This statistic illustrates the answers to a survey question on the usage of big data among SMEs in the Netherlands in 2018, by business unit. As of 2018, 26 percent of the respondents mentioned that they make use of big data with their marketing/sales department, whereas approximately 20 percent of the respondents indicated to use big data for pre-sales. Lowest use of big data is the HR department with six percent of the SME respondents.

The USDA Agricultural Research Service (ARS) recently established SCINet , which consists of a shared high performance computing resource, Ceres, and the dedicated high-speed Internet2 network used to access Ceres. Current and potential SCINet users are using and generating very large datasets so SCINet needs to be provisioned with adequate data storage for their active computing. It is not designed to hold data beyond active research phases. At the same time, the National Agricultural Library has been developing the Ag Data Commons, a research data catalog and repository designed for public data release and professional data curation. Ag Data Commons needs to anticipate the size and nature of data it will be tasked with handling. The ARS Web-enabled Databases Working Group, organized under the SCINet initiative, conducted a study to establish baseline data storage needs and practices, and to make projections that could inform future infrastructure design, purchases, and policies. The SCINet Web-enabled Databases Working Group helped develop the survey which is the basis for an internal report. While the report was for internal use, the survey and resulting data may be generally useful and are being released publicly. From October 24 to November 8, 2016 we administered a 17-question survey (Appendix A) by emailing a Survey Monkey link to all ARS Research Leaders, intending to cover data storage needs of all 1,675 SY (Category 1 and Category 4) scientists. We designed the survey to accommodate either individual researcher responses or group responses. Research Leaders could decide, based on their unit's practices or their management preferences, whether to delegate response to a data management expert in their unit, to all members of their unit, or to themselves collate responses from their unit before reporting in the survey.
Larger storage ranges cover vastly different amounts of data so the implications here could be significant depending on whether the true amount is at the lower or higher end of the range. Therefore, we requested more detail from "Big Data users," those 47 respondents who indicated they had more than 10 to 100 TB or over 100 TB total current data (Q5). All other respondents are called "Small Data users." Because not all of these follow-up requests were successful, we used actual follow-up responses to estimate likely responses for those who did not respond. We defined active data as data that would be used within the next six months. All other data would be considered inactive, or archival. To calculate per person storage needs we used the high end of the reported range divided by 1 for an individual response, or by G, the number of individuals in a group response. For Big Data users we used the actual reported values or estimated likely values.

Resources in this dataset:Resource Title: Appendix A: ARS data storage survey questions. File Name: Appendix A.pdfResource Description: The full list of questions asked with the possible responses. The survey was not administered using this PDF but the PDF was generated directly from the administered survey using the Print option under Design Survey. Asterisked questions were required. A list of Research Units and their associated codes was provided in a drop down not shown here. Resource Software Recommended: Adobe Acrobat,url: https://get.adobe.com/reader/ Resource Title: CSV of Responses from ARS Researcher Data Storage Survey. File Name: Machine-readable survey response data.csvResource Description: CSV file includes raw responses from the administered survey, as downloaded unfiltered from Survey Monkey, including incomplete responses. Also includes additional classification and calculations to support analysis. Individual email addresses and IP addresses have been removed. This information is that same data as in the Excel spreadsheet (also provided).Resource Title: Responses from ARS Researcher Data Storage Survey. File Name: Data Storage Survey Data for public release.xlsxResource Description: MS Excel worksheet that Includes raw responses from the administered survey, as downloaded unfiltered from Survey Monkey, including incomplete responses. Also includes additional classification and calculations to support analysis. Individual email addresses and IP addresses have been removed.Resource Software Recommended: Microsoft Excel,url: https://products.office.com/en-us/excel

WIDEa is R-based software aiming to provide users with a range of functionalities to explore, manage, clean and analyse "big" environmental and (in/ex situ) experimental data. These functionalities are the following, 1. Loading/reading different data types: basic (called normal), temporal, infrared spectra of mid/near region (called IR) with frequency (wavenumber) used as unit (in cm-1); 2. Interactive data visualization from a multitude of graph representations: 2D/3D scatter-plot, box-plot, hist-plot, bar-plot, correlation matrix; 3. Manipulation of variables: concatenation of qualitative variables, transformation of quantitative variables by generic functions in R; 4. Application of mathematical/statistical methods; 5. Creation/management of data (named flag data) considered as atypical; 6. Study of normal distribution model results for different strategies: calibration (checking assumptions on residuals), validation (comparison between measured and fitted values). The model form can be more or less complex: mixed effects, main/interaction effects, weighted residuals.

