![\"\"](\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2019\/07\/image17-300x276.png\")
<\/td>\nPeople remember 80% of what they see, only 20% of what they read<\/h3>\n<\/td>\n<\/tr>\n\n![\"\"](\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2019\/07\/image2-300x271.png\")
<\/td>\n\nAn infographic is 30x more likely to be read than pure text<\/h3>\n<\/td>\n<\/tr>\n\n![\"\"](\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2019\/07\/image3-300x276.png\")
<\/td>\n\nPeople are 17% more likely to be persuaded by imagery content<\/h3>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
<\/p>\n
PROJECT DESCRIPTION<\/h1>\nInformation graphics (infographics) can effectively deliver messages in an engaging and memorable fashion. Hence, they have now gained tremendous popularity in presentations, documents, dashboards, etc. However, as a task usually requiring high-level design creativity and good proficiency with graphic design tools, authoring a professional infographic remains a challenge for information workers. We proposed Text-to-Vis to lower the access barrier for them, as well as to unleash and inspire their creativity. With natural language statements as input, Text-to-Vis can automatically generate a diverse set of infographics that can well convey the information in the text. Text-to-Vis allows information workers to achieve expressive and compelling infographics effortlessly, and use them to enhance and enrich their presentations and documents.<\/h3>\n
<\/p>\n
APPROACH<\/h1>\n
\n\n\n\nSTATEMENT<\/h2>\n<\/td>\n<\/td>\n \nTECHNOLOGY<\/h2>\n<\/td>\n<\/td>\n \nINFOGRAPHICS<\/h2>\n<\/td>\n<\/tr>\n\n\n“More than 20% of smartphone users are social network users”<\/h3>\n<\/td>\n![\"\"](\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2019\/07\/arr.png\")
<\/td>\n\nNatural language analysis<\/h3>\nVisualization synthesis<\/h3>\n<\/td>\n<\/td>\n ![\"\"](\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2019\/07\/output-300x128.png\")
<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/br><\/p>\n
EXAMPLES<\/h1>\n
\t\t\t
\n\t\t\t\n\t\t\t\t\t\n\t\t3 in 5 Chinese people live in rural areas<\/h3>
\t<\/div>\n\t\t
\n\t\t<\/p>76% of students find math difficult<\/h3>
\t<\/div>\n\t\t
\n\t\t<\/p>40% of USA fresh water use is for agriculture<\/h3>
\t<\/div>\n\t<\/p>\t\t\t<\/div>\n\t\t<\/div>\n\t\t
\n\t\t\t
\n\t\t\t\n\t\t\t\t\t\n\t\t![\"\"](\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2019\/07\/3peoplechina-300x125.png\")
\t<\/div>\n\t\t\n\t\t![\"\"](\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2019\/07\/students-300x102.png\")
\t<\/div>\n\t\t\n\t\t![\"\"](\"https:\/\/www.microsoft.com\/en-us\/research\/uploads\/prod\/2019\/07\/water-300x114.png\")
\t<\/div>\n\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t<\/p>\n","protected":false},"excerpt":{"rendered":"Information graphics (infographics) can effectively deliver messages in an engaging and memorable fashion. Hence, they have now gained tremendous popularity in presentations, documents, dashboards, etc. However, as a task usually requiring high-level design creativity and good proficiency with graphic design tools, authoring a professional infographic remains a challenge for information workers. We proposed Text-to-Vis to lower the access barrier for them, as well as to unleash and inspire their creativity. With natural language statements as input, Text-to-Vis can automatically generate a diverse set of infographics that can well convey the information in the text. Text-to-Vis allows information workers to achieve expressive and compelling infographics effortlessly, and use them to enhance and enrich their presentations and documents.<\/p>\n","protected":false},"featured_media":599931,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"research-area":[13554],"msr-locale":[268875],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-599826","msr-project","type-msr-project","status-publish","has-post-thumbnail","hentry","msr-research-area-human-computer-interaction","msr-locale-en_us","msr-archive-status-active"],"msr_project_start":"","related-publications":[599919],"related-downloads":[],"related-videos":[],"related-groups":[],"related-events":[],"related-opportunities":[],"related-posts":[],"related-articles":[],"tab-content":[],"slides":[],"related-researchers":[{"type":"user_nicename","display_name":"Weiwei Cui","user_id":34808,"people_section":"Section name 1","alias":"weiweicu"},{"type":"user_nicename","display_name":"Ray Huang","user_id":33358,"people_section":"Section name 1","alias":"rayhuang"},{"type":"user_nicename","display_name":"Yun Wang","user_id":37827,"people_section":"Section name 1","alias":"wangyun"},{"type":"user_nicename","display_name":"Lei Fang","user_id":32635,"people_section":"Section name 1","alias":"leifa"},{"type":"user_nicename","display_name":"Jian-Guang Lou","user_id":32337,"people_section":"Section name 