1,720,958 research outputs found
Illustrations from the Turing Way book dashes
No full textIllustrations created by Scriberia as part of the Turing Way book dashes in Manchester on 17 May 2019 and London on 28 May 2019. They depict a variety of content of the handbook as well as book sprint activities and the Turing Way community in general. All illustrations are provided as .jpg and .svg files.
More information on the book dashes can be found at https://github.com/alan-turing-institute/the-turing-way/tree/master/workshops/book-dash
When using any of the images, please credit it with
"This image was created by Scriberia for The Turing Way community and is used under a CC-BY licence."
We encourage the use and re-use of these images as much as possible. This includes remixing the images, for example changing the colours or merging them together with additional (openly licensed) images. If you create something that others may benefit from, we encourage you to get in touch with the Turing Way team who can update this repository with the images you create.This work was supported by The UKRI Strategic Priorities Fund under the EPSRC Grant EP/T001569/1, particularly the "Tools, Practices and Systems" theme within that grant, and by The Alan Turing Institute under the EPSRC grant EP/N510129/1
The Turing Way: A Handbook for Reproducible Data Science
No full text<p>Reproducible research is necessary to ensure that scientific work can be trusted. Funders and publishers are beginning to require that publications include access to the underlying data and the analysis code. The goal is to ensure that all results can be independently verified and built upon in future work. This is sometimes easier said than done.</p>
<p>Sharing these research outputs means understanding data management, library sciences, software development, and continuous integration techniques: skills that are not widely taught or expected of academic researchers and data scientists.<em> </em><em>The Turing Way</em> is a handbook to support students, their supervisors, funders and journal editors in ensuring that reproducible data science is "too easy not to do".</p>
<p>It will include training material on version control, analysis testing, and open and transparent communication with future users, and build on Turing Institute case studies and workshops.</p>
<p>This project is openly developed and any and all questions, comments and recommendations are welcome at our github repository: <a href="https://github.com/alan-turing-institute/the-turing-way">https://github.com/alan-turing-institute/the-turing-way</a>.</p>
<p><strong>Release log</strong></p>
<ul>
<li><strong>v0.0.4:</strong> Continuous integration chapter merged to master.</li>
<li><strong>v0.0.3:</strong> Reproducible environments chapter merged to master.</li>
<li><strong>v0.0.2:</strong> Version control chapter merged to master.</li>
<li><strong>v0.0.1: </strong>Reproducibility chapter merged to master.</li>
</ul>This work was supported by The UKRI Strategic Priorities Fund under the EPSRC Grant EP/T001569/1, particularly the "Tools, Practices and Systems" theme within that grant, and by The Alan Turing Institute under the EPSRC grant EP/N510129/1
The Turing Way: A Handbook for Reproducible Data Science
No full text<p>Reproducible research is necessary to ensure that scientific work can be trusted. Funders and publishers are beginning to require that publications include access to the underlying data and the analysis code. The goal is to ensure that all results can be independently verified and built upon in future work. This is sometimes easier said than done.</p>
<p>Sharing these research outputs means understanding data management, library sciences, software development, and continuous integration techniques: skills that are not widely taught or expected of academic researchers and data scientists.<em> </em><em>The Turing Way</em> is a handbook to support students, their supervisors, funders and journal editors in ensuring that reproducible data science is "too easy not to do".</p>
<p>It will include training material on version control, analysis testing, and open and transparent communication with future users, and build on Turing Institute case studies and workshops.</p>
<p>This project is openly developed and any and all questions, comments and recommendations are welcome at our github repository: https://github.com/alan-turing-institute/the-turing-way.</p>
<p><strong>Release log</strong></p>
<ul>
<li><strong>v0.0.2:</strong> Version control chapter merged to master.</li>
<li><strong>v0.0.1: </strong>Reproducibility chapter merged to master.</li>
</ul>This work was supported by The UKRI Strategic Priorities Fund under the EPSRC Grant EP/T001569/1, particularly the "Tools, Practices and Systems" theme within that grant, and by The Alan Turing Institute under the EPSRC grant EP/N510129/1
The Turing Way: A Handbook for Reproducible Data Science
No full text<p>Reproducible research is necessary to ensure that scientific work can be trusted. Funders and publishers are beginning to require that publications include access to the underlying data and the analysis code. The goal is to ensure that all results can be independently verified and built upon in future work. This is sometimes easier said than done.</p>
