Document Database as a Service Market Outlook

As per our latest research, the global Document Database as a Service (DBaaS) market size reached USD 6.2 billion in 2024, and the market is poised to expand at a robust CAGR of 23.9% during the forecast period. By 2033, the market is projected to achieve a value of USD 52.6 billion, driven by the rapid adoption of cloud-based solutions and the escalating need for scalable data management across industries. The surge in digital transformation initiatives, coupled with growing enterprise demand for flexible, cost-effective, and high-performance database solutions, is fueling this growth trajectory.

The primary growth factor propelling the Document Database as a Service market is the exponential rise in unstructured and semi-structured data generated by enterprises worldwide. Organizations are increasingly seeking agile and scalable database solutions that can handle diverse data types, support real-time analytics, and seamlessly integrate with modern cloud-native applications. The proliferation of IoT devices, mobile applications, and digital services has further accelerated the volume and complexity of data, necessitating advanced DBaaS offerings. Document-oriented databases, with their flexible schema and scalability, are particularly well-suited for these requirements, positioning them as a cornerstone for modern data architectures in both large enterprises and SMEs.

Another significant driver is the cost efficiency and operational agility offered by DBaaS platforms. Traditional on-premises database management often incurs substantial capital expenditures, ongoing maintenance costs, and resource-intensive upgrades. In contrast, DBaaS solutions provide a pay-as-you-go model, automatic updates, and managed services, allowing businesses to focus on core operations rather than database administration. This shift not only reduces total cost of ownership but also enhances business continuity, scalability, and security. The integration of advanced features such as automated backups, disaster recovery, and real-time monitoring further enhances the value proposition of Document Database as a Service, making it an attractive option for organizations aiming to modernize their IT infrastructure.

The rapid evolution of artificial intelligence, machine learning, and big data analytics is also contributing to the expansion of the Document Database as a Service market. Enterprises are leveraging DBaaS platforms to power AI-driven applications, process large volumes of data, and derive actionable insights in real time. The ability of document databases to store and manage complex, hierarchical, and varied data structures aligns perfectly with the needs of next-generation analytics and data science projects. As a result, industries such as BFSI, healthcare, retail, and manufacturing are increasingly adopting DBaaS to enable innovation, improve customer experiences, and gain competitive advantages in their respective markets.

From a regional perspective, North America continues to dominate the global Document Database as a Service market, accounting for the largest revenue share in 2024. The presence of leading cloud service providers, high digital adoption rates, and a mature enterprise IT landscape are key factors driving regional growth. Meanwhile, Asia Pacific is emerging as the fastest-growing region, fueled by rapid cloud adoption, expanding digital economies, and increasing investments in IT infrastructure across countries such as China, India, and Japan. Europe, Latin America, and the Middle East & Africa are also witnessing steady growth, supported by regulatory initiatives, digital transformation projects, and the growing need for scalable data management solutions.

Database Type Analysis

The Database Type segment of the Document Database as a Service market is primarily categorized into NoSQL, NewSQL, Multi-Model, and Others. Among these, NoSQL databases have established a dominant position, thanks to their ability to efficiently handle unstructured and semi-structured data formats. The flexibility of NoSQL databases enables organizations to store various data types such as JSON, XML, and BSON, making them ideal for modern applications that require rapid development cycles and agile data models. The market demand for NoSQL DBaaS is further bolstered by the proliferation of web, mobile, and IoT applications, where scalability and performance are paramount. Enterpr

Document Database Platform Market Outlook

According to our latest research, the Global Document Database Platform market size was valued at $7.8 billion in 2024 and is projected to reach $26.4 billion by 2033, expanding at a CAGR of 14.5% during the forecast period of 2025–2033. The primary driver behind this robust growth is the exponential surge in unstructured data generation across various industries, which has significantly increased the need for scalable, flexible, and high-performance document database platforms. As enterprises transition to digital-first operations and cloud-native architectures, document database platforms are becoming critical for efficient data management, real-time analytics, and seamless integration with next-generation applications. This market is further propelled by the increasing adoption of artificial intelligence and machine learning technologies, which demand sophisticated data storage and retrieval solutions capable of handling diverse and complex data types.

Regional Outlook

North America holds the largest share of the global Document Database Platform market, accounting for nearly 39% of the total market value in 2024. This dominance stems from the region’s mature IT infrastructure, high cloud adoption rates, and a strong presence of leading technology vendors such as MongoDB, Amazon Web Services, and Microsoft. The United States, in particular, has seen a rapid uptake of document database platforms in sectors like BFSI, healthcare, and retail, driven by stringent regulatory compliance requirements and the need for robust data security. Furthermore, North America’s innovation ecosystem, characterized by substantial investments in R&D and a vibrant startup culture, continues to foster advancements in database technologies, ensuring sustained market leadership throughout the forecast period.

In contrast, the Asia Pacific region is projected to be the fastest-growing market for document database platforms, with a forecasted CAGR of 17.2% from 2025 to 2033. The surge in digital transformation initiatives across countries such as China, India, and Japan is fueling unprecedented demand for scalable data management solutions. Rapid urbanization, the proliferation of e-commerce, and the expansion of fintech and healthcare sectors are key contributors to this growth. Governments in the region are actively promoting digital infrastructure development, which, coupled with increasing investments from global cloud service providers, is accelerating the adoption of document database platforms. Notably, the region’s large population base and growing internet penetration present significant opportunities for market expansion, particularly among small and medium enterprises seeking cost-effective and agile database solutions.

Emerging economies in Latin America and the Middle East & Africa are also witnessing gradual adoption of document database platforms, albeit at a slower pace compared to mature markets. Localized challenges such as limited access to advanced IT infrastructure, budget constraints, and data sovereignty concerns hinder widespread implementation. However, increasing awareness about the benefits of cloud-based database solutions and supportive government policies aimed at digitalization are gradually mitigating these barriers. In Latin America, countries like Brazil and Mexico are experiencing a rise in demand from the retail and government sectors, while in the Middle East & Africa, the focus is on leveraging document databases for smart city initiatives and enhancing public sector efficiency.

