Correctly map and link all the data within the security master database to streamline the flow of information and reduce operational risk across internal and external production. Our Cross Reference Data package offers global instrument and market information for security identification, trading, settlement and transaction reporting. Retrieve common identifiers for a financial instrument and the related identifiers of the listings and respective markets.

Führen Sie mit der Wertpapierstammdatenbank ein korrektes Datenmapping durch, um den Informationsfluss zu optimieren und das operationelle Risiko innerhalb der internen und externen Produktion zu minimieren. Unser Paket für Cross Reference Data liefert globale Informationen zu Instrumenten und Märkten, die bei der Identifikation der Wertschriften, dem Trading, dem Settlement und den Transaktionsmeldungen eingesetzt werden. Erhalten Sie Identifikatoren für Finanzinstrumente sowie die zugehörigen Identifikatoren von Kotierungen und entsprechenden Märkten.

A listing of NYS counties with accompanying Federal Information Processing System (FIPS) and US Postal Service ZIP codes sourced from the NYS GIS Clearinghouse.

A translation service which contains a registry of pubicly available chemical information such as structures, chemical names, chemical synonyms, database identifiers, molecular masses, XlogP and proton-donor/acceptor data for compound-specific, structure-based cross references. It offers single ID conversion, batch ID conversion, InChI code conversion, and other services.

LSEG's Entity and Reference Data offers both static and dynamic data to help classify and describe financial instrument characteristics. Browse the datasets.

Data describing 1,903 well header data for Canada, obtained by the Canadian Geothermal Data Compilation. The data table includes general information on the location of the borehole, well construction information, location uncertainty and remarks. Information sources are included in the dataset. The HeaderURI for a particular borehole is the cross-referencing link used to associate the borehole with web based information on the well construction, pictures or other information, specific to one feature. Data processing to load and aggregate delimited text data from the OFR into a database, and web service deployment by SM Richard and Christy Caudill.

An official Digital Object Identifier (DOI) Registration Agency of the International DOI Foundation launched as a cooperative effort among publishers to enable persistent cross-publisher citation linking in online academic journals. The citation-linking network today covers over 65 million journal articles and other content items (books chapters, data, theses, technical reports) from thousands of scholarly and professional publishers around the globe. CrossRef does not aggregate full-text content but rather, it uses a system of distributed aggregation whereby full-text content is linked through a database consisting of minimal publisher metadata. Each record in the database is essentially a triplet: (metadata + URL+DOI). In addition to assigning DOIs to scholarly content, CrossRef has additional services: * Cited-By Linking * CrossRef Metadata Services * CrossCheck plagiarism screening (powered by iThenticate) * CrossMark update identification service * FundRef Funder identification service

This table relates Calgary Transit stops to Calgary Transit routes. The data source is the General Transit Feed Specification (GTFS) available on the City of Calgary Open Data which is released by Calgary Transit Service Design on at least a quarterly basis. A point will exist multiple times if the stop it used my multiple routes. One point per stop per route.

This feature class contains reference data points with specific site information on vegetation dominance type and tree size for the existing vegetation type mapping for the Northern portion of the Tongass National Forest. Reference data for this project came from numerous sources including: 1) Forest Service field crews collecting vegetation information specific to this project; 2) GO field crews collecting vegetation information for this project; 3) helicopter survey data; 4) Young-Growth Inventory data; 5) legacy data from previous Forest Service survey plots and the Forest Inventory and Analysis (FIA) program (FIA data are not included in this database); 6) legacy data from the prior Yakutat vegetation mapping project; and 7) image interpretation. This database contains reviewed legacy data for the Northern Tongass Existing Vegetation Type Map. Tongass National Forest personnel collected most of the ground data that was targeted for this mapping effort using a variety of means—primarily by foot using existing trail and road infrastructure, or by boat—to collect samples that capture the diversity of vegetation across the project area. Helicopter survey data were collected over the course of three weeks in July 2024 for the Northern Tongass, with the goal of reaching difficult to access areas. The Young-Growth Inventory information was leveraged as reference data from actively managed forest stands. Legacy data was cross-referenced with the classification key to label each plot with a vegetation type. All sites were reviewed within the context of their corresponding segment using high-resolution imagery. For more detailed information on reference data methodology please see the Central and Northern Tongass Existing Vegetation Project Report.

