Compliant Data Research Articles

Integrating Internet of Things, Provenance, and Blockchain to Enhance Trust in Last Mile Food Deliveries

In this article, we discuss our experience of realising a prototype IoT-based food safety monitoring solution which integrates inexpensive off-the-shelf open source IoT technology for monitoring food deliveries, semantic services for managing and reasoning about food safety provenance records, and private blockchain networks for persistent and secure storage of semantic provenance graphs. We describe how observation of real-world contexts was used to develop a prototype device, and the results of field trials deploying these prototypes as part of the food delivery process. Results indicate that continuous, context sensitive, trustworthy temperature measurement could provide benefits to multiple stakeholders across the delivery pathway. However, close attention has to be paid to the technology used - as cheap multi-functional IoT devices may produce low quality sensor observations which adversely affect the utility of the overall solution. Our experience also suggests that future food safety management systems may need to include machine-processable guidelines to support analysis of raw sensor data for food safety compliance.

Big data analytics in healthcare A systematic literature review and roadmap for practical implementation

The advent of healthcare information management systems (HIMSs) continues to produce large volumes of healthcare data for patient care and compliance and regulatory requirements at a global scale. Analysis of this big data allows for boundless potential outcomes for discovering knowledge. Big data analytics (BDA) in healthcare can, for instance, help determine causes of diseases, generate effective diagnoses, enhance QoS guarantees by increasing efficiency of the healthcare delivery and effectiveness and viability of treatments, generate accurate predictions of readmissions, enhance clinical care, and pinpoint opportunities for cost savings. However, BDA implementations in any domain are generally complicated and resource-intensive with a high failure rate and no roadmap or success strategies to guide the practitioners. In this paper, we present a comprehensive roadmap to derive insights from BDA in the healthcare (patient care) domain, based on the results of a systematic literature review. We initially determine big data characteristics for healthcare and then review BDA applications to healthcare in academic research focusing particularly on NoSQL databases. We also identify the limitations and challenges of these applications and justify the potential of NoSQL databases to address these challenges and further enhance BDA healthcare research. We then propose and describe a state-of-the-art BDA architecture called Med-BDA for healthcare domain which solves all current BDA challenges and is based on the latest zeta big data paradigm. We also present success strategies to ensure the working of Med-BDA along with outlining the major benefits of BDA applications to healthcare. Finally, we compare our work with other related literature reviews across twelve hallmark features to justify the novelty and importance of our work. The aforementioned contributions of our work are collectively unique and clearly present a roadmap for clinical administrators, practitioners and professionals to successfully implement BDA initiatives in their organizations.

Development and Evaluation of an Accelerometer-Based Protocol for Measuring Physical Activity Levels in Cancer Survivors: Development and Usability Study.

BackgroundThe collection of self-reported physical activity using validated questionnaires has known bias and measurement error.ObjectiveAccelerometry, an objective measure of daily activity, increases the rigor and accuracy of physical activity measurements. Here, we describe the methodology and related protocols for accelerometry data collection and quality assurance using the Actigraph GT9X accelerometer data collection in a convenience sample of ovarian cancer survivors enrolled in GOG/NRG 0225, a 24-month randomized controlled trial of diet and physical activity intervention versus attention control.MethodsFrom July 2015 to December 2019, accelerometers were mailed on 1337 separate occasions to 580 study participants to wear at 4 time points (baseline, 6, 12, and 24 months) for 7 consecutive days. Study staff contacted participants via telephone to confirm their availability to wear the accelerometers and reviewed instructions and procedures regarding the return of the accelerometers and assisted with any technology concerns.ResultsWe evaluated factors associated with wear compliance, including activity tracking, use of a mobile app, and demographic characteristics with chi-square tests and logistic regression. Compliant data, defined as ≥4 consecutive days with ≥10 hours daily wear time, exceeded 90% at all study time points. Activity tracking, but no other characteristics, was significantly associated with compliant data at all time points (P<.001). This implementation of data collection through accelerometry provided highly compliant and usable activity data in women who recently completed treatment for ovarian cancer.ConclusionsThe high compliance and data quality associated with this protocol suggest that it could be disseminated to support researchers who seek to collect robust objective activity data in cancer survivors residing in a wide geographic area.