Report Scope

EIA previously collected sales and revenue data in a category called "Other." This category was defined as including activities such as public street highway lighting, other sales to public authorities, sales to railroads and railways, and interdepartmental sales. EIA has revised its survey to separate the transportation sales and reassign the other activities to the commercial and industrial sectors as appropriate.

<p class="Bodypara">This is an electric utility data file that includes

utility level retail sales of electricity and associated revenue by end-use sector, State, and reporting month. The data source is the survey: Form EIA-826, "Monthly Electric Utility Sales and Revenue Report with State Distributions." The Form EIA-826 is used to collect retail sales of electricity and associated revenue, each month, from a statistically chosen sample of electric utilities in the United States. The respondents to the Form EIA-826 are chosen from the Form EIA-861, "Annual Electric Utility Report." The data also include, for each State, a record (UTILITYID "000000") containing data values which represent the arithmetic differences between the "estimated" State totals and the sum of the retail sales and associated revenue data reported by the respondents to the Form EIA-826.

The data are compressed into a self-extracting (f826yyyy.exe) zip file. This self-extracting zip file expands into one DBF file (f826utilyyyy.dbf) that contains the yearly data and an ASCII text file (f826layoutyyyy.txt) that contains the file description and record layout for the data base structure. The current year's file will be a year-to-date file and is maintained in this monthly format until the data for the final month is finalized.

To expand the self-extracting zip file, type f826yyyy.exe from a DOS window, or double click on the file name from File Manager in Windows 3x or Windows Explorer in either Windows 95, Windows 98, Windows 2000, XP, or ME. Or, click Start, then Run, then select name of .EXE file to open, then "OK." (Requires approx. 600K space). Usually, the current year's file will be a "year-to-date" file until the data for the final month is finalized.

^*Note: Substitute the applicable year for "yyyy" in the file name.

File Size: 200 k

Methodology is based on the "Model-Based Sampling, Inference and Imputation."

Contact:

NoSQL Database as a Service (DBaaS) Market Outlook

According to our latest research, the global NoSQL Database as a Service (DBaaS) market size reached USD 7.4 billion in 2024. The market is experiencing robust expansion, driven by the increasing adoption of cloud-native applications and the growing need for scalable, flexible data management solutions. The market is projected to reach USD 36.2 billion by 2033, growing at an impressive CAGR of 19.5% from 2025 to 2033. This growth is largely fueled by the proliferation of big data, the rise of Internet of Things (IoT) devices, and enterprises’ accelerating digital transformation initiatives.

The NoSQL Database as a Service market is being propelled by the exponential increase in unstructured and semi-structured data generated across industries. Traditional relational databases often struggle to handle the velocity, variety, and volume of modern data streams, creating a significant opportunity for NoSQL DBaaS solutions. These platforms offer high scalability, flexible schema design, and seamless integration capabilities, which are essential for businesses dealing with dynamic workloads and real-time analytics. As organizations prioritize digital agility and look to leverage data for competitive advantage, the adoption of NoSQL DBaaS is becoming a strategic imperative, especially among enterprises seeking to modernize their IT infrastructure and support next-generation applications.

Another critical growth factor for the NoSQL Database as a Service market is the widespread migration to cloud environments. Cloud-native architectures are designed to maximize scalability, availability, and performance, all of which are core strengths of NoSQL DBaaS platforms. Enterprises are increasingly shifting from on-premises databases to cloud-based solutions to benefit from reduced operational overhead, flexible pricing models, and global accessibility. The ability of NoSQL DBaaS to support multi-cloud and hybrid cloud strategies is further accelerating adoption, as businesses look to avoid vendor lock-in and ensure business continuity. The integration of advanced features such as automated scaling, backup, and disaster recovery is also contributing to the market’s sustained growth.

Moreover, the rising demand for real-time analytics, personalization, and IoT-driven applications is significantly impacting the NoSQL Database as a Service market. Modern applications require rapid data ingestion, low-latency processing, and high availability, which are areas where NoSQL DBaaS excels. Industries such as retail and e-commerce, BFSI, and healthcare are leveraging these platforms to deliver enhanced customer experiences, streamline operations, and drive innovation. The increasing focus on data security, compliance, and privacy is prompting vendors to offer robust security features and certifications, making NoSQL DBaaS an attractive option for regulated industries as well.

From a regional perspective, North America continues to dominate the NoSQL Database as a Service market, accounting for the largest revenue share in 2024 due to the presence of major technology providers and early adoption of cloud-based solutions. However, the Asia Pacific region is emerging as the fastest-growing market, driven by rapid digitalization, expanding IT infrastructure, and the growing presence of small and medium-sized enterprises (SMEs) embracing cloud technologies. Europe also holds a significant market share, supported by strong investments in digital transformation and data-driven initiatives across various industries. The Middle East & Africa and Latin America are witnessing steady growth, with increasing cloud adoption and government-led digitalization programs.

Database Type Analysis

The NoSQL Database as a Service market is segmented by database type into Document-Based, Key-Value Store, Column-Based, Graph-Based, and Others. Document-based databases, such as MongoDB and Couchbase, have gained substantial traction due to their flexibility in handling complex, semi-structured data like JSON and XML. These databases are widely used in content management systems, e-commerce platforms, and real-time analytics applications. Their ability to scale horizontally and support dynamic schema designs makes them ideal for rapidly evolving business requirements, thereby capturing a significant share of the market.

