Water quality data for the Refuge collected by volunteers collected once every two weeks: Turbidity, pH, Dissolved oxygen (DO), Salinity & Temperature. Sampling will occur at designated locations in the following water bodies: the Bay, D-Pool (fishing pond), C-Pool, B-Pool and A-Pool.

Explore detailed Swimming Pool import data of Pool in the USA—product details, price, quantity, origin countries, and US ports.

Get expert insights on Swimming Pool Market size, future trends, and business opportunities through 2034. Download the report now.

Gut content metabarcoding has provided important insights into the food web ecology of spiders, the most dominant terrestrial arthropod predators. In small invertebrates, like spiders, gut content analysis is often performed on whole body DNA extracts of individual predators, from which prey sequences are selectively enriched and sequenced. Since many spider species are generalist predators, large numbers of samples comprising individual spider specimens must be analyzed to recover an exhaustive image of a spider species’ prey spectrum, which is costly and time-consuming. Pooled processing of bulk samples of multiple specimens has been suggested to reduce the necessary workload and cost while still recovering a representative estimate of the prey diversity. However, it is still unclear if pooling approaches lead to bias in recovering the prey spectrum and if the results are comparable to data from individually processed spiders. Here, we test the effect of metabarcoding pooled spider gut content on the recovered taxonomic diversity and composition of prey. Using a newly adapted primer pair, which efficiently enriches COI barcode sequences of diverse arthropod prey groups while suppressing spider amplification, we test if pooling leads to reduced taxonomic diversity or skewed estimates of prey composition. Our results show that pooling and individual processing recover highly correlated taxonomic diversity and composition of prey. The only exception are very rare prey items which were less well recovered by pooling. Our results support pooling as a cost effective and time efficient approach to recover the diet of generalist predators for population-level studies of spider trophic interactions. Methods Individual spiders were collected using beat sheets, sweep nets, and hand sampling between June and July 2021 at a grassland site in Kimmlingen, Rhineland-Palatinate, Germany (49°49'58.4"N 6°36'05.8"E). Immediately after collection, all specimens were separated into individual tubes with pure ethanol and stored at room temperature. In the lab they were morphologically identified to species level, measured for body size (prosoma width), and stored at -20°C. Adult males were excluded from further analysis due to their reduced feeding activity (Pollard et al., 1995), as were specimens with visible damages to minimize the risk of contamination from external DNA fragments. DNA was extracted from each spider individually. Prior to DNA extraction, all samples were treated with 0.15% bleach (NaOCl) for 30 minutes according to Greenstone et al. (2012) to remove external DNA contamination. Since entire bodies were used for DNA extraction, they were homogenized for 30 seconds at maximum speed (SPEX 1600MiniG, Metuchen, New Jersey, USA), each with two sterile stainless steel beads in 600µl lysis buffer with 3µl Proteinase K (Invitrogen, Waltham, United States). Cell lysis was then performed at 55°C for 16 hours. Subsequent DNA extraction used the Qiagen Puregene Kit and followed the manufacturer's protocol (Qiagen, Hilden, Germany). GlycoBlue (Invitrogen, Waltham, United States) was added as coprecipitant (1:600) during the DNA precipitation step to visualize DNA and maximize its yield. To compare the performance of pooling and individual processing of gut content samples, we generated pools of DNA from a diverse set of spider species. We prepared eight pools, each consisting of equal volumes of ten DNA extracts from different spiders. Four pools were species-specific and comprised ten individuals of one species each (either Agelena labyrinthica, Evarcha arcuata, Mangora acalypha or Synema globosum). The other four pools were composed of ten individuals that belonged to different species, but with approximately similar size (XS = 0.5-1.0mm, S = 1.5-2.0mm, M = 2.5-3.0mm, L = 3.0-3.5mm prosoma width, Supplemental table 1). These DNA pools will be referred to as “Pool” hereafter. Each of the spiders in one pool were also amplified and sequenced individually. After DNA sequencing, the same combinations of samples as before were merged computationally (see Fig. 1B, Supplemental table 1), hence creating an exact individually processed replicate of the pooled sample. These computationally merged pools will be referred to as “Reference Pool” hereafter. Please note that we chose to use pools from extracted DNA rather than to pool spiders before DNA isolation. The latter approach would have made a comparison of the recovered prey richness and composition with individually processed spiders impossible. By pooling DNA extracts, and still processing the same extracts individually, an exact comparison of the effect of pooling on patterns of prey diversity can be observed. The incentive of this study however, is to provide insight into the suitability of extracting DNA from bulk samples instead of individual extractions. PCRs were performed using the Qiagen Multiplex PCR Kit in 10 µl volumes with 1 µl DNA and 0.5 µl of each 10 µM primer in 5µl Multiplex and 3µl RNAse free water. The PCR amplification was performed in two rounds. The first round consisted of an initial denaturation at 95°C for 15 min, and 32 cycles with an annealing temperature of 45°C (and additionally increments up to 50°C in the gradient PCR) for 90s and extension at 72°C for 90s, omitting final elongation. This PCR used the new primers with 20bp long tails added to the 5’-end as templates for the following indexing PCR. This indexing PCR consisted of 5 cycles of the same protocol as before, but with 56°C annealing temperature to introduce the Illumina TruSeq adapters and dual indices. Amplification success of each PCR step was verified on a 2% agarose gel stained with GelRed. Amplicons were combined into the final library using approximately equal amounts of DNA, depending on their band intensity on the agarose gel. Final libraries were purified with 1:1 AmPURE XP beads (Beckman and Coulter, California, USA) and sequenced in multiple runs on an Illumina Miseq platform with V3 chemistry 300 cycles. To control for contamination, blank extractions and blank PCRs were included in each respective batch and sequenced alongside the experimental samples. Data analysis Reads were demultiplexed using CASAVA (Illumina, San Diego, CA, USA) and allowing no mismatches in indices. The demultiplexed reads were then merged using PEAR (Zhang et al., 2014) with a minimum overlap of 50bp and a quality threshold of 20. The resulting merged reads were quality-filtered for at least 90% of bases exceeding Q30, and then converted to FASTA files using the FastX toolkit (Gordon and Hannon, 2010). Valid sequences were selected by retaining only sequences beginning with the forward primer and ending with the reverse primer, allowing for variation only in degenerate sites of the primer sequences. Primer sequences were then trimmed with sed in UNIX. Reads were dereplicated using USEARCH (Edgar, 2010) and the dereplicated sequences were clustered into zero-radius OTUs (zOTUs) using the unoise3 command (Edgar, 2016) with de novo chimera removal. Taxonomic identity was assigned to zOTU sequences using BLASTn (Altschul et al., 1990) against the complete NCBI nucleotide database (downloaded 12/2022), with the top 10 hits retained. A custom Python script (Schoeneberg, 2023) assigned taxonomy from the BLAST output. Sequences with non-arthropod hits among the top ten BLAST hits were excluded from further analyses. For all others, the first hit was used for zOTU annotation. This resulted in an OTU table consisting only of zOTUs belonging to Arthropoda. Annotated zOTUs were then filtered to a minimal percent identity of 90% and a minimal fragment length of 60bp.

