Search for a business by name. You can obtain business information and then proceed to purchase a certificate of good standing or other documents. The purpose of this search is simply to determine whether a company/entity exists and to provide basic information on the company/entity.

In order to facilitate the anonymisation of data, this list of first names and surnames was extracted from the SIRENE database of INSEE.

For each first name and surname, the number of appearances is indicated.

ATTENTION: No content check is done, and these lists may contain anomalies present in the original database!

Name Find Source LLC Whois Database, discover comprehensive ownership details, registration dates, and more for Name Find Source LLC with Whois Data Center.

The data (name, year of birth, sex, and number) are from a 100 percent sample of Social Security card applications for 1880 on.

Popular Baby Names by Sex and Ethnic Group Data were collected through civil birth registration. Each record represents the ranking of a baby name in the order of frequency. Data can be used to represent the popularity of a name. Caution should be used when assessing the rank of a baby name if the frequency count is close to 10; the ranking may vary year to year.

Investigate historical ownership changes and registration details by initiating a reverse Whois lookup for the name Database Marketing.

Content

This Data set contains various files containing over 1000 names and their respective gender information. This can be used to predict Gender using phonics from their respective names.

Inspiration

Searching for a person's name in a database is a unique challenge. Depending on the source and age of the data, you may not be able to count on the spelling of the name being correct, or even the same name being spelled the same way when it appears more than once. Discrepancies between stored data and search terms may be introduced due to personal choice or cultural differences in spellings, homophones, transcription errors, illiteracy, or simply lack of standardized spellings during some time periods. These sorts of problems are especially prevalent in transcriptions of handwritten historical records used by historians, genealogists, and other researchers.

A common way to solve the string-search problem is to look for values that are "close" to the same as the search target. Using a traditional fuzzy match algorithm to compute the closeness of two arbitrary strings is expensive, though, and it isn't appropriate for searching large data sets. A better solution is to compute hash values for entries in the database in advance, and several special hash algorithms have been created for this purpose. These phonetic hash algorithms allow you to compare two words or names based on how they sound, rather than the precise spelling.

Early Efforts: Soundex One such algorithm is Soundex, developed by Margaret K. Odell and Robert C. Russell in the early 1900s. The Soundex algorithm appears frequently in genealogical contexts because it's associated with the U.S. Census and is specifically designed to encode names. A Soundex hash value is calculated by using the first letter of the name and converting the consonants in the rest of the name to digits by using a simple lookup table. Vowels and duplicate encoded values are dropped, and the result is padded up to—or truncated down to—four characters.

The Fuzzy library includes a Soundex implementation for Python programs

This dataset can be used to explore the power of Fuzzy Source

This dataset contains 5M forenames (first names) and 8M surnames (last names) from 105 different countries. They are annotated with gender, country and the amount of occurrences in the original data. Since most names appear in multiple countries and sometimes not just for one gender, one name typically has multiple rows. The data is aggregated from https://github.com/philipperemy/name-dataset?tab=readme-ov-file#full-dataset .

A list of all countries can be found in "country_codes.csv".

If you want to look at names from your country, go to either "forenames.csv" or "surnames.csv" and click on the three horizontal bars in the head of the country column. Then search for your country_code with two capital letters and click apply.

This dataset contains a listing of individuals who have had their name formally changed in Ontario.

This data is made publicly available through the Ontario Gazette.

Investigate historical ownership changes and registration details by initiating a reverse Whois lookup for the name Digital database identification system.

This application provides information for active, inactive, and pre-registered firms. Query options are by FEI, Applicant Name, Establishment Name, Other Names, Establishment Type, Establishment Status, City, State, Zip Code, Country and Reporting Official First Name or Last Name.

• The first names file contains data on the first names given to children born in France between 1900 and 2021. This data is available at the France level and by department.
• The names file contains data on names by decade of birth from 1891 to 2000. These data are available at the France level and by department.

