Search

The dynamic network visualization design space consists of two major dimensions: network structural and temporal representation. As more techniques are developed and published, a clear need for evaluation and experimental comparisons between them emerges. Most studies explore the temporal dimension and diverse interaction techniques supporting the participants, focusing on a single structural representation. Empirical evidence about performance and preference for different visualization approaches is scattered over different studies, experimental settings, and tasks. This paper aims to comprehensively investigate the dynamic network visualization design space in two evaluations. First, a controlled study assessing participants’ response times, accuracy, and preferences for different combinations of network structural and temporal representations on typical dynamic network exploration tasks, with and without the support of standard interaction methods. Second, the best-performing combinations from the first study are enhanced based on participants’ feedback and evaluated in a heuristic-based qualitative study with visualization experts on a real-world network. Our results highlight node-link with animation and playback controls as the best-performing combination and the most preferred based on ratings. Matrices achieve similar performance to node-link in the first study but have considerably lower scores in our second evaluation. Similarly, juxtaposition exhibits evident scalability issues in more realistic analysis contexts.

Draco has been developed as an automated visualization recommendation system formalizing design knowledge as logical constraints in ASP (Answer-Set Programming). With an increasing set of constraints and incorporated design knowledge, even visualization experts lose overview in Draco and struggle to retrace the automated recommendation decisions made by the system. Our paper proposes an Visual Analytics (VA) approach to visualize and analyze Draco's constraints. Our VA approach is supposed to enable visualization experts to accomplish identified tasks regarding the knowledge base and support them in better understanding Draco. We extend the existing data extraction strategy of Draco with a data processing architecture capable of extracting features of interest from the knowledge base. A revised version of the ASP grammar provides the basis for this data processing strategy. The resulting incorporated and shared features of the constraints are then visualized using a hypergraph structure inside the radial-arranged constraints of the elaborated visualization. The hierarchical categories of the constraints are indicated by arcs surrounding the constraints. Our approach is supposed to enable visualization experts to interactively explore the design rules' violations based on highlighting respective constraints or recommendations. A qualitative and quantitative evaluation of the prototype confirms the prototype's effectiveness and value in acquiring insights into Draco's recommendation process and design constraints.

Guidance can support users during the exploration and analysis of complex data. Previous research focused on characterizing the theoretical aspects of guidance in visual analytics and implementing guidance in different scenarios. However, the evaluation of guidance-enhanced visual analytics solutions remains an open research question. We tackle this question by introducing and validating a practical evaluation methodology for guidance in visual analytics. We identify eight quality criteria to be fulfilled and collect expert feedback on their validity. To facilitate actual evaluation studies, we derive two sets of heuristics. The first set targets heuristic evaluations conducted by expert evaluators. The second set facilitates end-user studies where participants actually use a guidance-enhanced system. By following such a dual approach, the different quality criteria of guidance can be examined from two different perspectives, enhancing the overall value of evaluation studies. To test the practical utility of our methodology, we employ it in two studies to gain insight into the quality of two guidance-enhanced visual analytics solutions, one being a work-in-progress research prototype, and the other being a publicly available visualization recommender system. Based on these two evaluations, we derive good practices for conducting evaluations of guidance in visual analytics and identify pitfalls to be avoided during such studies.

Unexploded Ordnance (UXO) detection, the identification of remnant active bombs buried underground from archival aerial images, implies a complex workflow involving decision-making at each stage. An essential phase in UXO detection is the task of image selection, where a small subset of images must be chosen from archives to reconstruct an area of interest (AOI) and identify craters. The selected image set must comply with good spatial and temporal coverage over the AOI, particularly in the temporal vicinity of recorded aerial attacks, and do so with minimal images for resource optimization. This paper presents a guidance-enhanced visual analytics prototype to select images for UXO detection. In close collaboration with domain experts, our design process involved analyzing user tasks, eliciting expert knowledge, modeling quality metrics, and choosing appropriate guidance. We report on a user study with two real-world scenarios of image selection performed with and without guidance. Our solution was well-received and deemed highly usable. Through the lens of our task-based design and developed quality measures, we observed guidance-driven changes in user behavior and improved quality of analysis results. An expert evaluation of the study allowed us to improve our guidance-enhanced prototype further and discuss new possibilities for user-adaptive guidance.

