Collaborative Data Management Research Articles

An Update on the Six Recommendations from the 2012 IADMS Standard Measures Initiative: Assessing and Reporting Dancer Capacities, Risk Factors, and Injuries.

In 2012, the Standard Measures Consensus Initiative (SMCI) of the International Association for Dance Medicine and Science (IADMS) presented 6 recommendations regarding dance injury surveillance, definitions of injury and exposure, dance-specific screening, risk reduction strategies, and collaborative data management. The aim was to standardize risk factor measurement and injury reporting by researchers in dance medicine and science. Since then, numerous reports on the recording and reporting of injury data in sport and performing arts have been published. IADMS commissioned SMCI to update the 2012 recommendations, a process that involved 3 stages: (1) current field experts were invited to join SMCI, (2) SMCI members reviewed recent and relevant sport and performing arts literature, then drafted, discussed, and revised section updates, (3) IADMS invited individuals representing diverse backgrounds in the IADMS community to critically review drafted updates. The final update serves as a bridge from the 6 recommendations in the 2012 report to the current state of evidence. We continue to encourage use of dance injury surveillance systems and support that surveillance protocols be fit-for-purpose, and that failure to use clear and consistent injury definitions perpetuates a lack of rigor in dance injury research. Based on new evidence, we recommend that some aspects of injury surveillance be self-reported, that the choice of dance exposure measures be dependent on the research question, contextual factors, and type of injury/health problem(s) of interest, and that studies using dance-specific screening articulate specific objectives, validity, and reliability of each protocol. Future studies should focus on the development, implementation, and evaluation of strategies to minimize injury risk to improve consistency and rigor in data collection and research reporting on the health and wellness of dancer populations, thus facilitating a future dance injury consensus statement similar to recent statements published for sports and circus arts.

Blockchain-based cloud-edge collaborative data management for human-robot collaboration digital twin system

Human-robot collaboration demonstrates broad application prospects in product customization. Digital twin represents an advanced real-virtual interaction technology that plays an essential role in enhancing perception and interaction for human-robot collaboration. A digital twin-based human-robot collaboration system has been proposed to devise collaborative strategies, simulate collaborative processes, and ensure human safety. However, there exist research gaps in implementing human-robot collaboration digital twin systems. A significant challenge lies in constructing data models for describing data types and content in human-robot collaboration digital twin systems. Additionally, addressing data management aspects, including data sharing and storage, is crucial for the effective operation of human-robot collaboration digital twin systems. To bridge existing deficiencies, a novel approach is introduced for managing data in human-robot collaboration digital twin systems through a blockchain-based cloud-edge collaborative method. Initially, a conceptualization of the human-robot collaboration digital twin system alongside a cloud-edge data management framework is introduced. Subsequently, a data model is delineated to outline data categories and contents of human-robot collaboration digital twin systems. Following this, an exploration is conducted on methodologies for data sharing and storage utilizing blockchain and cloud technologies. Ultimately, the efficacy of the proposed approaches is validated through a case study.

Listening to patients, for the patients: The COVAD Study-Vision, organizational structure, and challenges.

The pandemic presented unique challenges for individuals with autoimmune and rheumatic diseases (AIRDs) due to their underlying condition, the effects of immunosuppressive treatments, and increased vaccine hesitancy. The COVID-19 vaccination in autoimmune diseases (COVAD) study, a series of ongoing, patient self-reported surveys were conceived with the vision of being a unique tool to gather patient perspectives on AIRDs. It involved a multinational, multicenter collaborative effort amidst a global lockdown. Leveraging social media as a research tool, COVAD collected data using validated patient-reported outcomes (PROs). The study, comprising a core team, steering committee, and global collaborators, facilitated data collection and analysis. A pilot-tested, validated survey, featuring questions regarding COVID-19 infection, vaccination and outcomes, patient demographics, and PROs was circulated to patients with AIRDs and healthy controls (HCs). We present the challenges encountered during this international collaborative project, including coordination, data management, funding constraints, language barriers, and authorship concerns, while highlighting the measures taken to address them. Collaborative virtual models offer a dynamic new frontier in medical research and are vital to studying rare diseases. The COVAD study demonstrates the potential of online platforms for conducting large-scale, patient-focused research and underscores the importance of integrating patient perspective into clinical care. Care of patients is our central motivation, and it is essential to recognize their voices as equal stakeholders and valued partners in the study of the conditions that affect them.

