October 1, 2021 Dataset Open Access

Fiedler, Lenz; Shah, Karan; Cangi, Attila; Bussmann, Michael

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{
  "@type": "Dataset", 
  "inLanguage": {
    "alternateName": "eng", 
    "@type": "Language", 
    "name": "English"
  }, 
  "version": "v1.0.0", 
  "datePublished": "2021-10-01", 
  "url": "https://rodare.hzdr.de/record/1197", 
  "creator": [
    {
      "affiliation": "HZDR / CASUS", 
      "@type": "Person", 
      "@id": "https://orcid.org/0000-0002-8311-0613", 
      "name": "Fiedler, Lenz"
    }, 
    {
      "affiliation": "HZDR / CASUS", 
      "@type": "Person", 
      "@id": "https://orcid.org/0000-0002-5480-2880", 
      "name": "Shah, Karan"
    }, 
    {
      "affiliation": "HZDR / CASUS", 
      "@type": "Person", 
      "@id": "https://orcid.org/0000-0001-9162-262X", 
      "name": "Cangi, Attila"
    }, 
    {
      "affiliation": "HZDR / CASUS", 
      "@type": "Person", 
      "@id": "https://orcid.org/0000-0002-8258-3881", 
      "name": "Bussmann, Michael"
    }
  ], 
  "@id": "https://doi.org/10.14278/rodare.1197", 
  "distribution": [
    {
      "fileFormat": "zip", 
      "@type": "DataDownload", 
      "contentUrl": "https://rodare.hzdr.de/api/files/df1c1124-89d8-40d5-bcd9-882886320c0a/dataset_scripts_deep_dive.zip"
    }
  ], 
  "name": "Dataset and scripts for A Deep Dive into Machine Learning Density Functional Theory for Materials Science and Chemistry", 
  "identifier": "https://doi.org/10.14278/rodare.1197", 
  "@context": "https://schema.org/", 
  "license": "https://creativecommons.org/licenses/by/4.0/legalcode", 
  "sameAs": [
    "https://www.hzdr.de/publications/Publ-33194"
  ], 
  "description": "<p>This dataset contains additional data for the publication &quot;A Deep Dive into Machine Learning Density Functional Theory for Materials Science and Chemistry&quot;. Its goal is to enable interested people to reproduce the citation analysis carried out in the aforementioned publication. &nbsp;</p>\n\n<p>&nbsp;</p>\n\n<p><strong>Prerequesites</strong></p>\n\n<p>The following software versions were used for the python version of this dataset:</p>\n\n<p>Python: 3.8.6</p>\n\n<p>Scholarly: 1.2.0</p>\n\n<p>Pyzotero: 1.4.24</p>\n\n<p>Numpy: 1.20.1</p>\n\n<p>&nbsp;</p>\n\n<p><strong>Contents</strong></p>\n\n<p>results/ : Contains the .csv files that were the results of the citation analysis.&nbsp; Paper groupings follow the ones outlined in the publication.</p>\n\n<p>scripts/ : Contains scripts to perform the citation analysis.</p>\n\n<p>Zotero.cached.pkl : Contains the cached Zotero library.</p>\n\n<p>&nbsp;</p>\n\n<p><strong>Usage</strong></p>\n\n<p>In order to reproduce the results of the citation analysis, you can use citation_analysis.py in conjunction with cached Zotero library. Manual additions can be verified using the check_consistency script.<br>\nPlease note that you will need a Tor key for the citation analysis, and access to our Zotero library if you don&#39;t want to use the cached version. If you need this access, simply contact us.</p>\n\n<p>&nbsp;</p>"
}