Portugal Number of Companies: excl Size: Large data was reported at 1.000 Unit th in 2016. This stayed constant from the previous number of 1.000 Unit th for 2015. Portugal Number of Companies: excl Size: Large data is updated yearly, averaging 1.000 Unit th from Dec 2006 (Median) to 2016, with 11 observations. The data reached an all-time high of 1.000 Unit th in 2016 and a record low of 0.900 Unit th in 2014. Portugal Number of Companies: excl Size: Large data remains active status in CEIC and is reported by Bank of Portugal. The data is categorized under Global Database’s Portugal – Table PT.O001: Number of Companies.

The Chinese Big Data market presents a compelling investment landscape, projected to experience robust growth. With a Compound Annual Growth Rate (CAGR) of 30% from 2019 to 2033, the market's value is expected to surge significantly. Several key drivers fuel this expansion. The burgeoning digital economy in China, coupled with increasing government initiatives promoting data-driven decision-making across sectors, is creating substantial demand for big data solutions. Furthermore, advancements in artificial intelligence (AI) and machine learning (ML) are inextricably linked to big data, fostering innovation and creating new applications across diverse industries, including BFSI, healthcare, retail, and manufacturing. The adoption of cloud-based big data solutions is accelerating, offering scalability and cost-effectiveness for businesses of all sizes. However, challenges remain, including data security concerns, a lack of skilled professionals, and the need for robust data governance frameworks. These restraints, while present, are not expected to significantly impede the overall market trajectory given the substantial opportunities and government support.
The market segmentation reveals diverse investment avenues. The cloud deployment model is projected to dominate due to its advantages, while the large enterprise segment presents the largest revenue pool. Within solutions, customer analytics, fraud detection, and predictive maintenance are currently high-growth areas, offering attractive ROI. Geographically, China itself represents a significant portion of the market, although international players are also gaining traction. Considering the robust CAGR and the diverse segments, strategic investments targeting cloud-based solutions, AI-powered analytics, and specific industry verticals (like BFSI and healthcare) hold significant promise for high returns. Careful consideration of regulatory landscapes and data privacy regulations is crucial for successful investment strategies within this dynamic market. Investment Opportunities of Big Data Technology in China This comprehensive report analyzes the burgeoning investment opportunities within China's Big Data Technology sector, offering a detailed forecast from 2019-2033. The report utilizes 2025 as its base and estimated year, covering the historical period (2019-2024) and forecasting market trends from 2025-2033. It delves into market dynamics, key players, and emerging trends shaping this rapidly expanding industry. This report is crucial for investors, businesses, and analysts seeking to understand and capitalize on the immense potential of China's big data market. Recent developments include: November 2022 - Alibaba announced the Innovative upgrade, and Greener 11.11 runs wholly on Alibaba Cloud, whereas Alibaba Cloud's dedicated processing unit powered 11.11 for the Apsara Cloud operating system. The upgraded infrastructure system significantly improved the efficiency of computing, storage, etc., October 2022 - Huawei Technologies Co.has unveiled its 4-in-1 hyper-converged enterprise gateway NetEngine AR5710, delved into the latest CloudCampus 3.0 + Simplified Solution, and launched a series of products for large enterprises and Small- and Medium-Sized Enterprises (SMEs). With these new offerings, Huawei aims to help enterprises simplify their campus networks and maximize digital productivity.. Key drivers for this market are: 6.1 Data Explosion: Unstructured, Semi-structured and Complex6.2 Improvement in Algorithm Development6.3 Need for Customer Analytics. Potential restraints include: 7.1 Lack of General Awareness And Expertise7.2 Data Security Concerns. Notable trends are: Need for Customer Analytics to Increase Exponentially Driving the Market Growth.

