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  • Digital Humanities and Cultural Heritage
  • 2013-2022
  • Research software
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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/

    gCube Catalogue (gCat) Client is a library designed to interact with REST API exposed by the gCat Service. gCube is an open-source software toolkit used for building and operating Hybrid Data Infrastructures enabling the dynamic deployment of Virtual Research Environments, such as the D4Science Infrastructure, by favouring the realisation of reuse-oriented policies. gCube has been used to successfully build and operate infrastructures and virtual research environments for application domains ranging from biodiversity to environmental data management and cultural heritage. gCube offers components supporting typical data management workflows including data access, curation, processing, and visualisation on a large set of data typologies ranging from primary biodiversity data to geospatial and tabular data. D4Science is a Hybrid Data Infrastructure combining over 500 software components and integrating data from more than 50 different data providers into a coherent and managed system of hardware, software, and data resources. The D4Science infrastructure drastically reduces the cost of ownership, maintenance, and operation thanks to the exploitation of gCube. The source code of this software version is available at: https://code-repo.d4science.org/gCubeSystem/gcat-client/releases/tag/v2.4.0

    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/ ZENODOarrow_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/
    ZENODO
    Software . 2022
    Data sources: Datacite
    ZENODO
    Software . 2022
    Data sources: ZENODO
    ZENODO
    Software . 2022
    Data sources: ZENODO
    ZENODO
    Software . 2022
    Data sources: ZENODO
    ZENODO
    Software . 2022
    Data sources: ZENODO
    ZENODO
    Software . 2022
    Data sources: ZENODO
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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/ ZENODOarrow_drop_down
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      ZENODO
      Software . 2022
      Data sources: Datacite
      ZENODO
      Software . 2022
      Data sources: ZENODO
      ZENODO
      Software . 2022
      Data sources: ZENODO
      ZENODO
      Software . 2022
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      ZENODO
      Software . 2022
      Data sources: ZENODO
      ZENODO
      Software . 2022
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  • Authors: Jinhang Jiang; Srinivasan, Karthik;

    MoreThanSentiments (Jiang and Srinivasan, 2022) is a python library written to help researchers calculate Boilerplate (Lang and Stice-Lawrence, 2015), Redundancy (Cazier and Pfeiffer, 2017), Specificity (Hope et al., 2016), Relative Prevalence (Blankespoor, 2019), etc. It is inspired by the idea that properly quantifying the text structure will also help researchers extract tons of meaningful information. And this domain-independent package is easy to be implemented in various projects for text quantification tasks.

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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: Barzaghi, Sebastian;

    A series of Python scripts that have been used to process the documents in the National Edition of Aldo Moro's works. In particular, the scripts are: recast.py, for refactoring the documents code, correcting possible errors, normalizing and finalizing resource URIs, and so on; TEIfy.py, for generating TEI documents from the HTML code. The TEI structure imitates the source code as closely as possible, and in addition integrates bibliographic metadata in the document header as well; PDFfy.py, for generating PDF documents from the HTML code. The PDF pagination and visualization is regulated by a CSS stylesheet (stylesheet.css); generate.py, for converting metadata into semantic statements collected in a knowledge base and organized on the basis of the standards defined in the data modelling phase; align.py, for integrating document markup in the knowledge base; main.py, for gathering and managing the functions defined in the other scripts; utils.py, for choosing which script to run and in which local directory. To start the program, run `python main.py` in the command prompt, then select the function you want to run and the directory you intend to use. You will either need a `config.json` file containing some variables in order to make it work (such as your local paths and names of the directories), or write them directly into the code as variables.

    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/ ZENODOarrow_drop_down
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    ZENODO
    Software . 2021
    License: CC BY
    Data sources: ZENODO
    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/
    ZENODO
    Software . 2021
    License: CC BY
    Data sources: Datacite
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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/ ZENODOarrow_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/
      ZENODO
      Software . 2021
      License: CC BY
      Data sources: ZENODO
      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/
      ZENODO
      Software . 2021
      License: CC BY
      Data sources: Datacite
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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: Giovanni Spitale; Federico Germani; Nikola Biller - Andorno;

