An Open Context "predicates" dataset item. Open Context publishes structured data as granular, URL identified Web resources. This "Variables" record is part of the "Kenan Tepe" data publication.

Accessing data in structured formats such as XML, CSV and JSON in statically typed languages is difficult, because the languages do not understand the structure of the data. Dynamically typed languages make this syntactically easier, but lead to error-prone code. Despite numerous efforts, most of the data available on the web do not come with a schema. The only information available to developers is a set of examples, such as typical server responses. We describe an inference algorithm that infers a type of structured formats including CSV, XML and JSON. The algorithm is based on finding a common supertype of types representing individual samples (or values in collections). We use the algorithm as a basis for an F# type provider that integrates the inference into the F# type system. As a result, users can access CSV, XML and JSON data in a statically-typed fashion just by specifying a representative sample document.

This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type "Anatomical Structure". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.

The Global Data Broker Market Size was valued at USD 275 Billion in 2024 and is projected to reach USD 568 Billion by 2032, growing at a CAGR of 9.05% during the forecast period 2026 to 2032.Global Data Broker Market DriversThe market drivers for the data broker market can be influenced by various factors. These may include:Growing Demand for Consumer and Enterprise Data: The need for actionable data across industries such as retail, finance, and healthcare is projected to drive demand for data broker services.Increasing Adoption of Programmatic Advertising: The shift towards automated ad buying using user behavior data is anticipated to boost reliance on data brokers for real-time audience segmentation.

The documents in this database are 12 different tax forms from the IRS 1040 Package X for the year 1988. These include Forms 1040, 2106, 2441, 4562, and 6251 together with Schedules A, B, C, D, E, F, and SE. Eight of these forms contain two pages or form faces; therefore, there are 20 different form faces represented in the database. The document images in this database appear to be real forms prepared by individuals, but the images have been automatically derived and synthesized using a computer.

Data Warehousing Solution Market size was valued at USD 28.5 Billion in 2024 and is projected to reach USD 65.0 Billion by 2032, growing at a CAGR of 10.2% during the forecast period 2026-2032.Global Data Warehousing Solution Market DriversThe market drivers for the data warehousing solution market can be influenced by various factors. These may include:Growing Data Volume: The exponential growth of data generated by organizations and digital platforms is driving demand for efficient data warehousing solutions.Cloud Adoption: The transition to cloud-based infrastructures accelerates the deployment of scalable and adaptable data warehousing systems.Advanced Analytics and BI: The increased usage of sophisticated analytics, AI, and business intelligence technologies is driving the demand for integrated data warehouses.

This dataset contains the entire concept structure of UMLS Metathesaurus for the semantic type "Embryonic Structure". One of the primary purposes of this dataset is to connect different names for all the concepts for a specific Semantic Type. There are 125 semantic types in the Semantic Network. Every Metathesaurus concept is assigned at least one semantic type; very few terms are assigned as many as five semantic types.

Data Warehouse Market size was valued at USD 27.68 Billion in 2024 and is projected to reach USD 63.9 Billion by 2032, growing at a CAGR of 11% from 2026 to 2032.Key Market Drivers:Increasing Volume of Data Generated across Industries: The exponential expansion of data generation is increasing the demand for robust data warehouse solutions. According to the International Data Corporation (IDC), the global datasphere is expected to increase from 33 zettabytes in 2018 to 175 zettabytes by 2025. This tremendous rise in data volume demands sophisticated data warehousing capabilities to ensure efficient storage, administration, and analysis.Growing Adoption of Cloud-based Data Warehousing: The shift to cloud-based solutions is a significant driver of the Data Warehouse Market.

CSV Table. This table includes coded descriptions for Commercial System Main Building Structure Codes in the St. Louis County, Missouri parcel dataset. Link to Metadata.

In 2023, instant coffee made up more than **** of the coffee market in China. However, freshly ground coffee had been rapidly gaining traction, reaching a market share of **** percent that year.