The 4U Rack-based Computer Data Unit (CDU) market is experiencing robust growth, driven by increasing demand for high-density computing infrastructure across various sectors. While the overall CDU market size is estimated at $828 million in 2025, the precise market share for the 4U rack-based segment isn't explicitly provided. However, considering the prevalence of rack-based solutions in data centers and the trend towards higher density deployments, we can reasonably estimate that the 4U rack-based CDU segment constitutes a significant portion of this overall market. Let's conservatively estimate this segment at 20% of the total market in 2025, resulting in a market size of approximately $165.6 million. Given the strong drivers like the expanding cloud computing infrastructure, edge computing deployments, and the growing need for efficient thermal management in data centers, we can project a healthy Compound Annual Growth Rate (CAGR) for this segment. A conservative estimate, considering market trends and technological advancements, would place the CAGR for 4U rack-based CDUs between 8% and 12% over the forecast period (2025-2033). This growth will be fueled by continuous innovation in cooling technologies, the adoption of liquid cooling solutions, and increasing demand for higher power density servers. The key applications driving this growth include internet data centers, telecommunications networks, and financial institutions, all of which require robust and efficient cooling solutions for their increasingly powerful IT infrastructure. Government and other sectors are also contributing to the market's expansion, further propelling the demand for 4U rack-based CDUs. Major players like nVent, CoolIT Systems, Boyd, Envicool, Delta Electronics, Schneider Electric (Motivair), Nidec, and DCX are actively shaping this market through technological innovations and strategic partnerships. Regional growth will be most prominent in North America and Asia-Pacific, driven by high IT infrastructure investment and a growing number of data centers in these regions. However, significant growth is expected across all regions, reflecting the global nature of the digital economy.

Data becomes an important issue in conducting research activities. Each research institute and R & D requires data was documented from previous research, whether derived from the institution itself or other institutions. Currently, each work unit already has several databases, but there is yet one means to store a safe and reliable. Therefore, a large scientific data repository system is required. In addition to being a means of sharing data, the repository is also intended to provide access and preserve data. The repository is expected to support intergovernmental research collaboration. The various data held by Indonesian Institute of Sciences (LIPI)'s work units, especially the Life Science and Earth Science can be categorized as big data because it has a very large volume, variety, and velocity (high speed) needed to process the data. The data are still scattered in part still managed by individually and partly. Individual data management causes lack of access, data is only accessible to a limited audience. Lack of access leads to duplication of research, wasted government funds, and lack of benefits for further research. ---------------------------------------------------------------------- Data menjadi masalah yang penting dalam melakukan kegiatan penelitian. Setiap lembaga penelitian dan badan litbang memerlukan data-data yang dokumentasi dari penelitian sebelumnya, baik yang berasal dari institusi sendiri atau institusi lain. Saat ini masing-masing satuan kerja sudah memiliki beberapa pangkalan data, akan tetapi belum ada satu sarana untuk menyimpan yang aman dan handal. Oleh karena itu, perlu dibuat sistem repositori big data ilmiah. Selain sebagai sarana berbagi data, repositori juga dimaksudkan untuk menyediakan akses dan melestarikan data. Dengan repositori diharapkan akan mendukung kolaborasi penelitian antar lembaga. Berbagai macam data yang dimiliki oleh satuan kerja di lingkungan LIPI, khususnya Kedeputian Ilmu Hayati dan Kedeputian Kebumian dapat dikategorikan big data imiah karena memiliki volume yang sangat besar, variety (jenis) yang sangat beragam, dan velocity (kecepatan) tinggi yang dibutuhkan untuk memproses data tersebut. Data-data tersebut masih tersebar sebagian masih dikelola secara individu dan sebagian sudah dikelola oleh satuan kerja. Pengelolaan data secara individu menyebabkan kurangnya akses, data hanya dapat diakses oleh kalangan terbatas. Kurangnya akses menyebabkan terjadinya duplikasi penelitian, dana pemerintah terbuang, dan kurangnya manfaat untuk penelitian lebih lanjut.

