Growth of the open science movement has drawn significant attention to data sharing and availability across the scientific community. In this study, we tested the ability to recover data collected under a particular funder-imposed requirement of public availability. We assessed overall data recovery success, tested whether characteristics of the data or data creator were indicators of recovery success, and identified hurdles to data recovery. Overall the majority of data were not recovered (26% recovery of 315 data projects), a similar result to journal-driven efforts to recover data. Field of research was the most important indicator of recovery success, but neither home agency sector nor age of data were determinants of recovery. While we did not find a relationship between recovery of data and age of data, age did predict whether we could find contact information for the grantee. The main hurdles to data recovery included those associated with communication with the researcher; loss of contact with the data creator accounted for half (50%) of unrecoverable datasets, and unavailability of contact information accounted for 35% of unrecoverable datasets. Overall, our results suggest that funding agencies and journals face similar challenges to enforcement of data requirements. We advocate that funding agencies could improve the availability of the data they fund by dedicating more resources to enforcing compliance with data requirements, providing data-sharing tools and technical support to awardees, and administering stricter consequences for those who ignore data sharing preconditions.

Most large-scale conservation policies are anticipated or announced in advance. This risks the possibility of preemptive resource extraction before the conservation intervention goes into force. We use a high-resolution dataset of satellite-based fishing activity to show that anticipation of an impending no-take marine reserve undermines the policy by triggering an unintended race-to-fish. We study one of the world's largest marine reserves, the Phoenix Islands Protected Area (PIPA), and find that fishers more than doubled their fishing effort once this area was earmarked for eventual protected status. The additional fishing effort resulted in an impoverished starting point for PIPA equivalent to 1.5 y of banned fishing. Extrapolating this behavior globally, we estimate that if other marine reserve announcements were to trigger similar preemptive fishing, this could temporarily increase the share of overextracted fisheries from 65% to 72%. Our findings have implications for general conservation efforts as well as the methods that scientists use to monitor and evaluate policy efficacy.

While forced labor in the world’s fishing fleet has been widely documented, its extent remains unknown. No methods previously existed for remotely identifying individual fishing vessels potentially engaged in these abuses on a global scale. By combining expertise from human rights practitioners and satellite vessel monitoring data, we show that vessels reported to use forced labor behave in systematically different ways from other vessels. We exploit this insight by using machine learning to identify high-risk vessels from among 16,000 industrial longliner, squid jigger, and trawler fishing vessels. Our model reveals that between 14% and 26% of vessels were high-risk, and also reveals patterns of where these vessels fished and which ports they visited. Between 57,000 and 100,000 individuals worked on these vessels, many of whom may have been forced labor victims. This information provides unprecedented opportunities for novel interventions to combat this humanitarian tragedy. More broadly, this research demonstrates a proof of concept for using remote sensing to detect forced labor abuses.

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