cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3546193
title: Webis Wikipedia Text Reuse Corpus 2018 (Webis-Wikipedia-Text-Reuse-18)

description: The Wikipedia Text Reuse Corpus 2018 (Webis-Wikipedia-Text-Reuse-18) containing text reuse cases extracted from within Wikipedia and in between Wikipedia and a sample of the Common Crawl. The corpus has following structure: wikipedia.jsonl.bz2: Each line, representing a Wikipedia article, contains a json array of article_id, article_title, and article_body within-wikipedia-tr-01.jsonl.bz2: Each line, representing a text reuse case, contains a json array of s_id (source article id), t_id (target article id), s_text (source text), t_text (target text) within-wikipedia-tr-02.jsonl.bz2: Each line, representing a text reuse case, contains a json array of s_id (source article id), t_id (target article id), s_text (source text), t_text (target text) preprocessed-web-sample.jsonl.xz: Each line, representing a web page, contains a json object of d_id, d_url, and content without-wikipedia-tr.jsonl.bz2: Each line, representing a text reuse case, contains a json array of s_id (Wikipedia article id), d_id (web page id), s_text (article text), d_content (web page content) The datasets were extracted in the work by Alshomary et al. 2018 that aimed to study the text reuse phenomena related to Wikipedia at scale. A pipeline for large scale text reuse extraction was developed and used on Wikipedia and the CommonCrawl.
description: {"references": ["Milad Alshomary, Michael V\u00f6lske, Tristan Licht, Henning Wachsmuth, Benno Stein, Matthias Hagen, and Martin Potthast. Wikipedia Text Reuse: Within and Without. In Leif Azzopardi et al, editors, Advances in Information Retrieval. 41st European Conference on IR Research (ECIR 2019) volume 11437 of Lecture Notes in Computer Science, pages 747-754, Berlin Heidelberg New York, April 2019. Springer."]}
DOI: 10.5281/zenodo.3372484
title: Webis Wikipedia Text Reuse Corpus 2018 (Webis-Wikipedia-Text-Reuse-18)

description: The Wikipedia Text Reuse Corpus 2018 (Webis-Wikipedia-Text-Reuse-18) containing text reuse cases extracted from within Wikipedia and in between Wikipedia and a sample of the Common Crawl. The corpus has following structure: wikipedia.jsonl.bz2: Each line, representing a Wikipedia article, contains a json array of article_id, article_title, and article_body within-wikipedia-tr-01.jsonl.bz2: Each line, representing a text reuse case, contains a json array of s_id (source article id), t_id (target article id), s_text (source text), t_text (target text) within-wikipedia-tr-02.jsonl.bz2: Each line, representing a text reuse case, contains a json array of s_id (source article id), t_id (target article id), s_text (source text), t_text (target text) preprocessed-web-sample.jsonl.xz: Each line, representing a web page, contains a json object of d_id, d_url, and content without-wikipedia-tr.jsonl.bz2: Each line, representing a text reuse case, contains a json array of s_id (Wikipedia article id), d_id (web page id), s_text (article text), d_content (web page content) The datasets were extracted in the work by Alshomary et al. 2018 that aimed to study the text reuse phenomena related to Wikipedia at scale. A pipeline for large scale text reuse extraction was developed and used on Wikipedia and the CommonCrawl.
description: {"references": ["Milad Alshomary, Michael V\u00f6lske, Tristan Licht, Henning Wachsmuth, Benno Stein, Matthias Hagen, and Martin Potthast. Wikipedia Text Reuse: Within and Without. In Leif Azzopardi et al, editors, Advances in Information Retrieval. 41st European Conference on IR Research (ECIR 2019) volume 11437 of Lecture Notes in Computer Science, pages 747-754, Berlin Heidelberg New York, April 2019. Springer."]}
DOI: 10.5281/zenodo.3598315
title: Webis-Argument-Framing-19

description: The dataset comprises 12,326 arguments that are labeled cover 465 topics and 1623 frames. The following fields are stored for each argument conclusion, premise, frame, topic, stance, argument_id, topic_id, frame_id For more information please refer to our paper "Modeling Frames in Argumentation".
description: If you use the dataset please cite our paper: @InProceedings{stein:2019q, author = {Yamen Ajjour and Milad Alshomary and Henning Wachsmuth and Benno Stein}, booktitle = {2019 Conference on Empirical Methods in Natural Language Processing and 9th Internationl Joint Conference on Natural Language Processing (EMNLP 2019)}, doi = {}, month = nov, publisher = {ACL}, site = {Hong Kong}, title = {{Modeling Frames in Argumentation}}, url = {}, year = 2019 }
DOI: 10.5281/zenodo.3373355
title: Webis-Argument-Framing-19