Galvanically Isolated DC-DC Converter Using a Single Isolation Transformer for Multi-Channel Communication

This paper presents a galvanically isolated dc-dc converter with multi-channel communication exploiting a single isolation transformer. The proposed architecture makes compliant both regulated output power and data transmission on the same isolation physical link by using ASK modulation of the power signal and data/control signal multiplexing. The dc-dc converter consists of two integrated chips fabricated in 0.18- $\mu \text{m}$ BCD and 0.13- $\mu \text{m}$ CMOS technologies, respectively, along with a thick polyimide transformer chip able to comply with reinforced isolation requirements. The dc-dc converter delivers an isolated output power up to 50 mW at 3.3-V output voltage, while providing three data channels with a data rate up to 10 Mb/s.

An ethico-legal framework for social data science

This paper presents a framework for research infrastructures enabling ethically sensitive and legally compliant data science in Europe. Our goal is to describe how to design and implement an open platform for big data social science, including, in particular, personal data. To this end, we discuss a number of infrastructural, organizational and methodological principles to be developed for a concrete implementation. These include not only systematically tools and methodologies that effectively enable both the empirical evaluation of the privacy risk and data transformations by using privacy-preserving approaches, but also the development of training materials (a massive open online course) and organizational instruments based on legal and ethical principles. This paper provides, by way of example, the implementation that was adopted within the context of the SoBigData Research Infrastructure.

EuroGeoSurveys: from a non-profit association to a geological service for Europe

Abstract EuroGeoSurveys (EGS) is a not-for-profit organization representing 37 national geological surveys and some regional surveys; it has an overall workforce of several thousand experts. EGS members provide official, interoperable, homogeneous, reliable, INSPIRE (infrastructure for spatial information in the European Community)-compliant public data on the subsurface for the benefit of society in terms of circular economy development, sustainable management of the subsurface resources, understanding and combatting climate change and the development of infrastructures and mitigation of geology-related natural hazards. The EGS is committed to establishing a geological service for Europe based on three pillars: (1) joint research with impact on EU policy level, which is being implemented through the GeoERA programme (Establishing the European Geological Surveys Research Area to deliver a Geological Service for Europe); (2) harmonizing and sharing pan-European geological data, through the European Geological Data Infrastructure (EGDI); and (3) sharing knowledge, capacities and infrastructure, through the pan-African support to the EGS-Organization of African Geological Surveys (OAGS) Partnership (PanAfGeo project). The EGS will continue to support the EU in its transition to a low-carbon, climate-neutral, resource-efficient, socially and environmentally resilient economy, in full compliance with the United Nations 2030 Agenda and the 17 Sustainable Development Goals.

A DIN Spec 91345 RAMI 4.0 Compliant Data Pipelining Model: An Approach to Support Data Understanding and Data Acquisition in Smart Manufacturing Environments

Today, data scientists in the manufacturing domain are confronted with various communication standards, protocols and technologies to save and transfer various kinds of data. These circumstances makes it hard to understand, find, access and extract data needed for use case depended applications. One solution could be a data pipelining approach enforced by a semantic model which describes smart manufacturing assets itself and the access to their data along their life-cycle. Many research contributions in smart manufacturing already came out with with reference architectures like the RAMI 4.0 or standards for meta data description or asset classification. Our research builds upon these outcomes and introduces a semantic model based DIN Spec 91345 (RAMI 4.0) compliant data pipelining approach with the smart manufacturing domain as exemplary use case. This paper has a focus on the developed semantic model used to enable an easy data exploration, finding, access and extraction of data, compatible with various used communication standards, protocols and technologies used to save and transfer data.

