As of June 2024, the most popular database management system (DBMS) worldwide was Oracle, with a ranking score of *******; MySQL and Microsoft SQL server rounded out the top three. Although the database management industry contains some of the largest companies in the tech industry, such as Microsoft, Oracle and IBM, a number of free and open-source DBMSs such as PostgreSQL and MariaDB remain competitive. Database Management Systems As the name implies, DBMSs provide a platform through which developers can organize, update, and control large databases. Given the business world’s growing focus on big data and data analytics, knowledge of SQL programming languages has become an important asset for software developers around the world, and database management skills are seen as highly desirable. In addition to providing developers with the tools needed to operate databases, DBMS are also integral to the way that consumers access information through applications, which further illustrates the importance of the software.

As of June 2024, the most popular open-source database management system (DBMS) in the world was MySQL, with a ranking score of ****. Oracle was the most popular commercial DBMS at that time, with a ranking score of ****.

Approximately ** percent of the surveyed software companies in Russia mentioned PostgreSQL, making it the most popular database management system (DBMS) in the period between February and May 2022. MS SQL and MySQL followed, having been mentioned by ** percent and ** percent of respondents, respectively.

As of June 2024, the most popular relational database management system (RDBMS) worldwide was Oracle, with a ranking score of *******. Oracle was also the most popular DBMS overall. MySQL and Microsoft SQL server rounded out the top three.

In 2023, over ** percent of surveyed software developers worldwide reported using PostgreSQL, the highest share of any database technology. Other popular database tools among developers included MySQL and SQLite.

The Database Market is Segmented by Database Type (Relational (RDBMS), Nosql, and More), Deployment (Cloud, On-Premsies), Service Model (Database-As-A-Service (DBaaS), License and Maintenance Software), Enterprise (SMEs, Large Enterprises), Workload Type (Transactional (OLTP), Analytical (OLAP), and More), End-User Vertical (BFSI, Retail, and More), and by Geography. The Market Forecasts are Provided in Terms of Value (USD).

Art Database Software Market size was valued at USD 751 Million in 2023 and is projected to reach USD 3,135 Million by 2031, growing at a CAGR of 16.9% during the forecast period 2024-2031.

Global Art Database Software Market Drivers

Increasing Need for Management of Digital Art: Effective management solutions are required due to the excessive number of artwork produced and the growing use of digital art forms. Digital tools are becoming more popular among art collectors, galleries, and museums for cataloging, arranging, and showcasing their holdings. The need for advanced art database software that can manage huge, varied portfolios is growing as a result of this trend. Additionally, galleries and artists are being forced to embrace digital systems that provide improved tracking, management, and sales analysis due to the surge in online art sales.

The statistic displays the most popular SQL databases used by software developers worldwide, as of **********. According to the survey, ** percent of software developers were using MySQL, an open-source relational database management system (RDBMS).

NoSQL Software Market Outlook

The global NoSQL software market size was valued at approximately USD 6 billion in 2023 and is projected to reach around USD 20 billion by 2032, growing at a compound annual growth rate (CAGR) of 14% during the forecast period. This market is driven by the escalating need for operational efficiency, flexibility, and scalability in database management systems, particularly in enterprises dealing with vast amounts of unstructured data.

One of the primary growth factors propelling the NoSQL software market is the exponential increase in data volumes generated by various digital platforms, IoT devices, and social media. Traditional relational databases often struggle to handle this surge efficiently, prompting organizations to shift towards NoSQL databases that offer more flexibility and scalability. The ability to store and process large sets of unstructured data without needing a predefined schema makes NoSQL databases an attractive choice for modern businesses seeking agility and speed in data management.

Moreover, the proliferation of cloud computing services has significantly contributed to the growth of the NoSQL software market. Cloud-based NoSQL databases provide cost-effective, scalable, and easily accessible solutions for enterprises of all sizes. The pay-as-you-go pricing model and the capacity to scale resources based on demand have made NoSQL databases a preferred option for startups and large enterprises alike. The seamless integration of NoSQL databases with cloud infrastructure enhances operational efficiencies and reduces the complexities associated with database management.

