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題名 Open Data and Open Science as Effective Ways to Revive Hard-to-reach Population: A Decade Research Applies to Taiwan Indigenous Peoples 作者 林季平
Lin, Ji-Ping貢獻者 社會系 關鍵詞 Big Data; Data Science; FAIR; Open Data; Open Science; TIPD 日期 2024-03 上傳時間 3-六月-2024 14:54:38 (UTC+8) 摘要 There has been a rich body of text and numerical archives about Taiwan indigenous peoples (TIPs) before 1940. Because detailed statistical archives and numerical data on TIPs were not available in the period between 1940-2010, we thus have very limited knowledge about developmental trajectory about TIPs. Such situation makes TIPs gradually become the so-called hard-to-reach population (HRP) and invisible in the real world. To overcome this issue, it becomes urgent to collect and build contemporary TIPs data. Supported by a decade research program form Council of Indigenous Peoples (https://www.cip.gov.tw/) in 2013, the author has been fully devoting himself to building a number of big open data on TIPS using scientific computing methods and techniques, based on the principles of open science and data science. The open data sets are built by integrating hacking skills, advanced math/statistics methods, and domain knowledge of various disciplines. Their repositories are hosted on OSF (Open Science Framework, https://osf.io/) and termed as TIPD (Taiwan Indigenous Peoples Open Research Data, for details, see https://osf.io/e4rvz/). TIPD complies with FARE data principle. It consists of the following categories of open data from 2013 to 2022: (1) categorical data, (2) multi-dimensional data, (3) population dynamics, (4) temporal geocoding data, (5) household structure data, (6) traditional TIPs community data (TICD), (7) generalized TICD query system, (8) genealogical data (not open to the public). The main contributions are as follows: (1) to enable TIPs who have been “invisible” to the world for seven decades to become “close” to “open” to the real world; (2) to empower TIPs researches from being “elite” to “ordinary” by using open data to reduce tech-barriers for researchers; (3) to extend TIPs studies from “local” to “global” arena by building bilingual open data repository; (4) to make research methods and techniques of TIPs switch from “macro” to “individual” level that make TIPS to revive from hard-to-reach to easy-to-reach population. 關聯 International Symposium on Grids & Clouds (ISGC) 2024, Academia Sinica Grid Computing Centre (ASGC) 資料類型 conference dc.contributor 社會系 dc.creator (作者) 林季平 dc.creator (作者) Lin, Ji-Ping dc.date (日期) 2024-03 dc.date.accessioned 3-六月-2024 14:54:38 (UTC+8) - dc.date.available 3-六月-2024 14:54:38 (UTC+8) - dc.date.issued (上傳時間) 3-六月-2024 14:54:38 (UTC+8) - dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/151577 - dc.description.abstract (摘要) There has been a rich body of text and numerical archives about Taiwan indigenous peoples (TIPs) before 1940. Because detailed statistical archives and numerical data on TIPs were not available in the period between 1940-2010, we thus have very limited knowledge about developmental trajectory about TIPs. Such situation makes TIPs gradually become the so-called hard-to-reach population (HRP) and invisible in the real world. To overcome this issue, it becomes urgent to collect and build contemporary TIPs data. Supported by a decade research program form Council of Indigenous Peoples (https://www.cip.gov.tw/) in 2013, the author has been fully devoting himself to building a number of big open data on TIPS using scientific computing methods and techniques, based on the principles of open science and data science. The open data sets are built by integrating hacking skills, advanced math/statistics methods, and domain knowledge of various disciplines. Their repositories are hosted on OSF (Open Science Framework, https://osf.io/) and termed as TIPD (Taiwan Indigenous Peoples Open Research Data, for details, see https://osf.io/e4rvz/). TIPD complies with FARE data principle. It consists of the following categories of open data from 2013 to 2022: (1) categorical data, (2) multi-dimensional data, (3) population dynamics, (4) temporal geocoding data, (5) household structure data, (6) traditional TIPs community data (TICD), (7) generalized TICD query system, (8) genealogical data (not open to the public). The main contributions are as follows: (1) to enable TIPs who have been “invisible” to the world for seven decades to become “close” to “open” to the real world; (2) to empower TIPs researches from being “elite” to “ordinary” by using open data to reduce tech-barriers for researchers; (3) to extend TIPs studies from “local” to “global” arena by building bilingual open data repository; (4) to make research methods and techniques of TIPs switch from “macro” to “individual” level that make TIPS to revive from hard-to-reach to easy-to-reach population. dc.format.extent 119 bytes - dc.format.mimetype text/html - dc.relation (關聯) International Symposium on Grids & Clouds (ISGC) 2024, Academia Sinica Grid Computing Centre (ASGC) dc.subject (關鍵詞) Big Data; Data Science; FAIR; Open Data; Open Science; TIPD dc.title (題名) Open Data and Open Science as Effective Ways to Revive Hard-to-reach Population: A Decade Research Applies to Taiwan Indigenous Peoples dc.type (資料類型) conference
