cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15151/esrf-es-885636378
title: Study of sulfur species in Li-S battery polymer electrolytes

description: This proposal suggests ex-situ HERDF-XANES and XES measurements on series of functionalized solid polymer electrolyte membranes to move toward safe and reliable solid-state batteries. To mimic battery failure, the electrolyte membranes are doped with Li2Sx of different chain length, i.e. 2 ≤ x ≤ 8, and concentration (over 3 decades) to determine the interlink between the nature of the polymer (functionality) and the polysulfide to determine the redox reactions at stake in such Li/S battery. In addition, several polymer host will be analyzed; i) polyethylene oxide (PEO) (reference material), ii) polystyrene-reinforced PEO (PS-PEO), and iii) a specifically designed single-ion conductor (PSTFSI-PEO) to gain insight into the local interactions between the polysulfides species and the structure of the host polymer matrices.
DOI: 10.15151/esrf-es-903499149
title: Functional oxides screening: CoO and LaCoO3.

description: The pristine state structural characterization of a CoO bulk crystal as received from manufacturer will be done at the hard X ray Microscope in ID06. This sample of 4x4x0.05 mm will be examined in the direct beam, in reflection and in transmission. Goal: provide a full characterization of the cubic phase. Strain relaxation trough point defects in multilayered systems. LaCoO3/SrTiO3 multi-layers will be examined. We expect to observe strain relaxation through point defects. This system will be observed in reflection conditions. The films grown by MBE should present a low defect concentration. Two stacks are to be analyzed where the thicknesses of the layers of LCO and STO vary.
DOI: 10.34740/kaggle/dsv/4133775
title: Contrastive Language Image Pretraining by openai
DOI: 10.6084/m9.figshare.20708950.v1
title: Supplementary document No.2

description: Supplementary document No.2
DOI: 10.6084/m9.figshare.20708950
title: Supplementary document No.2

description: Supplementary document No.2
DOI: 10.6084/m9.figshare.20708950.v3
title: Supplementary document No.2

description: Supplementary document No.2
DOI: 10.6084/m9.figshare.20708950.v2
title: Supplementary document No.2

description: Supplementary document No.2
DOI: 10.6084/m9.figshare.20709073
title: Supplementary document No.2

description: Supplementary document No.2
DOI: 10.6084/m9.figshare.20709073.v1
title: Supplementary document No.2

description: Supplementary document No.2
DOI: 10.6084/m9.figshare.20448234.v1
title: GNSS Data for “Physical Mechanism of Nonlinear Signals in GNSS Vertical 2 Coordinate Time Series in Taiwan ”

description: Data for “Physical Mechanism of Nonlinear Signals in GNSS Vertical 2 Coordinate Time Series in Taiwan ”
DOI: 10.5281/zenodo.3266052
title: Webis Tripad Sentiment Corpus 2013 (Webis-Tripad-13-Sentiment)

description: The Webis Tripad 2013 Sentiment Corpus is a English text corpus of 2100 hotel reviews for the development and evaluation of approaches to sentiment flow analysis. Each document in this corpus is assigned an overall rating score, some metadata, and two kinds of annotations. First, each statement of a review's text has been classified with respect to its sentiment polarity (positive, negative, objective) by Amazon Mechanical Turk (AMT) workers. Second, hotel aspects mentioned in the texts were tagged by in-house domain experts. To give an example, the sentence "The service was perfect and the rooms were clean." consists of two statements "The service was perfect" and "the rooms were clean", both with positive sentiment classification. The aspect in the first statement is "service" and "rooms" in the second, respectively.
description: {"references": ["Henning Wachsmuth, Martin Trenkmann, Benno Stein, Gregor Engels, and Tsvetomira Palakarska. A Review Corpus for Argumentation Analysis. In Alexander Gelbukh, editors, 15th International Conference on Intelligent Text Processing and Computational Linguistics (CICLing 2014), pages 115-127, Berlin Heidelberg New York, April 2014. Springer. ISBN 978-3-642-54902-1", "Henning Wachsmuth, Johannes Kiesel, and Benno Stein. Sentiment Flow\u2014A General Model of Web Review Argumentation. In Llu\u00eds M\u00e1rquez, Chris Callison-Burch, and Jian Su, editors, 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP 2015), pages 601-611, September 2015. Association for Computational Linguistics", "Henning Wachsmuth and Benno Stein. A Universal Model for Discourse-Level Argumentation Analysis. Special Section of the ACM Transactions on Internet Technology: Argumentation in Social Media (ACM TOIT), 17 (3) : 28:1-28:24, June 2017"]}
DOI: 10.5281/zenodo.3266053
title: Webis Tripad Sentiment Corpus 2013 (Webis-Tripad-13-Sentiment)

