CPDB, IBR, and SGLK2 represent ConsensusPathDB, InteractomeBrowser, and Signalink2, respectively. “Yes” indicates that a database includes a certain interaction, and “No” indicates that it does not. Note that the Reactome and KEGG databases contain mostly human and E. coli (in the case of KEGG) interaction data and map these interactions in other species based on gene orthology.

Pathway-centric approaches are widely used to interpret and contextualize -omics data. However, databases contain different representations of the same biological pathway, which may lead to different results of statistical enrichment analysis and predictive models in the context of precision medicine. We have performed an in-depth benchmarking of the impact of pathway database choice on statistical enrichment analysis and predictive modeling. We analyzed five cancer datasets using three major pathway databases and developed an approach to merge several databases into a single integrative one: MPath. Our results show that equivalent pathways from different databases yield disparate results in statistical enrichment analysis. Moreover, we observed a significant dataset-dependent impact on the performance of machine learning models on different prediction tasks. In some cases, MPath significantly improved prediction performance and also reduced the variance of prediction performances. Furthermore, MPath yielded more consistent and biologically plausible results in statistical enrichment analyses. In summary, this benchmarking study demonstrates that pathway database choice can influence the results of statistical enrichment analysis and predictive modeling. Therefore, we recommend the use of multiple pathway databases or integrative ones.

The Intermediate Data Structure (IDS) provides a standard format for storing and sharing individual-level longitudinal life-course data. Once the data are in the IDS format, a standard set of programs can be used to extract data for analysis, facilitating the analysis of data across multiple databases. However, individual-level longitudinal life-course data are currently held in many different databases and stored in many different formats, and the process of translating data into IDS can be long and tedious. The IDS Transposer is a software tool that automates this process for source data in many formats, allowing database administrators to specify how their datasets are to be represented in IDS.

The global market for academic research databases is experiencing robust growth, projected to reach $388.2 million in 2025. While the exact Compound Annual Growth Rate (CAGR) is not provided, considering the ongoing digitalization of research and education, a conservative estimate would place the CAGR in the range of 7-9% for the forecast period (2025-2033). This growth is fueled by several key drivers. The increasing reliance on digital resources by students, teachers, and researchers across all academic disciplines is a significant factor. Furthermore, the expanding volume of scholarly publications and the need for efficient access and management of research data are propelling market expansion. The rising adoption of cloud-based solutions and the development of sophisticated search and analytical tools within these databases are also contributing to this growth trajectory. The market segmentation highlights the diverse user base, with students, teachers, and experts representing major segments, each with varying needs and subscription models (charge-based or free access). The competitive landscape is characterized by established players like Scopus, Web of Science, and PubMed, alongside other significant contributors like ERIC, ProQuest, and IEEE Xplore, indicating a market with both established dominance and emerging players vying for market share. Geographic distribution shows a strong presence across North America and Europe, but with significant growth potential in Asia-Pacific regions. The market's future trajectory will likely be shaped by several trends. The increasing integration of artificial intelligence (AI) for enhanced search and data analysis capabilities will be a major factor. The ongoing development of open-access initiatives and the expansion of free databases will influence market dynamics, potentially impacting the revenue streams of subscription-based services. However, challenges such as data security concerns, the need for continuous content updates, and the varying levels of digital literacy across different user groups may act as restraints on market growth. Nevertheless, the overall outlook for the academic research database market remains positive, driven by the continued expansion of scholarly research and the growing demand for efficient and reliable access to research information globally.

This diploma thesis describes the conceptual development and implementation of a program for preparing research data of serveral scientific projects to import them into two different databases (PANGAEA and SeaIceDB). In addition the developed software makes it possible to start importing the prepared data into the database SeaIceDB and then visualize it on the local computer.

Academic Research Databases Market Outlook

The global academic research databases market size was valued at approximately USD 3.5 billion in 2023 and is projected to reach around USD 6.2 billion by 2032, growing at a CAGR of 6.5% during the forecast period. The increasing demand for digital resources in academic and research institutions, along with the growing emphasis on online learning and resource accessibility, are key factors driving market growth.