Another critical driver is the increasing adoption of NoSQL databases in various industry verticals such as retail, BFSI, IT, and healthcare. These industries require robust data management solutions to handle large volumes of diverse data types. NoSQL databases, with their flexible data models and high performance, cater to these requirements efficiently. In the retail sector, for example, NoSQL databases are used to manage customer data, product catalogs, and transaction histories, enabling more personalized and efficient customer services.

Regionally, North America holds a significant share of the NoSQL software market due to the presence of major technology companies and a mature IT infrastructure. The rapid digital transformation across enterprises in the region, alongside substantial investments in big data analytics and cloud computing, further fuels market growth. Additionally, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by the expanding IT sector, increased adoption of cloud services, and significant investments in digital technologies in countries like China and India.

Graph Databases Software has emerged as a crucial component in the landscape of NoSQL databases, particularly for applications that require understanding complex relationships between data entities. Unlike traditional databases that store data in tables, graph databases use nodes, edges, and properties to represent and store data, making them ideal for scenarios where relationships are as important as the data itself. This approach is particularly beneficial in fields such as social networking, where the ability to analyze connections between users can provide deep insights into social dynamics and influence patterns. As businesses increasingly seek to leverage data for competitive advantage, the demand for graph databases is expected to grow, driven by their ability to efficiently model and query interconnected data.

Type Analysis

The NoSQL software market is segmented into various types, including Document-Oriented, Key-Value Store, Column-Oriented, and Graph-Based databases. Document-oriented databases, such as MongoDB, store data in JSON-like documents, offering flexibility in data modeling and ease of use. These databases are widely used for content management systems, e-commerce applications, and real-time analytics. Their ability to handle semi-structured data and scalability features make them a popular choice among developers and enterprises seeking agile database solutions.

Key-Value Store databases, such as Redis and Amazon DynamoDB, store data as a collection of key-value pairs, providing ultra-fast read and write operations. These databases are ideal for applications requiring high-speed data retrieval, such as caching, session manag

A comparison table of popular database documentation tools, including supported DBMS, documentation formats, ease of use, customization options, and pricing.

Data Schema Management Software Market Outlook

According to our latest research, the global Data Schema Management Software market size was valued at USD 1.82 billion in 2024 and is expected to reach USD 4.86 billion by 2033, expanding at a robust CAGR of 11.4% during the forecast period from 2025 to 2033. This accelerated growth trajectory is primarily fueled by the rapid adoption of cloud-based data management solutions, the proliferation of data-driven business models, and the increasing complexity of enterprise data environments. As organizations across industries continue to prioritize data integrity, compliance, and efficiency, the demand for advanced schema management tools is set to escalate significantly.

One of the most influential growth factors in the Data Schema Management Software market is the exponential increase in data volume and diversity within modern enterprises. As organizations generate and consume vast amounts of structured and unstructured data from a multitude of sources, the challenge of maintaining consistent, accurate, and interoperable data schemas becomes more pronounced. Businesses are increasingly recognizing the need for sophisticated schema management solutions to streamline data integration, prevent data silos, and ensure seamless interoperability across disparate systems. This necessity is further amplified by the growing emphasis on real-time analytics, artificial intelligence, and machine learning initiatives, all of which require high-quality, well-structured data to deliver actionable insights and drive competitive advantage.

Another key driver propelling market growth is the tightening regulatory landscape surrounding data governance and compliance. Industries such as BFSI, healthcare, and government are subject to stringent data protection regulations like GDPR, HIPAA, and CCPA, which mandate rigorous data management practices and comprehensive audit trails. Data schema management software plays a pivotal role in helping organizations demonstrate compliance by enabling consistent data definitions, lineage tracking, and metadata management. As regulatory scrutiny intensifies and penalties for non-compliance become more severe, enterprises are ramping up their investments in schema management platforms to mitigate risk, enhance transparency, and safeguard sensitive information.

