{"id":19107,"date":"2016-10-01T15:05:00","date_gmt":"2016-10-01T15:05:00","guid":{"rendered":"https:\/\/technopolis-group.com\/the-next-step-in-data-linking\/"},"modified":"2020-04-08T15:04:30","modified_gmt":"2020-04-08T15:04:30","slug":"the-next-step-in-data-linking","status":"publish","type":"post","link":"https:\/\/technopolis-group.com\/de\/the-next-step-in-data-linking\/","title":{"rendered":"The next step in data linking"},"content":{"rendered":"\n

Integrating existing science and innovation databases to enrich our knowledge base for policy support.<\/p><\/blockquote><\/figure>\n\n\n\n

In the field of Science, Technology and Innovation (STI), there is a\ntradition of large-scale data collection. The EU launched its first large-scale\nmonitoring initiatives \u2014 the INNO Policy Trendchart and European Innovation Scoreboard\n\u2014 in 1999 and 2000 respectively, followed by several other STI-focused\nmonitoring platforms, observatories and projects. This has resulted in huge\ndatasets, collected through networks, surveys and statistical offices. Increasingly,\nthis data is available in open data formats, via open data portals. <\/p>\n\n\n\n

Is this big data? Probably not, but it is certainly a lot of data, especially if we link different types of datasets and use them to explore the interplay of large numbers of variables. <\/p>\n\n\n\n

\"\"
Example of Integrated database<\/em><\/figcaption><\/figure><\/div>\n\n\n\n

Multiplying mess <\/em><\/strong><\/h3>\n\n\n\n

Given the \u2018messiness\u2019 of individual STI datasets (missing data, etc.), one\nof the main challenges of such data linking is avoiding a \u2018messiness\nmultiplier\u2019 when linking datasets collected for different purposes using\ndifferent designs (e.g. different perspectives, definitions, data collection\nmethods, reliability levels, etc.). <\/p>\n\n\n\n

For example, consider the contrasts between business and policy-maker centred designs (e.g. Business Innovation Observatory vs Regional Innovation Monitor), or between thematically focused (i.e. policy, industry or technology-specific), STI-system encompassing (Key Enabling Technologies Observatory, European Cluster Observatory or European Innovation Scoreboard) and value-chain inspired designs (e.g. the up-coming RE-Confirm). <\/p>\n\n\n\n

So it is natural to question whether an integrated monitoring system would\nbe of greater value than the sum of its parts \u2014 and its ability to support\npolicy design. <\/p>\n\n\n\n

Technopolis Group recently used data linking in a comparative analysis of\nthe national \u2018Smart Specialisation Strategies\u2019 in Central Europe. The study\nidentified seven common \u2018technology priority areas\u2019 using data from five\ndifferent sources: the Smart Specialisation Platform (EYE@RIS3), FP7\/H2020\ndatabases, the European Cluster Observatory, the Key Enabling Technologies Observatory\nand OECD trade data. This selection was then verified based on analysis of\nR&D capacities (upstream), clustering and patenting (midstream) and\nbusiness or economic activity in the respective technology areas (downstream). <\/p>\n\n\n\n

The next step <\/em><\/strong><\/h3>\n\n\n\n

A takeaway from the project is that the public sector needs support in anticipating\nnew developments. Traditional STI databases are useful but have limitations\nwhen it comes to informing policy at a high level of granularity (e.g. in terms\nof industry segments\/niches) or capturing dynamics on the ground. <\/p>\n\n\n\n

An integrated monitoring system would make such work more efficient and\nenable more data to be consulted in one go (\u2018integrated dashboards\u2019) \u2014 e.g. to\ntest hypotheses and identify information gaps. But to make the next step, and\ntruly enrich our knowledge base, we need to turn the traditional STI indicators\ncollected by these observatories and other projects into \u2018real\u2019 big data: In\nother words, data linking that uses reliable STI concepts and structures for\nmarkets, industries and regions, but also acknowledges the strengths of using\nweb scraping, social media, and Wikipedia data to identify emerging\ntechnologies, new application areas and new markets.<\/p>\n","protected":false},"excerpt":{"rendered":"

Integrating existing science and innovation databases to enrich our knowledge […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[233,5],"tags":[],"solution":[],"sector":[94],"class_list":["post-19107","post","type-post","status-publish","format-standard","hentry","category-technopolitan-de","category-unkategorisiert","sector-digitalisation-de"],"acf":[],"yoast_head":"\nThe next step in data linking - Technopolis Group<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/technopolis-group.com\/de\/the-next-step-in-data-linking\/\" \/>\n<meta property=\"og:locale\" content=\"de_DE\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"The next step in data linking - Technopolis Group\" \/>\n<meta property=\"og:description\" content=\"Integrating existing science and innovation databases to enrich our knowledge […]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/technopolis-group.com\/de\/the-next-step-in-data-linking\/\" \/>\n<meta property=\"og:site_name\" content=\"Technopolis Group\" \/>\n<meta property=\"article:published_time\" content=\"2016-10-01T15:05:00+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2020-04-08T15:04:30+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/technopolis-group.com\/wp-content\/uploads\/2020\/02\/Datalinking.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1210\" \/>\n\t<meta property=\"og:image:height\" content=\"922\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"cedric\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Verfasst von\" \/>\n\t<meta name=\"twitter:data1\" content=\"cedric\" \/>\n\t<meta name=\"twitter:label2\" content=\"Gesch\u00e4tzte Lesezeit\" \/>\n\t<meta name=\"twitter:data2\" content=\"2\u00a0Minuten\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/technopolis-group.com\/de\/the-next-step-in-data-linking\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/technopolis-group.com\/de\/the-next-step-in-data-linking\/\"},\"author\":{\"name\":\"cedric\",\"@id\":\"https:\/\/technopolis-group.com\/nl\/#\/schema\/person\/eeed59af0b9e4a5e3a50d148502a3256\"},\"headline\":\"The next step in data linking\",\"datePublished\":\"2016-10-01T15:05:00+00:00\",\"dateModified\":\"2020-04-08T15:04:30+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/technopolis-group.com\/de\/the-next-step-in-data-linking\/\"},\"wordCount\":498,\"commentCount\":0,\"image\":{\"@id\":\"https:\/\/technopolis-group.com\/de\/the-next-step-in-data-linking\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/technopolis-group.com\/wp-content\/uploads\/2020\/02\/Datalinking-1024x780.png\",\"articleSection\":[\"Technopolitan\",\"Unkategorisiert\"],\"inLanguage\":\"de\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/technopolis-group.com\/de\/the-next-step-in-data-linking\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/technopolis-group.com\/de\/the-next-step-in-data-linking\/\",\"url\":\"https:\/\/technopolis-group.com\/de\/the-next-step-in-data-linking\/\",\"name\":\"The next step in data linking - 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