Terms of UseData Limitations and DisclaimerThe user’s use of and/or reliance on the information contained in the Document shall be at the user’s own risk and expense. MassDEP disclaims any responsibility for any loss or harm that may result to the user of this data or to any other person due to the user’s use of the Document.This is an ongoing data development project. Attempts have been made to contact all PWS systems, but not all have responded with information on their service area. MassDEP will continue to collect and verify this information. Some PWS service areas included in this datalayer have not been verified by the PWS or the municipality involved, but since many of those areas are based on information published online by the municipality, the PWS, or in a publicly available report, they are included in the estimated PWS service area datalayer.Please note: All PWS service area delineations are estimates for broad planning purposes and should only be used as a guide. The data is not appropriate for site-specific or parcel-specific analysis. Not all properties within a PWS service area are necessarily served by the system, and some properties outside the mapped service areas could be served by the PWS – please contact the relevant PWS. Not all service areas have been confirmed by the systems.Please use the following citation to reference these data:MassDEP, Water Utility Resilience Program. 2025. Community and Non-Transient Non-Community Public Water System Service Area (PubV2025_3).IMPORTANT NOTICE: This MassDEP Estimated Water Service datalayer may not be complete, may contain errors, omissions, and other inaccuracies and the data are subject to change. This version is published through MassGIS. We want to learn about the data uses. If you use this dataset, please notify staff in the Water Utility Resilience Program (WURP@mass.gov).This GIS datalayer represents approximate service areas for Public Water Systems (PWS) in Massachusetts. In 2017, as part of its “Enhancing Resilience and Emergency Preparedness of Water Utilities through Improved Mapping” (Critical Infrastructure Mapping Project ), the MassDEP Water Utility Resilience Program (WURP) began to uniformly map drinking water service areas throughout Massachusetts using information collected from various sources. Along with confirming existing public water system (PWS) service area information, the project collected and verified estimated service area delineations for PWSs not previously delineated and will continue to update the information contained in the datalayers. As of the date of publication, WURP has delineated Community (COM) and Non-Transient Non-Community (NTNC) service areas. Transient non-community (TNCs) are not part of this mapping project.Layers and Tables:The MassDEP Estimated Public Water System Service Area data comprises two polygon feature classes and a supporting table. Some data fields are populated from the MassDEP Drinking Water Program’s Water Quality Testing System (WQTS) and Annual Statistical Reports (ASR).The Community Water Service Areas feature class (PWS_WATER_SERVICE_AREA_COMM_POLY) includes polygon features that represent the approximate service areas for PWS classified as Community systems.The NTNC Water Service Areas feature class (PWS_WATER_SERVICE_AREA_NTNC_POLY) includes polygon features that represent the approximate service areas for PWS classified as Non-Transient Non-Community systems.The Unlocated Sites List table (PWS_WATER_SERVICE_AREA_USL) contains a list of known, unmapped active Community and NTNC PWS services areas at the time of publication.ProductionData UniversePublic Water Systems in Massachusetts are permitted and regulated through the MassDEP Drinking Water Program. The WURP has mapped service areas for all active and inactive municipal and non-municipal Community PWSs in MassDEP’s Water Quality Testing Database (WQTS). Community PWS refers to a public water system that serves at least 15 service connections used by year-round residents or regularly serves at least 25 year-round residents.All active and inactive NTNC PWS were also mapped using information contained in WQTS. An NTNC or Non-transient Non-community Water System refers to a public water system that is not a community water system and that has at least 15 service connections or regularly serves at least 25 of the same persons or more approximately four or more hours per day, four or more days per week, more than six months or 180 days per year, such as a workplace providing water to its employees.These data may include declassified PWSs. Staff will work to rectify the status/water services to properties previously served by declassified PWSs and remove or incorporate these service areas as needed.Maps of service areas for these systems were collected from various online and MassDEP sources to create service areas digitally in GIS. Every PWS is assigned a unique PWSID by MassDEP that incorporates the municipal ID of the municipality it serves (or the largest municipality it serves if it serves multiple municipalities). Some municipalities contain more than one PWS, but each PWS has a unique PWSID. The Estimated PWS Service Area datalayer, therefore, contains polygons with a unique PWSID for each PWS service area.A service area for a community PWS may serve all of one municipality (e.g. Watertown Water Department), multiple municipalities (e.g. Abington-Rockland Joint Water Works), all or portions of two or more municipalities (e.g. Provincetown Water Dept which serves all of Provincetown and a portion of Truro), or a portion of a municipality (e.g. Hyannis Water System, which is one of four PWSs in the town of Barnstable).Some service areas have not been mapped but their general location is represented by a small circle which serves as a placeholder. The location of these circles are estimates based on the general location of the source wells or the general estimated location of the service area - these do not represent the actual service area.Service areas were mapped initially from 2017 to 2022 and reflect varying years for which service is implemented for that service area boundary. WURP maintains the dataset quarterly with annual data updates; however, the dataset may not include all current active PWSs. A list of unmapped PWS systems is included in the USL table PWS_WATER_SERVICE_AREA_USL available for download with the dataset. Some PWSs that are not mapped may have come online after this iteration of the mapping project; these will be reconciled and mapped during the next phase of the WURP project. PWS IDs that represent regional or joint boards with (e.g. Tri Town Water Board, Randolph/Holbrook Water Board, Upper Cape Regional Water Cooperative) will not be mapped because their individual municipal service areas are included in this datalayer.PWSs that do not have corresponding sources, may be part of consecutive systems, may have been incorporated into another PWSs, reclassified as a different type of PWS, or otherwise taken offline. PWSs that have been incorporated, reclassified, or taken offline will be reconciled during the next data update.Methodologies and Data SourcesSeveral methodologies were used to create service area boundaries using various sources, including data received from the systems in response to requests for information from the MassDEP WURP project, information on file at MassDEP, and service area maps found online at municipal and PWS websites. When provided with water line data rather than generalized areas, 300-foot buffers were created around the water lines to denote service areas and then edited to incorporate generalizations. Some municipalities submitted parcel data or address information to be used in delineating service areas.Verification ProcessSmall-scale PDF file maps with roads and other infrastructure were sent to every PWS for corrections or verifications. For small systems, such as a condominium complex or residential school, the relevant parcels were often used as the basis for the delineated service area. In towns where 97% or more of their population is served by the PWS and no other service area delineation was available, the town boundary was used as the service area boundary. Some towns responded to the request for information or verification of service areas by stating that the town boundary should be used since all or nearly all of the municipality is served by the PWS.Sources of information for estimated drinking water service areasThe following information was used to develop estimated drinking water service areas:EOEEA Water Assets Project (2005) water lines (these were buffered to create service areas)Horsely Witten Report 2008Municipal Master Plans, Open Space Plans, Facilities Plans, Water Supply System Webpages, reports and online interactive mapsGIS data received from PWSDetailed infrastructure mapping completed through the MassDEP WURP Critical Infrastructure InitiativeIn the absence of other service area information, for municipalities served by a town-wide water system serving at least 97% of the population, the municipality’s boundary was used. Determinations of which municipalities are 97% or more served by the PWS were made based on the Percent Water Service Map created in 2018 by MassDEP based on various sources of information including but not limited to:The Winter population served submitted by the PWS in the ASR submittalThe number of services from WQTS as a percent of developed parcelsTaken directly from a Master Plan, Water Department Website, Open Space Plan, etc. found onlineCalculated using information from the town on the population servedMassDEP staff estimateHorsely Witten Report 2008Calculation based on Water System Areas Mapped through MassDEP WURP Critical Infrastructure Initiative, 2017-2022Information found in publicly available PWS planning documents submitted to MassDEP or as part of infrastructure planningMaintenanceThe