Blockchain in the agrifood sector: From storytelling to traceability fact-checking up to new economic models

This paper aims to demystify a lot of misconceptions still widely circulating today about the alleged properties of blockchain and then illustrate the real opportunities that this technology offers for "food system" and how it must be correctly implemented for it to be truly useful, for producers and consumers, particularly in the agrifood sector. The concepts of blockchain opportunities and incompleteness of agri-food chain projects based on blockchain technology are then explained, setting out the minimum and necessary characteristics required to make the use of this technology useful and effective (Minimum Viable Ecosystem). The process governance levels for the development and maintenance of a blockchain traceability project are then illustrated, focusing on the role and responsibility of each player in the supply chain.Finally, the structure of a blockchain solution is described, focusing on a number of structural and technological solutions by outlining the concepts of consistency checking for the validation of input data with appropriate smart contracts, and of information frameworks for the subsequent scrutiny of data in audit operations and the assignment of levels of reliability.These are essential prerequisites for a collaborative blockchain data management to pursue the objective of actual reliability and transparency of information.

A deep learning approach to photo–identification demonstrates high performance on two dozen cetacean species

Abstract Researchers can investigate many aspects of animal ecology through noninvasive photo–identification. Photo–identification is becoming more efficient as matching individuals between photos is increasingly automated. However, the convolutional neural network models that have facilitated this change need many training images to generalize well. As a result, they have often been developed for individual species that meet this threshold. These single‐species methods might underperform, as they ignore potential similarities in identifying characteristics and the photo–identification process among species. In this paper, we introduce a multi‐species photo–identification model based on a state‐of‐the‐art method in human facial recognition, the ArcFace classification head. Our model uses two such heads to jointly classify species and identities, allowing species to share information and parameters within the network. As a demonstration, we trained this model with 50,796 images from 39 catalogues of 24 cetacean species, evaluating its predictive performance on 21,192 test images from the same catalogues. We further evaluated its predictive performance with two external catalogues entirely composed of identities that the model did not see during training. The model achieved a mean average precision (MAP) of 0.869 on the test set. Of these, 10 catalogues representing seven species achieved a MAP score over 0.95. For some species, there was notable variation in performance among catalogues, largely explained by variation in photo quality. Finally, the model appeared to generalize well, with the two external catalogues scoring similarly to their species' counterparts in the larger test set. From our cetacean application, we provide a list of recommendations for potential users of this model, focusing on those with cetacean photo–identification catalogues. For example, users with high quality images of animals identified by dorsal nicks and notches should expect near optimal performance. Users can expect decreasing performance for catalogues with higher proportions of indistinct individuals or poor quality photos. Finally, we note that this model is currently freely available as code in a GitHub repository and as a graphical user interface, with additional functionality for collaborative data management, via Happywhale.com.

Cross Academia and Public Citizen Engagement for Developing Active Citizenship Competences

This paper explored the effect of the different open cross-university and public knowledge building activities on participants’ active citizenship competencies. A survey was conducted with the participants from 7 activities in higher education context in the INOS project. The results report that active citizenship competences could be better developed in cross-academia and public citizen engagement activities that target collaborative knowledge building and data management rather than in more individual crowdsourcing activities. The results indicate that the politico-legal competencies were the most difficult to develop through different activities and they require more specific design concerns in the future citizen science activities.

GraphSTEP: concurrent, cloud-based, lifecycle-oriented editing and updating of product data

A product's digital representation enables various activities in its lifecycle. In a neutral, file-based exchange format, like the STandard for the Exchange of Product model data (STEP), asynchronous access is possible throughout different software solutions, increasing the work interoperability across different stakeholders. However, the file-based handling of product data from the top to the shop floor and further lifecycle phases, subject to industrial practice, contradicts the Digital Twin (DT) concept embodying a centralized, single source of trust. To address this deficit, we propose GraphSTEP: a cloud-based platform to store, access, and manipulate product data, enabling concurrent, collaborative data management while preserving history through versioning.

