Big Data Research Research Articles

An efficient large‐scale whole‐genome sequencing analyses practice with an average daily analysis of 100Tbp: ZBOLT

AbstractBackgroundWith the advancement of whole‐genome sequencing (WGS) technology, massively parallel sequencing (MPS) remains the mainstream due to its accuracy, low cost, and high throughput. The development of the analytical pipeline corresponding to MPS has always been of great importance. Increasingly large population genomics studies, as a specific type of big data research, pose new challenges for analysis solutions.ResultsHere, we introduce ZBOLT, a comprehensive analysis system that incorporates both software and hardware advancements, making it an appropriate choice for large‐scale population genomic studies that require extensive data processing. In this study, we first evaluate ZBOLT's calling accuracy using the Genome in a Bottle (GIAB) benchmark dataset. Then we apply ZBOLT to a large‐scale population genomics study with 5,616 high sequencing depth samples totaling 1.16Pbp (base pair). As the results show, ZBOLT demonstrates exceptional efficiency and low energy consumption, processing 100Tbp per day and using 1kWh per 100Gbp sequenced sample.ConclusionThis research serves as a valuable reference for analyzing sequencing data from large population cohorts and underscores the significant potential of ZBOLT in large‐scale population genomics studies.

Big Data in Information Systems: A Review

The emergence of big data has brought about a significant transformation in the domain of Information Systems, presenting academics and companies with unparalleled prospects and complexities. This abstract examines the potential risks and benefits associated with conducting research in a dynamic and fast growing field. The field of Information Systems is characterized by the significant potential of big data research to bring about transformative effects on various sectors and societies. However, this promising development also gives rise to apprehensions surrounding issues of privacy, ethics, and data security. The potential benefits of big data research are many and varied. First and foremost, this technology offers the potential to extract practical and applicable knowledge from extensive and varied collections of data. This, in turn, facilitates decision-making based on data, fosters innovation, and enhances effectiveness across multiple industries. Furthermore, it enables the progression of cutting-edge technologies, such as machine learning and artificial intelligence, which possess the capacity to propel substantial improvements in the field of Information Systems. In conclusion, the utilization of big data research has the potential to augment our comprehension of intricate phenomena, facilitate predictive analytics, and stimulate the advancement of tailored services, consequently amplifying user experiences. Nevertheless, the potential risks associated with conducting big data research are equally substantial. The rapid expansion of data gathering and analysis has given rise to apprehensions regarding the protection of data privacy, security, and ownership. Academic researchers are confronted with the task of effectively addressing ethical quandaries pertaining to the acquisition and utilization of sensitive personal data. Furthermore, it is imperative to carefully contemplate the significant concern around algorithmic bias and discrimination in the context of data-driven decision-making. Furthermore, the considerable quantity and intricate nature of data provide obstacles in relation to the quality of data, the administration of data, and the ability to scale.

Global impacts of COVID-19 pandemic on sexual and reproductive health services: An international comparative study on primary care from the INTRePID Consortium.

To understand how the COVID-19 pandemic has impacted sexual and reproductive health (SRH) visits. An ecological study comparing SRH services volume in different countries before and after the onset of the COVID-19 pandemic. Seven countries from the INTernational ConsoRtium of Primary Care BIg Data Researchers (INTRePID) across four continents. Over 3.8 million SRH visits to primary care physicians in Australia, China, Canada, Norway, Singapore, Sweden and the USA. Difference in average SRH monthly visits before and during the pandemic, with negative binomial regression modelling to compare predicted and observed number of visits during the pandemic for SRH visits. Monthly number of visits to primary care physicians from 2018 to 2021. During the pandemic, the average volume of monthly SRH visits increased in Canada (15.6%, 99% CI 8.1-23.0%) where virtual care was pronounced. China, Singapore, Sweden and the USA experienced a decline (-56.5%, 99% CI -74.5 to -38.5%; -22.7%, 99% CI -38.8 to -6.5%; -19.4%, 99% CI -28.3 to -10.6%; and -22.7%, 99% CI -38.8 to -6.5%, respectively); while Australia and Norway showed insignificant changes (6.5%, 99% CI -0.7 to -13.8% and 1.7%, 99% CI -6.4 to -9.8%). The countries that maintained (Australia, Norway) or surpassed (Canada) pre-pandemic visit rates had the greatest use of virtual care. In-person SRH visits to primary care decreased during the pandemic. Virtual care seemed to counterbalance that decline. Although cervical cancer screening appeared insensitive to virtual care, strategies such as incorporating self-collected samples for HPV testing may provide a solution in a future pandemic.

