Web design service companies have experienced significant growth over the past few years, driven by the expanding use of the Internet. As online operations have become more widespread, businesses and consumers have increasingly recognized the importance of maintaining an online presence, leading to robust demand for web design services and boosting the industry’s profit. The rise in broadband connections and online business activities further spotlight this trend, making web design a vital component of modern commerce and communication. This solid foundation suggests the industry has been thriving despite facing some economic turbulence related to global events and shifting financial climates. Over the past few years, web design companies have navigated a dynamic landscape marked by both opportunities and challenges. Strong economic conditions have typically favored the industry, with rising disposable incomes and low unemployment rates encouraging both consumers and businesses to invest in professional web design. Despite this, the sector also faced hurdles such as high inflation, which made cost increases necessary and pushed some customers towards cheaper substitutes such as website templates and in-house production, causing a slump in revenue in 2022. Despite these obstacles, the industry has demonstrated resilience against rising interest rates and economic uncertainties by focusing on enhancing user experience and accessibility. Overall, revenue for web design service companies is anticipated to rise at a CAGR of 2.2% during the current period, reaching $43.5 billion in 2024. This includes a 2.2% jump in revenue in that year. Looking ahead, web design companies will continue to do well, as the strong performance of the US economy will likely support ongoing demand for web design services, bolstered by higher consumer spending and increased corporate profit. On top of this, government investment, especially at the state and local levels, will provide further revenue streams as public agencies seek to upgrade their web presence. Innovation remains key, with a particular emphasis on designing for mobile devices as more activities shift to on-the-go platforms. Companies that can effectively adapt to these trends and invest in new technologies will likely capture a significant market share, fostering an environment where entry remains feasible yet competitive. Overall, revenue for web design service providers is forecast to swell at a CAGR of 1.9% during the outlook period, reaching $47.7 billion in 2029.

The global marketing data analysis software market is projected to grow from XXX million in 2023 to XXX million by 2033, with a CAGR of XX% during the forecast period. The growth of the market is attributed to the increasing adoption of data-driven marketing strategies by businesses to improve their customer engagement and sales performance. Additionally, the growing popularity of cloud-based software solutions and the availability of advanced analytical tools are driving the market growth. The market is segmented based on application, type, company, and region. The retail and e-commerce segment holds the largest market share due to the high demand for data analysis in the industry. The website analysis software segment is expected to witness significant growth during the forecast period due to the increasing need for businesses to track and analyze website traffic and behavior. The North American region dominates the market, followed by Europe and Asia Pacific. The key players in the market are HubSpot, Semrush, Looker Data Sciences (Google), Insider, LeadsRx, SharpSpring, OWOX BI, and Whatagraph BV.

The global web design services market is experiencing tremendous growth, with a projected market size of USD 52.4 billion by the end of 2033. Driven by the rise of e-commerce and digital transformation, the market is expanding at a rapid CAGR of 12.2% from 2025 to 2033. North America and Asia Pacific are the leading regions, accounting for a significant share of the market. The growing adoption of mobile-first web design and the increasing demand for personalized user experiences are driving the demand for web design services. Market players such as Seller's Bay, WebFX, and Appnovation are key participants in the industry. These companies offer a range of web design services, including website design, website hosting, search engine optimization, and domain sales. The market is segmented based on application, with enterprise and private segments being the largest contributors. In terms of types, website design holds the dominant share, followed by website hosting. However, restraints such as security concerns, high development costs, and competition from open-source platforms may pose challenges to the market's growth.