Modern science and industry rely on computational models for simulation, prediction, and data analysis. Spatial blind source separation (SBSS) is a model used to analyze spatial data. Designed explicitly for spatial data analysis, it is superior to popular non-spatial methods, like PCA. However, a challenge to its practical use is setting two complex tuning parameters, which requires parameter space analysis. In this paper, we focus on sensitivity analysis (SA). SBSS parameters and outputs are spatial data, which makes SA difficult as few SA approaches in the literature assume such complex data on both sides of the model. Based on the requirements in our design study with statistics experts, we developed a visual analytics prototype for data type agnostic visual sensitivity analysis that fits SBSS and other contexts. The main advantage of our approach is that it requires only dissimilarity measures for parameter settings and outputs (Fig. 1). We evaluated the prototype heuristically with visualization experts and through interviews with two SBSS experts. In addition, we show the transferability of our approach by applying it to microclimate simulations. Study participants could confirm suspected and known parameter-output relations, find surprising associations, and identify parameter subspaces to examine in the future. During our design study and evaluation, we identified challenging future research opportunities.

Set visualization facilitates the exploration and analysis of set-type data. However, how sets should be visualized when the data are uncertain is still an open research challenge. To address the problem of depicting uncertainty in set visualization, we ask 1) which aspects of set type data can be affected by uncertainty and 2) which characteristics of uncertainty influence the visualization design. We answer these research questions by first describing a conceptual framework that brings together 1) the information that is primarily relevant in sets (i.e., set membership, set attributes, and element attributes) and 2) different plausible categories of (un)certainty (i.e., certainty, undefined uncertainty as a binary fact, and defined uncertainty as quantifiable measure). Following the structure of our framework, we systematically discuss basic visualization examples of integrating uncertainty in set visualizations. We draw on existing knowledge about general uncertainty visualization and previous evidence of its effectiveness.

In the past two decades, research in visual analytics (VA) applications has made tremendous progress, not just in terms of scientific contributions, but also in real-world impact across wide-ranging domains including bioinformatics, urban analytics, and explainable AI. Despite these success stories, questions on the rigor and value of VA application research have emerged as a grand challenge. This article outlines a research and development agenda for making VA application research more rigorous and impactful. We first analyze the characteristics of VA application research and explain how they cause the rigor and value problem. Next, we propose a research ecosystem for improving scientific value, and rigor and outline an agenda with 12 open challenges spanning four areas, including foundation, methodology, application, and community. We encourage discussions, debates, and innovative efforts toward more rigorous and impactful VA research.

Background: The best treatment strategy for fractures to the posterior malleolus (PM) is still intensively debated. The aim of this systematic review was to compare the patient-rated outcome following open reduction and internal fixation (ORIF) for fractures of the PM to either closed reduction using AP screws (CRIF) or no treatment in bi- or trimalleolar ankle fractures.Methods: Systematic literature research (MEDLINE (PubMed), CINAHL, Scopus, Central and EMBASE) according to the PICOS and PRISMA guidelines. Eligible were studies comparing the outcome following ORIF to any other treatment strategy for fractures to the posterior malleolus in isolated bi- or trimalleolar ankle fractures.Results: Twelve studies were eligible for a qualitative analysis and 6 for a quantitative analysis. Overall, a considerable heterogeneity among the studies was observed. The most commonly used outcome score was the American Orthopaedic Foot & Ankle Society ankle-hindfoot score (AOFAS). The final follow-up ranged from 12 to 160 months. Four studies compared ORIF to CRIF of the PM. The quantitative analysis revealed significantly better AOFAS scores for ORIF (90.9 vs 83.4 points; P< .001; I2= 0%). Three studies compared ORIF to untreated PM fragment. The quantitative analysis again revealed superior AOFAS scores for ORIF (92.0 vs 82.5 points; P< .001; I2= 99%). A similar trend was observed for the Ankle Fracture Scoring System and American Academy of Orthopaedic Surgeons Foot and Ankle Questionnaire scores as well as the quality of reduction.Conclusion: Despite a considerable heterogeneity, the data available point to a superior outcome following ORIF for fractures to the PM when compared to CRIF or no treatment.