A novel approach for standardizing clinical laboratory categorical test results using machine learning and string distance similarity

Standardizing clinical laboratory test results is critical for conducting clinical data science research and analysis. However, standardized data processing tools and guidelines are inadequate. In this paper, a novel approach for standardizing categorical test results based on supervised machine learning and the Jaro-Winkler similarity algorithm is proposed. A supervised machine learning model is used in this approach for scalable categorization of the test results into predefined groups or clusters, while Jaro-Winkler similarity is used to map text terms into standard clinical terms within these corresponding groups. The proposed method is applied to 75062 test results from two private hospitals in Bangladesh. The Support Vector Classification algorithm with a linear kernel has a classification accuracy of 98%, which is better than the Random Forest algorithm when categorizing test results. The experiment results show that Jaro-Winkler similarity achieves a remarkable 99.93% success rate in the test result standardization for the majority of groups with manual validation. The proposed method outperforms previous studies that concentrated on standardizing test results using rule-based classifiers on a smaller number of groups and distance similarities such as Cosine similarity or Levenshtein distance. Furthermore, when applied to the publicly available MIMIC-III dataset, our approach also performs excellently. All these findings show that the proposed standardization technique can be very beneficial for clinical big data research, particularly for national clinical research data hubs in low- and middle-income countries.

VISUAL ANALYSIS OF LOCAL EARTHQUAKE IN NORTH TAPANULI BASED ON DATA SCIENCE

Earthquakes are natural phenomena that occur when the Earth's tectonic plates move and release energy. Big Data's emergent epistemological and research paradigms, as well as data science, an increasingly integrated field of data research, are opening up new opportunities. Visualizing earthquake data is all about understanding earthquake characteristics such as size, location and depth. The result show that September was the quietest month in terms of earthquakes, and in this graph we can see the number of earthquakes for each month in 2022. The month of October is the one that has the highest number of earthquakes. We can see the average depth and magnitude of each year on the bubble chart. In addition, the size and color of the bubbles indicate the number of earthquakes that month. In general, most of the earthquakes occurred in the shallow earthquake range and the 1.8-3.85 magnitude range.

Association between total cholesterol and lumbar bone density in Chinese: a study of physical examination data from 2018 to 2023

BackgroundThe impact of total cholesterol (TC) on lumbar bone mineral density (BMD) is a topic of interest. However, empirical evidence on this association from demographic surveys conducted in China is lacking. Therefore, this study aimed to examine the relationship between serum TC and lumbar BMD in a sample of 20,544 Chinese adults between the ages of 20 and 80 years over a period of 5 years, from February 2018 to February 2023. Thus, we investigated the effect of serum TC level on lumbar BMD and its relationship with bone reduction in a Chinese adult population.MethodsThis cross-sectional study used data obtained from the Department of Health Management at Henan Provincial People’s Hospital between February 2018 and February 2023. The aim of this study was to examine the correlation between serum TC and lumbar BMD in individuals of different sexes. The research methodology encompassed population description, analysis of stratification, single-factor and multiple-equation regression analyses, smooth curve fitting, and analysis of threshold and saturation effects. The R and EmpowerStats software packages were used for statistical analysis.ResultsAfter adjusting for confounding variables, a multiple linear regression model revealed a significant correlation between TC and lumbar BMD in men. In subgroup analysis, serum TC was found to have a positive association with lumbar BMD in men, specifically those aged 45 years or older, with a body mass index (BMI) ranging from 24 to 28 kg/m2. A U-shaped correlation arose between serum TC and lumbar BMD was detected in women of different ages and BMI, the inflection point was 4.27 mmol/L for women aged ≥ 45 years and 4.35 mmol/L for women with a BMI of ≥ 28 kg/m2.ConclusionIn this study, Chinese adults aged 20–80 years displayed different effects of serum TC on lumbar BMD in sex-specific populations. Therefore, monitoring BMI and serum TC levels in women of different ages could prevent osteoporosis and osteopenia.Trial registrationThe research protocol was approved by the Ethics Committee of Beijing Jishuitan Hospital, in accordance with the Declaration of Helsinki guidelines (No. 2015-12-02). These data are part of the China Health Quantitative CT Big Data Research team, which has been registered at clinicaltrials.gov (code: NCT03699228).