The U.S. Geological Survey (USGS), in cooperation with the Missouri Department of Natural Resources (MDNR), collects data pertaining to the surface-water resources of Missouri. These data are collected as part of the Missouri Ambient Water-Quality Monitoring Network (AWQMN) and are stored and maintained by the USGS National Water Information System (NWIS) database. These data constitute a valuable source of reliable, impartial, and timely information for developing an improved understanding of the water resources of the State. Water-quality data collected between water years 1993 and 2017 were analyzed for long term trends and the network was investigated to identify data gaps or redundant data to assist MDNR on how to optimize the network in the future. This is a companion data release product to the Scientific Investigation Report: Richards, J.M., and Barr, M.N., 2021, General water-quality conditions, long-term trends, and network analysis at selected sites within the Ambient Water-Quality Monitoring Network in Missouri, water years 1993–2017: U.S. Geological Survey Scientific Investigations Report 2021–5079, 75 p., https://doi.org/10.3133/sir20215079. The following selected tables are included in this data release in compressed (.zip) format: AWQMN_EGRET_data.xlsx -- Data retrieved from the USGS National Water Information System database that was quality assured and conditioned for network analysis of the Missouri Ambient Water-Quality Monitoring Network AWQMN_R-QWTREND_data.xlsx -- Data retrieved from the USGS National Water Information System database that was quality assured and conditioned for analysis of flow-weighted trends for selected sites in the Missouri Ambient Water-Quality Monitoring Network AWQMN_R-QWTREND_outliers.xlsx -- Data flagged as outliers during analysis of flow-weighted trends for selected sites in the Missouri Ambient Water-Quality Monitoring Network AWQMN_R-QWTREND_outliers_quarterly.xlsx -- Data flagged as outliers during analysis of flow-weighted trends using a simulated quarterly sampling frequency dataset for selected sites in the Missouri Ambient Water-Quality Monitoring Network AWQMN_descriptive_statistics_WY1993-2017.xlsx -- Descriptive statistics for selected water-quality parameters at selected sites in the Missouri Ambient Water-Quality Monitoring Network The following selected graphics are included in this data release in .pdf format. Also included in this data release are web pages accessible for people with disabilities provided in compressed .zip format. The web pages present the same information as the .pdf files: Annual and seasonal discharge trends.pdf -- Graphics of discharge trends produced from the EGRET software for selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Annual_and_seasonal_discharge_trends_htm.zip -- Compressed web page presenting graphics of discharge trends produced from the EGRET software for selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Graphics of simulated quarterly sampling frequency trends.pdf -- Graphics of results of simulated quarterly sampling frequency trends produced by the R-QWTREND software at selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Graphics_of_simulated_quarterly_sampling_frequency_trends_htm.zip -- Compressed web page presenting graphics of results of simulated quarterly sampling frequency trends produced by the R-QWTREND software at selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Graphics of median parameter values.pdf -- Graphics of median values for selected parameters at selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Graphics_of_median_parameter_values_htm.zip -- Compressed web page presenting graphics of median values for selected parameters at selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Parameter value versus time.pdf -- Scatter plots of the value of selected parameters versus time at selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Parameter_value_versus_time_htm.zip -- Compressed web page presenting scatter plots of the value of selected parameters versus time at selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Parameter value versus discharge.pdf -- Scatter plots of the value of selected parameters versus discharge at selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Parameter_value_versus_discharge_htm.zip -- Compressed web page presenting scatter plots of the value of selected parameters versus discharge at selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Boxplot of parameter value distribution by season.pdf -- Seasonal boxplots of selected parameters from selected sites in the Missouri Ambient Water-Quality Monitoring Network. Seasons defined as Winter (December, January, and February), Spring (March, April, and May), Summer (June, July, and August), and Fall (September, October, and November). Graphics provided to support the interpretations in the Scientific Investigations Report. Boxplot_of_parameter_value_distribution_by_season_htm.zip -- Compressed web page presenting seasonal boxplots of selected parameters from selected sites in the Missouri Ambient Water-Quality Monitoring Network. Seasons defined as Winter (December, January, and February), Spring (March, April, and May), Summer (June, July, and August), and Fall (September, October, and November). Graphics provided to support the interpretations in the Scientific Investigations Report. Boxplot of sampled discharge compared with mean daily discharge.pdf -- Boxplots of the distribution of discharge collected at the time of sampling of selected parameters compared with the period of record discharge distribution from selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Boxplot_of_sampled_discharge_compared_with_mean_daily_discharge_htm.zip -- Compressed web page presenting boxplots of the distribution of discharge collected at the time of sampling of selected parameters compared with the period of record discharge distribution from selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Boxplot of parameter value distribution by month.pdf -- Monthly boxplots of selected parameters from selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report. Boxplot_of_parameter_value_distribution_by_month_htm.zip -- Compressed web page presenting monthly boxplots of selected parameters from selected sites in the Missouri Ambient Water-Quality Monitoring Network. Graphics provided to support the interpretations in the Scientific Investigations Report.