Alternative Data Provider Market Outlook

The global alternative data provider market size was valued at approximately USD 2.5 billion in 2023 and is expected to reach around USD 11 billion by 2032, growing at a robust CAGR of 18% during the forecast period. The surge in market size is primarily driven by the increasing demand for unique insights that alternative data provides to investment firms, hedge funds, and other financial institutions.

One of the prominent growth factors fueling the alternative data provider market is the escalating number of data sources. With the digital footprint expanding across social media, web scraping, credit card transactions, and satellite data, firms are constantly seeking new ways to gain a competitive edge. Social media platforms alone generate an immense volume of data daily, enabling businesses to derive real-time insights into consumer behavior, market trends, and sentiment analysis. This vast pool of unstructured data, when properly processed and analyzed, provides a goldmine of information for investment strategies and risk management.

Another significant growth driver is the increasing adoption of advanced analytical tools and artificial intelligence (AI). These technologies enable the efficient processing and analysis of large datasets, thus enhancing the accuracy and reliability of the insights derived. AI algorithms, in particular, are adept at identifying patterns and trends that may not be immediately apparent to human analysts. Moreover, the integration of machine learning techniques allows for continuous improvement in data analysis capabilities, making alternative data an indispensable tool for financial institutions aiming to stay ahead of the market.

Furthermore, the growing regulatory emphasis on transparency and accountability in financial markets is driving the adoption of alternative data. Regulatory bodies across the globe are increasingly scrutinizing traditional data sources to ensure fair trading practices and risk mitigation. In response, financial institutions are turning to alternative data providers to gain a more comprehensive view of market dynamics and to comply with stringent regulatory requirements. This shift toward greater transparency is expected to further bolster market growth.

Regionally, North America dominates the alternative data provider market, owing to the early adoption of advanced technologies and the presence of major financial hubs. However, other regions such as Asia Pacific and Europe are rapidly catching up. In Asia Pacific, the burgeoning fintech sector and the increasing number of start-ups are contributing significantly to market growth. Europe, on the other hand, is witnessing a surge in demand due to stringent regulatory frameworks and a growing emphasis on sustainable investing practices.

Data Type Analysis

The alternative data provider market can be segmented by data type into social media data, web scraped data, credit card transactions, satellite data, and others. Social media data is a significant segment that impacts the market due to the sheer volume and variety of data generated through various platforms like Facebook, Twitter, and LinkedIn. This data includes user posts, comments, likes, shares, and other forms of engagement that can be analyzed to gauge market sentiment and predict consumer behavior. Social media data is invaluable for real-time analysis and immediate insights, making it a crucial component for investment and marketing strategies.

Web scraped data is another vital segment, offering an extensive array of information collected from various online sources like e-commerce websites, news sites, blogs, and forums. This data type provides insights into market trends, product popularity, pricing strategies, and consumer preferences. Web scraping tools extract relevant information efficiently, which can then be analyzed to provide actionable insights for businesses looking to optimize their operations and investment strategies.

Credit card transaction data is a high-value segment, offering precise insights into consumer spending patterns and financial behaviors. This data can be used to track economic trends, monitor the performance of specific sectors, and forecast future spending habits. Financial institutions and hedge funds rely heavily on this type of data to make informed investment decisions and to develop targeted marketing campaigns. The granularity and accuracy of credit card transaction data make it a powerful tool for financial analysis.