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

The dataset contains information about the contents of 100 Terms of Service (ToS) of online platforms. The documents were analyzed and evaluated from the point of view of the European Union consumer law. The main results have been presented in the table titled "Terms of Service Analysis and Evaluation_RESULTS." This table is accompanied by the instruction followed by the annotators, titled "Variables Definitions," allowing for the interpretation of the assigned values. In addition, we provide the raw data (analyzed ToS, in the folder "Clear ToS") and the annotated documents (in the folder "Annotated ToS," further subdivided).

SAMPLE: The sample contains 100 contracts of digital platforms operating in sixteen market sectors: Cloud storage, Communication, Dating, Finance, Food, Gaming, Health, Music, Shopping, Social, Sports, Transportation, Travel, Video, Work, and Various. The selected companies' main headquarters span four legal surroundings: the US, the EU, Poland specifically, and Other jurisdictions. The chosen platforms are both privately held and publicly listed and offer both fee-based and free services. Although the sample cannot be treated as representative of all online platforms, it nevertheless accounts for the most popular consumer services in the analyzed sectors and contains a diverse and heterogeneous set.

CONTENT: Each ToS has been assigned the following information: 1. Metadata: 1.1. the name of the service; 1.2. the URL; 1.3. the effective date; 1.4. the language of ToS; 1.5. the sector; 1.6. the number of words in ToS; 1.7–1.8. the jurisdiction of the main headquarters; 1.9. if the company is public or private; 1.10. if the service is paid or free. 2. Evaluative Variables: remedy clauses (2.1– 2.5); dispute resolution clauses (2.6–2.10); unilateral alteration clauses (2.11–2.15); rights to police the behavior of users (2.16–2.17); regulatory requirements (2.18–2.20); and various (2.21–2.25). 3. Count Variables: the number of clauses seen as unclear (3.1) and the number of other documents referred to by the ToS (3.2). 4. Pull-out Text Variables: rights and obligations of the parties (4.1) and descriptions of the service (4.2)

ACKNOWLEDGEMENT: The research leading to these results has received funding from the Norwegian Financial Mechanism 2014-2021, project no. 2020/37/K/HS5/02769, titled “Private Law of Data: Concepts, Practices, Principles & Politics.”

Serverless NoSQL Database Market Outlook

According to our latest research, the Global Serverless NoSQL Database market size was valued at $2.8 billion in 2024 and is projected to reach $13.6 billion by 2033, expanding at a robust CAGR of 19.2% during the forecast period of 2025–2033. The primary driver fueling this remarkable growth is the surging demand for highly scalable, low-latency data management solutions that can seamlessly support modern, cloud-native applications across diverse industries. As organizations increasingly migrate to digital-first strategies, the need for flexible, cost-efficient, and maintenance-free database architectures is accelerating the adoption of serverless NoSQL databases worldwide. This transformation is further amplified by the proliferation of real-time analytics, IoT deployments, and mobile-first services, all of which require agile data storage and retrieval capabilities that traditional database models struggle to deliver.

Regional Outlook

North America currently dominates the serverless NoSQL database market, commanding the largest share with over 38% of global revenues in 2024. This leadership is largely attributed to the region’s mature cloud ecosystem, early adoption of serverless technologies, and the presence of tech giants such as Amazon Web Services, Google, and Microsoft, who are continuously innovating within this space. Favorable regulatory frameworks, a robust startup culture, and significant investments in digital transformation initiatives across industries like BFSI, healthcare, and retail further bolster the region’s dominance. Moreover, North American enterprises are at the forefront of leveraging advanced analytics, artificial intelligence, and IoT, all of which necessitate the high availability and scalability offered by serverless NoSQL databases.

Asia Pacific is emerging as the fastest-growing region in the serverless NoSQL database market, projected to expand at a remarkable CAGR of 23.5% through 2033. Rapid digitization, government-led smart city initiatives, and the exponential growth of mobile and IoT applications are key factors propelling market expansion in countries such as China, India, Japan, and South Korea. Additionally, the increasing penetration of cloud computing and the rising number of tech-savvy SMEs are driving demand for flexible, cost-effective database solutions. Major cloud service providers are also ramping up their investments and partnerships in the region, making advanced database technologies more accessible to a broader spectrum of enterprises.

In contrast, emerging economies in Latin America, the Middle East, and Africa are experiencing a gradual but steady uptake of serverless NoSQL database solutions. While these regions face challenges such as limited cloud infrastructure, skills shortages, and regulatory uncertainties, localized demand for digital services, especially in e-commerce, fintech, and media, is driving adoption. Governments are beginning to recognize the economic potential of digital transformation and are implementing supportive policies and incentives. However, organizations in these regions must navigate issues related to data sovereignty, connectivity, and vendor lock-in, which may moderate the pace of market penetration compared to more developed regions.

