Big Data and Society Abstract & Indexing - ResearchHelpDesk - Big Data & Society (BD&S) is open access, peer-reviewed scholarly journal that publishes interdisciplinary work principally in the social sciences, humanities and computing and their intersections with the arts and natural sciences about the implications of Big Data for societies. The Journal's key purpose is to provide a space for connecting debates about the emerging field of Big Data practices and how they are reconfiguring academic, social, industry, business, and government relations, expertise, methods, concepts, and knowledge. BD&S moves beyond usual notions of Big Data and treats it as an emerging field of practice that is not defined by but generative of (sometimes) novel data qualities such as high volume and granularity and complex analytics such as data linking and mining. It thus attends to digital content generated through online and offline practices in social, commercial, scientific, and government domains. This includes, for instance, the content generated on the Internet through social media and search engines but also that which is generated in closed networks (commercial or government transactions) and open networks such as digital archives, open government, and crowdsourced data. Critically, rather than settling on a definition the Journal makes this an object of interdisciplinary inquiries and debates explored through studies of a variety of topics and themes. BD&S seeks contributions that analyze Big Data practices and/or involve empirical engagements and experiments with innovative methods while also reflecting on the consequences for how societies are represented (epistemologies), realized (ontologies) and governed (politics). Article processing charge (APC) The article processing charge (APC) for this journal is currently 1500 USD. Authors who do not have funding for open access publishing can request a waiver from the publisher, SAGE, once their Original Research Article is accepted after peer review. For all other content (Commentaries, Editorials, Demos) and Original Research Articles commissioned by the Editor, the APC will be waived. Abstract & Indexing Clarivate Analytics: Social Sciences Citation Index (SSCI) Directory of Open Access Journals (DOAJ) Google Scholar Scopus

The global big data market is forecasted to grow to 103 billion U.S. dollars by 2027, more than double its expected market size in 2018. With a share of 45 percent, the software segment would become the large big data market segment by 2027.

What is Big data?

Big data is a term that refers to the kind of data sets that are too large or too complex for traditional data processing applications. It is defined as having one or some of the following characteristics: high volume, high velocity or high variety. Fast-growing mobile data traffic, cloud computing traffic, as well as the rapid development of technologies such as artificial intelligence (AI) and the Internet of Things (IoT) all contribute to the increasing volume and complexity of data sets.

Big data analytics

Advanced analytics tools, such as predictive analytics and data mining, help to extract value from the data and generate new business insights. The global big data and business analytics market was valued at 169 billion U.S. dollars in 2018 and is expected to grow to 274 billion U.S. dollars in 2022. As of November 2018, 45 percent of professionals in the market research industry reportedly used big data analytics as a research method.

China Big Data Technology Investment Opportunities Market was valued at USD 45.2 Billion in 2023 and is projected to reach USD 95.6 Billion by 2031, growing at a CAGR of 9.8% from 2024 to 2031.

China Big Data Technology Investment Opportunities Market: Definition/Overview

Big data technology is defined as the complex ecosystem of tools, processes, and methodologies that are utilized to handle extremely large datasets. These technologies are designed to extract valuable insights from structured and unstructured data that is generated at unprecedented volumes. Furthermore, the applications of big data technology are seen across multiple sectors, where data is processed, analyzed, and transformed into actionable intelligence. Advanced analytics, artificial intelligence, and machine learning capabilities are integrated into these systems, through which deeper insights are enabled, and predictive capabilities are enhanced.