One significant growth factor for the academic research databases market is the exponential increase in academic research activity worldwide. With the surge in the number of higher education institutions and research facilities, the demand for comprehensive and easily accessible databases has skyrocketed. These databases provide a centralized platform for researchers to access a wide array of scholarly articles, data sets, and other pertinent information, streamlining the research process and enhancing the quality of scholarly work.

Another driving force behind the market's expansion is the continuous technological advancements in database management and search functionalities. Modern academic research databases are equipped with sophisticated search algorithms, artificial intelligence, and machine learning capabilities that enable users to efficiently locate relevant information. These advancements not only improve user experience but also significantly reduce the time and effort required to conduct comprehensive literature reviews and gather data.

The increasing prevalence of interdisciplinary research is also contributing to the growth of the academic research databases market. Researchers today often work at the intersection of multiple disciplines, necessitating access to a diverse range of subject-specific databases. The availability of comprehensive databases that cover various fields such as science, technology, medicine, social sciences, and humanities supports this trend by providing researchers with the resources they need to explore and integrate knowledge from different domains.

From a regional perspective, North America holds the largest share of the academic research databases market, driven by the high concentration of leading academic and research institutions and substantial investments in research and development. Europe follows closely, with significant contributions from countries like the UK, Germany, and France. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, fueled by the rapid expansion of higher education infrastructure and increasing government support for research activities. Latin America and the Middle East & Africa, though smaller in market size, are also projected to experience steady growth due to rising academic and research initiatives in these regions.

Database Type Analysis

The academic research databases market is segmented by database type into bibliographic, full-text, numeric, multimedia, and others. Bibliographic databases, which include indexes and abstracts of research articles, play a crucial role in helping researchers locate relevant literature. These databases have been foundational in academic research, providing essential references and citation tracking that are pivotal for scholarly work. Their significance remains high due to the increasing volume of academic publications and the need for comprehensive literature searches.

Full-text databases provide complete access to research articles, journals, and other scholarly materials, making them indispensable for researchers who require in-depth study materials. The convenience of accessing entire articles, rather than just abstracts or summaries, significantly enhances the research process. Full-text databases are particularly valuable in fields such as medicine, where access to full clinical study reports, reviews, and case studies is critical for evidence-based practice.

Numeric databases, which offer access to statistical and numerical data, are essential for researchers in fields like economics, social sciences, and the natural sciences. These databases provide valuable data sets that can be used for quantitative analysis, modeling, and empirical research. The increasing emphasis on data-driven research and the availability of large data sets are propelling the demand for numeric databases.

Multimedia databases, which include audio, video, and other multimedia content, are gaining traction in academic research. These databases are particularly useful in disciplines such a

Biodiversity in many areas is rapidly shifting and declining as a consequence of global change. As such, there is an urgent need for new tools and strategies to help identify, monitor, and conserve biodiversity hotspots. One way to identify these areas is by quantifying functional diversity, which measures the unique roles of species within a community and is valuable for conservation because of its relationship with ecosystem functioning. Unfortunately, the trait information required to evaluate functional diversity is often lacking and is difficult to harmonize across disparate data sources. Biodiversity hotspots are particularly lacking in this information. To address this knowledge gap, we compiled Frugivoria, a trait database containing dietary, life-history, morphological, and geographic traits, for mammals and birds exhibiting frugivory, which are important for seed dispersal, an essential ecosystem service. Accompanying Frugivoria is an open workflow that harmonizes trait and taxonomic data from disparate sources and enables users to analyze traits in space. This version of Frugivoria contains mammal and bird species found in contiguous moist montane forests and adjacent moist lowland forests of Central and South America– the latter specifically focusing on the Andean states. In total, Frugivoria includes 45,216 unique trait values, including new values and harmonized values from existing databases. Frugivoria adds 23,707 new trait values (8,709 for mammals and 14,999 for birds) for a total of 1,733 bird and mammal species. These traits include diet breadth, habitat breadth, habitat specialization, body size, sexual dimorphism, and range-based geographic traits including range size, average annual mean temperature and precipitation, and metrics of human impact calculated over the range. Frugivoria fills gaps in trait categories from other databases such as diet category, home range size, generation time, and longevity, and extends certain traits, once only available for mammals, to birds. In addition, Frugivoria adds newly described species not included in other databases and harmonizes species classifications among databases. Frugivoria and its workflow enable researchers to quantify relationships between traits and the environment, as well as spatial trends in functional diversity, contributing to basic knowledge and applied conservation of frugivores in this region. By harmonizing trait information from disparate sources and providing code to access species occurrence data, this open-access database fills a major knowledge gap and enables more comprehensive trait-based studies of species exhibiting frugivory in this ecologically important region.

