A Critical Analysis on Obtaining and Using Data and Information for Pandemic Management

The chapter provides an in-depth overview and analysis for developing policies and strategies for managing a pandemic based on information and data. While looking for the credibility of an information source, various parameters are subjected for considerations (i.e., infection and death rates per given time, availability of personal protective equipment [PPE], overall population attitude, current strategy response rate, society behaviors, outcomes of policies interventions for curbing the spread of the virus, and many more). To critically analyze pandemic information and data usage along with issues and challenges that arise in collecting, extracting, or using various forms of information and data for pandemic management, numerous national action plans, world health databases, pandemic monitoring smart applications, government published infection-to-death ratios, and health cloud services are interpreted and discussed.

Design of Safety and Integrated Disaster Prevention System Based on Big Data Technology

With the development of Internet technology, with the continuous increase of data volume, it has become more and more difficult to maintain the traditional centralized data storage method. Data is easy to copy, difficult to share, high storage costs, and low data usage efficiency. Further trigger the demand for more efficient data storage technology. This article aims to study the application of blockchain technology in the data security storage and sharing system. On the basis of analyzing the problems of data sharing and cryptography, the functional modules of the data security storage and sharing system are designed. Encryption uses public key encryption algorithm to ensure encryption performance. The simulation experiment results show that the system is effective for file sharing, and the average generation time of the algorithm in this paper is within the controllable range.

Big Data for Prediction

Technical field of big data for prediction lures the attention of different stakeholders. The reasons are related to the potentials of the big data, which allows for learning from past behavior, discovering patterns and values, and optimizing business processes based on new insights from large databases. However, in order to fully utilize the potentials of big data, its stakeholders need to understand the scope and volume of patenting related to big data usage for prediction. Therefore, this chapter aims to perform an analysis of patenting activities related to big data usage for prediction. This is done by (1) exploring the timeline and geographic distribution of patenting activities, (2) exploring the most active assignees of technical content of interest, (3) detecting the type of the protected technical according to the international patent classification system, and (4) performing text-mining analysis to discover the topics emerging most often in patents' abstracts.

The role of data in sustainability assessment of urban mobility policies

Data have played a role in urban mobility policy planning for decades, especially in forecasting demand, but much less in policy evaluations and assessments. The surge in availability and openness of (big) data in the last decade seems to provide new opportunities to meet demand for evidence-based policymaking. This paper reviews how different types of data are employed in assessments published in academic journals by analyzing 74 cases. Our review finds that (a) academic literature has currently provided limited insight in new data developments in policy practice; (b) research shows that the new types of big data provide new opportunities for evidence-based policy-making; however, (c) they cannot replace traditional data usage (surveys and statistics). Instead, combining big data with survey and Geographic Information System data in ex-ante assessments, as well as in developing decision support tools, is found to be the most effective. This could help policymakers not only to get much more insight from policy assessments, but also to help avoid the limitations of one certain type of data. Finally, current research projects are rather data supply-driven. Future research should engage with policy practitioners to reveal best practices, constraints, and potential of more demand-driven data use in mobility policy assessments in practice.

The Notion of Continuing Education in Local Education Reports in Germany—An Analysis of Regional Disparities in Topics, Data, and Governance Recommendations

In Germany: continuing education (CE) is, to a large extent, controlled by municipalities (“Kommunen”). Municipalities have published an increasing number of education reports in recent years. These are intended as steering instruments for local education policy. Given that municipalities and the districts they represent differ in their structures, different challenges and opportunities associated with CE emerge. So far, it remains unclear which aspects of CE are considered by different types of municipalities in their reports and which steering potentials are seen. Based on a content analysis, we present findings on reported topics of CE, the extent of data usage, and derived recommendations for local governance. We separated four district types, showing different degrees of the dimension urban–rural. Although cities published education reports more frequently, rural districts were more likely to address CE issues. However, they had less data available, resulting in a lower range of topics and narrow overall opportunities for addressing CE. Therefore, improvement of data and accessibility of data are important to enhance the monitoring and governance of CE in municipalities. Regarding the reviewed recommendations, clear differences between district types emerged. For instance, expanding offers of CE for immigrants was mainly an issue of large cities, whereas rural districts emphasized the spatial and digital accessibility of offerings.

