The number of disclosed vulnerabilities has been steadily increasing over the years. At the same time, organizations face significant challenges patching their systems, leading to a need to prioritize vulnerability remediation in order to reduce the risk of attacks. Unfortunately, existing vulnerability scoring systems are either vendor-specific, proprietary, or are only commercially available. Moreover, these and other prioritization strategies based on vulnerability severity are poor predictors of actual vulnerability exploitation because they do not incorporate new information that might impact the likelihood of exploitation. In this paper we present the efforts behind building a Special Interest Group (SIG) that seeks to develop a completely data-driven exploit scoring system that produces scores for all known vulnerabilities, that is freely available, and which adapts to new information. The Exploit Prediction Scoring System (EPSS) SIG consists of more than 170 experts from around the world and across all industries, providing crowd-sourced expertise and feedback. Based on these collective insights, we describe the design decisions and trade-offs that lead to the development of the next version of EPSS. This new machine learning model provides an 82% performance improvement over past models in distinguishing vulnerabilities that are exploited in the wild and thus may be prioritized for remediation.

Organic remains from excavated sites include a wide range of materials, from distinct organisms (‘ecofacts’) to biomolecules. Biomolecules provide a variety of new research avenues, while ecofacts with longer histories of study are now being re-harnessed in unexpected ways. These resources are unlocking research potential, transcending what was previously imagined possible. However, this ‘organics revolution’ comes with a salutary corollary: our approaches to recovering and curating organics, and making accessible research data, are not developing as quickly as we need. In this paper, we review retention guidelines for institutions in Britain and Ireland, setting this against the backdrop of a ‘curation crisis’ that is affecting museums throughout Europe, and beyond. We suggest key themes, including the state of existing documentation and considerations of intrinsic and allied research potential, that should be used to open a discussion about the development of more comprehensive and standardised approaches to archiving in the future. Engaging in this conversation is the only way that we can hope to ensure the long-term retention and preservation of organics, while safeguarding associated research data. These changes are needed to ensure future global research collaborations across the academic, curatorial and professional archaeological sectors.

This data in brief article describes the data collection and analysis process undertaken to assess the effectiveness of land-use zoning for heritage protection at the UNESCO World Heritage Site of Bagan Myanmar [1]. In order to measure the expansion of urban areas within the Archaeological Zone of Bagan from 1987 to 2018, and thus assess whether development controls functioned effectively, imaging data was acquired from the archives of the US Landsat 5, and the European Space Agency's Sentinel 2 platforms. Shapefiles were generated in QGIS for each settlement within the zone for each of the eight sample years. The changing area, in square kilometres, of these settlements was then analysed over time, with further reference to two 'control' settlements that exist outside of the Archaeological Zone.