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Background and Objectives: Clinical data are essential for developing cloud platforms for intelligent diagnosis and treatment decision of diseases. However, cloud platforms for data sharing and exchange with clinicians are poorly suited. We aim to establish Eyecare-cloud, a platform which provide a novel method for clinical data and medical image sharing, to provide a convenient tool for clinicians., Methods: In this study, we displayed the main functions of Eyecare-cloud that we established. Based on clinical data from the cloud platform, we analyzed the incidence trend of the most common infantile retinal diseases, such as retinopathy of prematurity (ROP), over the past 20 years, as well as the associated risk factors for ROP occurrence. Statistical analyses were performed using GraphPad Prism (V.8.0) and SPSS software (V.26.0)., Results: The Eyecare-cloud offers numerous advantages, including systematic archiving of patient information, one-click export data, simplifying data collection and management, eliminating the need for manual input of clinical information, reducing clinical data migration time, and lowering data management costs significantly. A total of 22,913 premature infants from Eyecare-cloud were included in the data analysis. Based on 20 years of premature infant screening data analysis, we found that the ROP incidence began to slowly decline starting in 2003 but showed a gradual increase trend again in 2016. The incidence of severe ROP remained relatively stable at a low level since 2010. The number of premature infants increased steadily before 2016 but decreased since then. ROP occurrence was significantly associated with male sex, lower gestational age, and lower birth weight ( P  < 0.001)., Conclusion: Eyecare-cloud provides clinicians and researchers with convenient tools for big data analysis, which helps alleviate clinical workloads and integrate research data. This cloud platform supports the principles of predictive, preventive, and personalized medicine (PPPM/3PM), empowering clinicians and researchers to deliver more precise, proactive, and patient-centered eye care., Competing Interests: Competing interestsThe authors declare no competing interests.Conflict of interestThe authors declare no competing interests., (© The Author(s), under exclusive licence to European Association for Predictive, Preventive and Personalised Medicine (EPMA) 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

This paper presents a fully automated pipeline using a sparse convolutional autoencoder for quality control (QC) of affine registrations in large-scale T1-weighted (T1w) and T2-weighted (T2w) magnetic resonance imaging (MRI) studies. Here, a customized 3D convolutional encoder-decoder (autoencoder) framework is proposed and the network is trained in a fully unsupervised manner. For cross-validating the proposed model, we used 1000 correctly aligned MRI images of the human connectome project young adult (HCP-YA) dataset. We proposed that the quality of the registration is proportional to the reconstruction error of the autoencoder. Further, to make this method applicable to unseen datasets, we have proposed dataset-specific optimal threshold calculation (using the reconstruction error) from ROC analysis that requires a subset of the correctly aligned and artificially generated misalignments specific to that dataset. The calculated optimum threshold is used for testing the quality of remaining affine registrations from the corresponding datasets. The proposed framework was tested on four unseen datasets from autism brain imaging data exchange (ABIDE I, 215 subjects), information eXtraction from images (IXI, 577 subjects), Open Access Series of Imaging Studies (OASIS4, 646 subjects), and "Food and Brain" study (77 subjects). The framework has achieved excellent performance for T1w and T2w affine registrations with an accuracy of 100% for HCP-YA. Further, we evaluated the generality of the model on four unseen datasets and obtained accuracies of 81.81% for ABIDE I (only T1w), 93.45% (T1w) and 81.75% (T2w) for OASIS4, and 92.59% for "Food and Brain" study (only T1w) and in the range 88-97% for IXI (for both T1w and T2w and stratified concerning scanner vendor and magnetic field strengths). Moreover, the real failures from "Food and Brain" and OASIS4 datasets were detected with sensitivities of 100% and 80% for T1w and T2w, respectively. In addition, AUCs of > 0.88 in all scenarios were obtained during threshold calculation on the four test sets., (© 2024. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

Artificial intelligence (AI) is a machine science that can mimic human behaviour like intelligent analysis of data. AI functions with specialized algorithms and integrates with deep and machine learning. Living in the digital world can generate a huge amount of medical data every day. Therefore, we need an automated and reliable evaluation tool that can make decisions more accurately and faster. Machine learning has the potential to learn, understand and analyse the data used in healthcare systems. In the last few years, AI is known to be employed in various fields in pharmaceutical science especially in pharmacological research. It helps in the analysis of preclinical (laboratory animals) and clinical (in human) trial data. AI also plays important role in various processes such as drug discovery/manufacturing, diagnosis of big data for disease identification, personalized treatment, clinical trial research, radiotherapy, surgical robotics, smart electronic health records, and epidemic outbreak prediction. Moreover, AI has been used in the evaluation of biomarkers and diseases. In this review, we explain various models and general processes of machine learning and their role in pharmacological science. Therefore, AI with deep learning and machine learning could be relevant in pharmacological research., (© 2023. The Author(s) under exclusive licence to Maj Institute of Pharmacology Polish Academy of Sciences.)