Temporal Blind Source Separation (TBSS) is used to obtain the true underlying processes from noisy temporal multivariate data, such as electrocardiograms. TBSS has similarities to Principal Component Analysis (PCA) as it separates the input data into univariate components and is applicable to suitable datasets from various domains, such as medicine, finance, or civil engineering. Despite TBSS’s broad applicability, the involved tasks are not well supported in current tools, which offer only text-based interactions and single static images. Analysts are limited in analyzing and comparing obtained results, which consist of diverse data such as matrices and sets of time series. Additionally, parameter settings have a big impact on separation performance, but as a consequence of improper tooling, analysts currently do not consider the whole parameter space. We propose to solve these problems by applying visual analytics (VA) principles. Our primary contribution is a design study for TBSS, which so far has not been explored by the visualization community. We developed a task abstraction and visualization design in a user-centered design process. Task-specific assembling of well-established visualization techniques and algorithms to gain insights in the TBSS processes is our secondary contribution. We present TBSSvis, an interactive web-based VA prototype, which we evaluated extensively in two interviews with five TBSS experts. Feedback and observations from these interviews show that TBSSvis supports the actual workflow and combination of interactive visualizations that facilitate the tasks involved in analyzing TBSS results.

The phenomenon of a blockchain use case called initial coin offering (ICO) is drawing increasing attention as a novel funding mechanism. ICO is a crowdfunding type that utilizes blockchain tokens to allow for truly peer-to-peer investments. Although more than $7bn has been raised globally via ICOs as at 2018, the concept and its implications are not yet entirely understood. The research lags behind in providing in-depth analyses of ICO designs and their long-term success. We address this research gap by developing an ICO taxonomy, applying a cluster analysis to identify prevailing ICO archetypes, and providing an outlook on the token value market performance for individual archetypes. We identify five ICO design archetypes and display their secondary market development from both a short-term and a long-term perspective. We contribute to an in-depth understanding of ICOs and their implications. Further, we offer practitioners tangible design and success indications for future ICOs.

Visualizing big and complex multivariate data is challenging. To address this challenge, we propose flexible visual analytics(FVA) with the aim to mitigate visual complexity and interaction complexity challenges in visual analytics, while maintaining the strengths of multiple perspectives on the studied data. At the heart of our proposed approach are transitions that fluidly transform data between user-relevant views to offer various perspectives and insights into the data. While smooth display transitions have been already proposed, there has not yet been an interdisciplinary discussion to systematically conceptualize and formalize these ideas. As a call to further action, we argue that future research is necessary to develop a conceptual framework for flexible visual analytics. We discuss preliminary ideas for prioritizing multi-aspect visual representations and multi-aspect transitions between them, and consider the display user for whom such depictions are produced and made available for visual analytics. With this contribution we aim to further facilitate visual analytics on complex data sets for varying data exploration tasks and purposes based on different user characteristics and data use contexts.