Satellite data is an e

Routine reinspection of over 537 public pools and treated water aquatic facilities in the US State of Kentucky, totalling over 2,000 inspections per year. Inspections include spas (hot tubs), pools, wave pools, splash pads, theme park pools, and special purpose pools (such as dive pools) etc. Purpose of inspections is included in this data set.For more information on Louisville Metro’s aquatic inspection program and policies, see:https://louisvilleky.gov/government/health-wellness/swimming-poolsData will be updated weekly. Each week the data will be posted as a rolling five year table. That is the new data will be a set that includes inspections from the day of posting minus five years.Data Dictionary:Field Name-DefinitionInspectionID-Unique Identifier for the inspectionFacility ID-Unique Identifier for the FacilityFacility Name-Name of the FacilityFacility Address-Location of the FacilityFacility Address 2-Location of the FacilityFacility City-Location of the FacilityFacility State-Location of the FacilityFacility Postal Code-Location of the FacilityFacility County-Location of the FacilityVenue Type-* POOL-Any natural or artificial body or basin of water which is modified, improved, constructed or installed for the purpose of public swimming or bathing under the control or any person and includes but is not limited to the following: beaches, swimming pools, wave pools, competition swimming pools, diving pools, water slides and spray pools.* HOT TUB/SPA-A special facility designed for recreational and therapeutic use, and which is not drained, cleaned, or refilled after each individual use. It may include, but not limited to, units designed for hydrojet circulation, hot water, cold water, mineral bath, air induction bubbles, or any combination thereof. Common terminology for a spa includes but not limited to, therapeutic pool, hydrotherapy pool, whirlpool, hot spa* WADING POOL-A pool intended only for small children. The maximum depth is less than twenty-four (24) inches.* OTHER-Any other swimming facility not specifically definedInspection Date-Date the inspection was performedInspection Score-"value between 0-100%, 86% without critical issue is a passing facility.A facility facility may have a score of 0 if the purpose of the inspection was ""Other"""Inspection Purpose-* ROUTINE: Used to record routine inspection on an establishment and other facilities.* FOLLOW-UP: Used to record all follow-up inspections as a result of a previous inspection.* COMPLAINT: Used to record the investigation of a complaint received by the agency for regulated establishments, nuisances and the initial investigation for an animal bite.* OTHER: The inspections are not given a numerical score. This inspection type used to record monitoring inspections for swimming pools. "Inspection Passed-TRUE: Inspection Score >= 86% without any critical violations.No Imminent Health Hazards-TRUE: no conditions are present that required of immediate closure of facility.Disinfectant-Type of disinfectant used at the facility, either BROMINE or CHLORINEFree Chlorine-measured in parts per million (PPM), the amount of free Chlorine in the body of water.Free Bromine-measured in parts per million (PPM), the amount of free Bromine in the body of water.pH-The amount of acidity measured in the body of water.Enclosure-TRUE: Facility Enclosure: adequate, self closing gate, good repair or locked if no lifeguard on duty.Main Drain Visible-TRUE: Turbidity: the water is clear enough to see the main drain at the bottom of the body of water.Safety Equipment"-TRUE: First Aid, Safety Equipment, Spa Timer Switch, Telephone: readily accessible, adequate, maintained, good repairPOOLS and/or SPAS could include elevated lifeguard chair, ring buoy, life pole/shepherds crook, backboard with straps, first aid kit. "Disinfectant Level-TRUE: CHLORINE disinfectant levels maintained between 1.0 and 2.5ppm for pools or slides, 2.0 and 3.0ppm for spas. BROMINE disinfectant levels maintained between 1.0 and 2.5ppm for pools or slides, 3.0ppm for spas. Anything outside this range is considered a violation. When only a shallow end reading is taken or shallow and deep end reading the shallow end value is used. If only a deep end reading is taken that value is used.pH Balance-TRUE: pH value tested between 7.2 and 7.8, anything outside this range is considered a violation.Inspection Notes-Description of violations noted at the facility.Health and Wellness Protects and promotes the health, environment and well being of the people of Louisville, providing health-related programs and health office locations community wide.Contact:Gerald KaforskiLMPHWDataTeam@Louisvilleky.gov

Clinical Data Analytics Market Outlook

As per our latest research, the global clinical data analytics market size reached USD 12.8 billion in 2024, reflecting robust momentum driven by the increasing adoption of digital health technologies and the growing emphasis on data-driven decision-making in healthcare. The market is expected to expand at a CAGR of 24.1% from 2025 to 2033, with the forecasted market size projected to reach USD 86.7 billion by 2033. This remarkable growth trajectory is primarily fueled by the rising need for advanced analytics to improve patient outcomes, optimize operational efficiency, and comply with stringent regulatory requirements. The integration of artificial intelligence and machine learning into clinical data analytics platforms is further enhancing the market’s value proposition, making it an indispensable tool for modern healthcare organizations globally.

A key growth driver for the clinical data analytics market is the exponential increase in healthcare data generation, stemming from widespread adoption of electronic health records (EHRs), wearable devices, and connected health systems. Healthcare institutions are increasingly leveraging clinical data analytics solutions to extract actionable insights from these vast data pools, enabling more accurate diagnoses, personalized treatment plans, and proactive disease management. The need to reduce healthcare costs while maintaining high standards of patient care is compelling providers to adopt analytics-driven approaches. Clinical data analytics helps identify inefficiencies, detect patterns in patient care, and predict adverse events, which collectively contribute to improved clinical outcomes and operational savings.

