West African Policy Dialogue-impulse for better data: progress towards better analytics and better decisions.

Globally we are beginning to realise the need for policies built on evidence, and for environmental policy that entails science-based policy. Yet, any analysis is only as good as the data used to conduct it, thus understanding the limitations and assumptions in environmental data is crucial to ensuring it’s sensible and effective use. The digital revolution has seemingly changed the problem for many ecologists trying to understand the natural world from having insufficient data to approach many ecological questions, to one of how to usefully analyse ever growing volumes of data. Yet despite this huge volume of data, natural biases within the data require caution to ensure their sensible use, as those biases shape the outcomes of our analysis and may misrepresent true ecological patterns as a consequence. We also explore how different modes of data collection and the impact of citizen science in shaping our understanding of species patterns and how it may actually exacerbate rather than ameliorate existing biases. However guiding sensible use and providing more robust solutions is key, thus building from this we provide recommendations to help guide sensible and effective use and interpretation of data, frameworks to enable more effective use of data within its sensible limits, and better approaches for the generation of further data to aid the development of effective conservation, policy and management. We also demonstrate how data can be sensitively analysed and applied to help create better and more sensitive environmental targets, and can also help align, such as ecological conservation redline policy and to maximise the synergies between climate and biodiversity targets.

This study aims to determine the effect of Data Accuracy (DA) on Decision-Making Efficiency (DME), and examine the influence of Data Quality (DQ) on Decision-Making Efficiency (DME) in selected financial institutions in Delta State, Nigeria. The research adopts a quantitative approach, with a sample size of 222 respondents drawn from employees of various financial institutions in the region.The study employs structured questionnaires to gather data, utilizing statistical methods such as correlation and regression analysis to assess the relationships between data accuracy, data quality, and decision-making efficiency. The findings reveal that both Data Accuracy (DA) and Data Quality (DQ) have a significant positive impact on Decision-Making Efficiency (DME) in financial institutions. Specifically, higher levels of data accuracy contribute to more precise and timely decisions, while better data quality ensures more reliable and informed decision-making processes. Based on these findings, the study recommends that financial institutions invest in improving the accuracy and quality of their data systems, including regular data validation and cleaning procedures, to enhance decision-making processes. Training staff in data management and BI tools is also suggested to ensure effective use of data for decision-making. It highlights the importance of robust data systems in improving organizational performance and decision-making efficiency in financial institutions. The research concludes that the integration of high-quality data and accurate information systems is vital for enhancing the overall efficiency of decision-making processes within the financial sector in Nigeria.

Chapter 4 - Interoperability risks and health informatics

Smart city digital twins can provide useful insights by making effective use of multi disciplinary urban data from diverse sources. Whilst these insights provide new information that helps cities in decision making, verifying the authenticity, integrity, traceability and data ownership across various functional units have become critical characteristics to ensure the data is from an authentic and trustworthy source. However, these characteristics are rarely considered in a digital twin ecosystem. In this research we introduce a novel framework, namely, ‘SIGNED: Smart cIty diGital twiN vErifiable Data framework’ that is designed on the basis of data ownership, selective disclosure and verifiability principles. Using Verifiable Credentials, SIGNED ensures digital twin data are verifiably authentic i.e., it covers provenance, transparency, and reliability through verifiable presentation. A proof of concept is designed and evaluated based on a smart water management use case to demonstrate the effectiveness of SIGNED in securing verifiable exchange of digital twin data across multiple functional units. The proof-of-concept demonstrates that SIGNED successfully allows the exchange of data in a trusted and verifiable manner at negilgible performance cost, thus enhancing security and alleviating privacy issues when sharing data between various functional units in a smart city.

The principle of evidence-based decision-making for development policy and planning is now well accepted, and population data are of critical importance. Some ministries (eg., Health) are even including targets for program managers in the “use of reliable data in 75% of their decisions”. In 2008–2009, four African countries were selected for a study to assess the demand for, access to and use of demographic data for development decision making. In the Ethiopia case study presented here, the authors carried out nearly 100 key informant interviews of decision makers, key advisors, planners and media, at Federal and regional levels, plus follow up dialogue with selected and forthcoming policy advisors. The main finding is that demand for demographic data has increased, with the heightened need for monitoring international (eg, poverty, Cairo conference and MDG) targets and national results-based planning, as well as decentralized and locally empowered planning. However, there is still weak demand by international partners for developing strong and transparent national ME differing sources of information available on the same indicator (eg., contraceptive prevalence, ante-natal care) with contradictory estimates; old, unrepresentative and non-disaggregated data; research and survey findings not communicated well to policymakers, and skepticism and even mistrust of unexpected demographic statistics. Applied research, rigorous evaluation and data generation and analytical capacity in the country are weak, and the lack of demographic media expertise exacerbates the data use gap. The overall recommendation is advocacy for a culture of transparent information in order to rebuild trust and promote strategic use, as well as active involvement of the media to promote awareness of the importance of demographic data for development. Technical and institutional capacity building include the strengthening of key statistical, research and data collection institutions; improving true international partnerships towards increasing local ownership for large scale demographic data collection, research and M&E systems. It is also important to resolving key indicator contradictions between service statistics and household surveys through committed harmonization of sources, improve communications between data analysts, media and policymakers, and the creation of a well-functioning National Population Council. More research is needed on the socio-cultural and historical barriers to enabling a greater culture of reliable data.

