This TB supplements information in 45 CFR 1355.40, and the Appendices to Part 1355 by providing additional guidance on the AFCARS file format and the proper naming convention to be used for AFCARS files.

Metadata-only record linking to the original dataset. Open original dataset below.

This archival information package contains data file format descriptions and related codes needed to fully interpret NODC standard data formats developed and maintained by the NOAA National Oceanographic Data Center (NODC) from the 1960s until use of these standard formats, codes, and identifiers were largely deprecated in the mid-1990s. Most NODC standard formats were identified with a 'C' or 'F', followed by a 3-digit number, e.g., C022 was the identifier for Low Resolution CTD/STD format and F022 was the identifier for High Resolution CTD/STD format. Data from other formats sent to NODC were usually converted from the original format to one of the standard formats. When no standard format was appropriate, the original data was identified with using 'L', followed by a 3-digit number, e.g., L130 was the identifier for HYDROCARBONS data in a format for which NODC had no standard equivalent. Some NODC standard formats were only identified by a name (e.g., Station Data 2, Surface Current Underway Data "SCUDS"). The Universal BathyThermograph (UBT) format was used by multiple standard data file formats (e.g., C128 Mechanical BT, C118 Expendable BT, and other types of BathyThermographs). NODC briefly maintained a product database known as the 'Ocean Profile Data Base' of 'OPDB'. The OPDB use a non-standard data format known a 'P3'. The P3 data format and the OPDB were discontinued in the early 2000s, but the available format information for this data format is included in 0-data/P3 subdirectory for documentary purposes.

BioCTS for ISO/IEC Biometric Data Interchange Format Standards and Selected PIV Profiles is a conformance testing architecture that tests biometric data interchange records for conformance to ISO/IEC based biometric data interchange formats. The software includes a graphical user interface, several different conformance test suites, and provides detailed descriptions of any errors found.

A manually curated registry of standards, split into three types - Terminology Artifacts (ontologies, e.g. Gene Ontology), Models and Formats (conceptual schema, formats, data models, e.g. FASTA), and Reporting Guidelines (e.g. the ARRIVE guidelines for in vivo animal testing). These are linked to the databases that implement them and the funder and journal publisher data policies that recommend or endorse their use.

These data are the results of a systematic review that investigated how data standards and reporting formats are documented on the version control platform GitHub. Our systematic review identified 32 data standards in earth science, environmental science, and ecology that use GitHub for version control of data standard documents. In our analysis, we characterized the documents and content within each of the 32 GitHub repositories to identify common practices for groups that version control their documents on GitHub. In this data package, there are 8 CSV files that contain data that we characterized from each repository, according to the location within the repository. For example, in 'readme_pages.csv' we characterize the content that appears across the 32 GitHub repositories included in our systematic review. Each of the 8 CSV files has an associated data dictionary file (names appended with '_dd.csv' and here we describe each content category within CSV files. There is one file-level metadata file (flmd.csv) that provides a description of each file within the data package.

The Fish Pathology (F013) data set contains data from examinations of diseased fishes. Although these data may be from field observations, they derive primarily from laboratory analyses. Data include: location, and fishing duration, distance, and gear; catch statistics (e.g., total weight, number of individuals, age group, identity of diseases, and number of diseased individuals) by species for any number of species; and biological condition of selected specimens. The size, affected organ, location, and frequency of lesions may be reported for individual specimens. Specimens are identified with the NODC Taxonomic Code. These data may be characteristics of individual lesions or average lesion statistics.

This data package contains three templates that can be used for creating README files and Issue Templates, written in the markdown language, that support community-led data reporting formats. We created these templates based on the results of a systematic review (see related references) that explored how groups developing data standard documentation use the Version Control platform GitHub, to collaborate on supporting documents. Based on our review of 32 GitHub repositories, we make recommendations for the content of README Files (e.g., provide a user license, indicate how users can contribute) and so 'README_template.md' includes headings for each section. The two issue templates we include ('issue_template_for_all_other_changes.md' and 'issue_template_for_documentation_change.md') can be used in a GitHub repository to help structure user-submitted issues, or can be modified to suit the needs of data standard developers. We used these templates when establishing ESS-DIVE's community space on GitHub (https://github.com/ess-dive-community) that includes documentation for community-led data reporting formats. We also include file-level metadata 'flmd.csv' that describes the contents of each file within this data package. Lastly, the temporal range that we indicate in our metadata is the time range during which we searched for data standards documented on GitHub.

