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Metadata for Official Statistics

General Information


MISSY is an online service of the German Microdata Lab that addresses (social-)scientists working with official microdata. As this data is not primarily collected for scientific purposes, the documentation does not conform to established social science standards. This especially applies for the output-harmonized European surveys such as EU-SILC or EU-LFS which are conducted under varying circumstances in the different countries of the European Union.

MISSY closes this gap in documentation by providing a central source of structured documentation. This reduces the individual effort in data exploration and preparation as metadata, tool and links to the original documentation materials are accessible.

Support during all stages of the research process

MISSY supports researches on different levels throughout the research process in all phases of their project:

  • Find data: Answering the questions whether a series documented in MISSY is suitable to answer a certain research question.
  • Get data: Gathering information regarding regulations, restrictions and contact information to apply for data access.
  • Prepare data: i.e. by providing syntax-files to import the data into statistical software.
  • Understand data: by providing information about question wording, comparability over time, comparability over space etc.

Note: Unweighted frequencies

Please note that the statistics displayed in MISSY are calculated on the basis of unweighted data and therefore are not appropriate for factual conclusions. They are meant to be used for formal checks only. For example whether import process were successful by producing identical frequencies and to give a first impression on the number of cases and the distribution of a variable.

Hosting & Partners

MISSY is hosted by GESIS - Leibniz Institute for Social Sciences, in cooperation with different partners: statistical institutes such as Eurostat and Federal Statistical Office of Germany as well as external scientific projects such as Data Without Boundaries.