Navigating the Metaverse of Big Data: A Bibliometric Journey

The advent of the metaverse, an immersive digital universe, has caused a sea change in how people experience and share knowledge, fun, and community. This bibliometric study examines the vast realm of big data research into metaverses. We analyse the development, trends, and major players in this cross-disciplinary subject using bibliometric methods applied to a large database of academic publications, conference proceedings, and journals. Our study covers the years 2021–2023 and incorporates the most recent discoveries in the metaverse. In doing so, it sheds light on the intellectual landscape of metaverse-related studies by identifying the most relevant authors, publications, and journals. In particular, we show how particular authors' copious contributions have impacted the conversation. We also rank the conferences and journals that have the highest impact on the subject, illuminating the main distribution pathways for metaverse research. The dynamic aspect of metaverse scholarship is revealed by this examination, which also reveals its developing themes and issues. The report also delves into the ways in which scientists and academic institutions work together, drawing attention to the networks and collaborations that have spurred the development and expansion of understanding in the metaverse. We also investigate where metaverse research is being conducted, identifying centres of excellence and promising new frontiers. Researchers, policymakers, and industry stakeholders can use our findings to better understand the current state and future directions of this fast-growing field as they relate to the metaverse and big data. This bibliometric study acts as a compass, directing stakeholders towards well-informed decision-making and research goals in the metaverse and big data nexus as the metaverse continues to rise in popularity.

Trends of Cause-Specific Mortality and Association with Economic Status, Education Level, as Well as Health Investment among Adolescents Aged 10 to 24 Years in China, 2004–2019

Objective: To describe the secular trends of cause-specific mortality among adolescents aged 10 to 24 years from 2004 to 2019 and explore the association between mortality and economic status, education level as well as health investment. Methods: Mortality data of adolescents aged 10 to 24 years were obtained from the national disease surveillance points system. The age-standardized mortality rate (ASMR) was calculated by using the population data from the sixth national population census in 2010. GDP per capita, urbanization rate, illiteracy rate of the population over 15 years old, government education expenditure per capita, number of health service providers per 1000 people, and number of health beds per 1000 people were collected from China’s Economic and Social Big Data Research Platform. Age-period-cohort analysis was used to analyse the net age, period, and cohort effects of mortality among adolescents, while panel data regression was used to explore the association between mortality and economic status, education level as well as health investment. Results: Overall, the ASMR was 28.84 per 100,000 and the top five causes of mortality were road injuries, drowning, intentional self-harm and sequelae, leukaemia, and falls among adolescents aged 10 to 24 years in China in 2019. All-cause mortality declined with an annual percentage change of 4.02% (95% Confidence interval: 3.74% to 4.30%) from 2004 to 2019 yet with persistent differences across different demographic (gender and age) and geographical (urban-rural, and regional) subgroups. Notably, the ASMR for HIV/AIDS in males, lower respiratory infections in urban adolescents, and iron deficiency anaemia as well as cervical cancer in adolescents aged 20 to 24 years showed an increase over time. The multivariate panel data regression showed that the ASMR decreased by 5.18 (3.27, 7.08) per 100,000 for every increase in the number of health beds per 1000 population, but with insignificant association with GDP per capita and illiteracy rate in the total sample. Health beds investment was positively associated with ASMR at almost all subgroups except for adolescents aged 10 to 14 years; GDP per capita increase was helpful to males and rural adolescents while an increasing literacy rate was beneficial for females and adolescents aged 15 to 19 years. Conclusion: Given the persistent differences between subgroups, further investments including improving health services, especially increasing health bed investment, GDP per capita, and reducing the illiteracy rate and concern for adolescents in males, rural areas, the western regions, and aged 15 to 24 years are needed. Additionally, the increased burden of some diseases, such as HIV/AIDS, must be of further concern.