The Standardization of the CDISC Compliant Data Creation Process in CRO

The Standardization of the CDISC Compliant Data Creation Process in CRO

Quantitative implications of the updated EARL 2019 PET\u2013CT performance standards

PurposeRecently, updated EARL specifications (EARL2) have been developed and announced. This study aims at investigating the impact of the EARL2 specifications on the quantitative reads of clinical PET–CT studies and testing a method to enable the use of the EARL2 standards whilst still generating quantitative reads compliant with current EARL standards (EARL1).MethodsThirteen non-small cell lung cancer (NSCLC) and seventeen lymphoma PET–CT studies were used to derive four image datasets—the first dataset complying with EARL1 specifications and the second reconstructed using parameters as described in EARL2. For the third (EARL2F6) and fourth (EARL2F7) dataset in EARL2, respectively, 6 mm and 7 mm Gaussian post-filtering was applied. We compared the results of quantitative metrics (MATV, SUVmax, SUVpeak, SUVmean, TLG, and tumor-to-liver and tumor-to-blood pool ratios) obtained with these 4 datasets in 55 suspected malignant lesions using three commonly used segmentation/volume of interest (VOI) methods (MAX41, A50P, SUV4).ResultsWe found that with EARL2 MAX41 VOI method, MATV decreases by 22%, TLG remains unchanged and SUV values increase by 23–30% depending on the specific metric used. The EARL2F7 dataset produced quantitative metrics best aligning with EARL1, with no significant differences between most of the datasets (p>0.05). Different VOI methods performed similarly with regard to SUV metrics but differences in MATV as well as TLG were observed. No significant difference between NSCLC and lymphoma cancer types was observed.ConclusionsApplication of EARL2 standards can result in higher SUVs, reduced MATV and slightly changed TLG values relative to EARL1. Applying a Gaussian filter to PET images reconstructed using EARL2 parameters successfully yielded EARL1 compliant data.

Development of a Prospective Real-World Data Clinical Registry of Children and Adolescents With Migraine.

To develop a multicenter, multistakeholder, prospective clinical registry of children and adolescents with migraine to support the collection of real-world data of sufficient quality to support regulatory submissions and provide site-based infrastructure support for future clinical trials. As new migraine treatments come to market, pediatric efficacy and safety trials of these agents are needed. A clinical registry is an ideal regulatory strategy to provide both real-world data and site infrastructure to execute these trials. Multicenter, multistakeholder, prospective real-world data clinical registry of children and adolescents, 4-17years of age, diagnosed with migraine with or without aura. Participants will be followed for up to 12months at 3-month intervals, with interval recording of clinical data at study sites and self-reported data via mobile health application, as well as biobanking. We developed electronic case report forms that incorporated routinely collected clinical data with National Institute of Neurological Disorders and Stroke Headache Common Data Elements (Version 2.0). All data are captured in a 21 CFR Part 11 - compliant electronic data capture system - augmented by a real-time, web-based, and customizable data visualization platform. We engaged vendors to provide ancillary biobanking, patient data entry, and data visualization services. We used an iterative and highly collaborative multistakeholder approach to design and implement a streamlined registry protocol with input from all participating US sites. At each design and implementation step, we received input from therapeutic area experts, the US Food and Drug Administration (FDA), the National Institutes of Health, patient and parent advocates, health technology partners, drug developers, and site-based clinical investigators. The registry is governed by a multistakeholder steering committee with representation from sites, industry partners, patient advocates, and a member from the FDA (non-voting with respect to steering committee matters). The multistakeholder and site-driven approach to registry design and execution was highly efficient and resulted in the first patient enrolled within 6months of concept development. By ensuring regulatory compliant implementation of the registry, we created both a source of real-world data and a multisite platform for the conduct of future clinical trials that can be submitted to regulatory authorities to support inclusion of pediatric data in approved drug labeling. A highly collaborative approach with broad stakeholder engagement at all stages of the registry development was a key to our operational success.