The Big Data Technology Market size was valued at USD 349.40 USD Billion in 2023 and is projected to reach USD 918.16 USD Billion by 2032, exhibiting a CAGR of 14.8 % during the forecast period. Big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can’t manage them. But these massive volumes of data can be used to address business problems that wouldn’t have been able to tackle before. Big data technology is defined as software-utility. This technology is primarily designed to analyze, process and extract information from a large data set and a huge set of extremely complex structures. This is very difficult for traditional data processing software to deal with. Among the larger concepts of rage in technology, big data technologies are widely associated with many other technologies such as deep learning, machine learning, artificial intelligence (AI), and Internet of Things (IoT) that are massively augmented. In combination with these technologies, big data technologies are focused on analyzing and handling large amounts of real-time data and batch-related data. Recent developments include: February 2024: - SQream, a GPU data analytics platform, partnered with Dataiku, an AI and machine learning platform, to deliver a comprehensive solution for efficiently generating big data analytics and business insights by handling complex data., October 2023: - MultiversX (ELGD), a blockchain infrastructure firm, formed a partnership with Google Cloud to enhance Web3’s presence by integrating big data analytics and artificial intelligence tools. The collaboration aims to offer new possibilities for developers and startups., May 2023: - Vpon Big Data Group partnered with VIOOH, a digital out-of-home advertising (DOOH) supply-side platform, to display the unique advertising content generated by Vpon’s AI visual content generator "InVnity" with VIOOH's digital outdoor advertising inventories. This partnership pioneers the future of outdoor advertising by using AI and big data solutions., May 2023: - Salesforce launched the next generation of Tableau for users to automate data analysis and generate actionable insights., March 2023: - SAP SE, a German multinational software company, entered a partnership with AI companies, including Databricks, Collibra NV, and DataRobot, Inc., to introduce the next generation of data management portfolio., November 2022: - Thai Oil and Retail Corporation PTT Oil and Retail Business Public Company implemented the Cloudera Data Platform to deliver insights and enhance customer engagement. The implementation offered a unified and personalized experience across 1,900 gas stations and 3,000 retail branches., November 2022: - IBM launched new software for enterprises to break down data and analytics silos that helped users make data-driven decisions. The software helps to streamline how users access and discover analytics and planning tools from multiple vendors in a single dashboard view., September 2022: - ActionIQ, a global leader in CX solutions, and Teradata, a leading software company, entered a strategic partnership and integrated AIQ’s new HybridCompute Technology with Teradata VantageCloud analytics and data platform.. Key drivers for this market are: Increasing Adoption of AI, ML, and Data Analytics to Boost Market Growth . Potential restraints include: Rising Concerns on Information Security and Privacy to Hinder Market Growth. Notable trends are: Rising Adoption of Big Data and Business Analytics among End-use Industries.

The term Big Data is commonly used to describe a range of different concepts: from the collection and aggregation of vast amounts of data, to a plethora of advanced digital techniques designed to reveal patterns related to human behavior. In spite of its widespread use, the term is still loaded with conceptual vagueness. The aim of this study is to examine the understanding of the meaning of Big Data from the perspectives of researchers in the fields of psychology and sociology in order to examine whether researchers consider currently existing definitions to be adequate and investigate if a standard discipline centric definition is possible.MethodsThirty-nine interviews were performed with Swiss and American researchers involved in Big Data research in relevant fields. The interviews were analyzed using thematic coding.ResultsNo univocal definition of Big Data was found among the respondents and many participants admitted uncertainty towards giving a definition of Big Data. A few participants described Big Data with the traditional “Vs” definition—although they could not agree on the number of Vs. However, most of the researchers preferred a more practical definition, linking it to processes such as data collection and data processing.ConclusionThe study identified an overall uncertainty or uneasiness among researchers towards the use of the term Big Data which might derive from the tendency to recognize Big Data as a shifting and evolving cultural phenomenon. Moreover, the currently enacted use of the term as a hyped-up buzzword might further aggravate the conceptual vagueness of Big Data.

ChinaHighO3 is one of the series of long-term, full-coverage, high-resolution, and high-quality datasets of ground-level air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from the big data (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence by considering the spatiotemporal heterogeneity of air pollution.

This is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 10 km (i.e., D10K, M10K, and Y10K) ground-level maximum 8-hour average (MDA8) O3 dataset in China from 1979 to 2020. This dataset yields a high quality with a cross-validation coefficient of determination (CV-R2) of 0.87, a root-mean-square error (RMSE) of 17.10 µg m-3, and a mean absolute error (MAE) of 11.29 µg m-3 on a daily basis.

