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  • ZENODO

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  • Authors: Pandya, Mugdha; Bontcheva, Kalina; Maynard, Diana;

    This is a dataset with tweets from X. Each tweet mentions one or more UK MPs from a subset selected for our study to give a diverse representation of political leanings. Each tweet is labelled for hostility and the identity characteristic it targets (religion, race, gender). Each annotator also provides a confidence score for each label. Three annotators annotate each tweet. Annotators are UK-based students from Computer Science and Politics.

    ZENODOarrow_drop_down
    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
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  • Authors: ASReview LAB developers;

    ASReview Insights is an extension to ASReview LAB that extends the software with tools for plotting and extracting the statistical results of several performance metrics. The extension is especially useful in combination with the simulation functionality of ASReview LAB. If you use this software, please cite it using the metadata from this file.

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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: Lloyd, Montgomery; Clara, Lüders; Walid, Maalej;

    These artefacts accompany the "Mining Issue Trackers: Concepts and Techniques" chapter within the "Handbook of Natural Language Processing for Requirements Engineering" book. The book chapter includes a "Use Cases" section where natural language processing (NLP) techniques are applied to issue tracker data from Montgomery et al. [1]. The JupyterNotebooks in this replication package can be used to follow along with the use cases in the chapter. Lloyd Montgomery - lloyd.montgomery@uni-hamburg.deDr. Clara Lüders - clara.marie.lueders@gmail.comProf. Dr. Walid Maalej - walid.maalej@uni-hamburg.de All authors are affiliated with the University of Hamburg in Hamburg, Germany. Please cite this work as: Montgomery L, Lüders C, Maalej W. of Part, "Mining Issue Trackers: Concepts and Techniques," in "Handbook of Natural Language Processing for Requirements Engineering", 1st Edition, Ferrari A, Deshpande G. Eds. Springer Nature Switzerland AG, Cham, Switzerland, 2024, to appear. Title of Chapter: "Mining Issue Trackers: Concepts and Techniques" Abstract of the Chapter: An issue tracker is a software tool used by organisations to interact with users and manage various aspects of the software development lifecycle. With the rise of agile methodologies, issue trackers have become popular in open and closed-source settings alike. Internal and external stakeholders report, manage, and discuss “issues”, which represent different information such as requirements and maintenance tasks. Issue trackers can quickly become complex ecosystems, with dozens of projects, hundreds of users, thousands of issues, and often millions of issue evolutions. Finding and understanding the relevant issues for the task at hand and keeping an overview becomes difficult with time. Moreover, managing issue workflows for diverse projects becomes more difficult as organisations grow, and more stakeholders get involved. To help address these difficulties, software and requirements engineering research have suggested automated techniques based on mining issue tracking data. Given the vast amount of textual data in issue trackers, many of these techniques leverage natural language processing. This chapter discusses four major use cases for algorithmically analysing issue data to assist stakeholders with the complexity and heterogeneity of information in issue trackers. The chapter is accompanied by a follow-along demonstration package with JupyterNotebooks. [1] Montgomery, L., Lüders, C., Maalej, W.: An alternative issue tracking dataset of public jira repositories. In: Proceedings of the 19th International Conference on Mining Software Repositories. pp. 73–77 (2022)

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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: ZENODO
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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: ZENODO
    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: ZENODO
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      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: ZENODO
      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
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    Authors: Dobrecky, Leticia;

    Diccionario de Datos que corresponde al dataset "censo-1914-bibliotecas". En este material se documenta el proceso de preparación de los datos sobre las bibliotecas extraídos del Tercer Censo Nacional de la Argentina levantado el 1º de junio de 1914.

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    ZENODO
    Other ORP type . 2024
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    Data sources: ZENODO
    ZENODO
    Other ORP type . 2024
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Other ORP type . 2024
      License: CC BY
      Data sources: ZENODO
      ZENODO
      Other ORP type . 2024
      License: CC BY
      Data sources: Datacite
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    Authors: Dobrecky, Leticia;

    Datos sobre bibliotecas extraídos del Tercer Censo Nacional levantado el 1º de junio de 1914.

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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: ZENODO
    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: ZENODO
      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
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    Authors: Curry, Hannah;

    Digitised plan of Shudy Camps Anglo-Saxon cemetery, based on Lethbridge's 1936 publication. Includes orientation, sex and grave good data where possible.