description: The dataset comprises 12,326 arguments that are labeled cover 465 topics and 1623 frames. The following fields are stored for each argument conclusion, premise, frame, topic, stance, argument_id, topic_id, frame_id For more information please refer to our paper "Modeling Frames in Argumentation".
description: If you use the dataset please cite our paper: @InProceedings{stein:2019q, author = {Yamen Ajjour and Milad Alshomary and Henning Wachsmuth and Benno Stein}, booktitle = {2019 Conference on Empirical Methods in Natural Language Processing and 9th Internationl Joint Conference on Natural Language Processing (EMNLP 2019)}, doi = {}, month = nov, publisher = {ACL}, site = {Hong Kong}, title = {{Modeling Frames in Argumentation}}, url = {}, year = 2019 }
DOI: 10.5281/zenodo.3973258
title: ArguAna Counterargs

description: An English corpus for studying the retrieval of the best counterargument to an argument. It contains 6753 pairs of argument and best counterargument from the online debate portal idebate.org, along with different experiment files with up to millions of candidate pairs: arguana-counterargs-corpus.zip In case you publish any results related to the ArguAna Counterargs corpus, please cite our upcoming ACL 2018 paper on counterarguments.
DOI: 10.5281/zenodo.3973257
title: ArguAna Counterargs

description: An English corpus for studying the retrieval of the best counterargument to an argument. It contains 6753 pairs of argument and best counterargument from the online debate portal idebate.org, along with different experiment files with up to millions of candidate pairs: arguana-counterargs-corpus.zip In case you publish any results related to the ArguAna Counterargs corpus, please cite our upcoming ACL 2018 paper on counterarguments.
DOI: 10.5281/zenodo.3973297
title: Webis-ArgRank-17

description: An English benchmark dataset for studying argument relevance. It contains 32 rankings as well a ground-truth argument graph with more than 30,000 argument units. In addition, we provide the source code to reproduce our ranking experiments based on the dataset. In case you publish any results related to the Webis-ArgRank-17 dataset, please cite our EACL 2017 paper on argument relevance.
DOI: 10.5281/zenodo.3973298
title: Webis-ArgRank-17

description: An English benchmark dataset for studying argument relevance. It contains 32 rankings as well a ground-truth argument graph with more than 30,000 argument units. In addition, we provide the source code to reproduce our ranking experiments based on the dataset. In case you publish any results related to the Webis-ArgRank-17 dataset, please cite our EACL 2017 paper on argument relevance.
DOI: 10.5281/zenodo.1340629
title: Challenge or Empower: Revisiting Argumentation Quality in a News Editorial Corpus

description: The Webis-Editorial-Quality-18 corpus is a novel corpus with 1000 news editorials. The aim of this Corpus is to study a new notion for news editorials quality. It contains the quality assessments of 1000 news editorials, each annotated by three liberals and three conservatives. The annotators also reported free-text reasons for the effects they observed.
DOI: 10.5281/zenodo.1340628
title: Challenge or Empower: Revisiting Argumentation Quality in a News Editorial Corpus

description: The Webis-Editorial-Quality-18 corpus is a novel corpus with 1000 news editorials. The aim of this Corpus is to study a new notion for news editorials quality. It contains the quality assessments of 1000 news editorials, each annotated by three liberals and three conservatives. The annotators also reported free-text reasons for the effects they observed.
DOI: 10.5281/zenodo.3490948
title: Webis-Voice-based-and-Conversational-Argument-Search-20

description: Interface, questionnaires, and collected data for the paper "Investigating Expectations for Voice-based and Conversational Argument Search on the Web". Data is mostly in tab-separated values format (like csv, just with tabs). For the transcripts of the user study, only the sequence of action labels (in xml) is released for privacy reasons. The labels are described in user-study-tags-participant.txt and user-study-tags-system.txt.
DOI: 10.5281/zenodo.3490947
title: Webis-Voice-based-and-Conversational-Argument-Search-20

description: Interface, questionnaires, and collected data for the paper "Investigating Expectations for Voice-based and Conversational Argument Search on the Web". Data is mostly in tab-separated values format (like csv, just with tabs). For the transcripts of the user study, only the sequence of action labels (in xml) is released for privacy reasons. The labels are described in user-study-tags-participant.txt and user-study-tags-system.txt.
DOI: 10.5281/zenodo.3973240
title: ArguAna TripAdvisor