1","alias":"jlou"},{"type":"user_nicename","display_name":"Haidong Zhang","user_id":31953,"people_section":"Section name 1","alias":"haizhang"},{"type":"user_nicename","display_name":"Dongmei Zhang","user_id":31665,"people_section":"Section name 1","alias":"dongmeiz"}],"msr_research_lab":[199560],"msr_impact_theme":[],"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/599826"}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-project"}],"version-history":[{"count":22,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/599826\/revisions"}],"predecessor-version":[{"id":599934,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/599826\/revisions\/599934"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media\/599931"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=599826"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=599826"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=599826"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=599826"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=599826"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
<\/td>\nAn infographic is 30x more likely to be read than pure text<\/h3>\n<\/td>\n<\/tr>\n\n<\/td>\n \nPeople are 17% more likely to be persuaded by imagery content<\/h3>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
<\/p>\n
PROJECT DESCRIPTION<\/h1>\nInformation graphics (infographics) can effectively deliver messages in an engaging and memorable fashion. Hence, they have now gained tremendous popularity in presentations, documents, dashboards, etc. However, as a task usually requiring high-level design creativity and good proficiency with graphic design tools, authoring a professional infographic remains a challenge for information workers. We proposed Text-to-Vis to lower the access barrier for them, as well as to unleash and inspire their creativity. With natural language statements as input, Text-to-Vis can automatically generate a diverse set of infographics that can well convey the information in the text. Text-to-Vis allows information workers to achieve expressive and compelling infographics effortlessly, and use them to enhance and enrich their presentations and documents.<\/h3>\n
<\/p>\n
APPROACH<\/h1>\n
\n\n\n\nSTATEMENT<\/h2>\n<\/td>\n<\/td>\n \nTECHNOLOGY<\/h2>\n<\/td>\n<\/td>\n \nINFOGRAPHICS<\/h2>\n<\/td>\n<\/tr>\n\n\n“More than 20% of smartphone users are social network users”<\/h3>\n<\/td>\n<\/td>\n \nNatural language analysis<\/h3>\nVisualization synthesis<\/h3>\n<\/td>\n<\/td>\n <\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/br><\/p>\n
EXAMPLES<\/h1>\n
\t\t\t
\n\t\t\t\n\t\t\t\t\t\n\t\t3 in 5 Chinese people live in rural areas<\/h3>
\t<\/div>\n\t\t
\n\t\t<\/p>76% of students find math difficult<\/h3>
\t<\/div>\n\t\t
\n\t\t<\/p>40% of USA fresh water use is for agriculture<\/h3>
\t<\/div>\n\t<\/p>\t\t\t<\/div>\n\t\t<\/div>\n\t\t
\n\t\t\t
\n\t\t\t\n\t\t\t\t\t\n\t\t \t<\/div>\n\t\t\n\t\t\t<\/div>\n\t\t\n\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t<\/p>\n","protected":false},"excerpt":{"rendered":"Information graphics (infographics) can effectively deliver messages in an engaging and memorable fashion. Hence, they have now gained tremendous popularity in presentations, documents, dashboards, etc. However, as a task usually requiring high-level design creativity and good proficiency with graphic design tools, authoring a professional infographic remains a challenge for information workers. We proposed Text-to-Vis to lower the access barrier for them, as well as to unleash and inspire their creativity. With natural language statements as input, Text-to-Vis can automatically generate a diverse set of infographics that can well convey the information in the text. Text-to-Vis allows information workers to achieve expressive and compelling infographics effortlessly, and use them to enhance and enrich their presentations and documents.<\/p>\n","protected":false},"featured_media":599931,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"research-area":[13554],"msr-locale":[268875],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-599826","msr-project","type-msr-project","status-publish","has-post-thumbnail","hentry","msr-research-area-human-computer-interaction","msr-locale-en_us","msr-archive-status-active"],"msr_project_start":"","related-publications":[599919],"related-downloads":[],"related-videos":[],"related-groups":[],"related-events":[],"related-opportunities":[],"related-posts":[],"related-articles":[],"tab-content":[],"slides":[],"related-researchers":[{"type":"user_nicename","display_name":"Weiwei Cui","user_id":34808,"people_section":"Section name 1","alias":"weiweicu"},{"type":"user_nicename","display_name":"Ray Huang","user_id":33358,"people_section":"Section name 1","alias":"rayhuang"},{"type":"user_nicename","display_name":"Yun Wang","user_id":37827,"people_section":"Section name 1","alias":"wangyun"},{"type":"user_nicename","display_name":"Lei