Another significant growth factor is the rising prevalence of chronic diseases and the aging global population, which are placing unprecedented pressure on healthcare systems worldwide. Clinical data analytics empowers providers to stratify patient populations, monitor disease progression, and implement targeted interventions for high-risk groups. The ability to harness predictive analytics for early detection and prevention of complications is especially valuable in managing chronic conditions such as diabetes, cardiovascular diseases, and cancer. Moreover, the growing focus on value-based care models is incentivizing healthcare organizations to invest in analytics platforms that can demonstrate measurable improvements in quality and efficiency, further propelling market expansion.

The increasing regulatory scrutiny and demand for compliance with healthcare standards such as HIPAA, GDPR, and other regional data protection laws are also accelerating market growth. Clinical data analytics platforms are being designed with robust security and privacy features to ensure the safe handling of sensitive patient information. This not only helps organizations avoid costly penalties but also builds trust among patients, clinicians, and stakeholders. Additionally, the ongoing digital transformation in healthcare, supported by government initiatives and funding programs, is creating a favorable environment for the adoption of advanced analytics solutions across hospitals, clinics, research organizations, and pharmaceutical companies.

Regionally, North America continues to dominate the clinical data analytics market, accounting for the largest share due to its advanced healthcare infrastructure, high adoption of digital technologies, and supportive regulatory landscape. Europe follows closely, driven by strong government support for digital health initiatives and increasing investments in healthcare IT. The Asia Pacific region is emerging as a high-growth market, fueled by rapid healthcare modernization, rising healthcare expenditures, and growing awareness of the benefits of analytics. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as healthcare providers in these regions increasingly recognize the value of data-driven decision-making.

Component Analysis

This release presents provisional volcanic gas monitoring data from multi-GAS (multiple Gas Analyzer System) station "YELL_MUD", installed in July 2021 in the Obsidian Pool thermal area, Yellowstone National Park, USA. The multi-GAS station includes gas sensors to measure water vapor, carbon dioxide (CO2), sulfur dioxide (SO2), and hydrogen sulfide (H2S) in gas plumes, as well as meteorologic parameters (wind speed and direction, ambient temperature and relative humidity, ambient pressure), and the temperature of a nearby geothermal feature. The station is duty cycled to conserve power and collects data for 30 minutes every 6 hours beginning at 00:00, 06:00, 12:00, and 18:00 UTC. Before each measurement cycle the gas sensors' baseline responses are checked by recirculating trapped air through chemical scrubbers (soda lime and anhydrite) to remove acid gases and water vapor. On-site CO2, SO2, and H2S standard gases are sampled every 28.25 days to verify the sensor responses. High-r ...

In the last few years, the bed bug Cimex lectularius has been an increasing problem world-wide, mainly due to the development of insecticide resistance to pyrethroids. The characterization of resistance alleles is a prerequisite to improve surveillance and resistance management. To identify genomic variants associated with pyrethroid resistance in Cimex lectularius, we compared the genetic composition of two recent and resistant populations with that of two ancientsusceptible strains using a genome-wide pool-seq design. We identified a large 6 Mb "superlocus" showing particularly high genetic differentiation and association with the resistance phenotype. This superlocus contained several clustered resistance genes, andwas also characterized by a high density of structural variants (inversions, duplications). The possibility that this superlocus constitute a resistance "supergene" that evolved after the clustering of alleles adapted to insecticide and after reduction in recombination is discussed. Methods The four strains used in this studywere provided by CimexStore Ltd (Chepstow, United Kingdom). Two of these strains were susceptible to pyrethroids (S), as they were collected before their massive use and have been maintained under laboratory condition without insecticide exposure for more than 40 years : German Lab (GL, collected in Monheim, Germany) and London Lab (LL, collected in London, Great Britain). The other two resistant (R) populations were London Field (LF, collected in 2008 in London) moderately resistant to pyrethroids, and Sweden Field (SF, collected in 2015 in Malm., Sweden), with a moderate-to-high resistance level. For each strain, genomic DNA was extracted from 30 individual females (except for London Lab which had only 28) using NucleoSpin 96 Tissue Kit (Macherey Nagel, Hoerdt, France) and eluated in 100 μL of BE buffer. DNA concentration of these samples was measured using Quant-iT PicoGreen Kit (ThermoFisher, Waltham MASS, USA) according to manufacturer’s instructions. Samples were then gathered with an equal DNA quantity into pools. DNA purification was performed for each pool with 1.8 times the sample volume in AMPure XP beads (Beckman Coulter, Fullerton CA, USA). Purified DNAwere retrieved in 100 μL of ultrapure water. Pool concentrations were measured with Qubit using DNA HS Kit (Agilent, Santa Clara CA, USA). Final pool concentrations were as follow: 38.5 ng/μL for London Lab, 41.6 ng/μL for London Field, 40.3 ng/μL for German Lab and 38 ng/μL for Sweden Field. Sequencing was performed using TruSeq Nano Kit (Illumina, San Diego CA, USA) to produce paired-end read of 2 x 150 bp length and a coverage of 25 X for London Lab, 32 X for London Field, 39.5 X for German Lab and 25.4 X for Sweden Field by Genotoul (Castanet-Tolosan, France). The whole pipeline with the detail of parameters used is available on GitHub (https://github.com/chaberko-lbbe/clec-poolseq). Quality control analysis of reads obtained from each line was performed using FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc). The raw data have been submitted to the Sequence Read Archive (SRA) database of NCBI under BioProject PRJNA826750. Sequencing reads were filtered using Trimmomatic software v0.39 (Bolger et al., 2014), which removes adaptors. FastUniq v1.1 was then used to remove PCR duplicates (Xu et al., 2012). Reads were mapped on the C. lectularius reference genome (Clec_2.1 assembly, Harlan strain) performed as part of the i5K project (Poelchau et al., 2015), with an estimated size of 510.83 Mb. Mapping was performed using BWA mem v0.7.4 (Li and Durbin, 2009). Sam files were converted to bam format using samtools v1.9, and cleaned of unmapped reads (Li et al., 2009). The 1573 nuclear scaffolds were kept in this analysis, while the mitochondrial scaffold was not considered. Bam files corresponding to the four populations were converted into mpileup format with samtools v1.9. The mpileup file was then converted to sync format by PoPoolation2 version 1201 (Kofler et al., 2011). 8.03 million (M) SNPs were detected on this sync file using R/poolfstat package v2.0.0 (Hivert et al., 2018) and the following parameters: coverage per pool between 10 and 50. Fixation indexes (FST) were computed with R/poolfstat for each pairwise population comparison of each SNP. Global SNP pool was then trimmed on minor allele frequency (MAF) of 0.2 (computed as MAF = 0.5 − |p − 0.5|, with p being the average frequency across all four populations). This relatively high MAF value was chosen in order to remove loci for which we have very limited power to detect any association with the resistance phenotype in the BayPass analysis. BayPass v2.3 (Olazcuaga et al., 2020) was used with default parameters. The final dataset was thus reduced to 2.92M SNPs located on 990 scaffolds.