There have recently been intensive efforts aimed at addressing the challenges of environmental degradation and climate change through the applied innovative solutions of AI, IoT, and Big Data. Given the synergistic potential of these advanced technologies, their convergence is being embraced and leveraged by smart cities in an attempt to make progress toward reaching the environmental targets of sustainable development goals under what has been termed "environmentally sustainable smart cities." This new paradigm of urbanism represents a significant research gap in and of itself. To fill this gap, this study explores the key research trends and driving factors of environmentally sustainable smart cities and maps their thematic evolution. Further, it examines the fragmentation, amalgamation, and transition of their underlying models of urbanism as well as their converging AI, IoT, and Big Data technologies and solutions. It employs and combines bibliometric analysis and evidence synthesis methods. A total of 2,574 documents were collected from the Web of Science database and compartmentalized into three sub-periods: 1991-2015, 2016-2019, and 2020-2021. The results show that environmentally sustainable smart cities are a rapidly growing trend that markedly escalated during the second and third periods-due to the acceleration of the digitalization and decarbonization agendas-thanks to COVID-19 and the rapid advancement of data-driven technologies. The analysis also reveals that, while the overall priority research topics have been dynamic over time-some AI models and techniques and environmental sustainability areas have received more attention than others. The evidence synthesized indicates that the increasing criticism of the fragmentation of smart cities and sustainable cities, the widespread diffusion of the SDGs agenda, and the dominance of advanced ICT have significantly impacted the materialization of environmentally sustainable smart cities, thereby influencing the landscape and dynamics of smart cities. It also suggests that the convergence of AI, IoT, and Big Data technologies provides new approaches to tackling the challenges of environmental sustainability. However, these technologies involve environmental costs and pose ethical risks and regulatory conundrums. The findings can inform scholars and practitioners of the emerging data-driven technology solutions of smart cities, as well as assist policymakers in designing and implementing responsive environmental policies., Competing Interests: Competing interestsThe authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article., (© The Author(s) 2023.)

The early stages of the COVID-19 pandemic required a dramatic change in social practices, including distancing from social settings, to limit its spread. While social capital has considerable potential in facilitating the adoption of these norms, it also comes with considerable limitations that potentially undermine its effectiveness. We draw upon recently released mobility data from Google, network data from Facebook, and demographic data from the 2018 American Community Survey to determine how both organizational and networked measures of social capital relate to different forms of distancing. In addition, we employ geographically weighted regression to identify how these relationships vary across the nation. Findings indicate that while both forms of social capital can positively relate to distancing, the impacts are spatially inconsistent and, in some locations, social capital can discourage distancing. In sum, more policy efforts are needed to address not only low-social capital, but also unhelpful social capital., Competing Interests: Conflict of interestThe authors report no conflict of interest., (© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2021.)

Factors that determine individual age-related decline rates in physical performance are poorly understood and prediction poses a challenge. Linear and quadratic regression models are usually applied, but often show high prediction errors for individual athletes. Machine learning approaches may deliver more accurate predictions and help to identify factors that determine performance decline rates. We hypothesized that it is possible to predict the performance development of a master athlete from a single measurement, that prediction by a machine learning approach is superior to prediction by the average decline curve or an individually shifted decline curve, and that athletes with a higher starting performance show a slower performance decline than those with a lower performance. The machine learning approach was implemented using a multilayer neuronal network. Results showed that performance prediction from a single measurement is possible and that the prediction by a machine learning approach was superior to the other models. The estimated performance decline rate was highest in athletes with a high starting performance and a low starting age, as well as in those with a low starting performance and high starting age, while the lowest decline rate was found for athletes with a high starting performance and a high starting age. Machine learning was superior and predicted trajectories with significantly lower prediction errors compared to conventional approaches. New insights into factors determining decline trajectories were identified by visualization of the model outputs. Machine learning models may be useful in revealing unknown factors that determine the age-related performance decline., (© 2021. The Author(s).)