This data type contains data from field sampling of marine fish and shellfish. The data derive from analyses of midwater or bottom tow catches and provide information on population density and distribution. Cruise information, position, date, time, gear type, fishing distance and duration, and number of hauls are reported for each survey. Environmental data may include: meteorological conditions; surface temperature and salinity; bottom temperature and salinity; trawl depth temperature and salinity; current direction and speed. Bottom trawl or other gear dimensions and characteristics are also reported. Catch statistics (e.g., weight, volume, number of fish per unit volume) may be reported for both total haul and for individual species. Biological characteristics of selected specimens, predator/prey information (from stomach contents analysis) and growth data may also be included. Specimens are identified by an NODC Taxonomic Code. Data are very sparse prior to 1975.

This data set provides global gridded estimates of carbon, energy, and hydrologic fluxes between the land and atmosphere from 15 Terrestrial Biosphere Models (TBMs) in a standard format. Model estimates are at monthly and yearly time steps for the period 1900 to 2010, with a spatial resolution of 0.5 degree x 0.5 degree globally, excluding Antarctica.

This collection contains temperature, depth, and other data collected using mechanical bathythermograph (MBT) casts from numerous platforms in oceans and seas around the world from 1941-06-04 to 1988-02-16. Data were processed by NODC to the NODC Mechanical Bathythermograph (MBT/C128) format. The C128 format is used for temperature-depth profile data obtained using the mechanical bathythermograph (MBT) instrument. The maximum depth of MBT observations is approximately 285 m. Therefore, MBT data are useful only in studying the thermal structure of the upper layers of the ocean. Cruise information, date, position, and time are reported for each observation. The data record comprises pairs of temperature-depth values. Temperature data in this file are recorded at uniform 5 m depth intervals.

The ESS-DIVE reporting format for Comma-separated Values (CSV) file structure is based on a combination of existing guidelines and recommendations including some found within the Earth Science Community with valuable input from the Environmental Systems Science (ESS) Community. The CSV reporting format is designed to promote interoperability and machine-readability of CSV data files while also facilitating the collection of some file-level metadata content. Tabular data in the form of rows and columns should be archived in its simplest form, and we recommend submitting these tabular data following the ESS-DIVE reporting format for generic comma-separated values (CSV) text format files. In general, the CSV file format is more likely accessible by future systems when compared to a proprietary format and CSV files are preferred because this format is easier to exchange between different programs increasing the interoperability of a data file. By defining the reporting format and providing guidelines for how to structure CSV files and some field content within, this can increase the machine-readability of the data file for extracting, compiling, and comparing the data across files and systems. Data package files are in .csv, .png, and .md. Open the .csv with e.g. Microsoft Excel, LibreOffice, or Google Sheets. Open the .md files by downloading and using a text editor (e.g., notepad or TextEdit). Open the .png in e.g. a web browser, photo viewer/editor, or Google Drive.

Data has been processed by NODC to the NODC standard Fin Rot (F006) format. Full format descriptions are available from NCEI at https://www.ncei.noaa.gov/data/oceans/nodc/formats/. The Fin Rot (F006) format is used for data from examinations of the biological condition of diseased fishes. For tow samples collected, data include: total number of individuals of a given species, number of diseased fish of that species, and extent of damage to the body and various fins for up to three selected diseased individuals. The specimens are identified by the NODC Taxonomic Code. The survey, institution, senior scientist and station are identified. Parameters defining environmental conditions at the sample location at the time of collection (e.g., water temperature and salinity, air temperature, wind direction and speed) may also be recorded.

The ESS-DIVE reporting format for file-level metadata (FLMD) provides granular information at the data file level to describe the contents, scope, and structure of the data file to enable comparison of data files within a data package. The FLMD are fully consistent with and augment the metadata collected at the data package level. We developed the FLMD template based on a review of a small number of existing FLMD in use at other agencies and repositories with valuable input from the Environmental Systems Science (ESS) Community. Also included is a template for a CSV Data Dictionary where users can provide file-level information about the contents of a CSV data file (e.g., define column names, provide units). Files are in .csv, .xlsx, and .md. Templates are in both .csv and .xlsx (open with e.g. Microsoft Excel, LibreOffice, or Google Sheets). Open the .md files by downloading and using a text editor (e.g. Notepad or TextEdit). Though we provide Excel templates for the file-level metadata reporting format, our instructions encourage users to 'Save the FLMD template as a CSV following the CSV Reporting Format guidance'. In addition, we developed the ESS-DIVE File Level Metadata Extractor which is a lightweight python script that can extract some FLMD fields following the recommended FLMD format and structure.

Exposure Data Standards Market Outlook

According to our latest research, the global Exposure Data Standards market size reached USD 1.92 billion in 2024, with a robust compound annual growth rate (CAGR) of 11.7% projected throughout the forecast period. By 2033, the market is expected to attain a value of USD 5.24 billion, driven by the escalating need for standardized data frameworks across insurance, finance, healthcare, and regulatory sectors. This growth is underpinned by the increasing complexity of risk assessment, the proliferation of digital transformation initiatives, and heightened regulatory scrutiny, all of which necessitate reliable, interoperable exposure data standards for enhanced decision-making and compliance.