Bio Resource for array genes is a free online resource for easy access to collective and integrated information from various public biological resources for human, mouse, rat, fly and c. elegans genes. The resource includes information about the genes that are represented in Unigene clusters. This resource provides interactive tools to selectively view, analyze and interpret gene expression patterns against the background of gene and protein functional information. Different query options are provided to mine the biological relationships represented in the underlying database. Search button will take you to the list of query tools available. This Bio resource is a platform designed as an online resource to assist researchers in analyzing results of microarray experiments and developing a biological interpretation of the results. This site is mainly to interpret the unique gene expression patterns found as biological changes that can lead to new diagnostic procedures and drug targets. This interactive site allows users to selectively view a variety of information about gene functions that is stored in an underlying database. Although there are other online resources that provide a comprehensive annotation and summary of genes, this resource differs from these by further enabling researchers to mine biological relationships amongst the genes captured in the database using new query tools. Thus providing a unique way of interpreting the microarray data results based on the knowledge provided for the cellular roles of genes and proteins. A total of six different query tools are provided and each offer different search features, analysis options and different forms of display and visualization of data. The data is collected in relational database from public resources: Unigene, Locus link, OMIM, NCBI dbEST, protein domains from NCBI CDD, Gene Ontology, Pathways (Kegg, Genmapp and Biocarta) and BIND (Protein interactions). Data is dynamically collected and compiled twice a week from public databases. Search options offer capability to organize and cluster genes based on their Interactions in biological pathways, their association with Gene Ontology terms, Tissue/organ specific expression or any other user-chosen functional grouping of genes. A color coding scheme is used to highlight differential gene expression patterns against a background of gene functional information. Concept hierarchies (Anatomy and Diseases) of MESH (Medical Subject Heading) terms are used to organize and display the data related to Tissue specific expression and Diseases. Sponsors: BioRag database is maintained by the Bioinformatics group at Arizona Cancer Center. The material presented here is compiled from different public databases. BioRag is hosted by the Biotechnology Computing Facility of the University of Arizona. 2002,2003 University of Arizona.

The structure of the data in a mixed database can be a barrier when clustering that database into meaningful groups. A hierarchically structured database necessitates efficient distance measures and clustering algorithms to locate similarities between data objects. Therefore, existing literature proposes hierarchical distance measures to measure the similarities between the records in hierarchical databases. The main contribution of this research is to create and test a new distance measure for large hierarchical databases consisting of mixed data types and attributes, based on an existing tree-based (hierarchical) distance metric, the pq-gram distance metric. Several aims and objectives were pursued to fill a number of gaps in the current body of knowledge. One of these goals was to verify the validity of the pq-gram distance metric when applied to different data sets, and to compare and combine it with a number of different distance measures to demonstrate its usefulness across large mixed databases. To achieve this, further work focused on exploring how to exploit the existing method as a measure of hierarchical data attributes in mixed data sets, and to ascertain whether the new method would produce better results with large mixed databases. For evaluation purposes, the pq-gram metric was applied to The Health Improvement Network (THIN) database to determine if it could identify similarities between the records in the database. After this, it was applied to mixed data to examine different distance measures, which include non-hierarchical and other hierarchical measures, and to combine them to create a Combined Distance Function (CDF). The CDF improved the results when applied to different data sets, such as the hierarchical National Bureau of Economic Research of United States (NBER US) Patent data set and the mixed (THIN) data set. The CDF was then modified to create a New-CDF, which used only the hierarchical pq-gram metric to measure the hierarchical attributes in the mixed data set. The New-CDF worked well, finding the most similar data records when applied to the THIN data set, and grouping them in one cluster using the Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) clustering algorithm. The quality of the clusters was explored using two internal validation indices, Silhouette and C-Index, where the values showed good compactness and quality of the clusters obtained using the new method.

