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

Database that collects, integrates and links all relevant primary information from the GABI plant genome research projects and makes them accessible via internet. Its purpose is to support plant genome research in Germany, to yield information about commercial important plant genomes, and to establish a scientific network within plant genomic research. GreenCards is the main interface for text based retrieval of sequence, SNP, mapping data etc. Sharing and interchange of data among collaborating research groups, industry and the patent- and licensing agency are facilitated. * GreenCards: Text based search for sequence, mapping, SNP data etc. * Maps: Visualization of genetic or physical maps. * BLAST: Secure BLAST search against different public databases or non-public sequence data stored in GabiPD. * Proteomics: View interactive 2D-gels and view or download information for identified protein spots. Registered users can submit data via secure file upload.

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

Pupil Identity Data and School Enrolment Data for all primary school learners

A database of three-dimensional structural information about nucleic acids and their complexes. In addition to primary data, it contains derived geometric data, classifications of structures and motifs, standards for describing nucleic acid features, as well as tools and software for the analysis of nucleic acids. A variety of search capabilities are available, as are many different types of reports. NDB maintains the macromolecular Crystallographic Information File (mmCIF).

This dataset includes bibliographic information for 501 papers that were published from 2010-April 2017 (time of search) and use online biodiversity databases for research purposes. Our overarching goal in this study is to determine how research uses of biodiversity data developed during a time of unprecedented growth of online data resources. We also determine uses with the highest number of citations, how online occurrence data are linked to other data types, and if/how data quality is addressed. Specifically, we address the following questions:

1.) What primary biodiversity databases have been cited in published research, and which

 databases have been cited most often?

2.) Is the biodiversity research community citing databases appropriately, and are

 the cited databases currently accessible online?

3.) What are the most common uses, general taxa addressed, and data linkages, and how

 have they changed over time?

4.) What uses have the highest impact, as measured through the mean number of citations

 per year?

5.) Are certain uses applied more often for plants/invertebrates/vertebrates?

6.) Are links to specific data types associated more often with particular uses?

7.) How often are major data quality issues addressed?

8.) What data quality issues tend to be addressed for the top uses?

Relevant papers for this analysis include those that use online and openly accessible primary occurrence records, or those that add data to an online database. Google Scholar (GS) provides full-text indexing, which was important to identify data sources that often appear buried in the methods section of a paper. Our search was therefore restricted to GS. All authors discussed and agreed upon representative search terms, which were relatively broad to capture a variety of databases hosting primary occurrence records. The terms included: “species occurrence” database (8,800 results), “natural history collection” database (634 results), herbarium database (16,500 results), “biodiversity database” (3,350 results), “primary biodiversity data” database (483 results), “museum collection” database (4,480 results), “digital accessible information” database (10 results), and “digital accessible knowledge” database (52 results)--note that quotations are used as part of the search terms where specific phrases are needed in whole. We downloaded all records returned by each search (or the first 500 if there were more) into a Zotero reference management database. About one third of the 2500 papers in the final dataset were relevant. Three of the authors with specialized knowledge of the field characterized relevant papers using a standardized tagging protocol based on a series of key topics of interest. We developed a list of potential tags and descriptions for each topic, including: database(s) used, database accessibility, scale of study, region of study, taxa addressed, research use of data, other data types linked to species occurrence data, data quality issues addressed, authors, institutions, and funding sources. Each tagged paper was thoroughly checked by a second tagger.

The final dataset of tagged papers allow us to quantify general areas of research made possible by the expansion of online species occurrence databases, and trends over time. Analyses of this data will be published in a separate quantitative review.

TSA is an archive of computationally assembled transcript sequences from primary data such as ESTs and Next Generation Sequencing Technologies. The overlapping sequence reads from a complete transcriptome are assembled into transcripts by computational methods instead of by traditional cloning and sequencing of cloned cDNAs. The primary sequence data used in the assemblies must have been experimentally determined by the same submitter. TSA sequence records differ from GenBank records because there are no physical counterparts to the assemblies.

Operationalization of problematic pornography use.

Pupil Identity Data and School Enrolment Data for all post-primary school learners

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 results of analysis of shotgun proteomics mass spectrometry data can be greatly affected by the selection of the reference protein sequence database against which the spectra are matched. For many species there are multiple sources from which somewhat different sequence sets can be obtained. This can lead to confusion about which database is best in which circumstancesa problem especially acute in human sample analysis. All sequence databases are genome-based, with sequences for the predicted gene and their protein translation products compiled. Our goal is to create a set of primary sequence databases that comprise the union of sequences from many of the different available sources and make the result easily available to the community. We have compiled a set of four sequence databases of varying sizes, from a small database consisting of only the ∼20,000 primary isoforms plus contaminants to a very large database that includes almost all nonredundant protein sequences from several sources. This set of tiered, increasingly complete human protein sequence databases suitable for mass spectrometry proteomics sequence database searching is called the Tiered Human Integrated Search Proteome set. In order to evaluate the utility of these databases, we have analyzed two different data sets, one from the HeLa cell line and the other from normal human liver tissue, with each of the four tiers of database complexity. The result is that approximately 0.8%, 1.1%, and 1.5% additional peptides can be identified for Tiers 2, 3, and 4, respectively, as compared with the Tier 1 database, at substantially increasing computational cost. This increase in computational cost may be worth bearing if the identification of sequence variants or the discovery of sequences that are not present in the reviewed knowledge base entries is an important goal of the study. We find that it is useful to search a data set against a simpler database, and then check the uniqueness of the discovered peptides against a more complex database. We have set up an automated system that downloads all the source databases on the first of each month and automatically generates a new set of search databases and makes them available for download at http://www.peptideatlas.org/thisp/.