AUTOMATIC SAFETY EVALUATION AND VISUALIZATION OF SUBWAY STATION EXCAVATION BASED ON BIM-FEM/FDM INTEGRATED TECHNOLOGY

With the progressive promotion of BIM technology in collaborative design and engineering data management, there are large amounts of project information available for intelligent construction, engineering computation and design optimization. In the construction of subway station, BIM technology is still not mature and the engineering design is generally separate from the engineering safety evaluation. Thus, this paper proposed a technology that integrates BIM and numerical simulation (BIM-FEM/FDM integrated technology) to solve the problem of separation between engineering design and computation. A three-dimensional parametric modeling of subway station excavation was first carried out using the Revit® modeling software. Afterward, a FEM/FDM-process oriented data conversion interface was developed to extract and process the critical information from the parametric BIM model for numerical simulation. Then, under the impetus of an auto-simulation interface, the safety evaluation of subway station excavation was realized automatically and visualized graphically. The research of the BIM-FEM/FDM integrated technology presented in this paper has established a supporting platform to achieve the integration of BIM-based design and safety evaluation for subway station excavation.

IFC DATA MAPPING BASED ON A PARAMETRIC BIM CODING FOR AN EFFICIENT WORKFLOW IN THE QUANTIFICATION AND MANAGEMENT OF CONSTRUCTION COSTS

Abstract. In recent years there has been a great effort by the agents of the AECO sector aimed at an efficient use of data interoperability between the participating disciplines and the applicability in the specific software. In this context, the BIM (Building Information Modeling) methodology, moves in a collaborative data management environment to monitor and control the design, planning, cost control, construction processes. and building maintenance. But to enjoy the benefits of BIM in its fifth dimension (5D), cost and time savings, it is essential to properly plan each phase of the project and manage the information effectively within the same team.Therefore, this research aims to validate an effective method for the creation of the information of a constructive entity using codes, disaggregated by budget line items, necessary for the mapping of data in the structure of the IFC file (Industry Foundation Classes) and the confrontation with the filtering rules in quantification programs, in 1 to 1 relationship. Under normal conditions, the item to be measured would have a quality or parameter that distinguishes it from another item. All elements are mapped following an IFC structure using the eCOB standard. This information will be collected in the quantification program to compare it with the information established in the rule corresponding to each item.The analyzed and tested method, based on a proper structuring and classification of data of the elements that make up the BIM model, has been productive and efficient in avoiding random decision-making by the operator who analyzes the quantifications and that may lead to errors or inaccuracies when applying the metric. The result of the study shows that the combinations of parameters, influences by the possible variables of the constructive element, can be considerably reduced. The percentage of rules would decrease by a ratio of 17/120 in the IFC information mapping process to be validated in quantification and valuation software, improving data exchange and interoperability in an international environment.

Collaborative Big Data Management and Analytics in Complex Systems with Edge 2021 eaCamera: A Case Study on AI-Based Complex Attention Analysis with Edge System

As an extension of cloud computing, edge computing makes up for the deficiency of cloud computing to a certain extent. Edge computing reduces unnecessary data transmission and makes a significant contribution to the real-time and security of the system due to its characteristics that are closer to the terminal equipment. In this paper, we study the problem of attention detection. Attentional concentration during some specific tasks plays a vital role, which indicates the effectiveness and performance of human beings. Evaluation of attentional concentration status is essential in many fields. However, it is hard to define the behavior features related to the variety of tasks and behaviors. To solve this problem, we propose an intelligent edge system for attention concentration analysis, eaCamera, to recognize attentional concentration behaviors of students at the edge. To make objective measurements and save the label cost, eaCamera utilizes AI approaches to find the concentration behaviors based on a behavior analysis model with two perspectives, namely, individual perspective and group perspective. Individual perspective indicates personal behavior changes in time dimension while group perspective indicates the changes of the behavior within a group behavior manner. To evaluate the proposed system, a case study is done within a primary school to evaluate student’s performance in the classroom and offer teaching advice for teachers.