Innovation of Law Education Model in Applied Colleges and Universities Based on Big Data and Artificial Intelligence

Relying on the nation's first judicial big data research base for people's courts in Southeast University, Southeast University Law School has set up a training direction for graduate students in legal big data and artificial intelligence, and explored the “three-dimensional, small-scale, wide-ranging, and large-scale ecology.” The concept of “meta-integration” of legal postgraduate talent training will build a “four-in-one” legal big data and artificial intelligence postgraduate talent training system, so that the leading position of legal big data and artificial intelligence in leading disciplines will be gradually established, and the training effect of leading talents will be established. In addition, the establishment of a teaching feedback mechanism can also provide important guidance for how to improve artificial intelligence in legal education. This paper applies the data mining technology to the legal education in colleges and universities and comprehensively expounds the basic theories and methods of data mining.

Tekoäly tutkimuksen työkaluna

In this editorial we describe the advantages of AI use in research, such as help with quick processing of big research data, managing and analysing data, develop modelling methods and future scenarios. AI can support researchers’ work in many ways, but it should be used with caution. One of the most critical aspects in using AI is the importance of interpretation of the results that it offers. There are still questions about personal data and security risks in using AI. Perspectives on the use of AI in research vary significantly. While Italy has banned the use of ChatGPT, the University of Calgary offers courses on the use of ChatGPT in academic publishing and grant writing. AI is here to stay, so we have to learn our ways to understand and use it appropriately.

Public involvement and engagement in big data research: A scoping review

ObjectivesPublic involvement and engagement have been suggested as a way to establish public support for big data research, yet there has been no review exploring how these activities could facilitate this. Therefore, this scoping review aimed to explore public involvement and engagement in big data research.
 MethodsFollowing Arksey and O’Malley’s methodology, we systematically searched the following databases: CINAHL, Health Research Premium Collection, PubMed, Scopus and Web of Science for papers published between 2010-2021. Additional manual searches took place. These included the first 100 hits in Google search, journals (BMC Research Involvement and Engagement, International Journal of Population Data Science and Health Expectations) and grey literature (Patient Outcome Research Institute database, first 100 hits were screened). We extracted data using a standardised form. We then organised it in a descriptive and narrative way. A system logic model was developed to understand the complexity of this topic.
 ResultsFifty-three papers were identified as eligible for inclusion in our review. The findings indicate that public involvement and engagement have the potential to improve public trust and accountability for data resharing for research. However, there is limited literature actually evaluating these activities. The findings suggest that the public can be meaningfully involved and engaged in big data research, both in terms of individual research projects and data governance, but there is no one standardised approach to do it. Therefore, we developed an initial system logic model to map relevant aspects of the involvement and engagement activities. These include which communities to reach, the context (e.g. ethical, legal aspects or public views), the design and delivery of activities, and outcomes.
 ConclusionDespite the growing literature on public involvement and engagement in big data research, more research is needed as there are few primary empirical studies exploring involvement and engagement. We suggest using the system logic model we developed when reflecting on issues that might be relevant in organising these activities.

Understanding the effects of environmental perceptions on walking behavior by integrating big data with small data

Human-perceived landscape and amenities are not equivalent to the objective physical world. Traditional big data research that only uses objectively measured environments based on point-of-interest data has difficulty capturing the effect of human environmental perceptions on walking behavior. We proposed a conceptual framework to examine the effect of human perception on walking behavior by integrating travel trajectories from the “big data” derived from mobile phones and “small data” obtained from a questionnaire survey of residents living in Chengdu, China. Results show that the human subjective perceived environment is inconsistent with the objectively measured built environment. The walking behavior of individuals is completely regulated by their personal perception of the surrounding environment. Moreover, enhancing the environmental design of pathways leading to retail shops, recreational areas, and shopping centers could significantly enhance perceived accessibility and foster a more pedestrian-friendly neighborhood.

Anthracite memories: semantic tagging and coal mining oral histories

ABSTRACT Oral histories are a critical source of information about lived experiences of past events. They have been analysed both for their form – linguistically as texts, performances, and expressive accounts – and their content for understanding historic events and personal experiences. Here we focus on sentiment analysis approaches frequently applied to big data research questions, but less often utilised by anthropologists working with oral histories. Oral histories collected half a century ago in the anthracite mining communities of northeastern Pennsylvania are examined by considering methodological and historical questions. This project explores how oral history and data science might be productively combined to understand these now historic communities’ everyday lives and working conditions. Bakhtin’s (1981) concept of chronotope helps us understand the memory of these anthracite coal mining communities’ daily life and working conditions.