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).

A cross-national data archive located in Luxembourg that contains two primary databases: the Luxembourg Income Study Database (LIS Database) includes income microdata from a large number of countries at multiple points in time. The newer Luxembourg Wealth Study Database(LWS Database) includes wealth microdata from a smaller selection of countries. Both databases include labor market and demographic data as well. Our mission is to enable, facilitate, promote, and conduct cross-national comparative research on socio-economic outcomes and on the institutional factors that shape those outcomes. Since its beginning in 1983, the LIS has grown into a cooperative research project with a membership that includes countries in Europe, North America, and Australia. The database now contains information for more than 30 countries with datasets that span up to three decades. The LIS databank has a total of over 140 datasets covering the period 1968 to 2005. The primary objectives of the LIS are as follows: * Test the feasibility for creating a database containing social and economic data collected in household surveys from different countries; * Provide a method which allows researchers to use the data under restrictions required by the countries providing the data; * Create a system that allows research requests to be received from and returned to users at remote locations; and * Promote comparative research on the social and economic status of various populations and subgroups in different countries. Data Availability: The dataset is accessed globally via electronic mail networks. Extensive documentation concerning technical aspects of the survey data, variables list, and the social institutions of income provision in member countries are also available to users through the project Website. * Dates of Study: 1968-present * Study Features: International * Sample Size: 30+ Countries Link: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/00150

This database is part of the National Medical Information System (NMIS). The National Health Care Practitioner Database (NHCPD) supports Veterans Health Administration Privacy Act requirements by segregating personal information about health care practitioners such as name and social security number from patient information recorded in the National Patient Care Database for Ambulatory Care Reporting and Primary Care Management Module.

The Home Based Primary Care (HBPC) database receives and compiles data from local Hospital Based Home Care (HBHC) sanctioned programs at Veterans Affairs Medical Centers (VAMCs) that run home care programs under the Home Based Primary Care program. The primary purpose is to provide HBPC management with case mix, case load, and other performance information. The HBPC information system is referred to as HBC at the VA Austin Information Technology Center and as HBHC at the local level. The HBHC automated a paper-based system of reporting home care episodes. When an admission form is completed, an episode is opened and input into HBHC for a potential home care patient. The patient is evaluated and accepted to or rejected from the program. When a patient leaves the program for any reason an episode is closed and a discharge form completed and input into HBHC. HBHC runs a nightly extract of information within the Veterans Health Information Systems and Technology Architecture. Extractions include information on all Patient Care Encounters (PCEs) with the patient and home visits made by home care providers. Details of which provider(s) made the visit, the date, any diagnosis and any procedures performed are included. Each local application sends its data to the Austin HBC database on a monthly basis. A monthly report is prepared based on this information identifying the active cases at each VAMC. A more detailed quarterly report is produced that includes national comparisons among sites.

A database that contains sequences built from the existing primary sequence data in GenBank. The sequences and corresponding annotations are experimentally supported and have been published in a peer-reviewed scientific journal.

Database contains information on all patients for whom medical service was provided by primary health care providers in Croatia.

PhenomicDB is a multi-organism phenotype-genotype database including human, mouse, fruit fly, C.elegans, and other model organisms. The inclusion of gene indices (NCBI Gene) and orthologs (same gene in different organisms) from HomoloGene allows to compare phenotypes of a given gene over many organisms simultaneously. PhenomicDB contains data from publicly available primary databases: FlyBase, Flyrnai.org, WormBase, Phenobank, CYGD, MatDB, OMIM, MGI, ZFIN, SGD, DictyBase, NCBI Gene, and HomoloGene. We brought this wealth of data into a single integrated resource by coarse-grained semantic mapping of the phenotypic data fields, by including common gene indexes (NCBI Gene), and by the use of associated orthology relationships (HomoloGene). PhenomicDB is thought as a first step towards comparative phenomics and will improve the understanding of the gene functions by combining the knowledge about phenotypes from several organisms. It is not intended to compete with the much more dedicated primary source databases but tries to compensate its partial loss of depth by linking back to the primary sources. The basic functional concept of PhenomicDB is an integrated meta-search-engine for phenotypes. Users should be aware that comparison of genotypes or even phenotypes between organisms as different as yeast and man can have serious scientific hurdles. Nevertheless finding that the phenotype of a given mouse gene is described as ??similar to psoriasis?? and at the same time that the human ortholog has been described as a gene causing skin defects can lead to novelty and interesting hypotheses. Similarly, a gene involved in cancer in mammalian organisms could show a proliferation phenotype in a lower organism such as yeast and thus, give further insights to a researcher.

Additional file 1. Breakdown of disease areas where primary care databases are used in NICE technology appraisals.

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 16, 2013. A relational database that contains all the eukaryotic protein-encoding DNA sequences in GenBank. It provides detailed and comprehensive features about both the intron containing and the intron-less genes. In addition to the information found in the GenBank records, which includes properties such as sequence, position, length and description about introns, exons and protein coding regions, Xpro provides annotations on the splice sites motifs and intron phases. Furthermore, Xpro validates intron positions using alignment information between the records sequence and EST sequences found in dbEST. The entries in the XPro are also cross-referenced to SWISS-PROT/TrEMBL and Pfam databases. Unprecedented growth data in GenBank, the primary repository of nucleotide sequences due to the ever increasing number of genome and EST sequencing projects and the poor annotation of exon/intron details required for molecular evolution studies in the primary nucleotide database have made development of Xpro database. It is a specialized database that contains details about genomic features specific to eukaryotic genes and provides various web tools for analyzing/visualizing these features., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.