No abstract

The German National Educational Panel Study has been set up to collect longitudinal data for educational research. About 60,000 target persons will be questioned and tested within six starting cohorts. This will generate a very large amount of data. Accordingly, one of the most important challenges is to provide comfortable and user-friendly data access that simultaneously allows a high level of data protection. A better infrastructure for research data has been under construction in Germany since the 1990s. Research Data Centers offer a broad range of ways to access data. The National Educational Panel Study is not only matching these common access ways but also developing a remote access solution, called RemoteNEPS. This Secure Data Access will enable users to work with the data from their own computer via a terminal server solution. The data itself will not leave the National Educational Panel Study secure environment. This setting gives the opportunity to provide detailed data to researchers while guaranteeing a high level of data security. As well as providing data security, the concept of RemoteNEPS can be expanded to higher levels of data utility. A community for educational research can be set up to support good scientific practice. Additionally, RemoteNEPS has the capability to handle the structure of the National Educational Panel Study data even after multiple waves or when matching new data from different sources.

FORWARDThis paper is the product of one of the three working groups at Dagstuhl event 11382. The group was charged with producing a reference model for the process of longitudinal data production and use, with an emphasis on the specification and management of the supporting metadata. This model is designed to be useful for the gamut of study types where data are collected across time, including panel studies and repeated cross-sectional studies. It should also be useful for single cross-section studies.

In recent years, the storage of qualitative data has been a challenge to data archives using repositories that are based on relational databases, as large files cannot really be represented well in these structures. Most of the time, two or more structures have to be in place e.g. a fileserver that includes versioning for large files and a relational database for the tabular information. These structures necessitate the handling of multiple systems at the same time. With the arrival of Hadoop and other big data technologies, qualitative data and quantitative data can now be stored as mixed mode data in the same structures. This paper will discuss our findings in developing an early prototype version of MMRepo at the University of Applied Sciences Eastern Switzerland HTW Chur. Our prototype of MMRepo is a combination of the Invenio portal solution from CERN with a Hadoop 2.0 cluster using the DDI 3.3 beta metadata scheme for data documentation.

Spent brewer’s yeast (SBY) is a byproduct of the brewing industry traditionally used as a feed additive, although it could have much broader applications. In this paper, a comprehensive review of valorization of SBY for the production of high-value products, new materials, and biofuels, as well as environmental application, is presented. An economic perspective is given by mirroring marketing of conventional SBY with innovative high-value products. Cascading utilization of fine chemicals, biofuels, and nutrients such as proteins, carbohydrates, and lipids released by various SBY treatments has been proposed as a means to maximize the sustainable and circular economy.