Aspects Of Data Management Research Articles

Wireless 2.0: Toward an Intelligent Radio Environment Empowered by Reconfigurable Meta-Surfaces and Artificial Intelligence

In this article, we introduce Wireless 2.0, the future generation of wireless communication networks, in which the radio environment becomes controllable and intelligent by leveraging the emerging technologies of reconfigurable metasurfaces (RMSs) and artificial intelligence (AI). In particular, we emphasize AI-based computational methods and commence with an overview of the concept of intelligent radio environments (IREs) based on RMSs. Then, we elaborate on data management aspects, the requirements of supervised learning by examples, and the paradigm of reinforcement learning to learn by acting. Finally, we highlight numerous open challenges and research directions.

Extending Support for Publishing Sensitive Research Data at the University of Bristol


 The University of Bristol Research Data Service was set up in 2014 to provide support and training for academic staff and postgraduate researchers in all aspects of research data management. As part of this, the data.bris Research Data Repository was developed to provide a publication platform for research data generated at the University of Bristol. Initially launched in 2015 to provide open access to data, since 2017 it has also been possible to publish access-controlled datasets containing sensitive data via this platform.
 The vast majority (90%) of datasets published are openly accessible, but there has been steady demand for access-controlled release of datasets containing information that is ethically or commercially sensitive. These cases require careful management of additional risk: for example, where datasets contain information on human participants, balancing the risk of re-identification with the need to provide robust data that maximises research value through re-use. Many groups within the University of Bristol (for example, the Avon Longitudinal Study of Parents and Children) have extensive experience and expertise in this area, but it became apparent that there was a need to provide additional support for researchers who were not able to draw on the experience of these established groups. This practice paper describes the process of setting up a dedicated service to provide training and basic disclosure risk assessments in order to address these skills gaps, and outlines lessons learnt and future directions for the service.

Orbital debris ontology, terminology, and knowledge modeling

ABSTRACT The looming threat orbital debris poses to assets in orbit demands solutions. As the orbital population grows, so does the risk of collision, as well as the volume of associated data. It is, however, an opportunity for interdisciplinary innovation and cooperation. This paper focuses on the data management and knowledge modeling aspect of developing solutions for a sustainable and safe orbital space environment. An in-progress work to develop an orbital debris reference ontology [Rovetto, 2015] is summarized in order to discuss knowledge modeling for orbital debris. The formal analysis of this effort can contribute to standards development, as well as terminological and policy challenges. Leveraging the growing volumes of orbital debris and space situational awareness (SSA) data will create a more complete picture of the orbital space environment. Part of the solution will be: consistent and correct data interpretation, sharing orbital debris and SSA data in one form or another, terminology development & harmonization, and knowledge or domain modeling. To facilitate this, [Rovetto, 2015] proposed ontology development for the orbital debris and broader space domain. This paper summarizes concepts from that paper, and subsequently developed concepts [2-9]. See also https://purl.org/space-ontology and https://purl.org/space-ontology/odo . Ontology engineering is an interdisciplinary area of research related to knowledge representation and reasoning in artificial intelligence, model-based systems engineering, semantic technologies and the so-called semantic web. An ontology is effectively a computable and semantically rich terminology that presents a knowledge or domain model for a topic area. Expressions of knowledge, beliefs, or assertions are stored using formally defined terms. This knowledge base is reasoned over to yield answers to database queries, among other things. Ontologies have been developed in knowledge-based projects across various disciplines, and used for such things as search engines, chatbots, and enterprise knowledge graphs. Ontologies aim to support: interoperability, automated reasoning, data sharing and integration, data search and retrieval, and communicating the meaning of data. The Orbital Debris Ontology (ODO) [1], and related ontologies [Rovetto & Kelso 2016] [Rovetto 2016, 2017], were proposed to help achieve this. ODO, for instance, is intended as a domain ontology that can be used across federated databases, offering an explicitly specified set of concepts describing the orbital debris domain. Its meaning-rich taxonomy will provide a sharable semantics for orbital debris data to, in part, consistently communicate the meaning of data to both humans and machines, and tag data elements in space object catalogs to help afford automated reasoning tasks, decision support, knowledge discovery, and information integration. ODO and the SSA ontology (SSAO) [2] is part of the overall Orbital Space Ontology concept, which is conceived as a unifying domain reference ontology. It aims to provide a knowledge representation structure of orbital space, a common semantic model, and develop a sharable terminology. Collectively this will provide common meaning for datasets, a high-level taxonomy or classification for orbital space objects, and a means to characterize space objects. Ongoing efforts have included using visualizations, R, JSON-LD, and contemporary semantic technologies. Potential applications include web-based platforms, web apps, visualizations, modeling and simulations. Community input and participation may yield a more widely understood domain model as well as facilitate terminological standards. For example, conceptual, terminological, and ontological analysis can make helpful contributions to such efforts as the Space Debris Mitigation Requirements by developing more precise, consistent and coherent terms and definitions. Other projects can also use ODO (and its related ontologies) as a common knowledge model, metadata set, taxonomy or vocabulary. Readers interested in supporting development are encouraged to make contact.