To explore the application effect of the deep learning (DL) network model in the Internet of Things (IoT) database query and optimization. This study first analyzes the architecture of IoT database queries, then explores the DL network model, and finally optimizes the DL network model through optimization strategies. The advantages of the optimized model in this study are verified through experiments. Experimental results show that the optimized model has higher efficiency than other models in the model training and parameter optimization stages. Especially when the data volume is 2000, the model training time and parameter optimization time of the optimized model are remarkably lower than that of the traditional model. In terms of resource consumption, the Central Processing Unit and Graphics Processing Unit usage and memory usage of all models have increased as the data volume rises. However, the optimized model exhibits better performance on energy consumption. In throughput analysis, the optimized model can maintain high transaction numbers and data volumes per second when handling large data requests, especially at 4000 data volumes, and its peak time processing capacity exceeds that of other models. Regarding latency, although the latency of all models increases with data volume, the optimized model performs better in database query response time and data processing latency. The results of this study not only reveal the optimized model’s superior performance in processing IoT database queries and their optimization but also provide a valuable reference for IoT data processing and DL model optimization. These findings help to promote the application of DL technology in the IoT field, especially in the need to deal with large-scale data and require efficient processing scenarios, and offer a vital reference for the research and practice in related fields.

Data are part of the GULN Inventory and Monitoring Program Landbird monitoring project for BITH Turkey Creek Unit. Data were collected by Dr. Felipe Chavez-Ramirez through a contract with the Gulf Coast Bird Observatory in 2016. Data were entered into the Pointblue.org AKN database and exported to Excel 2010 format by GULN data manager, Whitney Granger. A separate reference is available for the Field Sheets that accompanies this data set.

This 2001 Population Census dataset contains statistics relevant to demographic, household, educational, economic, housing and internal migration characteristics of the Hong Kong population residing in the 139 Large Tertiary Planning Unit Groups in 2001. The dataset also contains the boundaries of individual Large Tertiary Planning Unit Groups. Since 1961, a population census has been conducted in Hong Kong every 10 years and a by-census in the middle of the intercensal period. The 2001 Population Census, which was conducted in March 2001, provides benchmark statistics on the socio-economic characteristics of the Hong Kong population vital to the planning and policy formulation of the government. This dataset will be incorporated into Population Distribution Framework Spatial Data Theme.

The Data Center Rack Power Distribution Unit (PDU) market is experiencing robust growth, projected to reach a market size of $2301 million in 2025, with a Compound Annual Growth Rate (CAGR) of 5.1% from 2025 to 2033. This expansion is driven by the increasing adoption of cloud computing and big data analytics, necessitating advanced power management solutions within data centers. The rising demand for high-density computing infrastructure and the need for efficient power distribution are key factors fueling market growth. Growth is further propelled by the increasing focus on data center optimization, energy efficiency initiatives, and the deployment of advanced monitoring and management tools integrated within PDUs. The market is segmented by PDU type (Basic, Metering, Monitoring, Switch, and Others) and application (Large and Small/Medium Data Centers), reflecting diverse user needs and deployment scenarios. Leading vendors like Schneider Electric APC, ABB, Cisco, Eaton, and Vertiv are actively competing through product innovation and strategic partnerships to cater to the growing market demand. The regional distribution of the market reveals strong growth potential across North America, Europe, and Asia Pacific. These regions are characterized by high data center density and a significant presence of major technology companies. The robust growth in these regions is primarily fueled by increased investment in data center infrastructure and a growing focus on energy-efficient solutions, especially in light of rising energy costs and environmental sustainability concerns. Further market expansion will likely be driven by continued advancements in PDU technology, integration with intelligent data center management systems, and the adoption of sustainable and eco-friendly designs. The competitive landscape is dynamic, with established players and emerging technology companies vying for market share through technological innovation and strategic partnerships.

Data Processing Unit (DPU) Chip Market Outlook

The global Data Processing Unit (DPU) chip market size is projected to grow from USD 1.8 billion in 2023 to USD 7.5 billion by 2032, exhibiting a Compound Annual Growth Rate (CAGR) of 17.2% during the forecast period. The rapid advancement in data-intensive applications and the escalating demand for efficient data management are significant factors propelling this market's growth. The increasing adoption of technologies such as artificial intelligence (AI), machine learning (ML), and big data analytics in various industries is also driving the need for advanced processing units capable of handling complex data processing tasks.

One of the primary growth factors for the DPU chip market is the exponential increase in data generation across various sectors. With the proliferation of IoT devices, social media, and digital transformation initiatives, organizations are generating massive volumes of data that need to be processed, stored, and analyzed efficiently. DPUs, with their specialized architecture designed for handling data-centric workloads, are becoming essential in scaling and optimizing data processing capabilities, thereby driving their demand in the market.