Teaching a neural network modeling socio-economic development of the region

The article is devoted to the formation of an array of data for the construction of an artificial neural network, designed to search for relationships between social and economic parameters of the development of regions of the Russian Federation. The relevance of research in this area is confirmed both by a large number of studies in the field of regional comparativistics and by the limited methods used in this kind of research, often limited to descriptive methods and basic techniques of parametric statistics. Under these conditions, the expansion of the mathematical apparatus and the more active introduction of information technologies (including in the area of Big Data analysis and the construction of predictive models based on artificial neural networks) can be viable. At the same time, however, it should be noted that the resources of an individual research team may be (and most likely will be) insufficient to create their own software solution for the implementation of machine learning algorithms from scratch. The use of third-party cloud-based software platforms (primarily IBM and Google infrastructures) allows to bypass the problem of the research team’s lack of expensive material and technical base, however they impose a number of limitations dictated by the requirements of the existing machine learning algorithms and the specific architecture provided platforms This puts the research team in front of the need to prepare the accumulated data set for processing: reducing the dimension, checking the data for compliance with the platform requirements and eliminating potential problem areas: “data leaks”, “learning distortions” and others. The paper was reported to the section “Sociology of Digital Society: Structures, Processes, Governance” of the International Conference Session “Public Administration and Development of Russia: National Goals and Institutions”.

A multidisciplinary-tailored digital solution to data capture in early phase clinical trials.

2 Background: Data capture in early phase cancer clinical trials (EPCCT) is usually via paper records with manual transcription to the sponsor’s case report form. Capturing real time trial data directly to computer (eSource) may reduce errors and increase completeness and timeliness of data entry. A simulated system pilot took place between Oct 2018 and Jan 2019 at an EPCCT facility to appraise Foundry Health’s eSource system “ClinSpark”. Aims were to assess consistency and effectiveness of creating electronic templates for source data capture and live data collection compliance. Methods: A multidisciplinary focus group (MFG) (2 research nurses, 1 doctor, 3 data managers) was created to collaborate with Foundry Health staff. Specialised features of the eSource system were adapted to handle the complex needs of EPCCT. The pilot incorporated a 5 day boot camp for familiarisation to the digital platform; a conference room test using simulated patient data; construction of a trial template including contingency planning; and a clinic floor test with live simulated patient data collection using digital tablets. The MFG agreed on a 52 item user acceptance test listing ideal features for a data collection tool, with items classified as high, medium or low priority. Results: During the 3 month pilot, templates for 2 EPCCT were planned and created by the MFG. Using eSource, 43 items (83%) of the acceptance test were passed compared with 27 items (52%) for the current (paper) system. For the 30 high-priority items, eSource passed 30 (100%) compared with 22 for the paper system (73%). The paper system was not superior to eSource for any items assessed. Time saving and potential error reduction were noted as additional benefits. Conclusions: This process demonstrates that a multidisciplinary approach can be used to successfully integrate a customised eSource system working with previously untrained staff. Improved performance across pre-specified domains and potential additional benefits were noted. As FDA encourages use of digital solutions in clinical trials, using eSource provides a potential solution for compliant and efficient data capture from protocol assessments at investigator sites and rapid data transfer to sponsors.

An Analysis of the Consequences of the General Data Protection Regulation on Social Network Research

This article examines the principles outlined in the General Data Protection Regulation in the context of social network data. We provide both a practical guide to General Data Protection Regulation--compliant social network data processing, covering aspects such as data collection, consent, anonymization, and data analysis, and a broader discussion of the problems emerging when the general principles on which the regulation is based are instantiated for this research area.

STEP-NC compliant data model for freeform surface manufacturing based on T-spline

ABSTRACTSTEP-NC is a new interface standard between CAD/CAM and CNC. Most of the studies focus on the 2.5D features machining using STEP-NC. However, freeform surface is widely used in the field of aerospace, automotive and dies/moulds. Beyond that, due to the drawbacks of NURBS that is the only representation for freeform surface in STEP, the research of STEP-NC for freeform surface is rare. In the paper, STEP-NC compliant data model is proposed for freeform surface manufacturing based on T-spline. We contribute three advantages: (1) a modified STEP data model is constructed that can represent and exchange arbitrary T-spline surface for STEP-NC-based manufacturing, (2) a general framework is proposed for evaluation of arbitrary T-spline surface that can be used for STEP-NC-based freeform surface evaluation, (3) a concept of Manufacturing-Suitable Region is proposed, which can divide T-spline surface into a set of regions for STEP-NC-based manufacturing. Some testing models show that the modified STEP data model is more efficient than the NURBS-based STEP data model for storage, and the feasibility to process arbitrary T-spline surface is verified using the general framework. In addition to this, a case study is researched to demonstrate the advantage of Manufacturing-Suitable Region for STEP-NC-based freeform surface manufacturing.