    The purpose of this tool is performing NLP analysis on Telegram chats. Telegram chats can be exported as .json files from the official client, Telegram Desktop (v. 2.9.2.0). The files are parsed, the content is used to populate a message dataframe, which is then anonymized. The software calculates and displays the following information: user count (n of users, new users per day, removed users per day); message count (n and relative frequency of messages, messages per day); autocoded messages (anonymized message dataframe with code weights assigned to each message based on a customizable set of regex rules); prevalence of codes (n and relative frequency); prevalence of lemmas (n and relative frequency); prevalence of lemmas segmented by autocode (n and relative frequency); mean sentiment per day; mean sentiment segmented by autocode. The software outputs: messages_df_anon.csv - an anonymized file containing the progressive id of the message, the date, the univocal pseudonym of the sender, and the text; usercount_df.csv - user count dataframe; user_activity_df.csv - user activity dataframe; messagecount_df.csv - message count dataframe; messages_df_anon_coded.csv - an anonymized file containing the progressive id of the message, the date, the univocal pseudonym of the sender, the text, the codes, and the sentiment; autocode_freq_df.csv - general prevalence of codes; lemma_df.csv - lemma frequency; autocode_freq_df_[rule_name].csv - lemma frequency in coded messages, one file per rule; daily_sentiment_df.csv - daily sentiment; sentiment_by_code_df.csv - sentiment segmented by code; messages_anon.txt - anonymized text file generated from the message data frame, for easy import in other software for text mining or qualitative analysis; messages_anon_MaxQDA.txt - anonymized text file generated from the message data frame, formatted specifically for MaxQDA (to track speakers and codes). Dependencies: pandas (1.2.1) json random os re tqdm (4.62.2) datetime (4.3) matplotlib (3.4.3) Spacy (3.1.2) + it_core_news_md wordcloud (1.8.1) Counter feel_it (1.0.3) torch (1.9.0) numpy (1.21.1) transformers (4.3.3) This code is optimized for Italian, however: Lemma analysis is based on spaCy, which provides several other models for other languages ( https://spacy.io/models ) so it can easily be adapted. Sentiment analysis is performed using FEEL-IT: Emotion and Sentiment Classification for the Italian Language (Kudos to Federico Bianchi <f.bianchi@unibocconi.it>; Debora Nozza <debora.nozza@unibocconi.it>; and Dirk Hovy <dirk.hovy@unibocconi.it>). Their work is specific for Italian. To perform sentiment analysis in other languages one could consider nltk.sentiment The code is structured in a Jupyter-lab notebook, heavily commented for future reference. The software comes with a toy dataset comprised of Wikiquotes copy-pasted in a chat created by the research group. Have fun exploring it. {"references": ["Bianchi F, Nozza D, Hovy D. FEEL-IT: Emotion and Sentiment Classification for the Italian Language. In: Proceedings of the 11th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. Association for Computational Linguistics; 2021. https://github.com/MilaNLProc/feel-it"]}

    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/ ZENODOarrow_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/
    ZENODO
    Software . 2021
    License: CC BY
    Data sources: ZENODO
    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/
    ZENODO
    Software . 2021
    License: CC BY
    Data sources: Datacite
    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/
    ZENODO
    Software . 2021
    License: CC BY
    Data sources: ZENODO
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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/
      ZENODO
      Software . 2021
      License: CC BY
      Data sources: ZENODO
      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/
      ZENODO
      Software . 2021
      License: CC BY
      Data sources: Datacite
      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/
      ZENODO
      Software . 2021
      License: CC BY
      Data sources: ZENODO
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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: Horton, Laurence;

    Code for checking the status of URL links in the dataset: Horton, Laurence (2018). UK Higher Education Institution Research Data Management Policies, 2009-2016. [Data Collection]. Colchester, Essex: UK Data Archive. https://doi.org/10.5255/UKDA-SN-851566 {"references": ["Horton, Laurence (2018). UK Higher Education Institution Research Data Management Policies, 2009-2016. [Data Collection]. Colchester, Essex: UK Data Archive. 10.5255/UKDA-SN-851566"]}

    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/ ZENODOarrow_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/
    ZENODO
    Software . 2021
    Data sources: Datacite
    ZENODO
    Software . 2021
    Data sources: ZENODO
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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/ ZENODOarrow_drop_down
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      Software . 2021
      Data sources: Datacite
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      Software . 2021
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  • Authors: Joseph Padfield;

    This is an interactive live modelling system which can automatically convert simple tab separated triples or JSON-LD into graphical models using the mermaid library. General updates and improvements have been carried out, with the addition of the Mermaid SubGraph functionality to aid the organisation of more complex models. Instructions: Full details and instructions relating to how this system works is still being developed, however several examples are included in the interactive web site: dynamic modelling, with all of the related code presented in GitHub.

