The dataset is comprised of: 1)VOC concentrations of soil gas and indoor air samples collected over the site; 2) the pressure readings used to monitor the pressure differential between subslab and indoor air.

The global academic research database market is booming, projected to hit $388.2 million in 2025, with a robust CAGR driving growth. This in-depth analysis explores market size, key players (Scopus, Web of Science, PubMed), and future trends shaping this vital sector for researchers and educators.

The dataset is comprised of VOC concentrations of soil gas, outdoor and indoor air samples collected at the site for the duration of this study. This dataset is associated with the following publication: Zimmerman, J., A. Williams, B. Schumacher, C. Lutes, L. Levy, G. Buckley, V. Boyd, C. Holton, T. McAlary, and R. Truesdale. The Representativeness of Subslab Soil Gas Collection as Effected by Probe Construction and Sampling Methods. Groundwater Monitoring & Remediation. Wiley-Blackwell Publishing, Hoboken, NJ, USA, 44(3): 106-121, (2024).

The Agency for Toxic Substances and Disease Registry (ATSDR) Hazardous Waste Site Polygon Data with CIESIN Modifications, Version 2 is a database providing georeferenced data for 1,572 National Priorities List (NPL) Superfund sites. These were selected from the larger set of the ATSDR Hazardous Waste Site Polygon Data, Version 2 data set with polygons from May 26, 2010. The modified data set contains only sites that have been proposed, currently on, or deleted from the final NPL as of October 25, 2013. Of the 2,080 ATSDR polygons from 2010, 1,575 were NPL sites but three sites were excluded - 2 in the Virgin Islands and 1 in Guam. This data set is modified by the Columbia University Center for International Earth Science Information Network (CIESIN). The modified polygon database includes all the attributes for these NPL sites provided in the ATSDR GRASP Hazardous Waste Site Polygon database and selected attributes from the EPA List 9 Active CERCLIS sites and SCAP 12 NPL sites databases. These polygons represent sites considered for cleanup under the Comprehensive Environmental Response, Compensation and Liability Act (CERCLA or Superfund). The Geospatial Research, Analysis, and Services Program (GRASP, Division of Health Studies, Agency for Toxic Substances and Disease Registry, Centers for Disease Control and Prevention) has created site boundary data using the best available information for those sites where health assessments or consultations have been requested.

Discover the booming academic research databases market! This comprehensive analysis reveals key trends, growth drivers, and leading players (Scopus, Web of Science, PubMed, etc.) impacting this multi-billion dollar industry from 2019-2033. Explore market size, CAGR, regional insights, and future forecasts.

A web-based database of protein interaction sites. PiSITE provides not only information of interaction sites of a protein from single PDB entry, but also information of interaction sites of a protein from multiple PDB entries including similar proteins. PiSite also provides a list of sociable proteins, proteins with multiple binding states and multiple binding partners.In PiSITE, the identification of the binding sites of protein chains is performed by searching the same proteins with different binding states in PDB at first, and then mapping those binding sites onto the query proteins. The database PiSITE provides real interaction sites of proteins using the complex structures in PDB. According to the progress of several structural genomic projects, we have a large amount of structural data in PDB. Consequently, we can observe different binding states of proteins in atomic resolutions, and can analyze actual interaction sites of proteins. It will lead better understandings of protein interaction sites in near future. Usual practice to identify the interaction site has been done using a representative complex in PDB. However, for the proteins with multiple partners, non-interaction sites identified by using a single complex structure is not enough, because some part of the non-binding sites may be involved in the interaction sites with another proteins. Therefore, the real interaction sites should be obtained by using all of the binding states in PDB. For the purpose, the identifications of the binding site in PiSITE are done by searching the same proteins with different binding sites in PDB at first, and then mapping the binding sites onto the query proteins. PiSITE also provides the lists of transient hub proteins, which we call sociable proteins to clarify the different of so-called hub proteins. The sociable proteins are identified as the proteins with multiple binding states and multiple binding partners. On the other hand, so-called hub proteins have been identified as the proteins at the hub position in protein-protein interaction networks obtained by large-scale experiments, but the definition of the hub proteins cannot differentiate transient hub proteins from stable ones, although the differentiation is critically important for the better understanding of protein interaction networks. In addition, the usual definition of hub proteins can contain supermolecules as hub proteins. The supermolecules can be identified as the proteins with a single binding state and multiple binding partners, which we call stable hub proteins.

NCBI, National Center for Biotechnology Information; KEGG, Kyoto Encyclopedia of Genes and Genomes.

The global academic research databases market is booming, projected to reach $259.3 million in 2025, with a CAGR of 5.9% through 2033. Discover key drivers, trends, and regional insights from this comprehensive market analysis covering Scopus, Web of Science, and more. Explore market segmentation by access type and user application.

