die
Ingenieurwissenschaften, ) vom 21.08.2019 bis
einschließlich 16.09.2019 durchgeführt.
Zielgruppe waren die Leiter von ingenieurwissenschaftlichen Forschungsgruppen,
im Sinne von Lehrstühlen / Professurenn / Fachgebieten an Hochschulen und
Universitäten sowie Verbundprojekte (z.B. SFBs) und einzelne
Forschungsabteilungen außeruniversitärer Forschungseinrichtungen (z.B.
Forschungsbereiche eines Fraunhofer-Instituts).
Es wurden alle ingenieurwissenschaftlichen Forschungseinrichtungen
Deutschlands angeschrieben (Fachbereiche von Universitäten und Hochschulen,
Verbundprojekte (SFBs, GRKs, Exzellenzcluster), Fraunhofer-, Leibniz-, Max-
Planck-Institute, Helmholtz-Zentren, Ressortforschungseinrichtungen,
Fachgesellschaften- und verbände).
Insgesamt wurden 701 Fragebögen ausgefüllt, davon 618 vollständig
Topic-Modeling- and Subject-Classification-Analyses of Articles from the EURASIP Journal on Advances in Signal Processing
No full textThis data set contains the results of topic-modeling- and subject-
classification-analyses of the abstracts of 87 articles from the EURASIP
Journal on Advances in Signal Processing (ISSN: 1687-6180). All of the
selected articles had in common that they were assigned the keyword “OFDM”
(Orthogonal Frequency-Division Multiplexing) by the authors or the publisher.
The topic modeling analyses were carried out with the program GibbsLDA++
() once with and once without stemming
(model-final.twords_w_stemming.txt and model-final.twords_wo_stemming.txt,
respectively). The program parameters were set to: src/lda -est -alpha 0.5
-beta 0.1 -ntopics 10 -niters 1000 -savestep 100 -twords 20
The subject classification analyses were carried out with the web-application
Annif.org (), which offers different algorithms for the
classification. The following algorithms were used (the name of the
corresponding result file is given in brackets): Annif prototype API English
(Annif.png), fastText English (fastText.png), Maui English (Maui.png), TF-IDF
English (TF-IDF.png), YSO ensemble English (YSO.png).
A list with the DOIs of the articles can be found in the file
"DOIs_analyzed_articles.txt" and the analyzed abstracts of these articles in
the zip archive "Abstracts_EURASIPJAdvSignalProcess.zip".
_
We noticed that at least 7 of the 87 abstracts analyzed had been incomplete in
the first version of our data set. These abstracts had in common, that they
contained numbers that were embedded as inline-graphics within an inline-
formula-element. Due to an error in the preprocessing of the texts, the
abstracts were cut off behind these elements. For this reason, we repeated the
analysis with the complete abstracts. Please notice, that the graphical
numbers themselves are still omitted, so that "complete" only refers to the
plain text.
DOIs of the articles that were affected:
* 10.1155/S1110865704403102
* 10.1155/S1110865704401140
* 10.1155/S1110865704311054
* 10.1155/S1110865703309060
* 10.1155/S1110865702000884
* 10.1155/ASP.2005.2730
* 10.1155/ASP.2005.525
Supplementary Data Table S1: Meta-analysis of Lithium-Ion Batteries
No full textThis data table contains literature values regarding the specific energy and greenhouse gas emissions during production and end-of-life of lithium-ion batteries
One Dimensional Magnetic Resonance Microscopy with Micrometer Resolution in Static Field Gradients : secondary data
No full textDBS Corpus
No full textThe DBS corpus contains 93 multi-document summaries for 293 German documents about 30 education-related topics. We sampled the topics from the Deutscher Bildungsserver (DBS) webpage and crawled the documents linked there. The documents are highly heterogeneous in terms of text type, genre, and style.
The multi-document summaries are the result of a seven step annotation process yielding coherent extracts – a novel type of summary that is based on phrases extracted from the original documents that have been ordered and minimally redacted to form a well-readable, coherent text. The data of all intermediate steps is part of the repository to allow for extensive system evaluation. If you use the corpus in academic works, please cite our COLING paper.2.
UKP Snopes Corpus
No full textThis corpus is based on the Snopes fact-checking website and provides annotations for training machine learning models for different tasks in the fact-checking process: document retrieval, stance detection, evidence identification and claim validation. The corpus contains 6,422 validated claims, 16,507 evidence text snippets (annotated with sentence level evidence), and 14,296 documents with their sources (URLs).
Please note: We crawled and provide the data according to the regulations of the German text and data mining policy, and we are allowed to share the corpus only for research purposes. Thus, in order to be able to download the corpus, you need to get in contact with us.
If you use the corpus in academic works, please cite our CoNLL paper
The crustal stress state of Germany - Results of a 3D geomechnical model
No full textThe datasets contain the 3D geomechanical model of Germany of Ahlers et al., 2022 (https://doi.org/10.1186/s40517-022-00222-6) and the complete stress tensor.v