CTOS Basis team works with our data partners and instituted a well defined methodology to produce comprehensive report with various analysis on credit information, financial, business operation, industries and credit risk.

Contents in the report include: • Latest registration details & capital structure of a company • Details of shareholders, directors and the management team • Litigation check & credit information on a company • Latest financial information • Various financial ratios & financial analysis • Payment records, Clientele, Business operations • Latest news check • Current investigation • Latest economic and industrial data • Industry analysis • Credit risk evaluation & credit rating

CTOS Basis team works with our data partners and instituted a well defined methodology to produce comprehensive report with various analysis on credit information, financial, business operation, industries and credit risk.

Contents in the report include: • Latest registration details & capital structure of a company • Details of shareholders, directors and the management team • Litigation check & credit information on a company • Latest financial information • Various financial ratios & financial analysis • Payment records, Clientele, Business operations • Latest news check • Current investigation • Latest economic and industrial data • Industry analysis • Credit risk evaluation & credit rating

Our consumer data is gathered and aggregated via surveys, digital services, and public data sources. We use powerful profiling algorithms to collect and ingest only fresh and reliable data points.

Our comprehensive data enrichment solution includes a variety of data sets that can help you address gaps in your customer data, gain a deeper understanding of your customers, and power superior client experiences. 1. Geography - City, State, ZIP, County, CBSA, Census Tract, etc. 2. Demographics - Gender, Age Group, Marital Status, Language etc. 3. Financial - Income Range, Credit Rating Range, Credit Type, Net worth Range, etc 4. Persona - Consumer type, Communication preferences, Family type, etc 5. Interests - Content, Brands, Shopping, Hobbies, Lifestyle etc. 6. Household - Number of Children, Number of Adults, IP Address, etc. 7. Behaviours - Brand Affinity, App Usage, Web Browsing etc. 8. Firmographics - Industry, Company, Occupation, Revenue, etc 9. Retail Purchase - Store, Category, Brand, SKU, Quantity, Price etc. 10. Auto - Car Make, Model, Type, Year, etc. 11. Housing - Home type, Home value, Renter/Owner, Year Built etc.

Consumer Graph Schema & Reach: Our data reach represents the total number of counts available within various categories and comprises attributes such as country location, MAU, DAU & Monthly Location Pings:

Data Export Methodology: Since we collect data dynamically, we provide the most updated data and insights via a best-suited method on a suitable interval (daily/weekly/monthly).

Consumer Graph Use Cases: 360-Degree Customer View: Get a comprehensive image of customers by the means of internal and external data aggregation. Data Enrichment: Leverage Online to offline consumer profiles to build holistic audience segments to improve campaign targeting using user data enrichment Fraud Detection: Use multiple digital (web and mobile) identities to verify real users and detect anomalies or fraudulent activity. Advertising & Marketing: Understand audience demographics, interests, lifestyle, hobbies, and behaviors to build targeted marketing campaigns.

The Intelligent Semantic Data Service market is experiencing robust growth, driven by the increasing need for organizations to extract actionable insights from rapidly expanding data volumes. The market's complexity necessitates sophisticated solutions that go beyond traditional data analytics, focusing on understanding the meaning and context of data. This demand is fueled by advancements in artificial intelligence (AI), particularly natural language processing (NLP) and machine learning (ML), which power semantic analysis engines. Key players like Google, IBM, Microsoft, Amazon, and others are heavily investing in this space, developing and deploying powerful solutions that cater to various industries, from finance and healthcare to retail and manufacturing. The market's projected Compound Annual Growth Rate (CAGR) suggests a significant expansion over the forecast period (2025-2033). We estimate the 2025 market size to be approximately $15 billion, based on industry reports and observed growth trajectories in related AI segments. This figure is expected to reach approximately $35 billion by 2033. Several factors contribute to this growth, including the rising adoption of cloud-based solutions, the need for improved data governance, and a growing emphasis on data-driven decision-making. However, the market also faces certain restraints. High implementation costs, the need for specialized expertise, and data security concerns can hinder widespread adoption. Furthermore, the market is characterized by a relatively high barrier to entry, favoring established players with significant R&D capabilities. Nevertheless, the potential benefits of unlocking the true value of unstructured data through intelligent semantic analysis are compelling enough to drive continued investment and innovation in this rapidly evolving market. Segmentation within the market is likely based on deployment type (cloud, on-premise), service type (data enrichment, knowledge graph creation, semantic search), and industry vertical. The geographic distribution shows a strong concentration in North America and Europe, followed by a steady growth in the Asia-Pacific region, driven by increasing digitalization efforts.

