The Data Science Platform market is experiencing robust growth, projected to reach $10.15 billion in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 23.50% from 2025 to 2033. This expansion is driven by several key factors. The increasing availability and affordability of cloud computing resources are lowering the barrier to entry for organizations of all sizes seeking to leverage data science capabilities. Furthermore, the growing volume and complexity of data generated across various industries necessitates sophisticated platforms for efficient data processing, analysis, and model deployment. The rise of AI and machine learning further fuels demand, as organizations strive to gain competitive advantages through data-driven insights and automation. Strong demand from sectors like IT and Telecom, BFSI (Banking, Financial Services, and Insurance), and Retail & E-commerce are major contributors to market growth. The preference for cloud-based deployment models over on-premise solutions is also accelerating market expansion, driven by scalability, cost-effectiveness, and accessibility. Market segmentation reveals a diverse landscape. While large enterprises are currently major consumers, the increasing adoption of data science by small and medium-sized enterprises (SMEs) represents a significant growth opportunity. The platform offering segment is anticipated to maintain a substantial market share, driven by the need for comprehensive tools that integrate data ingestion, processing, modeling, and deployment capabilities. Geographically, North America and Europe are currently leading the market, but the Asia-Pacific region, particularly China and India, is poised for significant growth due to expanding digital economies and increasing investments in data science initiatives. Competitive intensity is high, with established players like IBM, SAS, and Microsoft competing alongside innovative startups like DataRobot and Databricks. This competitive landscape fosters innovation and further accelerates market expansion. Recent developments include: November 2023 - Stagwell announced a partnership with Google Cloud and SADA, a Google Cloud premier partner, to develop generative AI (gen AI) marketing solutions that support Stagwell agencies, client partners, and product development within the Stagwell Marketing Cloud (SMC). The partnership will help in harnessing data analytics and insights by developing and training a proprietary Stagwell large language model (LLM) purpose-built for Stagwell clients, productizing data assets via APIs to create new digital experiences for brands, and multiplying the value of their first-party data ecosystems to drive new revenue streams using Vertex AI and open source-based models., May 2023 - IBM launched a new AI and data platform, watsonx, it is aimed at allowing businesses to accelerate advanced AI usage with trusted data, speed and governance. IBM also introduced GPU-as-a-service, which is designed to support AI intensive workloads, with an AI dashboard to measure, track and help report on cloud carbon emissions. With watsonx, IBM offers an AI development studio with access to IBMcurated and trained foundation models and open-source models, access to a data store to gather and clean up training and tune data,. Key drivers for this market are: Rapid Increase in Big Data, Emerging Promising Use Cases of Data Science and Machine Learning; Shift of Organizations Toward Data-intensive Approach and Decisions. Potential restraints include: Lack of Skillset in Workforce, Data Security and Reliability Concerns. Notable trends are: Small and Medium Enterprises to Witness Major Growth.

A listing of several open science contribution from Dr. David Tarboton, including Open Software, a Data-sharing platform, Online Education Resources, and Research Community Coordination and Leadership roles and efforts that supports open science.

Global Government Open Data Management Platform Market size was valued at USD 1.75 Billion in 2024 and is projected to reach USD 3.38 Billion by 2032, growing at a CAGR of 8.54% from 2026 to 2032.

Global Government Open Data Management Platform Market Drivers

Increasing Demand for Transparency and Accountability: There is a growing public demand for transparency in government operations, which drives the adoption of open data initiatives. According to a survey by the World Bank, 85% of respondents in various countries indicated that transparency in government decisions is crucial for reducing corruption, prompting governments to implement open data platforms.

Technological Advancements: Rapid advancements in information and communication technology (ICT) facilitate the development and deployment of open data management platforms. The International Telecommunication Union (ITU) reported that global Internet penetration reached approximately 64% in 2023, enabling more citizens to access open data and engage with government services online.

Government Initiatives and Policies: Many governments are actively promoting open data through policies and initiatives. For instance, the U.S. government's Open Data Initiative, launched in 2013, has led to the publication of over 300,000 datasets on Data.gov. Additionally, the European Union's Open Data Directive, which aims to make public sector data available, is further encouraging governments to embrace open data practices.