Report Scope

A dataset within the Harmonized Database of Western U.S. Water Rights (HarDWR). For a detailed description of the database, please see the meta-record v2.0. Changelog v2.0 - Switched source data from collecting records from each state independently to using the WestDAAT dataset v1.0 - Initial public release Description In order to hold a water right in the western United States, an entity, (e.g., an individual, corporation, municipality, sovereign government, or non-profit) must register a physical document with the state's water regulatory agency. State water agencies each maintain their own database containing all registered water right documents within the state, along with relevant metadata such as the point of diversion and place of use of the water. All western U.S. states have digitized their individual water rights databases, as well as geospatial data defining the areas in which water rights are managed. Each state maintains and provides their own water rights data in accordance with individual state regulations and standards. In addition, while all states make their water rights publicly available, each provides their records in unique formats, meaning that file types, field availability, and terms vary from state to state. This leads to additional challenges to managing resources which cross state lines, or conducting consistent multi-state water analyses. For the first version of HarDWR, we collected the water rights databases from 11 Western States of the United States. In order to preform regional analyses with the collected data, the raw records had to be harmonized into one single format. The Water Data Exchange (WaDE) is a program dedicated to the sharing of water-related data for the Western U.S. in a singular consistent format. Created by the Western States Water Council (WSWC) to facilitate the collection and dissemination of water data among WSWC's member states and the public, WaDE provides an important service for those interested in water resource planning and management in their focus region. Of the services which WaDE provides, the one of the most interesting is the WestDAAT dataset, which is a collection of water rights data provided by the 18 WSWC member states that have been standardized into a single format, much like we had done on a more limited scale with HarDWR v1. For this version of HarDWR we decided to use WestDAAT, specifically a snapshot created in Feburary 2024, as our water rights source data. A full explanation of the benefits gained from this switch can be found in the description of the updated Harmonized Water Rights Records v2.0, but in short it has allowed us to focus more of our efforts on answering research questions and gaining a more realistic understanding of how water rights are allocated. For more information on how the data for WestDAAT was collected, please see the WaDE data summary. Terms of Use While WaDE works directly with the state agencies to collect and standardize the water rights records, the ultimate authority for the water rights data remains the individual states. Each state, and their respective water right authorities, have made their water right records available for non-commercial reference uses. In addition, the states make no guarantees as to the completeness, accuracy, or timeliness of their respective databases, let alone the modifications which we, the authors of this paper, have made to the collected records. None of the states should be held liable for using this data outside of its intended use. As several of the states update their water rights databases daily, the information provided here is not the latest possible, and should not be used for legal purposes. WestDAAT itself has irregular updates. Additional questions about the data the source states provided should be directed to the respective state agencies (see methods.csv and organization.csv files described below). In addition, although data was presented here was not collected directly from the states, several states requested specifically worked disclaimers when sharing their data. These disclaimers are included here as an acknowledgement from where the water rights data is primarily sourced. Colorado: "The data made available here has been modified for use from its original source, which is the State of Colorado. THE STATE OF COLORADO MAKES NO REPRESENTATIONS OR WARRANTY AS TO THE COMPLETENESS, ACCURACY, TIMELINESS, OR CONTENT OF ANY DATA MADE AVAILABLE THROUGH THIS SITE. THE STATE OF COLORADO EXPRESSLY DISCLAIMS ALL WARRANTIES, WHETHER EXPRESS OR IMPLIED, INCLUDING ANY IMPLIED WARRANTIES OF MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. The data is subject to change as modifications and updates are complete. It is understood that the information contained in the Web feed is being used at one's own risk." Montana: "The Montana State Library provides this product/service for informational purposes only. The Library did not produce it for, nor is it suitable for legal, engineering, or surveying purposes. Consumers of this information should review or consult the primary data and information sources to ascertain the viability of the information for their purposes. The Library provides these data in good faith but does not represent or warrant its accuracy, adequacy, or completeness. In no event shall the Library be liable for any incorrect results or analysis; any direct, indirect, special, or consequential damages to any party; or any lost profits arising out of or in connection with the use or the inability to use the data or the services provided. The Library makes these data and services available as a convenience to the public, and for no other purpose. The Library reserves the right to change or revise published data and/or services at any time." Oregon: "This product is for informational purposes and may not have been prepared for, or be suitable for legal, engineering, or surveying purposes. Users of this information should review or consult the primary data and information sources to ascertain the usability of the information." File Descriptions The unmodified February, 2024 WestDAAT snapshot is composed of nine files. Below is a brief description of each file, as well as how they were utilized for HarDWR. WaDEDataDictionaryTerms.xlsx: As the file's name implies, this is a data dictionary for all of the below named files. This file describes the column names for each of the following files, with the exception of citation.txt which does not have any columns. The descriptions for each file are divided by tab,with the same name as their associated file, within this document. allocationamount.csv: The "main" file of the group, it contains the water right records for each state. Of particular note, each water right is broken down into one or more water allocations. Allocations may be withdrawn from one or more locations, or even multiple allocations associated with a particular location. This is a more subtle and realistic representation of how water is used than what was available in the first version of HarDWR. For the records from some states, this can mean that multiple allocations listed under a single right will appear as rows within this file. citation.txt: A combination of contact information for WaDE personnel, disclaimer about how the data should be used, and guidelines for citing WestDAAT. methods.csv: A file describing the source and method by which WaDE collected water rights data from each state. organization.csv: A file listing the water rights authoritative agencies for each state. sites.csv: This file provides the geographic, and other descriptors, of the physical location of allocations, called 'sites'. To reiterate, it is possible for one allocation to be associated with multiple sites, as well as one site to be associated with multiple allocations. The two descriptors which we were most interested in where the site's coordinates, as well as whether the site was classified as a Point of Diversion (POD) or a Place of Use (POU). As a general rule, PODs are geographic points, while POUs are areas typically represented as property boundaries or irregularly shaped polygons. sites_pouGeometry.csv: For those allocations with a POU site, this file contains the defining points for the associated polygons. variables.csv: A file describing the units in which an allocation's water amount is reported within WestDAAT. This information is essentially a repeat of the 'AllocationFlow_CFS' and 'AllocationVolume_AF' columns within allocationamount.csv, at least for our purposes. watersources: This file describes the source of water from which each site extracts from. For our purposes, this table was used to determine whether the water came from Surface Water, Groundwater, or Unspecified Water.

