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.

 

Scientific Publications

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’s examine their differences:

 

Feature	DOI (Digital Object Identifier)	ORCID (Open Researcher and Contributor ID)
What does it identify?	Scientific publications, datasets, reports, theses.	Researchers and scientific authors.
Main Purpose	Ensure a permanent link to a digital document.	Provide a unique identifier for each researcher.
Format	10.1234/abcd1234	0000-0001-2345-6789
Managed by	CrossRef, DataCite, other organizations.	ORCID (non-profit organization).
Used in	Journal articles, books, repositories, databases.	Researcher profiles, journals, academic institutions.

 

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.

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

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’s publications across various databases, ensuring proper attribution and visibility of their work.

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

 

Statical Data

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:

 

Feature	DDI (Data Documentation Initiative)	SDMX (Statistical Data and Metadata Exchange)
Approach	Documentation of data, metadata, and the data lifecycle in social sciences, surveys, and longitudinal data.	Exchange and harmonization of statistical data and metadata, focused on official statistics and time series.
Format	XML (DDI-Codebook and DDI-Lifecycle)	XML and JSON
Application Scope	Surveys, census data, longitudinal studies, qualitative data.	Macroeconomic data, official statistics, time series.
Main Users	Academic institutions, research centers, governments.	Central banks, international organizations (IMF, OECD, Eurostat).
Lifecycle Coverage	Documents the entire data lifecycle, from collection to analysis and preservation.	Standardizes statistical data exchange, facilitating comparisons between sources.

 

Here is an example of a survey in DDI (Data Documentation Initiative) format:

Finally, an example of statistical data in SDMX (Statistical Data and Metadata Exchange) format. This example represents the quarterly unemployment rate in a country: