Scientific Research Data Research Articles

A study on the big data scientific research model and the key mechanism based on blockchain

Abstract In an era of open data sharing, the scientific research field puts forward an urgent need for the value of big data. However, big data still form “data islands,” which seriously affects the level of scientific research and the progress of scientific research. In this regard, this article proposes the research and realization of the big data scientific research model and key mechanism based on blockchain. This article uses the K-means algorithm to cluster scientific research data and reasonably utilizes the decentralization, smart contracts, and non-tampering characteristics of the blockchain to design a distributed data model based on the blockchain. This article proposes that a BIZi network is formed based on a blockchain Interplanetary File System (IPFS) and Zigzag code (blockchain, IPF Sand Zigzag code, BIZi for short) to achieve reliable data connection and through a set of data access control mechanisms and data service customization mechanism to effectively provide data requirements for scientific research. Finally, IPFS network transmission speed performance can better meet the needs of scientific research. The larger the number of file blocks, the higher the fault tolerance rate of the scheme and the better the storage efficiency. In a completely open data-sharing scenario, the fault tolerance rate of Byzantine nodes is extremely high to ensure the stability of the blockchain. The current optimal consensus algorithm fault tolerance rate reaches 49%.

Twenty years of the Human Genome Diversity Project

In a seminal paper from 2005, Cavalli-Sforza describes the Human Genome Diversity Project (HGDP), an endeavour to collect the worldwide genetic diversity originating before the big diasporas and colonization following the fifteenth century. He recounts the project’s conception, its completion and first scientific impacts in 2002, the issues and criticism it faced and its possible role in the future of human genetics. Now, twenty years after its birth, we can take a look at that future and the long-term impact that the HGDP had on population and medical genetics, finding it still alive and kicking. We also show the role it played and its relationships with many other large initiatives that took place during these years. Finally, we examined the changed sensibilities on the ethical usage of genetic data for scientific research and how this affects the HGDP and other genetic efforts, both in population and medical genetics.

Assessment of the Dutch Rules on Health Data in the Light of the GDPR.

In 2021, the European Commission published its Assessment of the EU Member States' rules on health data in the light of General Data Protection Regulation. The Commission concluded that the GDPR has been interpreted in many ways in the EU as regards health research, and national implementation legislation has resulted in a fragmented legal landscape. Several lawful bases are used as a legitimation for the secondary use of health data. I address the Dutch legislation on the re-use, or secondary use of health data for scientific research where explicit consent is the general rule. However, both the GDPR, the Dutch GDPR Implementation Act and sectoral health legislation leave room for alternatives. I conclude that a further review of these alternatives is required to enhance scientific health research with the secondary use of health data, and I sketch a few avenues for further exploration.

Contentment with the Quality of Students’ Education of Russian Universities in a Pandemic (on the Example of the Financial University)

The paper reveals the features of contentment with the quality of education in higher educational institutions of the Russian Federation during the pandemic. The emphasis is on the forced transition first to the distance learning format of higher educational institutions in Russia, and then to the hybrid format chosen by some universities. The authors reveal the importance of conducting research aimed at studying satisfaction with the quality of education. The paper presents the data of scientific research characterizing the attitude of students and teachers to online classes. Also, there are described the advantages and disadvantages that the informants encountered during the distance learning format, and the disciplines that are especially difficult to conduct on online platforms are also highlighted. The authors paid particular attention to the results of a monitoring study on satisfaction with the quality of education of the Educational Sociological Laboratory of the Department of Sociology of the Financial University under the Government of the Russian Federation. The analysis of the research was based on compiling indices using its own calculation method. It is presented as a general index of contentment with the quality of education for the entire university, by courses and by the basis of education, as well as indices separately for full-time and online formats for conducting various groups of disciplines.

Feature Tracking and Segmentation in Real Time via Deep Learning in Vitreoretinal Surgery: A Platform for Artificial Intelligence-Mediated Surgical Guidance

This study investigated whether a deep-learning neural network can detect and segment surgical instrumentation and relevant tissue boundaries and landmarks within the retina using imaging acquired from a surgical microscope in real time, with the goal of providing image-guided vitreoretinal (VR) microsurgery. Retrospective analysis via a prospective, single-center study. One hundred and one patients undergoing VR surgery, inclusive of core vitrectomy, membrane peeling, and endolaser application, in a university-based ophthalmology department between July 1, 2020, and September 1,2021. A dataset composed of 606 surgical image frames was annotated by 3 VR surgeons. Annotation consisted of identifying the location and area of the following features, when present in-frame: vitrector-, forceps-, and endolaser tooltips, optic disc, fovea, retinal tears, retinal detachment, fibrovascular proliferation, endolaser spots, area where endolaser was applied, and macular hole. An instance segmentation fully convolutional neural network (YOLACT++) was adapted and trained, and fivefold cross-validation was employed to generate metrics for accuracy. Area under the precision-recall curve (AUPR) for the detection of elements tracked and segmented in the final test dataset; the frames per second (FPS) for the assessment of suitability for real-time performance of the model. The platform detected and classified the vitrector tooltip with a mean AUPR of 0.972 ± 0.009. The segmentation of target tissues, such as the optic disc, fovea, and macular hole reached mean AUPR values of 0.928 ± 0.013, 0.844 ± 0.039, and 0.916 ± 0.021, respectively. The postprocessed image was rendered at a full high-definition resolution of 1920× 1080 pixels at 38.77 ± 1.52 FPS when attached to a surgical visualization system, reaching up to 87.44 ± 3.8 FPS. Neural Networks can localize, classify, and segment tissues and instruments during VR procedures in real time. We propose a framework for developing surgical guidance and assessment platform that may guide surgical decision-making and help in formulating tools for systematic analyses of VR surgery. Potential applications include collision avoidance to prevent unintended instrument-tissue interactions and the extraction of spatial localization and movement of surgical instruments for surgical data science research. Proprietary or commercial disclosure may be found after the references.

How to “improve” prediction using behavior modification

Many internet platforms that collect behavioral big data use it to predict user behavior for internal purposes and for their business customers (e.g., advertisers, insurers, security forces, governments, political consulting firms) who utilize the predictions for personalization, targeting, and other decision-making. Improving predictive accuracy is therefore extremely valuable. Data science researchers design algorithms, models, and approaches to improve prediction. Prediction is also improved with larger and richer data. Beyond improving algorithms and data, platforms can stealthily achieve better prediction accuracy by pushing users’ behaviors towards their predicted values, using behavior modification techniques, thereby demonstrating more certain predictions. Such apparent “improved” prediction can result from employing reinforcement learning algorithms that combine prediction and behavior modification. This strategy is absent from the machine learning and statistics literature. Investigating its properties requires integrating causal with predictive notation. To this end, we incorporate Pearl’s causal do(.) operator into the predictive vocabulary. We then decompose the expected prediction error given behavior modification and identify the components impacting predictive power. Our derivation elucidates implications of such behavior modification to data scientists, platforms, their customers, and the humans whose behavior is manipulated. Behavior modification can make users’ behavior more predictable and even more homogeneous; yet this apparent predictability might not generalize when business customers use predictions in practice. Outcomes pushed towards their predictions can be at odds with customers’ intentions, and harmful to manipulated users.

Building health workforce capacity in Northern Australia

The Menzies Ramaciotti Regional and Remote Health Sciences Training Centre (Menzies-Ramaciotti Centre) is located within the Menzies School of Health Research (Menzies) in Darwin, Northern Territory (NT). The Menzies-Ramaciotti Centre is contributing to the development of a local health workforce in the NT, including a strong biomedical workforce. The Centre facilitates health workforce career progression for regional and remote youth, with a focus on career development for Aboriginal and Torres Strait Islander (First Nations) youth. The Centre works in collaboration with a range of industry and education partners, who also have strong workforce development goals and a commitment to serving a vital community need to build pathways into work and study with First Nations peoples. Part of the Centre’s focus entails delivery of high-quality training in biomedical sciences, including theoretical and practical skill development in microbiology, laboratory techniques, immunology, public health, data science, allied health, and health research. The Centre uses a non-linear, strengths-based approach to training with a multiplicity of entry and exit points including high school work experience placements, traineeships, vocational placements, as well as undergraduate and postgraduate placements.

The Analysis of Application Prospect and Sustainable Mode of Regional Resources in the Context of Tibetan Medicine Culture

At present, the Tibetan medicine culture formed by Tibetan medicine and Tibetan Buddhism has a state of aphasia in the global multicultural context. Furthermore, the medicinal resources bred in the special geographical location of the Qinghai-Tibet Plateau are not only the important material basis for the formation and development of Tibetan medicine culture but also provide potential value for modern drug discovery and medical research. Based on the existing research texts and scientific research data, this paper constructs the SPS sustainable development framework by combing the general situation of the medicinal resource and industry in the Qinghai-Tibet plateau, the background, and the prospect of the formation of Tibetan medicine culture, which provides the basic theoretical research and relevant countermeasures for the sustainable development and resource reuse of Tibetan medicine in the context of Tibetan medicine culture. It is meaningful that the research on the cultural propaganda of Tibetan medicine's national idea and medicinal value has found that the cultural industry code and the resource application potential in the Qinghai-Tibet Plateau region will have prominent geo-economic benefits in the near future, especially in the moment when the global epidemic is suddenly spreading.

Goal-setting and digital competencies as key elements of company development management system based on information and communication technologies

Digitalization is a popular and rather complex direction of modern company development. The article is devoted to justifying the relevance of managerial goal-setting and the need to form digital competencies of employees, as well as their features and provision in the management system of company development based on information and communication technologies. The study uses methods of analysis and synthesis of scientific literature and empirical data. The article summarizes the data of scientific and practical research to identify the main advantages of the company digitalization and the problems of its implementation. The author presents the popular goals of enterprise digital transformation. Using the modern information analysis of competent organizations, the author has constructed the model to justify the goals of the company digital development. The article provides suggestions for the development of digital competencies of company personnel.

Antimicrobial activity of natural products against MDR bacteria: A scientometric visualization analysis.

Objective: A growing number of studies have demonstrated the antimicrobial activity of natural products against multidrug-resistant bacteria. This study aimed to apply scientometric method to explore the current status and future trends in this field. Methods: All relevant original articles and reviews for the period 1997–2021 were retrieved from the Web of Science Core Collection database. VOSviewer, a scientometric software, and an online bibliometric analysis platform were used to conduct visualization study. Results: A total of 1,267 papers were included, including 1,005 original articles and 262 reviews. The United States and India made the largest contribution in this field. The University of Dschang from Cameroon produced the most publications. Coutinho HDM, Kuete V, and Gibbons S were key researchers, as they published a great many articles and were co-cited in numerous publications. Frontiers in Microbiology and Antimicrobial Agents and Chemotherapy were the most influential journals with the highest number of publications and co-citations, respectively. “Medicinal plants”, “methicillin-resistant Staphylococcus aureus”, “biofilm”, “minimum inhibitory concentration”, and “efflux pumps” were the most frequently used keywords, so these terms are presumed to be the current hot topics. All the included keywords could be roughly divided into four major themes, of which the theme of “natural product development approach” had attracted much attention in recent years. Furthermore, “Pseudomonas aeruginosa”, “nanoparticles”, “green synthesis”, “antimicrobial peptides”, “antibiofilm”, “biosynthetic gene clusters”, and “molecular dynamics simulation” had the latest average appearance year, indicating that these topics may become the research hot spots in the coming years. Conclusion: This study performed a scientometric analysis of the antibacterial activity of natural products against multidrug-resistant bacteria from a holistic perspective. It is hoped to provide novel and useful data for scientific research, and help researchers to explore this field more intuitively and effectively.

Use of climatic cold in scientific research

A review of scientific research data on the use of climatic cold carried out on the basis of institutes of the Federal research center «Yakut Scientific Center» Siberian Branch of the Russian Academy of Science is presented, natural low temperatures being used both as testing conditions and as a tool for experimental studies. The results of full-scale hydraulic tests of pressure vessels and pipes under conditions of low climatic temperatures, and under internal pressure created due to the effect of ice formation in closed cavities are presented. The results made it possible to determine the limit states of large-diameter pipelines, simulate the stress-strain state of the pipeline in thawed, freezing and frozen soils, substantiate the extension of the lifetime of pressure vessels, develop a technology for their welding, and determine the allowable size of defects in welded joints. It is shown that climatic tests of polymer, composite, construction, welding and other classes of materials provide for estimation of their service life under the impact of climatic factors. An experimental study of the response of solids to multiple collisions with ice granules at low climatic temperatures has been carried out along with the study of the processes of brittle fracture of their surface with the formation of splintery particles. Study of the effect of fibrous fillers of natural origin on the strength properties of fresh-ice based composition material revealed that introduction of fillers increases the strength characteristics of fresh ice by 2 – 3 times. It is shown that climatic features and a significant scientific potential of the Sakha (Yakutia) Republic provide for effective use of climatic cold as a tool for scientific research and solution of engineering problems in various sectors of the national economy.

From Zero to Python in 10.5 Hours: Building Foundational Programming Skills with Marine Biology Graduate Students and Researchers in an Introductory Workshop Series

ABSTRACT Programmatic processing, analysis, and visualization of scientific research data necessitate computational skills that scientists do not consistently acquire during their education. Python is a programming language currently in the ascendant among the scientific community. In early 2022, 25 marine scientists and marine biology graduate students were introduced to the fundamentals of Python programming in a 10.5-hour workshop series offered over seven weeks. A functional, web-based Python environment using Jupyter Notebooks, JupyterLab, Binder, and GitHub was paired with scaffolding and active learning pedagogy, to ensure an engaging learning experience. We describe the course design, adaptations, and outcomes, and offer recommendations for developing a similar introductory programming workshop.

Stochastic Zeroth-Order Riemannian Derivative Estimation and Optimization

We consider stochastic zeroth-order optimization over Riemannian submanifolds embedded in Euclidean space, where the task is to solve Riemannian optimization problems with only noisy objective function evaluations. Toward this, our main contribution is to propose estimators of the Riemannian gradient and Hessian from noisy objective function evaluations, based on a Riemannian version of the Gaussian smoothing technique. The proposed estimators overcome the difficulty of nonlinearity of the manifold constraint and issues that arise in using Euclidean Gaussian smoothing techniques when the function is defined only over the manifold. We use the proposed estimators to solve Riemannian optimization problems in the following settings for the objective function: (i) stochastic and gradient-Lipschitz (in both nonconvex and geodesic convex settings), (ii) sum of gradient-Lipschitz and nonsmooth functions, and (iii) Hessian-Lipschitz. For these settings, we analyze the oracle complexity of our algorithms to obtain appropriately defined notions of ϵ-stationary point or ϵ-approximate local minimizer. Notably, our complexities are independent of the dimension of the ambient Euclidean space and depend only on the intrinsic dimension of the manifold under consideration. We demonstrate the applicability of our algorithms by simulation results and real-world applications on black-box stiffness control for robotics and black-box attacks to neural networks. Funding: J. Li and S. Ma acknowledge the support of the National Science Foundation (NSF) [Grants DMS-1953210, CCF-1934568, and CCF-2007797]. K. Balasubramanian acknowledges the support of the NSF [Grant DMS-2053918]. K. Balasubramanian and S. Ma acknowledge the support of the UC Davis CeDAR (Center for Data Science and Artificial Intelligence Research) Innovative Data Science Seed Funding Program.

What Does Information Science Offer for Data Science Research?: A Review of Data and Information Ethics Literature

Abstract This paper reviews literature pertaining to the development of data science as a discipline, current issues with data bias and ethics, and the role that the discipline of information science may play in addressing these concerns. Information science research and researchers have much to offer for data science, owing to their background as transdisciplinary scholars who apply human-centered and social-behavioral perspectives to issues within natural science disciplines. Information science researchers have already contributed to a humanistic approach to data ethics within the literature and an emphasis on data science within information schools all but ensures that this literature will continue to grow in coming decades. This review article serves as a reference for the history, current progress, and potential future directions of data ethics research within the corpus of information science literature.

Patients' and Publics' Preferences for Data-Intensive Health Research Governance: Survey Study.

BackgroundPatients and publics are generally positive about data-intensive health research. However, conditions need to be fulfilled for their support. Ensuring confidentiality, security, and privacy of patients’ health data is pivotal. Patients and publics have concerns about secondary use of data by commercial parties and the risk of data misuse, reasons for which they favor personal control of their data. Yet, the potential of public benefit highlights the potential of building trust to attenuate these perceptions of harm and risk. Nevertheless, empirical evidence on how conditions for support of data-intensive health research can be operationalized to that end remains scant.ObjectiveThis study aims to inform efforts to design governance frameworks for data-intensive health research, by gaining insight into the preferences of patients and publics for governance policies and measures.MethodsWe distributed a digital questionnaire among a purposive sample of patients and publics. Data were analyzed using descriptive statistics and nonparametric inferential statistics to compare group differences and explore associations between policy preferences.ResultsStudy participants (N=987) strongly favored sharing their health data for scientific health research. Personal decision-making about which research projects health data are shared with (346/980, 35.3%), which researchers/organizations can have access (380/978, 38.9%), and the provision of information (458/981, 46.7%) were found highly important. Health data–sharing policies strengthening direct personal control, like being able to decide under which conditions health data are shared (538/969, 55.5%), were found highly important. Policies strengthening collective governance, like reliability checks (805/967, 83.2%) and security safeguards (787/976, 80.6%), were also found highly important. Further analysis revealed that participants willing to share health data, to a lesser extent, demanded policies strengthening direct personal control than participants who were reluctant to share health data. This was the case for the option to have health data deleted at any time (P<.001) and the ability to decide the conditions under which health data can be shared (P<.001). Overall, policies and measures enforcing conditions for support at the collective level of governance, like having an independent committee to evaluate requests for access to health data (P=.02), were most strongly favored. This also applied to participants who explicitly stressed that it was important to be able to decide the conditions under which health data can be shared, for instance, whether sanctions on data misuse are in place (P=.03).ConclusionsThis study revealed that both a positive attitude toward health data sharing and demand for personal decision-making abilities were associated with policies and measures strengthening control at the collective level of governance. We recommend pursuing the development of this type of governance policy. More importantly, further study is required to understand how governance policies and measures can contribute to the trustworthiness of data-intensive health research.