LotuS2: An ultrafast and highly accurate tool for amplicon sequencing analysis

Background: Amplicon sequencing is an established and cost-efficient method for profiling microbiomes. However, many available tools to process this data require both bioinformatics skills and high computational power to process big datasets. Furthermore, there are only few tools that allow for long read amplicon data analysis. To bridge this gap, we developed the LotuS2 (Less OTU Scripts 2) pipeline, enabling user-friendly, resource friendly, and versatile analysis of raw amplicon sequences. Results: In LotuS2, six different sequence clustering algorithms as well as extensive pre- and post-processing options allow for flexible data analysis by both experts, where parameters can be fully adjusted, and novices, where defaults are provided for different scenarios. We benchmarked three independent gut and soil datasets, where LotuS2 was on average 29 times faster compared to other pipelines - yet could better reproduce the alpha- and beta-diversity of technical replicate samples. Further benchmarking a mock community with known taxa composition showed that, compared to the other pipelines, LotuS2 recovered a higher fraction of correctly identified genera and species (98% and 57%, respectively). At ASV/OTU level, precision and F-score were highest for LotuS2, as was the fraction of correctly reconstructed 16S sequences. Conclusion: LotuS2 is a lightweight and user-friendly pipeline that is fast, precise and streamlined. High data usage rates and reliability enable high-throughput microbiome analysis in minutes. Availability: LotuS2 is available from GitHub, conda or via a Galaxy web interface, documented at http://lotus2.earlham.ac.uk/.

Ethical Considerations for the Use of Consumer Wearables in Health Research (Preprint)

UNSTRUCTURED Following the UN’s High Commissioner’s request for a moratorium on the use and adoption of specific Artificial Intelligence (AI) systems that pose serious risk to human rights, this commentary explores the current environment and future implications of using third-party wearable technologies in research for participants’ data privacy and data security. While wearables have been identified as tools in improving users’ physical and mental health and wellbeing by providing users with more personalized data and tailored interventions, the use of this technology does not come without concern. Primarily, as researchers we are concerned with enmeshment of corporate and research interests and what this can mean for participant data. By drawing on specific sections of the UN Report “The right to privacy in the digital age” we discuss the conflicts between corporate and research agendas and point out the current and future implications of the involvement of third-party companies for participant data privacy, data security, and data usage. Finally, we offer suggestions for researchers and third-party wearable developers for conducting ethical and transparent research with wearable tech. We propose that this commentary be used as a foothold for further discussions about the ethical implications of using third-party wearable tech in research.

The Obs-Services and their potentials for biodiversity data assessments with a test of the current reliability of photo-identification of Coleoptera in the field

The current reliability of species identifications by the Nature Identification API (NIA) of the app ObsIdentify is tested with a Coleoptera (Insecta) sample set from Germany. Seventy-five photographic beetle records taken with a smartphone camera under “average user” conditions are analysed in terms of correctness of the app’s identification result on various taxonomic levels, the displayed confidence level of the identification and the time until validation of the results. More than 60% of samples were identified correctly at the species level, but only 53% were validated within a month. The mechanisms by which users can upload pictures of their observations to be identified by the artificial intelligence and the validation process by experts are briefly explained. Regional specifics and further opportunities for data usage as well as currently existing problems are discussed and improvements are suggested. The expert validation of records is identified as a huge quality advantage of the Obs-Services. They are generally found to be a promising tool for lay people and professional institutions, despite still existing deficiencies such as identification failure in mutilated specimens, cryptic and rare species, doubtful species rarity ratings as well as the still insufficient capacity of validation. Experts and institutions are encouraged to volunteer as validators and collaborators.