Organizing and annotating biomedical data in structured ways has gained much interest and focus in the last 30 years. Driven by decreases in digital storage costs and advances in genetics sequencing, imaging, electronic data collection, and microarray technologies, data is being collected at an ever increasing rate. The need to store and exchange data in meaningful ways in support of data analysis, hypothesis testing and future collaborative use is pervasive. Because trans-disciplinary projects rely on effective use of data from many domains, there is a genuine interest in informatics community on how best to store and combine this data while maintaining a high level of data quality and documentation. The difficulties in sharing and combining raw data become amplified after post-processing and/or data analysis in which the new dataset of interest is a function of the original data and may have been collected by multiple collaborating sites. Simple meta-data, documenting which subject and version of data were used for a particular analysis, becomes complicated by the heterogeneity of the collecting sites yet is critically important to the interpretation and reuse of derived results. This manuscript will present a case study of using the XML-Based Clinical Experiment Data Exchange (XCEDE) schema and the Human Imaging Database (HID) in the Biomedical Informatics Research Network's (BIRN) distributed environment to document and exchange derived data. The discussion includes an overview of the data structures used in both the XML and the database representations, insight into the design considerations, and the extensibility of the design to support additional analysis streams.

Missing values are ubiquitous in industrial data sets because of multisampling rates, sensor faults, and transmission failures. The incomplete data obstruct the effective use of data and degrade the performance of data-driven models. Numerous imputation algorithms have been proposed to deal with missing values, primarily based on supervised learning, that is, imputing the missing values by constructing a prediction model with the remaining complete data. They have limited performance when the amount of incomplete data is overwhelming. Moreover, many methods have not considered the autocorrelation of time-series data. Thus, an adaptive-learned median-filled deep autoencoder (AM-DAE) is proposed in this study, aiming to impute missing values of industrial time-series data in an unsupervised manner. It continuously replaces the missing values by the median of the input data and its reconstruction, which allows the imputation information to be transmitted with the training process. In addition, an adaptive learning strategy is adopted to guide the AM-DAE paying more attention to the reconstruction learning of nonmissing values or missing values in different iteration periods. Finally, two industrial examples are used to verify the superior performance of the proposed method compared with other advanced techniques.

Many application tasks of multidimensional data analysis which describe the state of real physical or other systems face with difficulties. This is a consequence of the low-quality source data, including missing values, the probability of errors or unreliability of measurements. Incomplete data can become an obstacle for research using many modern informational methods. The current work examines the potential and capabilities of visual analytics tools for preliminary preparation, correction or complete analysis of primary data volumes. A promising area of application of the approach discussed in the study is the targeted use of visualization capabilities as a data analysis tool. The implementation of specialized visual metaphors is used to solve problems of processing and interpreting data, the sources of which are cyberphysical systems of different complexity levels. Such systems operate in an autonomous or partially controlled mode. A characteristic feature of these systems is the presence of a large number of sensors that collect various types of data. Such data differ in the capacity of the corresponding information channels, their speed and reliability. Examples of such cyberphysical systems are unmanned aerial vehicles (UAVs), robotic stations, and multimodal monitoring systems. These systems can function in conditions where it is difficult to obtain objective observation experience (deep-sea robots). The effective use of data collected by cyberphysical monitoring systems is a condition for solving a large number of application and research tasks.

