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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Wuttke, Ulrike; Klar, Jochen;

    Durch die zunehmende Digitalisierung nimmt die Bedeutung von Forschungsdaten in den Geisteswissenschaften, insbesondere in den Digital Humanities, zu. Dabei rücken in den letzten Jahren verstärkt Open Science-Paradigmen, wie der möglichst offene Zugang zu den Daten und die FAIR Data Principles (Findable, Accessible, Interoperable, Reuseable) in den Fokus. Diese bedeuten einen Paradigmenwechsel für Wissenschaftler*innen und Wissenschafts- und Gedächtnisinstitutionen und erfordern die Kooperation im Rahmen nationaler und internationaler Infrastrukturen. Zur Förderung guter Datenmanagementpraktiken verlangen verschiedene nationale und internationale Förderorganisationen bei Projektanträgen Auskunft über den Umgang mit nachgenutzten oder erstellten Forschungsdaten, z. B. in der Form von Datenmanagementplänen (DMPs). Inzwischen setzt sich die Erkenntnis durch, dass es nicht mit einem einmaligen Datenmanagementplan getan ist, sondern dass DMPs während des Projektverlaufs angepasst werden müssen, um das Forschungsdatenmanagement aktiv und unter Einbeziehung aller relevanten Stakeholder zu unterstützen und seine Potenziale auszuschöpfen (aktives Forschungsdatenmanagement). Hierfür wird im Rahmen eines DFG-Projekts das Werkzeug Research Data Management Organizer (RDMO) entwickelt. Der Vortrag gibt eine Einführung in die wichtigsten Begrifflichkeiten und Konzepte des geisteswissenschaftlichen Forschungsdatenmanagements und seine Bedeutung für die Sicherung der guten wissenschaftlichen Praxis, die Qualitätssicherung der Forschungsdaten und die Erhöhung des Impacts der Resultate digitaler geisteswissenschaftlicher Forschung und führt diesbezüglich in Funktionalitäten und Anwendungsmöglichkeiten des Werkzeugs RDMO ein. Präsentationsfolien für einen Vortrag im Rahmen des DH-Kolloquiums an der Berlin-Brandenburgischen Akademie der Wissenschaften (BBAW) am 02.08.2019.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Berlin Brandenburgis...arrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Straka, Milan; Straková, Jana;

    Tokenizer, POS Tagger, Lemmatizer and Parser models for 84 treebanks of 56 languages of Universal Depenencies 2.3 Treebanks, created solely using UD 2.3 data (http://hdl.handle.net/11234/1-2895). The model documentation including performance can be found at http://ufal.mff.cuni.cz/udpipe/models#universal_dependencies_23_models . To use these models, you need UDPipe binary version at least 1.2, which you can download from http://ufal.mff.cuni.cz/udpipe . In addition to models itself, all additional data and value of hyperparameters used for training are available in the second archive, allowing reproducible training.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ LINDAT/CLARIAH-CZ re...arrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    LINDAT/CLARIAH-CZ repository
    Other ORP type . 2018
    Data sources: B2FIND
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ LINDAT/CLARIAH-CZ re...arrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      LINDAT/CLARIAH-CZ repository
      Other ORP type . 2018
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  • Authors: Straka, Milan;

    Baseline UDPipe models for CoNLL 2018 Shared Task in UD Parsing, and supplementary material. The models require UDPipe version at least 1.2 and are evaluated using the official evaluation script. The models were trained using a custom data split for treebanks where no development data is provided. Also, we trained an additional "Mixed" model, which uses 200 sentences from every training data. All information needed to replicate the model training (hyperparameters, modified train-dev split, and pre-computed word embeddings for the parser) are included in the archive. Additionaly, we provide UD 2.2 CoNLL 2018 training data with automatically predicted morphology. We utilize the baseline models on development data and perform 10-fold jack-knifing (each fold is predicted with a model trained on the rest of the folds) on the training data.

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Ginter, Filip; Hajič, Jan; Luotolahti, Juhani; Straka, Milan; +1 Authors

    Automatic segmentation, tokenization and morphological and syntactic annotations of raw texts in 45 languages, generated by UDPipe (http://ufal.mff.cuni.cz/udpipe), together with word embeddings of dimension 100 computed from lowercased texts by word2vec (https://code.google.com/archive/p/word2vec/). For each language, automatic annotations in CoNLL-U format are provided in a separate archive. The word embeddings for all languages are distributed in one archive. Note that the CC BY-SA-NC 4.0 license applies to the automatically generated annotations and word embeddings, not to the underlying data, which may have different license and impose additional restrictions. Update 2018-09-03 =============== Added data in the 4 “surprise languages” from the 2017 ST: Buryat, Kurmanji, North Sami and Upper Sorbian. This has been promised before, during CoNLL-ST 2018 we gave the participants a link to this record saying the data was here. It wasn't, sorry. But now it is.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ LINDAT/CLARIAH-CZ re...arrow_drop_down
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    LINDAT/CLARIAH-CZ repository
    Other ORP type . 2017
    Data sources: B2FIND
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      LINDAT/CLARIAH-CZ repository
      Other ORP type . 2017
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  • Authors: Straka, Milan;