This series consists of a List of Temporary Employees of Government Departments from 1933 to 1934. The list appears to have been created by the Public Service Board.

Employees can be identified as being employed by a particular Department by checking the page number listed alongside their name in the alphabetically arranged list, and then cross referencing that number to the Index to Departments located at the front of the volume. This index contains a page number range for each Department.

Data related to 28,019 thermal conductivity observations at locations in Canada, obtained by the Canadian Geothermal Data Compilation. The data table includes general information on the location of the borehole or sample, measurement date, rock name and measurements. Information sources are included in the dataset. The SamplingFeatureURI for a particular sample is the cross-referencing link (foreign key) used to associate the observation with web based information on the feature of interest, including pictures, websites and documents. Data processing to load and aggregate delimited text data from the OFR into a database, and web service deployment by SM Richard and Christy Caudill.

Please see the current location for information exchange formats (Excel workbooks and schemas) at http://schemas.usgin.org/models. This repository is not the most updated location for schemas. THIS CONTENT MODEL SUPERSEDES THE DRILL STEM TEST CONTENT MODEL. This template defines the content model for a service that delivers well stem test data collected during development of Oil and Gas wells. Normal drilling procedures control formation pressures and fluids through the use of a hydrostatic head. Well testing brings these formation pressures and fluids to the surface. Data collected during the test procedure will be provided through the well test feature service. Types of tests include Production, G10, Drill Stem, Drawdown, Buildup, and other well test types. This spreadsheet defines the schema used for the interchange of well test observation results by the AASG geothermal data project for the National Geothermal Data System. The HeaderURI for a particular borehole (well for simple wells) is the cross-referencing link (foreign key) used to associate the header record, well logs, temperature measurements, and other information from a particular borehole.

This feature class contains reference data points with specific site information on vegetation dominance type and tree size for the existing vegetation type mapping for the Central portion of the Tongass National Forest. Reference data for this project came from numerous sources including: 1) Forest Service field crews collecting vegetation information specific to this project; 2) GO field crews collecting vegetation information for this project; 3) helicopter survey data; 4) Young-Growth Inventory data; 5) legacy data from previous Forest Service survey plots and the Forest Inventory and Analysis (FIA) program (FIA data are not included in this database); 6) legacy data from the prior Yakutat vegetation mapping project; and 7) image interpretation. This database contains reference data collected by GO staff for the Central Tongass Existing Vegetation Type Map. Tongass National Forest personnel collected most of the ground data that was targeted for this mapping effort using a variety of means—primarily by foot using existing trail and road infrastructure, or by boat—to collect samples that capture the diversity of vegetation across the project area. Helicopter survey data were collected over the course of three weeks in July 2024 for the Northern Tongass, with the goal of reaching difficult to access areas. The Young-Growth Inventory information was leveraged as reference data from actively managed forest stands. Legacy data was cross-referenced with the classification key to label each plot with a vegetation type. All sites were reviewed within the context of their corresponding segment using high-resolution imagery. For more detailed information on reference data methodology please see the Central and Northern Tongass Existing Vegetation Project Report.

Please see the current location for information exchange formats (Excel workbooks and schemas) at http://schemas.usgin.org/models. This repository is not the most updated location for schemas. WELL LOG DATA FOR SERVICE NEEDS TO GO INTO THE WELL LOG CONTENT MODEL (see http://schemas.usgin.org/models/#welllog ). This workbook is designed as a guideline for compiling information from well log headers. Information compiled in this workbook can be used to populate the Well Header data delivery template, Borehole Temperature template and to populate Metadata records for well log documents. Basic information from the header is used to create Well Header features used to index borehole locations with the information associated with the borehole. Typically maximum temperatures are recorded from log headers, and reported as'bottom hole temperature'using the borehole temperature observation service. Each row in the WellLogInformation sheet in this workbook will correspond to an individual log from a borehole; thus there may be multiple rows per well. For logs that are scanned or digitized and availble as online resources, metadata records will be created. The HeaderURI for a particular borehole (well for simple wells) is the cross-referencing link (foreign key) used to associate the header record, well log metadata, temperature measurements, and other information from a particular borehole.