An evident underestimation of the targeted prevention of dental diseases is strongly supported by alarming epidemiologic statistics globally. For example, epidemiologists demonstrated 100% prevalence of dental caries in the Russian population followed by clinical manifestation of periodontal diseases. Inadequately provided oral health services in populations are caused by multi-factorial deficits including but not limited to low socio-economic status of affected individuals, lack of insurance in sub-populations, insufficient density of dedicated medical units. Another important aspect is the "participatory" medicine based on the active participation of population in maintaining oral health: healthcare will remain insufficient as long as the patient is not motivated and does not feel responsible for their oral health. To this end, nearly half of chronically diseased people do not comply with adequate medical services suffering from severely progressing pathologies. Noteworthy, the prominent risk factors and comorbidities linked to the severe disease course and poor outcomes in COVID-19-infected individuals, such as elderly, diabetes mellitus, hypertension and cardiovascular disease, are frequently associated with significantly altered oral microbiome profiles, systemic inflammatory processes and poor oral health. Suggested pathomechanisms consider potential preferences in the interaction between the viral particles and the host microbiota including oral cavity, the respiratory and gastrointestinal tracts. Since an aspiration of periodontopathic bacteria induces the expression of angiotensin-converting enzyme 2, the receptor for SARS-CoV-2, and production of inflammatory cytokines in the lower respiratory tract, poor oral hygiene and periodontal disease have been proposed as leading to COVID-19 aggravation. Consequently, the issue-dedicated expert recommendations are focused on the optimal oral hygiene as being crucial for improved individual outcomes and reduced morbidity under the COVID-19 pandemic condition. Current study demonstrated that age, gender, socio-economic status, quality of environment and life-style, oral hygiene quality, regularity of dental services requested, level of motivation and responsibility for own health status and corresponding behavioural patterns are the key parameters for the patient stratification considering person-tailored approach in a complex dental care in the population. Consequently, innovative screening programmes and adapted treatment schemes are crucial for the complex person-tailored dental care to improve individual outcomes and healthcare provided to the population., Competing Interests: Competing interestsThe authors declare no competing interests., (© The Author(s) 2021.)

Biobanking as a quickly growing branch of personalised medicine has undergone enormous progress during last two decades. Nowadays it is a well developed and structured multidisciplinary field that reflects developments and advances of biomedical research based on principles of predictive, preventive and personalised medicine (PPPM/3PM). All these trends in PPPM progress have to be translated into practice and education of new generation of scientists and healthcare givers. The importance of biobanks for multitasking research, personalised treatment, and health care systems was emphasised by many scientists and health care experts. As biobanking carries multidisciplinary character currently including more professionals than ten-twenty years ago, new generation of professional biobankers is urgently needed. To create new generation of biobankers who are fully competent to answer more and more scientific and practical questions, new study programmes, novel university curricula, and topic-dedicated courses are essential. The aim of the review is to present basic forms, trends of biobanking education offered by various biobanking related bodies and to highlight future needs. The first step is to cover all activities and duties of biobanks: acquiring, collecting, storageing and sharing biological samples and associated data, using adequate assessment for both - materials and data, taking into consideration ethical, legal, and societal issues (ELSI), responding to all stakeholder needs including pharmaceutical and other related industries, patient organisations and many other interested groups, emerging technologies and innovations as well as current and future requirements of health care systems. To compile educational programmes is a comprehensive task for all actors involved in the field of biobanking who contribute to the harmonised process of creating high educational level for future generation of biobankers. The exchange of experience involving extensive international collaboration is the way how to facilitate the process of creating optimal biobanking education., Competing Interests: Ethics approvalThe author declares that she has no conflict of interest.Conflict of interestThe author declares that she has no conflict of interest., (© European Association for Predictive, Preventive and Personalised Medicine (EPMA) 2021.)

