Data-intensive Science Research Articles

BioTRY: A Comprehensive Knowledge Base for Titer, Rate, and Yield of Biosynthesis.

Synthetic biology is rapidly evolving into a data-intensive science that increasingly relies on massive data sets; one of its applications is the evaluation of the economic viability of fermentation processes. However, the key economic indicators, namely titer, rate, and yield (TRY), which respectively reflect the downstream processing, reactor size, and raw material costs, are not well captured in bioinformatics databases. In this paper, we present BioTRY, an intuitive and user-friendly tool that contains >5,000 biochemicals and >3,800 strains, along with over 52,000 corresponding TRY entries with original references. It is freely available at http://www.synbiohealth.cn/biotry. To our knowledge, BioTRY is the first available database on biosynthesis TRY data from original research. We anticipate that BioTRY will become a useful tool that aids researchers and decision-makers in understanding the current development state of biosynthesis and allows them to foresee potential prospects and applications for biosynthesis.

Innovative Scientific Discoveries: The Role of Intelligent Computing in the Fifth Paradigm Shift

This article explores the key role of intelligent computing in driving the paradigm shift of scientific discovery. The article first outlines the five paradigms of scientific discovery, from empirical observation to theoretical models, then to computational simulation and data intensive science, and finally introduces intelligent computing as the core of the fifth paradigm. Intelligent computing enhances the ability to understand, predict, and automate scientific discoveries of complex systems through technologies such as deep learning and machine learning. The article further analyzes the applications of intelligent computing in fields such as bioinformatics, astronomy, climate science, materials science, and medical image analysis, demonstrating its practical utility in solving scientific problems and promoting knowledge development. Finally, the article predicts that intelligent computing will play a more critical role in future scientific research, promoting interdisciplinary integration, open science, and collaboration, providing new solutions for solving complex problems.

Galaxy Helm chart: a standardized method for deploying production Galaxy servers.

The Galaxy application is a popular open-source framework for data intensive sciences, counting thousands of monthly users across more than 100 public servers. To support a growing number of users and a greater variety of use cases, the complexity of a production-grade Galaxy installation has also grown, requiring more administration effort. There is a need for a rapid and reproducible Galaxy deployment method that can be maintained at high-availability with minimal maintenance. We describe the Galaxy Helm chart that codifies all elements of a production-grade Galaxy installation into a single package. Deployable on Kubernetes clusters, the chart encapsulates supporting software services and implements the best-practices model for running Galaxy. It is also the most rapid method available for deploying a scalable, production-grade Galaxy instance on one's own infrastructure. The chart is highly configurable, allowing systems administrators to swap dependent services if desired. Notable uses of the chart include on-demand, fully-automated deployments on AnVIL, providing training infrastructure for the Bioconductor project, and as the AWS-recommended solution for running Galaxy on the Amazon cloud. The source code for Galaxy Helm is available at https://github.com/galaxyproject/galaxy-helm, the corresponding Helm package at https://github.com/CloudVE/helm-charts, and the required Galaxy container image https://github.com/galaxyproject/galaxy-docker-k8s.

Data-Intensive Science: Problems and Development of the Fourth Paradigm

Data-Intensive Science: Problems and Development of the Fourth Paradigm

Data as environment, environment as data: One Health in collaborative data-intensive science

This article analyses the operationalization of One Health in the context of data-intensive science in response to the COVID-19 outbreak. Building on ethnographic field research and revisiting the lives of a knowledge infrastructure of interdisciplinary collaboration set up online in the early phase of the COVID-19 health emergency, the article develops the notion of “data as environment.” This environment is a contact structure that entangles knowledge systems, subjects, processing tools, and mediated bio-socialities in processes of data-intensive knowledge co-production. Claims for new collaborative approaches between the biomedical, environmental, and social sciences are increasingly marked by the emergence of digital knowledge-making infrastructure that leverages data, knowledge, and expertise from different disciplines and sectors to increase scientific productivity via data-sharing technologies. Yet, digital knowledge-making infrastructures appear self-evident when they are in place, while data are often conceived as inert and disembodied information units separated from social relations of research. The argument that data are an environment expands anthropological thinking on data and digital knowledge-making infrastructures by enlightening political-ethical questions that are at stake in the emerging technoscientific worlds of the Anthropocene.