SECTIC-24K is a digital file of the Public Land Survey (PLS) section corners of Minnesota as recorded on the U.S. Geological Survey's 1:24,000 7.5-minute quadrangle maps (map dates ranging from the late 1940s - 1970s). The database attempts to best fit the section corner locations shown on the published 1:24,000 maps, even though better real-world data for the location of the section corner might be available elsewhere. The SECTIC-24K data set also includes a program which has the following utilities:

Utility A: Section corner extraction from the SECTIC-24K database by county, 1:24,000-scale quad, or township.
Utility B: Conversion among PLS, UTM, or LAT/LONG coordinates, either interactively or by file conversion. It also allows NAD27 - NAD83 conversions.
Utility C: Creation of a dBASE output file from SECTIC-24K.

This program does not run on Windows 7 - 64 bit computers (it does run on Windows - 32 bit). There is also a web service that generates much the same info as the SECTIC program. The main differences are it may not do NAD27/NAD83 shifts and it doesn't have a batch mode. A batch mode could be created using the web service and the scripting code of your choice. Find the web service at: https://gisdata.mn.gov/dataset/loc-pls-api-service

This dataset is a focus of the monitoring of urban planning documents in the territories of the municipalities concerned by the “Climimats of Burgundy”. This data was updated as of June 29, 2017 from the SUDOCUH database. Column DU_2015 shows the history of Urbanism documents applicable on 4 July 2015 when the “Climimats of Burgundy” was added to the UNESCO World Heritage site.

The Service Catalog (ServCat) is a digital library of secure, archived biological information (reports, surveys, databases, geospatial data and images) stored in a centralized, web-based database. The 2010 Strategic Plan for Inventories and Monitoring on National Wildlife Refuges highlights the importance of collecting and archiving legacy datasets for biological planning and conservation design.

A secondary objective of ServCat is to meet the Department’s Open Data Policy based on an Executive Order. Southeast region refuge managers will set document sensitivity levels (public or internal) in order to determine which records will be made available to the public. Documents made available to the public will be harvested by the government wide data.gov.

For more information about ServCat, please contact the Inventory and Monitoring branch chief, Janet Ertel, or download the ServCat fact sheet.

This report summarizes the ServCat pilot project and offers recommendations for the full-scale implementation of the database. During the pilot project a total of 2,473 documents from 10 different refuges were entered into the ServCat database. A document can take anywhere from 5 to 60 minutes to scan. Overall, a general approximation is that it takes 30 minutes to scan a document and enter it as a record in ServCat. Several lessons were learned throughout the course of the pilot project that will help guide the implementation of the data mining effort at the full-scale. Full-scale implementation of the ServCat database will require collaboration between refuges, regions, and the Natural Resource Program Center. Standardizing the metadata entered in ServCat will make it easier to find a document in the database and faster to create a record. Templates, the master keyword list, and the ServCat Guidance should be used whenever possible to expedite data entry.

Managed CouchDB Services Market Outlook

According to our latest research, the global managed CouchDB services market size reached USD 428 million in 2024, reflecting robust growth fueled by the increasing adoption of NoSQL databases and cloud-based solutions across diverse industries. The market is projected to expand at a CAGR of 18.7% during the forecast period, reaching an estimated USD 2,031 million by 2033. This accelerated growth is primarily driven by the rising demand for scalable, flexible, and cost-effective database management solutions, as well as the proliferation of digital transformation initiatives worldwide.

One of the primary growth factors propelling the managed CouchDB services market is the widespread digitalization of business processes and the exponential increase in unstructured data volumes. Organizations across sectors such as IT & telecommunications, BFSI, healthcare, and e-commerce are generating massive datasets that require robust, scalable, and highly available database solutions. Managed CouchDB services, with their ability to handle JSON-based document storage, horizontal scaling, and seamless replication, offer a compelling value proposition for enterprises seeking to modernize their data infrastructure without incurring the operational overhead of self-managed deployments. Furthermore, the growing need for real-time analytics, data synchronization across distributed environments, and agile application development is accelerating the shift toward managed NoSQL services, positioning CouchDB as a preferred choice for many businesses.

Another significant driver for the managed CouchDB services market is the increasing emphasis on cost optimization and operational efficiency. Enterprises, particularly small and medium-sized businesses, are looking to reduce their IT infrastructure costs by outsourcing database management to specialized service providers. Managed CouchDB services eliminate the need for in-house database administration, reduce capital expenditures on hardware, and ensure high availability and disaster recovery through professional support. This trend is complemented by the rising adoption of cloud-based deployment models, which further enhance scalability, flexibility, and access to advanced features such as automated backups, monitoring, and security compliance. As a result, managed CouchDB service providers are witnessing strong demand from organizations aiming to focus on their core competencies while leveraging enterprise-grade database capabilities.

The managed CouchDB services market is also benefiting from the growing regulatory landscape and the need for enhanced data security and compliance. With increasing concerns around data breaches, privacy regulations such as GDPR, HIPAA, and CCPA, and the need for secure data storage and transmission, organizations are turning to managed service providers that offer robust security and compliance features. Managed CouchDB services often include end-to-end encryption, role-based access controls, regular security audits, and compliance certifications, which are critical for industries handling sensitive data. This heightened focus on security and compliance is expected to further boost the adoption of managed CouchDB services, particularly in highly regulated sectors such as healthcare, finance, and government.

Regionally, North America continues to dominate the managed CouchDB services market, accounting for the largest revenue share in 2024, followed by Europe and Asia Pacific. The strong presence of technology giants, early adoption of cloud and NoSQL technologies, and a mature managed services ecosystem have contributed to North America's leadership position. Europe is witnessing significant growth, driven by stringent data protection regulations and increasing investments in digital infrastructure. Meanwhile, Asia Pacific is emerging as a high-growth market, fueled by rapid digital transformation, expanding IT sectors, and a surge in e-commerce and fintech startups. Latin America and the Middle East & Africa are also showing steady progress, albeit from a smaller base, as organizations in these regions increasingly recognize the benefits of managed database services.