A key growth factor for the Exposure Data Standards market is the accelerating adoption of digital technologies within highly regulated industries such as insurance, banking, and healthcare. As organizations digitize their operations, the volume and complexity of exposure data have surged, making standardized data formats essential for seamless data exchange and risk modeling. The insurance sector, in particular, is increasingly reliant on exposure data standards to streamline catastrophe modeling, underwriting, and claims management processes. This has led to widespread adoption of industry-standard data schemas, such as the Open Exposure Data (OED) format, which facilitates interoperability between insurers, reinsurers, and modeling platforms. The demand for real-time analytics and predictive modeling is further propelling the need for robust exposure data standards capable of supporting advanced data analytics, machine learning, and artificial intelligence applications.

Another significant driver is the tightening of regulatory requirements around risk reporting and data transparency. Financial regulators, government agencies, and international organizations are mandating the use of standardized data formats for reporting exposures, particularly in the wake of high-profile financial crises and natural disasters. These regulations compel organizations to adopt exposure data standards that ensure data accuracy, consistency, and auditability. The healthcare sector, for example, is witnessing a surge in demand for exposure data standards to manage and report on patient safety, public health risks, and pandemic response. The convergence of regulatory compliance and risk management imperatives is fostering innovation in exposure data standards, with vendors offering increasingly sophisticated solutions tailored to the evolving needs of diverse end-users.

Technological advancements are also playing a pivotal role in shaping the Exposure Data Standards market landscape. The proliferation of cloud computing, Internet of Things (IoT), and big data analytics is generating unprecedented volumes of exposure data from a multitude of sources, ranging from connected devices to remote sensors and digital platforms. Organizations are leveraging exposure data standards to integrate, aggregate, and analyze this data at scale, enabling more accurate risk assessment and mitigation strategies. The shift towards cloud-based deployment models, in particular, is facilitating the adoption of exposure data standards by providing scalable, cost-effective, and easily upgradable solutions. As organizations seek to harness the full potential of their data assets, the demand for comprehensive exposure data standards is expected to intensify across all major industry verticals.

Regionally, North America continues to dominate the Exposure Data Standards market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The strong presence of leading insurance companies, financial institutions, and regulatory bodies in these regions has fostered early adoption of exposure data standards. Meanwhile, Asia Pacific is emerging as a key growth engine, driven by rapid digitalization, expanding insurance and financial sectors, and increasing regulatory oversight. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as organizations in these regions recognize the benefits of standardized exposure data in managing emerging risks and complying with evolving regulations.

Standard definition of tourist information format.

This dataset consists of cartographic data in digital line graph (DLG) form for the northeastern states (Connecticut, Maine, Massachusetts, New Hampshire, New York, Rhode Island and Vermont). Information is presented on two planimetric base categories, political boundaries and administrative boundaries, each available in two formats: the topologically structured format and a simpler format optimized for graphic display. These DGL data can be used to plot base maps and for various kinds of spatial analysis. They may also be combined with other geographically referenced data to facilitate analysis, for example the Geographic Names Information System.

Examples of two types of Weibo data in standard format.

This collection contains temperature, depth and other data collected using bathythermograph (MBT/XBT) casts from numerous platforms in oceans and seas around the world from 1947-08-14 to 1992-01-04. Data were processed by NODC to the NODC Bathythermograph XBT Selected Depths (SBT/C125) format. The SBT format is used for temperature-depth data obtained from mechanical (MBT) and expendable (XBT) bathythermograph instruments. Data in this file were sent to NODC at depths selected by the originator - usually at standard horizons or some fixed interval. The MBT file holds data reported at a 5-meter depth interval, and depths in the XBT file are chosen at significant inflection points of the temperature-depth profile. SBT data can be selected from specific geographic regions or from specified cruises.

The data: Remission times (in weeks) for two groups of leukemia patients.

The values given for each group consist of time in weeks a patient is in remission, up to the point of the patient’s either going out of remission or being censored. Here, going out of remission is a failure. A person is censored if he or she remains in remission until the end of the study, is lost to follow-up, or withdraws before the end of the study.

If we pick out any individual and read across the table, we obtain the line of data for that person that gets entered in the computer. For example, person #3 has a survival time of 6 weeks, and since d = 1, this person failed, that is, went out of remission. The X value is 1 because person #3 is in group 1. As a second example, person #14, who has an observed survival time of 17 weeks, was censored at this time because d = 0. The X value is again 1 because person #14 is also in group 1.

As one more example, this time from group 2, person #32 survived 8 weeks and then failed, because d = 1; the X value is 0 because person #32 is in group 2.

This data file is in stata format which consists Nepal Living Standard data (NLSS)-IV