Database Migration Service Market Outlook

The global Database Migration Service market size was valued at approximately USD 4.5 billion in 2023 and is expected to reach around USD 14 billion by 2032, exhibiting a compound annual growth rate (CAGR) of 13.5% during the forecast period. The primary growth driver for this market is the increasing need for efficient management and migration of data across different platforms and systems, spurred by the growing adoption of cloud-based solutions among enterprises.

One of the major growth factors for the database migration service market is the rising adoption of cloud computing worldwide. Enterprises are increasingly moving their applications and databases to the cloud to leverage the benefits of scalability, flexibility, and cost reduction. Cloud-based database migration services are thus in high demand as they facilitate seamless and efficient data transfer from on-premises systems to cloud environments. The increasing use of cloud technologies across various industries is therefore a significant driver for the market.

Another contributing factor to the growth of this market is the rapid digital transformation across industries. Companies are modernizing their IT infrastructure to stay competitive, which often involves migrating legacy systems and databases to more modern and efficient platforms. This trend is particularly evident in sectors such as BFSI, healthcare, and retail, where large volumes of data need to be managed and utilized effectively. Database migration services enable these organizations to perform this transition smoothly, ensuring data integrity and minimizing downtime.

Furthermore, the growing trend of mergers and acquisitions among organizations is also boosting the demand for database migration services. When companies merge or acquire other businesses, they often face the challenge of integrating disparate systems and databases. Database migration services provide the necessary tools and expertise to merge these systems seamlessly, ensuring continuity of operations and data consistency. This factor is expected to continue driving the market growth in the coming years.

In the context of digital transformation, Application Modernization and Migration Service has emerged as a crucial enabler for businesses seeking to update their legacy systems. This service involves re-architecting, re-coding, and migrating applications to modern platforms, often leveraging cloud technologies. By doing so, organizations can enhance their operational efficiency, reduce costs, and improve scalability. The demand for these services is growing as companies recognize the need to stay competitive in an increasingly digital world. Application Modernization and Migration Service not only facilitates the seamless transition of applications but also ensures they are optimized for future technological advancements, thereby providing a strategic advantage.

Regionally, North America holds the largest share of the database migration service market, driven by the presence of major technology companies, high adoption of advanced technologies, and significant investments in IT infrastructure. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, at a CAGR of 15%, due to the rapid digital transformation, increasing adoption of cloud services, and growing number of SMEs in countries like China and India.

Type Analysis

The database migration service market by type is segmented into cloud-based and on-premises. Cloud-based database migration services are gaining substantial traction due to their scalability, flexibility, and cost-effectiveness. These services allow enterprises to migrate their databases without the need for extensive hardware investments, providing a more streamlined and efficient approach to data management. The cloud-based segment is expected to witness significant growth, driven by the increasing adoption of cloud technologies across various industries.

Cloud-based solutions also offer the advantage of reduced downtime during migration. Traditional on-premises migrations can be time-consuming and disruptive, but cloud-based services enable organizations to migrate their databases with minimal impact on their daily operations. This feature is particularly beneficial for enterprises that require high availability and cannot afford prolonged downtime. Furthermore, cloud-bas

Information

The diverse publicly available compound/bioactivity databases constitute a key resource for data-driven applications in chemogenomics and drug design. Analysis of their coverage of compound entries and biological targets revealed considerable differences, however, suggesting benefit of a consensus dataset. Therefore, we have combined and curated information from five esteemed databases (ChEMBL, PubChem, BindingDB, IUPHAR/BPS and Probes&Drugs) to assemble a consensus compound/bioactivity dataset comprising 1144803 compounds with 10915362 bioactivities on 5613 targets (including defined macromolecular targets as well as cell-lines and phenotypic readouts). It also provides simplified information on assay types underlying the bioactivity data and on bioactivity confidence by comparing data from different sources. We have unified the source databases, brought them into a common format and combined them, enabling an ease for generic uses in multiple applications such as chemogenomics and data-driven drug design.