Citizen Science Platform Market Outlook

According to our latest research, the global Citizen Science Platform market size reached USD 1.23 billion in 2024, reflecting robust engagement and technological advancement in public-driven scientific research. The market is projected to grow at a CAGR of 13.7% from 2025 to 2033, reaching a forecasted value of USD 3.84 billion by 2033. This impressive expansion is fuelled by increasing digital literacy, the proliferation of internet-connected devices, and growing awareness of the pivotal role that citizen science plays in data collection, environmental monitoring, and community-driven innovation.

The primary growth driver for the Citizen Science Platform market is the rising integration of digital technologies into scientific research, democratizing access and participation in scientific projects. The widespread adoption of smartphones, cloud computing, and user-friendly software platforms has empowered individuals and communities to contribute valuable data to scientific endeavors, breaking down traditional barriers between professional researchers and the general public. As scientific institutions and organizations increasingly recognize the value of crowd-sourced data, they are investing in scalable, secure, and interactive platforms that facilitate seamless collaboration, real-time data sharing, and robust analytics. This trend is further bolstered by the global push for open science and the growing necessity for large-scale data collection in fields such as ecology, astronomy, and public health.

Another significant factor fueling market growth is the expanding range of project types and end-user segments that leverage citizen science platforms. While environmental monitoring and ecology have historically dominated this space, there is a marked increase in projects related to health and medicine, astronomy, and social sciences. Academic institutions, government agencies, non-profit organizations, and even private enterprises are deploying citizen science platforms to harness the collective intelligence and observational power of the public. This diversification of applications not only broadens the market but also drives innovation in platform features, including data validation, participant engagement, and integration with emerging technologies such as artificial intelligence and machine learning.

The global focus on sustainability, urgent climate action, and public health crises has also contributed to the rapid adoption of citizen science platforms. Governments and international organizations are increasingly relying on citizen-generated data for real-time environmental monitoring, disease surveillance, and disaster response. The COVID-19 pandemic, in particular, underscored the importance of rapid, decentralized data collection, accelerating investments in digital infrastructure and collaborative platforms. As regulatory frameworks evolve to support data privacy and ethical participation, the market is expected to witness sustained growth, with an emphasis on inclusivity, transparency, and scientific rigor.

Regionally, North America currently leads the Citizen Science Platform market, accounting for the largest share in 2024, followed closely by Europe and the Asia Pacific. The dominance of North America can be attributed to strong governmental support, a mature research ecosystem, and the presence of leading technology providers. Europe’s market is propelled by a robust tradition of public engagement in science and significant funding from the European Union for citizen science initiatives. Meanwhile, the Asia Pacific region is emerging as a high-growth market, driven by increasing digital penetration, rising scientific literacy, and government-led initiatives to promote community participation in environmental and health monitoring. Latin America and the Middle East & Africa, though smaller in market share, are witnessing growing interest due to expanding internet access and efforts to address region-specific challenges through citizen-driven research.

Component Anal

The global data science platform market is projected to reach a value of $29.25 billion by 2033, growing at a CAGR of 19.8% from 2025 onwards. The market growth is primarily driven by the increasing demand for data-driven insights to optimize business operations and improve decision-making. Other factors contributing to the market expansion include the rising adoption of cloud computing, the proliferation of big data, and the growing need for data security and compliance. Key market segments include platform type (open and closed data science platforms), application (BFSI, healthcare, retail, and others), and region (North America, Europe, Asia Pacific, and the rest of the world). Major companies in the market include Microsoft, IBM, Google, Wolfram, and Datarobot. The competitive landscape is characterized by strategic partnerships, acquisitions, and new product launches. Market trends include the adoption of artificial intelligence (AI) and machine learning (ML) technologies, the rise of data science as a service (DSaaS), and the increasing emphasis on data governance.

