using-python-to-assess-serial-holdings-data-in-the-research-library-scipy-2018-kelly-thompson.json

{
  "copyright_text": null,
  "description": "Facing increasing demands on physical space, many academic libraries are\ninvestigating large-scale de-duplication of physical collections. These\ncollection management efforts require trustworthy, complete data and\nmust consider not just local needs, but shared print retention\ncommitments, preservation of scarce and at-risk materials, and\ncontinuous access to licensed electronic versions. While common\nsolutions exist for monographic resources, these questions remain much\nharder to answer for serials. The pymarc and pandas libraries offer\neffective tools for analyzing bibliographic data to generate \u201cfamilies\u201d\nof both print and electronic serial records, and compare holdings of\nthose titles as part of large-scale de-duplication efforts.Presenter(s):\nSpeaker: Kelly Thompson, University of Minnesota\n",
  "duration": 1652,
  "language": "eng",
  "recorded": "2018-07-11",
  "related_urls": [
    {
      "label": "Conference schedule",
      "url": "https://scipy2018.scipy.org/ehome/299527/721463/"
    },
    {
      "label": "Conference slides",
      "url": "https://github.com/deniederhut/Slides-SciPyConf-2018"
    }
  ],
  "speakers": [
    "Kelly Thompson"
  ],
  "tags": [],
  "thumbnail_url": "https://i.ytimg.com/vi/yrtiyWaWQUg/maxresdefault.jpg",
  "title": "Using Python to Assess Serial Holdings Data in the Research Library",
  "videos": [
    {
      "type": "youtube",
      "url": "https://www.youtube.com/watch?v=yrtiyWaWQUg"
    }
  ]
}