These data were used to examine grammatical structures and patterns within a set of geospatial glossary definitions. Objectives of our study were to analyze the semantic structure of input definitions, use this information to build triple structures of RDF graph data, upload our lexicon to a knowledge graph software, and perform SPARQL queries on the data. Upon completion of this study, SPARQL queries were proven to effectively convey graph triples which displayed semantic significance. These data represent and characterize the lexicon of our input text which are used to form graph triples. These data were collected in 2024 by passing text through multiple Python programs utilizing spaCy (a natural language processing library) and its pre-trained English transformer pipeline. Before data was processed by the Python programs, input definitions were first rewritten as natural language and formatted as tabular data. Passages were then tokenized and characterized by their part-of-speech, tag, dependency relation, dependency head, and lemma. Each word within the lexicon was tokenized. A stop-words list was utilized only to remove punctuation and symbols from the text, excluding hyphenated words (ex. bowl-shaped) which remained as such. The tokens’ lemmas were then aggregated and totaled to find their recurrences within the lexicon. This procedure was repeated for tokenizing noun chunks using the same glossary definitions.

Monthly building and demolition permits value of construction by type of structure and type of work.

The vastness of materials space, particularly that which is concerned with metal–organic frameworks (MOFs), creates the critical problem of performing efficient identification of promising materials for specific applications. Although high-throughput computational approaches, including the use of machine learning, have been useful in rapid screening and rational design of MOFs, they tend to neglect descriptors related to their synthesis. One way to improve the efficiency of MOF discovery is to data-mine published MOF papers to extract the materials informatics knowledge contained within journal articles. Here, by adapting the chemistry-aware natural language processing tool, ChemDataExtractor (CDE), we generated an open-source database of MOFs focused on their synthetic properties: the DigiMOF database. Using the CDE web scraping package alongside the Cambridge Structural Database (CSD) MOF subset, we automatically downloaded 43,281 unique MOF journal articles, extracted 15,501 unique MOF materials, and text-mined over 52,680 associated properties including the synthesis method, solvent, organic linker, metal precursor, and topology. Additionally, we developed an alternative data extraction technique to obtain and transform the chemical names assigned to each CSD entry in order to determine linker types for each structure in the CSD MOF subset. This data enabled us to match MOFs to a list of known linkers provided by Tokyo Chemical Industry UK Ltd. (TCI) and analyze the cost of these important chemicals. This centralized, structured database reveals the MOF synthetic data embedded within thousands of MOF publications and contains further topology, metal type, accessible surface area, largest cavity diameter, pore limiting diameter, open metal sites, and density calculations for all 3D MOFs in the CSD MOF subset. The DigiMOF database and associated software are publicly available for other researchers to rapidly search for MOFs with specific properties, conduct further analysis of alternative MOF production pathways, and create additional parsers to search for additional desirable properties.

Data and code for "Grace Patlewicz, Ann M. Richard, Antony J. Williams, Richard S. Judson, Russell S. Thomas, Towards reproducible structure-based chemical categories for PFAS to inform and evaluate toxicity and toxicokinetic testing, Computational Toxicology, Volume 24, 2022, 100250, ISSN 2468-1113, https://doi.org/10.1016/j.comtox.2022.100250.". This dataset is associated with the following publication: Patlewicz, G., A. Richard, A. Williams, R. Judson, and R. Thomas. Towards reproducible structure-based chemical categories for PFAS to inform and evaluate toxicity and toxicokinetic testing.. Computational Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 24: 100250, (2022).

This document mentions some fundamental concepts of structural analysis and establishes the classification of structures according to different parameters. Likewise, it defines the aspects required for the calculation of stability and structural determination.

The largest share of the advertising market in Slovakia belonged to online advertising. It made up a total of ***** percent of the entire market. It was closely followed by TV advertising, with a share of ***** percent. Third-placed radio advertising with a market share of **** percent, followed by print advertising at **** percent.

The statistic shows the recruiting structure of staffing firms in North America in 2017, by type of business. During the survey, ** percent of the respondents stated that their firm runs a split-desk model for contract hires.