Description of INSPIRE Download Service (predefined Atom): Youth hostel Swimming pool – The link(s) for downloading the records is/are generated dynamically from Get Map Calling a WMS Interface

Meter-Data Analytics Service Market Outlook

According to our latest research, the global meter-data analytics service market size reached USD 3.89 billion in 2024, with a robust year-on-year growth rate. The market is anticipated to witness a healthy CAGR of 14.2% during the forecast period from 2025 to 2033, propelling the market value to an estimated USD 12.12 billion by 2033. The primary growth factors driving this expansion include the accelerating adoption of smart meters, the increasing need for energy efficiency, and the ongoing digital transformation within utility sectors globally.

One of the most significant growth drivers for the meter-data analytics service market is the rapid deployment of smart grid infrastructure worldwide. Utilities and energy providers are increasingly leveraging advanced meter-data analytics to enhance their operational efficiency, reduce energy losses, and improve customer engagement. The proliferation of smart meters, which provide real-time and granular consumption data, has created a vast pool of data that requires sophisticated analytics solutions to extract actionable insights. This, in turn, has led to a surge in demand for both software and services that can process, analyze, and visualize meter data, thereby enabling utilities to optimize their energy distribution networks, detect anomalies, and support demand response initiatives effectively.

Another crucial factor fueling the growth of the meter-data analytics service market is the rising emphasis on sustainability and regulatory compliance. Governments and regulatory bodies across various regions are mandating utilities to adopt advanced metering infrastructure (AMI) and implement energy efficiency measures. These regulations are compelling utility companies to invest in robust analytics platforms to monitor consumption patterns, identify inefficiencies, and ensure adherence to environmental standards. Additionally, the integration of renewable energy sources into the grid is increasing the complexity of energy management, further necessitating advanced analytics capabilities to balance supply and demand dynamically. As organizations strive to meet stringent sustainability targets, the adoption of meter-data analytics services is expected to accelerate significantly.

The market is also benefiting from technological advancements such as cloud computing, artificial intelligence (AI), and machine learning (ML), which are transforming the landscape of meter-data analytics. Cloud-based deployment models are gaining traction due to their scalability, cost-effectiveness, and ability to facilitate real-time data processing across geographically dispersed assets. AI and ML algorithms are being employed to predict consumption trends, detect fraudulent activities, and automate decision-making processes. These innovations are enabling utilities to derive deeper insights from their meter data, improve operational agility, and deliver enhanced value to end-users. As digital transformation continues to reshape the utility sector, the adoption of advanced meter-data analytics services will remain a key enabler of business growth and competitive differentiation.

From a regional perspective, North America currently dominates the meter-data analytics service market, accounting for the largest share in 2024. This leadership position is attributed to the early adoption of smart grid technologies, strong regulatory support, and the presence of leading market players in the region. Europe follows closely, driven by ambitious energy transition goals and widespread smart meter rollouts. Meanwhile, the Asia Pacific region is emerging as a high-growth market, fueled by rapid urbanization, infrastructure modernization, and increasing investments in smart utility projects. Latin America and the Middle East & Africa are also witnessing steady growth, albeit at a slower pace, as utility companies in these regions gradually embrace digitalization and advanced analytics solutions.

Component Analysis

The meter-data analytics

A permit is required to install, operate or construct any indoor or outdoor bathing establishment with a pool in New York City. This permit may also include saunas, steam rooms, or spray grounds that are at the same location as the pool(s). This permit applies to bathing establishments owned or operated by city agencies, commercial interests or private entities including, but not limited to, public or private schools, corporations, hotels, motels, camps, apartment houses, condominiums, country clubs, gymnasia and health establishments. This dataset contains results of indoor and outdoor pool inspections.