A Literature Review of Location-Aware Computing Policies

Location-aware computing systems implementation has the challenge of maintaining its consistency due to frequent disconnections in the mobile environment. Many other exciting issues associated with this domain provide a fertile ground for many mobile database researchers to tackle. This work contributes by taking up-to-date literature on current location-aware mobile data management practices. The paper includes taxonomy for caching, map-matching, and location privacy protection with spatial-temporal queries, unlike other review papers, which only address a specific aspect of location-dependent data management. First, an in-depth analysis of state-of-the-art and classified literature is presented, taking into account the domain of application models and approaches for evaluation. Secondly, it provides a theoretical and analytical investigation of location-conscious computing approaches available in the literature, examining the merit and demerit of each method and the evolving processes between them. Finally, the paper highlighted the outstanding opportunities for future research directions.

Research on the management information of scientific research institutes

Through the analysis of the management information system, this paper analyzes the pain points in the process of enterprise information construction, and puts forward solutions from the aspects of master data management, process management, the application of ESB and other basic common platform construction. Finally, this paper analyzes the special situation of informatization construction in military research institutes, and probes into the difficulties and development direction of informatization construction.

Research data management at an African medical university: Implications for academic librarianship

The study investigates and provides results on the current status of research data management at a medical school in a developing African country. The study focusses on four aspects of research data management: generation; preservation and backup; competencies; and challenges. Quantitative data collected using a questionnaire from 84 health researchers and 16 librarians was complemented by qualitative data gathered through conducting structured interviews with the Director of Research of the university. The study results provide the research community with a better understanding of research data management perspectives amongst health researchers in a developing country. This could help research data management stakeholders to adopt better strategies for championing research data management activities in this particular country and in other developing countries. The study notes that the current research data management status creates an opportunity for an academic library to consolidate and fortify its widely viewed natural role in research data management. In view of the findings, the study proposes that the adoption of research data management policies is essential in inspiring the conceptualisation, popularisation, adoption, and operationalisation of various research data management activities within the African university environment.

Database Theory Column Report on PODS 2019

The 38th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems (PODS 2019) took place in Amsterdam, The Netherlands, from July 1 to July 3, 2019, in conjunction with the 2019 ACM SIGMOD International Conference on Management of Data. PODS focuses on theoretical aspects of data management, and the co-location with SIGMOD stimulates interaction between theory-oriented and systems-oriented research. Since the first edition of the symposium in 1982, the PODS papers are distinguished by a rigorous approach to widely diverse problems in data management, often bringing to bear techniques from a variety of different areas, including computational logic, finite model theory, computational complexity, algorithm design and analysis, programming languages, and artificial intelligence.