The consensus dataset provides increased target coverage and contains a higher number of molecules compared to the source databases which is also evident from a larger number of scaffolds. These features render the consensus dataset a valuable tool for machine learning and other data-driven applications in (de novo) drug design and bioactivity prediction. The increased chemical and bioactivity coverage of the consensus dataset may improve robustness of such models compared to the single source databases. In addition, semi-automated structure and bioactivity annotation checks with flags for divergent data from different sources may help data selection and further accurate curation.

Structure and content of the dataset

The dataset was created using the Konstanz Information Miner (KNIME) (https://www.knime.com/) and was exported as a CSV-file and a compressed CSV-file.

Except for the canonical SMILES columns, all columns are filled with the datatype ‘string’. The datatype for the canonical SMILES columns is the smiles-format. We recommend the File Reader node for using the dataset in KNIME. With the help of this node the data types of the columns can be adjusted exactly. In addition, only this node can read the compressed format.

Column content:

• ChEMBL ID, PubChem ID, IUPHAR ID: chemical identifier of the databases
• Target: biological target of the molecule expressed as the HGNC gene symbol
• Activity type: for example, pIC₅₀
• Assay type: Simplification/Classification of the assay into cell-free, cellular, functional and unspecified
• Unit: unit of bioactivity measurement
• Mean columns of the databases: mean of bioactivity values or activity comments denoted with the frequency of their occurrence in the database, e.g. Mean C = 7.5 *(15) -> the value for this compound-target pair occurs 15 times in ChEMBL database
• Activity check annotation: a bioactivity check was performed by comparing values from the different sources and adding an activity check annotation to provide automated activity validation for additional confidence
  • no comment: bioactivity values are within one log unit;
  • check activity data: bioactivity values are not within one log unit;
  • only one data point: only one value was available, no comparison and no range calculated;
  • no activity value: no precise numeric activity value was available;
  • no log-value could be calculated: no negative decadic logarithm could be calculated, e.g., because the reported unit was not a compound concentration
• Ligand names: all unique names contained in the five source databases are listed
• Canonical SMILES columns: Molecular structure of the compound from each database
• Structure check: To denote matching or differing compound structures in different source databases
  • match: molecule structures are the same between different sources;
  • no match: the structures differ;
  • 1 source: no structure comparison is possible, because the molecule comes from only one source database.
• Source: From which databases the data come from

Abstract

We had to delete V3 of MarketScan because of some unusual circumstances with the formats of some of the files we were sent (to prevent the duplication of records). V3.1 contains all of the info that was in V3, however V3.1 has 2022 data & a slightly different version of the 2021 data. The data on this page is the version of the 2021 data that was in V3. Our purpose in posting this is to enable researchers who completed analyses on V3 to replicate their work by combining the data here with the data on the main page.

FOR THE MAJORITY OF RESEARCHERS, however, we strongly recommend using V3.1, and ignoring this page, as it will be irrelevant for most research going forward. (Rule of thumb: If you are unsure whether you need the data on this page, then you probably don't need it.)

Usage

To recreate V3 of the data, use the data for 2020 and earlier that is on the main MarketScan Databases page, and combine it with the data on this page. That will give you the *exact *same data that was in V3.

The data documentation on the main MarketScan page also applies to the data on this page.

How is the data on this page different from the 2021 data on the main MarketScan page?

Metadata access is required to view this section.

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

NoSQL Database Market Outlook

According to our latest research, the global NoSQL database market size reached USD 9.8 billion in 2024, reflecting robust industry momentum driven by the exponential growth of unstructured and semi-structured data across enterprises. The market is experiencing a remarkable compound annual growth rate (CAGR) of 20.7% and is forecasted to attain a value of USD 63.6 billion by 2033. This exceptional growth trajectory is primarily fueled by the surging demand for scalable, flexible, and high-performance database solutions that can support modern application requirements, especially in the era of big data, real-time analytics, and cloud computing.

