Over the past decades, landscapes worldwide have experienced changes (e.g., urbanization, agricultural intensification, expansion of renewable energy uses) at magnitudes that put their sustainability at risk. The understanding of the drivers of these landscape changes remains challenging, partly because landscape research is spread across many domains and disciplines. We here provide a systematic synthesis of 144 studies that identify the proximate and underlying drivers of landscape change across Europe. First, we categorize how driving forces have been addressed and find that most studies consider medium-term time scales and local spatial scales. Most studies assessed only one case study area, one spatial scale, and less than four points in time. Second, we analyze geographical coverage of studies and reveal that countries with a non-European Union/European Free Trade Association membership; low Gross Domestic Product; boreal, steppic, and arctic landscapes; as well as forestland systems are underrepresented in the literature. Third, our review shows that land abandonment/extensification is the most prominent (62% of cases) among multiple proximate drivers of landscape change. Fourthly, we find that distinct combinations of mainly political/institutional, cultural, and natural/spatial underlying drivers are determining landscape change, rather than single key drivers. Our systematic review indicates knowledge gaps that can be filled by: (a) expanding the scope of studies to include underrepresented landscapes; (b) clarifying the identification and role of actors in landscape change; (c) deploying more robust tools and methods to quantitatively assess the causalities of landscape change; (d) setting up long-term studies that go beyond mapping land-cover change only; (e) strengthening cross-site and crosscountry comparisons of landscape drivers; (f) designing multi-scale studies that consider teleconnections; (g) considering subtle and novel processes of landscape change.

Dimensions was built as a platform to allow stakeholders in the research community, including academic bibliometricians, to more easily create and understand the context of different types of research object through the linkages between these objects. Links between objects are created via persistent identifiers and machine learning techniques, while additional context is introduced via data enhancements such as per-object categorisations and person and institution disambiguation. While these features make analytical use cases accessible for end users, the COVID-19 crisis has highlighted a different set of needs to analyze trends in scholarship as they occur: Real-time bibliometrics. The combination of full-text search, daily data updates, a broad set of scholarly objects including pre-prints and a wider set of data fields for analysis, broadens opportunities for a different style of analysis. A subset of these emerging capabilities is discussed and three basic analyses are presented as illustrations of the potential for real-time bibliometrics.

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