In the research of age-related performance declines, the value of cross-sectional versus longitudinal data is an ongoing debate. This paper analyses the largest longitudinal master track and field data set ever published to compare the age-related decline in performance between 16 athletics disciplines in cross-sectional and longitudinal data. The data set contained 83,209 results (64,948 from men, 78.1%; 18,261 from women, 21.9%) from 34,132 athletes (26,186 men, 76.7%; 7946 women, 23.3%), aged 35-97 years. In 61 athletes, 20 or more, and in 312 athletes, 15 or more results were available. The data were analyzed by regression statistics/ANCOVA. Men had a higher performance than women, irrespective of discipline in both cross-sectional and longitudinal data (p < 0.001). The performance in cross-sectional data was lower compared with the longitudinal data in all events and at any age (p ≤ 0.007) except for 1000 m men. The average age was lower in the cross-sectional than the longitudinal data (p < 0.001); men 46 and 58 years, women 44 and 56 years, respectively. The annual percentage rate of decline did not differ significantly between cross-sectional and longitudinal data, or between sexes in most disciplines. Performance declines after age 70 were 1.7 times (men) and 1.4 times (women) as steep as before. In conclusion, although longitudinal master athletics data of athletes with 10 and more results has higher average performance and age compared with cross-sectional data, cross-sectional data give a good impression of the annual percentage decline in performance, which was similar in men and women.

Background: The technological complexities and broad operational scope of eSource impede coordinated, inter-organizational action on advancing at-scale solutions., Methods: We introduce an architectural framework for articulating technological considerations across organizations. The architecture neither implies nor endorses solution implementations; rather, it proposes solution functionality based upon principles and good clinical practices., Results: Key technology considerations include patterns of anticipated use, implications to the current state of clinical trial operations, and the need for new technologies (i.e., IoT, Big Data, Predictive Analytics)., Conclusion: Technology considerations drive implications beyond technology-influencing regulatory, process, and ethical realms of clinical research.

Background: In 2006, a concept paper (ICH S2(R1)) describing the need for revision of the ICH guidelines on genotoxicity testing for new "small molecule" pharmaceuticals (then ICH S2A and ICH S2B) was finalised. As a result, testing strategy has changed, and flexibility has been introduced in the form of two "equally suitable" options for completing the battery of genotoxicity studies required to support clinical development and marketing of new products., Methods: The TIBCO Spotfire ® platform was used to create a specific view of available in-house data on genotoxicity studies conducted to support pharmaceutical product development over a period of approximately 12 years. The available in-house dataset comprised all reportable non-clinical data from Good Laboratory Practice (GLP) compliant and non-GLP/screening studies on regulated products (including pharmaceuticals, industrial chemicals, agrochemicals, and medical devices) across multiple testing sites, in different geographical locations., Results: The analysis showed clear trends in the numbers and types of genotoxicity studies conducted on small molecule pharmaceutical products during development, which correlated with the changes in the available regulatory guidance over time. More interestingly, where two "equally suitable" options for genotoxicity testing are described in the international guidance, there is a clear preference for ICH S2(R1) Option 1 (70-80% of testing) compared to Option 2 (20-30%)., Conclusion: Use of 'Big Data' identified trends in the newer approach to genotoxicity testing by industry in the light of the updated regulatory guidance.

Patient recruitment and retention are arguably the greatest challenges to the timely execution of clinical trials. This is particularly true in the case of trials involving biosimilars and those focused on rare diseases. For biosimilars, recruitment success typically hinges on difficulty of access to reimbursement for the originator product and may be hindered by competition from studies with other biosimilars and those with new chemical entities. For rare diseases, recruitment success depends not only on finding enough patients but also on retaining them for the duration of the trial. Historical success with patient recruitment-in addition to site qualifications-needs to be considered in parallel with current market competition and results in an ever-changing patient recruitment environment. Multiple companies supporting the biopharmaceutical industry, such as Contract Research Organizations, have begun to leverage sophisticated data modeling tools and techniques to find additional patients for biosimilar and rare disease trials and/or to find patients more quickly. In addition, these companies seek to better understand the population of interest and refine their statistical assumptions when conducting clinical trials or real-world analyses. The largest and most well-established companies that support the biopharmaceutical industry now have unparalleled access to big data from clinical trials, electronic health records, medical claims, laboratory tests, and prescriptions. This paper will discuss how big data can be harnessed to aid patient recruitment with a focus on clinical trials for biosimilars and orphan rare diseases.