Piloting a Safe Health Researcher course

Robust and standardised licensing and governance frameworks are used to ensure that datasets intended for research use are made available under the terms and conditions specified by a data owner.
 The UK Data Service makes use of the Five Safes framework to operate its 3-tier data access policy, and ensuring that data classified as personal data can be made available via appropriate legal gateways. This set of principles has gained traction with national statistics around the world, yet it is remarkably absent in the narrative of data access for health research. The health domain tends to focus on ‘data sharing agreements’, and less on training around trust, security and disclosure. The concept of a Safe Health Researcher is missing, yet is appealing.
 The UK Data Service has been piloting such a half day course, with colleagues in the health domain. The training helps consolidate the less well defined idea of a ‘bona fide’ researcher, typically required by funders such as the UK’s Medical Research Council when accessing their data assets. The term bonafide makes some assumptions about the credentials of the researchers, yet fails to ‘test’ them, instead relying on ‘trust ‘underwritten by the individual’s university, and maybe a short online security course.
 While purposeful breaches are certainly not common place, a researcher accessing personal or sensitive data would benefit from a structured half-day course that covers aspects of: potential/ actual disclosure risk in health data and appropriate access pathways; safeguards to be put in place when data with risk are shared; and what might constitute a published ‘unsafe’ output, i.e. with a risk of disclosure. The training focuses on health data and research examples, and draws on aspects of the research data management and publishing training undertaken by the UK Data Service (e.g. Corti et al, 2014) and on the UK Statistics Authority approved ‘Safe Research Training’ (SRT) course, which leads to the Accredited Researcher status.

Information and Communication Technology Applied to Continuing Quality Improvement for the Care and Cure of Childhood Severe Hematological Disorders in India

Introduction: Continued quality improvement systems are necessary not only to maintain minimum quality standards, but also contribute to improved outcomes (Snowden et al. 2017; Hashmi et al. 2017). In-spite of its importance, the implementation of a functional Quality Management System (QMS) in the routine daily activity oh health professionals may be a challenge, especially in resource limited settings (Hashmi et al. 2017). Methodology: BMTPlus is an information and communication technology (Agarwal et al. 2014) platform that has been used for knowledge management by Sankalp-People Tree Centre for Paediatric BMT, Bangalore (PTH) from July 2015 and Sankalp-CIMS Centre for Paediatric BMT, Ahmedabad (CIMS) from March 2017. BMTPlus brings together all aspects of transplant data management including clinical data management, personal records and QMS. The usage of these tools which directly relate to the continued quality improvement process was reviewed. Clinical outcomes for patients who underwent transplant for thalassemia in these centres from matched related donors were summarised using R statistical package (version 3.5.x) as an indicator of the effectiveness of the continued quality improvement process. Results: Table 1 summarises the program information, utilisation of various quality management-related records, staff turnover, outcomes and costs. Information technology-driven QMS provided the unique advantage of being able to audit all the data instead of sample data sets and thus widening the potential to detect issues. The inventory management and prescriptions system was audited in 2019 at PTH and the findings are that there were only 3 events in 4 years where important drugs/supplies came close to a critical shortage. Automated treatment plan imports contributed to 2 of the 1570 drug orders being detected with inaccuracies (0.13%). The PTH BMT service has been using BMTPlus for auto submission of data to CIBMTR; data audit in July 2019 showed that 92% of the 1263 due forms were successfully submitted. CIMS is catching up with 39% forms submitted so far. The impact of this quality improvement system on clinical outcomes of the program was measured on 172 successive first matched related transplants (103 from PTH and 69 from CIMS) upto April 15 2019. Outcomes were compared across 3 successive transplant protocols, all patients were transplanted at-least 100 days prior to this analysis. Both overall and disease-free survival improved significantly from protocol 1 to 3, i.e. from 88% to 100% (p = 0.002) and from 77% to 98% (p < 0.0001) respectively. Transplant-related mortality and rejection significantly dropped from 10% to 0% (p = 0.01) and from 22% to 3% (p < 0.0001) respectively. The outcomes achieved at this lower-middle income centre compared favourably with the 96.9% day-100 survival reported in USA for thalassemia ("U.S. Patient Survival Report" 2017). Progressively improved outcomes and good utilisation of quality management features were seen in spite of the fact that PTH had an attrition rate as high as 44% for physicians and 78% for nurses while in CIMS this was 74% and 57% respectively. The impact of quality management on the cost and sustainability of the program can be seen from the cost of the transplants. Though the programs experienced a 10% increase in operational costs on a year on year basis, the ongoing quality improvements led to fewer complications and cost escalations as a result of which the program at PTH and CIMS continue to offer matched-related transplants at USD 12.500 and USD 13.300 respectively which was the bundled cost set at the beginning of the programs in July-2015 and March-2017 respectively. Thanks to the use of online platform, only one quality/data manager is the only additional resource specific to continued quality improvement, quality assurance, outcome reporting and participation in international registries. Conclusion: Information and communication technology enabled comprehensive knowledge management and facilitated continuous quality improvement. This contributed to the delivery of cost-efficient and effective transplantations with progressively improving outcomes, which finally are at par with the best centres in the world in spite of the challenges of high staff turnover. Disclosures No relevant conflicts of interest to declare.