Advancing data science in drug development through an innovative computational framework for data sharing and statistical analysis

BackgroundNovartis and the University of Oxford’s Big Data Institute (BDI) have established a research alliance with the aim to improve health care and drug development by making it more efficient and targeted. Using a combination of the latest statistical machine learning technology with an innovative IT platform developed to manage large volumes of anonymised data from numerous data sources and types we plan to identify novel patterns with clinical relevance which cannot be detected by humans alone to identify phenotypes and early predictors of patient disease activity and progression.MethodThe collaboration focuses on highly complex autoimmune diseases and develops a computational framework to assemble a research-ready dataset across numerous modalities. For the Multiple Sclerosis (MS) project, the collaboration has anonymised and integrated phase II to phase IV clinical and imaging trial data from ≈35,000 patients across all clinical phenotypes and collected in more than 2200 centres worldwide. For the “IL-17” project, the collaboration has anonymised and integrated clinical and imaging data from over 30 phase II and III Cosentyx clinical trials including more than 15,000 patients, suffering from four autoimmune disorders (Psoriasis, Axial Spondyloarthritis, Psoriatic arthritis (PsA) and Rheumatoid arthritis (RA)).ResultsA fundamental component of successful data analysis and the collaborative development of novel machine learning methods on these rich data sets has been the construction of a research informatics framework that can capture the data at regular intervals where images could be anonymised and integrated with the de-identified clinical data, quality controlled and compiled into a research-ready relational database which would then be available to multi-disciplinary analysts. The collaborative development from a group of software developers, data wranglers, statisticians, clinicians, and domain scientists across both organisations has been key. This framework is innovative, as it facilitates collaborative data management and makes a complicated clinical trial data set from a pharmaceutical company available to academic researchers who become associated with the project.ConclusionsAn informatics framework has been developed to capture clinical trial data into a pipeline of anonymisation, quality control, data exploration, and subsequent integration into a database. Establishing this framework has been integral to the development of analytical tools.

The cardboard box study: understanding collaborative data management in the connected home

The home is a site marked by the increasing collection and use of personal data, whether online or from connected devices. This trend is accompanied by new data protection regulation and the development of privacy enhancing technologies (PETs) that seek to enable individual control over the processing of personal data. However, a great deal of the data generated within the connected home is interpersonal in nature and cannot therefore be attributed to an individual. The cardboard box study adapts the technology probe approach to explore with potential end users the salience of a PET called the Databox and to understand the challenge of collaborative rather than individual data management in the home. The cardboard box study was designed as an ideation card game and conducted with 22 households distributed around the UK, providing us with 38 participants. Demographically, our participants were of varying ages and had a variety of occupational backgrounds and differing household situations. The study makes it perspicuous that privacy is not a ubiquitous concern within the home as a great deal of data is shared by default of people living together; that when privacy is occasioned it performs a distinct social function that is concerned with human security and the safety and integrity of people rather than devices and data; and that current ‘interdependent privacy’ solutions that seek to support collaborative data management are not well aligned with the ways access control is negotiated and managed within the home.

BASIC FRAMEWORK OF DATA MANAGEMENT

This paper presents a framework for modeling and implementation of collaborative data management. The framework ensures information sharing and parallel process development. Building data infrastructure that you can trust necessitates adhering to a sound data management methodology. Reliable data comes from data infrastructures built on solid management concepts. Data analysis on the performance of these systems for integration and the motivation of implementing a data management framework. To acquire and preserve essential information such as conceptual metadata, the underlying culture that makes such a plan effective involves a partnership between business and IT, responsibility among data owners and stewards, and cooperation and collaboration throughout the data management process in an organization. This paper identifies the most appropriate data strategic approach and examines the current data management solutions.