Changes of Physical and Chemical Properties for Making Raw Materials and Reproductions According to Manufacturing Stages in Traditional Korean White Porcelain

We made a Korean white porcelain or Joseon Baekja jar and based on the raw materials used and reproductions of each stage, we aimed to compare and analyze the physicochemical changes of the raw materials such as clay at each manufacturing stage, as well as identify the characteristics and correlations. Although the basic main components of clay and glaze material are similar, their texture becomes denser in the process of bisque firing pottery (Chobeol-pyeon) and glaze firing pottery (Jaebeol-pyeon), and we confirmed that in addition to the tendency of increasing vitrification, low-temperature minerals such as mica and illite gradually disappeared, while high-temperature minerals such as cristobalite were newly created. This phenomenon has also been verified by the rapid decrease in absorption rate while the change in specific gravity was small. In addition, the color was greatly affected by the firing atmosphere, and the yellow-red chromaticity of the raw materials was higher during bisque firing but showed a rapidly decreasing characteristic during glaze firing. The value of magnetic susceptibility, which is related to iron (Fe) component, showed a tendency to decrease in glaze firing pottery. CT images were confirmed as a method that can indirectly estimate the change in the material properties of the object step-by-step for the entire object. In conclusion, the study of manufacturing stages of reproduction can provide basic data for scientific research on the estimation of porcelain and pottery making technology and changes in raw materials.

Paroxysmal Sympathetic Hyperactivity Syndrome (Review)

Paroxysmal sympathetic hyperactivity (PSH) is one of the complications of acute severe brain injuries (traumatic brain injury, intracranial hemorrhage, ischemia, and posthypoxic conditions) in both adults and children. Its high incidence and severe sequelae including organ dysfunction, infectious complications, impaired blood supply to organs and tissues associate with increased disability and mortality. The choice of effective therapy can be challenging because of multifaceted manifestations, diagnostic difficulties, and lack of a clear understanding of the pathophysiology of PSH. Currently, there are various local and international treatment strategies for PSH.The aim of the review is to summarize clinical and scientific research data on diagnosis and treatment of PSH to aid in the selection of an effective therapy.Material and methods. Web of Science, Scopus and RSCI databases were employed to select 80 sources containing relevant clinical and research data on the subject of this review.Results. The key principles of diagnosis and treatment of paroxysmal sympathetic hyperactivity have been reviewed. The current views on etiology and pathogenesis of paroxysmal sympathetic hyperactivity development were outlined. The clinical data concerning complications and sequelae of paroxysmal sympathetic hyperactivity were analyzed. We conclude the review with a discussion of current methods of the syndrome prevention.Conclusion. Preventing PSH and its adequate and prompt treatment could help avoid the abnormal pathway development following a severe brain injury, reduce its negative consequences and rate of complications, along with the duration of mechanical lung ventilation, patient's stay in ICU, disability and mortality rates. Careful selection of pathogenetic, symptomatic and supportive therapy significantly improves the rehabilitation potential of patients.

From data pipelines to FAIR data infrastructures: A vision for the new horizons of bio- and geodiversity data for scientific research