People Analytics and Performance of Deposit-Taking Micro Finance Institutions in Nyeri County, Kenya

People analytics is a data-driven approach to improving people-related decisions for advancing both individual and organizational success. While people have always been critical to the success of organizations, many business leaders still make key decisions about their workforce based on intuition, experience, advice, and guesswork. However, today leaders can improve their people decision-making based on the collection and systematic analysis of data. A closer look at the operations of many deposit taking micro-finance institutions reveals that they all face challenges related to human resources management. These firms invest in human development, only for the human capital to leave for greener pastures within a short period, impacting negatively and heavily on performance, survival and growth. It is therefore imperative that they undertake serious human resource evaluation, and people analytics can be a crucial tool for the success of this process. The aim of the study was thus to evaluate the effect of people analytics on the performance of Deposit Taking Micro Finance Institutions in Nyeri County, Kenya. The specific objectives guiding the study were: to determine the influence of technology adoption on the performance of deposit taking micro-finance institutions, effect of human resource data access on the performance of deposit taking micro-finance institutions, effect of data management capacity on the performance of deposit taking micro-finance institutions, and the effect of stewardship for people analytics on the performance of deposit taking micro-finance institutions in Nyeri County, Kenya. The study adopted the descriptive research design while targeting173 respondents comprising 8 human resource managers and 165 staff in the human resource department of 8 registered deposit taking micro-finance institutions in Nyeri County. Through stratified sampling method, all managers (8) and 30% (50) of the 165 staff comprised the sample size of 58 respondents. The selected respondents were considered key informants in the study area. Data was collected from primary sources using a semi-structured questionnaire. Data was analyzed with the aid of Statistical Package for Social Studies and excel computer software through descriptive (percentages, means, standard deviation), as well as inferential statistical methods (correlation and regression techniques). Tables and graphs were used for data presentation. Results showed that the micro finance institutions had established infrastructure for the application of technology. Descriptive and inferential analysis results indicated that technology adoption, human resource data access, data management and stewardship had a positive relationship with the performance of MFIs. Findings further indicated that out of the four independent variables, only three were significant: human resource data access, data management and stewardship. The study thus concluded that HR data access, data management and stewardship aspects of people analytics had significant effect on the performance of Microfinance Institutions. Technology adoption lowly affected people analytics and performance of micro finance institutions. To enhance data access and management, the study recommended that managers need to invest in new apps that are platforms for people analytics including cloud computing and artificial intelligence. They must also re-evaluate the techniques for human resource anaytics as well as capacity development in people analytics for managers and staff.

Data Management Capabilities of Extension Personnel in Kaduna Agricultural Development Agency Kaduna State, Nigeria

This study assessed the data management capabilities among extension personnel in Kaduna Agricultural Development Agency (KADA) in Kaduna State. Proportional and simple random sampling techniques were used to select 69 male and 28 female extension personnel to give a total sample size of 97 respondents. Data was collected using a structured questionnaire and analysed with descriptive statistics (means and percentages) and the Pearson Product Moment correlation analysis. The findings show that the majority of the respondents who were mostly educated to the level of diploma (73%) agreed that knowledge of mathematics/statistics is the most important ( x= 7.83) requirement for data management. It was also revealed that adequate training and workshops are important requirements ( x= 7.60) for successful data management and enhance performance of staff particularly those involved in data collection as enumerators considering that the work is cumbersome. Pearson correlation analysis shows that there is link between educational qualification, job experience and training with level of understanding of the important aspects of data management. Proficiency in mathematics and statistics is the most important requirement for an effective data management as perceived by the KADA staff. However, extension personnel performance can be improved through training and staff motivation through allowance and provision of transportation facilities. The Kaduna State ministry of agriculture in collaboration with KADA should be committed to ensuring the actualization of the above mentioned strategies. Keywords : Data management, capabilities, extension personnel