If you use the ChinaHighO3 dataset for related scientific research, please cite the corresponding reference (Wei et al., RSE, 2022; He et al., 2022):

Wei, J., Li, Z., Li, K., Dickerson, R., Pinker, R., Wang, J., Liu, X., Sun, L., Xue, W., and Cribb, M. Full-coverage mapping and spatiotemporal variations of ground-level ozone (O3) pollution from 2013 to 2020 across China. Remote Sensing of Environment, 2022, 270, 112775. https://doi.org/10.1016/j.rse.2021.112775

He, L., Wei, J., Wang, Y., Shang, Q., Liu, J., Yin, Y., Frankerberg, C., Jiang, J., Li, Z., and Yung, Y. Marked impacts of pollution mitigation on crop yields in China. Earth's Future, 2022, 10, e2022EF002936. https://doi.org/10.1029/2022EF002936

Note that access to this dataset is now restricted, as a longer-term (2000 to present), high-resolution (1 km), and higher quality ChinaHighO3 dataset is now available: http://doi.org/10.5281/zenodo.10477125

More CHAP datasets of different air pollutants can be found at: https://weijing-rs.github.io/product.html

Big Data As A Service Market size was valued at USD 18.23 Billion in 2023 and is projected to reach USD 120.09 Billion by 2030, growing at a CAGR of 29.31% during the forecast period 2024-2030.

Big Data-as-a-Service Market: Definition/ Overview

Big Data-as-a-Service (BDaaS) is a cloud-based approach that gives enterprises access to data management and analytics tools, allowing them to process, store, and analyze large amounts of data without requiring costly on-premises infrastructure. This solution enables firms to use advanced analytics for real-time decision-making, increasing operational efficiency and competitiveness. BDaaS has applications across a variety of industries, including finance for risk assessment, healthcare for patient data analysis, retail for customer behavior insights, and manufacturing for supply chain optimization.

Big Data and Society Acceptance Rate - ResearchHelpDesk - Big Data & Society (BD&S) is open access, peer-reviewed scholarly journal that publishes interdisciplinary work principally in the social sciences, humanities and computing and their intersections with the arts and natural sciences about the implications of Big Data for societies. The Journal's key purpose is to provide a space for connecting debates about the emerging field of Big Data practices and how they are reconfiguring academic, social, industry, business, and government relations, expertise, methods, concepts, and knowledge. BD&S moves beyond usual notions of Big Data and treats it as an emerging field of practice that is not defined by but generative of (sometimes) novel data qualities such as high volume and granularity and complex analytics such as data linking and mining. It thus attends to digital content generated through online and offline practices in social, commercial, scientific, and government domains. This includes, for instance, the content generated on the Internet through social media and search engines but also that which is generated in closed networks (commercial or government transactions) and open networks such as digital archives, open government, and crowdsourced data. Critically, rather than settling on a definition the Journal makes this an object of interdisciplinary inquiries and debates explored through studies of a variety of topics and themes. BD&S seeks contributions that analyze Big Data practices and/or involve empirical engagements and experiments with innovative methods while also reflecting on the consequences for how societies are represented (epistemologies), realized (ontologies) and governed (politics). Article processing charge (APC) The article processing charge (APC) for this journal is currently 1500 USD. Authors who do not have funding for open access publishing can request a waiver from the publisher, SAGE, once their Original Research Article is accepted after peer review. For all other content (Commentaries, Editorials, Demos) and Original Research Articles commissioned by the Editor, the APC will be waived. Abstract & Indexing Clarivate Analytics: Social Sciences Citation Index (SSCI) Directory of Open Access Journals (DOAJ) Google Scholar Scopus

39 percent of German managers from the chemical and pharmaceutical industries consider big data to already have a central meaning for their companies. The figures are based on a survey conducted in Germany in 2018.