The Canadian Geographical Names Data Base (CGNDB) is the authoritative national database of Canada's geographical names. The purpose of the CGNDB is to store place names and their attributes that have been approved by the Geographical Names Board of Canada (GNBC), the national coordinating body responsible for standards and policies on place names. The CGNDB is maintained by Natural Resources Canada, through the Canada Centre for Mapping and Earth Observation. The geographic extent of the CGNDB is the Canadian landmass and water bodies; the temporal extent is from 1897 to present. This dataset is extracted from the CGNDB on a weekly basis, and consists of current officially approved names, feature type, coordinates of the feature, decision date, source, and other attributes. The output file formats for this product are: text (CSV), Shape (SHP), and Keyhole Markup Language (KML). Content advisory: The Canadian Geographical Names Database contains historical terminology that is considered racist, offensive and derogatory. Geographical naming authorities are in the process of addressing many offensive place names, but the work is still ongoing. For more information, please contact the GNBC Secretariat.

The Washington State Department of Health presents this information as a service to the public. True and correct copies of legal disciplinary actions taken after July 1998 are available on our Provider Credential Search site. These records are considered certified by the Department of Health.

This includes information on health care providers.

Please contact our Customer Service Center at 360-236-4700 for information about actions before July 1998. The information on this site comes directly from our database and is updated daily at 10:00 a.m.. This data is a primary source for verification of credentials and is extracted from the primary database at 2:00 a.m. daily.

News releases about disciplinary actions taken against Washington State healthcare providers, agencies or facilities are on the agency's Newsroom webpage.

Disclaimer The absence of information in the Provider Credential Search system doesn't imply any recommendation, endorsement or guarantee of competence of any healthcare professional. The presence of information in this system doesn't imply a provider isn't competent or qualified to practice. The reader is encouraged to carefully evaluate any information found in this data set.

Investigate historical ownership changes and registration details by initiating a reverse Whois lookup for the name Web Database.

Dbfetch is an acronym for database fetch. Dbfetch provides an easy way to retrieve entries from various databases at the EBI in a consistent manner and allows you to retrieve up to 50 entries at a time from various up-to-date biological databases. It can be used from any browser as well as well as within a web-aware scripting tool that uses wget, lynx or similar. From the browser, follow these instructions... * Select a database: If you are using the first form to paste your search items: choose a database name from this form. If you are using the second form to upload your search items: the database name is included at the beginning of each line line of the upload file followed by a colon. * Enter search terms: These MUST BE in the appropriate database format, up to 200 search items can be queried in one run. If you are using the first form: separate search items with a comma or space. If you are using the second form: separate search items with a new line. * Choose an output format: Here you can choose the simpler fasta format, or the databases'''' default format for the chosen database. * Style: You can get your results as text or html. * Retrieve! - You are now ready to fetch your results, by pressing the Retrieve button.