This upload contains two Geopackage files of raw data used for urban analysis in the outskirts of Lille and Nice, France. 
The data include building footprints (layer "building"), roads (layer "road"), and administrative boundaries (layer "adm_boundaries")
extracted from version 3.3 of the French dataset BD TOPO®3 (IGN, 2023) for the municipalities of Santes, Hallennes-lez-Haubourdin,
Haubourdin, and Emmerin in northern France (Geopackage "DPC_59.gpkg") and Drap, Cantaron and La Trinité in southern France 
(Geopackage "DPC_06.gpkg").

Metadata for these layers is available here: https://geoservices.ign.fr/sites/default/files/2023-01/DC_BDTOPO_3-3.pdf

Additionally, this upload contains the results of the following algorithms available in GitHub (https://github.com/perezjoan/emc2-WP2?tab=readme-ov-file)

1. The identification of main streets using the QGIS plugin Morpheo (layers "road_morpheo" and "buffer_morpheo") 
https://plugins.qgis.org/plugins/morpheo/ 

2. The identification of main streets in local contexts – connectivity locally weighted (layer "road_LocRelCon")

3. Basic morphometry of buildings (layer "building_morpho")

4. Evaluation of the number of dwellings within inhabited buildings (layer "building_dwellings")

5. Projecting population potential accessible from main streets (layer "road_pop_results")

Project website: http://emc2-dut.org/

Publications using this sample data: 
Perez, J. and Fusco, G., 2024. Potential of the 15-Minute Peripheral City: Identifying Main Streets and Population Within Walking Distance. In: O. Gervasi, B. Murgante, C. Garau, D. Taniar, A.M.A.C. Rocha and M.N. Faginas Lago, eds. Computational Science and Its Applications – ICCSA 2024 Workshops. ICCSA 2024. Lecture Notes in Computer Science, vol 14817. Cham: Springer, pp.50-60. https://doi.org/10.1007/978-3-031-65238-7_4.

Acknowledgement. This work is part of the emc2 project, which received the grant ANR-23-DUTP-0003-01 from the French National Research Agency (ANR) within the DUT Partnership.

Most serum proteins are N-linked glycosylated, and therefore the glycoproteomic profiling of serum is essential for characterization of serum proteins. In this study, we profiled serum N-glycoproteome by our recently developed N-glycoproteomic method using solid-phase extraction of N-linked glycans and glycosite-containing peptides (NGAG) coupled with LC-MS/MS and site-specific glycosylation analysis using GPQuest software. Our data indicated that half of identified N-glycosites were modified by at least two glycans, with a majority of them being sialylated. Specifically, 3/4 of glycosites were modified by biantennary N-glycans and 1/3 of glycosites were modified by triantennary sialylated N-glycans. In addition, two novel atypical glycosites (with N–X–V motif) were identified and validated from albumin and α-1B-glycoprotein. The widespread presence of these two glycosites among individuals was further confirmed by individual serum analyses.

Seven sites examined in conjunction with the Towaoc Canal Reach III Excavations were selected for pollen analysis. The pollen record represents primarily floor and feature samples at these sites. Pollen was examined specifically to identify plants processed and/or stored at these sites.

Human tryptophanyl-tRNA synthetase (WRS) was digested by human MMPs, and digestion mixtures were subjected to terminal amine oriented mass spectrometry of substrates (ATOMS) protocol and LC-MS/MS analysis to identify MMP cleavage sites in WRS.