Technological advancements and the proliferation of cloud computing are also reshaping the Data Schema Management Software market landscape. The shift toward hybrid and multi-cloud environments has introduced new complexities in data architecture, necessitating dynamic schema management capabilities that can adapt to evolving business needs. Vendors are responding by offering cloud-native schema management solutions with features such as automated schema evolution, real-time synchronization, and seamless integration with leading cloud platforms. These innovations are not only reducing operational overhead but also empowering organizations to accelerate digital transformation initiatives, improve agility, and scale their data infrastructure with confidence.

From a regional perspective, North America currently dominates the Data Schema Management Software market, accounting for the largest revenue share in 2024. This leadership position is attributed to the region's advanced IT infrastructure, high adoption of cloud technologies, and the presence of major market players. However, the Asia Pacific region is expected to witness the fastest growth over the forecast period, driven by the digitalization of enterprises, burgeoning start-up ecosystems, and increasing investments in data-centric technologies. Europe, Latin America, and the Middle East & Africa are also experiencing steady growth, supported by rising awareness of data governance and the gradual modernization of legacy systems.

Component Analysis

The Data Schema Management Software market is segmented by component into software and services. Software solutions form the backbone of this market, representing the largest share in 2024 due to their critical role in automating schema design, validation, and change management processes. Advanced schema management software offers a comprehensive suite of features including visual modeling, version control, conflict resolution, and integration with popular database platforms. These capabilities are essential for enterprises seeking to maintain data consistency and integrity across complex, distributed env

Pakistan phone number database is a collection of phone numbers that are always correct and valid. Therefore, it can give you the most recent and up-to-date contact number list. Also, it can give you a 95% accuracy rate. However, you can get a high response rate. Similarly, we can give you an Excel or CSV file. Thus, buy our mobile number list and put it on any CRM software. Moreover, Pakistan phone number database is a helpful tool for reaching people in Pakistan. Pakistan mobile number data can help you in multiple ways to boost your business. This data is a well-known service in this segment so you can get it now. Furthermore, the List to Data website is a famous database service provider in this world. In addition, it can develop your business in a very short period. On the other hand, if you want to get a huge return on investment (ROI), buy our service right now. Also, it is the fastest way to get in touch with the right people. Most importantly, we strictly follow the GDPR rules and restrictions.

Vector Database as a Service Market Outlook

According to our latest research, the global Vector Database as a Service (DBaaS) market size reached USD 1.12 billion in 2024, driven by surging demand for AI-powered applications and data-intensive workloads. The market is expected to grow at a robust CAGR of 27.4% from 2025 to 2033, with the market size projected to reach USD 9.41 billion by 2033. This remarkable growth is primarily fueled by the increasing adoption of machine learning, generative AI, and advanced semantic search technologies across industries, as organizations seek scalable, real-time data solutions to power next-generation applications.

The primary growth factor for the Vector Database as a Service market is the exponential rise in unstructured and high-dimensional data generated by enterprises. Organizations across sectors such as BFSI, healthcare, e-commerce, and telecommunications are increasingly leveraging AI-driven applications that require rapid, accurate similarity search and retrieval from massive datasets. Traditional relational databases are ill-suited for these workloads, prompting a shift toward vector databases that can handle embeddings and facilitate real-time semantic search. This technological shift is further amplified by the proliferation of large language models (LLMs) and generative AI, both of which inherently depend on vector representations and require robust, scalable vector data infrastructure.

Another significant driver is the growing adoption of cloud-based solutions and managed services. Enterprises are rapidly moving away from on-premises database management due to the high costs, complexity, and lack of scalability associated with traditional systems. Vector Database as a Service enables organizations to deploy, scale, and manage high-performance vector databases with minimal operational overhead, allowing them to focus on core business and innovation. The pay-as-you-go pricing models and seamless integration with cloud-native AI/ML workflows further enhance the appeal of DBaaS offerings. This trend is particularly pronounced among small and medium enterprises (SMEs) that lack the resources for in-house data infrastructure but require advanced capabilities to stay competitive.