    Baseline UDPipe models for CoNLL 2017 Shared Task in UD Parsing, and supplementary material. The models require UDPipe version at least 1.1 and are evaluated using the official evaluation script. The models are trained on a slightly different split of the official UD 2.0 CoNLL 2017 training data, so called baselinemodel split, in order to allow comparison of models even during the shared task. This baselinemodel split of UD 2.0 CoNLL 2017 training data is available for download. Furthermore, we also provide UD 2.0 CoNLL 2017 training data with automatically predicted morphology. We utilize the baseline models on development data and perform 10-fold jack-knifing (each fold is predicted with a model trained on the rest of the folds) on the training data. Finally, we supply all required data and hyperparameter values needed to replicate the baseline models.

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Cherepnalkoski, Darko; Karpf, Andreas; Mozetič, Igor; Grčar, Miha;

    The resource consists of two datasets related to Members of the 8th European Parliament (MEPs). The first one is a dataset of 2,535 roll-call votes of MEPs until 2016-03-01. The second one is a dataset of 26,133 retweets between MEPs in the period between 2014-10-01 and 2016-03-01. The data can be used to examine the patterns of covoting and retweeting of MEPs and analyze the extent to which they are similar. The resource is presented and used in the paper: Darko Cherepnalkoski, Andreas Karpf, Igor Mozetič, Miha Grčar "Cohesion and coalition formation in the European Parliament: Roll-call votes and Twitter activities". PLoS ONE 11(11): e0166586, 2016. http://dx.doi.org/10.1371/journal.pone.0166586 The dataset contains 5 files, of which 3 contain metadata and 2 data. The metadata comprises information about the Members of 8th European Parliament (MEPs) until 2016-03-01, about roll-call votes (RCV) and possible actions during a RCV. The first data file contains a matrix with the votes of all MEPs during all RCVs while the second contains the retweets between the MEPs.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ CLARIN.SI repositoryarrow_drop_down
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    CLARIN.SI repository
    Other ORP type . 2016
    Data sources: B2FIND
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      CLARIN.SI repository
      Other ORP type . 2016
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Wuttke, Ulrike; Klar, Jochen;

    Durch die zunehmende Digitalisierung nimmt die Bedeutung von Forschungsdaten in den Geisteswissenschaften, insbesondere in den Digital Humanities, zu. Dabei rücken in den letzten Jahren verstärkt Open Science-Paradigmen, wie der möglichst offene Zugang zu den Daten und die FAIR Data Principles (Findable, Accessible, Interoperable, Reuseable) in den Fokus. Diese bedeuten einen Paradigmenwechsel für Wissenschaftler*innen und Wissenschafts- und Gedächtnisinstitutionen und erfordern die Kooperation im Rahmen nationaler und internationaler Infrastrukturen. Zur Förderung guter Datenmanagementpraktiken verlangen verschiedene nationale und internationale Förderorganisationen bei Projektanträgen Auskunft über den Umgang mit nachgenutzten oder erstellten Forschungsdaten, z. B. in der Form von Datenmanagementplänen (DMPs). Inzwischen setzt sich die Erkenntnis durch, dass es nicht mit einem einmaligen Datenmanagementplan getan ist, sondern dass DMPs während des Projektverlaufs angepasst werden müssen, um das Forschungsdatenmanagement aktiv und unter Einbeziehung aller relevanten Stakeholder zu unterstützen und seine Potenziale auszuschöpfen (aktives Forschungsdatenmanagement). Hierfür wird im Rahmen eines DFG-Projekts das Werkzeug Research Data Management Organizer (RDMO) entwickelt. Der Vortrag gibt eine Einführung in die wichtigsten Begrifflichkeiten und Konzepte des geisteswissenschaftlichen Forschungsdatenmanagements und seine Bedeutung für die Sicherung der guten wissenschaftlichen Praxis, die Qualitätssicherung der Forschungsdaten und die Erhöhung des Impacts der Resultate digitaler geisteswissenschaftlicher Forschung und führt diesbezüglich in Funktionalitäten und Anwendungsmöglichkeiten des Werkzeugs RDMO ein. Präsentationsfolien für einen Vortrag im Rahmen des DH-Kolloquiums an der Berlin-Brandenburgischen Akademie der Wissenschaften (BBAW) am 02.08.2019.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Berlin Brandenburgis...arrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Straka, Milan; Straková, Jana;