The Web-Scale IT market is experiencing robust growth, driven by the increasing adoption of cloud computing, big data analytics, and the Internet of Things (IoT). The market's expansion is fueled by the need for scalable, resilient, and cost-effective IT infrastructure to support the ever-growing demands of digital businesses across diverse sectors. Key application areas include healthcare (leveraging data for improved diagnostics and personalized medicine), energy and utilities (optimizing grid management and enhancing renewable energy integration), media and entertainment (powering streaming services and personalized content delivery), and financial services (ensuring secure and reliable transaction processing). The shift towards software-defined data centers (SDDCs) and the increasing implementation of self-healing software are significant trends shaping the market landscape, alongside automation and advanced analytics capabilities. While the market faces some restraints such as the high initial investment costs associated with Web-Scale IT infrastructure and the need for specialized skilled professionals, the long-term benefits of scalability, efficiency, and resilience are driving widespread adoption. We estimate the market size in 2025 to be $150 billion, growing at a CAGR of 15% from 2025 to 2033. This growth is projected across all geographic regions, with North America and Asia Pacific anticipated to be leading contributors due to the high concentration of technology companies and early adoption of innovative technologies. The competitive landscape is highly dynamic, with a mix of established technology giants (like Amazon, Google, Microsoft, and IBM) and specialized vendors offering a range of solutions. The market is characterized by strategic partnerships, acquisitions, and continuous innovation to cater to the evolving needs of businesses. The segment with the highest growth potential is likely self-healing software, driven by increasing demand for robust and autonomous IT management. Companies are investing heavily in research and development to enhance the capabilities of these solutions, focusing on AI and machine learning to optimize performance and minimize downtime. This ongoing technological advancement, coupled with the escalating digital transformation initiatives across industries, ensures a promising outlook for the Web-Scale IT market over the next decade.

Big Data and Society CiteScore 2024-2025 - ResearchHelpDesk - Big Data & Society (BD&S) is open access, peer-reviewed scholarly journal that publishes interdisciplinary work principally in the social sciences, humanities and computing and their intersections with the arts and natural sciences about the implications of Big Data for societies. The Journal's key purpose is to provide a space for connecting debates about the emerging field of Big Data practices and how they are reconfiguring academic, social, industry, business, and government relations, expertise, methods, concepts, and knowledge. BD&S moves beyond usual notions of Big Data and treats it as an emerging field of practice that is not defined by but generative of (sometimes) novel data qualities such as high volume and granularity and complex analytics such as data linking and mining. It thus attends to digital content generated through online and offline practices in social, commercial, scientific, and government domains. This includes, for instance, the content generated on the Internet through social media and search engines but also that which is generated in closed networks (commercial or government transactions) and open networks such as digital archives, open government, and crowdsourced data. Critically, rather than settling on a definition the Journal makes this an object of interdisciplinary inquiries and debates explored through studies of a variety of topics and themes. BD&S seeks contributions that analyze Big Data practices and/or involve empirical engagements and experiments with innovative methods while also reflecting on the consequences for how societies are represented (epistemologies), realized (ontologies) and governed (politics). Article processing charge (APC) The article processing charge (APC) for this journal is currently 1500 USD. Authors who do not have funding for open access publishing can request a waiver from the publisher, SAGE, once their Original Research Article is accepted after peer review. For all other content (Commentaries, Editorials, Demos) and Original Research Articles commissioned by the Editor, the APC will be waived. Abstract & Indexing Clarivate Analytics: Social Sciences Citation Index (SSCI) Directory of Open Access Journals (DOAJ) Google Scholar Scopus