Reverberation is the primary background interference of active sonar systems in shallow water environments, affecting target position detection accuracy. Reverberation suppression is a signal processing technique used to improve the clarity and accuracy of echo by eliminating the echoes, reverberations, and noise that occur during underwater propagation.This paper proposes an end-to-end network structure called the Reverberation Suppression Network (RS-U-Net) to suppress the reverberation of underwater echo signals. The proposed method effectively improves the signal-to-reverberation ratio (SRR) of the echo signal, outperforming existing methods in the literature. The RS-U-Net architecture uses sonar echo signal data as input, and a one-dimensional convolutional network (1D-CNN) is used in the network to train and extract signal features to learn the main features. The algorithm’s effectiveness is verified by the pool experiment echo data, which shows that the filter can improve the detection of echo signals by about 10 dB. The weights of reverberation suppression tasks are initialized with an auto-encoder, which effectively uses the training time and improves performance. By comparing with the experimental pool data, it is found that the proposed method can improve the reverberation suppression by about 2 dB compared with other excellent methods.

Context

I've been recently exploring Microsoft Azure and have been playing this game for the past 4 or so years. I am also a software developer by profession. I did a simple pipeline that gets data from the official Clash Royale API using (Python) Jupyter Notebooks and Azure VMs. I tried searching for public Clash Royale datasets, but the ones I saw don't quite have that much data from my perspective, so I decided to create one for the whole community.

I started pulling in the data at the beginning of the month of December until season 18 ended. This covers the season reset last December 07, and the latest balance changes last December 09. This dataset also contains ladder data for the new Legendary card Mother Witch.

The amount of data that I have, with the latest dataset, has ballooned to around 37.9 M distinct/ unique ladder matches that were (pseudo) randomly being pulled from a pool of 300k+ clans. If you think that this is A LOT, this could only be a percent of a percent (even lower) of the real amount of ladder battle data. It still may not reflect the whole population, also, the majority of my data are matches between players of 4000 trophies or more.

I don't see any reason for me not to share this to the public as the data is now considerably large that working on it and producing insights will take more than just a few hours of "hobby" time to do.

Feel free to use it on your own research and analysis, but don't forget to credit me.

Also, please don't monetize this dataset.

Stay safe. Stay healthy.

Happy holidays!

Content

Card Ids Master List is in the discussion, I also created a simple notebook to load the data and made a sample n=20 rows, so you can get an idea on what the fields are.

Inspiration

With this data, the following can possibly be answered 1. Which cards are the strongest? The weakest? 2. Which win-con is the most winning? 3. Which cards are always with a specific win-con? 4. When 2 opposing players are using maxed decks, which win-con is the most winning? 5. Most widely used cards? Win-Cons? 6. What are the different metas in different arenas and trophy ranges? 7. Is ladder matchmaking algorithm rigged? (MOST CONTROVERSIAL)

(and many more)

Implementation

I have 2 VMs running a total of 14 processes, and for each of these processes, I've divided a pool of 300k+ clans into the same number of groups. This went on 24/7, non-stop for the whole season. Each process will then randomize the list of clans it is assigned to and will iterate through each clan, and get that clan's members' ladder data. It is important to note that I also have a pool of 470 hand-picked clans that I always get data from, as these clans were the starting point that eventually enabled me to get the 300k+ clans. There are clans who have minimal ladder data, there are some clans who have A LOT.

To prevent out of memory exceptions, as my VMs are not really that powerful (I'm using Azure free credits), I've put on a time and limit of battles extracted per member.

My Clan and Handle

My account: https://royaleapi.com/player/89L2CLRP My clan: https://royaleapi.com/clan/J898GQ

Acknowledgements

Thank you to SUPERCELL for creating this FREEMIUM game that has tested countless people's patience, as well as the durability of countless mobile devices after being smashed against a wall, and thrown on the floor.

Thank you to Microsoft for Azure and free monthly credits

Thank you to Python and Jupyter notebooks.

Thank you Kaggle for hosting this dataset.

Measurement data of aboveground litterfall and littermass and litter carbon, nitrogen, and nutrient concentrations were extracted from 685 original literature sources and compiled into a comprehensive database to support the analysis of global patterns of carbon and nutrients in litterfall and litter pools. Data are included from sources dating from 1827 to 1997.

The reported data include the literature reference, general site information (description, latitude, longitude, and elevation), site climate data (mean annual temperature and precipitation), site vegetation characteristics (management, stand age, ecosystem and vegetation-type codes), annual quantities of litterfall (by class, kg m-2 yr-1), litter pool mass (by class and litter layer, kg m-2), and concentrations of nitrogen (N), phosphorus (P), and base cations for the litterfall (g m-2 yr-1) and litter pool components (g m-2).

The investigators intent was to compile a comprehensive data set of individual direct field measurements as reported by researchers. While the primary emphasis was on acquiring C data, measurements of N, P, and base cations were also obtained, although the database is sparse for elements other than C and N. Each of the 1,497 records in the database represents a measurement site. Replicate measurements were averaged according to conventions described in Section 5 and recorded for each site in the database. The sites were at 575 different locations.