Another significant driver for the DPU chip market is the rising demand for enhanced network security and data privacy. As cyber threats become more sophisticated, enterprises are increasingly looking for solutions that offer robust security mechanisms without compromising performance. DPUs offer integrated security features such as encryption, secure boot, and isolated processing environments, making them ideal for securing data in transit and at rest. This growing emphasis on data security is contributing to the market's expansion.

The shift towards edge computing is also playing a pivotal role in the growth of the DPU chip market. Edge computing requires efficient data processing at the edge of the network to reduce latency and improve real-time decision-making. DPUs, with their ability to offload and accelerate data-intensive tasks, are becoming crucial components in edge data centers and devices. This trend is expected to further fuel the adoption of DPUs across various applications and industries.

From a regional perspective, North America is poised to dominate the DPU chip market due to the presence of major technology companies and a robust IT infrastructure. The region's focus on innovation and early adoption of advanced technologies are key factors driving the market. Meanwhile, Asia Pacific is anticipated to witness the highest growth rate, attributed to the rapid digitization, growing investments in data centers, and the expansion of cloud services in countries like China and India.

Component Analysis

The DPU chip market can be segmented into three main components: hardware, software, and services. The hardware segment includes the physical DPU chips and related peripherals that facilitate data processing. This segment is expected to hold the largest market share due to the continuous advancements in semiconductor technologies and the increasing demand for high-performance computing solutions. The development of more powerful and energy-efficient DPUs is driving the growth of this segment.

The software segment encompasses the operating systems, firmware, and application software that enable the functionality of DPU chips. As DPUs become more integrated into data centers and enterprise networks, there is a growing need for specialized software solutions that can optimize their performance and manage workloads effectively. This segment is expected to witness substantial growth as companies invest in software development to harness the full potential of DPU hardware.

The services segment includes consulting, integration, maintenance, and support services related to DPU chip deployment. With the increasing complexity of data processing tasks and the need for seamless integration of DPUs into existing IT infrastructures, the demand for professional services is on the rise. Service providers are focusing on offering customized solutions to meet the specific needs of different industries, driving the growth of this segment.

Overall, the hardware segment is anticipated to maintain its dominance throughout the forecast period, while the software and services segments are expected to exhibit robust growth. The synergy between these components is crucial for the successful implementation and utilization of DPU chips in various applications.

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The Digital Geologic Map of the Big Sandy Creek Unit, Big Thicket National Preserve and Vicinity, Texas is composed of GIS data layers complete with ArcMap 9.3 layer (.LYR) files, two ancillary GIS tables, a Map PDF document with ancillary map text, figures and tables, a FGDC metadata record and a 9.3 ArcMap (.MXD) Document that displays the digital map in 9.3 ArcGIS. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) funded program that is administered by the NPS Geologic Resources Division (GRD). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: Big Thicket NPres staff and Texas Bureau of Economic Geology staff. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation sections(s) of this metadata record (bisa_metadata.txt; available at http://nrdata.nps.gov/bith/nrdata/geology/gis/bisa_metadata.xml). All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.1. (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The GIS data is available as a 9.3 personal geodatabase (bisa_geology.mdb), and as shapefile (.SHP) and DBASEIV (.DBF) table files. The GIS data projection is NAD83, UTM Zone 15N. That data is within the area of interest of Big Thicket National Preserve.

This dataset contains the administrative units of the Grand-Duchy of Luxembourg. The dataset is structured according to the INSPIRE Annex I Theme - Administrative Units. The data has been derived from the Cadastral database and contains the shape of the country, the districts, the cantons and the municipalities.

Estonia Payments: Vol: Payable: Cross Border: Large data was reported at 0.500 Unit th in Jun 2018. This records an increase from the previous number of 0.400 Unit th for May 2018. Estonia Payments: Vol: Payable: Cross Border: Large data is updated monthly, averaging 0.900 Unit th from Dec 1997 (Median) to Jun 2018, with 247 observations. The data reached an all-time high of 4.800 Unit th in Dec 2003 and a record low of 0.300 Unit th in Apr 2017. Estonia Payments: Vol: Payable: Cross Border: Large data remains active status in CEIC and is reported by Bank of Estonia. The data is categorized under Global Database’s Estonia – Table EE.KA005: Payment Statistics: Value and Volume of Payments.