Opinion: New directions in atmospheric research offered by research infrastructures combined with open and data-intensive science

Abstract. The acquisition and dissemination of essential information for understanding global biogeochemical interactions between the atmosphere and ecosystems and how climate–ecosystem feedback loops may change atmospheric composition in the future comprise a fundamental prerequisite for societal resilience in the face of climate change. In particular, the detection of trends and seasonality in the abundance of greenhouse gases and short-lived climate-active atmospheric constituents is an important aspect of climate science. Therefore, easy and fast access to reliable, long-term, and high-quality observational environmental data is recognised as fundamental to research and the development of environmental forecasting and assessment services. In our opinion article, we discuss the potential role that environmental research infrastructures in Europe (ENVRI RIs) can play in the context of an integrated global observation system. In particular, we focus on the role of the atmosphere-centred research infrastructures ACTRIS (Aerosol, Clouds and Trace Gases Research Infrastructure), IAGOS (In-service Aircraft for a Global Observing System), and ICOS (Integrated Carbon Observation System), also referred to as ATMO-RIs, with their capabilities for standardised collection and provision of long-term and high-quality observational data, complemented by rich metadata. The ATMO-RIs provide data through open access and offer data interoperability across different research fields including all fields of environmental sciences and beyond. As a result of these capabilities in data collection and provision, we elaborate on the novel research opportunities in atmospheric sciences which arise from the combination of open-access and interoperable observational data, tools, and technologies offered by data-intensive science and the emerging collaboration platform ENVRI-Hub, hosted by the European Open Science Cloud (EOSC).

The Global Network Advancement Group A Next Generation System for the LHC Program and Data Intensive Sciences

This paper presents the rapid progress, vision and outlook across multiple state of the art development lines within the Global Network Advancement Group (GNA-G) and its Data Intensive Sciences and SENSE/AutoGOLE working groups, which are designed to meet the present and future needs and address the challenges of the Large Hadron Collider and other science programs with global reach. Since it was founded in the Fall of 2019 and the working groups were formed in 2020, in partnership with ESnet, Internet2, CENIC, GEANT, ANA, RNP, StarLight, NRP, N-DISE, AmLight, and many other leading research and education networks and network R&D projects, as well as Caltech, UCSD/SDSC, Fermilab, CERN, LBL, and many other leading universities and laboratories, the GNA-G working groups have deployed two virtual circuit and programmable testbeds spanning six continents which supports continuous developments aimed at the next generation of programmable networks interworking with the science programs’ computing and data management systems. The talk covers examples of recent progress in developing and deploying new methods and approaches in multidomain virtual circuits, flow steering, path selection, load balancing and congestion avoidance, segment routing and machine learning based traffic prediction and optimization.

Pillars of biology: Boolean modeling of gene-regulatory networks

Boolean modeling is a mathematical modeling framework used for defining and studying gene-regulatory networks (GRNs). It serves as a means to develop mechanistic models, offering insights into the trajectories and dynamic properties of GRNs. In this review, I delve into seminal papers published in the Journal of Theoretical Biology that have spearheaded this field. Additionally, I explore the application of these modeling methods in the current era of data-intensive science.