The word ‘pattern’ frequently appears in the visualisation and visual analytics literature, but what do we mean when we talk about patterns? We propose a practicable definition of the concept of a pattern in a data distribution as a combination of multiple interrelated elements of two or more data components that can be represented and treated as a unified whole. Our theoretical model describes how patterns are made by relationships existing between data elements. Knowing the types of these relationships, it is possible to predict what kinds of patterns may exist. We demonstrate how our model underpins and refines the established fundamental principles of visualisation. The model also suggests a range of interactive analytical operations that can support visual analytics workflows where patterns, once discovered, are explicitly involved in further data analysis.

In the application domain of digital humanities network visualization is increasingly being used to conduct research as the main interests of the domain experts lie in exploring and analyzing relationships between entities and their changes over time. Visualizing the dynamics and different perspectives of such data is a non-trivial task but it enables researchers to explore connections between disparate entities and investigate historical narratives that emerge. In this paper we present Circular, an interactive exploration environment to visualize event-based networks and support research in digital humanities through visualization of historical subjects in space and time. Our radial design is the result of iterative collaboration with domain experts, and we discuss the process of collaborative development and exploration of public music festivities in Vienna as an example of immersive development methodology. We validate our approach by means of both domain and visualization expert interviews and show the potential of this approach in supporting the visual exploration of historical subjects. We discuss our design rationales, visual encodings, and interactions as to allow the reproducibility of this approach within a framework of transdisciplinary collaboration with digital humanities.

The economy of a country can be modeled as a complex system in which several players buy and sell goods from each other. By analyzing the investment flows, it is possible to reconstruct the supply chain for the production of most goods, whose understanding is important to analysts and public officials interested in creating and evaluating strategies for informed and strategic decision making, for instance, adjusting tax policies. Those networks of players and investments, however, tend to be complex and very dense, which leads to over-plotted visualizations that obfuscate precious information such as the dependencies between productive sectors and regions. In this paper, we propose Hermes, a guidance-enriched Visual Analytics environment (named after the Greek God of Commerce) for the exploration of complex economic networks, to uncover supply chains, regions’ productivity, and sector-to-sector relationships. With practical knowledge regarding guidance, we designed and implemented a visual sub-graph querying approach to extract patterns from such complex investment graphs obtained from real-world data.

Preoperative care is a critical, yet complex, time-sensitive process. Optimization of workflow is challenging for many reasons, including a lack of standard workflow analysis methods. We sought to comprehensively characterize electronic health record–mediated preoperative nursing workflow. We employed a structured methodological framework to investigate and explain variations in the workflow. Video recording software captured 10 preoperative cases at Arizona and Florida regional referral centers. We compared the distribution of work for electronic health record tasks and off-screen tasks through quantitative analysis. Suboptimal patterns and reasons for variation were explored through qualitative analysis. Although both settings used the same electronic health record system, electronic health record tasks and off-screen tasks time distribution and patterns were notably different across two sites. Arizona nurses spent a longer time completing preoperative assessment. Electronic health record tasks occupied a higher proportion of time in Arizona, while off-screen tasks occupied a higher proportion in Florida. The contextual analysis helped to identify the variation associated with the documentation workload, preparation of the patient, and regional differences. These findings should seed hypotheses for future optimization efforts and research supporting standardization and harmonization of workflow across settings, post–electronic health record conversion.

Chronic pancreatitis (CP) is associated with alcohol abuse in 80% of cases. The primary treatment goals in CP are pain reduction and avoidance of pancreatitis-associated complications. CP should be treated in an interdisciplinary approach. A recent randomized clinical trial showed that early surgery compared with an endoscopy-first approach resulted in reduced pain levels. Surgical resections are, therefore, the most efficient treatment of pancreatitis-associated pain as well as other complications and should be performed early in the course of the disease. Since most of the patients pre­sent with chronic inflammation of the pancreatic head, pancreatic head resection is the most common treatment option. Duodenum-preserving pancreatic head resections are the surgical procedure of choice, but pancreaticoduodenectomies (Kausch-Whipple procedures) demonstrate similar outcome with regard to pain control, quality of life, and metabolic parameters. Other surgical procedures, including drainage procedures, pancreatic segmental resections, or left resections, are rarely indicated.