On behalf of the North Coast Resource Partnership (NCRP) and the Watershed Research and Training Center (WRTC), Tukman Geospatial conducted a set of regional assessments targeted at identifying opportunities for new woody biomass utilization within California's North Coast Region as part of the North Coast Forest Biomass Strategy. We used an initial candidate site selection process to locate potentially suitable sites for biomass utilization in the form of wood processing or biomass energy generation. Once we located candidate sites for further investigation, we performed an additional viability check on each site by using Google Street View and aerial imagery to rule out sites with obvious disqualifying factors. Following this initial selection process, we used a network analysis to examine recoverable woody residues within an economically feasible travel time to each selected candidate site, based on estimates of residues generated from a hypothetical 40% thin from below silvicultural treatment. For this analysis, we used outputs from the Schatz Energy Research Center's C-BREC model as the data source for biomass residues. We conducted this feedstock analysis for four scenarios, each representing a unique combination of the proposed use for the site (sawmill versus biomass power plant) and the presence or absence of an additional screen to limit recoverable residues to areas deemed feasible for mechanical treatment. These analyses resulted in a suite of deliverables, including features classes containing the candidate site locations as points and polygons, as well as feature classes and summary tables displaying the results of the feedstock analysis for each scenario. The candidate site selection and feedstock analyses are intended to be part of a larger, ongoing effort to locate opportunities for biomass utilization in the region. They are not meant to be comprehensive, but rather to serve as the first steps in a process involving subsequent regionwide assessments that incorporate other critical considerations such as impacts on equity and forest health. Accessing Deliverables Table 1 lists deliverables from the candidate site selection and feedstock analyses along with their respective links: Table 1: List of Deliverables and Links for Candidate Site Selection and Feedstock Analyses

  Description


  Type


  Link




  File GDB Containing Candidate Site and Feedstock Analysis Feature Classes


  File Geodatabase


  https://ncrp.online/North_Coast_Biomass_CandidateSites_Feedstock_GDB




  Candidate Sites (Parcel Groups)


  Feature Service


  https://ncrp.online/North_Coast_Biomass_Candidate_Sites_Parcel_Groups




  Candidate Sites (Points)


  Feature Service


  https://ncrp.online/North_Coast_Biomass_Candidate_Sites_Points




  Candidate Sites Stakeholder Review App


  Web App


  https://ncrp.online/North_Coast_Biomass_Candidate_Sites_Review_App




  Feedstock Analysis Results Summary Table: Biomass Power Plant Scenario


  Excel Workbook


  https://ncrp.online/North_Coast_Feedstock_Summary_Biomass




  Feedstock Analysis Results Summary Table: Biomass Power Plant with Mechanical Treatment Feasibility Screen Scenario


  Excel Workbook


  https://ncrp.online/North_Coast_Feedstock_Summary_Biomass_Feasible




  Feedstock Analysis Results Summary Table: Sawmill Scenario


  Excel Workbook


  https://ncrp.online/North_Coast_Feedstock_Summary_Sawmill




  Feedstock Analysis Results Summary Table: Sawmill with Mechanical Treatment Feasibility Screen Scenario


  Excel Workbook


  https://ncrp.online/North_Coast_Feedstock_Summary_Sawmill_Feasible




  North Coast Biomass Utilization: Data Products and Attribute Definitions


  PDF Reference Document


  https://ncrp.online/North_Coast_Biomass_Data_Dictionary




  North Coast Biomass Utilization: Candidate Site Selection and Feedstock Assessment Memo


  PDF Reference Document


  https://ncrp.online/North_Coast_Biomass_Candidate_Sites_Feedstock_Memo

Table 2 lists file names and descriptions for deliverables: Table 2: List of Deliverables and Links for Candidate Site Selection and Feedstock Analyses

  Name


  Type


  Description






  NorthCoast_CandidateSites_Points_v2_20240514


  Point Feature Class


  A feature class containing point locations and attributes for all considered candidate sites in the North Coast Region.