Raw sequencing data from Denitrifying Anaerobic Methane Oxidation (DAMO) experiments and the relevant statistical data generated by various bioinformatics tools. This dataset is not publicly accessible because: All the experiments for this study were not performed in EPA but in co-authors' institution which has managed the project and prepared a manuscript for peer-reviewed journal submission. It can be accessed through the following means: The raw data will be made available by the authors on request (Dr. Yaohuan Gao, gaoyaohuan@xjtu.edu.cn). Format: Not available because the raw data was not generated in EPA. This dataset is associated with the following publication: Xia, L., Y. Wang, P. Yao, H. Ryu, Z. Dong, C. Tan, S. Deng, H. Liao, and Y. Gao. The effects of model insoluble copper compounds in anoxic sedimentary environment on denitrifying anaerobic methane oxidation (DAMO) activity. Microorganisms. MDPI, Basel, SWITZERLAND, 12(11): 2259, (2024).

Performance measures are data metrics defined and tracked by city departments to measure the city government’s effectiveness and efficiency of service delivery. Data for the performance measures are derived from the biennial Resident Survey as well as department data tracking systems. Each performance measure is connected to one of the strategic goals and objectives that the City has defined as a high priority. The performance measures will be reviewed and refined annually to ensure they are representative of the priorities set out by City Council and the community. The performance measures in this filtered view is for the "Learning and Enrichment Opportunities" strategic goal and objective.

A compressed file containing the biological processes and KEGG pathways for each of the three clusters obtained using k-means as tab separated lists. Columns in each file are the same as in RSeqFlow-OlivePollen8 for biological processes (files starting by BP-) and RSeqFlow-OlivePollen9 for KEGG pathways (files starting by KEGG-).

We describe a bibliometric network characterizing co-authorship collaborations in the entire Italian academic community. The network, consisting of 38,220 nodes and 507,050 edges, is built upon two distinct data sources: faculty information provided by the Italian Ministry of University and Research and publications available in Semantic Scholar. Both nodes and edges are associated with a large variety of semantic data, including gender, bibliometric indexes, authors' and publications' research fields, and temporal information. While linking data between the two original sources posed many challenges, the network has been carefully validated to assess its reliability and to understand its graph-theoretic characteristics. By resembling several features of social networks, our dataset can be profitably leveraged in experimental studies in the wide social network analytics domain as well as in more specific bibliometric contexts. , The proposed network is built starting from two distinct data sources:

the entire dataset dump from Semantic Scholar (with particular emphasis on the authors and papers datasets) the entire list of Italian faculty members as maintained by Cineca (under appointment by the Italian Ministry of University and Research).

By means of a custom name-identity recognition algorithm (details are available in the accompanying paper published in Scientific Data), the names of the authors in the Semantic Scholar dataset have been mapped against the names contained in the Cineca dataset and authors with no match (e.g., because of not being part of an Italian university) have been discarded. The remaining authors will compose the nodes of the network, which have been enriched with node-related (i.e., author-related) attributes. In order to build the network edges, we leveraged the papers dataset from Semantic Scholar: specifically, any two authors are said to be connected if there is at least one pap..., , # Data cleaning and enrichment through data integration: networking the Italian academia

https://doi.org/10.5061/dryad.wpzgmsbwj

Manuscript published in Scientific Data with DOI .

Description of the data and file structure

This repository contains two main data files:

• edge_data_AGG.csv, the full network in comma-separated edge list format (this file contains mainly temporal co-authorship information);
• Coauthorship_Network_AGG.graphml, the full network in GraphML format.Â

along with several supplementary data, listed below, useful only to build the network (i.e., for reproducibility only):

• University-City-match.xlsx, an Excel file that maps the name of a university against the city where its respective headquarter is located;
• Areas-SS-CINECA-match.xlsx, an Excel file that maps the research areas in Cineca against the research areas in Semantic Scholar.

Description of the main data files

The `Coauthorship_Networ...

Establishment of a syndication flow from the department of Vendée (85) with the data from the “Restoration” schedule with enriched data: name, address, type of restaurant, means of communication (landline phone, e-mail and website), social networks, GPS coordinates, labels, languages spoken, payment methods accepted, opening dates, rates, details of visits, videos.

CTOS Basis team works with our data partners and instituted a well defined methodology to produce comprehensive report with various analysis on credit information, financial, business operation, industries and credit risk.