United States agricultural researchers have many options for making their data available online. This dataset aggregates the primary sources of ag-related data and determines where researchers are likely to deposit their agricultural data. These data serve as both a current landscape analysis and also as a baseline for future studies of ag research data. Purpose As sources of agricultural data become more numerous and disparate, and collaboration and open data become more expected if not required, this research provides a landscape inventory of online sources of open agricultural data. An inventory of current agricultural data sharing options will help assess how the Ag Data Commons, a platform for USDA-funded data cataloging and publication, can best support data-intensive and multi-disciplinary research. It will also help agricultural librarians assist their researchers in data management and publication. The goals of this study were to establish where agricultural researchers in the United States-- land grant and USDA researchers, primarily ARS, NRCS, USFS and other agencies -- currently publish their data, including general research data repositories, domain-specific databases, and the top journals compare how much data is in institutional vs. domain-specific vs. federal platforms determine which repositories are recommended by top journals that require or recommend the publication of supporting data ascertain where researchers not affiliated with funding or initiatives possessing a designated open data repository can publish data Approach The National Agricultural Library team focused on Agricultural Research Service (ARS), Natural Resources Conservation Service (NRCS), and United States Forest Service (USFS) style research data, rather than ag economics, statistics, and social sciences data. To find domain-specific, general, institutional, and federal agency repositories and databases that are open to US research submissions and have some amount of ag data, resources including re3data, libguides, and ARS lists were analysed. Primarily environmental or public health databases were not included, but places where ag grantees would publish data were considered. Search methods We first compiled a list of known domain specific USDA / ARS datasets / databases that are represented in the Ag Data Commons, including ARS Image Gallery, ARS Nutrition Databases (sub-components), SoyBase, PeanutBase, National Fungus Collection, i5K Workspace @ NAL, and GRIN. We then searched using search engines such as Bing and Google for non-USDA / federal ag databases, using Boolean variations of “agricultural data” /“ag data” / “scientific data” + NOT + USDA (to filter out the federal / USDA results). Most of these results were domain specific, though some contained a mix of data subjects. We then used search engines such as Bing and Google to find top agricultural university repositories using variations of “agriculture”, “ag data” and “university” to find schools with agriculture programs. Using that list of universities, we searched each university web site to see if their institution had a repository for their unique, independent research data if not apparent in the initial web browser search. We found both ag specific university repositories and general university repositories that housed a portion of agricultural data. Ag specific university repositories are included in the list of domain-specific repositories. Results included Columbia University – International Research Institute for Climate and Society, UC Davis – Cover Crops Database, etc. If a general university repository existed, we determined whether that repository could filter to include only data results after our chosen ag search terms were applied. General university databases that contain ag data included Colorado State University Digital Collections, University of Michigan ICPSR (Inter-university Consortium for Political and Social Research), and University of Minnesota DRUM (Digital Repository of the University of Minnesota). We then split out NCBI (National Center for Biotechnology Information) repositories. Next we searched the internet for open general data repositories using a variety of search engines, and repositories containing a mix of data, journals, books, and other types of records were tested to determine whether that repository could filter for data results after search terms were applied. General subject data repositories include Figshare, Open Science Framework, PANGEA, Protein Data Bank, and Zenodo. Finally, we compared scholarly journal suggestions for data repositories against our list to fill in any missing repositories that might contain agricultural data. Extensive lists of journals were compiled, in which USDA published in 2012 and 2016, combining search results in ARIS, Scopus, and the Forest Service's TreeSearch, plus the USDA web sites Economic Research Service (ERS), National Agricultural Statistics Service (NASS), Natural Resources and Conservation Service (NRCS), Food and Nutrition Service (FNS), Rural Development (RD), and Agricultural Marketing Service (AMS). The top 50 journals' author instructions were consulted to see if they (a) ask or require submitters to provide supplemental data, or (b) require submitters to submit data to open repositories. Data are provided for Journals based on a 2012 and 2016 study of where USDA employees publish their research studies, ranked by number of articles, including 2015/2016 Impact Factor, Author guidelines, Supplemental Data?, Supplemental Data reviewed?, Open Data (Supplemental or in Repository) Required? and Recommended data repositories, as provided in the online author guidelines for each the top 50 journals. Evaluation We ran a series of searches on all resulting general subject databases with the designated search terms. From the results, we noted the total number of datasets in the repository, type of resource searched (datasets, data, images, components, etc.), percentage of the total database that each term comprised, any dataset with a search term that comprised at least 1% and 5% of the total collection, and any search term that returned greater than 100 and greater than 500 results. We compared domain-specific databases and repositories based on parent organization, type of institution, and whether data submissions were dependent on conditions such as funding or affiliation of some kind. Results A summary of the major findings from our data review: Over half of the top 50 ag-related journals from our profile require or encourage open data for their published authors. There are few general repositories that are both large AND contain a significant portion of ag data in their collection. GBIF (Global Biodiversity Information Facility), ICPSR, and ORNL DAAC were among those that had over 500 datasets returned with at least one ag search term and had that result comprise at least 5% of the total collection. Not even one quarter of the domain-specific repositories and datasets reviewed allow open submission by any researcher regardless of funding or affiliation. See included README file for descriptions of each individual data file in this dataset. Resources in this dataset:Resource Title: Journals. File Name: Journals.csvResource Title: Journals - Recommended repositories. File Name: Repos_from_journals.csvResource Title: TDWG presentation. File Name: TDWG_Presentation.pptxResource Title: Domain Specific ag data sources. File Name: domain_specific_ag_databases.csvResource Title: Data Dictionary for Ag Data Repository Inventory. File Name: Ag_Data_Repo_DD.csvResource Title: General repositories containing ag data. File Name: general_repos_1.csvResource Title: README and file inventory. File Name: README_InventoryPublicDBandREepAgData.txt