  NorthCoast_CandidateSites_Points_Selected_v2_20240514


  Point Feature Class


  A feature class containing point locations and attributes for selected candidate sites in the North Coast Region.




  NorthCoast_CandidateSites_ParcelGroups_v2_20240514


  Polygon Feature Class


  A feature class containing parcel group polygons and attributes for all considered candidate sites in the North Coast Region.




  NorthCoast_CandidateSites_ParcelGroups_Selected_v2_20240514


  Polygon Feature Class


  A feature class containing parcel group polygons and attributes for selected candidate sites in the North Coast Region.




  NorthCoast_Feedstock_Biomass


  Polygon Feature Class


  A feature class containing service areas and corresponding residue totals at 30-minute travel time increments for the "biomass" scenario for each candidate site.




  NorthCoast_Feedstock_Biomass_Feasible


  Polygon Feature Class


  A feature class containing service areas and corresponding residue totals at 30-minute travel time increments for the "biomass with mechanical feasibility screen" scenario for each candidate site.




  NorthCoast_Feedstock_Sawmill


  Polygon Feature Class


  A feature class containing service areas and corresponding residue totals at 30-minute travel time increments for the "sawmill" scenario for each candidate site.




  NorthCoast_Feedstock_Sawmill_Feasible


  Polygon Feature Class


  A feature class containing service areas and corresponding residue totals at 30-minute travel time increments for the "sawmill with mechanical feasibility screen" scenario for each candidate site.




  NorthCoast_Feedstock_Biomass_Summary_20240521.xlsx


  Excel Workbook


  A workbook with the results of the "biomass" scenario in tabular format (versions with unsorted data and data sorted by priority within each subregion are both included).




  NorthCoast_Feedstock_Sawmill_Feasible_Summary_20240521.xlsx


  Excel Workbook


  A workbook with the results of the "biomass with mechanical feasibility screen" scenario in tabular format (versions with unsorted data and data sorted by priority within each subregion are both included).




  NorthCoast_Feedstock_Sawmill _Summary_20240521.xlsx


  Excel Workbook


  A workbook with the results of the "sawmill" scenario in tabular format (versions with unsorted data and data sorted by priority within each subregion are both included).




  NorthCoast_Feedstock_Sawmill_Feasible_Summary_20240521.xlsx


  Excel Workbook


  A workbook with the results of the "sawmill with mechanical feasibility screen" scenario in tabular format (versions with unsorted data and data sorted by priority within each subregion are both included).




  North Coast Biomass Utilization Regional Assessment Candidate Sites and Feedstock Memo May 2024.pdf


  PDF Reference Document


  A memo outlining the methods and results of the candidate site selection and feedstock analyses.




  North Coast Biomass Utilization Regional Assessment Data Products and Attribute Definitions May 2024.pdf


  PDF Reference Document


  A reference document containing notes about data products and definitions for their attributes (this document).