ABSTRACTPrimary care physicians (general practitioners (GPs)) are burdened for various reasons and are particularly affected by stress-related complaints and an increasing prevalence of burnout. Thus, the prevention of physician burnout has become a major interest for health care services. Although many studies have addressed this issue in recent years, little seems to be known about the work strain and burnout rates in GP trainees. Therefore the objective of this article is to review the psychosocial burden and relevant prevention strategies for GPs with a special emphasis on GP trainees. Regardless of the specialty, burnout is more prevalent among medical trainees and so-called ‘early career’ physicians than among the age-matched population. Accordingly, burnout seems to be frequent among GP trainees, although there is some evidence that there are fewer doctors working in general medicine who were already heavily burdened at the time of choosing their career. The sudden assumption of responsibility in patient care as well as the fear of showing imperfection in front of their supervisors, or lack of recognition from senior doctors, the medical team, or patients might be stressors typical to this career stage. GP trainees might also feel burdened by the new level of personal involvement and thus have to develop or increase their individual level of professionality to deal with the patients’ medical and personal problems. In conclusion, interventions to promote physical and mental health of GP trainees are a necessity to ensure passionate GPs in the future and should therefore be integrated into any postgraduate training curriculum in general practice.

The detection of anomalous events in huge amounts of data is sought in many domains. For instance, in the context of financial data, the detection of suspicious events is a prerequisite to identify and prevent attempts to defraud. Hence, various financial fraud detection approaches have started to exploit Visual Analytics techniques. However, there is no study available giving a systematic outline of the different approaches in this field to understand common strategies but also differences. Thus, we present a survey of existing approaches of visual fraud detection in order to classify different tasks and solutions, to identify and to propose further research opportunities. In this work, fraud detection solutions are explored through five main domains: banks, the stock market, telecommunication companies, insurance companies, and internal frauds. The selected domains explored in this survey were chosen for sharing similar time-oriented and multivariate data characteristics. In this survey, we (1) analyze the current state of the art in this field; (2) define a categorization scheme covering different application domains, visualization methods, interaction techniques, and analytical methods which are used in the context of fraud detection; (3) describe and discuss each approach according to the proposed scheme; and (4) identify challenges and future research topics.

Proteases are involved in almost every important cellular activity, from embryonic morphogenesis to apoptosis. To study protease activity in situ, hydrogels provide a synthetic mimic of the extracellular matrix (ECM) and have utility as a platform to study activity, such as those related to cell migration, in three-dimensions. Although three-dimensional visualization of protease activity could prove quite useful to elucidate the proteolytic interaction at the interface between cells and their surrounding environment, there has been no versatile tool to visualize local proteolytic activity in real time. Here, micrometer-sized gels were synthesized by inverse suspension polymerization using thiol–ene photoclick chemistry. The size distribution was selected to avoid cellular uptake and to lower cytotoxicity, while simultaneously allowing the integration of peptide-based FRET sensors of local cell activity. Proteolytic activity of collagenase was detected within an hour via changes in fluorescence of embedded microgels; incubation of microgel sensors with A375 melanoma cells showed upregulated MMP activity in the presence of soluble fibronectins in media. The microgel sensors were readily incorporated into both gelatin and poly(ethylene glycol) (PEG) hydrogels and used to successfully detect spatiotemporal proteolytic activity of A375 melanoma cells. Finally, a tumor model was constructed from a hydrogel microwell array that was used to aggregate A375 melanoma cells, and local variations in proteolytic activity were monitored as a function of distance from the cell aggregate center.

