CfP: “The Role and Benefit of Metadata Capture, Discovery and Harmonization in Survey Research”


This session invites presentations dealing with structured metadata in a standardized form across the data life-cycle: case studies, systems and tools for i.e. instrument design, data entry, data processing, maintaining data documentation, and capturing and storing the metadata within a repository for later re-use. Capturing metadata as early on in the survey life-cycle as possible in a structured way enhances transparency and quality, supports harmonization and comparison of studies, and enables reproducible research and reuse of survey components for other waves or surveys. Metadata management can be seen as an integrated part of the survey research process. A wide range of different products and services for different audiences can be generated on the basis of metadata like web-based information systems, traditional codebooks, command setups for statistical packages, question banks, and searching and locating of data.  Papers are invited on, but not limited to, the following topics: reuse of metadata across space, time, and studies, metadata banks such as for questions and classifications, and metadata-driven information systems, possibly using DDI Lifecycle (Data Documentation Initiative). The session is aimed at survey designers and implementers, data and metadata managers, information system managers of cross-national surveys, metadata experts, and others.

Session at the 5th Biennial ACSPRI Social Science Methodology Conference 2016
ACSPRI – Australian Consortium for Social and Political Research Incorporated
Theme: Social science in Australia: 40 years on
Conference dates: Tuesday July 19 – Friday July 22, 2016
Venue: The University of Sydney, Sydney, Australia
Deadline: Friday March 4, 2016

[via DDI-users]

Dictionary with terms for the Research Data Domain

Screenshot: CASRAI dictionary for the Research Data Domain

The Consortia Advancing Standards in Research Administration Information (CASRAI) provides a dictionary containing terms for the Research Data Domains. Each term has a unique identifier (UUID) and a URL that can be used as references to enhance reading comprehension of documents by hyperlinking terms to their definition. The URL for each term contains a link to a Discussion page to complete the feedback loop with the community of users.

The Glossary has been developed in consultation with vocabulary experts and practitioners from a wide cross-section of stakeholder groups. It is meant to be a practical reference for individuals and working groups concerned with the improvement of research data management, and as a meeting place for further discussion and development of terms. The aim is to create a stable and sustainably governed glossary of community accepted terms and definitions, and to keep it relevant by maintaining it as a ‘living document’ that is updated when necessary.

Form other sections of the dictionary one can return to this pilot section using the top-menu item Filter by and selecting Research Data Domain. To see all terms in the CASRAI dictionary (including the RDC terms), go here:

In addition to direct comments on specific terms in the Glossary CASRAI is very interested in receiving feedback about the Glossary in general. Here is a short survey:

This section of the dictionary is developed and maintained by Research Data Canada’s (RDC) Standards & Interoperability Committee ( in collaboration with CASRAI. It is made publicly available under a Creative Commons Attribution Only license (CC-BY).

(via [DDI-users])

Save the date: EDDI2015 am 2./3. Dezember in Kopenhagen

Die 7. Konferenz der europäischen DDI-Nutzer_innen findet am 2. und 3. Dezember in Kopenhagen statt. Die Deadline für die Einreichung von Beiträgen ist der 6. September, der Call-for-papers wird am 22. Mai veröffentlicht. Die Konferenzwebseite enthält schon jetzt die wichtigsten Hinweise.

Paper: User-focused threat identification for anonymised microdata


When producing anonymised microdata for research, national statistics institutes (NSIs) identify a number of ‚risk scenarios‘ of how intruders might seek to attack a confidential dataset. Hans-Peter Hafner, Felix Ritchie and Rainer Lenz argue in their paper „User-focused threat identification for anonymised microdata“ (PDF) that the strategy used to identify confidentiality protection measures can be seriously misguided, mainly since scenarios focus on data protection without sufficient reference to other aspects of data. This paper brings together a number of findings to see how the above problem can be addressed in a practical context. Using as an example the creation of a scientific use file, the paper demonstrates that an alternative perspective can have dramatically different outcomes. (Source: Authors‘ abstract)

do-Dateien und R-Skripte: Style-Guides helfen, den Code zu verstehen

Wer zusammen Daten aufbereitet, wird wohl oder übel auch einmal in die Lage kommen, Skripte verstehen zu müssen, die andere geschrieben haben. Das ist nicht immer ganz einfach.

Die Situation verbessert sich, wenn der Aufbau des Codes vereinbarten Kriterien folgt und deshalb empfiehlt z.B. Google für die Softwareentwicklung, sich an bestimmte Regeln zu halten: Es gibt Style-Guides für die verschiedensten Programmiersprachen.

Verschiedene Style-Guides

Aber auch für Stata und R lassen sich entsprechende Empfehlungen finden:

Code, der Style-Guides entspricht, ist leichter (wieder) zu verstehen und zu warten. Es ist sicher eine gute Idee, sich an solchen Style Guides zu orientieren oder sie für die eigenen Arbeiten anzupassen.