This metadata record will contain the results of analyses of tissue samples from Antarctic Rock-cod (Trematomus bernacchii) collected at sites around Davis station to determine wastewater exposure and sub-lethal impact. AAS Project 4177. The results of metal analysis, stable isotope analysis and images of histological analysis of fish from Davis Station are in this dataset. Sample sites and fish collection Antarctic Rock-cod were collected at 6 sites from Prydz Bay near Davis Station East Antarctica, during the 2012/13 summer. Approximately twenty fish were collected from each site by line and in box traps from four sites along a (9 km) spatial gradient starting from the Davis Station wastewater outfall, southward 0km (within 250m of the point of discharge), 1km, 4km and 9km, in the direction of the predominant current. Additionally, two reference sites were sampled 9 km and 16 km north of the discharge point. Once collected, fish were immediately returned to the Davis Station laboratories and sacrificed individually by immersion in an Aqui-s solution (~15ml/L). Once no signs of life were present (approximately 5 min), fish length and weight were measured. Tissues were preserved in various ways for a number of analyses to be conducted at a later date. Stable Isotope analysis. Davis Station Laboratory Dorsolateral muscle tissue from the left side of each individual was removed, placed in aluminium foil and frozen at -20 degrees C for later analysis. Tissue processing A section of frozen tissue was removed (approximately 1 x 1 cm cubed), placed into a clean, acid washed glass crucible and cut into small pieces. This was then dried at 80 degrees C for 48 h. Tissue from each fish was carefully removed and placed into separate 2 ml Eppendorf tubes, each containing an washed, dried stainless steel ball bearing and the lids closed tightly to ensure no moisture could enter. Tissue was crushed into a fine powder by shaking in a Tissue II Lyser. Ball bearings were removed from vials and crushed tissue samples were sent to Cornell University Stable Isotope laboratory for d13C (carbon stable isotope) and d15N (nitrogen stable isotope) analysis. Stable isotope ratios are expressed in parts per thousand units using the standard delta (d) notation d13C and d15N. Data Set This data set consists of an Excel spreadsheet containing raw data of Nitrogen and Carbon Stable Isotope analysis from 6 sites in the Prydz Bay area of East Antarctica. It includes site distance and direction from wastewater discharge point. The file name code stable isotope analysis is; Project number_Season_Taxa_analysis type AAS_4177_12_13_Trematomus_Isotopes Project number : AAS_4177 Season : 2012/13 season Taxa: Trematomus Analysis type: Stable Isotope Metal analysis. Davis Station Laboratory Dorsolateral muscle tissue from the right side of each individual was removed, placed in a plastic zip lock bag and frozen at -20 degrees C for later analysis. Tissue processing 10g of frozen muscle tissue was sent to Advanced Analytical Australia for metal analysis of a suite of metals (Cd, Cr, Cu, Hg, Mn, Zn, Al, Ni, Pb). Data Set This data set consists of an Excel spreadsheet containing raw data of metal analysis (mg/kg) from 6 sites in the Prydz Bay area of East Antarctica. It includes site distance and direction from wastewater discharge point. The file name code stable isotope analysis is; Project number_Season_Taxa_analysis type AAS_4177_12_13_Trematomus_Metals Project number : AAS_4177 Season : 2012/13 season Taxa: Trematomus Analysis type: Metal analysis Histological analysis Davis Station Laboratory A small piece of a number of fish tissues (gill, liver, spleen, head kidney, gonad), were collected immediately after death of the fish to ensure no degradation of tissue and preserved in 10% seawater buffered formalin for later analysis Tissue processing Each piece of tissue was dehydrated in ascending grades of ethanol (30-100%), cleared in Histolene and embedded in paraffin wax. Tissue was sectioned using a HM 32 Micron microtome at 4 microns. Standard haematoxylin and eosin (H and E) stain was used to stain all tissue sections. Each section was examined blind (i.e. the examiner did not know the field location of the tissue samples) using a Zeiss AxioPlan microscope at 100-400 x magnification. Histological analysis is ongoing. Data Set This data set consists of a pdf file with images of normal and potential sub-lethal histological alterations. The file name code stable isotope analysis is; Project number_Season_Taxa_analysis type AAS_4177_12_13_Trematomus_Histology Project number : AAS_4177 Season : 2012/13 season Taxa: Trematomus Analysis type: Histopathology