🇺🇸 미국 English The dataset contains constructed unique geospatial identifier for buildings. A buildings UBID is the north axis aligned "bounding box" of its footprint represented as the centroid (in the GDAL grid reference system format), which is represented by the first set of characters before the first dash, and four cardinal extents, which are represented by the four sets of numbers after the first dash (North, East, South, West),The data has been constructed by spatially joining the latest (2019) building footprints published in DC Open Data with the Common Ownership Lot shapefile. The UBIDs were coded using US DOE’s Implementation code. Please note that the current data set may include some unnecessary structures identified as buildings. These included sheds, overhangs, bus stops, and other structures that do not need to be assigned a UBID. An updated version of the UBID dataset will be released when this issue is resolved. This project is the result of the US DOE Better Buildings Building Energy Data Analysis (BEDA) Accelerator. US DOE is working with stakeholders including state and local governments, commercial and residential building data aggregators, property owners, and product and service providers to develop the UBID system and to pilot it in real-world settings. US DOE and its partners are demonstrating the benefits of UBID in managing and cross-referencing large building datasets and in reducing the costs and enhancing the value proposition of leveraging building energy data. UBIDs For more information regarding UBIDs please visit: https://www.energy.gov/eere/buildings/unique-building-identifier-ubid

AI Autofill Cosmetic Ingredient Safety File Market Outlook

According to our latest research, the AI Autofill Cosmetic Ingredient Safety File market size reached USD 412 million in 2024, demonstrating robust momentum driven by the increasing adoption of AI-powered solutions across the cosmetic industry. The market is expected to expand at a CAGR of 18.7% from 2025 to 2033, propelling the market value to approximately USD 1.97 billion by 2033. This impressive growth is primarily attributed to the rising demand for automation in regulatory documentation, enhanced safety assessments, and the growing complexity of cosmetic formulations, which necessitate advanced digital solutions for compliance and ingredient management.

The AI Autofill Cosmetic Ingredient Safety File market is experiencing transformative growth due to the rapid digitalization of the cosmetic industry and the increasing regulatory scrutiny surrounding product safety. As cosmetic manufacturers introduce more innovative and complex formulations, the need for accurate, efficient, and compliant ingredient safety files has become paramount. AI-powered autofill solutions streamline the creation and management of these files by automating data entry, cross-referencing regulatory databases, and flagging potential compliance issues in real-time. This automation not only reduces the risk of human error but also accelerates the time-to-market for new products, giving manufacturers a competitive advantage. Furthermore, the integration of AI with existing product lifecycle management systems enables seamless updates and ensures that all documentation remains current with evolving global regulations.

Another significant growth driver for the AI Autofill Cosmetic Ingredient Safety File market is the increasing complexity and volume of regulatory requirements imposed by authorities such as the US FDA, the European Commission, and various Asian regulatory bodies. These agencies are continually updating their lists of approved and restricted ingredients, requiring cosmetic companies to maintain meticulous records for each product. AI-driven solutions alleviate the burden of manual compliance checks by automatically updating ingredient lists, safety data, and compliance reports in line with the latest regulations. This not only enhances operational efficiency but also minimizes the risk of costly non-compliance penalties. As a result, both large multinational cosmetic manufacturers and smaller independent brands are investing heavily in AI-powered compliance tools to future-proof their operations.

The proliferation of AI in cosmetic ingredient safety file management is further fueled by the growing emphasis on product safety and transparency among consumers. Today’s consumers are more informed and demand greater visibility into the ingredients used in their personal care products. In response, cosmetic companies are leveraging AI to provide comprehensive safety documentation and ingredient traceability. AI-powered platforms facilitate the aggregation and analysis of safety data from multiple sources, enabling companies to offer transparent, easily accessible information to both regulators and end-users. This increased transparency not only builds consumer trust but also strengthens brand reputation in an increasingly competitive marketplace. The convergence of regulatory, operational, and consumer-driven factors is expected to sustain the high growth trajectory of the AI Autofill Cosmetic Ingredient Safety File market in the coming years.