Critical mass: the creation of Pajarero/Birder communities in Mexico for citizen science

ABSTRACT Birding (pajareo) precedes the field of citizen science by decades, if not centuries. However, by being incorporated into institutional programs and digital platforms [Invernizzi, N. 2020. “Public Participation and Democratization: Effects on the Production and Consumption of Science and Technology.” Tapuya: Latin American Science, Technology and Society 3 (1): 227–253. https://do.org/10.1080/25729861.2020.1835225], birding has been inscribed into the logic of data-intensive science [Scroggins, M. J., and I. V. Pasquetto. 2020. “Labor Out of Place: On the Varieties and Valences of (In)Visible Labor in Data-Intensive Science.” Engaging Science, Technology, and Society 6:111–132. https://do.org/10.17351/ests2020.341], with the premise of increasing the number of volunteers to accumulate more and more data. The history of these practices in Mexico is very recent, dating back only about 15 years. The explosion in the number of practitioners since then has been largely the product of institutional arrangements and top-down initiatives carried out by Mexican government environmental agencies. Building on recent STS approaches to public participation that conceive citizen science as a situated and contextual phenomenon, I propose that public policies can be legitimately used to create publics. From the study of interviews, visual materials, participant observation in birding field trips, and other ethnographic materials, I recover the different stories that have led to the creation of brigades, groups, and observation clubs in Mexico. I argue that this approach allows us to go beyond normative positions and present a much more complex reality than the mere instrumental relationship between institutions and communities.

PANGAEA - Data Publisher for Earth & Environmental Science

The information system PANGAEA provides targeted support for research data management as well as long-term data archiving and publication. PANGAEA is operated as an open access library for archiving, publishing, and distributing georeferenced data from earth and environmental sciences. It focuses on observational and experimental data. Citability, comprehensive metadata descriptions, interoperability of data and metadata, a high degree of structural and semantic harmonization of the data inventory as well as the commitment of the hosting institutions ensures the long-term usability of archived data. PANGAEA is a pioneer of FAIR and open data infrastructures to enable data intensive science and an integral component of national and international science and technology activities. This paper provides an overview of the recent organisational, structural, and technological advancements in developing and operating the information system.

AIDTN: Towards a Real-Time AI Optimized DTN System With NVMeoF

Large-scale data transport for data-intensive sciences is a complex multidimensional challenge. The challenge includes optimizing the end-to-end Big Data movement performance in real-time, supporting direct remote data access using NVMe over Fabrics (NVMeoF) and deploying to existing research platforms. AIDTN is the first effort to provide a unique AI system designed to incorporate NVMe over Fabrics (NVMeoF) and optimize coordination among multiple components supporting large-scale, multi-domain Wide Area Network (WAN) data-intensive science. AIDTN's research objective is to integrate next-generation storage architecture using NVMeoF, specialized network design using high-performance network appliances, Data Transfer Nodes (DTNs), catalysts in driving data transport, and a unique AI system explicitly designed for high-performance data movement challenges. AIDTN is the first system that uses network and system features to predict the end-to-end performance of high-performance data movement and further extends the model with NVMe-specific features for NVMeoF remote data access. As a result, AIDTN improves data movement performance by up to 284% while minimizing packet loss compared to other heuristics approaches. It also has a prediction error rate as low as 0.16 compared to AI models with the only network (error rate = 0.29) or network and system features (error rate = 0.19).

Machine Learning in Geology: Challenges and Prospects

Machine Learning methods, along with high-performance computing, is creating chances for data intensive science in various domains such as Geology. Known for its precision and efficiency, Machine learning has renovated many fields of science thoroughly. However, when it comes to Geology, Machine Learning methods aren’t fully applied, lacking an overall frame to arrange those works with scattered aims and methods. This paper presents a comprehensive review of research focusing on application of Machine Learning methods in Geology studies. The works analyzed were categorized in three aspects of Geology studies: mineral prospecting and mapping, land-cover monitoring; geochemical anomalies detection; earthquake monitoring and geological sample identification. The selection and classification of the presented articles demonstrate how Geology benefits from Machine Learning methods.