Clinical Outcomes of the Lund, Toronto, and Organ Care System Protocols for Normothermic Ex Vivo Lung Perfusion: A Systematic Review and Pursuit of Network Meta-Analysis

Purpose Ex Vivo Lung Perfusion (EVLP) is an excellent method to preserve and recondition donor lungs, thus increasing the lung donor pool. This systematic review summarizes studies comparing both primary graft dysfunction (PGD) and survival between EVLP and conventional lung transplantation (c-LTx), or between different EVLP protocols. The objective is to evaluate outcomes of the three main EVLP protocols: ‘Lund’, ‘Toronto’ and ‘Organ Care System’ (OCS), through a network meta-analysis (NMA). Methods A systematic search was performed in Pubmed, Embase, the Cochrane library and Web of Science. Studies performing EVLP on initially discarded donor lungs were independently assessed for eligibility by two researchers. Data on study design, participants, and EVLP methods were extracted, together with outcomes such as survival and PGD. The generated database was assessed on meeting the assumptions for NMA. Results Nine studies were included: two non-randomised clinical trials (n = 240), and four prospective (n = 1360) and three retrospective observational cohort studies (n = 540). The study protocols could be divided into: Toronto (5/9), Lund (3/9) and OCS (1/9). Most frequently reported outcomes were PGD grade 3 at 72 hours post-LTx, and one-month and one-year survival. All included studies compared EVLP to c-LTx, providing solely indirect evidence for comparison between protocols and thus no loops within the NMA model. In the absence of loops, consistency within the model cannot be statistically assessed. Thus, thorough examination for differences that could modify the treatment effect is required. Substantial variations were found in study design, type of LTx, and participant characteristics. It was therefore not feasible to construct a subset of studies that met the similarity and consistency assumptions of NMA. Conclusion Currently, no substantiated statistical comparison can be made between different EVLP protocols. Further attempts at NMA are dependent on the introduction of studies comparing multiple EVLP protocols, or an increase in randomised controlled trials performing EVLP on standard donor lungs. Alternatively, collaborative data management between EVLP centres would be a promising way to explore the effect of different protocols, and their individual components, on patient outcomes.

Digital Twin-enabled Collaborative Data Management for Metal Additive Manufacturing Systems

Metal Additive Manufacturing (AM) has been attracting a continuously increasing attention due to its great advantages compared to traditional subtractive manufacturing in terms of higher design flexibility, shorter development time, lower tooling cost, and fewer production wastes. However, the lack of process robustness, stability and repeatability caused by the unsolved complex relationships between material properties, product design, process parameters, process signatures, post AM processes and product quality has significantly impeded its broad acceptance in the industry. To facilitate efficient implementation of advanced data analytics in metal AM, which would support the development of intelligent process monitoring, control and optimisation, this paper proposes a novel Digital Twin (DT)-enabled collaborative data management framework for metal AM systems, where a Cloud DT communicates with distributed Edge DTs in different product lifecycle stages. A metal AM product data model that contains a comprehensive list of specific product lifecycle data is developed to support the collaborative data management. The feasibility and advantages of the proposed framework are validated through the practical implementation in a distributed metal AM system developed in the project MANUELA. A representative application scenario of cloud-based and deep learning-enabled metal AM layer defect analysis is also presented. The proposed DT-enabled collaborative data management has shown great potential in enhancing fundamental understanding of metal AM processes, developing simulation and prediction models, reducing development times and costs, and improving product quality and production efficiency.

Digital Twin: Research Framework to Support Preventive Conservation Policies

Preventive strategies for the conservation of heritage sites have gradually been preferred to curative approaches because of their ability to maintain their significance. Furthermore, most experts now agree that conservation management of heritage places based on a common understanding of their cultural values is essential to address all the particularities of their contexts. Recently, significant research has demonstrated the potential of Heritage Building Information Modelling (HBIM) for the collaborative data management in conjunction with conservation projects. The recent development of HBIM web platforms illustrates the value of strengthening the link between the digital model and the physical realm of heritage assets. This paper advocates the application of Digital Twin’s (DT) principles, using HBIM models as a digital replica, to support the preventive conservation of heritage places. Based on an extensive literature review, a comprehensive framework that integrates the DT into the management plan process for the preventive conservation of built heritage is proposed. Several recommendations for its implementation are finally discussed, such as the identification of tangible features of significance, the threats associated with their integrity and the corresponding mitigation strategies, with particular emphasis on the value assessment process. The result is a data model for structuring information on preventive conservation strategies. This framework provides the basis for future implementation and demonstrates the need for a DT approach in this context.