Motivation is of great importance in the teaching-learning process, because motivated students seek out opportunities and show interest and enthusiasm in carrying out their tasks. The objective of this review is to identify and present the information available in the literature on the status quo of motivation among nursing program entrants. This is a qualitative scoping review study, a type of literature review designed to map out and find evidence to address a specific research objective, following the Joanna Briggs Institute methodology. The objective was outlined using the PCC (Population, Concept, Context) acronym. The protocol was developed and registered on the Open Science Framework (OSF) platform under DOI 10.17605/OSF.IO/EJNGY. The search strategy and database selection were defined by a library and information science professional together with the authors. The search will be carried out in the following databases: Cumulative Index to Nursing and Allied Health Literature, Literatura Latino Americana e do Caribe em Ciências da Saúde, Lilacs Esp, National Library of Medicine (PubMed), ScienceDirect, Scopus, and the Web of Science platform. The researchers will meet to discuss discrepancies and make decisions using a consensus model, and a third researcher will be tasked with independently resolving any conflicts. Data extraction will involve two independent researchers reviewing each article. Documents such as original articles; theoretical studies; experience reports; clinical study articles; case studies; normative, integrative, and systematic reviews; meta-analyses; meta-syntheses; monographs; theses; and dissertations in English, Portuguese, and Spanish from 2017 to 2023 were included. The results will be presented in tabular and/or diagrammatic format, along with a narrative summary.

Content

How does your organization use this dataset? What other NYSERDA or energy-related datasets would you like to see on Open NY? Let us know by emailing OpenNY@nyserda.ny.gov.

Research project summaries provide brief descriptions of research projects supported by New York State Energy Research and Development Authority’s (NYSERDA) Technology and Business Innovation portfolio of programs. Each summary includes the research project’s background, objective, and expected benefits. Closed research projects also include a section on the project results.

Context

This is a dataset hosted by the State of New York. The state has an open data platform found here and they update their information according the amount of data that is brought in. Explore New York State using Kaggle and all of the data sources available through the State of New York organization page!

• Update Frequency: This dataset is updated annually.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

This dataset presents the assessment tool used to analyze 20 Data Management Plan (DMP) templates on the Argos platform, along with the pre-print of the manuscript for an article that is about to be published in the Journal Biblios of the University of Pittsburgh. The main objective of this study was to investigate the need to implement a DMP at Universidad Centroamericana José Simeón Cañas (UCA) to improve accessibility, discovery, and reuse of research. Using a qualitative case study methodology, we worked with 10 selected research groups to evaluate and adapt a base model for the DMP. The results indicated a significant improvement in research data management and a positive perception from users regarding the processing and organization of their data. This set includes the DMP format generated for UCA, as well as recommendations for other institutions interested in adopting similar data management practices, contributing to the continued growth of scholarly output and the ethical and..., Method: A qualitative case study methodology was employed, which included participant observation of researchers and administrative staff from various 2024 research groups, along with an analysis of documentation and LibGuides. A benchmarking process was also conducted, comparing 20 PGDI templates to extract the best structure and practices from various research institutions. Content analysis: This method was used to examine a set of 20 PGDI templates from the ARGOS initiative, a platform developed by OpenAIRE and EUDAT for planning and managing research data. A systematic review of the structure and content of each of these templates was conducted, assessing the clarity, consistency, and adequacy of the information presented. Through this content analysis, key elements were identified that needed to be incorporated or improved in the base template provided to UCA research groups. This process allowed us to highlight best practices and identify areas that required additional attention, ..., , # Data from: Data management plan (DMP): Towards more efficient scientific management at the Universidad Centroamericana José Simeón Cañas

https://doi.org/10.5061/dryad.1zcrjdg25

Description of the Data and File Structure

README for the Dataset: Implementation of a Data Management Plan (DMP)

Dataset Description

This dataset includes the evaluation instrument used to analyze 20 Data Management Plan (DMP) templates on the Argos platform. Additionally, the pre-print of the manuscript of the article that is set to be published in the Journal Biblios at the University of Pittsburgh has been attached. Furthermore, the format of the Data Management Plan generated for the Universidad Centroamericana José Simeón Cañas (UCA), developed from this research, is included.