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    ZENODO
    Software . 2021
    Data sources: ZENODO
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      Software . 2021
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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: Joseph Padfield;

    This is a simple set of processes for creating a standard set of webpages based on a simple set of json files. This project is intended to work along side other projects to provide a simple way of creating a set of consistent webpages, which can be delivered as part of your own GitHub project using GitHub pages. It has been extended to allow more complex features such as presenting IIIIF (https://iiif.io) viewers, Timelines, and ordered Lists & Galleries. Various updates to the related JavaScript libraries have been added along with an option to use the whole system to create dynamic as well as static websites. For more detail and the most current version of the code see: https://github.com/jpadfield/simple-site

    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/ ZENODOarrow_drop_down
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    Software . 2021
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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/ ZENODOarrow_drop_down
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      Software . 2021
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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: Tobias Hey; Fei Chen; Sebastian Weigelt; Walter F. Tichy;

    This is the supplementary material of the paper "Improving Traceability Link Recovery Using Fine-grained Requirements-to-Code Relations". In this paper, we explore the performance of word embedding-based fine-grained requirements-to-code relations in automated traceability link recovery. The repository includes the code as well as the datasets used. To fully enable the potential of a word embedding based approach, we translated the identifier in two datasets (SMOS and eTour) to match the prevalent language. Attribution (of datasets used): The original SMOS and eAnci datasets can be attributed to Gethers et al., On integrating orthogonal information retrieval methods to improve traceability recovery. In 2011 27th IEEE International Conference on Software Maintenance (ICSM), Sep. 2011. Available: https://doi.org/10.1109/ICSM.2011.6080780 The original eTour dataset was provided for the TEFSE challenge at 6th International Workshop on Traceability in Emerging Forms of Software Engineering (TEFSE), 2011 and was retrieved from http://coest.org/

    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/ ZENODOarrow_drop_down
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    ZENODO
    Software . 2021
    License: CC BY
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    Authors: Mahmud, Junayed; Faisal, Fahim; Islam Arnob, Raihan; Anastasopoulos, Antonios; +1 Authors

    This Zenodo repository contains the code, data, and replication package for our paper entitled "Code to Comment Translation: An Empirical Study on Model Effectiveness and Errors". 

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    Authors: Pachankis, Yang I.;

    This research is based on observations made with the NASA/ESA Hubble Space Telescope obtained from the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5–26555. This research is based on observations made with the Galaxy Evolution Explorer (GALEX) satellite, a NASA mission led by the California Institute of Technology, obtained from the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5–26555. The scientific results reported in this article are based [in part] on observations made by the Chandra X-ray Observatory. This work is based [in part] on observations made with the Spitzer Space Telescope, which was operated by the Jet Propulsion Laboratory, California Institute of Technology under a contract with NASA. The observations are performed on Harvard-Smithsonian Micro-observatory, and the data of this upload are processed by SAOImage DS9 MacOS. The set of imaging plate analysis served as orbital and trajectory analysis in relation to the angular momenta of the observations. Cyber-based targeting and intrusion were experienced during the experiment and analysis, with specific mocks on the contours with male genital. The ZScales served for the extra dimension analysis on the antimatter induction on the theoretical Van Der Waals Force in antimatter electromagnetism.

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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/

    gCube Catalogue (gCat) Client is a library designed to interact with REST API exposed by the gCat Service. gCube is an open-source software toolkit used for building and operating Hybrid Data Infrastructures enabling the dynamic deployment of Virtual Research Environments, such as the D4Science Infrastructure, by favouring the realisation of reuse-oriented policies. gCube has been used to successfully build and operate infrastructures and virtual research environments for application domains ranging from biodiversity to environmental data management and cultural heritage. gCube offers components supporting typical data management workflows including data access, curation, processing, and visualisation on a large set of data typologies ranging from primary biodiversity data to geospatial and tabular data. D4Science is a Hybrid Data Infrastructure combining over 500 software components and integrating data from more than 50 different data providers into a coherent and managed system of hardware, software, and data resources. The D4Science infrastructure drastically reduces the cost of ownership, maintenance, and operation thanks to the exploitation of gCube. The source code of this software version is available at: https://code-repo.d4science.org/gCubeSystem/gcat-client/releases/tag/v2.4.0

