This document introduces observational studies and provides examples. It defines observation as watching, inspecting, and taking note of behaviors and environments. There are two main types of observational studies: qualitative or unstructured observations which do not require hypotheses and rely on observer skills, and quantitative or structured observations which require hypotheses and trained observers to count predetermined behaviors. Examples of observational situations discussed include observing people in supermarkets and at fairs. Tips for unobtrusive observation are also provided.
医療データベース研究の信頼性・透明性・再生性を高めるための研究の手続きに関する、ISPOR&ISPE合同タスクフォースのリコメンデーション「Good Practices for Real-World Data Studies of Treatment and/or Comparative Effectiveness: Recommendations from the Joint ISPOR-ISPE Special Task Force on Real-World Evidence in Health Care Decision Making 」のまとめです。REQUIRE研究会での報告内容になります。
This document introduces observational studies and provides examples. It defines observation as watching, inspecting, and taking note of behaviors and environments. There are two main types of observational studies: qualitative or unstructured observations which do not require hypotheses and rely on observer skills, and quantitative or structured observations which require hypotheses and trained observers to count predetermined behaviors. Examples of observational situations discussed include observing people in supermarkets and at fairs. Tips for unobtrusive observation are also provided.
医療データベース研究の信頼性・透明性・再生性を高めるための研究の手続きに関する、ISPOR&ISPE合同タスクフォースのリコメンデーション「Good Practices for Real-World Data Studies of Treatment and/or Comparative Effectiveness: Recommendations from the Joint ISPOR-ISPE Special Task Force on Real-World Evidence in Health Care Decision Making 」のまとめです。REQUIRE研究会での報告内容になります。
Test slide for the lab - Target prioritization Maori Ito
This document discusses candidate gene prioritization for large-scale experiments. It notes that there are challenges in combining available information from different data sources due to lack of annotations for individual genes. The process involves taking many candidate genes as input and applying integrated knowledge discovery to output a smaller number of selected target genes.
This presentation discusses ways to improve biomedical database search services through the use of semantic web technologies like schema.org and metadata tagging. It explains how marking up database entries with metadata and declaring vocabularies allows search engines to better understand and return more informative results for biomedical queries. The presentation provides examples of how Sagace and other search collaboration organizations are already reflecting semantic metadata in their results.
Life Science Database Cross Search and MetadataMaori Ito
Life science databases are sometimes difficult to understand due to lack of information. I'd like to add metadata into databases and improve search results.