description: The Webis Tripad 2013 Sentiment Corpus is a English text corpus of 2100 hotel reviews for the development and evaluation of approaches to sentiment flow analysis. Each document in this corpus is assigned an overall rating score, some metadata, and two kinds of annotations. First, each statement of a review's text has been classified with respect to its sentiment polarity (positive, negative, objective) by Amazon Mechanical Turk (AMT) workers. Second, hotel aspects mentioned in the texts were tagged by in-house domain experts. To give an example, the sentence "The service was perfect and the rooms were clean." consists of two statements "The service was perfect" and "the rooms were clean", both with positive sentiment classification. The aspect in the first statement is "service" and "rooms" in the second, respectively.
description: {"references": ["Henning Wachsmuth, Martin Trenkmann, Benno Stein, Gregor Engels, and Tsvetomira Palakarska. A Review Corpus for Argumentation Analysis. In Alexander Gelbukh, editors, 15th International Conference on Intelligent Text Processing and Computational Linguistics (CICLing 2014), pages 115-127, Berlin Heidelberg New York, April 2014. Springer. ISBN 978-3-642-54902-1", "Henning Wachsmuth, Johannes Kiesel, and Benno Stein. Sentiment Flow\u2014A General Model of Web Review Argumentation. In Llu\u00eds M\u00e1rquez, Chris Callison-Burch, and Jian Su, editors, 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP 2015), pages 601-611, September 2015. Association for Computational Linguistics", "Henning Wachsmuth and Benno Stein. A Universal Model for Discourse-Level Argumentation Analysis. Special Section of the ACM Transactions on Internet Technology: Argumentation in Social Media (ACM TOIT), 17 (3) : 28:1-28:24, June 2017"]}
DOI: 10.5281/zenodo.3274636
title: args.me corpus

description: args.me corpus comprises 387 606 arguments crawled from four debate portals in the middle of 2019. The debate portals are Debat ewise, IDebate.org, Debatepedia, and Debate.org. The arguments are extracted using heuristics that are designed for each debate portal.
DOI: 10.5281/zenodo.3734893
title: args.me corpus

