## Randomness


It's funny how humans are forced to be organized while pacing through the randomness of life. Brief of chaos, brief of enlightenment, a little sprinkle of sadness all in a piece at every moment, and sometimes in
sinusoidal occurrence.

Well that is not the basis of this document, this is just to test the  use of datahub, and it's very random of me to pick this topic

<Catalog
  datasets={[
    {
      _id: '07026b22d49916754df1dc8ffb9ccd1c31878aae',
      metadata: {
        'details-of-task': 'Detect and categorise abusive language in social media data',
        language: 'Albanian',
        'level-of-annotation': [
          'Posts'
        ],
        'link-to-data': 'https://doi.org/10.6084/m9.figshare.19333298.v1',
        'link-to-publication': 'https://arxiv.org/abs/2107.13592',
        medium: [
          'Text'
        ],
        'percentage-abusive': 13.2,
        platform: [
          'Instagram',
          'Youtube'
        ],
        reference: 'Nurce, E., Keci, J., Derczynski, L., 2021. Detecting Abusive Albanian. arXiv:2107.13592',
        'size-of-dataset': 11874,
        'task-description': 'Hierarchical (offensive/not; untargeted/targeted; person/group/other)',
        title: 'Detecting Abusive Albanian'
      },
      url_path: 'dataset-4'
    },
    {
      _id: '42c86cf3c4fbbab11d91c2a7d6dcb8f750bc4e19',
      file_path: 'content/dataset-1/index.md',
      metadata: {
        'details-of-task': 'Enriched versions of the OffensEval/OLID dataset with the distinction of explicit/implicit offensive messages and the new dimension for abusive messages. Labels for offensive language: EXPLICIT, IMPLICT, NOT; Labels for abusive language: EXPLICIT, IMPLICT, NOTABU',
        language: 'English',
        'level-of-annotation': [
          'Tweets'
        ],
        'link-to-data': 'https://github.com/tommasoc80/AbuseEval',
        'link-to-publication': 'http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.760.pdf',
        medium: [
          'Text'
        ],
        'percentage-abusive': 20.75,
        platform: [
          'Twitter'
        ],
        reference: 'Caselli, T., Basile, V., Jelena, M., Inga, K., and Michael, G. 2020. "I feel offended, don’t be abusive! implicit/explicit messages in offensive and abusive language". The 12th Language Resources and Evaluation Conference (pp. 6193-6202). European Language Resources Association.',
        'size-of-dataset': 14100,
        'task-description': 'Explicitness annotation of offensive and abusive content',
        title: 'AbuseEval v1.0'
      },
      url_path: 'dataset-1'
    },
    {
      _id: '80001dd32a752421fdcc64e91fbd237dc31d6bb3',
      file_path: 'content/dataset-2/index.md',
      metadata: {
        'details-of-task': 'Incivility',
        language: 'Arabic',
        'level-of-annotation': [
          'Posts'
        ],
        'link-to-data': 'http://alt.qcri.org/~hmubarak/offensive/AJCommentsClassification-CF.xlsx',
        'link-to-publication': 'https://www.aclweb.org/anthology/W17-3008',
        medium: [
          'Text'
        ],
        'percentage-abusive': 0.81,
        platform: [
          'AlJazeera'
        ],
        reference: 'Mubarak, H., Darwish, K. and Magdy, W., 2017. Abusive Language Detection on Arabic Social Media. In: Proceedings of the First Workshop on Abusive Language Online. Vancouver, Canada: Association for Computational Linguistics, pp.52-56.',
        'size-of-dataset': 32000,
        'task-description': 'Ternary (Obscene, Offensive but not obscene, Clean)',
        title: 'Abusive Language Detection on Arabic Social Media (Al Jazeera)'
      },
      url_path: 'dataset-2'
    },
    {
      _id: '96649d05d8193f4333b10015af76c6562971bd8c',
      file_path: 'content/dataset-3/index.md',
      metadata: {
        'details-of-task': 'Detectioning CDC from abusive comments',
        language: 'Croatian',
        'level-of-annotation': [
          'Posts'
        ],
        'link-to-data': 'https://github.com/shekharRavi/CoRAL-dataset-Findings-of-the-ACL-AACL-IJCNLP-2022',
        'link-to-publication': 'https://aclanthology.org/2022.findings-aacl.21/',
        medium: [
          'Newspaper Comments'
        ],
        'percentage-abusive': 100,
        platform: [
          'Posts'
        ],
        reference: 'Ravi Shekhar, Mladen Karan and Matthew Purver (2022). CoRAL: a Context-aware Croatian Abusive Language Dataset. Findings of the ACL: AACL-IJCNLP.',
        'size-of-dataset': 2240,
        'task-description': 'Multi-class based on context dependency categories (CDC)',
        title: 'CoRAL: a Context-aware Croatian Abusive Language Dataset'
      },
      url_path: 'dataset-3'
    }
  ]}
  facets={[
    'language',
    'platform'
  ]}
/>


Wow, this product really looks good, data-journalism on steriod. Okay trying another component to see how it works

<FlatUiTable
  data={{
    url: 'https://storage.openspending.org/alberta-budget/__os_imported__alberta_total.csv'
  }}
 />