The increasing focus on personalized user experiences and intelligent automation is also propelling the Vector Database as a Service market. Recommendation engines, semantic search platforms, fraud detection systems, and advanced analytics all rely on the ability to process and analyze high-dimensional vectors in real time. As organizations strive to deliver hyper-personalized content and services, the need for scalable, low-latency vector search capabilities becomes paramount. This demand is further bolstered by the rise of hybrid and multi-cloud environments, where DBaaS solutions offer flexibility, reliability, and seamless integration across diverse IT landscapes. As a result, the market is witnessing heightened investment from both established technology vendors and innovative startups aiming to capture a share of this rapidly expanding landscape.

Regionally, North America remains at the forefront of the Vector Database as a Service market, accounting for the largest share in 2024 due to the early adoption of AI technologies, strong presence of leading cloud providers, and a mature digital ecosystem. However, Asia Pacific is emerging as the fastest-growing region, driven by rapid digital transformation, burgeoning AI research, and increasing investments in cloud infrastructure. Europe is also witnessing significant growth, supported by stringent data regulations and a growing focus on enterprise AI adoption. Latin America and the Middle East & Africa are gradually catching up, with local enterprises and governments recognizing the value of advanced vector data solutions for economic modernization and digital competitiveness.

Offering Analysis

The Vector Database as a Service market is segmented by offering into Solutions and Services. Solutions encompass the core vector database platforms, APIs, and software tools that enable organizations to store, index, and search high-dimensional vectors at scale. These offerings are rapidly evolving to support advanced features such as hybrid search (combining vector and keyword search), real-time analytics, and integration with popular AI/ML frameworks. As enterprises increasingly demand seamless, end-to-end data pipelin

County Business Patterns is an annual series that provides subnational economic data by industry. The series is useful for studying the economic activity of small areas, analyzing economic changes over time, and as a benchmark for statistical series, surveys, and databases between economic censuses. Businesses use the data for analyzing market potential, measuring the effectiveness of sales and advertising programs, setting sales quotas, and developing budgets. Government agencies use the dat a for administration and planning. County Business Patterns covers most of the country's economic activity. The series excludes data on self-employed individuals, employees of private households, railroad employees, agricultural production employees, and most government employees. Data from the County Business Patterns series are published primarily on the basis of the North American Industry Classification System (NAICS). Earlier County Business Patterns data were published according to the Standard Industrial Classification (SIC)system. While many of the individual NAICS industries correspond directly to industries as defined under the SIC system, most of the aggregate NAICS groupings do not. Particular care should be taken in comparing data for retail trade, wholesale trade, and manufacturing, which are sector titles used in both NAICS and SIC, but cover somewhat different groups of industries. For more information on NAICS and changes from the SIC system, go to www.census.gov/epcd/www/naics.html. The 1999 County Business Patterns series includes the following NAICS sectors: Forestry, Fishing, Hunting, and Agriculture, Mining, Utilities, Construction, Manufacturing, Wholesale Trade, Retail Trade, Transportation and Warehousing, Information, Finance and Insurance, Real Estate and Rental and Leasing, Professional, Scientific, and Technical Services, Management of Companies and Enterprises, Administrative and Support and Waste, Educational Services, Health Care and Social Assistance, Arts, Entertainment, and Recreation, Accommodation and Food Services, Other Services, Auxiliaries and Unclassified. Data Extraction: The CD-ROM in addition of including PDF tables, also includes software for creating County Business Patterns data files compatible with popular database and spreadsheet software. Note to Users: This CD is part of a collection located in the Data Archive of the Odum Institute for Research in Social Science, at the University of North Carolina at Chapel Hill. The collection is located in Room 10, Manning Hall. Users may check out the CDs, subscribing to the honor system. Items can be checked out for a period of two weeks. Loan forms are located adjacent to the collection.

Database as Code Platform Market Outlook

According to our latest research, the Database as Code Platform market size reached USD 1.65 billion in 2024 globally, reflecting robust adoption across multiple industries. The market is experiencing a strong compound annual growth rate (CAGR) of 22.4%, and is forecasted to reach USD 11.67 billion by 2033. This remarkable growth is primarily driven by the increasing demand for automated, scalable, and agile database management solutions that align with modern DevOps practices and cloud-native architectures.