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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: ZENODO
    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: ZENODO
      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
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    Authors: Rinne, Christoph;

    Data from the literature containing information on the age at death of Neolithic burials from Germany. The data were taken from mortality tables (Dx), individual data or bar charts. The data set does not claim to be complete, but focuses on larger burial groups.

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    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: ZENODO
    ZENODO
    Dataset . 2024
    License: CC BY
    Data sources: Datacite
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      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: ZENODO
      ZENODO
      Dataset . 2024
      License: CC BY
      Data sources: Datacite
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    Authors: Kalodner, Jacob;

    Geopackage containing data on Lethbridge's (1936) record of graves at Shudy Camps.

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    Dataset . 2024
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    Data sources: ZENODO
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      Dataset . 2024
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    Authors: Julian, Laabs; Brozio, Jan Piet; Kneisel, Jutta; Schaefer-Di Maida, Stefanie; +3 Authors

    This is the supplementary material of the paper “Perspectives on Socio-environmental Transformations in Ancient Europe” (DOI: https://doi.org/10.1007/978-3-031-53314-3_5). Please consult the publication for in depth description of the data, its context and for the method applied on the data, as well as references to primary sources. Requirements to be installed to run the scripts: Python 3 (https://www.python.org/) with the packages numpy (https://numpy.org/), pandas (https://pandas.pydata.org/), matplotlib (https://matplotlib.org/), seaborn (https://seaborn.pydata.org/), sklearn (https://scikit-learn.org/stable/) and scipy (https://scipy.org/); all included in Ancaonda (Python-Distribution, https://www.anaconda.com/). R (https://cran.r-project.org/) with the packages here (https://cran.r-project.org/web/packages/here/index.html) and rcarbon (https://cran.r-project.org/web/packages/rcarbon/index.html). 

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    ZENODO
    Other ORP type . 2024
    License: CC BY
    Data sources: ZENODO
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      Other ORP type . 2024
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    Authors: Leemans, Inger; Tonelli, Sara; Christlein, Vincent; Troncy, Raphaël; +2 Authors

    This resource of the Olfactory Heritage Toolkit (DOI 10.5281/zenodo.10700210) presents an overview of places and spaces that are often mentioned in European digitized texts or depicted in images (1600-1920) as having a significant smellscape. The list is compiled by the Odeuropa project, to support cultural heritage policy makers to envision where smell may play a significant role and help heritage communities to acknowledge the value of olfaction in heritage contexts. Furthermore, the data may enable heritage documentation systems (e.g. digital collection management systems used by libraries, archives, museums, (in)tangible heritage lists, heritage classification systems) to include olfaction in their documents and resources.

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    ZENODO
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    ZENODO
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      ZENODO
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  • Authors: Pandya, Mugdha; Bontcheva, Kalina; Maynard, Diana;

    This is a dataset with tweets from X. Each tweet mentions one or more UK MPs from a subset selected for our study to give a diverse representation of political leanings. Each tweet is labelled for hostility and the identity characteristic it targets (religion, race, gender). Each annotator also provides a confidence score for each label. Three annotators annotate each tweet. Annotators are UK-based students from Computer Science and Politics.

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    ZENODO
    Dataset . 2024
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    ZENODO
    Dataset . 2024
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      ZENODO
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      ZENODO
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  • Authors: ASReview LAB developers;

    ASReview Insights is an extension to ASReview LAB that extends the software with tools for plotting and extracting the statistical results of several performance metrics. The extension is especially useful in combination with the simulation functionality of ASReview LAB. If you use this software, please cite it using the metadata from this file.

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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: Lloyd, Montgomery; Clara, Lüders; Walid, Maalej;