The global data center equipment market is experiencing robust growth, driven by the increasing demand for cloud computing, big data analytics, and the expansion of the Internet of Things (IoT). The market size in 2025 is estimated at $200 billion USD, exhibiting a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This significant expansion is fueled by several key factors. Firstly, the widespread adoption of cloud-based services necessitates substantial investment in data center infrastructure, including servers, networking equipment, and storage solutions. Secondly, the exponential growth of data generated by IoT devices necessitates high-capacity data centers and advanced data management systems. Finally, the shift towards hybrid and multi-cloud environments further fuels the demand for scalable and adaptable data center equipment. This growth is not uniform across all segments; the database machine segment is experiencing the fastest growth due to increasing demand for high-performance computing and data processing, while the IoT application segment shows strong growth potential due to the increasing number of connected devices. While geographical expansion and technological advancements are major drivers, challenges remain. These include escalating energy costs associated with powering large data centers, the complexity of managing increasingly intricate infrastructures, and the need for robust cybersecurity measures to safeguard sensitive data. Despite these challenges, the market is poised for continued expansion. Key growth opportunities lie in the development of energy-efficient equipment, the integration of artificial intelligence (AI) for data center management, and the adoption of edge computing to process data closer to its source. The increasing adoption of software-defined networking (SDN) and network function virtualization (NFV) is also expected to contribute significantly to market growth in the coming years. Companies specializing in advanced networking solutions, high-performance computing, and data storage are well-positioned to capitalize on these opportunities. The competitive landscape is dynamic, with both established players and innovative startups contributing to the market's evolution. Strategic partnerships and acquisitions are expected to further shape the industry landscape.

Additional file 2. R Code. All of the R scripts used for the analysis in “Analyzing health insurance coverage using the 2015 planningdatabase†section.

Big data approaches to psychology have become increasing popular (Jones, 2017). Two of the main developments of this line of research is the advent of distributional models of semantics (e.g., Landauer and Dumais, 1997), which learn the meaning of words from large text corpora, and the collection of mega datasets of human behavior (e.g., The English lexicon project; Balota et al., 2007). The current article combines these two approaches, with the goal being to understand the consistency and preference that people have for word meanings. This was accomplished by mining a large amount of data from an online, crowdsourced dictionary and analyzing this data with a distributional model. Overall, it was found that even for words that are not an active part of the language environment, there is a large amount of consistency in the word meanings that different people have. Additionally, it was demonstrated that users of a language have strong preferences for word meanings, such that definitions to words that do not conform to people’s conceptions are rejected by a community of language users. The results of this article provides insights into the cultural evolution of word meanings, and sheds light on alternative methodologies that can be used to understand lexical behavior.

1. Summary

Global estimates of reach-level bankfull river width generated in the article by Peirong Lin, Ming Pan, George H. Allen, Renato Frasson, Zhenzhong Zeng, Dai Yamazaki, Eric F. Wood entitled "Global reach-level bankfull river width leveraging big-data geospatial analysis", Geophysical Research Letters (accepted).

2. File Description

Shapefile storing machine learning-derived bankfull river width, and environmental covariates used to predict the width (~1.4GB). The polylines were vectorized by Lin et al. (2019) based on the Multi-Error Removed Improved-Terrain (MERIT) DEM and MERIT Hydro (Yamazaki et al., 2017, 2019), under a channelization threshold of 25 km². Only rivers wider than 30 m are shown here; these locations were determined by jointly using the Global River Widths from Landsat (GRWL) database (Allen & Pavelsky, 2018) and the MERIT Hydro width estimates (Yamazaki et al., 2019).

3. Attribute Description

• COMID: identification number of the river reach, same as that used in global river modeling by Lin et al., (2019);
• Order: Strahler-Horton stream order, with stream order 1 starting from those with an upstream drainage area of 25 km²;
• Area: Upstream drainage basin area in km²;
• Sin: Sinuosity of the river segment (unitless);
• Slp: mean slope of the river segment (unitless);
• Elev: mean elevation of the river segment;
• K: mean bedrock permeability of the unit catchment surrounding the river segment, with data extracted from Huscroft et al. (2018);
• P: mean bedrock porosity of the unit catchment surrounding the river segment, with data extracted from Huscroft et al. (2018);
• AI: mean aridity index of the unit catchment; data extracted from Trabucco & Zomer (2019);
• LAI: mean leaf area index of the unit catchment; data extracted from Zhu et al. (2013);
• SND: mean sand content (mass percentage, %) of the unit catchment; data extracted from Hengl et al. (2017);
• CLY: mean clay content (mass percentage, %) of the unit catchment; data extracted from Hengl et al. (2017);
• SLT: mean silt content (mass percentage, %) of the unit catchment; data extracted from Hengl et al. (2017);
• Urb: mean urban fraction of the unit catchment; data extracted from Liu et al. (2018);
• WTD: mean water table depth (m below surface) of the unit catchment; data extracted from Fan et al. (2013);
• HW: mean human water use (irrigational + industrial + domestic) of the unit catchment; data extracted from Wada et al. (2016)
• DOR: degree of dam regulation for the river segment; the definition of DOR and data were sourced from Grill et al. (2019)
• QMEAN: mean annual discharge (m³/s) for the river segment; the multi-year averaged were calculated from Lin et al. (2019);
• Q2: 2-year return period flood discharge (m³/s) for the river segment; the 35-year data used to calculate the field was sourced from Lin et al. (2019);
• Width_m: bankfull river width (m) estimated by using the optimized machine learning model of this study, applied to Q2 and other environmental covariates;
• Width_DHG: bankfull river width (m) estimated by using the Moody & Troutman (2002) equation applied to Q2 estimated in this study

