Suchergebnisse

Preservation of Knowledge- Data processing in the Danish Data Archives

Intro -- Preface -- Contents -- New Challenges in Air Quality Measurements -- 1 Introduction -- 2 Current Standard and Equivalence Measuring Methods -- 2.1 Problems Related to Current Air Quality Monitoring -- 3 Role of Low-Cost Sensors (LCS) in the Future Air Quality Networks -- 3.1 Low-Cost Sensor Technology -- 4 Data Elaboration of Low-Cost Sensors -- 4.1 Advantages of LCSs -- 4.2 Caveats of LCSs -- 5 Satellite Remote Sensors -- 5.1 Data Treatment -- 5.2 Advantages of Satellite Data -- 5.3 Caveats -- 5.4 Temporal and Spatial Resolution -- 5.5 Conclusion About Satellite Data -- 6 New Networks for Air Quality Monitoring -- 7 Conclusions -- References -- A Data Processing Architecture for Intelligent Hierarchical Air Quality Monitoring Networks in Urban Innovation and Citizen Science Applications -- 1 Introduction -- 2 General Architecture -- 2.1 AirHeritage IoT Inception and Storage Architecture -- 3 Data Processing Pipeline -- 3.1 Sensor Data Capture Stage: The MONICA Device -- 3.2 Calibration Stage -- 3.3 Sensor Fusion Stage -- 3.4 Personalized Feedback Stage -- 4 Conclusions -- References -- Using Continuous Integration Processes to Build Environments for Processing Air Quality Data from IoT Devices -- 1 Introduction -- 2 Software Engineering Processes in the Development of Complex Systems -- 3 Building an Environment for the Development of IoT Systems Using Good Software Engineering Practices -- 4 Verification of the IoT System Manufacturing Environment for the Construction of a Hybrid Information System on Air Quality for Gdańsk -- 5 Summary -- References -- AQ Mapping Through Low-Cost Sensor Networks -- 1 Introduction -- 2 Pollution Variables and Low-Cost Sensors -- 2.1 Common Pollutants in Urban Areas -- 2.2 Sensors -- 2.3 Low-Cost Commercial Sensors and Stations -- 3 AQ Data Collection Initiatives -- 3.1 Governmental and Private Efforts.

IntroductionThe Department of Veterans Affairs (VA) Million Veteran Program (MVP) nutrition data is derived from dietary food/beverage intake information collected through a semiquantitative food frequency questionnaire (SFFQ).
MethodsEstimates of dietary energy, nutrient, and non-nutritive food components intakes data were derived from an extensively validated SFFQ, which assessed the habitual frequency of consumption of 61 food items, added sugar, fried food frequency, and 21 nutritional supplements over the 12 months preceding questionnaire administration.
ResultsComplete nutrition data was available for 353,418 MVP participants as of 30th September 2021. Overall, 91.5% of MVP participants with nutrition data were male with an average age of 65.7 years at enrollment. Participants who completed the SFFQ were primarily White (82.5%), and Blacks accounted for 13.2% of the responders. Mean ± SD energy intake for 353, 418 MVP participants was 1428 ±616 kcal/day, which was 1434 ±617 kcal/day for males and 1364 ±601 kcal/day for females. Energy intake and information on 322 nutrients and non-nutritive food components is available through contact with MVP for research collaborations at www.research.va.gov/mvp.
ConclusionsThe energy and nutrient data derived from MVP SFFQ are an invaluable resource for Veteran health and research. In conjunction with the MVP Lifestyle Survey, electronic health records, and genomic data, MVP nutrition data may be used to assess nutritional status and related risk factors, disease prevalence, and determinants of health that can provide scientific support for the development of evidence-based public health policy and health promotion programs and services for Veterans and general population.

AbstractHealth data have enormous potential to transform healthcare, health service design, research, and individual health management. However, health data collected by institutions tend to remain siloed within those institutions limiting access by other services, individuals or researchers. Further, health data generated outside health services (e.g., from wearable devices) may not be easily accessible or useable by individuals or connected to other parts of the health system. There are ongoing tensions between data protection and the use of data for the public good (e.g., research). Concurrently, there are a number of data platforms that provide ways to disrupt these traditional health data siloes, giving greater control to individuals and communities. Through four case studies, this paper explores platforms providing new ways for health data to be used for personal data sharing, self-health management, research, and clinical care. The case-studies include data platforms: PatientsLikeMe, Open Humans, Health Record Banks, and unforgettable.me. These are explored with regard to what they mean for data access, data control, and data governance. The case studies provide insight into a shift from institutional to individual data stewardship. Looking at emerging data governance models, such as data trusts and data commons, points to collective control over health data as an emerging approach to issues of data control. These shifts pose challenges as to how "traditional" health services make use of data collected on these platforms. Further, it raises broader policy questions regarding how to decide what public good data should be put towards.

Social media shatters the barrier to communicate anytime anywhere for people of all walks of life. The publicly available, virtually free information in social media poses a new challenge to consumers who have to discern whether a piece of information published in social media is reliable. For example, it can be difficult to understand the motivations behind a statement passed from one user to another, without knowing the person who originated the message. Additionally, false information can be propagated through social media, resulting in embarrassment or irreversible damages. Provenance data associated with a social media statement can help dispel rumors, clarify opinions, and confirm facts. However, provenance data about social media statements is not readily available to users today. Currently, providing this data to users requires changing the social media infrastructure or offering subscription services. Taking advantage of social media features, research in this nascent field spearheads the search for a way to provide provenance data to social media users, thus leveraging social media itself by mining it for the provenance data. Searching for provenance data reveals an interesting problem space requiring the development and application of new metrics in order to provide meaningful provenance data to social media users. This lecture reviews the current research on information provenance, explores exciting research opportunities to address pressing needs, and shows how data mining can enable a social media user to make informed judgements about statements published in social media

There is a growing need for data on the sustainability of agriculture, not only with industry but especiallyalso with researchers and policy makers who have to monitor and evaluate the Common AgriculturalPolicy (CAP), including its cross‐compliance, greening and rural development measures. The FLINT project('Farm Level Indicators for New Topics in policy evaluation') has investigated options to collect such data.In nine member states of the European Union (EU), with different systems of data collection at farm level,it has collected and analysed sustainability data from 1,099 farms in the Farm Accountancy Data Network(FADN). The additional farm‐level data was collected in nine countries ‐ The Netherlands, Hungary,Finland, Poland, Spain, Ireland, Greece, France and Germany – and for eight types of farming (TF).Appendix A shows the distribution of farms within the FLINT sample. Within the FLINT project, theobjective of Workpackage (WP) 5 is to analyse the added value of the newly collected farm level indicatorsfor policy evaluation.The data available in the FLINT project includes accountancy data from FADN, the 'FADN data', as well asadditional data on economic, environmental and social sustainability of farms, 'FLINT data', andsustainability indicators computed with this data.Several WP5 analyses have been performed on specific topics, leading to 15 case study deliverables 5.2.The present deliverable, the last one of WP5, uses all this to draw lessons on the usefulness of theinformation collected during the FLINT project and in particular whether the farm level indicatorsdeveloped in FLINT can help improve policy evaluation.