Coastal shelf sediments are hot spots of organic matter mineralization. They receive up to 50% of primary production, which, in higher latitudes, is strongly seasonal. Polar and temperate benthic bacterial communities, however, show a stable composition based on comparative 16S rRNA gene sequencing despite different microbial activity levels. Here, we aimed to resolve this contradiction by identifying seasonal changes at the functional level, in particular with respect to algal polysaccharide degradation genes, by combining metagenomics, metatranscriptomics, and glycan analysis in sandy surface sediments from Isfjorden, Svalbard. Gene expressions of diverse carbohydrate-active enzymes changed between winter and spring. For example, β-1,3-glucosidases (e.g. GH30, GH17, GH16) degrading laminarin, an energy storage molecule of algae, were elevated in spring, while enzymes related to α-glucan degradation were expressed in both seasons with maxima in winter (e.g. GH63, GH13_18, and GH15). Also, the expression of GH23 involved in peptidoglycan degradation was prevalent, which is in line with recycling of bacterial biomass. Sugar extractions from bulk sediments were low in concentrations during winter but higher in spring samples, with glucose constituting the largest fraction of measured monosaccharides (84% ± 14%). In porewater, glycan concentrations were ~18-fold higher than in overlying seawater (1107 ± 484 vs. 62 ± 101 μg C l−1) and were depleted in glucose. Our data indicate that microbial communities in sandy sediments digest and transform labile parts of photosynthesis-derived particulate organic matter and likely release more stable, glucose-depleted residual glycans of unknown structures, quantities, and residence times into the ocean, thus modulating the glycan composition of marine coastal waters.

Since it can be challenging for users to effectively utilize interactive visualizations, guidance is usually provided to assist users in solving tasks. Guidance is mentioned as an effective mean to overcome stall situations occurring during the analysis. However, the effectiveness of a peculiar guidance solution usually varies for different analysis scenarios. The same guidance may have different effects on users with (1) different levels of expertise. The choice of the appropriate (2) degree of guidance and the type of (3) task under consideration also affect the positive or negative outcome of providing guidance. Considering these three factors, we conducted a user study to investigate the effectiveness of variable degrees of guidance with respect to the user’s previous knowledge in different analysis scenarios. Our results shed light on the appropriateness of certain degrees of guidance in relation to different tasks, and the overall influence of guidance on the analysis outcome in terms of user’s mental state and analysis performance.

Plasmonic molecules are building blocks of metallic nanostructures that give rise to intriguing optical phenomena with similarities to those seen in molecular systems. The ability to design plasmonic hybrid structures and molecules with nanometric resolution would enable applications in optical metamaterials and sensing that presently cannot be demonstrated, because of a lack of suitable fabrication methods allowing the structural control of the plasmonic atoms on a large scale. Here we demonstrate a wafer-scale “lithography-free” parallel fabrication scheme to realize nanogap plasmonic meta-molecules with precise control over their size, shape, material, and orientation. We demonstrate how we can tune the corresponding coupled resonances through the entire visible spectrum. Our fabrication method, based on glancing angle physical vapor deposition with gradient shadowing, permits critical parameters to be varied across the wafer and thus is ideally suited to screen potential structures. We obtain billions of aligned dimer structures with controlled variation of the spectral properties across the wafer. We spectroscopically map the plasmonic resonances of gold dimer structures and show that they not only are in good agreement with numerically modeled spectra, but also remain functional, at least for a year, in ambient conditions.

Ceramic fibers are just as glass and basalt member of the group of inorganic fibers. Like most types of inorganic fibers ceramic fibers have a high tear resistance but a limited flexibility. [1] Ceramic fibers are characterized by their extraordinary high temperature and chemical resistance. These properties make them interesting for different high technical applications, as they occur in aerospace, chemical-and energy technology. In this field, they are applied especially as a reinforcement component in composite materials. Not only the partially high material price, but although the typical brittleness of ceramic fibers bring huge problems during the textile production chain, which limits the availability of complex textile preforms in the market. Often, a radical revision of the machine and processing concept is necessary to enable an economical production process. The Application Center for Textile Fiber Ceramics TFK at Fraunhofer-Center for High Temperature Materials and Design HTL develops and modifies textile production processes to make them suitable for the special requirements of ceramic fibers. One and multilayer woven fabrics, braids and tape structures for the winding process have already been successfully implemented. A further development complex is the intensive investigation of three-dimensional textile reinforcement structures. Regarding the high material costs, these research activities are very important. If the textile reinforcement is placed only where needed, the amount of used fiber material can be reduced significantly.

