BELIT provides access to about 350.000 records from the integrated German, American and French databases. It is an extensive bibliographic directory of literature in the area of bioethics unique world wide, containing references to monographs, grey literature, legal documents, journal articles, newspaper articles and book contributions. It is an integrated online database developed by the German Reference Centre for Ethics in the Life Sciences. BELIT Search enables the retrieval of bibliographical data filtered by categories. Choose a category from the drop down lists in the top section of the main screen. Enter the query term(s) in the text field next to the search category chosen. Help texts are available by clicking on Help. Users can combine different search categories with and or or respectively as well as restrict their search by selecting further categories from the drop down lists in the lower section of the main screen. The default display of the search results is an abbreviated title list. Click on Full details to see the complete bibliographical record, including the short code of the source database in the top right hand corner of each record. Refer to the BELIT intro screen for an explanation of the short codes. Using the arrow keys users can navigate through the lists. Users may also carry out searches using the various indexes. An index is an alphabetically ordered register containing all query terms assigned to a search category. There is an index for each category.

Retrieving sets of citation contexts for papers with PubMed ID 18996891, 21071411, 22135297, and 23376192. (XLSX 66 kb)

Functional Annotation of Variants - Online Resource (FAVOR, https://favor.genohub.org) is a comprehensive whole-genome variant annotation database and a variant browser, providing hundreds of functional annotation scores from a variety of aspects of variant biological function. This FAVOR Essential Database is comprised of a collection of essential annotation scores for all possible SNVs (8,812,917,339) and observed indels (79,997,898) in Build GRCh38/hg38, including variant info, chromosome, position, reference allele, alternative allele, aPC-Conservation, aPC-Epigenetics, aPC-Epigenetics-Active, aPC-Epigenetics-Repressed, aPC-Epigenetics-Transcription, aPC-Local-Nucleotide-Diversity, aPC-Mappability, aPC-Mutation-Density, aPC-Protein-Function, aPC-Proximity-To-TSSTES, aPC-Transcription-Factor, CAGE promoter, CAGE, MetaSVM, rsID, FATHMM-XF, Gencode Comprehensive Category, Gencode Comprehensive Info, Gencode Comprehensive Exonic Category, Gencode Comprehensive Exonic Info, GeneHancer, LINSIGHT, CADD, rDHS. These annotation scores can be integrated into FAVORannotator (https://github.com/zhouhufeng/FAVORannotator) to create an annotated GDS (aGDS) file by storing the genotype data and their functional annotation data in an all-in-one file. The aGDS file can then facilitate a wide range of functionally-informed downstream analyses.

THIS RESOURCE IS NO LONGER IN SERVICE, documented on May 16, 2016. The establishment of open access literature makes it possible for knowledge to be extracted from scholarly articles and included in other resources. BioLit aims to extract database identifiers and rich meta-data from open access articles in the life sciences and integrate that information with existing biological databases. We have begun prototyping this effort using a clone of the RCSB Protein Data Bank, a database of macromolecular structures. Cyberinfrastructure is integral to all aspects of conducting experimental research and distributing those results. However, it has yet to make a similar impact on the way we communicate that information. Peer-reviewed publications have long been the currency of scientific research as they are the fundamental unit through which scientists communicate with and evaluate each other. However, in striking contrast to the data, publications have yet to benefit from the opportunities offered by cyberinfrastructure. While the means of distributing publications has vastly improved, publishers have done little else to capitalize on the electronic medium. In particular, semantic information describing the content of these publications is sorely lacking, as is the integration of this information with data in public repositories. This is confounding considering that many basic tools for marking-up and integrating publication content in this manner already exist, such as a centralized literature database, relevant ontologies, and machine-readable document standards. We believe that the research community is ripe for a revolution in scientific communication and that the current generation of scientists will be the one to push it forward. These scientists, generally graduate students and new post-docs and have grown up with cyberinfrastructure as a part of their daily lives, not just a specialized aspect of their profession. They have a natural ability to do science in an electronic environment without the need for printed publications or static documents and, in fact, can feel quite limited by the traditional format of a publication. Perhaps most importantly, they appreciate that the sheer amount of data and the number of publications is prohibitive to the traditional methods of keeping current with the literature. Fink, L., Bourne, P. Reinventing Scholarly Communication for the Electronic Age, CTWatch Quarterly, Volume 3, Number 3, August 2007., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