description: The args.me corpus comprises 387 740 arguments. They are crawled from the debate portals Debatewise (14 353 arguments), IDebate.org (13 522 arguments), Debatepedia (21 197 arguments), and Debate.org (338 620 arguments). Moreover, the corpus contains 48 arguments from Canadian Parliament discussions. The arguments are extracted using heuristics that are designed for each debate portal. These arguments are the ones currently provided through the args.me search engine. Note that the args API does not return the sourceText (which is indexed by args.me an included in this dataset) due to its size. Cite args.me as Henning Wachsmuth, Martin Potthast, Khalid Al-Khatib, Yamen Ajjour, Jana Puschmann, Jiani Qu, Jonas Dorsch, Viorel Morari, Janek Bevendorff, and Benno Stein. Building an Argument Search Engine for the Web. In 4th Workshop on Argument Mining (ArgMining 2017) at EMNLP, pages 49-59, September 2017. Association for Computational Linguistics. Cite this dataset as Yamen Ajjour, Henning Wachsmuth, Johannes Kiesel, Martin Potthast, Matthias Hagen, and Benno Stein. Data Acquisition for Argument Search: The args.me corpus. In 42nd German Conference on Artificial Intelligence (KI 2019), September 2019. Springer. and with the DOI of Zenodo. The development for args.me is hosted in our Gitlab. This collection is licensed with the Creative Commons Attribution 4.0 International. Individual rights to the content still apply.
description: {"references": ["Yamen Ajjour, Henning Wachsmuth, Johannes Kiesel, Martin Potthast, Matthias Hagen, and Benno Stein. Data Acquisition for Argument Search: The args.me corpus. In 42nd German Conference on Artificial Intelligence (KI 2019), September 2019. Springer.", "Henning Wachsmuth, Martin Potthast, Khalid Al-Khatib, Yamen Ajjour, Jana Puschmann, Jiani Qu, Jonas Dorsch, Viorel Morari, Janek Bevendorff, and Benno Stein. Building an Argument Search Engine for the Web. In 4th Workshop on Argument Mining (ArgMining 2017) at EMNLP, pages 49-59, September 2017. Association for Computational Linguistics."]}
DOI: 10.5281/zenodo.3274635
title: args.me corpus

description: The args.me corpus (version 1.0, cleaned) comprises 382 545 arguments crawled from four debate portals in the middle of 2019. The debate portals are Debatewise, IDebate.org, Debatepedia, and Debate.org. The arguments are extracted using heuristics that are designed for each debate portal. This version contains the same arguments as Version 1.0, but cleaned as described in the corresponding publication. Cite args.me as Henning Wachsmuth, Martin Potthast, Khalid Al-Khatib, Yamen Ajjour, Jana Puschmann, Jiani Qu, Jonas Dorsch, Viorel Morari, Janek Bevendorff, and Benno Stein. Building an Argument Search Engine for the Web. In 4th Workshop on Argument Mining (ArgMining 2017) at EMNLP, pages 49-59, September 2017. Association for Computational Linguistics. Cite this dataset as Yamen Ajjour, Henning Wachsmuth, Johannes Kiesel, Martin Potthast, Matthias Hagen, and Benno Stein. Data Acquisition for Argument Search: The args.me corpus. In 42nd German Conference on Artificial Intelligence (KI 2019), September 2019. Springer. and with the DOI of Zenodo. Cite the cleaning procedure as TODO. The development for args.me is hosted in our Gitlab. This collection is licensed with the Creative Commons Attribution 4.0 International. Individual rights to the content still apply.
description: {"references": ["Yamen Ajjour, Henning Wachsmuth, Johannes Kiesel, Martin Potthast, Matthias Hagen, and Benno Stein. Data Acquisition for Argument Search: The args.me corpus. In 42nd German Conference on Artificial Intelligence (KI 2019), September 2019. Springer.", "Henning Wachsmuth, Martin Potthast, Khalid Al-Khatib, Yamen Ajjour, Jana Puschmann, Jiani Qu, Jonas Dorsch, Viorel Morari, Janek Bevendorff, and Benno Stein. Building an Argument Search Engine for the Web. In 4th Workshop on Argument Mining (ArgMining 2017) at EMNLP, pages 49-59, September 2017. Association for Computational Linguistics."]}
DOI: 10.5281/zenodo.4139439
title: args.me corpus