Regionally, North America and Europe continue to dominate the AI Autofill Cosmetic Ingredient Safety File market, accounting for a combined share of over 65% in 2024. These regions are characterized by stringent regulatory environments, advanced technological infrastructure, and a high concentration of leading cosmetic manufacturers. The Asia Pacific region is emerging as a key growth engine, propelled by the rapid expansion of the cosmetics industry in countries such as China, Japan, and South Korea. Meanwhile, Latin America and the Middle East & Africa are witnessing steady adoption, driven by increasing regulatory harmonization and the entry of global cosmetic brands. The regional dynamics reflect the interplay between regulatory pressures, industry maturity, and technological readiness, shaping the global outlook for AI-powered cosmetic ingredient safety file solutions.

[Superseded]This dataset is a single layer from [Superseded] City Plan 2014 – v26.00–2023 collection. Not all layers were updated in this amendment, for more information on past Adopted City Plan amendments.This feature class is shown on the Airport environs overlay map - Procedures for Air Navigation Services - Aircraft operations surfaces (map reference: OM-001.3).This feature class includes the following sub-categories:(a) Procedures for Air Navigation Services–Aircraft Operational Surfaces (PANS-OPS) sub-categories:(i) procedures for air navigation surfaces (PANS) sub-category.For information about the overlay and how it is applied, please refer to the Brisbane City Plan 2014 document. Additional information to assist with cross referencing the Airport environs overlay datasets is available in the City Plan 2014 — Airport Environs overlay — reference dataset on Open Data website.The Airport environs overlays contain information derived from data that is created or owned by BAC and licensed to Brisbane City Council. Its use by any person for purposes not associated with planning and development in Brisbane is not authorised.

profil.AT 3.02 serves as the basis for ensuring a uniform description of the content, origin, spatial reference, access, quality, terms of use, attributes, etc. of geographical resources in Austria. Resources are referred to as data sets, data series, spatially located documents, applications or geographical services (e.g. Open Geospatial Web Map Services [OGC WMS]). profil.AT 3.02 describes the subset of metadata elements extracted from the current (2016) requirements of the INSPIRE Directive 2007 after a demand survey in Austria and a comparison with the requirements. In order to ensure an ambivalent interpretation of this profile adapted to ON/EN/ISO 19115, a reference to the corresponding assignment in ON/EN/ISO 19110, ON/EN/ISO 19119, ON/EN/ISO 19139 and the cross reference to the whitepaper OGD metadata v2.3 is shown.

This series comprises file reference cards arranged by traders' names. The address of each trader is also recorded. Additionally, the cards include complainants' names and a cross reference to the appropriate Complaints file.

These cards have not been added to since 1976, neither is the card system used for reference as Consumer complaints files are now accessed through the Consumer complaint card index.

There is, however, a discrepancy between the commencing dates of the current Card index and the Complaints files, with the former commencing in 1978 and the latter in 1974.

(11/15062-77). 16 boxes.

Note:
This description is extracted from Concise Guide to the State Archives of New South Wales, 3rd Edition 2000.

[Superseded] This dataset is a single layer from [Superseded] City Plan 2014 – v32.00–2025 collection. Not all layers were updated in this amendment, for more information on past Adopted City Plan amendments.This feature class is shown on the Airport environs overlay map - Procedures for Air Navigation Services - Aircraft operations surfaces (map reference: OM-001.3).This feature class includes the following sub-categories:(a) Procedures for Air Navigation Services–Aircraft Operational Surfaces (PANS-OPS) sub-categories:(i) procedures for air navigation surfaces (PANS) sub-category.For information about the overlay and how it is applied, please refer to the Brisbane City Plan 2014 document. Additional information to assist with cross referencing the Airport environs overlay datasets is available in the City Plan 2014 — Airport Environs overlay — reference dataset on Open Data website.The Airport environs overlays contain information derived from data that is created or owned by BAC and licensed to Brisbane City Council. Its use by any person for purposes not associated with planning and development in Brisbane is not authorised.