This dataset is comprised of a collection of example DMPs from a wide array of fields; obtained from a number of different sources outlined below. Data included/extracted from the examples include the discipline and field of study, author, institutional affiliation and funding information, location, date created, title, research and data-type, description of project, link to the DMP, and where possible external links to related publications or grant pages. This CSV document serves as the content for a McMaster Data Management Plan (DMP) Database as part of the Research Data Management (RDM) Services website, located at https://u.mcmaster.ca/dmps. Other universities and organizations are encouraged to link to the DMP Database or use this dataset as the content for their own DMP Database. This dataset will be updated regularly to include new additions and will be versioned as such. We are gathering submissions at https://u.mcmaster.ca/submit-a-dmp to continue to expand the collection.

The DOI list of the documents retrieved from the WOS Medline database in the research area of Life Sciences & Biomedicine from 2014-2016.

This study explores the extent to which bibliometric indicators based on counts of highly-cited documents could be affected by the choice of data source. The initial hypothesis is that databases that rely on journal selection criteria for their document coverage may not necessarily provide an accurate representation of highly-cited documents across all subject areas, while inclusive databases, which give each document the chance to stand on its own merits, might be better suited to identify highly-cited documents. To test this hypothesis, an analysis of 2,515 highly-cited documents published in 2006 that Google Scholar displays in its Classic Papers product is carried out at the level of broad subject categories, checking whether these documents are also covered in Web of Science and Scopus, and whether the citation counts offered by the different sources are similar. The results show that a large fraction of highly-cited documents in the Social Sciences and Humanities (8.6%-28.2%) are invisible to Web of Science and Scopus. In the Natural, Life, and Health Sciences the proportion of missing highly-cited documents in Web of Science and Scopus is much lower. Furthermore, in all areas, Spearman correlation coefficients of citation counts in Google Scholar, as compared to Web of Science and Scopus citation counts, are remarkably strong (.83-.99). The main conclusion is that the data about highly-cited documents available in the inclusive database Google Scholar does indeed reveal significant coverage deficiencies in Web of Science and Scopus in some areas of research. Therefore, using these selective databases to compute bibliometric indicators based on counts of highly-cited documents might produce biased assessments in poorly covered areas.

We conducted a systematic search using broad and case study-derived keywords, detailed in the appendix. This resulted in 318 sources from 28 databases, encompassing peer-reviewed articles analyzing internal documents of chemical corporations. We complemented our efforts with a snowball sampling method to uncover additional case studies and journal articles not initially captured by our search. Results were categorized and analyzed using Marc-Andre Gagnon and Sergio Sismondo's ghost management framework. The final results included and analyzed 15 scientific papers (3–17). Legal proceedings served as the primary source of internal document data for all examined articles. We uncovered and categorized dynamic strategies employed by chemical corporations to protect and advance their interests, including scientific capture (n=13), regulatory capture (n=13), professional capture (n=7), civil society capture (n=6), media capture (n=4), legal capture (n=4), technological capture (n=3), and market capture (n=2). The limited scientific literature meeting our criteria confirms early findings by Wieland et al (18), highlighting a research gap in the chemical industry. Our analysis, building on the ghost-management framework, unveils a different emphasis in the way internal documents were used in scientific literature to understand corporate strategies at play in the chemical sector as compared to the pharmaceutical sector. In contrast to Gagnon and Dong's pharmaceutical corporate capture review, which identified 37 papers before 2022 (1), our chemical industry findings reveal a lower count, with only 15 papers identified. Comparing pharmaceutical and chemical scoping reviews, lower variations emerge across scientific (n=28 vs. n=13), professional (n=16 vs. n=7), and market captures (n=4 vs. n=2). The chemical industry shows higher instances of regulatory (n=6 vs. n=13), civil society (n=4 vs. n=6), media (n=3 vs. n=4), and technological captures (n=2 vs. n=3) compared to the pharmaceutical industry. Both industries employ conflict of interests and legitimization strategies to deflect public policy inquiries and protect their interests. However, a notable distinction lies in their objectives. While the analysis of the pharmaceutical industry focuses on profit maximization through biased promotion of health products, the analysis of the chemical sector emphasizes the institutionalization of ignorance, the evasion of liability, and the pre-emption of regulatory actions.