description: An English corpus for studying local sentiment flows and aspect-based sentiment analysis. It contains 2100 hotel reviews balanced with respect to the reviews’ sentiment scores. All reviews are segmented into subsentence-level statements that have then been manually classified as a fact, a positive, or a negative opinion. Also, all hotel aspects mentioned in the reviews have been annotated as such: arguana-tripadvisor-annotated-plus-software-v1.zip arguana-tripadvisor-annotated-v2.zip In addition, we provide nearly 200k further hotel reviews without manual annotations: v1 upon request arguana-tripadvisor-unannotated-v2.zip The corpus is free-to-use for scientific purposes, not for commercial applications. In version 2, the annotated XMI files have been changed according to a new underlying type system that is more easily extendable. Notice that some adaptations of the software of version 1 are necessary to make it work with version 2. In case you publish any results related to the ArguAna TripAdvisor corpus, please cite our CICLing 2014 paper.
DOI: 10.5281/zenodo.3251803
title: Webis-Debate-16

description: A collection of phrases classified as argumentative or non-argumentative. The phrases have the topics culture, economy, education, environment, free speech, health, international, law , politics, religion, science, society and sport. The classifications and the phrases were seperated by tabs in text files. There are in total 10.846 argumentative phrases and 5556 non-argumentative ones.
DOI: 10.5281/zenodo.3251804
title: Webis-Debate-16

description: A collection of phrases classified as argumentative or non-argumentative. The phrases have the topics culture, economy, education, environment, free speech, health, international, law , politics, religion, science, society and sport. The classifications and the phrases were seperated by tabs in text files. There are in total 10.846 argumentative phrases and 5556 non-argumentative ones.
DOI: 10.6084/m9.figshare.20708278.v1
title: Total protein and carbohydrate content in whole silk worm

description: The above data shows the presence of total protein and carbohydate content in whoe 5<sup>th</sup> instar spermidine fed silk worm larvae after heat shock treatment.
DOI: 10.5281/zenodo.4818133
title: Webis-ConcluGen-2021

description: <strong>Update:</strong> Patch 1.0.1 removes duplicates (up to 5% of the examples in the base version) and re-indexes the examples with new "id" values. The argument and conclusion are now merged into a single csv file for each split to be easily loaded as dataframes. We also include the test set used for manual evaluation (<em>manual_evaluation_arguments.csv</em>) to support qualitative comparisons. The corpus contains 130.519 (argumentative text, conclusion) pairs. There are four variations of this corpus: <em>base:</em> (argumentative text, conclusion) pairs <em>topic: </em>(topic encoded argumentative text, conclusion) pairs <em>targets: </em>(topic and target encoded argumentative text, conclusion) pairs <em>aspects: </em>(topic and aspect encoded argumentative text, conclusion) pairs Each variation is split into train, val, and test files. Additionally, the test set used for automatic evaluation is provided (<em>automatic_evaluation_test_set.csv</em>) which must be used for any quantitative evaluation.
DOI: 10.5281/zenodo.6513329
title: Webis-ConcluGen-2021

description: <strong>Update:</strong> Patch 1.0.1 removes duplicates (up to 5% of the examples in the base version) and re-indexes the examples with new "id" values. The argument and conclusion are now merged into a single csv file for each split to be easily loaded as dataframes. We also include the test set used for manual evaluation (<em>manual_evaluation_arguments.csv</em>) to support qualitative comparisons. The corpus contains 130.519 (argumentative text, conclusion) pairs. There are four variations of this corpus: <em>base:</em> (argumentative text, conclusion) pairs <em>topic: </em>(topic encoded argumentative text, conclusion) pairs <em>targets: </em>(topic and target encoded argumentative text, conclusion) pairs <em>aspects: </em>(topic and aspect encoded argumentative text, conclusion) pairs Each variation is split into train, val, and test files. Additionally, the test set used for automatic evaluation is provided (<em>automatic_evaluation_test_set.csv</em>) which must be used for any quantitative evaluation.
DOI: 10.5281/zenodo.3339152
title: Webis-WikiDiscussions-18

