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.

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.

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 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.

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.

Tower Structure Type

## Overview

Tower Structure Type is a dataset for instance segmentation tasks - it contains Object Identification Tower 2 Object Identification Pole 9m2f QmU9 annotations for 432 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

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.

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.

Contains physical, manmade structures used for diverting, storing, releasing, or measuring water; structures are associated with specific bodies of water.

This map shows the predominant structure type of housing units, and number of housing units in structure, by tract for the most recent American Community Survey.. Data from American Community Survey.This map helps illustrated where there are predominantly higher or lower density housing structures in a given area.Single-family detached houses are single-family detached homes (90% confidence). 1-unit structure detached from any other house, that is, with open space on all four sides. Such structures are considered detached even if they have an adjoining shed or garage. A one-family house that contains a business is considered detached as long as the building has open space on all four sides. A housing unit may be a house, an apartment, a mobile home, or rooms which have direct access from outside the building or through a common hall. Boats, recreational vehicles (RVs), vans, tents, railroad cars, and the like are included only if they are occupied as someone's current place of residence.Low-Density Multifamily Attached houses are comprised of single-family attached houses, and multifamily housing that are 2-9 units in a structure such as townhomes/row houses, apartments, double houses, or houses attached to nonresidential structures, each house is a separate, attached structure if the dividing or common wall goes from ground to roof.Medium-Density Multifamily Attached are comprised of multifamily housing that are 10-49 units in a structure, such as many med rise apartments.High-Density Multifamily Attached are comprised of multifamily housing that are 50+ units in a structure, generally considered high-rise apartments.Mobile homes are a prefabricated structure, built in a factory on a permanently attached chassis before being transported to site.

Building permits, dwelling units by type of structure, seasonally adjusted data and at annual rate for Canada and urban centres, 10,000 and over, monthly data 1960 to today.

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.

This statistic presents the distribution of the financial assets owned by French households between 2013 and 2017, according to the type of assets. In 2017, insurance represented around ** percent of the financial wealth of French people.

Model parameters and code used in the manuscript "Fluctuation-dependent coexistence of stage-structured species" by CM Werner, LM Hallett, and LG Shoemaker. Additional information in read me file.

Economic structure absolute values by type of indicator and type of institute. National.