The Data Mining Tools Market size was valued at USD 1.01 USD billion in 2023 and is projected to reach USD 1.99 USD billion by 2032, exhibiting a CAGR of 10.2 % during the forecast period. The growing adoption of data-driven decision-making and the increasing need for business intelligence are major factors driving market growth. Data mining refers to filtering, sorting, and classifying data from larger datasets to reveal subtle patterns and relationships, which helps enterprises identify and solve complex business problems through data analysis. Data mining software tools and techniques allow organizations to foresee future market trends and make business-critical decisions at crucial times. Data mining is an essential component of data science that employs advanced data analytics to derive insightful information from large volumes of data. Businesses rely heavily on data mining to undertake analytics initiatives in the organizational setup. The analyzed data sourced from data mining is used for varied analytics and business intelligence (BI) applications, which consider real-time data analysis along with some historical pieces of information. Recent developments include: May 2023 – WiMi Hologram Cloud Inc. introduced a new data interaction system developed by combining neural network technology and data mining. Using real-time interaction, the system can offer reliable and safe information transmission., May 2023 – U.S. Data Mining Group, Inc., operating in bitcoin mining site, announced a hosting contract to deploy 150,000 bitcoins in partnership with major companies such as TeslaWatt, Sphere 3D, Marathon Digital, and more. The company is offering industry turn-key solutions for curtailment, accounting, and customer relations., April 2023 – Artificial intelligence and single-cell biotech analytics firm, One Biosciences, launched a single cell data mining algorithm called ‘MAYA’. The algorithm is for cancer patients to detect therapeutic vulnerabilities., May 2022 – Europe-based Solarisbank, a banking-as-a-service provider, announced its partnership with Snowflake to boost its cloud data strategy. Using the advanced cloud infrastructure, the company can enhance data mining efficiency and strengthen its banking position.. Key drivers for this market are: Increasing Focus on Customer Satisfaction to Drive Market Growth. Potential restraints include: Requirement of Skilled Technical Resources Likely to Hamper Market Growth. Notable trends are: Incorporation of Data Mining and Machine Learning Solutions to Propel Market Growth.

Abstract

The dataset was derived by the Bioregional Assessment Programme from the Streamflow data and locations for selected gauges in the Hunter subregion dataset. The source dataset is identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

HUN Streamflow Baseflow Analysis

Baseflow Index (BFI) was derived for a number of Hunter gauging stations. They are created from synthesised data based on the New South Wales Office of Water (NOW) hydstra surface water database.

The BFI is a measure of the proportion of the river runoff that derives from stored sources; the more permeable the rock, superficial deposits and soils in a catchment, the higher the baseflow and the more sustained the river's flow during periods of low rainfall.

Data was extracted from the Hydstra format to .csv files for surface water sites within the Hunter region. Data were presented as a chart in the context report.

Dataset History

HUN Streamflow Baseflow Analysis

Baseflow Index (BFI) was derived for a number of Hunter gauging stations. They are are created from synthesised data based on the New South Wales Office of Water (NOW) hydstra surface water database.

Data was extracted from the Hydstra format to .csv files for surface water sites within the Hunter region.

Dataset Citation

Bioregional Assessment Programme (2014) HUN Streamflow Baseflow Analysis. Bioregional Assessment Derived Dataset. Viewed 07 February 2017, http://data.bioregionalassessments.gov.au/dataset/5233faa3-33b9-40b9-9483-18cc9b6648e1.

Dataset Ancestors

• Derived From Bioregional Assessment areas v02

• Derived From Gippsland Project boundary

• Derived From Bioregional Assessment areas v04

• Derived From SYD ALL Raw Stream Gauge Data BoM v01

• Derived From Bioregional Assessment areas v03

• Derived From Bioregional Assessment areas v05

• Derived From GEODATA TOPO 250K Series 3

• Derived From Bioregional Assessment areas v01

• Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)

• Derived From Victoria - Seamless Geology 2014

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From Geological Provinces - Full Extent

• Derived From Streamflow data and locations for selected gauges in the Hunter subregion

• Derived From Natural Resource Management (NRM) Regions 2010

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Analysis of ‘Erosion Susceptibility Sites’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/60e77050-c12b-4018-b32e-12862d24fc14 on 27 January 2022.

--- Dataset description provided by original source is as follows ---

Connecticut Erosion Susceptibility a 1:24,000-scale, polygon feature-based layer that was developed as a predictive tool to show areas most susceptible to terrace escarpment type erosion. The layer compiled from the soils and quaternary geology data layers and was field tested during October-December, 2005. The Erosion Susceptilibity layer was developed as part of Project #03-02 Statewide GIS Analysis and Mapping of the Geologic Conditions Contributing to Eroding Terrace Escarpments. The layer does not represent eroding conditions at any one particular point in time, but rather base or general conditions which can be accounted for during planning or management strategies. The layer includes 4 types of areas susceptible to erosion, ranked 1 (most susceptible) through 4, and their descriptive attribute. Areas outside of the mapped polygons can be considered less susceptible to erosion. Data is compiled at 1:24,000 scale. This data is not updated.