description: Webis-WikiDiscussions-18 Corpus is the output of parsing the entire set of Wikipedia talk pages. The corpus contains about six million discussions, consisting of about 20 million turns. The turns comprise around 74,000 different tags with a total of about 100,000 instances, around 7000 different shortcuts with about 400,000 instances, and around 51,000 different inline templates with about 3.3 million instances. The database has the following structure: <strong>PAGES:</strong> PAGE-ID, URL, TITLE <strong>DISCUSSIONS:</strong> DISCUSSION-ID, PAGE-ID, TITLE <strong>COMMENTS:</strong> COMMENT-ID, DISCUSSION-ID, PARENT-ID, TEXT-RAW, TEXT-CLEAN, USER <strong>TAGS:</strong> TAG-ID, COMMENT-ID, TAG-TEXT, TAG-CLASS <strong>TEMPLATES:</strong> TEMPLATE-ID, DISCUSSION-ID, TEMPLATE-TEXT <strong>SHORTCUTS:</strong> SHORTCUT-ID, COMMENT-ID, SHORTCUT-TEXT, SHORTCUT-CLASS <strong>LINKS:</strong> LINK-ID, COMMENT-ID, LINK-TEXT <strong>INLINE-TEMPLATES:</strong> IL-TEMPLATE-ID, COMMENT-ID, IL-TEMPLATE-TEXT, TYPE, DESCRIPTION
DOI: 10.5281/zenodo.3339151
title: Webis-WikiDiscussions-18

description: Webis-WikiDiscussions-18 Corpus is the output of parsing the entire set of Wikipedia talk pages. The corpus contains about six million discussions, consisting of about 20 million turns. The turns comprise around 74,000 different tags with a total of about 100,000 instances, around 7000 different shortcuts with about 400,000 instances, and around 51,000 different inline templates with about 3.3 million instances. The database has the following structure: <strong>PAGES:</strong> PAGE-ID, URL, TITLE <strong>DISCUSSIONS:</strong> DISCUSSION-ID, PAGE-ID, TITLE <strong>COMMENTS:</strong> COMMENT-ID, DISCUSSION-ID, PARENT-ID, TEXT-RAW, TEXT-CLEAN, USER <strong>TAGS:</strong> TAG-ID, COMMENT-ID, TAG-TEXT, TAG-CLASS <strong>TEMPLATES:</strong> TEMPLATE-ID, DISCUSSION-ID, TEMPLATE-TEXT <strong>SHORTCUTS:</strong> SHORTCUT-ID, COMMENT-ID, SHORTCUT-TEXT, SHORTCUT-CLASS <strong>LINKS:</strong> LINK-ID, COMMENT-ID, LINK-TEXT <strong>INLINE-TEMPLATES:</strong> IL-TEMPLATE-ID, COMMENT-ID, IL-TEMPLATE-TEXT, TYPE, DESCRIPTION
DOI: 10.5281/zenodo.3339136
title: Webis-WikiDebate-18

description: Webis-WikiDebate-18 is a large-scale corpus for the argumentation model. The corpus is generated automatically based on the metadata in discussions and then verified partly by an expert. The table has the following attributes: comment-ID discussion-ID reference comment-ID comment (wiki format with tag) comment (plain text without tag) username timestamp hierarchy level in discussion The IDs consist of up to 3 numbers. For example, the comment-ID "3203277-22-11" consists of the page-ID "3203277" with the 23rd discussion and the 12th comment inside the discussion. Please note that the counting starts at 0. The page-ID is from the MediaWiki's internal article ID and can be called by the curid attribute (e.g. http://en.wikipedia.org/?curid=3203277). The article on Wikipedia and the corresponding talk page have two different IDs.<br> Sometimes the value is "\N" when the comment or the structure of the discussion was ill-formed and there was no previous comment, user or timestamp statement. This can happen quite often because no editor checks the meta data and everything has to be managed by the users themselves.<br> The reference comment-ID is the last comment on the higher hierarchy level which the current one refers to with its statement.<br> The hierarchy level of a comment in a discussion is identified by the number of the ":" in wiki format at the beginning of a comment and shows how deep the discussion already involves.
DOI: 10.5281/zenodo.3339135
title: Webis-WikiDebate-18