A key growth factor in the NoSQL database market is the rapid proliferation of digital transformation initiatives across industries. Organizations are increasingly generating vast volumes of data from diverse sources such as social media, IoT devices, mobile applications, and e-commerce platforms. Traditional relational database management systems (RDBMS) often struggle to accommodate the scale, variety, and velocity of this data, which has led to a pronounced shift toward NoSQL solutions. NoSQL databases provide the flexibility to store, process, and analyze both structured and unstructured data without the rigid schema constraints of RDBMS, enabling businesses to derive actionable insights and enhance decision-making processes. This adaptability is particularly crucial for industries like retail, finance, and healthcare, where real-time customer engagement and data-driven services are key competitive differentiators.

Another significant driver propelling the NoSQL database market is the growing adoption of cloud computing and the increasing need for highly available, distributed database architectures. Cloud-based NoSQL solutions offer organizations the ability to scale resources dynamically, reduce infrastructure costs, and ensure high availability and disaster recovery capabilities. As enterprises embrace hybrid and multi-cloud strategies, NoSQL databases have become integral to supporting mission-critical workloads, global application deployments, and seamless data integration across disparate environments. The rise of microservices and containerized applications has further accelerated the demand for NoSQL databases, as these architectures require agile, horizontally scalable data storage solutions to meet the evolving needs of modern businesses.

The emergence of advanced analytics, artificial intelligence (AI), and machine learning (ML) applications is further amplifying the demand for NoSQL database market solutions. These technologies require the ability to ingest, process, and analyze massive datasets in real time, often with complex relationships and diverse data types. NoSQL databases, with their support for flexible data models and high-throughput operations, are uniquely positioned to power next-generation analytics and AI-driven applications. This trend is particularly evident in sectors such as BFSI, healthcare, and telecommunications, where organizations are leveraging NoSQL databases to enhance fraud detection, personalize customer experiences, and optimize operational efficiencies. The ongoing evolution of data privacy regulations and the need for secure, compliant data management practices further reinforce the strategic importance of NoSQL solutions in the global data ecosystem.

From a regional perspective, North America continues to dominate the NoSQL database market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The United States, in particular, is home to leading technology vendors and a mature digital infrastructure, which has facilitated widespread adoption of NoSQL solutions across various industry verticals. Meanwhile, Asia Pacific is emerging as a high-growth market, driven by rapid digitalization, increasing investments in cloud infrastructure, and the proliferation of internet-connected devices. The region is witnessing a surge in demand from sectors such as e-commerce, fintech, and telecommunications, as businesses seek to harness the power of big data and real-time analytics to drive innovation and competitiveness. As organizations across the globe continue to embrace digital transformation, the NoSQL database market is poised for sustained growth and technological advancement over the forecast period.

This dataset contains vulnerability duplicate information from two sources: the cross-database duplicates and the GitHub Advisory Database duplicates. The dataset is provided in JSON format and is intended for use in research related to vulnerability matching and duplication detection.

## Dataset Overview

The dataset consists of two files:

1. **cross_database_duplicates.json**: Contains 22,145 pairs of duplicate vulnerabilities identified across multiple databases.
2. **github_advisory_database_duplicates.json**: Contains 133 pairs of duplicate vulnerabilities specifically from the GitHub Advisory Database.

## File Format

Both files are in JSON format. Each record consists of four attributes:

- `id_1`: The ID of the first vulnerability report.
- `id_2`: The ID of the second vulnerability report.
- `record_1`: The first vulnerability report.
- `record_2`: The second vulnerability report.

These attributes are designed to help users identify and compare vulnerability reports that are considered duplicates.

## Usage

This dataset can be used for studies in vulnerability matching, natural language processing (NLP) applications, and the development of tools for detecting duplicate vulnerabilities in different databases.