    Tokenizer, POS Tagger, Lemmatizer and Parser models for 84 treebanks of 56 languages of Universal Depenencies 2.3 Treebanks, created solely using UD 2.3 data (http://hdl.handle.net/11234/1-2895). The model documentation including performance can be found at http://ufal.mff.cuni.cz/udpipe/models#universal_dependencies_23_models . To use these models, you need UDPipe binary version at least 1.2, which you can download from http://ufal.mff.cuni.cz/udpipe . In addition to models itself, all additional data and value of hyperparameters used for training are available in the second archive, allowing reproducible training.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ LINDAT/CLARIAH-CZ re...arrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    LINDAT/CLARIAH-CZ repository
    Other ORP type . 2018
    Data sources: B2FIND
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      LINDAT/CLARIAH-CZ repository
      Other ORP type . 2018
      Data sources: B2FIND
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  • Authors: Straka, Milan;

    Baseline UDPipe models for CoNLL 2018 Shared Task in UD Parsing, and supplementary material. The models require UDPipe version at least 1.2 and are evaluated using the official evaluation script. The models were trained using a custom data split for treebanks where no development data is provided. Also, we trained an additional "Mixed" model, which uses 200 sentences from every training data. All information needed to replicate the model training (hyperparameters, modified train-dev split, and pre-computed word embeddings for the parser) are included in the archive. Additionaly, we provide UD 2.2 CoNLL 2018 training data with automatically predicted morphology. We utilize the baseline models on development data and perform 10-fold jack-knifing (each fold is predicted with a model trained on the rest of the folds) on the training data.

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    Authors: Ginter, Filip; Hajič, Jan; Luotolahti, Juhani; Straka, Milan; +1 Authors

    Automatic segmentation, tokenization and morphological and syntactic annotations of raw texts in 45 languages, generated by UDPipe (http://ufal.mff.cuni.cz/udpipe), together with word embeddings of dimension 100 computed from lowercased texts by word2vec (https://code.google.com/archive/p/word2vec/). For each language, automatic annotations in CoNLL-U format are provided in a separate archive. The word embeddings for all languages are distributed in one archive. Note that the CC BY-SA-NC 4.0 license applies to the automatically generated annotations and word embeddings, not to the underlying data, which may have different license and impose additional restrictions. Update 2018-09-03 =============== Added data in the 4 “surprise languages” from the 2017 ST: Buryat, Kurmanji, North Sami and Upper Sorbian. This has been promised before, during CoNLL-ST 2018 we gave the participants a link to this record saying the data was here. It wasn't, sorry. But now it is.

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    LINDAT/CLARIAH-CZ repository
    Other ORP type . 2017
    Data sources: B2FIND
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  • Authors: Straka, Milan;

    Baseline UDPipe models for CoNLL 2017 Shared Task in UD Parsing, and supplementary material. The models require UDPipe version at least 1.1 and are evaluated using the official evaluation script. The models are trained on a slightly different split of the official UD 2.0 CoNLL 2017 training data, so called baselinemodel split, in order to allow comparison of models even during the shared task. This baselinemodel split of UD 2.0 CoNLL 2017 training data is available for download. Furthermore, we also provide UD 2.0 CoNLL 2017 training data with automatically predicted morphology. We utilize the baseline models on development data and perform 10-fold jack-knifing (each fold is predicted with a model trained on the rest of the folds) on the training data. Finally, we supply all required data and hyperparameter values needed to replicate the baseline models.

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    Authors: Cherepnalkoski, Darko; Karpf, Andreas; Mozetič, Igor; Grčar, Miha;

    The resource consists of two datasets related to Members of the 8th European Parliament (MEPs). The first one is a dataset of 2,535 roll-call votes of MEPs until 2016-03-01. The second one is a dataset of 26,133 retweets between MEPs in the period between 2014-10-01 and 2016-03-01. The data can be used to examine the patterns of covoting and retweeting of MEPs and analyze the extent to which they are similar. The resource is presented and used in the paper: Darko Cherepnalkoski, Andreas Karpf, Igor Mozetič, Miha Grčar "Cohesion and coalition formation in the European Parliament: Roll-call votes and Twitter activities". PLoS ONE 11(11): e0166586, 2016. http://dx.doi.org/10.1371/journal.pone.0166586 The dataset contains 5 files, of which 3 contain metadata and 2 data. The metadata comprises information about the Members of 8th European Parliament (MEPs) until 2016-03-01, about roll-call votes (RCV) and possible actions during a RCV. The first data file contains a matrix with the votes of all MEPs during all RCVs while the second contains the retweets between the MEPs.

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    CLARIN.SI repository
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