Biobanking is entering the new era-era of big data. New technologies, techniques, and knowledge opened the potential of the whole domain of biobanking. Biobanks collect, analyse, store, and share the samples and associated data. Both samples and especially associated data are growing enormously, and new innovative approaches are required to handle samples and to utilize the potential of biobanking data. The data reached the quantity and quality of big data, and the scientists are facing the questions how to use them more efficiently, both retrospectively and prospectively with the aim to discover new preventive methods, optimize treatment, and follow up and to optimize healthcare processes. Biobanking in the era of big data contribute to the development of predictive, preventive, and personalised medicine, for every patient providing the right treatment at the right time. Biobanking in the era of big data contributes to the paradigm shift towards personalising of healthcare., Competing Interests: Conflict of interestThe authors declare that they have no conflicts of interest., (© European Association for Predictive, Preventive and Personalised Medicine (EPMA) 2020.)

Objective: The objective of this study is to assess public attitudes toward pharmaceutical companies' secondary uses of patient records and public preferences regarding consent approaches., Method: 3000 responses to an online survey were collected from adults in Japan. The questionnaire included 32 items related to (1) awareness of "clinical trials"; (2) awareness of the processes of drug development, such as cost, time, and the number of candidate substances in a new drug; (3) knowledge of the laws and regulations for use of patient records in Japan; (4) assessment of the public benefit of the secondary use of patient records; (5) preferences for consent for the secondary use of patient records; and (6) basic characteristics of the respondents., Results: The public benefit from secondary use of records by academic institutions for scientific research was rated highest. All of the activities by pharmaceutical companies were rated higher than those by governmental institutions and other for-profit companies. Regarding consent approaches, 37.9% preferred an "opt-in" approach for new drug development by pharmaceutical companies, 79.7% of whom would change their preference to an "opt-out" approach under specific conditions, such as ensuring intended uses only., Conclusion: Our respondents consider the "public benefit" as dependent on the relative distance from "promoting public health" when assessing the secondary purpose of patient record use. Pharmaceutical companies should include the beneficial purposes when using patient records with "opt-out" approach. Policy makers should pay more attention to the purposes of use when developing personal information protection policies.

The collection and classification of data into meaningful categories is a key step in the process of knowledge making. In the life sciences, the design of data discovery and integration tools has relied on the premise that a formal classificatory system for expressing a body of data should be grounded in consensus definitions for classifications. On this approach, exemplified by the realist program of the Open Biomedical Ontologies Foundry, progress is maximized by grounding the representation and aggregation of data on settled knowledge. We argue that historical practices in systematic biology provide an important and overlooked alternative approach to classifying and disseminating data, based on a principle of coordinative rather than definitional consensus. Systematists have developed a robust system for referring to taxonomic entities that can deliver high quality data discovery and integration without invoking consensus about reality or "settled" science.

Computational models using text corpora have proved useful in understanding the nature of language and human concepts. One appeal of this work is that text, such as from newspaper articles, should reflect human behaviour and conceptual organization outside the laboratory. However, texts do not directly reflect human activity, but instead serve a communicative function and are highly curated or edited to suit an audience. Here, we apply methods devised for text to a data source that directly reflects thousands of individuals' activity patterns. Using product co-occurrence data from nearly 1.3-m supermarket shopping baskets, we trained a topic model to learn 25 high-level concepts (or topics ). These topics were found to be comprehensible and coherent by both retail experts and consumers. The topics indicated that human concepts are primarily organized around goals and interactions (e.g. tomatoes go well with vegetables in a salad), rather than their intrinsic features (e.g. defining a tomato by the fact that it has seeds and is fleshy). These results are consistent with the notion that human conceptual knowledge is tailored to support action. Individual differences in the topics sampled predicted basic demographic characteristics. Our findings suggest that human activity patterns can reveal conceptual organization and may give rise to it., (© The Author(s) 2019.)

Objective: The objective of this study is to assess public attitudes toward pharmaceutical companies' secondary uses of patient records and public preferences regarding consent approaches., Method: 3000 responses to an online survey were collected from adults in Japan. The questionnaire included 32 items related to (1) awareness of "clinical trials"; (2) awareness of the processes of drug development, such as cost, time, and the number of candidate substances in a new drug; (3) knowledge of the laws and regulations for use of patient records in Japan; (4) assessment of the public benefit of the secondary use of patient records; (5) preferences for consent for the secondary use of patient records; and (6) basic characteristics of the respondents., Results: The public benefit from secondary use of records by academic institutions for scientific research was rated highest. All of the activities by pharmaceutical companies were rated higher than those by governmental institutions and other for-profit companies. Regarding consent approaches, 37.9% preferred an "opt-in" approach for new drug development by pharmaceutical companies, 79.7% of whom would change their preference to an "opt-out" approach under specific conditions, such as ensuring intended uses only., Conclusion: Our respondents consider the "public benefit" as dependent on the relative distance from "promoting public health" when assessing the secondary purpose of patient record use. Pharmaceutical companies should include the beneficial purposes when using patient records with "opt-out" approach. Policy makers should pay more attention to the purposes of use when developing personal information protection policies.