4. References

Allen, G. H., & Pavelsky, T. M. (2018). Global extent of rivers and streams. Science, 361(6402), 585–588. https://doi.org/10.1126/science.aat0636

Fan, Y., Li, H., & Miguez-Macho, G. (2013). Global Patterns of Groundwater Table Depth. Science, 339(6122), 940–943. https://doi.org/10.1126/science.1229881

Grill, G., Lehner, B., Thieme, M., Geenen, B., Tickner, D., Antonelli, F., et al. (2019). Mapping the world’s free-flowing rivers. Nature, 569(7755), 215. https://doi.org/10.1038/s41586-019-1111-9

Hengl, T., Mendes de Jesus, J., Heuvelink, G. B. M., Ruiperez Gonzalez, M., Kilibarda, M., Blagotić, A., et al. (2017). SoilGrids250m: Global gridded soil information based on machine learning. PLOS ONE, 12(2), e0169748. https://doi.org/10.1371/journal.pone.0169748

Huscroft, J., Gleeson, T., Hartmann, J., & Börker, J. (2018). Compiling and Mapping Global Permeability of the Unconsolidated and Consolidated Earth: GLobal HYdrogeology MaPS 2.0 (GLHYMPS 2.0). Geophysical Research Letters, 45(4), 1897–1904. https://doi.org/10.1002/2017GL075860

Lin, P., Pan, M., Beck, H. E., Yang, Y., Yamazaki, D., Frasson, R., et al. (2019). Global Reconstruction of Naturalized River Flows at 2.94 Million Reaches. Water Resources Research, 0(0). https://doi.org/10.1029/2019WR025287

Liu, X., Hu, G., Chen, Y., Li, X., Xu, X., Li, S., et al. (2018). High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform. Remote Sensing of Environment, 209, 227–239. https://doi.org/10.1016/j.rse.2018.02.055

Trabucco, A., & Zomer, R. (2019, January 18). Global Aridity Index and Potential Evapotranspiration (ET0) Climate Database v2. https://doi.org/10.6084/m9.figshare.7504448.v3

Wada, Y., Graaf, I. E. M. de, & Beek, L. P. H. van. (2016). High-resolution modeling of human and climate impacts on global water resources. Journal of Advances in Modeling Earth Systems, 8(2), 735–763. https://doi.org/10.1002/2015MS000618

Yamazaki, D., Ikeshima, D., Tawatari, R., Yamaguchi, T., O’Loughlin, F., Neal, J. C., et al. (2017). A high-accuracy map of global terrain elevations. Geophysical Research Letters, 44(11), 5844–5853. https://doi.org/10.1002/2017GL072874

Yamazaki, D., Ikeshima, D., Sosa, J., Bates, P. D., Allen, G. H., & Pavelsky, T. M. (2019). MERIT Hydro: A High-Resolution Global Hydrography Map Based on Latest Topography Dataset. Water Resources Research. https://doi.org/10.1029/2019WR024873

Zhu, Z., Bi, J., Pan, Y., Ganguly, S., Anav, A., Xu, L., et al. (2013). Global Data Sets of Vegetation Leaf Area Index (LAI)3g and Fraction of Photosynthetically Active Radiation (FPAR)3g Derived from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) for the Period 1981 to 2011. Remote Sensing, 5(2), 927–948. https://doi.org/10.3390/rs5020927

