metapub.org

the official Metapub website

https://github.com/metapub/metapub.org

Science Score: 31.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
    Found CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
  • DOI references
  • Academic publication links
  • Academic email domains
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (0.2%) to scientific vocabulary
Last synced: 10 months ago · JSON representation ·

Repository

the official Metapub website

Basic Info
  • Host: GitHub
  • Owner: metapub
  • License: mit
  • Language: Hack
  • Default Branch: master
  • Size: 780 KB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 1
  • Releases: 0
Created about 6 years ago · Last pushed almost 2 years ago
Metadata Files
Readme Funding License Citation

README.md

metapub.org

the official Metapub website

Owner

  • Name: metapub
  • Login: metapub
  • Kind: organization

Citation (citations/citations/adera2.php)

<article class="citation">
    <p><strong><a href="https://www.mdpi.com/1420-3049/27/19/6453">Adera2.0: A Drug Repurposing Workflow for Neuroimmunological Investigations Using Neural Networks</a></strong></p>
    <p><strong>Authors:</strong> Marzena Lazarczyk et al</p>
    <p><strong>DOI:</strong> <a href="https://doi.org/10.3390/molecules27196453">10.3390/molecules27196453</a></p>
    <blockquote>
        <p>"The first phase of the workflow (phase I) covers the aim of building a database of the JSON format containing parsed PDFs (Figure 1). This phase consists of five steps. The first step’s objective is to fetch the PubMed IDs related to the search query. This is accomplished by using the PubMed fetcher function available through the Metapub python library. This step uses the input query to search for recent PubMed articles that match the query terms. After that, in the second step, the workflow fetches the abstracts and keywords of the retrieved PubMed IDs. This is achieved through the use of the python library Keybert. The third step in this phase involves downloading the identified PDFs; this is done using the fetch_PDFs library."</p>
    </blockquote>
</article>

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