The nutrition values of fish and other aquatic foods have recently gained global recognition for their potential to alleviate ‘hidden hunger’ in many contexts and for many nutritional vulnerable people. Yet, data for most fish, aquatic species, and forms of aquatic foods (particularly those of lower commercial value) are unavailable and unattainable due to the prohibitive cost of high-quality nutrient analysis. This means the databases that house the data that do exist are simultaneously incredibly valuable and riddled with gaps. Many initiatives have risen to address this challenge of compiling the best quality, to all available, data on the nutrient qualities of fish and other aquatic foods. There are multiple databases that now exist through which a researcher or policy maker might locate or contribute data. These include (1) Analytical Food Composition Database; (2) Food Composition Database for Biodiversity Global food composition database for fish and shellfish (3) Seafood Data (4) FishNutrients (5) Aquatic Food Composition Database (6) FoodEXplorer (7) the many different National Food Composition Databases. With input from experts from the fields of food sciences, nutrition and fisheries, and with a rapid review process by database curators, we compiled the metadata for seven different databases that contain large data set on nutrient qualities of fish and other aquatic foods. By summarising metadata, and generating a comparison between databases, we envisage that this tool will help researchers navigate these different tools, and better understand their different strengths and limitations.

Database Covering the Previously Excluded Daily Life Activities

In biomedical engineering, implants are designed according to the boundary conditions of gait data and tested against. However, due to diversity in cultural backgrounds, religious rituals might cause different ranges of motion and different loading patterns. Especially in the Eastern part of the world, diverse Activities of Daily Living (ADL) consist of salat, yoga rituals, and different style sitting postures. Although databases cover ADL for the Western population, a database covering these diverse activities of the Eastern world, specific to these populations is non-existent. To include previously excluded ADL is a key step in understanding the kinematics and kinetics of these activities. By means of developments in motion capture technologies, excluded ADL data are captured to obtain the coordinate values to calculate the range of motion and the joint reaction forces. This study focuses on data collection protocol and the creation of an online database of previously excluded ADL activities, targeting 200 healthy subjects via Qualisys and IMU motion capture systems, and force plates, from West and Middle East Asian populations. Anthropometrics are known to affect kinematics and kinetics which are also included in the collected data. The current version of the database covers 50 volunteers for 12 different activities, the database aims for 100- male and 100- female healthy volunteers as the final target including C3D and BVH file types. The tasks are defined and listed in a table to create a database to make a query based on age, gender, BMI, type of activity and motion capture system. The data is collected only from a healthy population to understand healthy motion patterns during these previously excluded ADLs. The collected data is to be used for designing implants to allow these sorts of activities to be performed without compromising the quality of life of patients performing these activities in the future.

SQL In-Memory Database Market size was valued at USD 9.26 Billion in 2024 and is projected to reach USD 35.7 Billion by 2032, growing at a CAGR of 20.27% from 2026 to 2032.

SQL In-Memory Database Market Drivers

Demand for Real-Time Analytics and Processing: Businesses increasingly require real-time insights from their data to make faster and more informed decisions. SQL In-Memory databases excel at processing data much faster than traditional disk-based databases, enabling real-time analytics and operational dashboards.

Growth of Big Data and IoT Applications: The rise of Big Data and the Internet of Things (IoT) generates massive amounts of data that needs to be processed quickly. SQL In-Memory databases can handle these high-velocity data streams efficiently due to their in-memory architecture.

Improved Performance for Transaction Processing Systems (TPS): In-memory databases offer significantly faster query processing times compared to traditional databases. This translates to improved performance for transaction-intensive applications like online banking, e-commerce platforms, and stock trading systems.

Reduced Hardware Costs (in some cases): While implementing an in-memory database might require an initial investment in additional RAM, it can potentially reduce reliance on expensive high-performance storage solutions in specific scenarios.

Focus on User Experience and Application Responsiveness: In today's digital landscape, fast and responsive applications are crucial. SQL In-Memory databases contribute to a smoother user experience by enabling quicker data retrieval and transaction processing.

However, it's important to consider some factors that might influence market dynamics:

Limited Data Capacity: In-memory databases are typically limited by the amount of available RAM, making them less suitable for storing massive datasets compared to traditional disk-based solutions.

Higher Implementation Costs: Setting up and maintaining an in-memory database can be more expensive due to the additional RAM requirements compared to traditional databases.

