{
  "id": 236638,
  "title": "Multiple datasets in one publication?",
  "url": "/competitions/coleridgeinitiative-show-us-the-data/discussion/236638",
  "author_name": "",
  "post_date": "2021-05-05T07:46:23.532631100Z",
  "votes": 3,
  "comment_count": 2,
  "views": 0,
  "content": "<p>For each publication, if there are some excerpts noting one dataset, and some excerpts noting another dataset. Do we arrange those of different datasets in one single PredictionString, or split them into two entries?</p>\n<p>i.e. which way:<br>\n(1) <code>\"&lt;pub_id_1&gt;\", \"&lt;dataset_1_excerpt_1&gt;|&lt;dataset_1_excerpt_2&gt;|&lt;dataset_2_excerpt_1&gt;\"</code><br>\nor<br>\n(2) <br>\n<code>\"&lt;pub_id_1&gt;\", \"&lt;dataset_1_excerpt_1&gt;|&lt;dataset_1_excerpt_2&gt;\",</code><br>\n<code>\"&lt;pub_id_1&gt;\", \"&lt;dataset_2_excerpt_1&gt;\"</code></p>\n<p>Some hints please, thanks! </p>",
  "messages": [
    {
      "id": "1293837",
      "postDate": "05/05/2021 07:46:23",
      "content": "<p>For each publication, if there are some excerpts noting one dataset, and some excerpts noting another dataset. Do we arrange those of different datasets in one single PredictionString, or split them into two entries?</p>\n<p>i.e. which way:<br>\n(1) <code>\"&lt;pub_id_1&gt;\", \"&lt;dataset_1_excerpt_1&gt;|&lt;dataset_1_excerpt_2&gt;|&lt;dataset_2_excerpt_1&gt;\"</code><br>\nor<br>\n(2) <br>\n<code>\"&lt;pub_id_1&gt;\", \"&lt;dataset_1_excerpt_1&gt;|&lt;dataset_1_excerpt_2&gt;\",</code><br>\n<code>\"&lt;pub_id_1&gt;\", \"&lt;dataset_2_excerpt_1&gt;\"</code></p>\n<p>Some hints please, thanks! </p>",
      "rawMarkdown": "For each publication, if there are some excerpts noting one dataset, and some excerpts noting another dataset. Do we arrange those of different datasets in one single PredictionString, or split them into two entries?\n\ni.e. which way:\n(1) `\"<pub_id_1>\", \"<dataset_1_excerpt_1>|<dataset_1_excerpt_2>|<dataset_2_excerpt_1>\"`\nor\n(2) \n`\"<pub_id_1>\", \"<dataset_1_excerpt_1>|<dataset_1_excerpt_2>\", `\n`\"<pub_id_1>\", \"<dataset_2_excerpt_1>\"`\n\nSome hints please, thanks!",
      "votes": null
    },
    {
      "id": "1295926",
      "postDate": "05/06/2021 20:07:09",
      "content": "<p>In my opinion, second option should throw an error. As the number of rows in test submission exceed the expected count. </p>",
      "rawMarkdown": "In my opinion, second option should throw an error. As the number of rows in test submission exceed the expected count.",
      "votes": null
    },
    {
      "id": "1297229",
      "postDate": "05/07/2021 20:10:09",
      "content": "<p>Submission File</p>\n<p>For each publication Id in the test set, you must predict excerpts (multiple excerpts divided by a pipe character) for PredictionString variable. The file should contain a header and have the following format:</p>\n<p>Id,PredictionString<br>\n000e04d6-d6ef-442f-b070-4309493221ba,space objects dataset|small objects data<br>\n0176e38e-2286-4ea2-914f-0583808a98aa,small objects dataset<br>\n01860fa5-2c39-4ea2-9124-74458ae4a4b4,large objects<br>\n01e4e08c-ffea-45a7-adde-6a0c0ad755fc,space location data|national space objects|national space dataset<br>\n01fea149-a6b8-4b01-8af9-51e02f46f03f,a dataset of large objects<br>\netc.</p>",
      "rawMarkdown": "Submission File\n\nFor each publication Id in the test set, you must predict excerpts (multiple excerpts divided by a pipe character) for PredictionString variable. The file should contain a header and have the following format:\n\nId,PredictionString\n000e04d6-d6ef-442f-b070-4309493221ba,space objects dataset|small objects data\n0176e38e-2286-4ea2-914f-0583808a98aa,small objects dataset\n01860fa5-2c39-4ea2-9124-74458ae4a4b4,large objects\n01e4e08c-ffea-45a7-adde-6a0c0ad755fc,space location data|national space objects|national space dataset\n01fea149-a6b8-4b01-8af9-51e02f46f03f,a dataset of large objects\netc.",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 1295926,
      "author_name": "mannyjain",
      "author_url": "",
      "post_date": "05/06/2021 20:07:09",
      "content": "<p>In my opinion, second option should throw an error. As the number of rows in test submission exceed the expected count. </p>",
      "votes": null,
      "replies": []
    },
    {
      "id": 1297229,
      "author_name": "mlconsult",
      "author_url": "",
      "post_date": "05/07/2021 20:10:09",
      "content": "<p>Submission File</p>\n<p>For each publication Id in the test set, you must predict excerpts (multiple excerpts divided by a pipe character) for PredictionString variable. The file should contain a header and have the following format:</p>\n<p>Id,PredictionString<br>\n000e04d6-d6ef-442f-b070-4309493221ba,space objects dataset|small objects data<br>\n0176e38e-2286-4ea2-914f-0583808a98aa,small objects dataset<br>\n01860fa5-2c39-4ea2-9124-74458ae4a4b4,large objects<br>\n01e4e08c-ffea-45a7-adde-6a0c0ad755fc,space location data|national space objects|national space dataset<br>\n01fea149-a6b8-4b01-8af9-51e02f46f03f,a dataset of large objects<br>\netc.</p>",
      "votes": null,
      "replies": []
    }
  ],
  "raw_markdown_by_id": {
    "1293837": "For each publication, if there are some excerpts noting one dataset, and some excerpts noting another dataset. Do we arrange those of different datasets in one single PredictionString, or split them into two entries?\n\ni.e. which way:\n(1) `\"<pub_id_1>\", \"<dataset_1_excerpt_1>|<dataset_1_excerpt_2>|<dataset_2_excerpt_1>\"`\nor\n(2) \n`\"<pub_id_1>\", \"<dataset_1_excerpt_1>|<dataset_1_excerpt_2>\", `\n`\"<pub_id_1>\", \"<dataset_2_excerpt_1>\"`\n\nSome hints please, thanks!",
    "1295926": "In my opinion, second option should throw an error. As the number of rows in test submission exceed the expected count.",
    "1297229": "Submission File\n\nFor each publication Id in the test set, you must predict excerpts (multiple excerpts divided by a pipe character) for PredictionString variable. The file should contain a header and have the following format:\n\nId,PredictionString\n000e04d6-d6ef-442f-b070-4309493221ba,space objects dataset|small objects data\n0176e38e-2286-4ea2-914f-0583808a98aa,small objects dataset\n01860fa5-2c39-4ea2-9124-74458ae4a4b4,large objects\n01e4e08c-ffea-45a7-adde-6a0c0ad755fc,space location data|national space objects|national space dataset\n01fea149-a6b8-4b01-8af9-51e02f46f03f,a dataset of large objects\netc."
  },
  "source": "meta"
}