Machine-Learning-Generated Synthetic Data for Regulation Best Interest Compliance

Big Data hype has not missed investment management, the reality is that price data from U.S. financial markets are not really Big Data. Price data is Small Data. The fact that sellers and advisors in financial markets use Small Data to generate and test investment strategies creates two major problems. First, the economic mechanisms that generate prices (and therefore returns) may change through time, so that historical data from an earlier time may tell us little or nothing about future prices and returns. Second, even if data-generating-mechanisms are somewhat stable through time, inferences about the profitability of investment strategies may be sensitive to a handful of outliers in the data that get picked up again and again in different strategies mined from the same Small Data set. In this article, we present an answer to the financial Small Data problem: using machine-learning methods to generate “synthetic” financial data. The essential part of our approach to developing synthetic data is the use of machine learning methods to generate data that might have been generated by financial markets but was not. Synthetic price and return data have numerous uses, including testing new investment strategies and helping investors plan for retirement and other personal investment goals with more realistic future return scenarios. In this article, we focus on a particularly important use of synthetic data: meeting legal and regulatory requirements such as best interest and fiduciary requirements.

Survey results from a patient’s perspective during a phase I/research clinic visit.

216 Background: The University of Miami’s Phase I Program is South Florida’s only academic Phase I center dedicated to drug development for cancer patients. On April 2018, Sylvester Comprehensive Cancer Center opened the Phase I/Experimental Therapeutics (ET) Clinic dedicated to research patients enrolled in Phase I clinical trials and established a multi-disciplinary care team. As Phase I clinical trials are evolving, it is necessary to improve on the patient experience as it pertains to research care. Methods: Using a Likert-type scale, dual language survey (English and Spanish), patients were asked 11 questions about the Phase I/ET Clinic visit ranging from the beginning of their Phase I clinical trial enrollment all the way to provider care during the visit. The survey was conducted from January 2019-April 2019 through a secure, HIPAA compliant online Research Electronic Data Capture software (REDCap). Results: Of the 30 patients that were approached, 26 responded (Renal 26.9%, Myelofibrosis 19.2%, Bladder 15.4%, Head and Neck 7.7%, Acute Myeloid Leukemia 15.4%, Myelodysplastic Syndrome 7.7%, and Other 7.7%). Our results found that 100% of research patients would recommend a Phase I clinical trial to another participant. In addition, 96.2% of patients felt there was continuation of their care through their clinical trial process. As a note, 19.2% of patients positively recognized a Phase I provider including Nurse Practitioners (NPs) and physicians. Of these 19.2% of patients, 50% praised an NP in the free text area of the survey. Lastly, 42.3% of patients positively mentioned the team support received in the Phase I Clinic in the free text area of the survey. Conclusions: Our conclusion based on survey results show that having committed providers and clinic space for research patients on a Phase I clinical trial provided an overall positive and personalized experience. Additionally, the survey reflects on how patients are more inclined to refer to a clinical trial due to a good experience. Future directions should look at developing a similar clinic for all phases of research considering the exceptional patient feedback emphasizing on provider care.

ECMM CandiReg-A ready to use platform for outbreaks and epidemiological studies.