Here you find the transcripts of interviews collected by Sabina Leonelli as part of the ERC project "The Epistemology of Data-Intensive Science". You also find the information sheet provided to interviewees, which gives you the context for this project. Further information and related publications can be found at www.datastudies.eu. One paper that specifically makes use of these interviews was published by Sabina Leonelli in the journal Philosophy of Science in 2018, under the title "Data in Time: Time-Scales of Data Use in the Life Sciences." The transcripts document yeast researchers' attitudes to data curation and the use of databases in their field. Researchers have consented to have these transcripts made available as Open Data. Other interviewees did not give consent, so those transcripts are held securely by the research team in Exeter.

home cage data female mice cage 6 all text file and database information

PLBD (Protein Ligand Binding Database) table description XML file

General

The provided ZIP archive contains an XML file "main-database-description.xml" with the description of all tables (VIEWS) that are exposed publicly at the PLBD server (https://plbd.org/). In the XML file, all columns of the visible tables are described, specifying their SQL types, measurement units, semantics, calculation formulae, SQL statements that can be used to generate values in these columns, and publications of the formulae derivations.

The XML file conforms to the published XSD schema created for descriptions of relational databases for specifications of scientific measurement data. The XSD schema ("relational-database_v2.0.0-rc.18.xsd") and all included sub-schemas are provided in the same archive for convenience. All XSD schemas are validated against the "XMLSchema.xsd" schema from the W3C consortium.

The ZIP file contains the excerpt from the files hosted in the https://plbd.org/ at the moment of submission of the PLBD database in the Scientific Data journal, and is provided to conform the journal policies. The current data and schemas should be fetched from the published URIs:

https://plbd.org/
https://plbd.org/doc/db/schemas
https://plbd.org/doc/xml/schemas

Software that is used to generate SQL schemas, RestfulDB metadata and the RestfulDB middleware that allows to publish the databases generated from the XML description on the Web are available at public Subversion repositories:

svn://www.crystallography.net/solsa-database-scripts
svn://saulius-grazulis.lt/restfuldb

Usage

The unpacked ZIP file will create the "db/" directory with the tree layout given below. In addition to the database description file "main-database-description.xml", all XSD schemas necessary for validation of the XML file are provided. On a GNU/Linux operating system with a GNU Make package installed, the XML file validity can be checked by unpacking the ZIP file, entering the unpacked directory, and running 'make distclean; make'. For example, on a Linux Mint distribution, the following commands should work:

unzip main-database-description.zip
cd db/release/v0.10.0/tables/
sh -x dependencies/Linuxmint-20.1/install.sh
make distclean
make

If necessary, additional packages can be installed using the 'install.sh' script in the 'dependencies/' subdirectory corresponding to your operating system. As of the moment of writing, Debian-10 and Linuxmint-20.1 OSes are supported out of the box; similar OSes might work with the same 'install.sh' scripts. The installation scripts require to run package installation command under system administrator privileges, but they use only the standard system package manager, thus they should not put your system at risk. For validation and syntax checking, the 'rxp' and 'xmllint' programs are used.

The log files provided in the "outputs/validation" subdirectory contain validation logs obtained on the system where the XML files were last checked and should indicate validity of the provided XML file against the references schemas.

Layout of the archived file tree

db/
└── release
  └── v0.10.0
    └── tables
      ├── Makeconfig-validate-xml
      ├── Makefile
      ├── Makelocal-validate-xml
      ├── dependencies
      ├── main-database-description.xml
      ├── outputs
      └── schema

Journals & Ranking

An academic journal or research journal is a periodical publication in which research articles relating to a particular academic discipline is published, according to Wikipedia. Currently, there are more than 25,000 peer-reviewed journals that are indexed in citation index databases such as Scopus and Web of Science. These indexes are ranked on the basis of various metrics such as CiteScore, H-index, etc. The metrics are calculated from yearly citation data of the journal. A lot of efforts are given to make a metric that reflects the journal's quality.

Journal Ranking Dataset

This is a comprehensive dataset on the academic journals coving their metadata information as well as citation, metrics, and ranking information. Detailed data on their subject area is also given in this dataset. The dataset is collected from the following indexing databases: - Scimago Journal Ranking - Scopus - Web of Science Master Journal List

The data is collected by scraping and then it was cleaned, details of which can be found in HERE.

