1,720,958 research outputs found
Outputs of the Jupyter Notebook - Variational data assimilation with deep prior (CIRC23)
No full textThe repository contains the outputs of the notebook "Variational data assimilation with deep prior (CIRC23)" published in The Environmental Data Science Book
Template (Jupyter Notebook) published in the Environmental Data Science book - snapshot
No full textThe research object refers to the Template notebook published in the Environmental Data Science book.Research Object in rohub2020: https://w3id.org/ro-id/92654099-ca41-4bc3-8450-0b5b267861a
Environmental Data Science book: A computational notebook community for open environmental data science.
No full textWhat's Changed
<ul>
<li>docs: add ViktorDomazetoski as a contributor for blog, and code by @allcontributors in <a href="https://github.com/alan-turing-institute/environmental-ds-book/pull/206">https://github.com/alan-turing-institute/environmental-ds-book/pull/206</a></li>
<li>docs: add ancazugo as a contributor for blog, and code by @allcontributors in <a href="https://github.com/alan-turing-institute/environmental-ds-book/pull/207">https://github.com/alan-turing-institute/environmental-ds-book/pull/207</a></li>
<li>docs: add SkirOwen as a contributor for blog, and code by @allcontributors in <a href="https://github.com/alan-turing-institute/environmental-ds-book/pull/208">https://github.com/alan-turing-institute/environmental-ds-book/pull/208</a></li>
<li>docs: add asthanameghna as a contributor for review by @allcontributors in <a href="https://github.com/alan-turing-institute/environmental-ds-book/pull/209">https://github.com/alan-turing-institute/environmental-ds-book/pull/209</a></li>
<li>docs: add NHomer-Edi as a contributor for review by @allcontributors in <a href="https://github.com/alan-turing-institute/environmental-ds-book/pull/210">https://github.com/alan-turing-institute/environmental-ds-book/pull/210</a></li>
<li>docs: add dbhatedin as a contributor for review by @allcontributors in <a href="https://github.com/alan-turing-institute/environmental-ds-book/pull/211">https://github.com/alan-turing-institute/environmental-ds-book/pull/211</a></li>
<li>Notebook - CIRC23 Deep learning and variational inversion to quantify and attribute climate change by @acocac in <a href="https://github.com/alan-turing-institute/environmental-ds-book/pull/186">https://github.com/alan-turing-institute/environmental-ds-book/pull/186</a></li>
<li>docs: add Mukulikaa as a contributor for blog, and code by @allcontributors in <a href="https://github.com/alan-turing-institute/environmental-ds-book/pull/213">https://github.com/alan-turing-institute/environmental-ds-book/pull/213</a></li>
<li>docs: add Rutika-16 as a contributor for blog, and code by @allcontributors in <a href="https://github.com/alan-turing-institute/environmental-ds-book/pull/214">https://github.com/alan-turing-institute/environmental-ds-book/pull/214</a></li>
<li>docs: add tinaok as a contributor for review by @allcontributors in <a href="https://github.com/alan-turing-institute/environmental-ds-book/pull/215">https://github.com/alan-turing-institute/environmental-ds-book/pull/215</a></li>
<li>docs: add crlna16 as a contributor for review by @allcontributors in <a href="https://github.com/alan-turing-institute/environmental-ds-book/pull/216">https://github.com/alan-turing-institute/environmental-ds-book/pull/216</a></li>
<li>docs: add polpel as a contributor for review by @allcontributors in <a href="https://github.com/alan-turing-institute/environmental-ds-book/pull/217">https://github.com/alan-turing-institute/environmental-ds-book/pull/217</a></li>
<li>Notebook - CIRC23 Variational data assimilation with deep prior by @acocac in <a href="https://github.com/alan-turing-institute/environmental-ds-book/pull/212">https://github.com/alan-turing-institute/environmental-ds-book/pull/212</a></li>
<li>Add script to generate UUID by @acocac in <a href="https://github.com/alan-turing-institute/environmental-ds-book/pull/218">https://github.com/alan-turing-institute/environmental-ds-book/pull/218</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a href="https://github.com/alan-turing-institute/environmental-ds-book/compare/v0.2.0...v0.2.1">https://github.com/alan-turing-institute/environmental-ds-book/compare/v0.2.0...v0.2.1</a></p>To reference the latest version of The Environmental Data Science Book, please cite it as below
Environmental Data Science book: A computational notebook community for open environmental data science.
No full text<h2>What's Changed</h2>
<ul>
<li>Update CONTRIBUTING.md by @acocac in https://github.com/alan-turing-institute/environmental-ds-book/pull/219</li>
<li>docs: add timo0thy as a contributor for blog, and code by @allcontributors in https://github.com/alan-turing-institute/environmental-ds-book/pull/220</li>
<li>Fixes #222: Update NBI preview link in CONTRIBUTING.md by @bnubald in https://github.com/alan-turing-institute/environmental-ds-book/pull/223</li>
<li>Upgrade Jupyter Book Version by @acocac in https://github.com/alan-turing-institute/environmental-ds-book/pull/226</li>
<li>docs: add bnubald as a contributor for test by @allcontributors in https://github.com/alan-turing-institute/environmental-ds-book/pull/227</li>
<li>Fix username in fosstodon by @acocac in https://github.com/alan-turing-institute/environmental-ds-book/pull/228</li>
<li>Fix corrupted thumbnail in Malhotra et al. notebook by @acocac in https://github.com/alan-turing-institute/environmental-ds-book/pull/229</li>
<li>Bump version: v0.2.1 → v0.3.0 by @acocac in https://github.com/alan-turing-institute/environmental-ds-book/pull/230</li>
</ul>
<h2>New Contributors</h2>
<ul>
<li>@bnubald made their first contribution in https://github.com/alan-turing-institute/environmental-ds-book/pull/223</li>
</ul>
<p><strong>Full Changelog</strong>: https://github.com/alan-turing-institute/environmental-ds-book/compare/v0.2.1...v0.3.0</p>To reference the latest version of The Environmental Data Science Book, please cite it as below
ESIP 2023 - ROHub: Research Object Hub - Manage and preserve your research work, make it available and discover new knowledge - archive
No full textPresentation given at ESIP January meeting 2023 to the session entitled Bringing Cloud-Native Science "Down To Earth".
For over 20 years, ESIP meetings have brought together the most innovative thinkers and leaders around Earth science data, forming a community dedicated to making Earth science data more discoverable, accessible and useful to researchers, practitioners, policymakers, and the public. The theme of the January meeting is "Opening Doors to Open Science."Research Object in rohub2020: https://w3id.org/ro-id/e744c06f-087a-4456-9007-fd130464a2a
AGU 2022 - Environmental Data Science Book: a community-driven resource showcasing open-source Environmental science - snapshot
No full textThis Research Object aggregates all the different Research Objects and resources used for presenting the Environmental Data Science Book at AGU 2022.
The Environmental Data Science book is a living, open and community-driven online resource to showcase and support the publication of data, research and open-source tools for collaborative, reproducible and transparent Environmental Data Science.
The Environmental Data Science is:
a book
a community
a global collaboration
We target to make sense of:
environmental systems
environmental data and sensors
innovative research in Environmental Data Science
open-source tools for Environmental Data Science
We hope you find the content in the resource helpful.
The resource and executable notebooks are free under a CC-BY licence and OSI-approved MIT license, respectively.Research Object in rohub2020: https://w3id.org/ro-id/871a1786-bc6a-4e60-a160-3f57e3869d3
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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