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        "event_title":"Data Accessibility",
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        "combined_title":"Data Accessibility: Alexis Castellano",
        "event_subtitle":"Alexis Castellano",
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        "description":"Tutorial Overview\nFocus: Using AI to improve access to, understanding of, and readiness for working with data.\n\nThis session would focus on helping researchers navigate the early stages of working with data, especially when datasets are large, complex, poorly documented, or unfamiliar. AI can assist with interpreting data dictionaries, summarizing metadata, identifying missing documentation, and making datasets more approachable for new users.\n\nPossible hands-on activities:\n\nSummarize a dataset description or codebook into plain language\nGenerate a checklist for evaluating whether a dataset is usable for a project\nIdentify likely data limitations or missing documentation\nDraft questions to ask a data provider or collaborator\nUse AI to map dataset fields to possible research questions\nReminder: All attendees, please bring a laptop.",
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        "tags":["Data Management","Academic Technology At Michigan","Ai Literacy","Artificial Intelligence","data","Data Analysis","school of information","Data Science","Genai","information"],
        "website":"https:\/\/midas.umich.edu\/events\/data-accessibility\/",
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    {
        "datetime_modified":"20261007T212123",
        "datetime_start":"20261008T130000",
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        "event_title":"Data Accessibility",
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        "event_type":"Workshop \/ Seminar",
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        "description":"Description:This session focuses on the early stages of working with data, when a dataset is large, complex, poorly documented, or unfamiliar. Participants will use AI to interpret data dictionaries, surface undocumented assumptions, and assess whether a dataset can answer their research question. They will also learn how to determine which AI tools they are permitted to use with a given dataset, since research data frequently carries terms of use that restrict AI tools. Participant exercises use a public-domain dataset and U-M's own AI services.- ICPSR policy on the use of large language models: https:\/\/www.icpsr.umich.edu\/sites\/icpsr\/about\/policies\/large-language-models-and-ai\n- ICPSR redistribution policy: https:\/\/www.icpsr.umich.edu\/sites\/icpsr\/about\/policies\/redistribution\n- Using AI with MIDUS, NSHAP, Add Health and other NACDA-hosted data: https:\/\/www.icpsr.umich.edu\/sites\/icpsr\/news\/using-ai-with-midus-nshap-add-health-and-other-nacda-hosted-data\n- U-M guidance on AI and U-M data: https:\/\/safecomputing.umich.edu\/protect-the-u\/safely-use-sensitive-data\/AI-and-UM-Data\n- U-M generative AI services: https:\/\/genai.umich.edu\/\n- ACS PUMS documentation: https:\/\/www.census.gov\/programs-surveys\/acs\/microdata\/documentation.htmlPossible hands-on activities:- Determine which tier of AI tool you may use with a dataset you are currently working with, and where to check\n- Translate a cryptic codebook into plain language, with the model flagging what it cannot determine instead of guessing\n- Extract documentation into a structured format that records, field by field, what the model could not determine from the documentation alone\n- Generate a fitness-for-purpose checklist against your own research question, and draft specific questions to send a data provider\nBefore the session:1. All attendees please bring a laptop.2. Sign in at genai.umich.edu to make sure your U-M GPT access is working. We'll use it for all hands-on exercises.\u00a03. Come with a research question you're working on, and the name of a dataset you're using or considering for it. If the dataset has a data use agreement or terms of use page, have the link handy. Please don't plan to upload your own data during the session. We'll use a public dataset for the exercises, and part of the session covers how to check which AI tools you're permitted to use with your own data.\n\n",
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