Institutions, funding bodies, and national research organizations are pushing for more data sharing and FAIR data. Institutions typically implement data policies, frequently supported by an institutional data repository. Funders typically mandate data sharing. So where does this leave the researcher? How can researchers benefit from doing the additional work to share their data? In order to make sure that researchers and institutions get credit for sharing their data, the data needs to be tracked and attributed first. In this paper we investigated where the research data ended up for 11 research institutions, and how this data is currently tracked and attributed. Furthermore, we also analysed the gap between the research data that is currently in institutional repositories, and where their researchers truly share their data. We found that 10 out of 11 institutions have most of their public research data hosted outside of their own institution. Combined, they have 12% of their institutional research data published in the institutional data repositories. According to our data, the typical institution had 5% of their research data (median) published in the institutional repository, but there were 4 universities for which it was 10% or higher. By combining existing data-to-article graphs with existing article-to- researcher and article-to-institution graphs it becomes possible to increase tracking of public research data and therefore the visibility of researchers sharing their data typically by 17x. The tracking algorithm that was used to perform analysis and report on potential improvements has subsequently been implemented as a standard method in the Mendeley Data Monitor product. The improvement is most likely an under-estimate because, while the recall for datasets in institutional repositories is 100%, that is not the case for datasets published outside the institutions, so there are even more datasets still to be discovered.
No abstract
Traditional citation and download metrics have long been the standard by which we measure the use and value of scholarly articles. However, these methods neglect the usage and real-world impact of newer technologies to access, store, and share downloaded scholarly articles. This session's speakers will share the results of interviews, focus groups, and an international survey with 1,000 scholars to investigate the ways in which they now access, store, share, and use downloaded scholarly articles. By identifying and measuring what traditional metrics fail to examine, the Beyond Downloads project attempts to capture a more complete picture of the use and value of scholarly articles, which is critical for librarians, publishers, and vendors to understand in developing scholarly tools and services. Complete usage can no longer be measured by traditional means alone. The speakers will discuss the findings of their research and the implications for metrics that take into account scholars' changing access, reading, and sharing behaviors.
Dutch freeways suffer from severe congestion during rush hours or incidents. Research shows that 64% of congested traffic during rush hour consists of commuter traffic [30]. A traffic congestion increases travel time, resulting in a delay for travelers. Reliable travel time predictions are essential for Dynamic Routing, in which travelers can be rerouted to avoid congestions. Travel times can be calculated from vehicle speed [41] in case of free flowing traffic. In case of congestion, we will make an estimation error regarding the travel time. Therefore, an accurate speed prediction model is necessary.In this thesis, the predictability of the average vehicle speed by Bayesian Networks is investigated. A case study is conducted where several Bayesian Network models we propose are evaluated for a well known traffic bottleneck in the Netherlands. We show that Bayesian Networks are capable of predicting the start or end of a congestion at the bottleneck reasonably accurate for a prediction horizon until 30 minutes. Further, we propose a prediction model based on historical data, which is able to predict the average vehicle speed at the bottleneck location for longer prediction horizons. In the end, we propose a hybrid model which combines our Bayesian Network and our prediction model based on historical data. This hybrid model is able to predict a traffic congestion with an accuracy of 85% for a prediction horizon of 2.5 hours.The results of our case study show that modeling traffic using Bayesian Networks is promising. Our models can form the input for a travel time prediction model for Dynamic Routing.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.