Dominant forms of contemporary big-data based digital citizen science do not question the institutional divide between qualified experts and lay-persons. In our paper, we turn to the historical case of a large-scale amateur project on biogeographical birdwatching in the late nineteenth and early twentieth century to show that networked amateur research (that produces a large set of data) can operate in a more autonomous mode. This mode depends on certain cultural values, the constitution of specific knowledge objects, and the design of self-governed infrastructures. We conclude by arguing that the contemporary quest for autonomous citizen science is part of a broader discourse on the autonomy of scientific research in general. Just as the actors in our historical case positioned themselves against the elitism of gentlemen scientists, avant-garde groups of the twenty first century like biohackers and civic tech enthusiasts position themselves against the system of professional science-while "digital citizen science" remains to oscillate between claims for autonomy and realities of heteronomy, constantly reaffirming the classic lay-expert divide.

Introduction: Alzheimer's disease (AD) is the first cause of dementia. Diagnostic criteria have evolved: proposals to revise the NINCDS-ADRDA criteria were published in 2007. Our aim was to analyze the evolution in the coding of AD in the French nationwide exhaustive hospital discharge database (PMSI) between 2007 and 2017., Methods: We analyzed evolution of International Classification of Diseases and Related Health Problems, 10th edition (ICD-10) coding for AD and AD dementia in the PMSI database from 2008 to 2017 (285,748,938 inpatient stays)., Results: We observed a 44% decrease in the number of inpatient stays with a principal diagnosis of AD or AD dementia from 2007 (46,313 inpatient stays) to 2017 (25,856 inpatient stays) in France. Over the same period, we observed a 49% increase in the number of inpatient stays with a principal diagnosis of related dementias (other organic mental disorders or other degenerative disorders). Overall, the number of inpatient stays for dementia remained stable despite the increase in the total number of inpatient stays: 95,377 in 2007 (0.409% of inpatient stays) and 99,190 in 2017 (0.344%)., Conclusion: We therefore note a shift from AD and AD dementia to other dementia diagnoses since 2007. This study suggests a more accurate use of AD related ICD-10 codes since the revised criteria in 2007., Competing Interests: The authors do not have any conflict of interest to declare.

Biobanks have during last two decades gained an important role in the whole process of biomedical research and health care not only in Europe but also worldwide. Biobanks are one of the pillars in personalised medicine tackling all its aspects such as prevention, diagnosis, treatment and monitoring closely the specific characteristics of an individual patient. The current and future power of biobanks is the amount of samples of high-quality and related information available for current and future research of diseases, for optimising patients´ prevention, diagnosis, treatment and monitoring. The material stored in biobanks is a treasure for future technologies that will be able to utilise the currently uncovered information and knowledge. A great and growing number of samples and related information also opens new ways in how to tackle the big data problems and population studies. Biobanks play a substantial role in drug discovery, development and validation. Biobanks are not only an issue of biomedical research, but are becoming a public issue involving patients, to actively participate in biobanking with respect to ethical, legal and social issues. And, finally, biobanking as a multidisciplinary and modern field of science requires appropriate education at all levels of society. To be a world leader in the field of biobanking requires wide international and interdisciplinary collaboration. The topic-dedicated programmes released by the European Commission sustainably support biobank development in Europe and the main tool is the biggest European Union (EU) research and innovation programme ever-Horizon 2020. This article reviews the main Horizon 2020 biobanking projects, financing schemes and the future perspectives., Competing Interests: The authors declare that they have no conflict of interest.The authors provide consent for publication.For this type of study, formal consent is not required.

