This dataset provides data at the national level from federal fiscal year 2006 onwards for the accuracy of the assignment of Social Security numbers (SSN) based on an end-of line sample review of transactions that result in the release of SSN cards.

Analysis of ‘Social Security Administration Data for Enumeration Accuracy’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/e568c9ad-f5c4-4627-81fd-37229a259af2 on 11 February 2022.

--- Dataset description provided by original source is as follows ---

This dataset provides data at the national level from federal fiscal year 2006 onwards for the accuracy of the assignment of Social Security numbers (SSN) based on an end-of line sample review of transactions that result in the release of SSN cards.

--- Original source retains full ownership of the source dataset ---

These data are provided to allow users for reproducibility of an open source tool entitled 'automated Accumulation Threshold computation and RIparian Corridor delineation (ATRIC)'

The digitalised social safety net programmes of Bangladesh are evaluated with a sample of 531 beneficiaries and non-beneficiaries.

LDSF soil data for Lewa.

SSN: Sample Serial Number (in the lab)

SOC: Soil organic carbon

TN: Total nitrogen

ExBas: Sum of exchangeable bases (Ca+Mg+K+Na; cmolc kg-1)

ExCa: Exchangeable Ca (cmolc kg-1)

ExMg: Exchangeable Mg (cmolc kg-1)

ExK: Exchangeable K (cmolc kg-1)

ExNa: Exchangeable Na (cmolc kg-1)

Sand content (%)

Clay content (%)

This ontology is based on the SSN Ontology by the W3C Semantic Sensor Networks Incubator Group (SSN-XG), together with considerations from the W3C/OGC Spatial Data on the Web Working Group.

To identify sensitive genes for space radiation, we integrated the transcriptomic samples of spaceflight mice from GeneLab and predicted the radiation doses absorbed by individuals in space. A single-sample network (SSN) for each individual sample was constructed. Then, using machine learning and genetic algorithms, we built the regression models to predict the absorbed dose equivalent based on the topological structure of SSNs. Moreover, we analyzed the SSNs from each tissue and compared the similarities and differences among them. Our model exhibited excellent performance with the following metrics: R2=0.980, MSE=6.74e−04, and the Pearson correlation coefficient of 0.990 (p value

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Submarine Market Size 2024-2028

The submarine market size is forecast to increase by USD 9.56 billion at a CAGR of 5.52% between 2023 and 2028.

Fleet replacement programs are the key driver of the submarine market, as nations look to modernize their naval forces with advanced, more capable submarines. The upcoming trend is the development of multi-mission submarines. These versatile submarines are designed to perform a range of operations, including intelligence gathering, surveillance, reconnaissance, and combat, making them more adaptable and cost-effective for various military needs. 
Furthermore, the increasing demand for submarine power cables and submarine fiber cables is influencing the industry's development, highlighting the need for advanced infrastructure. These factors are shaping the market's future, making it a compelling space for stakeholders and investors. The market analysis report provides a comprehensive overview of these trends and challenges, offering valuable insights for businesses looking to capitalize on growth opportunities.

What will be the Size of the Submarine Market During the Forecast Period?

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The market encompasses the production and sale of submarines for various applications, primarily In the defense sector. Key drivers in this market include new submarine acquisitions by defense forces worldwide and the need for advanced underwater detection systems to secure maritime boundaries and offshore resources. High-strength alloy steel and titanium are commonly used in submarine construction due to their durability and resistance to corrosion. 
Layoffs and order deliveries can impact the market, while geopolitical tensions and military systems modernization fuel demand. Technological advancements continue to shape the market, with innovations in propulsion, sonar systems, and missile attacks. Seafaring nations with significant submarine fleets, such as those involved In the nuclear triad or maritime disputes, are major players.
Submarines serve diverse roles, from covert operations and surprise attacks to deterrents against naval forces and groundwater bodies In the ocean coast and offshore territories.

How is this Submarine Industry segmented and which is the largest segment?

The submarine industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Type

  SSN
  SSBN
  SSK


Application

  Military
  Commercial


Geography

  North America

    US


  APAC

    China
    Japan
    South Korea


  Europe



  Middle East and Africa



  South America

By Type Insights

The SSN segment is estimated to witness significant growth during the forecast period. Nuclear-powered submarines (SSNs) offer military forces significant advantages with their independent power source, enabling prolonged underwater operations without the need for frequent surface visits. The nuclear propulsion system delivers increased power for high-speed travel and extended voyage times, making SSNs an essential component of naval military segments. Despite controversy surrounding their deployment due to nuclear proliferation concerns, the demand for SSNs remains strong due to their superior capabilities. These submarines can host advanced weapons and sensors, extend their operational life beyond 25 years without refueling, and operate covertly in enemy territories or offshore resources. Technological advancements, including nuclear technology, 3D printing, and complex submarine parts like missile fins and guidance systems, contribute to the cost-effectiveness and strategic importance of SSNs.

The defense sector continues to invest in submarine capabilities, with acquisition programs and military budgets prioritizing the development of attack submarines, combat SSNs, and crewless vessels. The naval industry's focus on strengthening submarine capabilities includes the integration of intelligence, surveillance, and reconnaissance roles, as well as addressing maritime disputes, geopolitical conflicts, and naval forces.