An exploratory approach to data driven knowledge creation

This paper describes a new approach to knowledge creation that is instrumental for the emerging paradigm of data-intensive science. The proposed approach enables the acquisition of new insights from the data by exploiting existing relationships between diverse types of datasets acquired through various modalities. The value of data consistently improves when it can be linked to other data because linking multiple types of datasets allows creating novel data patterns within a scientific data space. These patterns enable the exploratory data analysis, an analysis strategy that emphasizes incremental and adaptive access to the datasets constituting a scientific data space while maintaining an open mind to alternative possibilities of data interconnectivity. A technology, the Linked Open data (LOD), was developed to enable the linking of datasets. We argue that the LOD technology presents several limitations that prevent the full exploitation of this technology to acquire new insights. In this paper, we outline a new approach that enables researchers to dynamically create data patterns in a research data space by exploiting explicit and implicit/hidden relationships between distributed research datasets. This dynamic creation of data patterns enables the exploratory data analysis strategy.

Deep Learning Approach for Predicting the Therapeutic Usages of Unani Formulas towards Finding Essential Compounds

The use of herbal medicines in recent decades has increased because their side effects are considered lower than conventional medicine. Unani herbal medicines are often used in Southern Asia. These herbal medicines are usually composed of several types of medicinal plants to treat various diseases. Research on herbal medicine usually focuses on insight into the composition of plants used as ingredients. However, in the present study, we extended to the level of metabolites that exist in the medicinal plants. This study aimed to develop a predictive model of the Unani therapeutic usage based on its constituent metabolites using deep learning and data-intensive science approaches. Furthermore, the best prediction model was then utilized to extract important metabolites for each therapeutic usage of Unani. In this study, it was observed that the deep neural network approach provided a much better prediction model than other algorithms including random forest and support vector machine. Moreover, according to the best prediction model using the deep neural network, we identified 118 important metabolites for nine therapeutic usages of Unani.

Prediction of winter wheat yield and dry matter in North China Plain using machine learning algorithms for optimal water and nitrogen application

Accurate prediction of crop yield and dry matter as well as optimized water and nitrogen management can favor rational decision-making for farming systems. Combining high-performance computing with innovative technologies of big data processing, machine learning (ML) advances data-intensive science and provides an important supporting frame for crop yield prediction. This paper evaluated the performance of five ML algorithms, including linear regression (LR), decision tree (DT), support vector machine (SVM), ensemble learning (EL), and Gaussian process regression (GPR), for winter wheat (Triticum aestivum L.) yield and dry matter prediction using data collected from previous studies conducted within the last twenty years in the North China Plain (NCP). In addition, winter wheat yield and dry matter were explored using the best algorithm, while polynomial functions were proposed that could describe the relationship of water and nitrogen application with winter wheat yield and dry matter. Results confirmed that the GPR model outperformed all other models for predicting the yield (R2 = 0.87) and dry matter (R2 = 0.86) of winter wheat. The prediction errors of the GPR model for maximum yield and dry matter of winter wheat were 5.8 % and 1.1 %, respectively. The yield and dry matter of winter wheat in the NCP could be predicted by the GPR model and polynomial functions, and the optimal water and nitrogen application for maximum yield and dry matter could be obtained. The results provide insight into site-specific crop management.

The Craft and Coordination of Data Curation: Complicating Workflow Views of Data Science

Data curation is the process of making a dataset fit-for-use and archivable. It is critical to data-intensive science because it makes complex data pipelines possible, studies reproducible, and data reusable. Yet the complexities of the hands-on, technical, and intellectual work of data curation is frequently overlooked or downplayed. Obscuring the work of data curation not only renders the labor and contributions of data curators invisible but also hides the impact that curators' work has on the later usability, reliability, and reproducibility of data. To better understand the work and impact of data curation, we conducted a close examination of data curation at a large social science data repository, the Inter-university Consortium for Political and Social Research (ICPSR). We asked: What does curatorial work entail at ICPSR, and what work is more or less visible to different stakeholders and in different contexts? And, how is that curatorial work coordinated across the organization? We triangulated accounts of data curation from interviews and records of curation in Jira tickets to develop a rich and detailed account of curatorial work. While we identified numerous curatorial actions performed by ICPSR curators, we also found that curators rely on a number of craft practices to perform their jobs. The reality of their work practices defies the rote sequence of events implied by many life cycle or workflow models. Further, we show that craft practices are needed to enact data curation best practices and standards. The craft that goes into data curation is often invisible to end users, but it is well recognized by ICPSR curators and their supervisors. Explicitly acknowledging and supporting data curators as craftspeople is important in creating sustainable and successful curatorial infrastructures.