ABSTRACT: Construction and demolition waste comprises a significant portion of global waste streams, ranging from 26% to 40% in countries like the US, Europe, and China. As researchers become more conscious of material efficiency and responsible resource use, the Circular Economy (CE) concept, including construction and design practices, is getting more attention. Tools like Life Cycle Assessment (LCA) and Building Information Modeling (BIM) are expected to be crucial for activating CE practices. LCA provides essential data on the environmental impact of materials and energy by quantitatively assessing their flows, which is key for making informed decisions in line with CE. Meanwhile, BIM offers a technological framework that enables the digital representation of places' physical and functional characteristics, facilitating building data management throughout its lifecycle. However, a key challenge lies in the limited semantic interoperability between BIM and LCA tools. This issue hampers the seamless exchange and effective use of data, making it harder to fully harness the potential of both approaches in promoting CE. This study aims to identify and address the barriers that prevent the effective integration of BIM and LCA tools in construction and design practices to enhance the coordination between these tools and promote CE principles. The study systematically reviewed academic journals and conference publications to discover methodologies and technologies to bridge interoperability gaps. The review identifies emerging sample technologies as critical enablers for enhanced integration. Innovations are streamlining data exchange, reducing errors, and enabling automated flow between BIM and LCA platforms. Waste reduction and resource efficiency applications will require universally accepted data standards and open-source tools to enhance interoperability for advancing CE principles in construction.

The large amounts of client behavioral data collected by residential treatment programs represent an information processing challenee. If programs are to make effective use of data, they need processing systems which are flexible and cost-efficient, and which generate results applicable to program information needs. This report describes a computer-based system which utilizes data from daily behavioral observations by staff to produce varying types of statistical results, including behavioral progress charts of individual clicnts, behavioral charts of client groups, and more general research data. The report describes the steps involved in data collection, processing and dissemination within a residential treatment program.

Lacking a coordinated effort in utilizing data and tracking program outcomes, one agency developed a Quality Management (QM) division to facilitate and manage more effective data use. To support this process, the agency sought to develop a collective, agency-wide understanding and investment in improving and measuring client outcomes. Similarly, the agency also focused efforts on creating a culture of transparency and accountability, with goals of improving service, increasing agency integrity, meeting regulatory compliance, and engaging in effective risk management. Operationalizing the QM initiative involved developing procedures, systems, and guidelines that would facilitate the generation of reliable and accurate data that could be used to inform program change and decision-making. This case study describes this agency's experience in successfully creating and implementing a QM initiative aimed at engaging in greater knowledge sharing.

The liberalisation of aviation policies across Africa has had a significant impact on air traffic flow within the region. This study aims to examine the effects of this policy, with a specific focus on Nigeria’s regional connectivity to other West African countries. By analyzing data from the pre and post-liberalisation eras, the study aims to determine the changes in passenger and aircraft movement and evaluate the significance of the policy on Nigeria’s sub-region. The study utilizes a longitudinal data set covering the period between 1988 and 2018. This data is divided into two eras: the pre-liberalisation era (1988-2000) and the post-liberalisation era (2001-2018). To assess the impact of liberalisation, a t-test is conducted on the data, comparing the air traffic flow patterns before and after the implementation of liberalisation policies.The findings reveal a substantial improvement in the air traffic flow of passengers and aircraft from Nigeria to other West African countries since the introduction of liberalisation policies. The growth patterns observed in the post-liberalisation era exhibit a linear trend, indicating sustained progress. However, uncertainties arising from hazards in air transport operations can influence the growth patterns.The study underscores the positive impact of African aviation liberalisation policy on air traffic flow, particularly in the regional context of Nigeria’s connectivity with other West African countries. The results demonstrate significant improvements in passenger and aircraft movement. However, to fully capitalize on the benefits of liberalisation, regional efforts and an improved framework need to be intensified. This will ensure sustained growth and maximize the advantages of liberalisation in the aviation sector.

The Ethics of Transparency: Publication of Cardiothoracic Surgical Outcomes in the Lay Press

SummaryThis article is part of the Focus Theme of Methods of Information in Medicine on the German Medical Informatics Initiative. The Medical Informatics Initiative (MII) was launched within the scope of the German Federal Ministry of Education and Research’s (BMBF) Medical Informatics Funding Scheme, with the goal of developing infrastructure for the integration of clinical data from patient care and medical research in Germany. Its work is to be performed over the course of a decade (2016–2025) across three funding phases, with the first two concentrating on university hospitals. During the conceptual phase (now concluded), a central supporting project ensured coordination – and laid the ground for standardised solutions for all the initiative’s sites and scientific consortia that will enable effective data use and exchange, both for health care as well as research. The conceptual phase focused on the following: a) interoperability, through the consistent use of international standards (from an early stage, i.e. primary IT systems in patient care); b) standardised templates for patient consent and harmonised data protection; and c) standard rules for data use and access (monitoring and safeguarding access to data). On this basis, the initiative aims in the long term to improve medical research (particularly health care research, using data from treatments), to accelerate the transfer of knowledge from research to patient care – and to provide important impetus for the digitalization of medicine in Germany.

The accuracy of race & ethnicity data in US based healthcare databases: A systematic review