A major growth factor for the Database as Code Platform market is the accelerating shift towards automation and Infrastructure as Code (IaC) in software development lifecycles. Organizations are increasingly embracing DevOps and CI/CD pipelines to streamline application development and deployment processes. By integrating database management into code repositories and automating schema changes, companies can achieve unprecedented levels of agility, consistency, and collaboration between development and operations teams. This transformation is further fueled by the proliferation of microservices, containerization, and cloud-native application patterns, all of which require robust and flexible database automation tools to support rapid scaling and frequent updates.

Another significant driver is the growing emphasis on compliance, data security, and governance in response to evolving regulatory frameworks such as GDPR, HIPAA, and CCPA. Database as Code Platforms enable organizations to codify security policies, automate compliance checks, and maintain immutable audit trails, thereby reducing the risk of human error and unauthorized changes. This capability is especially critical for industries handling sensitive data, such as banking, financial services, healthcare, and e-commerce. The ability to enforce standardized processes and demonstrate compliance seamlessly across distributed environments is increasingly viewed as a business imperative, further propelling market growth.

The surging adoption of cloud computing and hybrid cloud strategies is also a pivotal factor shaping the Database as Code Platform market. Enterprises are seeking solutions that offer seamless integration across on-premises, private, and public cloud infrastructures while ensuring consistent database operations and governance. Database as Code Platforms address this need by providing unified management, version control, and automation capabilities regardless of the underlying environment. As organizations continue to modernize their IT landscapes, the demand for flexible, scalable, and interoperable database automation tools is expected to surge, supporting both legacy systems and next-generation digital initiatives.

Regionally, North America leads the global Database as Code Platform market, driven by early technology adoption, a mature DevOps ecosystem, and substantial investments in digital transformation. Europe follows closely, with strong growth in regulated sectors and a focus on data privacy. The Asia Pacific region is emerging as a high-growth market, fueled by rapid cloud adoption and the expansion of digital enterprises across countries such as China, India, and Japan. Latin America and the Middle East & Africa are also witnessing increased adoption, albeit at a slightly slower pace, as organizations in these regions embark on modernization journeys and seek to enhance operational efficiencies through automation.

Component Analysis

The Database as Code Platform market is segmented by component into Platform and Services. The Platform segment encompasses the core software solutions that enable database schema versioning, automation, integration with CI/CD pipelines, and policy enforcement. This segment currently dominates the market, accounting for the majority of the global revenue share, as organizations prioritize investments in robust, feature-rich platforms that can support their evolving DevOps and data management needs. Leading platform providers are continuously enhancing their offerings with advanced capabilities such as AI-driven automation, real-time monitoring, and seamless integration with popular development tools, which further accelerates market adoption.

The Services segment, which includes consulting, implementation, training, and support services, is experiencing signific

Childcare System Market size was valued at USD 1.4 Billion in 2024 and is projected to reach USD 6.8 Billion by 2032, growing at a CAGR of 30% from 2026-2032.

Major factors that are driving the market growth include a decrease in child mortality rate, increasing labor force participation of women, and positive government initiatives. Governments have a significant role in driving revenues for the global Childcare System Market. The implementation of stringent regulations by governments, coupled with increased funding to daycare centers, has helped parents, especially from low-income families. One of the major factors driving the growth of the Childcare System Market is the rise in the working women population. Changes in lifestyle as well as standard of living especially in developing economies have seen a surge in the women working for the population.

Global Childcare System Market Definition

Childcare system software is a tool that helps in managing the daily operations of daycare centers or preschools to save childcare time and make work and life easier. It is specifically designed for child care centers and other similar child-oriented facilities. The software supports, guides, and automates administrative tasks like parents' contact information database, scheduled appointments, attendance records, and children's health data management. The Childcare system saves time for childcare centers or pre-schools by automating administrative tasks such as invoicing, reporting, and admissions.