Medication reconciliation (MedRec) is a mission-critical process which can serve to reduce adverse drug events (ADEs) in surgical settings. However, providing quality care is limited by current health information technology (IT), which is often inefficient and unintuitive due to poor usability, resulting in high cognitive burden. We have been characterizing EHR mediated workflow in the Mayo Clinic enterprise prior to a system-wide electronic health records (EHR) conversion in order to harmonize workflows. We compared and evaluated MedRec processes in pre-operative nursing assessments across two different EHRs in place in different locales at baseline. The interfaces differed both in their modes of interaction and cognitive support. Analyses surfaced interface elements that were unintuitive and inefficient, creating unnecessary complexities in clinicians’ interactive behavior. Keystroke level models (KLM), a modeling tool for predicting task completion time, showed that to access medication lists required a different series of operations across the two systems. Different designs can differentially mediate task performance, which can aid in the mitigation of errors for complex cognitive tasks. Identification of barriers in EHR-mediated workflow and barriers to interface usability could lead to system redesigns that minimize cognitive load while improving patient safety and efficiency.

During data preprocessing, analysts spend a significant part of their time and effort profiling the quality of the data along with cleansing and transforming the data for further analysis. While quality metrics—ranging from general to domain-specific measures—support assessment of the quality of a dataset, there are hardly any approaches to visually support the analyst in customizing and applying such metrics. Yet, visual approaches could facilitate users’ involvement in data quality assessment. We present MetricDoc, an interactive environment for assessing data quality that provides customizable, reusable quality metrics in combination with immediate visual feedback. Moreover, we provide an overview visualization of these quality metrics along with error visualizations that facilitate interactive navigation of the data to determine the causes of quality issues present in the data. In this article, we describe the architecture, design, and evaluation of MetricDoc, which underwent several design cycles, including heuristic evaluation and expert reviews as well as a focus group with data quality, human-computer interaction, and visual analytics experts.

A promising strategy to counteract the progressing plastic pollution of the environment can involve the replacement of persistent plastics with biodegradable materials. Biodegradable polymers are enzymatically degradable by various hydrolytic enzymes. However, these materials can reach the environment in the same way as conventional plastics. Therefore, they are accessible to terrestrial, freshwater, and marine biota. Once ingested by marine organisms, highly active enzymes in their digestive tracts may break down biodegradable compounds. We incubated microparticles of five different biodegradable plastics, based on polylactictic acid (PLA), polybutylene succinate (PBS), polybutylene adipate terephthalate (PBAT) and polyhydroxybutyrate-co-valerate (PHBV), in-vitrowith the gastric fluid of the edible crab Cancer pagurusand evaluated the hydrolysis rates by pH Stat titration. A plastic blend of PLA with PBAT showed the highest hydrolysis rate. The enzymes in the gastric fluid of crabs were separated by anion exchange chromatography. Fractions with carboxylesterase activity were identified using fluorescent methylumbelliferyl (MUF)-derivatives. Pooled fractions with high carboxylesterase activity also hydrolyzed a PLA/PBAT plastic blend. Carboxylesterases showed molecular masses of 40–45 kDa as determined by native gel electrophoresis (SDS-PAGE). Our study demonstrated that digestive carboxylesterases in the gastric fluid of C. pagurusexhibit a high potential for hydrolyzing certain biodegradable plastics. Since esterases are common in the digestive tract of organisms, it seems likely that other invertebrates possess the ability to hydrolyze biodegradable plastics.