Developing an ethical framework-guided instrument for assessing bias in EHR-based Big Data studies: a research protocol

IntroductionThe emergence of Big Data health research has exponentially advanced the fields of medicine and public health but has also faced many ethical challenges. One of most worrying but still...

Trends and hotspots of publications on ferroptosis: A 10 Year overview

BackgroundFerroptosis was proposed to be a type of programmed cell death in 2012. Ferroptosis plays a significant role in a variety of illnesses. ObjectiveTo better understand the direction of future research, we performed a bibliometric analysis to identify research hotspots with a focus on ferroptosis. MethodsThe search terms [TI = “ferroptosis” OR (“GSH” AND “GPX4”) OR “lipid peroxidation” OR “iron homeostasis” OR “iron metabolism”] AND [PY = “2012–2022”] AND [DT = “Article OR Review”] AND [LA = “English”] were used to retrieve publications related to ferroptosis for a bibliometric analysis. We utilized Microsoft Excel to calculate the frequency and proportion of the published articles, VOSviewer to perform a co-occurrence analysis and for visualizing the data, CiteSpace to obtain a timeline of keywords and institutions, and RStudio to calculate citation metrics. As indicated by the analysis, indicators such as the number of publications, the most productive authors and coauthorship status, the distribution of publications by country, favoured journals, the most influential institutions and the most frequently cited documents are reported in this article. ResultsA total of 8009 publications were retrieved from the WOS core collection, and 197 papers published in 2023 were removed from this analysis. The remaining 7812 papers, which included 118 in the WOS collection, were incorporated into the bibliometric study. ConclusionThe number of annual scientific publications on ferroptosis have been increasing each year. The academic communities represented by Tang, Daolin, Stockwell, Brent R., Wang, Fudi, and Conrad, Marcus were the most authoritative. China, USA, and Germany were the front-runners in the field of ferroptosis. Free Radical Biology and Medicine was the largest contributor of ferroptosis-related research, and Cell and Nature were the most influential journals to publish articles on ferroptosis. Columbia Univ and Univ Pittsburgh were the institutions that received the most attention. Recent research on ferroptosis has been focused on molecular mechanisms, particularly those in the contexts of various diseases, which will be a hotspot of future research. In addition, interdisciplinary ferroptosis and big-data research is expected to be a new frontier.

What is “big data” and how should we use it? The role of large datasets, secondary data, and associated analysis techniques in outdoor recreation research

With researchers increasingly interested in big data research, this conceptual paper describes how large datasets, secondary data, and associated analysis techniques can be used to understand outdoor recreation. Some types of large, secondary datasets that have been increasingly used in outdoor recreation research include social media, mobile device data, and trip reports or online reviews. First, we give a brief overview of big data terms and outline the steps involved in conducting big data research. In doing so, we describe data sources and analysis techniques relevant for outdoor recreation, and review how they have been applied in previous published works. We then describe opportunities, limitations, and considerations of using big data. Finally, we outline several questions researchers may consider when designing, conducting, reporting, and reviewing outdoor recreation research using big data. Overall, big data approaches can expand our understanding of outdoor recreation and, by addressing key questions, may help researchers harness the strengths of big data while ensuring quality and integrity.

Measuring visitor flows using mobile positioning data in three Hungarian second-tier cities

PurposeThe research analyses the spatial-temporal behaviour of international visitors using mobile positioning data (MPD) in three second-tier cities in Hungary: Szeged, Debrecen and Pécs. The purpose of this study is to identify the origins of visitors, length of stay, seasonal concentrations and mobility between cities.Design/methodology/approachA large volume of data generated by foreign mobile phone users was processed and analysed for six consecutive months in 2018. The movements of around 3.5 million foreigners visiting Hungary were captured.FindingsThe data showed significant differences in tourist turnover, mobility patterns and flows indicating different levels of tourism activity and potential in the three cities. This included day trips, cross-border tourism, seasonal fluctuations and activity-driven demand (e.g. special events and cultural tourism).Research limitations/implicationsMPD cannot fully explain or predict drivers of visitor behaviour, such as purpose of visit, the precise activities that they undertake there and the experiences that they gain. It can, however, provide information on spatial and temporal flows, patterns and concentrations of visitors.Practical implicationsThe investigated cities should follow distinct tourism development policies to attract more tourists and strengthen the links with their neighbouring destinations at the same time as differentiating themselves through marketing as competitive alternative destinations.Social implicationsAugmenting tourism has socio-economic implications for residents and communities. The development of cultural tourism and local event-based tourism must incorporate community needs.Originality/valueThe paper analyses the extent to which MPD can provide insights into visitors’ spatio-temporal mobility and flows in relatively under-visited second-tier cities and it highlights opportunities and gaps for big data research in an urban context.