The Legal Entity Identifier (LEI) is a 20-character, alpha-numeric code based on the ISO 17442 standard developed by the International Organization for Standardization (ISO). It connects to key reference information that enables clear and unique identification of legal entities participating in financial transactions. Each LEI contains information about an entity’s ownership structure and thus answers the questions of 'who is who’ and ‘who owns whom’. Simply put, the publicly available LEI data pool can be regarded as a global directory, which greatly enhances transparency in the global marketplace. The Financial Stability Board (FSB) has reiterated that global LEI adoption underpins “multiple financial stability objectives” such as improved risk management in firms as well as better assessment of micro and macro prudential risks. As a result, it promotes market integrity while containing market abuse and financial fraud. Last but not least, LEI rollout “supports higher quality and accuracy of financial data overall”. The publicly available LEI data pool is a unique key to standardized information on legal entities globally. The data is registered and regularly verified according to protocols and procedures established by the Regulatory Oversight Committee. In cooperation with its partners in the Global LEI System, the Global Legal Entity Identifier Foundation (GLEIF) continues to focus on further optimizing the quality, reliability and usability of LEI data, empowering market participants to benefit from the wealth of information available with the LEI population. The drivers of the LEI initiative, i.e. the Group of 20, the FSB and many regulators around the world, have emphasized the need to make the LEI a broad public good. The Global LEI Index, made available by GLEIF, greatly contributes to meeting this objective. It puts the complete LEI data at the disposal of any interested party, conveniently and free of charge. The benefits for the wider business community to be generated with the Global LEI Index grow in line with the rate of LEI adoption. To maximize the benefits of entity identification across financial markets and beyond, firms are therefore encouraged to engage in the process and get their own LEI. Obtaining an LEI is easy. Registrants simply contact their preferred business partner from the list of LEI issuing organizations available on the GLEIF website.

LAILLAIL is a Large language model-Aware selection approach for In-context-Learning-based code generation named LAIL. LAIL uses LLMs themselves to select examples. It requires LLMs themselves to label a candidate example as a positive example or a negative example for a requirement.## Requirements- openai- tqdm- javaWe also privide a scripts (/Evaluation/evaluation_setup.sh) to help set up programming language dependencies that are used in evaluation.bashbash evaluation_setup.sh###### DatasetThe datasets contain DevEval, MBJP, MBPP, MBCPP, and HumanEval. DevEval is a repository-level code generation dataset, which is collected from real-word code repositories. The dataset aligns with real-world code repositories in multiple dimensions. Thus, we take DevEval as the example to demonstrate how to process the dataset. Take ../Dataset/DevEval as example:train.jsonl and test.jsonl:(1) We randomly select two domains to evaluate LAIL and baselines, including the scientific engineering domain and text processing domain. (2) We randomly split the tasks of the two domains into the training set and the test set. Finally, we acquire 101 examples in the training set and 49 examples in the test set. (3) Given a requirement from a repository, we use tree-sitter to parse the repository and acquire all functions of the repository. (4) We treat functions contained in the repository as the candidate pool. Then LAIL and baselines retrieve a few functions from thecandidate pool as demonstration examples. source data and test_source data folders consist of the original code repositories collected from Github.estimate_prompt folder contain the constructed prompts to estimate candidate examples.generation_prompt folder contains the constructed prompts where the demonstration examples are selected by LAIL and different baselines. For example:(1) ICL_LAIL folder provides the selected examples' id in LAIL_id by our LAIL. Developers can directly use these provided prompts through codellama_completion.py to generate programs. (2) After generating programs, developers need to process generated programs with process_generation.py. (3) Finally, developers evaluate the generated programs with the source code in Evaluation folder.############ LAIL ### Estimate candidate examples by LLMs themselvesWe leverage LLM themseleves to estimate candidate examples. The code is storaged in the LAIL/estimate_examples package.Take DevEval as example:(1) /Dataset/DevEval/estimate_prompt folder contains the constructed prompts to estimate candidate examples.(2) Developers run the following command to estimate candidate examples by CodeLlama-7B:bashbash make_estimation_prompt.sh ../Dataset/DevEval/estimation_prompt(3) According to the probability feedback of LLMs, we acquire the positive and negative examples.###### Train a neural retriever(1) We use the labeled positive and negative examples to train a neural retriever with contrastive learning. The code is storaged in the /LAIL/LAIL/retriever/train folder.bashexport CUDA_VISIBLE_DEVICES=0nohup python run.py \ --output_dir=/saved_models \ --model_type=roberta \ --config_name=microsoft/graphcodebert-base \ --model_name_or_path=microsoft/graphcodebert-base \ --tokenizer_name=microsoft/graphcodebert-base \ --do_train \ --train_data_file=/id.jsonl \ --epoch 100 \ --block_size 128 \ --train_batch_size 16 \ --learning_rate 1e-4 \ --max_grad_norm 1.0 \ --seed 123456 >mbpp.txt 2>&1 &## Select a few demonstration examples using the trained retriever(2) Given a test requirement, developers use the trained retriever to select a few demonstration examples.The code is storaged in the /LAIL/LAIL/retriever/train folder.bashbash run_inference.sh ../Dataset/DevEval###### Code Generation(1) After acquired the prompt context consisting of a few selected examples, developers input a test requirement and the prompt context into LLMs and acquire desired programs.For example, developers use CodeLlama ( ../LAIL/ICL_LAIL/codellama_completion.py) to generate programs:bashexport CUDA_VISIBLE_DEVICES=0torchrun --nproc_per_node=1 --master_port=16665 codellama_completion.py Salesforce/CodeLlama-7b ../Dataset/DevEval/prompt_LAIL.jsonl --temperature=0.8 --max_batch_size=4 --output_base=output_random --get_logits=False(2) After generating programs, developers need to process generated programs with ../LAIL/ICL_LAIL/process_generation.py. bashpython process_generation.py###### BaselinesThis paper contains seven baselines that use different approaches to select demonstration examples for ICL_based code generation.(1) The source code is in the baselines folder and each baseline is in a individual folder.Developers can acquire the selected examples of all baselines by runing source code as follows:bashpython baselines.py(2) Then, developers use /baselines/make_prompt.py to contruct a prompt context using the selected candidate examples as follows:bashpython make_prompt.py ICLCoder ICLCoder -1###### EvaluationIn this paper, we use Pass@k to evaluate the performances of LAIL and baselines by the source code in LAIL/EvaluationSince the DevEval dataset is a repository-level code generation which is complex to evaluate, developers can use the following pipeline to evaluate different approaches by the source code in /LAIL/Evaluation/.## CitationIf you have any questions or suggestions, please email us at lijiaa@pku.edu.cn.