A Framework for Managing and Analyzing Big Data in Indian School Education System with Reference to Jammu & Kashmir

Big Data storm has reached all most in every sector whether be public or private and is an important decision making factor for the administration or governing body of any system or organization, because with the advancements in technologies, various public, private, and social organizations are creating or exchanging a huge volume of data through different sources in various formats. Big Data can be applied in various sectors of India, wherein one of the essential sectors is education system where Big Data analytics is slowly and steadily finding its place for betterment of the services being provided by this system. India has one of the biggest school systems in the world which is spread over different states of the country and one part of this system is functional in one of the northern states of India known as Jammu and Kashmir (J&K). This study is based on Big Data management and analysis in School Education System of J&K. In the area of School Education in J&K, computers are finding an important place for data management as well as imparting learning through digital means. There is a need of applying Big Data technologies to various aspects of the School Education in J&K, as huge amount of data with variety and high frequency of generation is available in the institutes under this system. This paper will analyze various aspects of Big Data management and analysis system for School Education of J&K. This paper will also highlight the current scenarios of School Education System of J&K in handling Big Data such as sources of Big Data, Scope of Big Data analytics in School Education System of J&K, opportunities of Big Data analytics, challenges and issues that can be faced in this system, Applications of Big Data analytics in School Education of J&K, and finally discussing the proposed architecture of Big Data management and analysis system for School Education of J&K.

A BGC-Argo Guide: Planning, Deployment, Data Handling and Usage

The Biogeochemical-Argo program (BGC-Argo) is a new profiling-float-based, ocean wide, and distributed ocean monitoring program which is tightly linked to, and has benefited significantly from, the Argo program. The community has recommended for BGC-Argo the addition of six measurements in addition to pressure, temperature and salinity measured by Argo, to include oxygen, pH, nitrate, downwelling light, chlorophyll fluorescence and the optical backscattering coefficient. The purpose of this addition is to enable the monitoring of ocean biogeochemistry and health, and in particular, monitor major processes such as ocean deoxygenation, acidification and warming and their effect on phytoplankton, the main source of energy of marine ecosystems. Here we describe the salient issues associated with the operation of the BGC-Argo network, with information useful for those interested in deploying and using the data it produces. These include, float testing, deployment and increasingly, recovery. Aspect of data management, processing and quality control are covered as well as specific issues associated with each of the six BGC-Argo sensors. In particular, it is recommended that water samples be collected during float deployment to be used for validation of sensor output.

Technical, Political, and Social Issues in Archaeological Collections Data Management

AbstractManaging collections means ensuring that the data about them are useful, available, and accurate. In addition to the technical aspects of data management, there are layers of political and social structure that direct the construction and use of collections data. The Minnesota Historical Society (MNHS) employs a set of data standards that allows us to gather electronic cataloging data from a wide community of archaeology researchers who are depositing collections at our institution. Though met with initial resistance, these standards have facilitated publication in Open Context as linked open data. Furthermore, institutional discussions concerning Creative Commons licensing and the cultural sensitivity of collections data were precipitated by publication, highlighting the role of social agreement in data management. We found that successful employment of data standards must take into account the needs of the various stakeholders and further their interests. Standards will be most useful and successful when they are lightweight, are supported by training and documentation, and exist as part of a system that allows for more than one way to characterize the collections.