Managing collaborative research data for integrated, interdisciplinary environmental research

The consistent management of research data is crucial for the success of long-term and large-scale collaborative research. Research data management is the basis for efficiency, continuity, and quality of the research, as well as for maximum impact and outreach, including the long-term publication of data and their accessibility. Both funding agencies and publishers increasingly require this long term and open access to research data. Joint environmental studies typically take place in a fragmented research landscape of diverse disciplines; researchers involved typically show a variety of attitudes towards and previous experiences with common data policies, and the extensive variety of data types in interdisciplinary research poses particular challenges for collaborative data management. In this paper, we present organizational measures, data and metadata management concepts, and technical solutions to form a flexible research data management framework that allows for efficiently sharing the full range of data and metadata among all researchers of the project, and smooth publishing of selected data and data streams to publicly accessible sites. The concept is built upon data type-specific and hierarchical metadata using a common taxonomy agreed upon by all researchers of the project. The framework’s concept has been developed along the needs and demands of the scientists involved, and aims to minimize their effort in data management, which we illustrate from the researchers’ perspective describing their typical workflow from the generation and preparation of data and metadata to the long-term preservation of data including their metadata.

Group targets a platform for collaborative data management in drug discovery

A consortium of pharmaceutical companies, academic researchers, and technology suppliers has announced a 3-year, $20 million project to apply machine learning and blockchain security to data manage...

EdgeCare: Leveraging Edge Computing for Collaborative Data Management in Mobile Healthcare Systems

With the wide application of mobile healthcare systems, the total amount of healthcare data is ever increasing rapidly as users interact with healthcare service providers frequently. This leads to a challenging task to manage healthcare data. Existing work mainly pay attention to centralized and blockchain-based mechanisms. But they cannot adapt to the increasing amount of global healthcare data and suffer from complex application challenges, respectively. Decentralized and collaborative data management assisted by edge computing exhibits major advantages in improving overall system performance. We present a secure and efficient data management system named as EdgeCare for mobile healthcare systems. Local authorities are established to schedule edge servers for processing healthcare data and facilitating data trading. A hierarchical architecture with collaboration is designed for feasible implementation of EdgeCare. After that, we investigate secure data uploading and sharing in the system. We use an electronic medical record to show how healthcare data is processed with security considerations. We also conduct the Stackelberg game-based optimization algorithm to approach the optimal incentive mechanism for a data collector and users in the fair decentralized data trading. The numerical results with security analysis are provided to demonstrate that EdgeCare offers effective solutions to protect healthcare data, and support efficient data trading.

Errors in Electronic Health Record-Based Data Query of Statin Prescriptions in Patients With Coronary Artery Disease in a Large, Academic, Multispecialty Clinic Practice.

BackgroundWith the recent implementation of the Medicare Quality Payment Program, providers face increasing accountability for delivering high‐quality care. Such pay‐for‐performance programs aim to leverage systematic data captured by electronic health record (EHR) systems to measure performance; however, the fidelity of EHR query for assessing performance has not been validated compared with manual chart review. We sought to determine whether our institution's methodology of EHR query could accurately identify cases in which providers failed to prescribe statins for eligible patients with coronary artery disease.Methods and ResultsA total of 9459 patients with coronary artery disease were seen at least twice at the Emory Clinic between July 2014 and June 2015, of whom 1338 (14.1%, 95% confidence interval 13.5–14.9%) had no statin prescription or exemption per EHR query. A total of 120 patient cases were randomly selected and reviewed by 2 physicians for further adjudication. Of the 120 cases initially classified as statin prescription failures, only 21 (17.5%; 95% confidence interval, 11.7–25.3%) represented true failure following physician review.ConclusionsSole reliance on EHR data query to measure quality metrics may lead to significant errors in assessing provider performance. Institutions should be cognizant of these potential sources of error, provide support to medical providers, and form collaborative data management teams to promote and improve meaningful use of EHRs. We propose actionable steps to improve the accuracy of EHR data query that require hypothesis testing and prospective validation in future studies.