Hybrid Solutions: Many organizations opt for hybrid database solutions that combine in-memory and disk-based storage, leveraging the strengths of both for different data sets and applications.

An updated and improved version of a global, vertically resolved, monthly mean zonal mean ozone database has been calculated – hereafter referred to as the BSVertOzone database, the BSVertOzone database. Like its predecessor, it combines measurements from several satellite-based instruments and ozone profile measurements from the global ozonesonde network. Monthly mean zonal mean ozone concentrations in mixing ratio and number density are provided in 5 latitude zones, spanning 70 altitude levels (1 to 70km), or 70 pressure 5 levels that are approximately 1km apart (878.4hPa to 0.046hPa). Different data sets or "Tiers" are provided: "Tier 0" is based only on the available measurements and therefore does not completely cover the whole globe or the full vertical range uniformly; the "Tier 0.5" monthly mean zonal means are calculated from a filled version of the Tier 0 database where missing monthly mean zonal mean values are estimated from correlations at level 20 against a total column ozone database and then at levels above and below on correlations with lower and upper levels respectively. The Tier 10 0.5 database includes the full range of measurement variability and is created as an intermediate step for the calculation of the "Tier 1" data where a least squares regression model is used to attribute variability to various known forcing factors for ozone. Regression model fit coefficients are expanded in Fourier series and Legendre polynomials (to account for seasonality and latitudinal structure, respectively). Four different combinations of contributions from selected regression model basis functions result in four different "Tier 1" data set that can be used for comparisons with chemistry-climate model simulations that do not 15 exhibit the same unforced variability as reality (unless they are nudged towards reanalyses). Compared to previous versions of the database, this update includes additional satellite data sources and ozonesonde measurements to extend the database period to 2016. Additional improvements over the previous version of the database include: (i) Adjustments of measurements to account for biases and drifts between different data sources (using a chemistry-transport model simulation as a transfer standard), (ii) a more objective way to determine the optimum number of Fourier and Legendre expansions for the basis 20 function fit coefficients, and (iii) the derivation of methodological and measurement uncertainties on each database value are traced through all data modification steps. Comparisons with the ozone database from SWOOSH (Stratospheric Water and OzOne Satellite Homogenized data set) show excellent agreements in many regions of the globe, and minor differences caused by different bias adjustment procedures for the two databases. However, compared to SWOOSH, BSVertOzone additionally covers the troposphere.

The global market for academic research databases is experiencing robust growth, projected to be valued at $259.3 million in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 5.9% from 2025 to 2033. This expansion is driven by several key factors. The increasing digitization of scholarly publications and the growing reliance on online research resources across universities, research institutions, and corporations are significant contributors. Furthermore, the expanding availability of open-access journals and repositories, while presenting challenges to some established players, ultimately broadens the overall market by increasing accessibility and usage. The rising demand for advanced search functionalities, data analytics tools integrated within these databases, and robust citation management systems also fuels market growth. Different subscription models, including free and charge-based access, cater to diverse user needs – students, teachers, experts, and others – further driving market segmentation and overall growth. The North American market currently holds a significant share due to the presence of major research institutions and established database providers. However, increasing research activities in Asia-Pacific and other regions are poised to fuel future growth, with a potentially significant increase in the market share in these regions over the forecast period. Competition remains intense among established players like Scopus, Web of Science, and PubMed, alongside newer entrants. Differentiation through superior indexing, advanced search capabilities, and specialized content areas is vital for success in this competitive landscape. The market segmentation by application (Student, Teacher, Expert, Others) and type of access (Charge, Free) provides valuable insights into the diverse user base and revenue streams. The "charge" segment is expected to maintain a significant market share, driven by the demand for comprehensive and specialized research content requiring paid subscriptions. However, the "free" segment, fueled by the increasing availability of open-access resources, will also show considerable growth, broadening accessibility and market penetration. Regional growth patterns will likely reflect existing research infrastructure and investments in higher education and research across different geographic areas. Continued technological advancements and innovation in areas such as artificial intelligence-powered search and data analysis will further shape the market landscape, leading to more sophisticated and efficient research tools in the years to come.