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  • Authors: Jinhang Jiang; Srinivasan, Karthik;

    MoreThanSentiments (Jiang and Srinivasan, 2022) is a python library written to help researchers calculate Boilerplate (Lang and Stice-Lawrence, 2015), Redundancy (Cazier and Pfeiffer, 2017), Specificity (Hope et al., 2016), Relative Prevalence (Blankespoor, 2019), etc. It is inspired by the idea that properly quantifying the text structure will also help researchers extract tons of meaningful information. And this domain-independent package is easy to be implemented in various projects for text quantification tasks.

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    Authors: Barzaghi, Sebastian;

    A series of Python scripts that have been used to process the documents in the National Edition of Aldo Moro's works. In particular, the scripts are: recast.py, for refactoring the documents code, correcting possible errors, normalizing and finalizing resource URIs, and so on; TEIfy.py, for generating TEI documents from the HTML code. The TEI structure imitates the source code as closely as possible, and in addition integrates bibliographic metadata in the document header as well; PDFfy.py, for generating PDF documents from the HTML code. The PDF pagination and visualization is regulated by a CSS stylesheet (stylesheet.css); generate.py, for converting metadata into semantic statements collected in a knowledge base and organized on the basis of the standards defined in the data modelling phase; align.py, for integrating document markup in the knowledge base; main.py, for gathering and managing the functions defined in the other scripts; utils.py, for choosing which script to run and in which local directory. To start the program, run `python main.py` in the command prompt, then select the function you want to run and the directory you intend to use. You will either need a `config.json` file containing some variables in order to make it work (such as your local paths and names of the directories), or write them directly into the code as variables.

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    Authors: Giovanni Spitale; Federico Germani; Nikola Biller - Andorno;

    The purpose of this tool is performing NLP analysis on Telegram chats. Telegram chats can be exported as .json files from the official client, Telegram Desktop (v. 2.9.2.0). The files are parsed, the content is used to populate a message dataframe, which is then anonymized. The software calculates and displays the following information: user count (n of users, new users per day, removed users per day); message count (n and relative frequency of messages, messages per day); autocoded messages (anonymized message dataframe with code weights assigned to each message based on a customizable set of regex rules); prevalence of codes (n and relative frequency); prevalence of lemmas (n and relative frequency); prevalence of lemmas segmented by autocode (n and relative frequency); mean sentiment per day; mean sentiment segmented by autocode. The software outputs: messages_df_anon.csv - an anonymized file containing the progressive id of the message, the date, the univocal pseudonym of the sender, and the text; usercount_df.csv - user count dataframe; user_activity_df.csv - user activity dataframe; messagecount_df.csv - message count dataframe; messages_df_anon_coded.csv - an anonymized file containing the progressive id of the message, the date, the univocal pseudonym of the sender, the text, the codes, and the sentiment; autocode_freq_df.csv - general prevalence of codes; lemma_df.csv - lemma frequency; autocode_freq_df_[rule_name].csv - lemma frequency in coded messages, one file per rule; daily_sentiment_df.csv - daily sentiment; sentiment_by_code_df.csv - sentiment segmented by code; messages_anon.txt - anonymized text file generated from the message data frame, for easy import in other software for text mining or qualitative analysis; messages_anon_MaxQDA.txt - anonymized text file generated from the message data frame, formatted specifically for MaxQDA (to track speakers and codes). Dependencies: pandas (1.2.1) json random os re tqdm (4.62.2) datetime (4.3) matplotlib (3.4.3) Spacy (3.1.2) + it_core_news_md wordcloud (1.8.1) Counter feel_it (1.0.3) torch (1.9.0) numpy (1.21.1) transformers (4.3.3) This code is optimized for Italian, however: Lemma analysis is based on spaCy, which provides several other models for other languages ( https://spacy.io/models ) so it can easily be adapted. Sentiment analysis is performed using FEEL-IT: Emotion and Sentiment Classification for the Italian Language (Kudos to Federico Bianchi <f.bianchi@unibocconi.it>; Debora Nozza <debora.nozza@unibocconi.it>; and Dirk Hovy <dirk.hovy@unibocconi.it>). Their work is specific for Italian. To perform sentiment analysis in other languages one could consider nltk.sentiment The code is structured in a Jupyter-lab notebook, heavily commented for future reference. The software comes with a toy dataset comprised of Wikiquotes copy-pasted in a chat created by the research group. Have fun exploring it. {"references": ["Bianchi F, Nozza D, Hovy D. FEEL-IT: Emotion and Sentiment Classification for the Italian Language. In: Proceedings of the 11th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. Association for Computational Linguistics; 2021. https://github.com/MilaNLProc/feel-it"]}