description: Webis-WikiDebate-18 is a large-scale corpus for the argumentation model. The corpus is generated automatically based on the metadata in discussions and then verified partly by an expert. The table has the following attributes: comment-ID discussion-ID reference comment-ID comment (wiki format with tag) comment (plain text without tag) username timestamp hierarchy level in discussion The IDs consist of up to 3 numbers. For example, the comment-ID "3203277-22-11" consists of the page-ID "3203277" with the 23rd discussion and the 12th comment inside the discussion. Please note that the counting starts at 0. The page-ID is from the MediaWiki's internal article ID and can be called by the curid attribute (e.g. http://en.wikipedia.org/?curid=3203277). The article on Wikipedia and the corresponding talk page have two different IDs.<br> Sometimes the value is "\N" when the comment or the structure of the discussion was ill-formed and there was no previous comment, user or timestamp statement. This can happen quite often because no editor checks the meta data and everything has to be managed by the users themselves.<br> The reference comment-ID is the last comment on the higher hierarchy level which the current one refers to with its statement.<br> The hierarchy level of a comment in a discussion is identified by the number of the ":" in wiki format at the beginning of a comment and shows how deep the discussion already involves.
DOI: 10.5281/zenodo.3973270
title: Arg-Microtexts Synthesis Benchmark

description: 130 logos-oriented and 130 pathos-oriented benchmark arguments for 10 topics, manually synthesized by 26 experts based on a pool of argumentative discourse units from the Arg-Microtexts corpus. In case you publish any results related to the this benchmark data, please cite our upcoming COLING 2018 paper on argumentation synthesis.
DOI: 10.5281/zenodo.3973271
title: Arg-Microtexts Synthesis Benchmark

description: 130 logos-oriented and 130 pathos-oriented benchmark arguments for 10 topics, manually synthesized by 26 experts based on a pool of argumentative discourse units from the Arg-Microtexts corpus. In case you publish any results related to the this benchmark data, please cite our upcoming COLING 2018 paper on argumentation synthesis.
DOI: 10.5281/zenodo.7003489
title: How does a Moka Pot work? 2D X-Ray video gives insights!

description: This sequence of X-Ray images shows how one of the most common Italian moka pots actually work! The sequence starts with a completely prepared moka pot (water in the bottom part, coffee in the middle and hot plate on). During the process the water starts to boil and the steam pressure pushes the hot water through the coffee into the bassin at the top of the pot. This video sequence and additional explanations can also be found on Wikipedia under: Wikipedia Moka pot english Wikipedia Moka pot german The data set contains: TIF-stack of the raw footage (sequence of X-Ray images) 3 artificially colored images in the beginning (Bottom), mid (Mid) and end (Top) of the process. The colored images are based on an image processing workflow which includes the time-derivative of the raw seqeunce, minima and maxima projections, HUE color-space transformation, etc.
DOI: 10.5281/zenodo.7003490
title: How does a Moka Pot work? 2D X-Ray video gives insights!

description: This sequence of X-Ray images shows how one of the most common Italian moka pots actually work! The sequence starts with a completely prepared moka pot (water in the bottom part, coffee in the middle and hot plate on). During the process the water starts to boil and the steam pressure pushes the hot water through the coffee into the bassin at the top of the pot. This video sequence and additional explanations can also be found on Wikipedia under: Wikipedia Moka pot english Wikipedia Moka pot german The data set contains: TIF-stack of the raw footage (sequence of X-Ray images) 3 artificially colored images in the beginning (Bottom), mid (Mid) and end (Top) of the process. The colored images are based on an image processing workflow which includes the time-derivative of the raw seqeunce, minima and maxima projections, HUE color-space transformation, etc.
DOI: 10.5281/zenodo.6067666
title: Touché20-Argument-Retrieval-for-Controversial-Questions

description: Data for the Argument Retrieval for Controversial Questions task at Touché 2020.
DOI: 10.5281/zenodo.6067667
title: Touché20-Argument-Retrieval-for-Controversial-Questions

description: Data for the Argument Retrieval for Controversial Questions task at Touché 2020.
DOI: 10.5281/zenodo.6862281
title: Touché20-Argument-Retrieval-for-Controversial-Questions

description: Data for the Argument Retrieval for Controversial Questions task at Touché 2020.
DOI: 10.5281/zenodo.6873564
title: Touché20-Argument-Retrieval-for-Controversial-Questions

description: Data for the Argument Retrieval for Controversial Questions task at Touché 2020.
DOI: 10.5281/zenodo.6801439
title: Touché22-Argument-Retrieval-for-Controversial-Questions

description: Data for the Argument Retrieval for Controversial Questions task at Touché 2022.
DOI: 10.5281/zenodo.6801440
title: Touché22-Argument-Retrieval-for-Controversial-Questions

description: Data for the Argument Retrieval for Controversial Questions task at Touché 2022.
DOI: 10.5281/zenodo.6873574
title: Touché22-Argument-Retrieval-for-Controversial-Questions

description: Data for the Argument Retrieval for Controversial Questions task at Touché 2022.