Most childcare software is interconnected with social media tools so that childcare centers can communicate with parents on social media through the software. For instance, Procare is a popular childcare software, which stores information regarding the child and their family. Childcare software can be either operated from local computers or via mobile if it has been given access to other systems running somewhere else. The system is mainly used to increase staff productivity by storing information regarding the child and family.

Early child care is an equally important and often overlooked component of child development. Child care providers can be children's first teachers and therefore play an integral role in systems of early childhood education. Quality care from a young age can have a substantial impact on the future successes of children. The main focus of the childcare system is on the development of the child, whether that be mental, social, or psychological.

Growing Demand for Childcare Services: Increasing workforce participation and changing family structures drive the need for childcare solutions.

Technological Advancements: Adoption of digital tools for managing enrollment, scheduling, and communication streamlines childcare operations.

Regulatory Compliance: Stringent regulations regarding safety, staffing ratios, and educational standards necessitate the adoption of comprehensive childcare systems.

Focus on Early Childhood Education: Rising awareness of the importance of early childhood education fuels demand for systems that support educational programming and developmental tracking.

Shift towards Flexible and Remote Work: The trend towards remote work increases the demand for flexible childcare options, driving the need for systems that enable remote monitoring and communication between parents and caregivers.

Electrical Estimating Software Market size was valued at USD 2 Billion in 2024 and is projected to reach USD 4.08 Billion by 2031, growing at a CAGR of 10.23% from 2024 to 2031.

Global Electrical Estimating Software Market Drivers

Increasing Construction Activities: The global increase in construction projects, driven by urbanization, infrastructure development, and commercial building expansions, fuels the demand for precise and efficient estimating software to improve project planning and budgeting procedures.

Building Information Modeling (BIM) Adoption: Using BIM technology improves collaboration and coordination among project stakeholders, generating demand for estimating software that interacts easily with BIM platforms to increase project accuracy and efficiency.

Focus on Cost Efficiency and Sustainability: Businesses are investing in improved estimating tools as the emphasis shifts toward cost efficiency and sustainable construction methods. The United States Green Building Council said that LEED-certified buildings use 25% less energy and 11% less water than non-certified buildings. Electrical estimation software that can precisely assess and improve energy-efficient systems is becoming increasingly popular as a means of meeting sustainability targets while being cost-effective.

Technological Advancements: Software capabilities, such as artificial intelligence (AI) and machine learning (ML) algorithms for predictive analytics, are revolutionizing estimation software. These technologies provide more accurate forecasting, risk management, and decision-making capabilities, resulting in widespread use across sectors.

Graph Database Market size was valued at USD 2.86 Billion in 2024 and is projected to reach USD 14.58 Billion by 2032, growing at a CAGR of 22.6% from 2026 to 2032. Global Graph Database Market DriversThe growth and development of the Graph Database Market is attributed to certain main market drivers. These factors have a big impact on how Graph Database are demanded and adopted in different sectors. Several of the major market forces are as follows:Growth of Connected Data: Graph databases are excellent at expressing and querying relationships as businesses work with datasets that are more complex and interconnected. Graph databases are becoming more and more in demand as connected data gains significance across multiple industries.Knowledge Graph Emergence: In fields like artificial intelligence, machine learning, and data analytics, knowledge graphs—which arrange information in a graph structure—are becoming more and more popular. Knowledge graphs can only be created and queried via graph databases, which is what is causing their widespread use.Analytics and Machine Learning Advancements: Graph databases handle relationships and patterns in data effectively, enabling applications related to advanced analytics and machine learning. Graph databases are becoming more and more in demand when combined with analytics and machine learning as businesses want to extract more insights from their data.Real-Time Data Processing: Graph databases can process data in real-time, which makes them appropriate for applications that need quick answers and insights. In situations like fraud detection, recommendation systems, and network analysis, this is especially helpful.Increasing Need for Security and Fraud Detection: Graph databases are useful for fraud security and detection applications because they can identify patterns and abnormalities in linked data. The growing need for graph databases in security solutions is a result of the ongoing evolution of cybersecurity threats.

