Accounting fraud poses significant financial and reputational risks for organizations. Traditional detection methods - such as manual audits and red-flag indicators - struggle to keep pace with the growing volume and complexity of financial data. In contrast, artificial intelligence...

Background: Accurate, real-time data about response technologies, capabilities, and availabilities are key to effective emergency response logistics; this is particularly important in remote settings, such as in the Arctic, where limited infrastructure, logistics, and technologies occasion the...

Supervisory data are typically not conceived for statistical purposes or considered "official statistics", but they are disclosed to the public, either directly by the supervised institutions or indirectly by the competent authorities. This disclosure is required under Pillar 3 of the Basel...

Supervised machine learning methods, in which no error labels are present, are increasingly popular methods for identifying potential data errors. Such algorithms rely on the tenet of a 'ground truth' in the data, which in other words assumes correctness in the majority of the cases. Points...

Micro-data on robots have been very sparse in Germany so far. Consequently, a dedicated section has been introduced in the IAB Establishment Panel 2019 that includes questions on the number and type of robots used. This article describes the background and development of the survey questions,...

Customer reviews from digital platforms are a vital data resource for recommender and other decision support systems. The performance of these systems is highly dependent on the quality of the underlying data–particularly its currency. Existing metrics for assessing the currency of customer...

Accounting fraud poses significant financial and reputational risks for organizations. Traditional detection methods - such as manual audits and red-flag indicators - struggle to keep pace with the growing volume and complexity of financial data. In contrast, artificial intelligence...

We propose an observation-driven dynamic common factor model for missing value imputation in high-dimensional panel data. The model exploits both serial and cross-sectional information in the data and can easily cope with time-variation in conditional means and variances, as well as with either...

We use a rigorous three-stage many-analysts design to assess how different researcher decisions-specifically data cleaning, research design, and the interpretation of a policy question-affect the variation in estimated treatment effects. A total of 146 research teams each completed the same...

This report discusses the findings and recommendations of the 2024 assessment of the data quality of the public sector debt statistics (PSDS) of the Republic of Zambia against the IMF's Data Quality Assessment Framework (DQAF) for PSDS. The assessment was undertaken as part of a project to...

This report discusses the findings and recommendations of the 2024 assessment of the data quality of the public sector debt statistics (PSDS) of the United Republic of Tanzania against the IMF's Data Quality Assessment Framework (DQAF) for PSDS. The assessment was undertaken as part of a project...

This document provides technical details and support for the implementation of air quality monitoring under the Directive (EU) 2024/2881 of the European Parliament and of the Council of 23 October 2024 on ambient air quality and cleaner air for Europe (recast) (AAQD, Directive (EU) 2024/2881)....

Commission Decision of 25 February 2016 setting up a Scientific, Technical and Economic Committee for Fisheries, C(2016) 1084, OJ C 74, 26.2.2016, p. 4-10. The Commission may consult the group on any matter relating to marine and fisheries biology, fishing gear technology, fisheries economics,...

Machine learning algorithms are typically evaluated using benchmark datasets under the assumption that these datasets are clean. However, recent studies have revealed the presence of label noise in many benchmark datasets, indicating a biased evaluation to date. Confident learning (CL), an...

Damit ein Register wie das geplante bundesweite Bildungsverlaufsregister optimal genutzt werden kann, sind vorab die technischen Probleme zu klären, die bei der Zusammenführung der Daten einer Person aus verschiedenen Datenquellen, dem sogenannten Record-Linkage, auftreten können. Einige der...

Damit ein Register wie das geplante bundesweite Bildungsverlaufsregister optimal genutzt werden kann, sind vorab die technischen Probleme zu klären, die bei der Zusammenführung der Daten einer Person aus verschiedenen Datenquellen, dem sogenannten Record-Linkage, auftreten können. Einige der...

This report discusses the findings and recommendations of a diagnostic assessment of the quality of public sector debt statistics (PSDS) of Malawi based on the IMF's Data Quality Assessment Framework for PSDS. The assessment was undertaken in February 2025 as part of a project to strengthen the...

In January/February 2025, an assessment was undertaken of the data quality of the public sector debt statistics (PSDS) of Malawi against the IMF's Data Quality Assessment Framework (DQAF) for PSDS. The mission was undertaken as part of a project to strengthen the quality of public sector debt in...

In February 2025, an assessment was undertaken of the data quality of the public sector debt statistics (PSDS) of the Republic of Zimbabwe against the IMF's Data Quality Assessment Framework (DQAF) for PSDS. The assessment was undertaken as part of a project to strengthen the quality of public...

This report discusses the findings and recommendations of the 2025 assessment of the data quality of the public debt statistics of Zimbabwe against the IMF's Data Quality Assessment Framework (DQAF). The assessment was undertaken as part of a project to strengthen the quality of public sector...

This paper examines the quality of quarterly records on work hours collected from employers in the State of Washington to administer the unemployment insurance (UI) system, specifically to determine eligibility for UI. We subject the administrative records to four "trials," all of which suggest...

Supervisory data are typically not conceived for statistical purposes or considered "official statistics", but they are disclosed to the public, either directly by the supervised institutions or indirectly by the competent authorities. This disclosure is required under Pillar 3 of the Basel...

Data-centric artificial intelligence (data-centric AI) represents an emerging paradigm that emphasizes the importance of enhancing data systematically and at scale to build effective and efficient AI-based systems. The novel paradigm complements recent model-centric AI, which focuses on...

Der Bedarf an schnell verfügbaren Daten zur kurzfristigen Entwicklung des Arbeitsmarktes steigt. Das Statistische Bundesamt analysiert diesbezüglich bereits seit Jahren das Potenzial von Online-Stellenanzeigen. Insbesondere in Projekten des "ESSnet Big Data" wurden verschiedene...

Monitoring soil quality provides indispensable inputs for effective policy advice, but very few poorer countries can implement high-quality surveys on soil. We offer an alternative, low-cost imputation-based approach to generating various soil quality indicators. The estimation results validate...

In September 2024, an assessment was undertaken of the data quality of the public sector debt statistics (PSDS) of the United Republic of Tanzania against the IMF's Data Quality Assessment Framework (DQAF) for PSDS. The mission was undertaken as part of a project to strengthen the quality of...

In October 2024, an assessment was undertaken of the data quality of the public sector debt statistics (PSDS) of the Republic of Zambia against the IMF's Data Quality Assessment Framework (DQAF) for PSDS. The assessment was undertaken as part of a project to strengthen the quality of public...