{"id":250532,"date":"2022-03-23T10:00:33","date_gmt":"2022-03-23T10:00:33","guid":{"rendered":"https:\/\/itelligent.es\/?p=250532"},"modified":"2025-02-10T13:56:26","modified_gmt":"2025-02-10T13:56:26","slug":"standards-in-statistical-data-and-research-orcid-doi-ddi-and-sdmx","status":"publish","type":"post","link":"https:\/\/itelligent.es\/en\/standards-in-statistical-data-and-research-orcid-doi-ddi-and-sdmx\/","title":{"rendered":"Standards in Statistical Data and Research: ORCID, DOI, DDI, and SDMX"},"content":{"rendered":"

[et_pb_section fb_built=”1″ admin_label=”section” _builder_version=”4.16″ global_colors_info=”{}”][et_pb_row admin_label=”row” _builder_version=”4.16″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text admin_label=”Text” _builder_version=”4.23″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” hover_enabled=”0″ global_colors_info=”{}” sticky_enabled=”0″] In our experience developing IT technologies, we have undertaken various projects in fields related to Social Sciences and scientific research. In these projects, we have had to “struggle” with different standards. In this post, we will attempt to clarify some common doubts when working with scientific publications and statistical data.<\/p>\n

 <\/p>\n

Scientific Publications<\/h2>\n

When working with scientific publications, the first standards we encounter are Open Researcher and Contributor ID (ORCID) and Digital Object Identifier (DOI). Both are unique identifiers in the academic field, but they serve different purposes. Let\u2019s examine their differences:<\/p>\n

 <\/p>\n\n\n\n\n\n\n\n\n\n
Feature<\/strong><\/span><\/td>\nDOI (Digital Object Identifier)<\/strong><\/span><\/td>\nORCID (Open Researcher and Contributor ID)<\/strong><\/span><\/td>\n<\/tr>\n<\/thead>\n
What does it identify?<\/strong><\/span><\/td>\nScientific publications, datasets, reports, theses.<\/span><\/td>\nResearchers and scientific authors.<\/span><\/td>\n<\/tr>\n
Main Purpose<\/strong><\/span><\/td>\nEnsure a permanent link to a digital document.<\/span><\/td>\nProvide a unique identifier for each researcher.<\/span><\/td>\n<\/tr>\n
Format<\/strong><\/span><\/td>\n10.1234\/abcd1234<\/span><\/td>\n0000-0001-2345-6789<\/span><\/td>\n<\/tr>\n
Managed by<\/strong><\/span><\/td>\nCrossRef, DataCite, other organizations.<\/span><\/td>\nORCID (non-profit organization).<\/span><\/td>\n<\/tr>\n
Used in<\/strong><\/span><\/td>\nJournal articles, books, repositories, databases.<\/span><\/td>\nResearcher profiles, journals, academic institutions.<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n

DOI is an identifier used to permanently locate digital publications, ensuring their accessibility over time. Its main advantage is preventing broken links to documents, facilitating the consultation of academic and scientific information without the risk of loss.<\/p>\n

Below is an example of an article published in a journal with the following DOI: https:\/\/doi.org\/10.1016\/j.heliyon.2023.e22739<\/a>.<\/p>\n

\"\"<\/p>\n

On the other hand, ORCID is a unique identifier that allows a researcher to be recognized throughout their career, even if they change their name or affiliation. Its use is essential for linking all an author\u2019s publications across various databases, ensuring proper attribution and visibility of their work.<\/p>\n

Below is an example of a researcher’s ORCID: https:\/\/orcid.org\/0000-0003-4878-3107.<\/a><\/p>\n

\"\"<\/p>\n

 <\/p>\n

Statical Data<\/h2>\n

In the projects we have conducted with statistical data, we have encountered two standards that are often confused: Data Documentation Initiative (DDI) and Statistical Data and Metadata Exchange (SDMX). Both are standards for data documentation and exchange, but they have different approaches:<\/p>\n

 <\/p>\n\n\n\n\n\n\n\n\n\n
Feature<\/strong><\/span><\/td>\nDDI (Data Documentation Initiative)<\/strong><\/span><\/td>\nSDMX (Statistical Data and Metadata Exchange)<\/strong><\/span><\/td>\n<\/tr>\n<\/thead>\n
Approach<\/strong><\/span><\/td>\nDocumentation of data, metadata, and the data lifecycle in social sciences, surveys, and longitudinal data.<\/span><\/td>\nExchange and harmonization of statistical data and metadata, focused on official statistics and time series.<\/span><\/td>\n<\/tr>\n
Format<\/strong><\/span><\/td>\nXML (DDI-Codebook and DDI-Lifecycle)<\/span><\/td>\nXML and JSON<\/span><\/td>\n<\/tr>\n
Application Scope<\/strong><\/span><\/td>\nSurveys, census data, longitudinal studies, qualitative data.<\/span><\/td>\nMacroeconomic data, official statistics, time series.<\/span><\/td>\n<\/tr>\n
Main Users<\/strong><\/span><\/td>\nAcademic institutions, research centers, governments.<\/span><\/td>\nCentral banks, international organizations (IMF, OECD, Eurostat).<\/span><\/td>\n<\/tr>\n
Lifecycle Coverage<\/strong><\/span><\/td>\nDocuments the entire data lifecycle, from collection to analysis and preservation.<\/span><\/td>\nStandardizes statistical data exchange, facilitating comparisons between sources.<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n

Here is an example of a survey in DDI (Data Documentation Initiative)<\/strong> format:<\/p>\n

\"\"<\/p>\n

Finally, an example of statistical data in SDMX (Statistical Data and Metadata Exchange)<\/strong> format. This example represents the quarterly unemployment rate in a country:<\/p>\n

\"\"<\/p>\n","protected":false},"excerpt":{"rendered":"

[et_pb_section fb_built=”1″ admin_label=”section” _builder_version=”4.16″ global_colors_info=”{}”][et_pb_row admin_label=”row” _builder_version=”4.16″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text admin_label=”Text” _builder_version=”4.23″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” hover_enabled=”0″ global_colors_info=”{}” sticky_enabled=”0″] In our experience developing IT technologies, we have undertaken various projects in fields related to Social Sciences and scientific research. In these projects, we have had to “struggle” with different standards. […]<\/p>\n","protected":false},"author":9,"featured_media":250543,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"off","_et_pb_old_content":"

\u00bfPor qu\u00e9 son necesarios los espacios de datos?<\/strong><\/p>\r\n

Hoy en d\u00eda, hay sectores donde es requisito indispensable la compartici\u00f3n de datos entre los distintos actores de un mismo sector. Un ejemplo protot\u00edpico de esto, son las aduanas. En las fronteras, si se comparten datos entre los pa\u00edses lim\u00edtrofes, no s\u00f3lo se puede reducir el esfuerzo, evitando un doble chequeo, sino que adem\u00e1s se puede conseguir un funcionamiento \u00f3ptimo si se comparten datos. Por ejemplo, poder detectar de forma temprana un posible problema veterinario si el pa\u00eds de entrada conoce con antelaci\u00f3n la carga que trae un cami\u00f3n gracias a la informaci\u00f3n facilitada por el pa\u00eds de salida<\/p>\r\n

\"data-spaces-netitelligent\"<\/p>\r\n

Sin embargo, esto tambi\u00e9n puede suponer introducir nuevas complejidades que deben ser resueltas. Algunas de ellas son:<\/p>\r\n\r\n