Natural science collections are vast repositories of bio- and geodiversity specimens. These collections, originating from natural history cabinets or expeditions, are increasingly becoming unparalleled sources of data facilitating multidisciplinary research (Meineke et al. 2018, Heberling et al. 2019, Cook et al. 2020, Thompson et al. 2021). Due to various global data mobilization and digitisation efforts (Blagoderov et al. 2012,Nelson and Ellis 2018), this digitised information about specimens includes database records along with two/three-dimensional images, sonograms, sound or video recordings, computerised tomography scans, machine-readable texts from labels on the specimens as well as media items and notes related to the discovery sites and acquisition (Hedrick et al. 2020,Phillipson 2022). The scope and practice of specimen gathering are also evolving. The term extended specimen was coined to refer to the specimen and associated data extending beyond the singular physical object to other physical or digital entities such as chemical composition, genetic sequence data or species data. Thus the specimen becomes an interconnected network of data resources that have incredible potential to enhance integrative and data-driven research (Webster 2017,Lendemer et al. 2019,Hardisty et al. 2022). These practices also reflect the role of data and the curatorial data life-cycle starting from the initial material sampling process to the downstream analysis. We are also seeing growing acknowledgement that disparate and domain specific data elements prevent interdisciplinarity which is crucial for a holistic understanding of biodiversity and climate crisis (Hicks et al. 2010, Craven et al. 2019, Folk and Siniscalchi 2021). Thus the data elements are not just records or rows in a database or data pipelines going from one repository to another. They have the potential to become self-describing digital artefacts that can revolutionise how machines interpret and work with specimen data. Within this context, the Distributed System of Scientific Collections (DiSSCo), a new European Research Infrastructure for natural science collections, envisions an infrastructure based on FAIR Digital Objects (FDO) that can unify more than 170 European natural science collections under common and FAIR-compliant (Findable, Accessible, Interoperable, Reusable) (Wilkinson et al. 2016) access and curation policies and practices. DiSSCo’s key element in achieving FAIR is the implementation of Digital Specimen (a domain specific FDO) that closely aligns with the extended specimen practices. The idea behind Digital Specimen – an FDO that acts as a digital surrogate for a specific physical specimen in a natural science collection – was influenced by global conversations around the implementation of the Digital Object Architecture for biodiversity data (De Smedt et al. 2020, Islam et al. 2020,Hardisty et al. 2020). The main purpose of this talk is to explain the vision of how FAIR and FDO can create a data infrastructure that can not only take advantage of existing databases and repositories but at the same time provide support for innovative services such as AI and digital twinning. With scientific use cases in mind, the talk will highlight a few key FAIR and FDO components (persistent identifiers, metadata, ontologies) within the collaborative modelling activity of Digital Specimen specification. These components provide the template for specifying how a Digital Specimen should look so DiSSCo can build a FAIR service ecosystem based on FDOs (Addink et al. 2021). We will also give examples of envisioned services that can help with image feature extraction, and model training (Grieb et al. 2021,Hardisty et al. 2022) and digital twinning (Schultes et al. 2022). We believe this is an exciting new paradigm powered by FAIR and FDO that can help both humans and machines to accelerate the use of specimen data. From physical objects curated over hundred years, we have developed data pipelines, aggregators and repositories (Barberousse 2021). Now is the time to look for solutions where these data records can become FAIR Digital Objects to enable wider access and multidisciplinary research.

Data science and climate risk analytics

AbstractWith influences from different communities, data science has evolved to provide insights in many different data‐driven environments, including climate science. In this article, a brief review of data science and its connection to climate science will be presented. Additionally, two data science pipelines for quantifying risks from climate change are discussed. These pipelines focus on flooding due to tropical cyclone storm surge and changes in the distribution of temperature or precipitation or wind due to climate change via downscaling climate models. Finally, some key data science research areas in climate risk analytics are discussed.

Improving child health through Big Data and data science.

Child health is defined by a complex, dynamic network of genetic, cultural, nutritional, infectious, and environmental determinants at distinct, developmentally determined epochs from preconception to adolescence. This network shapes the future of children, susceptibilities to adult diseases, and individual child health outcomes. Evolution selects characteristics during fetal life, infancy, childhood, and adolescence that adapt to predictable and unpredictable exposures/stresses by creating alternative developmental phenotype trajectories. While child health has improved in the United States and globally over the past 30 years, continued improvement requires access to data that fully represent the complexity of these interactions and to new analytic methods. Big Data and innovative data science methods provide tools to integrate multiple data dimensions for description of best clinical, predictive, and preventive practices, for reducing racial disparities in child health outcomes, for inclusion of patient and family input in medical assessments, and for defining individual disease risk, mechanisms, and therapies. However, leveraging these resources will require new strategies that intentionally address institutional, ethical, regulatory, cultural, technical, and systemic barriers as well as developing partnerships with children and families from diverse backgrounds that acknowledge historical sources of mistrust. We highlight existing pediatric Big Data initiatives and identify areas of future research.Impact Big Data and data science can improve child health.This review highlights the importance for child health of child-specific and life course-based Big Data and data science strategies.This review provides recommendations for future pediatric-specific Big Data and data science research.