Service Type Analysis

The managed CouchDB services market is segmented by service type into database hosting, backup & recovery, monitoring & maintenance, security & compliance, and others. Database hosting remains the cor

Watershed Data Management (WDM) database file ARGN20.WDM is an update of ARGN19.WDM (Bera, 2020) with the processed data from October 1, 2019 through September 30, 2020, appended to it. The primary data were downloaded from the Argonne National Laboratory (ANL) (Argonne National Laboratory, 2020) and processed following the guidelines documented in Over and others (2010). ARGN20.WDM file contains nine data series: air temperature, in degrees Fahrenheit (dsn 400), dewpoint temperature, in degrees Fahrenheit (dsn 500), wind speed, in miles per hour (dsn 300), solar radiation, in Langleys (dsn 600), computed potential evapotranspiration, in thousandths of an inch (dsn 200), and four data-source flag series for air temperature (dsn 410), dewpoint temperature (dsn 510), wind speed (dsn 310), and solar radiation (dsn 610), respectively, from January 1,1948, to September 30, 2020. Daily potential evapotranspiration (PET) were computed from average daily air temperature, average daily dewpoint temperature, daily total wind speed, and daily total solar radiation and disaggregated to hourly PET, in thousandths of an inch, using the Fortran program LXPET (Murphy, 2005). Missing and apparently erroneous data values were replaced with adjusted values from nearby weather stations used as “backup”. The Illinois Climate Network (Water and Atmospheric Resources Monitoring Program, 2020) station at St. Charles, Illinois, was used as "backup" for the hourly air temperature, solar radiation, and wind speed data. The Midwestern Regional Climate Center (Midwestern Regional Climate Center, 2020) provided the hourly dewpoint temperature and wind speed data collected by the National Weather Service from the station at O'Hare International Airport and used as "backup". Each data source flag is of the form "xyz", which allows the user to determine its source and the methods used to process the data (Over and others, 2010). To open this file user needs to install any of the utilities described in the section "Related External Resources" on this page. References Cited: Argonne National Laboratory, 2020, Meteorological data, accessed on November 17, 2020, at http://www.atmos.anl.gov/ANLMET/. Bera, M., 2020, Meteorological Database, Argonne National Laboratory, Illinois, January 1, 1948 - September 30, 2019: U.S. Geological Survey data release, ​https://doi.org/10.5066/P9X0P4HZ. Midwestern Regional Climate Center, 2020, Meteorological data, accessed on November 3, 2020, at https://mrcc.illinois.edu/CLIMATE/. Murphy, E.A., 2005, Comparison of potential evapotranspiration calculated by the LXPET (Lamoreux Potential Evapotranspiration) Program and by the WDMUtil (Watershed Data Management Utility) Program: U.S. Geological Survey Open-File Report 2005-1020, 20 p., https://pubs.er.usgs.gov/publication/ofr20051020. Over, T.M., Price, T.H., and Ishii, A.L., 2010, Development and analysis of a meteorological database, Argonne National Laboratory, Illinois: U.S. Geological Survey Open-File Report 2010-1220, 67 p., http://pubs.usgs.gov/of/2010/1220/. Water and Atmospheric Resources Monitoring Program. Illinois Climate Network, 2020. Illinois State Water Survey, 2204 Griffith Drive, Champaign, IL 61820-7495. Data accessed on November 9, 2020, at http://dx.doi.org/10.13012/J8MW2F2Q.

Watershed Data Management (WDM) database file ARGN18.WDM is an update of ARGN17.WDM (Bera and Over, 2018) with the processed data from October 1, 2017 through September 30, 2018 appended to it. The primary data were downloaded from the Argonne National Laboratory (ANL) (Argonne National Laboratory, 2018) and processed following the guidelines documented in Over and others (2010). ARGN18.WDM file contains nine data series: air temperature, in degrees Fahrenheit (dsn 400), dewpoint temperature, in degrees Fahrenheit (dsn 500), wind speed, in miles per hour (dsn 300), solar radiation, in Langleys (dsn 600), computed potential evapotranspiration, in thousandths of an inch (dsn 200), and four data-source flag series for air temperature (dsn 410), dewpoint temperature (dsn 510), wind speed (dsn 310), and solar radiation (dsn 610), respectively, from January 1,1948, to September 30, 2018. Daily potential evapotranspiration (PET) were computed from average daily air temperature, average daily dewpoint temperature, daily total wind speed, and daily total solar radiation and disaggregated to hourly PET, in thousandths of an inch, using the Fortran program LXPET (Murphy, 2005). Missing and apparently erroneous data values were replaced with adjusted values from nearby weather stations used as “backup”. The Illinois Climate Network (Water and Atmospheric Resources Monitoring Program, 2018) station at St. Charles, Illinois, was used as "backup" for the hourly air temperature, solar radiation, and wind speed data. The Midwestern Regional Climate Center (Midwestern Regional Climate Center, 2018) provided the hourly dewpoint temperature and wind speed data collected by the National Weather Service at the station at O'Hare International Airport and used as "backup". Each data source flag is of the form "xyz", which allows the user to determine its source and the methods used to process the data (Over and others, 2010). To open this file user needs to install any of the utilities described in the section "Related External Resources" on this page. References Cited: Argonne National Laboratory, 2018, Meteorological data, accessed on October 10, 2018, at http://www.atmos.anl.gov/ANLMET/. Bera, M., and Over, T.M., 2018, Meteorological Database, Argonne National Laboratory, Illinois, January 1, 1948 - September 30, 2017: U.S. Geological Survey data release, ​https://doi.org/10.5066/F7H1318R. Midwestern Regional Climate Center, 2018, Meteorological data, accessed on October 12, 2018, at https://mrcc.illinois.edu/CLIMATE/. Murphy, E.A., 2005, Comparison of potential evapotranspiration calculated by the LXPET (Lamoreux Potential Evapotranspiration) Program and by the WDMUtil (Watershed Data Management Utility) Program: U.S. Geological Survey Open-File Report 2005-1020, 20 p., https://pubs.er.usgs.gov/publication/ofr20051020. Over, T.M., Price, T.H., and Ishii, A.L., 2010, Development and analysis of a meteorological database, Argonne National Laboratory, Illinois: U.S. Geological Survey Open-File Report 2010-1220, 67 p., http://pubs.usgs.gov/of/2010/1220/. Water and Atmospheric Resources Monitoring Program. Illinois Climate Network, 2018. Illinois State Water Survey, 2204 Griffith Drive, Champaign, IL 61820-7495. Data accessed on October 30, 2018, at http://dx.doi.org/10.13012/J8MW2F2Q