The add-on had been designed for the VANTED framework and used to create QSDB Database's collection of clickable networks. Each network is laid out according to SBGN standards, showing quorum sensing and quorum quenching interactions between organisms and signaling molecules. This data set constitutes the source code of the add-on, developed to visualise the SBGN graphs of the QSDB Database using as input tabular aggregated data collected from existing literature.

Paper abstract: The human microbiome is largely shaped by the chemical interactions of its microbial members, which includes cross-talk via shared signals or quenching of the signalling of other species. Quorum sensing is a process that allows microbes to coordinate their behaviour in dependence of their population density and to adjust gene expression accordingly. We present the Quorum Sensing Database (QSDB), a comprehensive database of all published sensing and quenching relations between organisms and signalling molecules of the human microbiome, as well as an interactive web interface that allows browsing the database, provides graphical depictions of sensing mechanisms as Systems Biology Graphical Notation diagrams and links to other databases.

Database URL: QSDB (Quorum Sensing DataBase) is freely available via an interactive web interface and as a downloadable csv file at http://qsdb.org.

The organizations that contribute to the longevity of 67 long-lived molecular biology databases published in Nucleic Acids Research (NAR) between 1991-2016 were identified to address two research questions 1) which organizations fund these databases? and 2) which organizations maintain these databases? Funders were determined by examining funding acknowledgements in each database's most recent NAR Database Issue update article published (prior to 2017) and organizations operating the databases were determine through review of database websites.

This layer of data locates the sites contained in the Sites & Organisations database managed by Rennes Métropole services. In the long term, other actors will be able to contribute to the updating of this database (territorial authorities, equipment managers, association...). This layer is the result of merging 3 layers of data that were managed by 3 different services. To streamline their management and pool updating efforts, it was decided to group them into a single database (the Sites & Organisations database) and set up an appropriate management tool. The Sites & Organisations database aims to bring together public, semi-public and private facilities receiving from the public as well as many associations that use them at the Rennes Métropole scale. The Sites & Organisations described may also be provided with business data specific to the profile of these various contributors. A “site” is a geographically located “container” (by an address and geographical coordinates) to which an “activity” described by an organisation (content) is mandatory. When a site is not limited to a building but is represented by a large area (e.g. sports complex, university, hospitals...), the notion of “father site” is used. An “organisation” represents an “activity” specified by a description, a nomenclature, keywords, timetables and, where applicable, business data. Its location will result from its association with a site. An organisation may not have a linked site (no location) when it comes to a phone number, a website... Description of the fields awarded: id_site: Site Identifier name_site: Site name name_pvci: Name of the site used on the Plan de Ville Communal et Intercommunal de Rennes Métropole etat_site: Site Status (active/inactive/project) id_level_site: Level (0 by default) id_site_pere: Father site identifier name_site_pere: Father site name id_org_main: Principal organism identifier name_org_main: Name of main organisation id_theme_main: Nomenclature (identifying the theme attached) name_theme_main: Nomenclature (name of theme attached) id_activite_main: Nomenclature (identifying the activity attached) name_activite_main: Nomenclature (name of activity attached) id_specialite_main: Nomenclature (identifying the speciality attached) name_specialite_main: Nomenclature (name of speciality attached) name_org_secondary: Name of organisation(s) associated with the site Access to the nomenclature is available below in the metadata.

A database and storage service resource which allows users to create, view, share, and download information from companion websites. RunMyCode allows users to create companion websites for their scientific publications. Users can share and download computer code and data from companion websites made with RunMyCode. Any software and data format is compatible with RunMyCode.

TassDB stores extensive data about alternative splice events at GYNGYN donors and NAGNAG acceptors. Currently, 114,554 tandem splice sites of eight species are contained in the database, 5,209 of which have EST/mRNA evidence for alternative splicing. Users can search by Transcript Accession Number and Gene Symbol, SQL Query, and Tandem Donor/Tandem Acceptor pairs.

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

🧠 About Dataset

Overview

The AIToolBuzz — 16,763 AI Tools Dataset is a comprehensive collection of publicly available information on artificial intelligence tools and platforms curated from AIToolBuzz.com.
It compiles detailed metadata about each tool, including name, description, category, founding year, technologies used, website, and operational status.

The dataset serves as a foundation for AI trend analysis, product discovery, market research, and NLP-based categorization projects.
It enables researchers, developers, and analysts to explore the evolution of AI tools, detect emerging sectors, and study keyword trends across industries.

Dataset Composition

• Total Entries: 16,763 AI tools
• Time Period: Data collected in October 2025
• Source: AIToolBuzz.com — a curated directory of AI products and services
• Format: CSV (comma-separated), UTF-8 encoded
• Columns: 13 descriptive fields covering both tool metadata and website status

Use Cases

• 🧩 Market & Trend Analysis — Examine growth and patterns in AI categories, technologies, and geographies.
• 🤖 NLP & ML Projects — Use keywords and descriptions for text clustering or embedding tasks.
• 🏷️ Tool Discovery & Classification — Build AI tool recommenders or taxonomies.
• 📊 Data Visualization — Create dashboards showing trends over time or by region.

