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When I started my PhD, I wanted to do something related to systems but I wasn't sure exactly what. I didn't consider data management systems initially, because I was unaware of the richness of the systems work that data management systems were build on. I thought the field was mainly about SQL. Luckily, that view changed quickly.
The Hasso Plattner Institute (HPI), academically structured as the independent Faculty of Digital Engineering at the University of Potsdam, unites computer science research and teaching with the advantages of a privately financed institute and a tuition-free study program. Founder and namesake of the institute is the SAP co-founder Hasso Plattner, who also heads the Enterprise Platform and Integration Concepts (EPIC) research center which focuses on the technical aspects of business software with a vision to provide the fastest way to get insights out of enterprise data. Founded in 2006, the EPIC combines three research groups comprising autonomous data management, enterprise software engineering, and data-driven decision support.
German and European migration policy operates in permanent crisis mode. Sudden increases in irregular immigration create a sense of loss of control, which is instrumentalised by populist forces. This has generated great interest in quantitative migration predictions. High expectations are placed in the AI-based tools currently under devel­op­ment for forecasting irregular migration. The potential applications of these tools are manifold. They range from managing and strengthening the EU's reception capacity and border protections to configuring humanitarian aid provision and longer-term planning of development programmes. There is a significant gap between the expectations placed in the new instruments and their practical utility. Technical limits exist, medium-term forecasts are methodologically implausible, and channels for feeding the results into political decision-making processes are lacking. The great demand for predictions is driven by the political functions of migration prediction, which include its uses in political communication, funding acquisition and legitimisation of political decisions. Investment in the quality of the underlying data will be more productive than developing a succession of new prediction tools. Funding for applications in emergency relief and development cooperation should be prioritised. Crisis early warning and risk analysis should also be strengthened and their networking improved.
Die deutsche und europäische Migrationspolitik befindet sich im permanenten Krisenmodus. Plötzliche Anstiege ungeregelter Zuwanderung nähren ein Gefühl von Kontrollverlust, das wiederum von populistischen Kräften instrumentalisiert wird. Daher hat die Politik großes Interesse an quantitativen Migrationsprognosen. Besondere Erwartungen wecken KI-gestützte Instrumente zur Vorhersage ungeregelter Wanderungsbewegungen, wie sie zurzeit entwickelt werden. Die Anwendungsfelder dieser Instrumente sind vielfältig. Sie reichen von einer Stärkung der Aufnahmekapazitäten in der EU über die präventive Verschärfung von Grenzschutzmaßnahmen und eine bedarfsgerechte Bereitstellung von Ressourcen in humanitären Krisen bis zur längerfristigen entwicklungspolitischen Programmplanung. Allerdings besteht eine deutliche Kluft zwischen den Erwartungen an die neuen Instrumente und ihrem praktischen Mehrwert. Zum einen sind die technischen Möglichkeiten begrenzt, und mittelfristige Vorhersagen zu ungeregelten Wanderungen sind methodisch kaum möglich. Zum anderen mangelt es an Verfahren, um die Ergebnisse in politische Entscheidungsprozesse einfließen zu lassen. Die hohe Nachfrage nach Prognosen erklärt sich aus den politischen Funktionen quantitativer Migrationsvorhersage - beispielsweise ihrem Potential für die politische Kommunikation, die Mitteleinwerbung und die Legitimierung politischer Entscheidungen. Investitionen in die Qualität der den Prognosen zugrunde liegenden Daten sind sinnvoller als die Entwicklung immer neuer Instrumente. Bei der Mittelvergabe für Prognosen sollten Anwendungen in der Nothilfe und der Entwicklungszusammenarbeit priorisiert werden. Zudem sollten die Krisenfrüherkennung und die Risikoanalyse gestärkt werden, und die beteiligten Akteure sollten sich besser vernetzen.
This study pushes our understanding of research reliability by reproducing and replicating claims from 110 papers in leading economic and political science journals. The analysis involves computational reproducibility checks and robustness assessments. It reveals several patterns. First, we uncover a high rate of fully computationally reproducible results (over 85%). Second, excluding minor issues like missing packages or broken pathways, we uncover coding errors for about 25% of studies, with some studies containing multiple errors. Third, we test the robustness of the results to 5,511 re-analyses. We find a robustness reproducibility of about 70%. Robustness reproducibility rates are relatively higher for re-analyses that introduce new data and lower for re-analyses that change the sample or the definition of the dependent variable. Fourth, 52% of re-analysis effect size estimates are smaller than the original published estimates and the average statistical significance of a re-analysis is 77% of the original. Lastly, we rely on six teams of researchers working independently to answer eight additional research questions on the determinants of robustness reproducibility. Most teams find a negative relationship between replicators' experience and reproducibility, while finding no relationship between reproducibility and the provision of intermediate or even raw data combined with the necessary cleaning codes.
Using novel longitudinal data, this paper studies the short- and medium-term effects of Russia's invasion of Ukraine on February 24, 2022 on social trust of adolescents in Germany. Comparing adolescents who responded to our survey shortly before the start of the war with those who responded shortly after the conflict began and applying difference-in-differences (DiD) models over time, we find a significant decline in the outcome after the war started. These findings provide new evidence on how armed conflicts influence social trust and well-being among young people in a country not directly involved in the war.
We present a catalogue of 362 million stellar parameters, distances, and extinctions derived from Gaia's Early Data Release (EDR3) cross-matched with the photometric catalogues of Pan-STARRS1, SkyMapper, 2MASS, and All WISE. The higher precision of the Gaia EDR3 data, combined with the broad wavelength coverage of the additional photometric surveys and the new stellar-density priors of the StarHorse code, allows us to substantially improve the accuracy and precision over previous photo-astrometric stellar-parameter estimates. At magnitude G = 14 (17), our typical precisions amount to 3% (15%) in distance, 0.13 mag (0.15 mag) in V-band extinction, and 140 K (180 K) in effective temperature. Our results are validated by comparisons with open clusters, as well as with asteroseismic and spectroscopic measurements, indicating systematic errors smaller than the nominal uncertainties for the vast majority of objects. We also provide distance- and extinction-corrected colour-magnitude diagrams, extinction maps, and extensive stellar density maps that reveal detailed substructures in the Milky Way and beyond. The new density maps now probe a much greater volume, extending to regions beyond the Galactic bar and to Local Group galaxies, with a larger total number density. We publish our results through an ADQL query interface (gaia . aip . de) as well as via tables containing approximations of the full posterior distributions. Our multi-wavelength approach and the deep magnitude limit render our results useful also beyond the next Gaia release, DR3.