Recent outbreaks of Candida auris further exemplify that invasive Candida infections are a substantial threat to patients and healthcare systems. Even short treatment delays are associated with higher mortality rates. Epidemiological shifts towards more resistant Candida spp. require careful surveillance. Triggered by the emergence of C auris and by increasing antifungal resistance rates the European Confederation of Medical Mycology developed an international Candida Registry (FungiScope™ CandiReg) to allow contemporary multinational surveillance. CandiReg serves as platform for international cooperation to enhance research regarding invasive Candida infections. CandiReg uses the General Data Protection Regulation compliant data platform ClinicalSurveys.net that holds the electronic case report forms (eCRF). Data entry is supported via an interactive macro created by the software that can be accessed via any Internet browser. CandiReg provides an eCRF for invasive Candida infections that can be used for a variety of studies from cohort studies on attributable mortality to evaluations of guideline adherence, offering to the investigators of the 28 ECMM member countries the opportunity to document their cases of invasive Candida infection. CandiReg allows the monitoring of epidemiology of invasive Candida infections, including monitoring of multinational outbreaks. Here, we describe the structure and management of the CandiReg platform. CandiReg supports the collection of clinical information and isolates to improve the knowledge on epidemiology and eventually to improve management of invasive Candida infections. CandiReg promotes international collaboration, improving the availability and quality of evidence on invasive Candida infection and contributes to improved patient management.

A Climate Event Portal for Citizen Science

Abstract. Climate change as both a local and global phenomenon causes numerous societal and financial implications every year. Its perception among citizens, however, is highly controversial. For example, in August 2018, the negative effects of climate change were very prominent in the state of Bavaria, when farmers lost large amounts of feed crop due to a drought in the same year. While governments can initiate policies in response to this, implementing these policies in a democracy requires the approval of the public, which is mainly composed of non-experts. In order to mobilize citizens for their own benefit, thus promote citizen science, the Bavarian Climate Research Network (BAYKLIF) was launched. One of the objectives deals with the creation of a portal and a number of mobile services for citizens, domain specialists and decision makers. This paper is dedicated to a project within this network, which is relevant to cartography. It aims to create a Climate Event Portal accessible by normal citizens who may also contribute with their own data to the portal or validate existing climate events based on their knowledge and supported by visualization tools. The anticipated portal consists of five core concepts. First, the OpenEventCollector, which will search for climate events by categories and collect event data in an interactive and intuitive way. Second, an OpenEventCrawler, that should automatically search open digital archives and social media, thus keep generating data about climate events. Third, an OpenEventEditor, to visualize the data for the user, allow them to interact with the data, and contribute their own (Fig. 1). This, however, must all be possible within the technical limitations of smartphones or web-applications. Fourth, an OpenEventDetector, which should automatically detect climate events from datasets, via machine learning. Fifth, an OpenEventAnalysor that explains the context of climate events, the visualizations and the process of generating this data. The purpose of this Climate Event Portal is to involve the user, thus ensuring that all data permits the re-use is necessary. Its development reveals three interrelated challenges: (1) examining user-generated data for relevance and legal compliance, (2) improving representative user feedback, which requires the participation of diverse user groups and (3) sustaining users’ interest and attention, which requires innovative incentives for users’ contributions (e.g. gamification-oriented visualization, visual storytelling etc.). This work will contribute to the knowledge on how to adapt presentations based on citizens’ interaction, as well as create an event portal, where citizen-contributed data and expert-generated data are coalesced. The authors aim to share their experiences and findings with researchers engaged in similar projects of citizen science.

SEINet: A Centralized Specimen Resource Managed by a Distributed Network of Researchers