Biodiversidata: A Collaborative Initiative Towards Open Data Availability in Uruguay

Data-sharing has become a key component in the modern scientific era of large-scale research, with numerous advantages for both data collectors and users. However, data-sharing in Uruguay remains neglected given that major public sources of biodiversity information (government and academia) are not open-access. As a consequence, the patterns and drivers of biodiversity in this country remain poorly understood and so does our ability to manage and conserve its biodiversity. To overcome this critical gap, collaborative strategies are needed to communicate the importance and benefits of data openness, exchange and provide technical tools and training on all aspects of data management, sharing practices, focus on incentives, and motivation structures for data-holders. Here, we introduce the Biodiversidata initiative (www.biodiversidata.org) – a novel Uruguayan Consortium of Biodiversity Data. Biodiversidata is a collaboration among experts with the aim of improving the country’s biodiversity knowledge and the open-access of the vast resources they generate. Biodiversidata aims to collate the first comprehensive open-access database on Uruguay's whole biodiversity, to support advancements in scientific research and conservation actions. Currently, Biodiversidata consists of over 30 experts from across national and international institutions, studying diverse biodiversity groups. After less than two years, we have collected, curated and standardised a dataset of ~70,000 records of primary biodiversity data of tetrapod species – the first and most comprehensive open biodiversity database ever gathered for Uruguay to date. However, the process is hampered by multiple challenges: the lack of support for sampling of specimens and maintenance of collections has contributed to the situation were data are often perceived as personal property rather than collective resources; institutions have no plans or strategies directed to digitisation of their collections which actually places biodiversity data in Uruguay ‘at risk’ of being lost; the scarce governmental and academic incentive structures towards open scientific research relegates data-sharing to a personal decision; although scientists individually are willing to share their research data, the lack of data management plans within their research groups hampers the capacity to digitise the data and thus, to make them available; former initiatives aimed to create comprehensive biodiversity databases did not consider the balance between openness and gain for researchers, setting the subject of data-sharing more of an obligation than a path of promotion, which impacted negatively in the perception of scientist to open their data. the lack of support for sampling of specimens and maintenance of collections has contributed to the situation were data are often perceived as personal property rather than collective resources; institutions have no plans or strategies directed to digitisation of their collections which actually places biodiversity data in Uruguay ‘at risk’ of being lost; the scarce governmental and academic incentive structures towards open scientific research relegates data-sharing to a personal decision; although scientists individually are willing to share their research data, the lack of data management plans within their research groups hampers the capacity to digitise the data and thus, to make them available; former initiatives aimed to create comprehensive biodiversity databases did not consider the balance between openness and gain for researchers, setting the subject of data-sharing more of an obligation than a path of promotion, which impacted negatively in the perception of scientist to open their data. To overcome some of these challenges, we decided to direct Biodiversidata to individual researchers/experts and not institutions. We called them with the plan of collecting the maximum possible amount of data from vertebrate, invertebrate and plant species, use it to collaboratively generate impactful scientific research. An important aspect was that we requested data only to fit the premise of being primary biodiversity data (i.e., data records that document the occurrence of a species in space and time). This meant cleaning and standardising very heterogeneous information, from a variety of source types and formats, including updating scientific names and georeferentiating sampling locations. However, centralising the cleaning process allowed researchers to send their raw records without spending time cleaning them themselves and, as a consequence, enlarged the amount of data being collated. Collectively, Biodiversidata’s approach towards changing the culture of data-sharing practices has relied on the reinforcement of a scientific collaboration culture that benefits not only researchers at the individual level, but the progress of larger-scale issues as a whole. There is a long way to go on the subject of open research data in Uruguay, though, aiming strategies to people, capitalising data management and progressing with step-by-step rewards, is already showing some preliminary encouraging results.

Aspects of Body Metrics Data Management in the Long Term for the European Fitness Industry

AbstractThe dawn of the fourth industrial revolution, mostly known through the German initiative “Industrie 4.0”, builds on a set of technologies emerging from software and information and communication technologies (ICT); paired with the growth of the Internet-of-Things (IoT), the so-called “smart products” are expanding on the market. These smart products integrate data collection and processing capacities. Additionally, the collected data have their own lifecycle, and can be classified as sensitive data. In that sense, companies developing hardware products may need support to step in “smart products” development. Digital transformation strategy is a possible overall support for companies. However, regarding smart product development and IoT data management, no studies to date have addressed formalized guidelines to support companies. This article proposes a set of guidelines focusing on IoT data management to support hardware companies in their transformation towards smart products. The proposed guidelines are exemplified on a fitness industry case which is using wearable devices collecting body metrics, considered as sensitive data.