Leveraging Artificial Intelligence and Robotics to Improve Mental Health

Artificial Intelligence (AI) is one of the oldest fields of computer science used in building structures that look like human beings in terms of thinking, learning, solving problems, and decision making. AI technologies and techniques have been in application in various aspects to aid in solving problems and performing tasks more reliably, efficiently, and effectively than what would happen without their use. These technologies have also been reshaping the health sector's field, particularly digital tools and medical robotics. The new reality has been feasible since there has been exponential growth in the patient health data collected globally. The different technological approaches are revolutionizing medical sciences into data-intensive sciences. Notably, with digitizing medical records supported the increasing cloud storage, the health sector created a vast and potentially immeasurable volume of biomedical data necessary for implementing robotics and AI. Despite the notable use of AI in healthcare sectors such as dermatology and radiology, its use in psychological healthcare has neem models. Considering the increased mortality and morbidity levels among patients with psychiatric illnesses and the debilitating shortage of psychological healthcare workers, there is a vital requirement for AI and robotics to help in identifying high-risk persons and providing measures that avert and treat mental disorders. This discussion is focused on understanding how AI and robotics could be employed in improving mental health in the human community. The continued success of this technology in other healthcare fields demonstrates that it could also be used in redefining mental sicknesses objectively, identifying them at a prodromal phase, personalizing the treatments, and empowering patients in their care programs.

Plant-pollinator Interaction Data: A case study of the WorldFAIR project

Biodiversity is a data-intensive science and relies on data from a large number of disciplines in order to build up a coherent picture of the extent and trajectory of life on earth (Bowker 2000). The ability to integrate such data from different disciplines, geographic regions and scales is crucial for making better decisions towards sustainable development. As the Biodiversity Information Standards (TDWG) community tackles standards development and adoption beyond its initial emphases on taxonomy and species distributions, expanding its impact and engaging a wider audience becomes increasingly important. Biological interactions data (e.g., predator-prey, host-parasite, plant-pollinator) have been a topic of interest within TDWG for many years and a Biological Interaction Data Interest Group (IG) was established in 2016 to address that issue. The IG has been working on the complexity of representing interactions data and surveying how Darwin Core (DwC, Wieczorek 2012) is being used to represent them (Salim 2022). The importance of cross-disciplinary science and data inspired the recently funded WorldFAIR project—Global cooperation on FAIR data policy and practice—coordinated by the Committee on Data of the International Science Council (CODATA), with the Research Data Alliance (RDA) as a major partner. WorldFAIR will work with a set of case studies to advance implementation of the FAIR data principles (Fig. 1). The FAIR data principles promote good practices in data management, by making data and metadata Findable, Accessible, Interoperable, and Reusable (Wilkinson 2016). Interoperability will be a particular focus to facilitate cross-disciplinary research. A set of recommendations and a framework for FAIR assessment in a set of disciplines will be developed (Molloy 2022). One of WorldFAIR's case studies is related to plant-pollinator interactions data. Its starting point is the model and schema proposed by Salim (2022) based on the DwC standard, which adheres to the diversifying GBIF data model strategy and on the Plant-Pollinator vocabulary described by Salim (2021). The case study on plant-pollinator interactions originated in the TDWG Biological Interaction Data Interest Group (IG) and within the RDA Improving Global Agricultural Data (IGAD) Community of Practice. IGAD is a forum for sharing experiences and providing visibility to research and work in food and agricultural data and has become a space for networking and blending ideas related to data management and interoperability. This topic was chosen because interoperability of plant-pollinator data is needed for better monitoring of pollination services, understanding the impacts of cultivated plants on wild pollinators and quantifying the contribution of wild pollinators to cultivated crops, understanding the impact of domesticated bees on wild ecosystems, and understanding the behaviour of these organisms and how this influences their effectiveness as pollinators. In addition to the ecological importance of these data, pollination is economically important for food production. In Brazil, the economic value of the pollination service was estimated at US$ 12 billion in 2018 (Wolowski 2019). All eleven case studies within the WorldFAIR project are working on FAIR Implementation Profiles (FIPs), which capture comprehensive sets of FAIR principle implementation choices made by communities of practice and which can accelerate convergence and facilitate cross-collaboration between disciplines (Schultes 2020). The FIPs are published through the FIP Wizard, which allows the creation of FAIR Enabling Resources. The FIPs creation will be repeated by the end of the project and capture results obtained from each case study in order to advance data interoperability. In the first FIP, resources from the Global Biodiversity Information Facility (GBIF) and Global Biotic Interactions (GloBI) were catalogued by the Plant-Pollinator Case Study team, and we expect to expand the existing FAIR Enabling Resources by the end of the project and contribute to plant-pollinator data interoperability and reuse. To tackle the challenge of promoting FAIR data for plant-pollinator interactions within the broad scope of the several disciplines and subdisciplines that generate and use them, we will conduct a survey of existing initiatives handling plant-pollinator interactions data and summarise the current status of best practices in the community. Once the survey is concluded, we will choose at least five agriculture-specific plant-pollination initiatives from our partners, to serve as targets for standards adoption. For data to be interoperable and reusable, it is essential that standards and best practices are community-developed to ensure adoption by the tool builders and data scientists across the globe. TDWG plays an important role in this scenario and we expect to engage the IG and other interested parties in that discussion.