The SEINet Portal Network has a complex social and development history spanning nearly two decades. Initially established as a basic online search engine for a select handful of biological collections curated within the southwestern United States, SEINet has since matured into a biodiversity data network incorporating more than 330 institutions and 1,900 individual data contributors. Participating institutions manage and publish over 14 million specimen records, 215,000 observations, and 8 million images. Approximately 70% of the collections make use of the data portal as their primary "live" specimen management platform. The SEINet interface now supports 13 regional data portals distributed across the United States and northern Mexico (http://symbiota.org/docs/seinet/). Through many collaborative efforts, it has matured into a tool for biodiversity data exploration, which includes species inventories, interactive identification keys, specimen and field images, taxonomic information, species distribution maps, and taxonomic descriptions. SEINet’s initial developmental goals were to construct a read-only interface that integrated specimen records harvested from a handful of distributed natural history databases. Intermittent network conductivity and inconsistent data exchange protocols frequently restricted data persistence. National funding opportunities supported a complete redesign towards the development of a centralized data cache model with periodic "snapshot" updates from original data sources. A service-based management infrastructure was integrated into the interface to mobilize small- to medium-sized collections (&amp;lt;1 million specimen records) that commonly lack consistent infrastructure and technical expertise to maintain a standard compliant specimen database. These developments were the precursors to the Symbiota software project (Gries et al. 2014). Through further development of Symbiota, SEINet transformed into a robust specimen management system specifically geared toward specimen digitization with features including data entry from label images, harvesting data from specimen duplicates, batch georeferencing, data validation and cleaning, generating progress reports, and additional tools to improve the efficiency of the digitization process. The central developmental paradigm focused on data mobilization through the production of: a versatile import module capable of ingesting a diverse range of data structures, a robust toolkit to assist in digitizing and managing specimen data and images, and a Darwin Core Archive (DwC-A) compliant data publishing and export toolkit to facilitate data distribution to global aggregators such as Global Biodiversity Information Facility (GBIF) and iDigBio. a versatile import module capable of ingesting a diverse range of data structures, a robust toolkit to assist in digitizing and managing specimen data and images, and a Darwin Core Archive (DwC-A) compliant data publishing and export toolkit to facilitate data distribution to global aggregators such as Global Biodiversity Information Facility (GBIF) and iDigBio. User interfaces consist of a decentralized network of regional data portals, all connecting to a centralized shared data source. Each of the 13 data portals are configured to present a regional perspective specifically tailored to represent the needs of the local research community. This infrastructure has supported the formation of regional consortia, who provide network support to aid local institutions in digitizing and publishing their collections within the network. The community-based infrastructure creates a sense of ownership – perhaps even good-natured competition – by the data providers and provides extra incentive to improve data quality and expand the network. Certain areas of development remain challenging in spite of the project's overall success. For instance, data managers continuously struggle to maintain a current local taxonomic thesaurus used for name validation, data cleaning, and to resolve taxonomic discrepancies commonly encountered when integrating collection datasets. We will discuss the successes and challenges associated with the long-term sustainability model and explore potential future paths for SEINet that support the long-term goal of maintaining a data provider that is in full compliance with the FAIR use principles of making the datasets findable, accessible, interoperable, and reusable (Wilkinson et al. 2016).

Pharmacology beyond the patient – The environmental risks of human drugs

BackgroundThe presence of pharmaceuticals in the environment is a growing global concern and although environmental risk assessment is required for approval of new drugs in Europe and the USA, the adequacy of the current triggers and the effects-based assessments has been questioned. ObjectiveTo provide a comprehensive analysis of all regulatory compliant aquatic ecotoxicity data and evaluate the current triggers and effects-based environmental assessments to facilitate the development of more efficient approaches for pharmaceuticals toxicity testing. MethodsPublicly-available regulatory compliant ecotoxicity data for drugs targeting human proteins was compiled together with pharmacological information including drug targets, Cmax and lipophilicity. Possible links between these factors and the ecotoxicity data for effects on, growth, mortality and/or reproduction, were evaluated. The environmental risks were then assessed based on a combined analysis of drug toxicity and predicted environmental concentrations based on European patient consumption data. ResultsFor most (88%) of the of 975 approved small molecule drugs targeting human proteins a complete set of regulatory compliant ecotoxicity data in the public domain was lacking, highlighting the need for both intelligent approaches to prioritize legacy human drugs for a tailored environmental risk assessment and a transparent database that captures environmental data. We show that presence/absence of drug-target orthologues are predictive of susceptible species for the more potent drugs. Drugs that target the endocrine system represent the highest potency and greatest risk. However, for most drugs (>80%) with a full set of ecotoxicity data, risk quotients assuming worst-case exposure assessments were below one in all European countries indicating low environmental risks for the endpoints assessed. ConclusionWe believe that the presented analysis can guide improvements to current testing procedures, and provide valuable approaches for prioritising legacy drugs (i.e. those registered before 2006) for further ecotoxicity testing. For drugs where effects of possible concern (e.g. behaviour) are not captured in regulatory tests, additional mechanistic testing may be required to provide the highest confidence for avoiding environmental impacts.