Currently active biological databases aiming to archive data related to oral biology.

Content of the Bioinformatics for Dentistry, with its respective primary sources.

The datasets (hgnc_complete_set and withdrawn) used to create this ID mapping database were downloaded from HGNC (HUGO Gene Nomenclature Committee at the European Bioinformatics Institute, website URL: https://www.genenames.org/) on 09/05/2022.

This database was used for the BridgeDb demo at BioSB 2022 conference.

The scripts used to create this database based on HGNC: https://github.com/tabbassidaloii/create-bridgedb-secondary2primary

This work was funded by the FAIRplus project (grant agreement no 802750) and NWO Open Science Fund (grant no 203.001.121).

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Primary roads are generally divided, limited-access highways within the interstate highway system or under State management, and are distinguished by the presence of interchanges. These highways are accessible by ramps and may include some toll highways. The MAF/TIGER Feature Classification Code (MTFCC) is S1100 for primary roads. Secondary roads are main arteries, usually in the U.S. Highway, State Highway, and/or County Highway system. These roads have one or more lanes of traffic in each direction, may or may not bedivided, and usually have at-grade intersections with many other roads and driveways. They usually have both a local name and a route number. The MAF/TIGER Feature Classification Code (MTFCC) is S1200 for secondary roads.

Link: https://figshare.com/articles/dataset/S2_Table/25546426. (XLSX)

The data (hmdb_metabolites, released on 17/11/2021) used to create this ID mapping database was downloaded from HMDB (Human Metabolome Database, website URL: https://hmdb.ca/).

This database was used for the BridgeDb demo at BioSB 2022 conference.

The scripts used to create this database based on HGNC: https://github.com/tabbassidaloii/create-bridgedb-secondary2primary

This work was funded by the FAIRplus project (grant agreement no 802750) and NWO Open Science Fund (grant no 203.001.121).

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Primary roads are generally divided, limited-access highways within the interstate highway system or under State management, and are distinguished by the presence of interchanges. These highways are accessible by ramps and may include some toll highways. The MAF/TIGER Feature Classification Code (MTFCC) is S1100 for primary roads. Secondary roads are main arteries, usually in the U.S. Highway, State Highway, and/or County Highway system. These roads have one or more lanes of traffic in each direction, may or may not bedivided, and usually have at-grade intersections with many other roads and driveways. They usually have both a local name and a route number. The MAF/TIGER Feature Classification Code (MTFCC) is S1200 for secondary roads.

Dataset

This dataset was created by sadi ela

Released under Database: Open Database, Contents: Database Contents

Contents

The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Primary roads are generally divided, limited-access highways within the interstate highway system or under State management, and are distinguished by the presence of interchanges. These highways are accessible by ramps and may include some toll highways. The MAF/TIGER Feature Classification Code (MTFCC) is S1100 for primary roads. Secondary roads are main arteries, usually in the U.S. Highway, State Highway, and/or County Highway system. These roads have one or more lanes of traffic in each direction, may or may not be divided, and usually have at-grade intersections with many other roads and driveways. They usually have both a local name and a route number. The MAF/TIGER Feature Classification Code (MTFCC) is S1200 for secondary roads.

PROSITE consists of documentation entries describing protein domains, families and functional sites as well as associated patterns and profiles to identify them [More... / References / Commercial users ]. PROSITE is complemented by ProRule , a collection of rules based on profiles and patterns, which increases the discriminatory power of profiles and patterns by providing additional information about functionally and/or structurally critical amino acids [More...].

Abstract

Well-functioning financial systems serve a vital purpose, offering savings, credit, payment, and risk management products to people with a wide range of needs. Yet until now little had been known about the global reach of the financial sector - the extent of financial inclusion and the degree to which such groups as the poor, women, and youth are excluded from formal financial systems. Systematic indicators of the use of different financial services had been lacking for most economies.

The Global Financial Inclusion (Global Findex) database provides such indicators. This database contains the first round of Global Findex indicators, measuring how adults in more than 140 economies save, borrow, make payments, and manage risk. The data set can be used to track the effects of financial inclusion policies globally and develop a deeper and more nuanced understanding of how people around the world manage their day-to-day finances. By making it possible to identify segments of the population excluded from the formal financial sector, the data can help policy makers prioritize reforms and design new policies.

Geographic coverage

The sample excludes the Northeast states and remote islands. The excluded area represents approximately 10% of the total adult population.

Analysis unit

Individual

Universe

The target population is the civilian, non-institutionalized population 15 years and above.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Global Findex indicators are drawn from survey data collected by Gallup, Inc. over the 2011 calendar year, covering more than 150,000 adults in 148 economies and representing about 97 percent of the world's population. Since 2005, Gallup has surveyed adults annually around the world, using a uniform methodology and randomly selected, nationally representative samples. The second round of Global Findex indicators was collected in 2014 and is forthcoming in 2015. The set of indicators will be collected again in 2017.

Surveys were conducted face-to-face in economies where landline telephone penetration is less than 80 percent, or where face-to-face interviewing is customary. The first stage of sampling is the identification of primary sampling units, consisting of clusters of households. The primary sampling units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households by means of the Kish grid.

Surveys were conducted by telephone in economies where landline telephone penetration is over 80 percent. The telephone surveys were conducted using random digit dialing or a nationally representative list of phone numbers. In selected countries where cell phone penetration is high, a dual sampling frame is used. Random respondent selection is achieved by using either the latest birthday or Kish grid method. At least three attempts are made to teach a person in each household, spread over different days and times of year.