Example Entries

Provenance Summary

• Source: AIToolBuzz.com — public web directory.
• Collection Method: Automated web scraping via requests + BeautifulSoup, extracting metadata from each tool’s public page.
• Date Collected: October 2025.
• License: Derived dataset — redistribution permitted with attribution (CC BY 4.0 recommended).
• Collector: Swathik Devadiga.
• Frequency: Planned quarterly updates.

Citation

If you use this dataset, please cite as: AIToolBuzz — 16,763 AI Tools (Complete Directory with Metadata). Kaggle. https://aitoolbuzz.com

License

License: CC BY 4.0 — Creative Commons Attribution 4.0 International

You are free to share and adapt the data for research or analysis with proper attribution to AIToolBuzz.com as the original source.

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on October 29,2025. Relational database of all significant published qualitative and quantitative information on cell signaling proteins. The Molecule Pages database was developed with the specific aim of allowing interactions, and indeed whole pathways, to be modeled. The goal is to filter the data to present only validated information. In addition, the Gateway is the home of Signaling Update, which provides a one-stop overview of the latest and hottest research in cell signaling for both the specialist and non-specialist alike.

• IP Addresses Mapped to 3M+ Company Domains
• 100m+ IP seen from Employees in Last 30 days
• 1st Party IP Data Generated by Email Newsletter Interactions
• Sales, leads and marketing intelligence
• Cybersecurity and threat intelligence
• Company IP Datasets
• Company IP Databases

A dataset compiling all known terrestrial haul-out sites for the Atlantic walrus. The dataset comprises of the following documents: (1) the database in .csv format: walrus_haulout_database_Atlantic_caff_v4.csv; (2) the database in .shp format: walrus_haulout_database_Atlantic_caff_v4.shp; and (3) a user guidance document: user_guidance_atlantic_walrus_db.docx. The dataset will be updated annually. The latest update was 14th May 2025.

The set of these datasets are made to analyze information credibility in general (rumor and disinformation for English and French documents), and occuring on the social web. Target databases about rumor, hoax and disinformation helped to collect obviously misinformation. Some topic (with keywords) helps us to made corpora from the micrroblogging platform Twitter, great provider of rumors and disinformation.1 corpus describes Texts from the web database about rumors and disinformation. 4 corpora from Social Media Twitter about specific rumors (2 in English, 2 in French). 4 corpora from Social Media Twitter randomly built (2 in English, 2 in French). 4 corpora from Social Media Twitter about specific rumors (2 in English, 2 in French).Size of different corpora :Social Web Rumorous corpus: 1,612French Hollande Rumorous corpus (Twitter): 371 French Lemon Rumorous corpus (Twitter): 270 English Pin Rumorous corpus (Twitter): 679 English Swine Rumorous corpus (Twitter): 1024French 1st Random corpus (Twitter): 1000 French 2st Random corpus (Twitter): 1000 English 3st Random corpus (Twitter): 1000 English 4st Random corpus (Twitter): 1000French Rihanna Event corpus (Twitter): 543 English Rihanna Event corpus (Twitter): 1000 French Euro2016 Event corpus (Twitter): 1000 English Euro2016 Event corpus (Twitter): 1000A matrix links tweets with most 50 frequent wordsText data :_id : message id body text : string text dataMatrix data :52 columns (first column is id, second column is rumor indicator 1 or -1, other columns are words value is 1 contain or 0 does not contain) 11,102 lines (each line is a message)Hidalgo corpus: lines range 1:75 Lemon corpus : lines range 76:467 Pin rumor : lines range 468:656 swine : lines range 657:1311random messages : lines range 1312:11103Sample contains : French Pin Rumorous corpus (Twitter): 679 Matrix data :52 columns (first column is id, second column is rumor indicator 1 or -1, other columns are words value is 1 contain or 0 does not contain) 189 lines (each line is a message)

The Business Websites Database of European Companies serves as an invaluable and comprehensive resource, meticulously curated to include an extensive and diverse collection of links directing users to the official websites of prominent and influential companies headquartered or operating within Europe. This database spans a wide array of industries and sectors, ranging from technology and finance to manufacturing, healthcare, retail, and beyond, ensuring that users have access to a broad spectrum of business information. By offering direct access to these companies' online platforms, the database not only facilitates seamless navigation to their digital presence but also provides users with the opportunity to explore detailed insights about their products, services, corporate values, and market activities, making it an essential tool for researchers, professionals, and anyone seeking to engage with the European business landscape.