Use our G2 dataset to collect product descriptions, ratings, reviews, and pricing information from the world's largest tech marketplace. You may purchase a full or partial dataset depending on your business needs. The G2 Software Products Dataset, with a focus on top-rated products, serves as a valuable resource for software buyers, businesses, and technology enthusiasts. This use case highlights products that have received exceptional ratings and positive reviews on the G2 platform, offering insights into customer satisfaction and popularity. For software buyers, this dataset acts as a trusted guide, presenting a curated selection of G2's top-rated software products, ensuring a higher likelihood of satisfaction with purchases. Businesses and technology professionals can leverage this dataset to identify popular and well-reviewed software solutions, optimizing their decision-making process. This use case emphasizes the dataset's utility for those specifically interested in exploring and acquiring top-rated software products from G2's Product Overview The G2 software products and reviews dataset offer a detailed and thorough overview of leading software companies. The dataset includes all major data points: Product descriptions Average rating (1-5) Sellers number of reviews Key features (highest and lowest rated) Competitors Website & social media links and more.

Over the last decade, the application of hyperspectral data to estimate foliar traits in vegetation has exploded in use, including applications in agriculture and phenotyping, ecosystem functioning and biodiversity. There is an urgent need for a common set of software tools and open-source repository for those tools and spectral models derived from spectral data. Our effort focuses on developing the software, database/accessibility tools and web front- and back-ends needed to ensure open-source usage of spectral data among a large user community, including: data repositories, data processing tools, and spectral model distribution. Building on our previous EcoSIS.org effort, we will greatly expand the functionality of open-source tools for spectral data analysis, thus reducing barriers to entry for new researchers wanting to make use of field and/or imaging spectroscopy. This proposal is being submitted under the Data-Centric Technologies core topic area. Our primary target audiences are users wishing to scale from ground measurements to imagery or simply to use existing published algorithms to predict foliar traits from new ground reflectance or imaging spectroscopy data. EcoSIS.org is an easy-to-use online database that we developed with NASA support for storing, documenting, and distributing vegetation-themed spectroscopic datasets. The EcoSIS data portal makes contribution of rigorously-attributed datasets intuitive and uncomplicated. Ancillary data, such as chemical and physiological traits, as well as spectroscopy metadata, are easily added to make datasets discoverable across the internet, facilitate synergistic studies, and provide data to inform remote sensing research. Datasets published via EcoSIS are eligible to receive a DOI, providing persistent access by the user community as required by peer-reviewed journals and funding agencies. We propose to expand participation in EcoSIS by creating a suite of complementary open-source tools — the EcoSIS Toolkit — that make processing and preparation of spectral data straightforward, further removing the potential barriers to entry for those whose research would greatly benefit from the inclusion spectroscopy datasets and models. Throughout the development process for all tools we will employ Agile practices to iteratively add and test new features. All software developed through this project will use only open-source technologies and will be licensed under the Apache Licence 2.0 (http://www.apache.org/licenses/LICENSE-2.0). We will develop: 1) the Ecological Spectral Model Library — EcoSML.org — an online repository that distributes model parameters, example code, and other supporting resources related to spectra-derived models used to predict sample traits from spectra; 2) EcoSIS SDK library packages for popular scientific open-source languages to interface with EcoSIS; 3) the Spectroscopy Data Abstraction Library (SpecDAL, https://specdal.github.io/) will be an open-source Python library. Inspired by GDAL (http://www.gdal.org/), it will provide functions and classes to work with data files from industry-standard portable field spectrometers as well as custom built instruments; 4) HyTools, a toolbox of open-source Python programs used to perform necessary processing of hyperspectral images, providing a source to new users for code that has either been part of closed-source software packages or developed by individual researchers on an ad hoc basis. No comparable resources for these proposed functions are widely available, although there are some disparate sources on the web providing these functions. During this 2-year project, EcoSML will enter at TRL 2 and exit at TRL 4, while the SDK libraries, SpecDAL and HyTools will enter at TRL 3 and exit at TRL 5-7.