Every three months, ZBW – Leibniz Information Centre for Economics invites you to their INCONECSS Community Meetings where you can delve into current topics in the field of economics and business information and share your experience with others.

The upcoming INCONECSS Community Meetings will take place on:

Sep 27, 2021: Research Data: Representations, Analytics and Visualizations
Dec 13, 2021: Trainings and Games Related to Research Data

Sign up and further information: https://www.inconecss.eu/community.html

Sudden and unforeseen shocks can cause incalculable and fast-changing economic dynamics to which policy makers need to respond quickly – as we have experienced during the COVID-19 pandemic. At the same time, data from traditional statistics that usually guide policy decisions are only available with non-negligible time delays. This leaves policy makers uncertain about how to most effectively manage their economic countermeasures to support businesses.

Given this information deficit, our colleagues from ZEW – Leibniz Centre for European Economic Research propose a framework that guides policy makers throughout all stages of an unforeseen economic shock by providing timely and reliable data as a basis to make informed decisions. They do so by combining early stage ‘ad hoc’ web analyses, ‘follow-up’ business surveys, and ‘retrospective’ analyses of firm outcomes.

Learn more about the proposed integrated data framework in the recently published discussion paper.

Company websites are an important source of economic data and can be used for various scientific approaches, such as predicting firm innovativeness or examining market entry strategies. But the content of those websites changes over time, which requires a continuous monitoring to capture this (change of) information.

For this reason, the ZEW – Leibniz Centre for European Economic Research scrapes the content of corporate websites since 2018 in a panel format, updated every three to six months.

Find out more about the data and how you can access it on our new Mannheim Web Panel page!

Working with data involves attention to data privacy issues in order to protect the individual. But for researchers it can be very demanding to identify which privacy protection regulation is binding and under which conditions it applies to their own work as legal issues are usually not central to their area of expertise.

To offer researchers and other people working with data an entry point to understand those important privacy law issues, the BERD@BW team developed an interactive Virtual Assistant (iVA). iVA leads you with a series of questions through the regulations and provides a result based on your answers.

The first part of iVA, which examines with you if privacy protection regulations apply to your data project, was recently updated and can be accessed here: https://www.berd-bw.de/iva/ (german)

While iVA currently addresses if privacy protection regulations apply to you, we are already working on an extension to cover the issue more profoundly. We are looking forward to present you a second part of iVA, that will let you check if you are allowed to process personal data and what requirements you have to keep in mind.

Great news! BERD and UB Mannheim have joined the Wikibase Stakeholder Group (WBSG). It is a group of a few members who actively use Wikibase and develop extensions for it. The mission of the group is to commission production and maintenance of open source extensions to Wikibase as well as documentation.

BERD is honoured to join the WBSG and looking forward to shape the future of the Wikibase Ecosystem.

European Semantic Web Conference (ESWC) is a major venue on semantic technologies. ESWC 2021 took place online on 6-10 June and had many interesting contributions. BERD was happy to attend ESWC 2021 and to present our new tool RaiseWikibase.

Our poster RaiseWikibase: Fast inserts into the BERD instance was presented by Renat Shigapov. RaiseWikibase is a Python tool for speeding up knowledge graph construction and data integration using Wikibase. In our paper we made performance analysis and showed an example of knowledge graph construction with a few millions of German companies. Take a look at our open source code, one minute video and preprint.