This is the updated version of the dataset from 10.5281/zenodo.6320761 Information The diverse publicly available compound/bioactivity databases constitute a key resource for data-driven applications in chemogenomics and drug design. Analysis of their coverage of compound entries and biological targets revealed considerable differences, however, suggesting benefit of a consensus dataset. Therefore, we have combined and curated information from five esteemed databases (ChEMBL, PubChem, BindingDB, IUPHAR/BPS and Probes&Drugs) to assemble a consensus compound/bioactivity dataset comprising 1144648 compounds with 10915362 bioactivities on 5613 targets (including defined macromolecular targets as well as cell-lines and phenotypic readouts). It also provides simplified information on assay types underlying the bioactivity data and on bioactivity confidence by comparing data from different sources. We have unified the source databases, brought them into a common format and combined them, enabling an ease for generic uses in multiple applications such as chemogenomics and data-driven drug design. The consensus dataset provides increased target coverage and contains a higher number of molecules compared to the source databases which is also evident from a larger number of scaffolds. These features render the consensus dataset a valuable tool for machine learning and other data-driven applications in (de novo) drug design and bioactivity prediction. The increased chemical and bioactivity coverage of the consensus dataset may improve robustness of such models compared to the single source databases. In addition, semi-automated structure and bioactivity annotation checks with flags for divergent data from different sources may help data selection and further accurate curation. This dataset belongs to the publication: https://doi.org/10.3390/molecules27082513 Structure and content of the dataset Dataset structure ChEMBL ID PubChem ID IUPHAR ID Target Activity type Assay type Unit Mean C (0) ... Mean PC (0) ... Mean B (0) ... Mean I (0) ... Mean PD (0) ... Activity check annotation Ligand names Canonical SMILES C ... Structure check (Tanimoto) Source The dataset was created using the Konstanz Information Miner (KNIME) (https://www.knime.com/) and was exported as a CSV-file and a compressed CSV-file. Except for the canonical SMILES columns, all columns are filled with the datatype ‘string’. The datatype for the canonical SMILES columns is the smiles-format. We recommend the File Reader node for using the dataset in KNIME. With the help of this node the data types of the columns can be adjusted exactly. In addition, only this node can read the compressed format. Column content: ChEMBL ID, PubChem ID, IUPHAR ID: chemical identifier of the databases Target: biological target of the molecule expressed as the HGNC gene symbol Activity type: for example, pIC50 Assay type: Simplification/Classification of the assay into cell-free, cellular, functional and unspecified Unit: unit of bioactivity measurement Mean columns of the databases: mean of bioactivity values or activity comments denoted with the frequency of their occurrence in the database, e.g. Mean C = 7.5 *(15) -> the value for this compound-target pair occurs 15 times in ChEMBL database Activity check annotation: a bioactivity check was performed by comparing values from the different sources and adding an activity check annotation to provide automated activity validation for additional confidence no comment: bioactivity values are within one log unit; check activity data: bioactivity values are not within one log unit; only one data point: only one value was available, no comparison and no range calculated; no activity value: no precise numeric activity value was available; no log-value could be calculated: no negative decadic logarithm could be calculated, e.g., because the reported unit was not a compound concentration Ligand names: all unique names contained in the five source databases are listed Canonical SMILES columns: Molecular structure of the compound from each database Structure check (Tanimoto): To denote matching or differing compound structures in different source databases match: molecule structures are the same between different sources; no match: the structures differ. We calculated the Jaccard-Tanimoto similarity coefficient from Morgan Fingerprints to reveal true differences between sources and reported the minimum value; 1 structure: no structure comparison is possible, because there was only one structure available; no structure: no structure comparison is possible, because there was no structure available. Source: From which databases the data come from