Contents in the report include: • Latest registration details & capital structure of a company • Details of shareholders, directors and the management team • Litigation check & credit information on a company • Latest financial information • Various financial ratios & financial analysis • Payment records, Clientele, Business operations • Latest news check • Current investigation • Latest economic and industrial data • Industry analysis • Credit risk evaluation & credit rating

Table S7. Subtype-specific CNV and their values in TCGA and external dataset. The table show the previously reported subtype-specific CNV and their values (mean and standard deviation) for (A) breast cancer and (B) GBM TCGA and external dataset. The values were CDFs (ranged from 0 to 1) for TCGA data, log2 of estimated copy numbers (centered at 0) for METABRIC, and estimated copy numbers (centered at 2) for REMBRANDT, respectively. (XLSX 11 kb)

Flow of syndication with data from the “sports and cultural activities”, “Leisure equipment”, “Cultural heritage”, “Natural heritage” and “Testations” with enriched data: name, address, type of activity/equipment, means of communication (landline phone, e-mail and website), social networks, GPS coordinates, labels, languages spoken, payment methods accepted, opening dates, rates, details of visits, videos.

Digitalizing highway infrastructure is gaining interest in Germany and other countries due to the need for greater efficiency and sustainability. The maintenance of the built infrastructure accounts for nearly 30% of greenhouse gas emissions in Germany. To address this, Digital Twins are emerging as tools to optimize road systems. A Digital Twin of a built asset relies on a geometric-semantic as-is model of the area of interest, where an essential step for automated model generation is the semantic segmentation of reality capture data. While most approaches handle data without considering real-world context, our approach leverages existing geospatial data to enrich the data foundation through an adaptive feature extraction workflow. This workflow is adaptable to various model architectures, from deep learning methods like PointNet++ and PointNeXt to traditional machine learning models such as Random Forest. Our four-step workflow significantly boosts performance, improving overall accuracy by 20% and unweighted mean Intersection over Union (mIoU) by up to 43.47%. The target application is the semantic segmentation of point clouds in road environments. Additionally, the proposed modular workflow can be easily customized to fit diverse data sources and enhance semantic segmentation performance in a model-agnostic way.

This study aimed to (1) enrich microbial acetylenotrophs from trichloroethylene (TCE) contaminated groundwater and (2) evaluate whether these enrichments could degrade TCE coupled to acetylene degradation. Acetylenotrophs are microorganisms that use acetylene as their carbon and energy source. TCE contaminated groundwater was collected from wells at the Naval Air Warfare Center (NAWC) in West Trenton, New Jersey. Microbial acetylene uptake in groundwater samples was established by mixing the groundwater with a defined mineral medium to supply nutrients and providing acetylene as the sole electron donor and carbon source. The structure of the microbial community in those enrichments was characterized as shown by 16S rRNA gene sequencing and analysis. The acetylenotrophic groundwater enrichment cultures were then tested to assess whether they could utilize acetylene to drive reduction of TCE and tetrachloroethene (PCE) to vinyl chloride.

Determining the optimal targets of genomic sub-sampling for phylogenomics, phylogeography, and population genomics remains a challenge for evolutionary biologists. Of the available methods for sub-sampling the genome, hybrid enrichment (sequence capture) has become one of the primary means of data collection for systematics, due to the flexibility and cost efficiency of this approach. Despite the utility of this method, information is lacking as to what genomic targets are most appropriate for addressing questions at different evolutionary scales. In this study, first we compare the benefits of target loci developed for deep- and shallow-scales by comparing these loci at each of three taxonomic levels: within a genus (phylogenetics), within a species (phylogeography) and within a hybrid zone (population genomics). Specifically, we target evolutionary conserved loci that are appropriate for deep phylogenetic scales and more rapidly evolving loci that are informative for phylogeographic a...