Connecticut Erosion Sites is a site specific, point feature-based layer developed at 1:24,000-scale that includes decriptive information regarding the character of the erosion (severity, slope, geologic factors) at selected locations through out the state. The layer is based on information collected and compiled during October-December, 2005 while field testing the applicability of the Erosion Susceptilibity layer developed as part of Project #03-02 Statewide GIS Analysis and Mapping of the Geologic Conditions Contributing to Eroding Terrace Escarpments. The layer represents conditions at a particular point in time. The layer includes 83 locations and descriptive attributes (site name, severity of erosion, description, etc) as well as attributes from a spatial join with merged soils and quaternary geology layers. Features are point locations that represent the selected study areas within the state; it is NOT a comprehensive inventory of erosion locations. Data is compiled at 1:24,000 scale. This data is not updated.

--- Original source retains full ownership of the source dataset ---

This dataset is about: (Table C2) Grain size analysis of ODP Site 151-909. Please consult parent dataset @ https://doi.org/10.1594/PANGAEA.712694 for more information.

Stratigraphic pollen samples from two prehistoric sites in Eureka County, Nevada, located near the Humboldt River, were examined to provide information concerning vegetation growing at the time these sites were occupied. Site 26Eu3654, a small, dispersed lithic scatter was located on an alluvial fan south of the Humboldt River, while site 26Eu3655, another dispersed lithic scatter, was situated on a small knoll south of the river. Five pollen samples were examined from each site.

Human plasma fibronectin is an adhesive protein that plays a crucial role in wound healing. Many studies had indicated that glycans may mediate the expression and functions of fibronectin, yet a comprehensive understanding of its glycosylation is still missing. Here, we performed a comprehensive N- and O-glycosylation mapping of human plasma fibronectin, and quantified the occurrence of each glycoform in a site-specific manner. Intact N-glycopeptides were enriched by zwitterionic hydrophilic interaction chromatography, and N-glycosites sites were localized by the 18O-labeling method. O-glycopeptide enrichment and O-glycosite identification were achieved by an enzyme-assisted site-specific extraction method. An RP–LC–MS/MS system functionalized with Collision-Induced Dissociation and stepped normalized collision energy (sNCE)-HCD tandem mass was applied to analyze the glycoforms of fibronectin. A total of 6 N-glycosites and 53 O-glycosites were identified, which were occupied by 3842 N-glycoforms and 16 O-glycoforms, respectively. Furthermore, 81.4% of N-glycans were either fucosylated, sialylated, or with both modifications77.31% of N-glycans were sialylated, while O-glycosylation was dominated by the sialyl-T antigen. These site-specific glycosylation patterns on human fibronectin can facilitate functional analyses of fibronectin and therapeutics development.

Market Size and Segmentation: The global bullet screen video sharing site market was valued at USD XXX million in 2025. It is projected to reach USD XXX million by 2033, exhibiting a CAGR of XX% from 2025 to 2033. The market is segmented by application (media and entertainment, sports and gaming, others) and type (web-based, app-based). Media and entertainment account for the largest market share due to the popularity of online video streaming platforms. Drivers and Restraints: Key market drivers include the increasing popularity of video content, the surge in broadband internet penetration, and the proliferation of social media. Additionally, the gamification of video sharing platforms, where users engage with each other through real-time comments, has also contributed to market growth. However, factors such as privacy concerns, intellectual property rights, and bandwidth limitations may restrain market development.

[309 Pages Report] The network traffic analysis solution market is expected to expand its roots in the global market at a moderate CAGR of 11.3% through 2032.

What is the Regional Analysis for the Network Traffic Analysis Solutions Market?

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