    These artefacts accompany the "Mining Issue Trackers: Concepts and Techniques" chapter within the "Handbook of Natural Language Processing for Requirements Engineering" book. The book chapter includes a "Use Cases" section where natural language processing (NLP) techniques are applied to issue tracker data from Montgomery et al. [1]. The JupyterNotebooks in this replication package can be used to follow along with the use cases in the chapter. Lloyd Montgomery - lloyd.montgomery@uni-hamburg.deDr. Clara Lüders - clara.marie.lueders@gmail.comProf. Dr. Walid Maalej - walid.maalej@uni-hamburg.de All authors are affiliated with the University of Hamburg in Hamburg, Germany. Please cite this work as: Montgomery L, Lüders C, Maalej W. of Part, "Mining Issue Trackers: Concepts and Techniques," in "Handbook of Natural Language Processing for Requirements Engineering", 1st Edition, Ferrari A, Deshpande G. Eds. Springer Nature Switzerland AG, Cham, Switzerland, 2024, to appear. Title of Chapter: "Mining Issue Trackers: Concepts and Techniques" Abstract of the Chapter: An issue tracker is a software tool used by organisations to interact with users and manage various aspects of the software development lifecycle. With the rise of agile methodologies, issue trackers have become popular in open and closed-source settings alike. Internal and external stakeholders report, manage, and discuss “issues”, which represent different information such as requirements and maintenance tasks. Issue trackers can quickly become complex ecosystems, with dozens of projects, hundreds of users, thousands of issues, and often millions of issue evolutions. Finding and understanding the relevant issues for the task at hand and keeping an overview becomes difficult with time. Moreover, managing issue workflows for diverse projects becomes more difficult as organisations grow, and more stakeholders get involved. To help address these difficulties, software and requirements engineering research have suggested automated techniques based on mining issue tracking data. Given the vast amount of textual data in issue trackers, many of these techniques leverage natural language processing. This chapter discusses four major use cases for algorithmically analysing issue data to assist stakeholders with the complexity and heterogeneity of information in issue trackers. The chapter is accompanied by a follow-along demonstration package with JupyterNotebooks. [1] Montgomery, L., Lüders, C., Maalej, W.: An alternative issue tracking dataset of public jira repositories. In: Proceedings of the 19th International Conference on Mining Software Repositories. pp. 73–77 (2022)

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    ZENODO
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    ZENODO
    Dataset . 2024
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    ZENODO
    Dataset . 2024
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      ZENODO
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      ZENODO
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    Authors: Dobrecky, Leticia;

    Diccionario de Datos que corresponde al dataset "censo-1914-bibliotecas". En este material se documenta el proceso de preparación de los datos sobre las bibliotecas extraídos del Tercer Censo Nacional de la Argentina levantado el 1º de junio de 1914.

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    Authors: Dobrecky, Leticia;

    Datos sobre bibliotecas extraídos del Tercer Censo Nacional levantado el 1º de junio de 1914.

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    ZENODO
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    Authors: Curry, Hannah;

    Digitised plan of Shudy Camps Anglo-Saxon cemetery, based on Lethbridge's 1936 publication. Includes orientation, sex and grave good data where possible.

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    ZENODO
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    Authors: Rinne, Christoph;

    Data from the literature containing information on the age at death of Neolithic burials from Germany. The data were taken from mortality tables (Dx), individual data or bar charts. The data set does not claim to be complete, but focuses on larger burial groups.

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    Authors: Kalodner, Jacob;

    Geopackage containing data on Lethbridge's (1936) record of graves at Shudy Camps.

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    Authors: Julian, Laabs; Brozio, Jan Piet; Kneisel, Jutta; Schaefer-Di Maida, Stefanie; +3 Authors

    This is the supplementary material of the paper “Perspectives on Socio-environmental Transformations in Ancient Europe” (DOI: https://doi.org/10.1007/978-3-031-53314-3_5). Please consult the publication for in depth description of the data, its context and for the method applied on the data, as well as references to primary sources. Requirements to be installed to run the scripts: Python 3 (https://www.python.org/) with the packages numpy (https://numpy.org/), pandas (https://pandas.pydata.org/), matplotlib (https://matplotlib.org/), seaborn (https://seaborn.pydata.org/), sklearn (https://scikit-learn.org/stable/) and scipy (https://scipy.org/); all included in Ancaonda (Python-Distribution, https://www.anaconda.com/). R (https://cran.r-project.org/) with the packages here (https://cran.r-project.org/web/packages/here/index.html) and rcarbon (https://cran.r-project.org/web/packages/rcarbon/index.html). 

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    Authors: Leemans, Inger; Tonelli, Sara; Christlein, Vincent; Troncy, Raphaël; +2 Authors

    This resource of the Olfactory Heritage Toolkit (DOI 10.5281/zenodo.10700210) presents an overview of places and spaces that are often mentioned in European digitized texts or depicted in images (1600-1920) as having a significant smellscape. The list is compiled by the Odeuropa project, to support cultural heritage policy makers to envision where smell may play a significant role and help heritage communities to acknowledge the value of olfaction in heritage contexts. Furthermore, the data may enable heritage documentation systems (e.g. digital collection management systems used by libraries, archives, museums, (in)tangible heritage lists, heritage classification systems) to include olfaction in their documents and resources.

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