Key Features

• Rank: Overall rank of journal (derived from sorted SJR index).
• Title: Name or title of journal.
• OA: Open Access or not.
• Country: Country of origin.
• SJR-index: A citation index calculated by Scimago.
• CiteScore: A citation index calculated by Scopus.
• H-index: Hirsh index, the largest number h such that at least h articles in that journal were cited at least h times each.
• Best Quartile: Top Q-index or quartile a journal has in any subject area.
• Best Categories: Subject areas with top quartile.
• Best Subject Area: Highest ranking subject area.
• Best Subject Rank: Rank of the highest ranking subject area.
• Total Docs.: Total number of documents of the journal.
• Total Docs. 3y: Total number of documents in the past 3 years.
• Total Refs.: Total number of references of the journal.
• Total Cites 3y: Total number of citations in the past 3 years.
• Citable Docs. 3y: Total number of citable documents in the past 3 years.
• Cites/Doc. 2y: Total number of citations divided by the total number of documents in the past 2 years.
• Refs./Doc.: Total number of references divided by the total number of documents.
• Publisher: Name of the publisher company of the journal.
• Core Collection: Web of Science core collection name.
• Coverage: Starting year of coverage.
• Active: Active or inactive.
• In-Press: Articles in press or not.
• ISO Language Code: Three-letter ISO 639 code for language.
• ASJC Codes: All Science Journal Classification codes for the journal.

Rest of the features provide further details on the journal's subject area or category: - Life Sciences: Top level subject area. - Social Sciences: Top level subject area. - Physical Sciences: Top level subject area. - Health Sciences: Top level subject area. - 1000 General: ASJC main category. - 1100 Agricultural and Biological Sciences: ASJC main category. - 1200 Arts and Humanities: ASJC main category. - 1300 Biochemistry, Genetics and Molecular Biology: ASJC main category. - 1400 Business, Management and Accounting: ASJC main category. - 1500 Chemical Engineering: ASJC main category. - 1600 Chemistry: ASJC main category. - 1700 Computer Science: ASJC main category. - 1800 Decision Sciences: ASJC main category. - 1900 Earth and Planetary Sciences: ASJC main category. - 2000 Economics, Econometrics and Finance: ASJC main category. - 2100 Energy: ASJC main category. - 2200 Engineering: ASJC main category. - 2300 Environmental Science: ASJC main category. - 2400 Immunology and Microbiology: ASJC main category. - 2500 Materials Science: ASJC main category. - 2600 Mathematics: ASJC main category. - 2700 Medicine: ASJC main category. - 2800 Neuroscience: ASJC main category. - 2900 Nursing: ASJC main category. - 3000 Pharmacology, Toxicology and Pharmaceutics: ASJC main category. - 3100 Physics and Astronomy: ASJC main category. - 3200 Psychology: ASJC main category. - 3300 Social Sciences: ASJC main category. - 3400 Veterinary: ASJC main category. - 3500 Dentistry: ASJC main category. - 3600 Health Professions: ASJC main category.

A benchmarking analysis of the researchers of the Faculty of Chemistry of the Wrocław University of Life Sciences was completed on 10.10.2022 by the staff of the Main Library. The analysis was based on indicators from the Web of Science database and the InCites tool, along with the Scopus database and the Scival tool. The breakdown was prepared for the purpose of selecting the highest performing professionals in terms of bibliometric indicators from the above databases. In the excel file "Benchmarking analysis of the researchers of the Faculty of Chemistry of Wrocław University of Life Sciences", personal data of the faculty members were anonymised. The analysis considers publications published since 2017. The dataset also includes 3 column charts in PDF format. Documents show a comparison of the number of publications, h-index, and times cited (with and without self-citations) by faculty researchers from the Web of Science and Scopus databases.

A database containing more than 6,000 variables for U.S. counties. ARF contains information on health facilities, health professions, measures of resource scarcity, health status, economic activity, health training programs, and socioeconomic and environmental characteristics. In addition, the basic file contains geographic codes and descriptors which enable it to be linked to many other files and to aggregate counties into various geographic groupings.

SPRC19 seeks to document as completely as possible all U.S. state policy responses to the COVID-19 pandemic. This includes all policy actions originating from the executive (governor’s office as well as executive agencies), legislative, and judicial branches. An action represents any change in current COVID-19 policy set at the state level. Actions are identified by reading through source documents collected from state websites and other sources according to their effects on any of over two hundred different policy areas. Each action is coded on a variety of features. These include its policy topic area, the branch that made the action, the announcement date, the effective date, an expiration date (if given), and the relationship to prior actions in the same policy area. To access the data and documentation quickly, search the Table view for "SPRC19" or switch to the Tree view. SPRC19 contains over 40,000 policy actions covering over 200 different policy areas. The current version is completed through December 31, 2020. We are currently in the process of updating through March 2021. The current release extends the previous release by adding actions from September through December 2020. The SPRC19 database was assembled with the support of the National Science Foundation through the following grants (grants #1558509, #1637095, #1558661, #1558781, #1558561, #2028724, #2028675, and #2028674, #2148216) and the NIH (grant #1R21AI164391-01).