description: The args.me corpus (version 1.0, cleaned) comprises 382 545 arguments crawled from four debate portals in the middle of 2019. The debate portals are Debatewise, IDebate.org, Debatepedia, and Debate.org. The arguments are extracted using heuristics that are designed for each debate portal. This version contains the same arguments as Version 1.0, but cleaned as described in the corresponding publication. Cite args.me as Henning Wachsmuth, Martin Potthast, Khalid Al-Khatib, Yamen Ajjour, Jana Puschmann, Jiani Qu, Jonas Dorsch, Viorel Morari, Janek Bevendorff, and Benno Stein. Building an Argument Search Engine for the Web. In 4th Workshop on Argument Mining (ArgMining 2017) at EMNLP, pages 49-59, September 2017. Association for Computational Linguistics. Cite this dataset as Yamen Ajjour, Henning Wachsmuth, Johannes Kiesel, Martin Potthast, Matthias Hagen, and Benno Stein. Data Acquisition for Argument Search: The args.me corpus. In 42nd German Conference on Artificial Intelligence (KI 2019), September 2019. Springer. and with the DOI of Zenodo. Cite the cleaning procedure as TODO. The development for args.me is hosted in our Gitlab. This collection is licensed with the Creative Commons Attribution 4.0 International. Individual rights to the content still apply.
description: {"references": ["Yamen Ajjour, Henning Wachsmuth, Johannes Kiesel, Martin Potthast, Matthias Hagen, and Benno Stein. Data Acquisition for Argument Search: The args.me corpus. In 42nd German Conference on Artificial Intelligence (KI 2019), September 2019. Springer.", "Henning Wachsmuth, Martin Potthast, Khalid Al-Khatib, Yamen Ajjour, Jana Puschmann, Jiani Qu, Jonas Dorsch, Viorel Morari, Janek Bevendorff, and Benno Stein. Building an Argument Search Engine for the Web. In 4th Workshop on Argument Mining (ArgMining 2017) at EMNLP, pages 49-59, September 2017. Association for Computational Linguistics."]}
DOI: 10.5281/zenodo.3244090
title: Language Function Analysis 2011 Corpus (LFA-11)

description: The Language Function Analysis 2011 Corpus (LFA-11) is a German text corpus of promotional text, reviews and blog posts on music and smartphones. The texts were manually classified with respect to their topic relevance, language function, and sentiment polarity. The purpose of the corpus is to provide textual data for the development and evaluation of approaches to language function analysis and sentiment analysis. Therefore, each text is classified by language function (personal, commercial, or informational) as well as by sentiment (positive, negative, neutral). The corpus consists of two separated collections, which contain the texts about <em>music</em> and <em>smartphones</em> respectively. The music collection consists of 2,713 promotional texts and reviews from both users and professionals. The smartphone collection contains 2,093 blog posts on smartphones from the Spinn3r corpus.
description: {"references": ["Henning Wachsmuth and Kathrin Bujna (2011). Back to the Roots of Genres: Text Classification by Language Function"]}
DOI: 10.5281/zenodo.3244089
title: Language Function Analysis 2011 Corpus (LFA-11)

description: The Language Function Analysis 2011 Corpus (LFA-11) is a German text corpus of promotional text, reviews and blog posts on music and smartphones. The texts were manually classified with respect to their topic relevance, language function, and sentiment polarity. The purpose of the corpus is to provide textual data for the development and evaluation of approaches to language function analysis and sentiment analysis. Therefore, each text is classified by language function (personal, commercial, or informational) as well as by sentiment (positive, negative, neutral). The corpus consists of two separated collections, which contain the texts about <em>music</em> and <em>smartphones</em> respectively. The music collection consists of 2,713 promotional texts and reviews from both users and professionals. The smartphone collection contains 2,093 blog posts on smartphones from the Spinn3r corpus.
description: {"references": ["Henning Wachsmuth and Kathrin Bujna (2011). Back to the Roots of Genres: Text Classification by Language Function"]}
DOI: 10.5281/zenodo.4382353
title: Same Side Stance Classification Challenge

description: The dataset contains argument pairs which are sampled from args.me dataset and cover two topics: abortion and gay marriage. The dataset is used in the same side stance classification challenge which consists of two experiments (cross-topics and within topics)
DOI: 10.5281/zenodo.5504019
title: Same Side Stance Classification Challenge

