The Time Efficiency Gain in Sharing and Reuse of Research Data

Among the frequently stated benefits of sharing research data are time efficiency or increased productivity. The assumption is that reuse or secondary use of research data saves researchers time in not having to produce data for a publication themselves. This can make science more efficient and productive. However, if there is no reuse, time costs in making data available for reuse will have been made with no return on this investment. In this paper a mathematical model is used to calculate the break-even point for time spent sharing in a scientific community, versus time gain by reuse. This is done for several scenarios; from simple to complex datasets to share and reuse, and at different sharing rates. The results indicate that sharing research data can indeed cause an efficiency revenue for the scientific community. However, this is not a given in all modeled scenarios. The scientific community with the lowest reuse needed to reach a break-even point is one that has few sharing researchers and low time investments for sharing and reuse. This suggests it would be beneficial to have a critical selection of datasets that are worth the effort to prepare for reuse in other scientific studies. In addition, stimulating reuse of datasets in itself would be beneficial to increase efficiency in scientific communities

Organic micropollutant removal in full-scale rapid sand filters used for drinking water treatment in The Netherlands and Belgium

Biological treatment processes have the potential to remove organic micropollutants (OMPs) during water treatment. The OMP removal capacity of conventional drinking water treatment processes such as rapid sand filters (RSFs), however, has not been studied in detail. We investigated OMP removal and transformation product (TP) formation in seven full-scale RSFs all treating surface water, using high-resolution mass spectrometry based quantitative suspect and non-target screening (NTS). Additionally, we studied the microbial communities with 16S rRNA gene amplicon sequencing (NGS) in both influent and effluent waters as well as the filter medium, and integrated these data to comprehensively assess the processes that affect OMP removal. In the RSF influent, 9 to 30 of the 127 target OMPs were detected. The removal efficiencies ranged from 0 to 93%. A data-driven workflow was established to monitor TPs, based on the combination of NTS feature intensity profiles between influent and effluent samples and the prediction of biotic TPs. The workflow identified 10 TPs, including molecular structure. Microbial community composition analysis showed similar community composition in the influent and effluent of most RSFs, but different from the filter medium, implying that specific microorganisms proliferate in the RSFs. Some of these are able to perform typical processes in water treatment such as nitrification and iron oxidation. However, there was no clear relationship between OMP removal efficiency and microbial community composition. The innovative combination of quantitative analyses, NTS and NGS allowed to characterize real scale biological water treatments, emphasizing the potential of bio-stimulation applications in drinking water treatment. © 2020 The Author

Operon structure of Staphylococcus aureus

In bacteria, gene regulation is one of the fundamental characteristics of survival, colonization and pathogenesis. Operons play a key role in regulating expression of diverse genes involved in metabolism and virulence. However, operon structures in pathogenic bacteria have been determined only by in silico approaches that are dependent on factors such as intergenic distances and terminator/promoter sequences. Knowledge of operon structures is crucial to fully understand the pathophysiology of infections. Presently, transcriptome data obtained from growth curves in a defined medium were used to predict operons in Staphylococcus aureus. This unbiased approach and the use of five highly reproducible biological replicates resulted in 93.5% significantly regulated genes. These data, combined with Pearson’s correlation coefficients of the transcriptional profiles, enabled us to accurately compile 93% of the genome in operon structures. A total of 1640 genes of different functional classes were identified in operons. Interestingly, we found several operons containing virulence genes and showed synergistic effects for two complement convertase inhibitors transcribed in one operon. This is the first experimental approach to fully identify operon structures in S. aureus. It forms the basis for further in vitro regulation studies that will profoundly advance the understanding of bacterial pathophysiology in vivo

Het toepassen van bioassays binnen Nederland: Deltafact

Deze Deltafact beschrijft de onderdelen en de opbouw van het bioassay-spoor van de sleutelfactor toxiciteit 2. Het bioassay-spoor biedthulpmiddelen voor het toepassen van bioassays voor het bepalen van dechemische waterkwaliteit. Het Deltafact is opgesteld in het kader van hetKennisimpuls Waterkwaliteit, project Toxiciteit

A game theoretic analysis of research data sharing

While reusing research data has evident benefits for the scientific community as a whole, decisions to archive and share these data are primarily made by individual researchers. In this paper we analyse, within a game theoretical framework, how sharing and reuse of research data affect individuals who share or do not share their datasets. We construct a model in which there is a cost associated with sharing datasets whereas reusing such sets implies a benefit. In our calculations, conflicting interests appear for researchers. Individual researchers are always better off not sharing and omitting the sharing cost, at the same time both sharing and not sharing researchers are better off if (almost) all researchers share. Namely, the more researchers share, the more benefit can be gained by the reuse of those datasets. We simulated several policy measures to increase benefits for researchers sharing or reusing datasets. Results point out that, although policies should be able to increase the rate of sharing researchers, and increased discoverability and dataset quality could partly compensate for costs, a better measure would be to directly lower the cost for sharing, or even turn it into a (citation-) benefit. Making data available would in that case become the most profitable, and therefore stable, strategy. This means researchers would willingly make their datasets available, and arguably in the best possible way to enable reuse

Taking the example of computer systems engineering for the analysis of biological cell systems

In this paper, we discuss the potential for the use of engineering methods that were originally developed for the design of\ud embedded computer systems, to analyse biological cell systems. For embedded systems as well as for biological cell systems,\ud design is a feature that defines their identity. The assembly of different components in designs of both systems can vary widely.\ud In contrast to the biology domain, the computer engineering domain has the opportunity to quickly evaluate design options and\ud consequences of its systems by methods for computer aided design and in particular design space exploration. We argue that there\ud are enough concrete similarities between the two systems to assume that the engineering methodology from the computer systems\ud domain, and in particular that related to embedded systems, can be applied to the domain of cellular systems. This will help to\ud understand the myriad of different design options cellular systems have. First we compare computer systems with cellular systems.\ud Then, we discuss exactly what features of engineering methods could aid researchers with the analysis of cellular systems, and what\ud benefits could be gained

Evaluating the design of biological cells using a computer workbench

For embedded systems as well as for biological cell systems, design is a feature that defines their identity. The assembly of different components in designs of both systems can vary widely. Given the similarities between computers and cellular systems, methods and models of computation from the domain of computer systems engineering might be applied to modeling cellular systems. Our aim is to construct a framework that focuses on understanding the design options and consequences within a cell, taking an in silico (forward-) engineering approach rather than a reverse engineering approach that is used in this domain as a default now. We take our ideas from the domain of embedded computer systems. The most important features of our approach, as taken from this domain, are a variable abstraction level of components that allows for addition of components when detailed information is lacking, and a separation of concerns between function and performance by components in the design. This allows for efficient and flexible modeling. Also, there is a strict separation between computation within- and communication between components, reducing complexity. As a proof of principle, we show that we can make a statement regarding the design of the gene expression machinery of the cell to produce a protein, using such a method. 1