Of interest to pharmaceutical, nutritional, and biomedical researchers, as well as individuals and companies involved with alternative therapies and and herbal products, this database is one of the world's leading repositories of ethnobotanical data, evolving out of the extensive compilations by the former Chief of USDA's Economic Botany Laboratory in the Agricultural Research Service in Beltsville, Maryland, in particular his popular Handbook of phytochemical constituents of GRAS herbs and other economic plants (CRC Press, Boca Raton, FL, 1992). In addition to Duke's own publications, the database documents phytochemical information and quantitative data collected over many years through research results presented at meetings and symposia, and findings from the published scientific literature. The current Phytochemical and Ethnobotanical databases facilitate plant, chemical, bioactivity, and ethnobotany searches. A large number of plants and their chemical profiles are covered, and data are structured to support browsing and searching in several user-focused ways. For example, users can

get a list of chemicals and activities for a specific plant of interest, using either its scientific or common name download a list of chemicals and their known activities in PDF or spreadsheet form find plants with chemicals known for a specific biological activity display a list of chemicals with their LD toxicity data find plants with potential cancer-preventing activity display a list of plants for a given ethnobotanical use find out which plants have the highest levels of a specific chemical

References to the supporting scientific publications are provided for each specific result. Resources in this dataset:Resource Title: Duke-Source-CSV.zip. File Name: Duke-Source-CSV.zipResource Description: Dr. Duke's Phytochemistry and Ethnobotany - raw database tables for archival purposes. Visit https://phytochem.nal.usda.gov/phytochem/search for the interactive web version of the database.Resource Title: Data Dictionary (preliminary). File Name: DrDukesDatabaseDataDictionary-prelim.csvResource Description: This Data Dictionary describes the columns for each table. [Note that this is in progress and some variables are yet to be defined or are unused in the current implementation. Please send comments/suggestions to nal-adc-curator@ars.usda.gov ]

The German Library Statistics (DBS) is the national statistics of the German library system and contains statistical key figures. It includes public libraries, scientific libraries, as well as specialized scientific libraries. More information can be found at DBS. This dataset contains the following information on academic libraries in Bavaria 2019: Borrowings by total physical units, borrowings, of which: Extensions upon user request, reservations, attendance, requests for information, library visits, 1. ... Virtual visits (visits) input blocked, user training sessions (hours), participants in user training sessions, 1. Calls for e-learning offers from the library, 2. Accepted dissertations of the own university, 3. Accepted dissertations of your own university, of which: Online dissertations, 4. Open access green and gold publications provided on own repositories , searches in local online catalogues and discovery systems, searches in databases, access to journal titles, full advertisements of journal articles, full advertisements of individual digital documents, 1. Full display of individual digital documents, including: Full ads from commercially distributed e-books, 2. Full display of individual digital documents, including: Full display of individual documents on the institutional repository

ARGN17.WDM contains nine data series: air temperature in degrees Fahrenheit (dsn 400), dewpoint temperature in degrees Fahrenheit (dsn 500), wind speed in miles per hour (dsn 300), solar radiation in Langleys (dsn 600), computed potential evapotranspiration in thousandths of an inch (dsn 200), and four data-source flag series for air temperature (dsn 410), dewpoint temperature (dsn 510), wind speed (dsn 310) and solar radiation (dsn 610) respectively from January 1,1948, to September 30, 2017. The primary source of the data is Argonne National Laboratory (Argonne National Laboratory, 2017) and is processed following the guidelines documented in Over and others (2010). Daily potential evapotranspiration (PET) in thousandths of an inch is computed from average daily air temperature in degrees Fahrenheit (°F), average daily dewpoint temperature in degrees Fahrenheit (°F), daily total wind movement in miles (mi), and daily total solar radiation in Langleys per day (Lg/d) and disaggregated to hourly PET in thousandths of an inch using the Fortran program LXPET (Murphy, 2005). Missing and apparently erroneous data values were replaced with adjusted values from nearby stations used as “backup”. The Illinois Climate Network (Water and Atmospheric Resources Monitoring Program, 2015) station at St. Charles, Illinois is used as "backup" for the air temperature, solar radiation and wind speed data. Midwestern Regional Climate Center (Midwestern Regional Climate Center, 2017) station at Chicago O'Hare International Airport is used as "backup" for the dewpoint temperature and wind speed data. Each data source flag is of the form "xyz" that allows the user to determine its source and the methods used to process the data (Over and others, 2010). To open this file user needs to install any of the utilities described in the section "Related External Resources" in this page. References Cited: Argonne National Laboratory, 2017, Meteorological data, accessed on October 25, 2017, at URL http://gonzalo.er.anl.gov/ANLMET/. Midwestern Regional Climate Center, 2017, Meteorological data, accessed on December 5, 2017, at URL http://mrcc.isws.illinois.edu/CLIMATE/welcome.jsp. Murphy, E.A., 2005, Comparison of potential evapotranspiration calculated by the LXPET (Lamoreux Potential Evapotranspiration) Program and by the WDMUtil (Watershed Data Management Utility) Program: U.S. Geological Survey Open-File Report 2005-1020, 20 p., https://pubs.er.usgs.gov/publication/ofr20051020. Over, T.M., Price, T.H., and Ishii, A.L., 2010, Development and analysis of a meteorological database, Argonne National Laboratory, Illinois: U.S. Geological Survey Open-File Report 2010-1220, 67 p., http://pubs.usgs.gov/of/2010/1220/. Water and Atmospheric Resources Monitoring Program, 2015, Illinois Climate Network: Champaign, Ill., Illinois State Water Survey, accessed on December 5, 2017, at http://dx.doi.org/10.13012/J8MW2F2Q.