Snapshot img

Online Data Science Training Programs Market Size 2025-2029

The online data science training programs market size is forecast to increase by USD 8.67 billion, at a CAGR of 35.8% between 2024 and 2029.

The market is experiencing significant growth due to the increasing demand for data science professionals in various industries. The job market offers lucrative opportunities for individuals with data science skills, making online training programs an attractive option for those seeking to upskill or reskill. Another key driver in the market is the adoption of microlearning and gamification techniques in data science training. These approaches make learning more engaging and accessible, allowing individuals to acquire new skills at their own pace. Furthermore, the availability of open-source learning materials has democratized access to data science education, enabling a larger pool of learners to enter the field. However, the market also faces challenges, including the need for continuous updates to keep up with the rapidly evolving data science landscape and the lack of standardization in online training programs, which can make it difficult for employers to assess the quality of graduates. Companies seeking to capitalize on market opportunities should focus on offering up-to-date, high-quality training programs that incorporate microlearning and gamification techniques, while also addressing the challenges of continuous updates and standardization. By doing so, they can differentiate themselves in a competitive market and meet the evolving needs of learners and employers alike.

What will be the Size of the Online Data Science Training Programs Market during the forecast period?

Request Free SampleThe online data science training market continues to evolve, driven by the increasing demand for data-driven insights and innovations across various sectors. Data science applications, from computer vision and deep learning to natural language processing and predictive analytics, are revolutionizing industries and transforming business operations. Industry case studies showcase the impact of data science in action, with big data and machine learning driving advancements in healthcare, finance, and retail. Virtual labs enable learners to gain hands-on experience, while data scientist salaries remain competitive and attractive. Cloud computing and data science platforms facilitate interactive learning and collaborative research, fostering a vibrant data science community. Data privacy and security concerns are addressed through advanced data governance and ethical frameworks. Data science libraries, such as TensorFlow and Scikit-Learn, streamline the development process, while data storytelling tools help communicate complex insights effectively. Data mining and predictive analytics enable organizations to uncover hidden trends and patterns, driving innovation and growth. The future of data science is bright, with ongoing research and development in areas like data ethics, data governance, and artificial intelligence. Data science conferences and education programs provide opportunities for professionals to expand their knowledge and expertise, ensuring they remain at the forefront of this dynamic field.

How is this Online Data Science Training Programs Industry segmented?

The online data science training programs industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. TypeProfessional degree coursesCertification coursesApplicationStudentsWorking professionalsLanguageR programmingPythonBig MLSASOthersMethodLive streamingRecordedProgram TypeBootcampsCertificatesDegree ProgramsGeographyNorth AmericaUSMexicoEuropeFranceGermanyItalyUKMiddle East and AfricaUAEAPACAustraliaChinaIndiaJapanSouth KoreaSouth AmericaBrazilRest of World (ROW)

By Type Insights

The professional degree courses segment is estimated to witness significant growth during the forecast period.The market encompasses various segments catering to diverse learning needs. The professional degree course segment holds a significant position, offering comprehensive and in-depth training in data science. This segment's curriculum covers essential aspects such as statistical analysis, machine learning, data visualization, and data engineering. Delivered by industry professionals and academic experts, these courses ensure a high-quality education experience. Interactive learning environments, including live lectures, webinars, and group discussions, foster a collaborative and engaging experience. Data science applications, including deep learning, computer vision, and natural language processing, are integral to the market's growth. Data analysis, a crucial application, is gaining traction due to the increasing demand

Explore the historical Whois records related to data-pool.com (Domain). Get insights into ownership history and changes over time.

Data presented here to evaluate the effects of CrossFit intervention on students’ health-related physical fitness during physical education process through a meta-analysis. The procedure started with systematic screening following PRISMA guidelines using databases: Web of Science, SCOPUS, ScienceDirect, PubMed. Searching strategy and eligibility criteria of the literatures used in the meta-analysis are described. In all, 60 literatures were identified through predefined database and 6 of which fit for eligibility criteria. Meta-analysis was conducted by using RevMan-5.4.1. The random effects model was used to evaluate the effects of CF intervention on students. The outcomes from comparison between experiment and control groups were descripted with studies’ 95% confidence intervals (CI). The heterogeneity within comparison groups was estimated based on p-value of chi-squared (Q) test. I2 value also used for quantifying heterogeneity. Publication bias was estimated by using Rosenthal’s fail-safe number test (Nfs-T). Nfs-T = 19S - N (S = number of studies, p < .05, N = number of studies, p > .05). Tolerance level (TL) = 5K + 10 (K = all included studies). Sensitivity and subgroup analysis were conducted to remove study (studies) with deficiency that might influence effect in pooled group. The procedure illustrated here allows scholars to conveniently access and expand the pooled literatures and to utilize these data in future meta-analyses.