Master Data Management Maturity Assessment : A Case Study of a Pasar Rebo Public Hospital

Patient’s master data is an important data for hospital’s operational. If the master data quality does not meet the expectation, it will affect the billing process and then will affect the income for the hospital. Master data management can help the hospital to integrate all their master data they have so for each data there will be only single source of truth and validity of data will be complied. By doing assessment of master data management maturity, hospital will be able to identify what aspect of master data management that they lack so the process can be improved as well as the quality of master data. Assessment of master data management maturity level is done using Master Data Management Maturity Model (MD3M) by Spruitz and Pietzka. The assessment showed 90 percent of activity related to master data management has been implemented. Some policies need to be implemented regarding to the master data management, such as documentation of the policies and the definition of master data, formal appointment of the data stewards, and the implementation of master data quality management.

Collection and Storage of Assembly Quality Data of Complex Electromechanical Products

In the context of made in China 2025 and intelligent manufacturing, “data-based decision” is one of the important concepts for quality management promotion, and data integrity and availability is an important aspect of data collection and data management. Combined with the characteristics of complex electromechanical products with complex structure, multiple development links and high quality requirements, this paper discusses the management category of quality big data from the perspective of manufacturing process operation management and product life cycle, studies the acquisition strategy by manual and automatic methods, acquisition process, the quality data modeling and expression specifications based on unified coding specifications and business flow and data flow framework and provides references for enterprises to carry out quality big data construction.

PO 8495 DATA SHARING IS PART OF DATA MANAGEMENT: THE NEED FOR A HOLISTIC AND COHERENT VIEW ON RESEARCH DATA MANAGEMENT

BackgroundAwareness of data management (DM) is often restricted to ‘the cost of computers’ or ‘the need for a database’. Recently, ‘data sharing’ can be added to this shortlist. Indeed, in recent years data sharing became often required or so strongly promoted that the importance of all other aspects related to DM or data handling in clinical tended still to be overlooked. However, the development of data sharing guidelines and associated privacy regulations (e.g. the EU General Data Protection Regulation) created a new momentum for highlighting the importance of qualitative data management.MethodsAn overview of DM processes is given, within the framework and challenges of conducting non-commercial clinical trials in North-South partnerships.ResultsThe DM workflow of a clinical trial is presented, highlighting essential DM tasks, deliverables and milestones. Pre-study tasks and deliverables are addressed: SOPs, a data management plan, the implementation of a GCP-compliant validated data management system and compliance to data quality, privacy, security and standards (e.g. MedDRA, CDISC). Subsequent study-specific processes including the collection, entry, querying and cleaning of the data are discussed. In addition, DM metrics important to guide quality, productivity and timelines are reviewed while considering their impact on post-study activities such as data sharing.ConclusionData sharing is only one of many DM tasks, at the end of the DM workflow. Focusing too much on data sharing while neglecting other DM aspects might lead to underestimating the workload, resources, quality assurance and time needed for data management and by and large for the trial itself. Integrating data sharing into a holistic vision on data management is paramount for clinical research.

The PAU Survey: Operation and orchestration of multi-band survey data

The Physics of the Accelerating Universe (PAU) Survey is an international project for the study of cosmological parameters associated with Dark Energy. PAU's 18-CCD camera (PAUCam), installed at the prime focus of the William Herschel Telescope at the Roque de los Muchachos Observatory (La Palma, Canary Islands), scans part of the northern sky, to collect low resolution spectral information of millions of galaxies with its unique set of 40 narrow-band filters in the optical range from 450 nm to 850 nm, and a set of 6 standard broad band filters. The PAU data management (PAUdm) team is in charge of treating the data, including data transfer from the observatory to the PAU Survey data center, hosted at Port d'Informaci\'o Cient\'ifica (PIC). PAUdm is also in charge of the storage, data reduction and, finally, of making the results available to the scientific community. We describe the technical solutions adopted to cover different aspects of the PAU Survey data management, from the computing infrastructure to support the operations, to the software tools and web services for the data process orchestration and exploration. In particular we will focus on the PAU database, developed for the coordination of the different PAUdm tasks, and to preserve and guarantee the consistency of data and metadata.