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The SSN segment was valued at USD 10.74 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

APAC is estimated to contribute 42% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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The North American the market is predominantly driven by the United States, which holds the largest market share due to its substantial defense budget and strate

The EMI ontology is used to structure spectrum annotation provenance by reusing the PROV-O ontology (a W3C recommendation) and sample and observation data by applying the SOSA ontology. EMI reuses the SOSA ontology as a data schema for struturing the Sample and Observation data. SOSA (Sensor, Observation, Sample, and Actuator) is a subset of SSN (Semantic Sensor Network Ontology) that is a W3C recommendation. [from homepage]

Description:This data deposit contains the Numerical Identification Death Files (National Archives Identifier 23845618), the NUMIDENT SS-5 Application Files (National Archives Identifier 23845613), the NUMIDENT Claims Files (National Archives Identifier 23852747), and the associated technical documentation. Data Acquisition:These files were e-delivered to Anthony Wray via secure link by the Electronic Records Division of the National Archives and Records Administration (NARA) on 17 October 2019, as per a digitized reproduction order (Quote QO1-525370500 and Quote QO1-528389077). The packing slip is included in the data deposit (docs/Packing Slip.PDF).Rights to Publish:The data are in the public domain, as confirmed by emails received from NARA on 28 December 2023 and 3 January 2024 (see docs/permission_to_publish_email.pdf).How to Cite: Please adhere to the citation and data usage guidelines when using this dataset. See the included LICENSE.txt and README.md files for details. Details:The Numerical Identification Files (NUMIDENT), 1936–2007, series contains records for every Social Security number (SSN) assigned to individuals with a verified death or who would have been over 110 years old by December 31, 2007. There are three types of entries in NUMIDENT: application (SS-5), claim, and death records. A NUMIDENT record may contain more than one entry. Information contained in NUMIDENT records includes: each applicant's full name, SSN, date of birth, place of birth, citizenship, sex, father's name, mother's maiden name, and race/ethnic description (optional). NUMIDENT includes information regarding any subsequent changes made to the applicant's record, including name changes and life or death claims. The death records in NUMIDENT do not include any State reported deaths in accordance with the Social Security Act section 205(r). There are 72,182,729 SS-5 records entries; 25,230,486 claim record entries; and 49,459,293 death record entries.See https://catalog.archives.gov/id/12004494 for more information.Related Data:Visit the CenSoc Project for public micro datasets linked to NUMIDENT: https://censoc.berkeley.edu/.

Contact: Jakob Franke, jakob.franke@botanik.uni-hannover.deDescription of files:1 - OSC search scriptContains all files for searching OSC sequences from the 1,000 plant transcriptome (1KP) data (https://www.nature.com/articles/s41586-019-1693-2 and https://gigascience.biomedcentral.com/articles/10.1186/2047-217X-3-17)1 - OSC search script / 230418_osc_mining_blast100_hmmer30.RScript for extracting OSC sequences from 1KP data.Important: All sequences to be screened must be provided in subfolder "sequences" as amino acid sequence fasta files. For 1KP data, there should be 1455 files named XXXX-translated-protein.fa. Assembled 1KP data is available here: https://sites.google.com/a/ualberta.ca/onekp and https://drive.google.com/drive/folders/175nB8kf1UQushuEzv7UaJLPNNwdOrxh51 - OSC search script / input / 1kp sample list.xlsxOriginally provided sample list from 1KP: https://sites.google.com/a/ualberta.ca/onekp/home-page1 - OSC search script / input / reference_OSCs_Chen_NPR.fastaAmino acid sequences of 170 reference OSCs reported by Chen et al. in this paper: https://pubs.rsc.org/en/content/articlelanding/2021/np/d1np00015b1 - OSC search script / output / 1kp_sample_list_with_OSCs_blast100_hmmer30.xlsxExtended version of 1kp sample list.xlsx generated by the R script. Includes the numbers of OSCs found by different search strategies.1 - OSC search script / output / OSCs_blast100_hmmer30.xlsxTable with names and further data for all OSC hits found by the R script.1 - OSC search script / output / OSCs_full_length_blast100_hmmer30.faFasta file with amino acid sequences of all full length OSCs found by the R script (full length is defined in the R script as full_length_cutoff; here 700 AA)1 - OSC search script / output / OSCs_total_blast100_hmmer30.faFasta file with amino acid sequences of all OSCs found by the R script, not just full length. Includes many partial sequences which are most likely artefacts.2 - SSN analysisContains all files for analysing OSC sequences with sequence similarity networks.2 - SSN analysis / 123887_240429_OSCs_references_e360_repnode-1.00_ssn.xgmmlOutput from EFI-EST website generated with "Option C - Fasta" with fasta file 230425_OSCs+references.fa, an E value of 360, and representative node network at 100% ID: https://efi.igb.illinois.edu/efi-est/Analogous xgmml files for E 350 and 370 (shown in SI) are provided.2 - SSN analysis / 230425_OSCs+references.faInput fasta sequence file for EFI-EST website: https://efi.igb.illinois.edu/efi-est/Combines reference sequences from reference_OSCs_Chen_NPR.fasta and found OSC sequences from OSCs_full_length_blast100_hmmer30.fa (see above).2 - SSN analysis / 240429_full_length_OSCs_blast100_hmmer30+references_shared_names.xlsxAdjusted Cytoscape node table for visual annotation of SSN.2 - SSN analysis / 240430 SSN network.cysCytoscape (3.10.2) session containing networks, styles, and adjusted node table used in the manuscript. Visualisation using the yFiles Organic Layout (version 1.1.4).

Demographics of the samples for analyses.