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Determinants of credit growth in Saudi Arabia are investigated.

A panel approach is applied to macroeconomic and bank-level data spanning 2000 ‐15.

Bank lending is supported by strong bank balance sheet conditions (high capital ratio, and growth of NPL provisioning and deposits), and higher growth of both oil prices and non-oil private sector GDP. Lower bank concentration also helps, likely through greater competition, so does stronger institution. Consistent with the literature, lending by Islamic banks may be more responsive to economic activity. Lending remained robust in 2015 despite oil prices having declined, helped by strong bank balance sheets and as banks reduced their holdings of “excess liquidity”. To support bank lending in the period ahead, bank balance sheets need to remain strong. Fiscal adjustment and a reduced reliance on banks to finance the budget deficit would support credit provision to the private sector.

The paper is first to analyze in detail determinants of bank lending in Saudi Arabia applying a panel approach to bank level data, and draws critical policy implications.

Using cross-country data on the 1,000 largest global banks for 2019, the paper aims to examine the response of bank risk and returns to the pandemic.

The author employs weighted least squares (WLS) techniques for the purposes of analysis.

The findings suggest that banks with Islamic windows increased their riskiness in response to the pandemic, although there was not much impact on profitability. Additionally, the author categorizes banks based on certain major characteristics and find that these findings are manifest primarily for well-capitalized and less liquid banks.

Research as to the impact of the pandemic on banks' balance sheets has been an unaddressed area of research. By focusing on a large sample of banks across countries with both Islamic and conventional banking presence, the analysis sheds light on the balance sheet response of banks to the pandemic, an aspect that has not been addressed earlier.

Semantic retrieval and discovery of datasets published as open data remains a challenging task. The datasets inherently originate in the globally distributed web jungle, lacking the luxury of centralized database administration, database schemes, shared attributes, vocabulary, structure and semantics. The existing dataset catalogs provide basic search functionality relying on keyword search in brief, incomplete or misleading textual metadata attached to the datasets. The search results are thus often insufficient. However, there exist many ways of improving the dataset discovery by employing content-based retrieval, machine learning tools, third-party (external) knowledge bases, countless feature extraction methods and description models and so forth.

In this paper, the authors propose a modular framework for rapid experimentation with methods for similarity-based dataset discovery. The framework consists of an extensible catalog of components prepared to form custom pipelines for dataset representation and discovery.

The study proposes several proof-of-concept pipelines including experimental evaluation, which showcase the usage of the framework.

To the best of authors’ knowledge, there is no similar formal framework for experimentation with various similarity methods in the context of dataset discovery. The framework has the ambition to establish a platform for reproducible and comparable research in the area of dataset discovery. The prototype implementation of the framework is available on GitHub.