Fang","user_id":32635,"people_section":"Section name 1","alias":"leifa"},{"type":"user_nicename","display_name":"Jian-Guang Lou","user_id":32337,"people_section":"Section name 1","alias":"jlou"},{"type":"user_nicename","display_name":"Haidong Zhang","user_id":31953,"people_section":"Section name 1","alias":"haizhang"},{"type":"user_nicename","display_name":"Dongmei Zhang","user_id":31665,"people_section":"Section name 1","alias":"dongmeiz"}],"msr_research_lab":[199560],"msr_impact_theme":[],"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/599826"}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-project"}],"version-history":[{"count":22,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/599826\/revisions"}],"predecessor-version":[{"id":599934,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/599826\/revisions\/599934"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media\/599931"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=599826"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=599826"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=599826"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=599826"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=599826"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
<\/td>\nPeople are 17% more likely to be persuaded by imagery content<\/h3>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
<\/p>\n
PROJECT DESCRIPTION<\/h1>\nInformation graphics (infographics) can effectively deliver messages in an engaging and memorable fashion. Hence, they have now gained tremendous popularity in presentations, documents, dashboards, etc. However, as a task usually requiring high-level design creativity and good proficiency with graphic design tools, authoring a professional infographic remains a challenge for information workers. We proposed Text-to-Vis to lower the access barrier for them, as well as to unleash and inspire their creativity. With natural language statements as input, Text-to-Vis can automatically generate a diverse set of infographics that can well convey the information in the text. Text-to-Vis allows information workers to achieve expressive and compelling infographics effortlessly, and use them to enhance and enrich their presentations and documents.<\/h3>\n
<\/p>\n
APPROACH<\/h1>\n
\nSTATEMENT<\/h2>\n<\/td>\nTECHNOLOGY<\/h2>\n<\/td>\nINFOGRAPHICS<\/h2>\n<\/td>\n<\/tr>\n\n | “More than 20% of smartphone users are social network users”<\/h3>\n<\/td>\n<\/td>\n |
Natural language analysis<\/h3>\nVisualization synthesis<\/h3>\n<\/td>\n<\/td>\n <\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/br><\/p>\n
EXAMPLES<\/h1>\n
\t\t\t
\n\t\t\t\n\t\t\t\t\t\n\t\t3 in 5 Chinese people live in rural areas<\/h3>
\t<\/div>\n\t\t
\n\t\t<\/p>76% of students find math difficult<\/h3>
\t<\/div>\n\t\t
\n\t\t<\/p>40% of USA fresh water use is for agriculture<\/h3>
\t<\/div>\n\t<\/p>\t\t\t<\/div>\n\t\t<\/div>\n\t\t
\n\t\t\t
\n\t\t\t\n\t\t\t\t\t\n\t\t \t<\/div>\n\t\t\n\t\t\t<\/div>\n\t\t\n\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t<\/p>\n","protected":false},"excerpt":{"rendered":"Information graphics (infographics) can effectively deliver messages in an engaging and memorable fashion. Hence, they have now gained tremendous popularity in presentations, documents, dashboards, etc. However, as a task usually requiring high-level design creativity and good proficiency with graphic design tools, authoring a professional infographic remains a challenge for information workers. We proposed Text-to-Vis to lower the access barrier for them, as well as to unleash and inspire their creativity. With natural language statements as input, Text-to-Vis can automatically generate a diverse set of infographics that can well convey the information in the text. Text-to-Vis allows information workers to achieve expressive and compelling infographics effortlessly, and use them to enhance and enrich their presentations and documents.<\/p>\n","protected":false},"featured_media":599931,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"research-area":[13554],"msr-locale":[268875],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-599826","msr-project","type-msr-project","status-publish","has-post-thumbnail","hentry","msr-research-area-human-computer-interaction","msr-locale-en_us","msr-archive-status-active"],"msr_project_start":"","related-publications":[599919],"related-downloads":[],"related-videos":[],"related-groups":[],"related-events":[],"related-opportunities":[],"related-posts":[],"related-articles":[],"tab-content":[],"slides":[],"related-researchers":[{"type":"user_nicename","display_name":"Weiwei Cui","user_id":34808,"people_section":"Section name 1","alias":"weiweicu"},{"type":"user_nicename","display_name":"Ray Huang","user_id":33358,"people_section":"Section name 1","alias":"rayhuang"},{"type":"user_nicename","display_name":"Yun Wang","user_id":37827,"people_section":"Section name 