Our dataset provides detailed and precise insights into the business, commercial, and industrial aspects of any given area in the USA (Including Point of Interest (POI) Data and Foot Traffic. The dataset is divided into 150x150 sqm areas (geohash 7) and has over 50 variables. - Use it for different applications: Our combined dataset, which includes POI and foot traffic data, can be employed for various purposes. Different data teams use it to guide retailers and FMCG brands in site selection, fuel marketing intelligence, analyze trade areas, and assess company risk. Our dataset has also proven to be useful for real estate investment.- Get reliable data: Our datasets have been processed, enriched, and tested so your data team can use them more quickly and accurately.- Ideal for trainning ML models. The high quality of our geographic information layers results from more than seven years of work dedicated to the deep understanding and modeling of geospatial Big Data. Among the features that distinguished this dataset is the use of anonymized and user-compliant mobile device GPS location, enriched with other alternative and public data.- Easy to use: Our dataset is user-friendly and can be easily integrated to your current models. Also, we can deliver your data in different formats, like .csv, according to your analysis requirements. - Get personalized guidance: In addition to providing reliable datasets, we advise your analysts on their correct implementation.Our data scientists can guide your internal team on the optimal algorithms and models to get the most out of the information we provide (without compromising the security of your internal data).Answer questions like: - What places does my target user visit in a particular area? Which are the best areas to place a new POS?- What is the average yearly income of users in a particular area?- What is the influx of visits that my competition receives?- What is the volume of traffic surrounding my current POS?This dataset is useful for getting insights from industries like:- Retail & FMCG- Banking, Finance, and Investment- Car Dealerships- Real Estate- Convenience Stores- Pharma and medical laboratories- Restaurant chains and franchises- Clothing chains and franchisesOur dataset includes more than 50 variables, such as:- Number of pedestrians seen in the area.- Number of vehicles seen in the area.- Average speed of movement of the vehicles seen in the area.- Point of Interest (POIs) (in number and type) seen in the area (supermarkets, pharmacies, recreational locations, restaurants, offices, hotels, parking lots, wholesalers, financial services, pet services, shopping malls, among others). - Average yearly income range (anonymized and aggregated) of the devices seen in the area.Notes to better understand this dataset:- POI confidence means the average confidence of POIs in the area. In this case, POIs are any kind of location, such as a restaurant, a hotel, or a library. - Category confidences, for example"food_drinks_tobacco_retail_confidence" indicates how confident we are in the existence of food/drink/tobacco retail locations in the area. - We added predictions for The Home Depot and Lowe's Home Improvement stores in the dataset sample. These predictions were the result of a machine-learning model that was trained with the data. Knowing where the current stores are, we can find the most similar areas for new stores to open.How efficient is a Geohash?Geohash is a faster, cost-effective geofencing option that reduces input data load and provides actionable information. Its benefits include faster querying, reduced cost, minimal configuration, and ease of use.Geohash ranges from 1 to 12 characters. The dataset can be split into variable-size geohashes, with the default being geohash7 (150m x 150m).

Additional file 1: Table S1. GO biological process enrichment analysis of Arabidopsis thaliana met1-1 RNA sequence data.

Dragonflies and damselflies are a charismatic, medium-sized insect order (~6300 species) with a unique potential to approach comparative research questions. Their taxonomy and many ecological traits for a large fraction of extant species are relatively well understood. However, until now, the lack of a large-scale phylogeny based on high throughput data with the potential to connect both perspectives has precluded comparative evolutionary questions for these insects. Here, we provide an ordinal hypothesis of classification based on anchored hybrid enrichment using a total of 136 species representing 46 of the 48 families or incertae sedis, and a total of 478 target loci. Our analyses recovered the monophyly for all three suborders: Anisoptera, Anisozygoptera and Zygoptera. Although the backbone of the topology was reinforced and showed the highest support values to date, our genomic data was unable to fully resolve portions of the topology. The latter suggests that increasing the taxon sampling might resolve these issues rather than increasing molecular data. In addition, a quartet sampling approach highlights the potential evolutionary scenarios that may have shaped evolutionary phylogeny (e.g., incomplete lineage sorting and introgression) in the evolutionary history of this taxon. Finally, in light of our phylogenomic reconstruction and previous morphological and molecular information we proposed an updated odonate classification and define five new families (Amanipodagrionidae fam. nov., Mesagrionidae fam. nov., Mesopodagrionidae fam. nov., Priscagrionidae fam. nov., Protolestidae fam. nov.) and reinstate another two (Rhipidolestidae stat. res., Tatocnemididae stat. res.). Additionally, we feature the problematic taxonomic groupings for examination in future studies to improve our current phylogenetic hypothesis.

Methods Both raw sequence files (FASTQ) and multiple sequence alignments for Odonata targeted enrichment data

Establishment of a syndication flow from the department of Vendée (85) with data from the “Festivals and Events” schedule with enriched data: name, address, type of FMA (Fests and events), means of communication (landline phone, e-mail and website), social networks, GPS coordinates, labels, languages spoken, methods of payment accepted, opening dates, rates, details of visits, videos.

The search terms 'phosphorus', 'nitrogen', 'nutrient-enrichment' and 'limitation' were used in BIOSIS. From this search a collection of peer-reviewed literature spanning 1992-2008 was compiled. Specifically, we were looking for nutrient enrichment experiment data for coastal and wetland sites. From the literature we extracted nutrient dosages, significant functional trait changes, the associated 'new' means and associatd standard errors/variances and p-values.