Street Name Master List - contains all the reserved and active street names.

This is the HQSNP DB (high-quality SNP database) developed by CHG bioinformatics group. The high-quality SNP is defined as a SNP having allele frequency or genotyping data. The majority of the HQSNPs come from HapMap, others come from JSNP (Japanese SNP database), TSC (The SNP Consortium), Affymetrix 120K SNP, and Perlegen SNP. There are four kinds of SNP search you can do: * Get SNPs by dbSNP rs#: Choose this search if you have already selected a list of SNPs and you just want to get the SNP information. The program will generate a Excel file containing the SNP flanking sequence, variation, quality, function, etc. In the Excel file, there are 10 highlighted fields. You can send only those highlighted information to Illumina to get SNP pre-score. (The same fields are presented in other types of searches as well.) * Get gene SNPs by gene names: Choose this search if you have a list of gene names and you want to get the SNP information in these genes. The gene name can be official gene symbol, Ensembl gene ID, RefSeq accession ID, LocusLink number, etc. * Get gene SNPs by genome regions: Choose this search if you have a list of genome regions and you want to get all gene SNP information in these regions. The software will find all the Ensembl genes in the regions and find SNPs associated to each Ensembl gene. * Get genome scan SNPs by genome regions: Choose this search if you have a list of genome regions and you want to get evenly spaced SNPs in these regions. A SNP selection tool (SNPselector) was built upon HQSNP. It took snp ID list, gene name list, or genome region list as input and searched SNPs for genome scan or gene assoctiation study. It could take an optional ABI SNP file (exported from ABI SNP search web page) as input for checking whether the candidate SNP is available from ABI. It could also take an optional Illumina SNP pre-score file as input to select SNP for Illumina SNP assay. It generated results sorted by tag SNP in LD block, SNP quality, SNP function, SNP regulatory potential, and SNP mutation risk. SNPselector is now retired from public use (as of September 30, 2010).

Address Lookup Service is a web service providing lookup function on Hong Kong address records in both Chinese and English aggregated from various Government Bureaux/Departments. This web service allows data consumers to look up address records in machine-readable format (XML or JSON) using address element information. It aims to facilitate development of applications with the need to capture Hong Kong address information more efficiently and accurately. The Address Lookup Service provides formatted addresses of the premises in Hong Kong, including private and public housing estates, commercial and industrial buildings, government buildings and offices, markets and shopping centers; and common facilities such as recreation and sports centres. Some addresses provided are representing a complex of buildings, such as schools or universities. Most of the addresses in Address Lookup Service are available in 2-Dimensional format which typically includes up to street name, building number and building name. 3-Dimensional formatted addresses, such as addresses include flat number and floor number, they are only available for public housing estates. For more details, please refer the Data Dictionary. This service also includes unofficial descriptions of buildings which are long established addresses in rural areas of the New Territories and are generally accepted by the public. The choice of name for a building is a matter for the owner, and at present there is no controlling legislation. The inclusion of a building name in this service confers no proprietary right to it or any part of it.