1","alias":"wangyun"},{"type":"user_nicename","display_name":"Lei Fang","user_id":32635,"people_section":"Section name 1","alias":"leifa"},{"type":"user_nicename","display_name":"Jian-Guang Lou","user_id":32337,"people_section":"Section name 1","alias":"jlou"},{"type":"user_nicename","display_name":"Haidong Zhang","user_id":31953,"people_section":"Section name 1","alias":"haizhang"},{"type":"user_nicename","display_name":"Dongmei Zhang","user_id":31665,"people_section":"Section name 1","alias":"dongmeiz"}],"msr_research_lab":[199560],"msr_impact_theme":[],"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/599826"}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-project"}],"version-history":[{"count":22,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/599826\/revisions"}],"predecessor-version":[{"id":599934,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/599826\/revisions\/599934"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media\/599931"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=599826"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=599826"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=599826"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=599826"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=599826"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
<\/td>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/br><\/p>\n
EXAMPLES<\/h1>\n
\t\t\t
3 in 5 Chinese people live in rural areas<\/h3>
\t<\/div>\n\t\t
76% of students find math difficult<\/h3>
\t<\/div>\n\t\t
40% of USA fresh water use is for agriculture<\/h3>
\t<\/div>\n\t<\/p>\t\t\t<\/div>\n\t\t<\/div>\n\t\t
\n\t\t\t
\t<\/div>\n\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t<\/p>\n","protected":false},"excerpt":{"rendered":"Information graphics (infographics) can effectively deliver messages in an engaging and memorable fashion. Hence, they have now gained tremendous popularity in presentations, documents, dashboards, etc. However, as a task usually requiring high-level design creativity and good proficiency with graphic design tools, authoring a professional infographic remains a challenge for information workers. We proposed Text-to-Vis to lower the access barrier for them, as well as to unleash and inspire their creativity. With natural language statements as input, Text-to-Vis can automatically generate a diverse set of infographics that can well convey the information in the text. Text-to-Vis allows information workers to achieve expressive and compelling infographics effortlessly, and use them to enhance and enrich their presentations and documents.<\/p>\n","protected":false},"featured_media":599931,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"research-area":[13554],"msr-locale":[268875],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-599826","msr-project","type-msr-project","status-publish","has-post-thumbnail","hentry","msr-research-area-human-computer-interaction","msr-locale-en_us","msr-archive-status-active"],"msr_project_start":"","related-publications":[599919],"related-downloads":[],"related-videos":[],"related-groups":[],"related-events":[],"related-opportunities":[],"related-posts":[],"related-articles":[],"tab-content":[],"slides":[],"related-researchers":[{"type":"user_nicename","display_name":"Weiwei Cui","user_id":34808,"people_section":"Section name 1","alias":"weiweicu"},{"type":"user_nicename","display_name":"Ray Huang","user_id":33358,"people_section":"Section name 1","alias":"rayhuang"},{"type":"user_nicename","display_name":"Yun Wang","user_id":37827,"people_section":"Section name 1","alias":"wangyun"},{"type":"user_nicename","display_name":"Lei Fang","user_id":32635,"people_section":"Section name 1","alias":"leifa"},{"type":"user_nicename","display_name":"Jian-Guang Lou","user_id":32337,"people_section":"Section name 1","alias":"jlou"},{"type":"user_nicename","display_name":"Haidong Zhang","user_id":31953,"people_section":"Section name 1","alias":"haizhang"},{"type":"user_nicename","display_name":"Dongmei Zhang","user_id":31665,"people_section":"Section name 1","alias":"dongmeiz"}],"msr_research_lab":[199560],"msr_impact_theme":[],"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/599826"}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-project"}],"version-history":[{"count":22,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/599826\/revisions"}],"predecessor-version":[{"id":599934,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/599826\/revisions\/599934"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media\/599931"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=599826"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=599826"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=599826"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=599826"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=599826"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}