The Software Defined Storage (SDS) market is experiencing robust growth, driven by the increasing need for agile, scalable, and cost-effective data storage solutions. The market's Compound Annual Growth Rate (CAGR) of 25.80% from 2019 to 2024 indicates significant expansion, a trend projected to continue throughout the forecast period (2025-2033). Key drivers include the rising adoption of cloud computing, the proliferation of big data, and the need for improved data management efficiency across various sectors. The shift towards hyper-converged infrastructure (HCI) is further fueling market expansion, enabling businesses to consolidate their IT infrastructure and streamline operations. Large enterprises, particularly in the BFSI (Banking, Financial Services, and Insurance) and Telecom & IT sectors, are major adopters of SDS solutions due to their stringent data security and compliance requirements. While the market faces certain restraints such as the complexities associated with implementing SDS and potential security concerns, the overall positive market dynamics suggest a continued upward trajectory. The competitive landscape is diverse, with established players like IBM, NetApp, and Dell EMC alongside emerging players vying for market share. Segmentation by storage type (block, file, object, hyper-converged), enterprise size (SME, large enterprise), and end-user industry offers granular insights into the market's structure and growth potential within specific niches. The SDS market's regional distribution is expected to mirror global trends, with North America and Europe maintaining a significant market share due to early adoption and strong technological advancements. However, Asia, particularly regions like China and India, is poised for substantial growth due to rising digitalization and expanding IT infrastructure. Latin America and the Middle East & Africa are also predicted to show moderate growth as digital transformation initiatives accelerate in these regions. The continued innovation in SDS technologies, including advancements in automation, AI-powered data management, and improved security features, will be pivotal in shaping the future of the market. The focus on hybrid and multi-cloud deployments is also expected to drive the demand for SDS solutions capable of seamless integration across various cloud environments. The market is predicted to reach a substantial valuation by 2033, reflecting a continuing strong demand for flexible, scalable, and cost-effective data storage management. Recent developments include: March 2022 - Nvidia acquired Excelero, a high-performance block storage provider whose core product, Excelero NVMesh, offers software-defined block storage via networked NVMe SSDs and operates through networked drives on-prem or in private clouds and started supporting Microsoft Azure form last year. This acquisition of Excelero could mark another win for its increasingly diversified portfolio of assets as the GPU giant works to broaden its reach beyond components and into integrated systems and software., October 2021 - Exxact Corporation, a leading provider of high-performance computing (HPC), artificial intelligence (AI), and data center solutions, and SoftIron, a leader in task-specific data infrastructure solutions, announced a partnership to create solutions for the modern-day enterprise that make software-defined storage (SDS) simple.. Key drivers for this market are: Rapidly Growing Volume of Data Across Enterprises, Increased Demand for Industrial Mobility for Remotely Managing the Process Industry. Potential restraints include: Lack of Security Awareness in Virtualization Environment, Industry Standard Deficiency. Notable trends are: BFSI Sector to Witness Significant Growth.

ChinaHighSO2 is one of the series of long-term, full-coverage, high-resolution, and high-quality datasets of ground-level air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from the big data (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence by considering the spatiotemporal heterogeneity of air pollution.

This is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 10 km (i.e., D10K, M10K, and Y10K) ground-level SO2 dataset in China from 2013 to 2020. This dataset yields a high quality with a cross-validation coefficient of determination (CV-R2) of 0.84, a root-mean-square error (RMSE) of 10.07 µg m-3, and a mean absolute error (MAE) of 4.68 µg m-3 on a daily basis.

If you use the ChinaHighSO2 dataset for related scientific research, please cite the corresponding reference (Wei et al., ACP, 2023):

Wei, J., Li, Z., Wang, J., Li, C., Gupta, P., and Cribb, M. Ground-level gaseous pollutants (NO2, SO2, and CO) in China: daily seamless mapping and spatiotemporal variations. Atmospheric Chemistry and Physics, 2023, 23, 1511–1532. https://doi.org/10.5194/acp-23-1511-2023

More CHAP datasets of different air pollutants can be found at: https://weijing-rs.github.io/product.html

Global Software Defined Storage(SDS) Market reached US$ 31.1 billion in 2022 and is expected to reach US$ 645.2 billion by 2030