description: The dataset contains argument pairs which are sampled from args.me dataset and cover two topics: abortion and gay marriage. The dataset is used in the same side stance classification challenge which consists of two experiments (cross-topics and within topics). Cite the dataset as Benno Stein, Yamen Ajjour, Roxanne El Baff, Khalid Al-Khatib, Philipp Cimiano, and Henning Wachsmuth. Same Side Stance Classification. In Yamen Ajjour et al, editors, Same Side Stance Classification Shared Task 2019, volume 2921, July 2021
DOI: 10.5281/zenodo.4382352
title: Same Side Stance Classification Challenge

description: The dataset contains argument pairs which are sampled from args.me dataset and cover two topics: abortion and gay marriage. The dataset is used in the same side stance classification challenge which consists of two experiments (cross-topics and within topics). Cite the dataset as Benno Stein, Yamen Ajjour, Roxanne El Baff, Khalid Al-Khatib, Philipp Cimiano, and Henning Wachsmuth. Same Side Stance Classification. In Yamen Ajjour et al, editors, Same Side Stance Classification Shared Task 2019, volume 2921, July 2021
DOI: 10.5281/zenodo.3254404
title: Webis-Editorials-16

description: Newer Version of this corpus in the 2018 version can be found here: https://doi.org/10.5281/zenodo.1340629 The Webis-Editorials-16 corpus is a novel corpus with 300 news editorials evenly selected from three diverse online news portals: Al Jazeera, Fox News, and The Guardian. The aim of the corpus is to study (1) the mining and classification of fine-grained types of argumentative discourse units and (2) the analysis of argumentation strategies pursued in editorials to achieve persuasion. To this end, each editorial contains manual type annotations of all units that capture the role that a unit plays in the argumentative discourse, such as assumption or statistics. The corpus consists of 14,313 units of six different types, each annotated by three professional annotators from the crowdsourcing platform <em>upwork.com.</em>
description: {"references": ["Khalid Al-Khatib, Henning Wachsmuth, Johannes Kiesel, Matthias Hagen, and Benno Stein. A News Editorial Corpus for Mining Argumentation Strategies. In 26th International Conference on Computational Linguistics (COLING 2016), pages 3433-3443, December 2016. Association for Computational Linguistics"]}
DOI: 10.5281/zenodo.3254405
title: Webis-Editorials-16

description: Newer Version of this corpus in the 2018 version can be found here: https://doi.org/10.5281/zenodo.1340629 The Webis-Editorials-16 corpus is a novel corpus with 300 news editorials evenly selected from three diverse online news portals: Al Jazeera, Fox News, and The Guardian. The aim of the corpus is to study (1) the mining and classification of fine-grained types of argumentative discourse units and (2) the analysis of argumentation strategies pursued in editorials to achieve persuasion. To this end, each editorial contains manual type annotations of all units that capture the role that a unit plays in the argumentative discourse, such as assumption or statistics. The corpus consists of 14,313 units of six different types, each annotated by three professional annotators from the crowdsourcing platform <em>upwork.com.</em>
description: {"references": ["Khalid Al-Khatib, Henning Wachsmuth, Johannes Kiesel, Matthias Hagen, and Benno Stein. A News Editorial Corpus for Mining Argumentation Strategies. In 26th International Conference on Computational Linguistics (COLING 2016), pages 3433-3443, December 2016. Association for Computational Linguistics"]}
DOI: 10.5281/zenodo.3257460
title: Webis-Revenue-10