<p>Sharing these research outputs means understanding data management, library sciences, software development, and continuous integration techniques: skills that are not widely taught or expected of academic researchers and data scientists.<em> </em><em>The Turing Way</em> is a handbook to support students, their supervisors, funders and journal editors in ensuring that reproducible data science is "too easy not to do".</p>
<p>It will include training material on version control, analysis testing, and open and transparent communication with future users, and build on Turing Institute case studies and workshops.</p>
<p>This project is openly developed and any and all questions, comments and recommendations are welcome at our github repository: https://github.com/alan-turing-institute/the-turing-way.</p>
<p><strong>Release log</strong></p>
<p><strong>v0.0.3:</strong> Reproducible environments chapter merged to master.</p>
<p><strong>v0.0.2:</strong> Version control chapter merged to master.</p>
<p><strong>v0.0.1: </strong>Reproducibility chapter merged to master.</p>
<p> </p>This work was supported by The UKRI Strategic Priorities Fund under the EPSRC Grant EP/T001569/1, particularly the "Tools, Practices and Systems" theme within that grant, and by The Alan Turing Institute under the EPSRC grant EP/N510129/1
The Turing Way: A Handbook for Reproducible Data Science
No full text<p>Reproducible research is necessary to ensure that scientific work can be trusted. Funders and publishers are beginning to require that publications include access to the underlying data and the analysis code. The goal is to ensure that all results can be independently verified and built upon in future work. This is sometimes easier said than done.</p>
<p>Sharing these research outputs means understanding data management, library sciences, software development, and continuous integration techniques: skills that are not widely taught or expected of academic researchers and data scientists.<em> </em><em>The Turing Way</em> is a handbook to support students, their supervisors, funders and journal editors in ensuring that reproducible data science is "too easy not to do".</p>
<p>It will include training material on version control, analysis testing, and open and transparent communication with future users, and build on Turing Institute case studies and workshops.</p>
<p>This project is openly developed and any and all questions, comments and recommendations are welcome at our github repository: https://github.com/alan-turing-institute/the-turing-way.</p>
<p><strong>Release log</strong></p>
<ul>
<li><strong>v0.0.1: </strong>Reproducibility chapter merged to master.</li>
</ul>This work was supported by The UKRI Strategic Priorities Fund under the EPSRC Grant EP/T001569/1, particularly the "Tools, Practices and Systems" theme within that grant, and by The Alan Turing Institute under the EPSRC grant EP/N510129/1
The Turing Way: A Handbook for Reproducible Data Science
No full textPoster presentation of the Turing Way at the 2019 Open Science Fair.
Abstract:
The Turing Way is a handbook to support students, their supervisors, funders and journal editors in ensuring that reproducible data science is "too easy not to do" (https://the-turing-way.netlify.com). It includes training material on topics such as version control and analysis testing, and will build upon Alan Turing Institute case studies and workshops. The project also demonstrates open and transparent project management and communication with future users, as it is openly developed at our GitHub repository: https://github.com/alan-turing-institute/the-turing-way. All resources associated with workshops we have delivered, as well as how to organise a Book Dash (a one-day book sprint), are also openly available.
Reproducible research is necessary to ensure that scientific work can be trusted. Funders and publishers are beginning to require that publications include access to the underlying data and the analysis code. The goal is to ensure that all results can be independently verified and built upon in future work, which is sometimes easier said than done. Sharing these research outputs means understanding data management, library sciences, software development, and continuous integration techniques: skills that are not widely taught or expected of academic researchers and data scientists.
This poster will present an overview of the handbook so far and show Open Science Fair participants how they can contribute their knowledge to make it even better going forwards or how to open up their own projects to a wider contributor community. This poster relates to the overall theme of the conference, as the Turing Way provides the tools to improve research habits in a self-contained handbook. It will also ensure that PhD students, postdocs, PIs and funding teams know which parts of the "responsibility of reproducibility" they can affect, and what they should do to nudge research and data science to being more efficient, effective and understandable
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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