Additional file 8. List of species name entries with strain-like elements, collected from COCONUT and NPASS databases.

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. DB-PABP is an attempt to document the publicly available experimentally determined polyanion binding proteins (PABPs). The purpose of the database is to provide life scientists who are interested in PA/PABP interactions with a comprehensive data repository, as well as computer scientists with a publicly available dataset to perform knowledge discovery and datamining studies. The database is manually curated. It uses protein annotations from NCBI protein database and literature information is retrieved from PubMed. Whenever applicable, links to NCBI protein database and PubMed are provided so users may access additional information available in these public databases.

Global Regulatory Intelligence Databases Market Outlook

According to our latest research, the global Regulatory Intelligence Databases market size reached USD 1.85 billion in 2024, reflecting robust demand from highly regulated industries and a growing need for real-time compliance insights. The market is projected to exhibit a CAGR of 13.2% during the forecast period, reaching an estimated USD 5.42 billion by 2033. This impressive growth is primarily driven by increasing regulatory complexities, especially in the pharmaceutical and biotechnology sectors, and the urgent need for organizations to streamline compliance processes and mitigate risks associated with global operations.

A key growth driver for the Regulatory Intelligence Databases market is the accelerating pace of regulatory changes worldwide. As governments and regulatory agencies introduce new compliance frameworks and update existing ones, organizations face significant challenges in keeping pace with evolving requirements. Regulatory Intelligence Databases provide a centralized, real-time repository of global regulations, enabling companies to monitor, interpret, and implement changes efficiently. The pharmaceutical and life sciences industries, in particular, are experiencing a surge in demand for such solutions due to frequent updates in drug approval processes, clinical trial protocols, and safety reporting standards. The adoption of these databases helps organizations avoid costly compliance lapses, reduce manual monitoring efforts, and foster a proactive approach to regulatory management.

Another major factor fueling market expansion is the digital transformation of compliance and risk management functions. Organizations are increasingly shifting from manual, paper-based regulatory tracking to automated platforms powered by artificial intelligence and machine learning. This shift is not only improving the accuracy and timeliness of regulatory intelligence but also enabling predictive analytics to anticipate future changes and their potential impact. Cloud-based deployment models are gaining traction, offering scalability, rapid implementation, and seamless updates. The integration of Regulatory Intelligence Databases with other enterprise systems, such as document management, workflow automation, and business intelligence tools, further enhances their value proposition, making them indispensable for organizations operating in multiple jurisdictions.

The growing emphasis on global harmonization of regulations and the expansion of multinational clinical trials are also contributing to market growth. As pharmaceutical, biotechnology, and contract research organizations increasingly conduct cross-border operations, the complexity of managing diverse regulatory requirements multiplies. Regulatory Intelligence Databases serve as a critical resource for these entities, providing up-to-date information on country-specific regulations, submission guidelines, and reporting obligations. Additionally, regulatory agencies themselves are leveraging these databases to streamline internal workflows, improve transparency, and facilitate international collaboration. The trend towards digital health, personalized medicine, and accelerated drug approvals is expected to further amplify the need for advanced regulatory intelligence solutions over the coming years.

Regionally, North America continues to hold the largest share of the Regulatory Intelligence Databases market, driven by stringent regulatory frameworks, high adoption of digital compliance solutions, and the presence of leading pharmaceutical and biotechnology firms. However, the Asia Pacific region is witnessing the fastest growth, propelled by expanding healthcare infrastructure, increasing clinical research activities, and evolving regulatory landscapes across emerging markets such as China, India, and Southeast Asia. Europe remains a significant contributor, characterized by harmonized regulations under the European Medicines Agency (EMA) and proactive adoption of regulatory technology. Latin America and the Middle East & Africa are also emerging as promising markets, supported by growing investments in healthcare and regulatory modernization initiatives.

Additional file 5. PhyloSophos analysis results of species entries (n=25,899) from NPASS database.

Additional file 4. PhyloSophos analysis results of species entries (n=36,803) from LOTUS database.