description: The corpus consists of 1,128 German news articles from the years 2003 to 2009, collected from 29 general and business news websites. In each article, statements on the revenue of companies or markets were manually annotated, i.e., sentences and entities that refer to a statement are tagged and linked to each other. Here is an example of a revenue statement from the corpus: Loewe AG: Vorläufige Neun-Monats-Zahlen<br> Kronach, [6. November 2007]<sub>REF</sub> - Das Ergebnis vor Zinsen und Steuern (EBIT) des Loewe Konzerns konnte in den ersten 9 Monaten 2007 um 41% gesteigert werden. Vor diesem Hintergrund hebt die [Loewe AG]<sub>ORG </sub>ihre EBIT-Prognose für das laufende Geschäftsjahr auf 20 Mio. Euro an. <strong>Beim Umsatz strebt Konzernchef [Rainer Hecker]</strong><sub><strong>AUTH</strong></sub><strong> </strong><strong>[für das Gesamtjahr]</strong><sub><strong>TIME</strong></sub><strong> ein höher als ursprünglich geplantes [Wachstum]</strong><sub><strong>TREND</strong></sub><strong> [von 10% auf ca. 380 Mio. Euro]</strong><sub><strong>MONEY</strong></sub><strong> an.</strong> (...) A revenue statement comprises seven attributes: <em>Forecast/Declaration</em>: A sentence that represents a forecast or declaration on revenue. <em>Organization/Market</em>: The subject of the statement, i.e., either an organization or market. <em>Time Expression</em>: The period of time referenced by the statement. <em>Reference Point</em>: The point in time when the statement was issued (used to resolute relative time expressions). <em>Money Expression</em>: The monetary value referenced by the statement. <em>Author</em>: The holder of the statement. <em>Trend</em>: A word that indicates the trend of the monetary entity. A total of 2,075 statements have been annotated by domain experts. For more information on the construction of the dataset see the documentation.
description: {"references": ["Henning Wachsmuth, Peter Prettenhofer, and Benno Stein. Efficient Statement Identification for Automatic Market Forecasting. In Chu-Ren Huang and Dan Jurafsky, editors, 23rd International Conference on Computational Linguistics (COLING 10), pages 1128-1136, Stroudsburg, Pennsylvania, August 2010. Association for Computational Linguistics."]}
DOI: 10.5281/zenodo.3257461
title: Webis-Revenue-10

description: The corpus consists of 1,128 German news articles from the years 2003 to 2009, collected from 29 general and business news websites. In each article, statements on the revenue of companies or markets were manually annotated, i.e., sentences and entities that refer to a statement are tagged and linked to each other. Here is an example of a revenue statement from the corpus: Loewe AG: Vorläufige Neun-Monats-Zahlen<br> Kronach, [6. November 2007]<sub>REF</sub> - Das Ergebnis vor Zinsen und Steuern (EBIT) des Loewe Konzerns konnte in den ersten 9 Monaten 2007 um 41% gesteigert werden. Vor diesem Hintergrund hebt die [Loewe AG]<sub>ORG </sub>ihre EBIT-Prognose für das laufende Geschäftsjahr auf 20 Mio. Euro an. <strong>Beim Umsatz strebt Konzernchef [Rainer Hecker]</strong><sub><strong>AUTH</strong></sub><strong> </strong><strong>[für das Gesamtjahr]</strong><sub><strong>TIME</strong></sub><strong> ein höher als ursprünglich geplantes [Wachstum]</strong><sub><strong>TREND</strong></sub><strong> [von 10% auf ca. 380 Mio. Euro]</strong><sub><strong>MONEY</strong></sub><strong> an.</strong> (...) A revenue statement comprises seven attributes: <em>Forecast/Declaration</em>: A sentence that represents a forecast or declaration on revenue. <em>Organization/Market</em>: The subject of the statement, i.e., either an organization or market. <em>Time Expression</em>: The period of time referenced by the statement. <em>Reference Point</em>: The point in time when the statement was issued (used to resolute relative time expressions). <em>Money Expression</em>: The monetary value referenced by the statement. <em>Author</em>: The holder of the statement. <em>Trend</em>: A word that indicates the trend of the monetary entity. A total of 2,075 statements have been annotated by domain experts. For more information on the construction of the dataset see the documentation.
description: {"references": ["Henning Wachsmuth, Peter Prettenhofer, and Benno Stein. Efficient Statement Identification for Automatic Market Forecasting. In Chu-Ren Huang and Dan Jurafsky, editors, 23rd International Conference on Computational Linguistics (COLING 10), pages 1128-1136, Stroudsburg, Pennsylvania, August 2010. Association for Computational Linguistics."]}