The sample size in India was 3,518 individuals.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup, Inc. also provided valuable input. The questionnaire was piloted in over 20 countries using focus groups, cognitive interviews, and field testing. The questionnaire is available in 142 languages upon request.

Questions on insurance, mobile payments, and loan purposes were asked only in developing economies. The indicators on awareness and use of microfinance insitutions (MFIs) are not included in the public dataset. However, adults who report saving at an MFI are considered to have an account; this is reflected in the composite account indicator.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country- and indicator-specific standard errors, refer to the Annex and Country Table in Demirguc-Kunt, Asli and L. Klapper. 2012. "Measuring Financial Inclusion: The Global Findex." Policy Research Working Paper 6025, World Bank, Washington, D.C.

Data were provided by geological Survey of Serbia - GSS.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Primary roads are generally divided, limited-access highways within the interstate highway system or under State management, and are distinguished by the presence of interchanges. These highways are accessible by ramps and may include some toll highways. The MAF/TIGER Feature Classification Code (MTFCC) is S1100 for primary roads. Secondary roads are main arteries, usually in the U.S. Highway, State Highway, and/or County Highway system. These roads have one or more lanes of traffic in each direction, may or may not bedivided, and usually have at-grade intersections with many other roads and driveways. They usually have both a local name and a route number. The MAF/TIGER Feature Classification Code (MTFCC) is S1200 for secondary roads.

Dataset

This dataset was created by Lay Christian

Released under Database: Open Database, Contents: © Original Authors

Contents

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CyanoMetDB is a comprehensive database of secondary metabolites from cyanobacteria manually curated from primary references described in Jones et al (2021), DOI: 10.1016/j.watres.2021.117017 (preprint DOI: 10.1101/2020.04.16.038703). This upload contains the 2023 release. Please cite Jones et al (2021) DOI: 10.1016/j.watres.2021.117017 and this record Janssen et al (2023) DOI: 10.5281/zenodo.7922070 when using this CyanoMetDB Version 2!

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The Missouri Spatial Data Information Service (MSDIS) has modified the selected dataset to display data for the state of Missouri for distribution and use. It is updated annually as new data are published by the United States Census Bureau. For complete metadata, please download the original file at https://msdis-archive.missouri.edu/archive/Missouri_Vector_Data/TIGER_Data/

The working conditions for teachers in Vietnam were characterized by increased workload and pressure, burdening teachers’ well-being. The study aims to investigate anxiety prevalence and identify some related factors among primary and secondary school teachers in Hanoi after the first COVID-19 outbreak in 2020. This paper analyzed data of 481 teachers working at ten primary and secondary schools in Hanoi city. Anxiety was measured using the anxiety component of the Depression, Anxiety, and Stress scale 42 items. Multivariable logistics regression was performed to examine anxiety-related factors using SPSS 20.0 at a significant level p less than 0.05. The prevalence of anxiety symptoms was 42.4% and similar between primary and secondary school teachers. More secondary teachers reported moderate to severe anxiety symptoms than primary teachers did (31.6% and 27.7%). Primary school teachers who felt discomfort with their supervisor’s assessment, high responsibility for student safety, and ever thinking of leaving their current job were more likely to report anxiety symptoms (OR (95%CI) = 2.8 (1.2–6.5), 3.6 (1.0–12.8), and 2.6 (1.3–5.4), respectively). Meanwhile, the discomfort of caring for many students or problematic students, repetitive work, and disagreement with coworkers were risk factors of anxiety among secondary school teachers (OR (95%CI) = 2.6 (1.2–5.8), 3.2 (1.1–9.2), 3.4 (1.3–8.8), and 3.7 (1.1–12.6), respectively). In conclusion, the prevalence of teachers with anxiety symptoms is on the rise, caused by the characteristics of the job and professional relationships. Tailored support for teachers in different grades is necessary to improve and prevent teachers’ anxiety.

Additional file 1: Table S1. Presence/absence of flag-2 loci among 4028 strains belonging to eight families and 72 genera. The presence of flag-1 loci is also indicated. The previous taxonomy denotes the taxonomy according to the NCBI genome database, while the current taxonomy is according to the Genome Taxonomy Database (GTDB). The isolation source as well as habitat/lifestyles of the different strains are given. Table S2. Molecular characteristics of the flag-2 loci among 592 taxa in the Enterobacterales. The sizes of the flag-2 loci, variable regions VR1 and VR2, their G + C contents (%) and G + C deviation (%) from the genome are shown. The number of predicted proteins encoded on each of these flag-2 fractions are also shown. Table S3. Characteristics of the cargo genes encoded in the variable regions VR1 and VR2 and elsewhere in the enterobacterial flag-2 loci. The number of strains and the families/genera in which each protein occurs within the flag-2 loci are indicated, as well as the average amino acid identities among enterobacterial orthologues. Conserved domains present in each cargo protein as determined by BlastP analysis against the Conserved Domain Database are shown.

The purpose of this study was to evaluate the prognostic value of serum uric acid levels in a large cohort of men and women at high risk of cardiovascular disease. Serum uric acid levels were determined in all patients seen for primary/secondary cardiovascular disease prevention at the Cleveland Clinic Section of Preventive Cardiology and Rehabilitation between 1998 and 2004, and all data were entered into the Preventive Cardiology Information System (PreCIS) database. Vital status of the patients was determined through the Social Security Death Index. A total of 2,003 men and 1,095 women with a mean age of 55.4 years, with a range of 18 to 87 years, were included.