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    Authors: Horton, Laurence;

    Code for checking the status of URL links in the dataset: Horton, Laurence (2018). UK Higher Education Institution Research Data Management Policies, 2009-2016. [Data Collection]. Colchester, Essex: UK Data Archive. https://doi.org/10.5255/UKDA-SN-851566 {"references": ["Horton, Laurence (2018). UK Higher Education Institution Research Data Management Policies, 2009-2016. [Data Collection]. Colchester, Essex: UK Data Archive. 10.5255/UKDA-SN-851566"]}

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  • Authors: Joseph Padfield;

    This is an interactive live modelling system which can automatically convert simple tab separated triples or JSON-LD into graphical models using the mermaid library. General updates and improvements have been carried out, with the addition of the Mermaid SubGraph functionality to aid the organisation of more complex models. Instructions: Full details and instructions relating to how this system works is still being developed, however several examples are included in the interactive web site: dynamic modelling, with all of the related code presented in GitHub.

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    Authors: Joseph Padfield;

    This is a simple set of processes for creating a standard set of webpages based on a simple set of json files. This project is intended to work along side other projects to provide a simple way of creating a set of consistent webpages, which can be delivered as part of your own GitHub project using GitHub pages. It has been extended to allow more complex features such as presenting IIIIF (https://iiif.io) viewers, Timelines, and ordered Lists & Galleries. Various updates to the related JavaScript libraries have been added along with an option to use the whole system to create dynamic as well as static websites. For more detail and the most current version of the code see: https://github.com/jpadfield/simple-site

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    Authors: Tobias Hey; Fei Chen; Sebastian Weigelt; Walter F. Tichy;

    This is the supplementary material of the paper "Improving Traceability Link Recovery Using Fine-grained Requirements-to-Code Relations". In this paper, we explore the performance of word embedding-based fine-grained requirements-to-code relations in automated traceability link recovery. The repository includes the code as well as the datasets used. To fully enable the potential of a word embedding based approach, we translated the identifier in two datasets (SMOS and eTour) to match the prevalent language. Attribution (of datasets used): The original SMOS and eAnci datasets can be attributed to Gethers et al., On integrating orthogonal information retrieval methods to improve traceability recovery. In 2011 27th IEEE International Conference on Software Maintenance (ICSM), Sep. 2011. Available: https://doi.org/10.1109/ICSM.2011.6080780 The original eTour dataset was provided for the TEFSE challenge at 6th International Workshop on Traceability in Emerging Forms of Software Engineering (TEFSE), 2011 and was retrieved from http://coest.org/

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    Authors: Mahmud, Junayed; Faisal, Fahim; Islam Arnob, Raihan; Anastasopoulos, Antonios; +1 Authors

    This Zenodo repository contains the code, data, and replication package for our paper entitled "Code to Comment Translation: An Empirical Study on Model Effectiveness and Errors". 

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    Authors: Pachankis, Yang I.;

    This research is based on observations made with the NASA/ESA Hubble Space Telescope obtained from the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5–26555. This research is based on observations made with the Galaxy Evolution Explorer (GALEX) satellite, a NASA mission led by the California Institute of Technology, obtained from the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5–26555. The scientific results reported in this article are based [in part] on observations made by the Chandra X-ray Observatory. This work is based [in part] on observations made with the Spitzer Space Telescope, which was operated by the Jet Propulsion Laboratory, California Institute of Technology under a contract with NASA. The observations are performed on Harvard-Smithsonian Micro-observatory, and the data of this upload are processed by SAOImage DS9 MacOS. The set of imaging plate analysis served as orbital and trajectory analysis in relation to the angular momenta of the observations. Cyber-based targeting and intrusion were experienced during the experiment and analysis, with specific mocks on the contours with male genital. The ZScales served for the extra dimension analysis on the antimatter induction on the theoretical Van Der Waals Force in antimatter electromagnetism.

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