China Industrial Enterprise: Large & Medium: State Holding: Number of Enterprise data was reported at 7,035.000 Unit in Oct 2018. This records a decrease from the previous number of 7,074.000 Unit for Sep 2018. China Industrial Enterprise: Large & Medium: State Holding: Number of Enterprise data is updated monthly, averaging 7,987.500 Unit from Jan 2001 (Median) to Oct 2018, with 186 observations. The data reached an all-time high of 12,720.000 Unit in Jun 2001 and a record low of 6,969.000 Unit in Feb 2008. China Industrial Enterprise: Large & Medium: State Holding: Number of Enterprise data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BF: Industrial Financial Data: Large and Medium: State Holding Enterprise.

The global Data Communication Unit (DCU) market is experiencing robust growth, driven by the increasing demand for high-speed data transmission across various sectors. The market size in 2025 is estimated at $15 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 8% from 2025 to 2033. This growth is fueled by several key factors, including the proliferation of IoT devices necessitating efficient data communication, the expanding adoption of cloud computing and big data analytics, and the ongoing digital transformation across industries like manufacturing, healthcare, and transportation. The rising need for reliable and secure data transmission in enterprise settings and government infrastructure further bolsters market expansion. Key segments driving growth include WLAN and Ethernet technologies, owing to their widespread compatibility and scalability. Within applications, the enterprise and government sectors are major contributors, with significant investment in advanced communication networks and infrastructure upgrades. While the market presents significant opportunities, certain restraints exist. These include the high initial investment costs associated with DCU implementation, the complexity of integrating different DCU technologies within existing infrastructure, and the potential security vulnerabilities associated with large-scale data transmission networks. However, advancements in technology, particularly in areas like 5G and edge computing, are anticipated to mitigate these challenges and drive further market expansion. The competitive landscape is marked by a mix of established players and emerging technology companies, with key players focusing on product innovation, strategic partnerships, and geographical expansion to gain a competitive edge. The market is expected to witness further consolidation in the coming years as companies strive to meet the evolving needs of businesses and governments worldwide.

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 NYC Department of City Planning’s (DCP) Housing Database contains all NYC Department of Buildings (DOB) approved housing construction and demolition jobs filed or completed in NYC since January 1, 2010. It includes the three primary construction job types that add or remove residential units: new buildings, major alterations, and demolitions, and can be used to determine the change in legal housing units across time and space. Records in the Housing Database Project-Level Files are geocoded to the greatest level of precision possible, subject to numerous quality assurance and control checks, recoded for usability, and joined to other housing data sources relevant to city planners and analysts.

Data are updated semiannually, at the end of the second and fourth quarters of each year.

Please see DCP’s annual Housing Production Snapshot summarizing findings from the 21Q4 data release here. Additional Housing and Economic analyses are also available.

The NYC Department of City Planning’s (DCP) Housing Database Unit Change Summary Files provide the net change in Class A housing units since 2010, and the count of units pending completion for commonly used political and statistical boundaries (Census Block, Census Tract, City Council district, Community District, Community District Tabulation Area (CDTA), Neighborhood Tabulation Area (NTA). These tables are aggregated from the DCP Housing Database Project-Level Files, which is derived from Department of Buildings (DOB) approved housing construction and demolition jobs filed or completed in NYC since January 1, 2010. Net housing unit change is calculated as the sum of all three construction job types that add or remove residential units: new buildings, major alterations, and demolitions. These files can be used to determine the change in legal housing units across time and space.

Global Data Centre Rack Market size was valued at USD 5.01 Billion in 2024 and is projected to reach USD 9.22 Billion by 2031, growing at a CAGR of 7.92 % from 2024 to 2031.

Data Center Rack Market Drivers

Rapid Digital Transformation: The increasing adoption of cloud computing, IoT, and big data technologies is driving the demand for efficient data center infrastructure, including data center racks.

Data Center Consolidation: To reduce operational costs and improve energy efficiency, organizations are consolidating their data centers, necessitating the use of high-density data center racks.