Exploring how to improve the involvement of Polish and South Asian communities around big data research. A qualitative study using COM-B model.

Involving public contributors helps researchers to ensure that public views are taken into consideration when designing and planning research, so that it is person-centred and relevant to the public. This paper will consider public involvement in big data research. Inclusion of different communities is needed to ensure everyone's voice is heard. However, there remains limited evidence on how to improve the involvement of seldom-heard communities in big data research. This study aims to understand how South Asians and Polish communities in the UK can be encouraged to participate in public involvement initiatives in big data research. Forty interviews were conducted with Polish (n=20) and South Asian (n=20) participants on Zoom. The participants were living in the United Kingdom and had not previously been involved as public contributors. Transcribed interviews were analysed using reflexive thematic analysis. We identified eight themes. The 'happy to reuse data' theme sets the scene by exploring our participants' views towards big data research and under what circumstances they thought that data could be used. The remaining themes were mapped under the capability-opportunity-motivation-behaviour (COM-B) model, as developed by Michie and colleagues. This allowed us to discuss multiple factors that could influence people's willingness to become public contributors. Our study is the first to explore how to improve the involvement and engagement of seldom-heard communities in big data research using the COM-B model. The results have the potential to support researchers who want to identify what can influence members of the public to be involved. By using the COM-B model, it is possible to determine what measures could be implemented to better engage these communities.

Metadata-independent classification of MRI sequences using convolutional neural networks: Successful application to prostate MRI

PurposeThe ever-increasing volume of medical imaging data and interest in Big Data research brings challenges to data organization, categorization, and retrieval. Although the radiological value chain is almost entirely digital, data structuring has been widely performed pragmatically, but with insufficient naming and metadata standards for the stringent needs of image analysis. To enable automated data management independent of naming and metadata, this study focused on developing a convolutional neural network (CNN) that classifies medical images based solely on voxel data. MethodA 3D CNN (3D-ResNet18) was trained using a dataset of 31,602 prostate MRI volumes with 10 different sequence types of 1243 patients. A five-fold cross-validation approach with patient-based splits was chosen for training and testing. Training was repeated with a gradual reduction in training data assessing classification accuracies to determine the minimum training data required for sufficient performance. The trained model and developed method were tested on three external datasets. ResultsThe model achieved an overall accuracy of 99.88 % ± 0.13 % in classifying typical prostate MRI sequence types. When being trained with approximately 10 % of the original cohort (112 patients), the CNN still achieved an accuracy of 97.43 % ± 2.10 %. In external testing the model achieved sensitivities of > 90 % for 10/15 tested sequence types. ConclusionsThe herein developed CNN enabled automatic and reliable sequence identification in prostate MRI. Ultimately, such CNN models for voxel-based sequence identification could substantially enhance the management of medical imaging data, improve workflow efficiency and data quality, and allow for robust clinical AI workflows.

Diversity and neocolonialism in Big Data research: Avoiding extractivism while struggling with paternalism

The extractive logic of Big Data-driven technology and knowledge production has raised serious concerns. While most criticism initially focused on the impacts on Western societies, attention is now increasingly turning to the consequences for communities in the Global South. To date, debates have focused on private-sector activities. In this article, we start from the conviction that publicly funded knowledge and technology production must also be scrutinized for their potential neocolonial entanglements. To this end, we analyze the dynamics of collaboration in an European Union-funded research project that collects data for developing a social platform focused on diversity. The project includes pilot sites in China, Denmark, the United Kingdom, India, Italy, Mexico, Mongolia, and Paraguay. We present the experience at four field sites and reflect on the project’s initial conception, our collaboration, challenges, progress, and results. We then analyze the different experiences in comparison. We conclude that while we have succeeded in finding viable strategies to avoid contributing to the dynamics of unilateral data extraction as one side of the neocolonial circle, it has been infinitely more difficult to break through the much more subtle but no less powerful mechanisms of paternalism that we find to be prevalent in data-driven North–South relations. These mechanisms, however, can be identified as the other side of the neocolonial circle.