SNAG: SDN-Managed Network Architecture for GridFTP Transfers Using Application-Awareness

Increasingly, academic campus networks support large-scale data transfer workflows for data-intensive science. These data transfers rely on high-performance, scalable, and reliable protocols for moving large amounts of data over a high-bandwidth, high-latency network. GridFTP is a widely used protocol for wide area network (WAN) data movement. However, as the GridFTP protocol does not share connection information with the network-layer, network operators have reduced flexibility, particularly in identifying/managing flows across the network. We address this problem by deploying a production “ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">application-aware</i> ” software defined network (SDN) for managing GridFTP transfers for data-intensive science workflows. We first propose a novel application-aware architecture called SNAG (SDN-managed Network Architecture for GridFTP transfers). SNAG combines <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">application-layer and network-layer collaboration</i> (termed “application-awareness”) with SDN-enabled network management to classify, monitor and to manage network resources actively. Until now, our SNAG deployment has successfully classified over <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1.5 Billion</i> GridFTP connections at the Holland Computing Center (HCC), University of Nebraska-Lincoln (UNL). Next, we develop an application-aware SDN system to provide differentiated network services for distributed computing workflows. At HCC, we also demonstrate how our system ensures the quality of service (QoS) for high-throughput workflows such as Compact Muon Solenoid (CMS) and Laser Interferometer Gravitational-Wave Observatory (LIGO). Further, we also demonstrate how application-aware SDN can be exploited to create <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">policy-driven</i> approaches to achieve accurate resource accounting for each workflow. We present strategies for implementing differentiated network services and discuss their capacity improvement benefits. Lastly, we provide some guidelines and recommendations for developing application-aware SDN architectures for general-purpose applications.

A Study on Traffic Prediction for the Backbone of Korea’s Research and Science Network Using Machine Learning

To fix network congestion resulting from the increase in high volume traffic in data-intensive science and the increase in internet traffic due to COVID-19, there has been a necessity of traffic engineering through traffic prediction. For this, there have been various attempts from a statistical method such as ARIMA to machine learning including LSTM and GRU. This study aimed to collect and learn KREOENT backbone and subscribers’ traffic volume through diverse machine learning techniques (e.g., SVR, LSTM, GRU, etc.) and predict maximum traffic on the following day.