In this paper, we analyze the millions of referral paths of patients' interactions with the healthcare system for each year in the 2006-2011 time period and relate them to U.S. cardiovascular treatment records. For a patient, a "referral path" records the chronological sequence of physicians encountered by a patient (subject to certain constraints on the times between encounters). It provides a basic unit of analysis in a broader referral network that encodes the flow of patients and information between physicians in a healthcare system. We consider referral networks defined over a range of interactions as well as the characteristics of referral paths, producing a characterization of the various networks as well as the physicians they comprise. We further relate these metrics and findings to outcomes in the specific area of cardiovascular care. In particular, we match a referral path to occurrences of Acute Myocardial Infarction (AMI) and use the summary measures of the referral path to predict the treatment a patient receives and medical outcomes following treatment. Some referral path features are more significant with respect to their ability to boost a tree-based predictive model, and have stronger correlations with numerical treatment outcome variables. The patterns of referral paths and the derived informative features illustrate the potential for using network science to optimize patient referrals in healthcare systems for improved treatment outcomes and more efficient utilization of medical resources., Competing Interests: The authors declare that they have no competing interests.Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Genome data, the core of the 2008 proclaimed big data revolution in biology, are automatically generated and analyzed. The transition from the manual laboratory practice of electrophoresis sequencing to automated DNA-sequencing machines and software-based analysis programs was completed between 1982 and 1992. This transition facilitated the first data deluge, which was considerably increased by the second and third generation of DNA-sequencers during the 2000s. However, the strategies for evaluating sequence data were also transformed along with this transition. The paper explores both the computational strategies of automation, as well as the data evaluation culture connected with it, in order to provide a complete picture of the complexity of today's data generation and its intrinsic data positivism. This paper is thereby guided by the question, whether this data positivism is the basis of the big data revolution of molecular biology announced today, or it marks the beginning of its data hubris.

This article looks at how games and play contribute to the big data-driven production of knowledge in High-Energy Physics, with a particular focus on the Large Hadron Collider (LHC) at the European Organization for Nuclear Research (CERN), where the author has been conducting anthropological fieldwork since 2014. The ludic (playful) aspect of knowledge production is analyzed here in three different dimensions: the Symbolic, the Ontological, and the Epistemic. The first one points towards CERN as place where a cosmological game of probability is played with the help of Monte-Carlo simulations. The second one can be seen in the agonistic infrastructures of competing experimental collaborations. The third dimension unfolds in ludic platforms, such as online Challenges and citizen science games, which contribute to the development of machine learning algorithms, whose function is necessary in order to process the huge amount of data gathered from experimental events. Following Clifford Geertz, CERN itself is characterized as a site of deep play, a concept that contributes to understanding wider social and cultural orders through the analysis of ludic collective phenomena. The article also engages with Peter Galison's idea of the trading zone, proposing to comprehend it in the age of big data as a Playground. Thus the author hopes to contribute to a wider discussion in the historiographical and social study of science and technology, as well as in cultural anthropology, by recognizing the ludic in science as a central element of understanding collaborative knowledge production.

This paper is a critical assessment of the epistemological impact of the systematic quantification of nature with the accumulation of big datasets on the practice and orientation of ecological science. We examine the contents of big databases and argue that it is not just accumulated information; records are translated into digital data in a process that changes their meanings. In order to better understand what is at stake in the 'datafication' process, we explore the context for the emergence and quantification of biodiversity in the 1980s, along with the concept of the global environment. In tracing the origin and development of the global biodiversity information facility (GBIF) we describe big data biodiversity projects as a techno-political construction dedicated to monitoring a new object: the global diversity. We argue that, biodiversity big data became a powerful driver behind the invention of the concept of the global environment, and a way to embed ecological science in the political agenda.

Academic research is driven by several factors causing different disciplines to act as "sources" or "sinks" of knowledge. However, how the flow of authors' research interests - a proxy of human knowledge - evolved across time is still poorly understood. Here, we build a comprehensive map of such flows across one century, revealing fundamental periods in the raise of interest in areas of human knowledge. We identify and quantify the most attractive topics over time, when a relatively significant number of researchers moved from their original area to another one, causing what we call a "diaspora of the knowledge" towards sinks of scientific interest, and we relate these points to crucial historical and political events. Noticeably, only a few areas - like Medicine, Physics or Chemistry - mainly act as sources of the diaspora, whereas areas like Material Science, Chemical Engineering, Neuroscience, Immunology and Microbiology or Environmental Science behave like sinks.