Objective

The primary objective of this study was to investigate the need to implement a Data Management Plan (DMP) to improve the accessibility, discoverability...

Content

More details about each file are in the individual file descriptions.

Context

This is a dataset hosted by the City of New York. The city has an open data platform found here and they update their information according the amount of data that is brought in. Explore New York City using Kaggle and all of the data sources available through the City of New York organization page!

• Update Frequency: This dataset is updated monthly.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

This dataset is distributed under the following licenses: Public Domain

The National Transportation Library (NTL) provides national and international access to transportation information, coordinates information creation and dissemination, and provides reference services for Department of Transportation (DOT) employees and public stakeholders. Established in 1998 by the Transportation Equity Act for the 21st Century (TEA-21; P.L. 105-178), NTL’s authorized role was expanded in 2012’s Moving Ahead for Progress in the 21st Century (MAP-21; P.L. 112- 141). NTL’s primary product and service is the Repository and Open Science Access Portal (ROSA P) (https://rosap.ntl.bts.gov). NTL’s collections in ROSA P are full-text digital publications, datasets, and other resources. Legacy print materials that have been digitized are collected if they have historic, technical, or national significance. The repository is also designated as the full-text repository for USDOT-funded research under the USDOT Public Access Plan. Collections in ROSA P are available without restriction to transportation researchers, statistical organizations, the media, and the general public.

Snapshot img

Government Open Data Management Platform Market Size 2025-2029

The government open data management platform market size is valued to increase by USD 189.4 million, at a CAGR of 12.5% from 2024 to 2029. Rising demand for digitalization in government operations will drive the government open data management platform market.

Market Insights

North America dominated the market and accounted for a 38% growth during the 2025-2029.
By End-user - Large enterprises segment was valued at USD 108.50 million in 2023
By Deployment - On-premises segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 138.56 million 
Market Future Opportunities 2024: USD 189.40 million
CAGR from 2024 to 2029 : 12.5%

Market Summary

The market witnesses significant growth due to the increasing demand for digitalization in government operations. Open data management platforms enable governments to make large volumes of data available to the public in a machine-readable format, fostering transparency and accountability. The adoption of advanced technologies such as artificial intelligence (AI) and machine learning (ML) in these platforms enhances data analysis capabilities, leading to more informed decision-making. However, data privacy concerns remain a major challenge in the open data management market. Governments must ensure the protection of sensitive information while making data publicly available. A real-world business scenario illustrating the importance of open data management platforms is supply chain optimization in the public sector.
By sharing data related to procurement, logistics, and inventory management, governments can streamline their operations and improve efficiency. For instance, a city government could share real-time traffic data to optimize public transportation routes, reducing travel time and improving overall service delivery. Despite these benefits, it is crucial for governments to address data security concerns and establish robust data management policies to ensure the safe and effective use of open data platforms.

What will be the size of the Government Open Data Management Platform Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free Sample

The market continues to evolve, with recent research indicating a significant increase in data reuse initiatives among government agencies. The use of open data platforms in the public sector has grown by over 25% in the last two years, driven by a need for transparency and improved data-driven decision making. This trend is particularly notable in areas such as compliance and budgeting, where accurate and accessible data is essential. Data replication strategies, data visualization libraries, and data portal design are key considerations for government agencies looking to optimize their open data management platforms.
Effective data discovery tools and metadata schema design are crucial for ensuring data silos are minimized and data usage patterns are easily understood. Data privacy regulations, such as GDPR and HIPAA, also require robust data governance frameworks and data security audits to maintain data privacy and protect against breaches. Data access logs, data consistency checks, and data quality dashboards are essential components of any open data management platform, ensuring data accuracy and reliability. Data integration services and data sharing platforms enable seamless data exchange between different agencies and departments, while data federation techniques allow for data to be accessed in its original source without the need for data replication.
Ultimately, these strategies contribute to a more efficient and effective data lifecycle, allowing government agencies to make informed decisions and deliver better services to their constituents.

Unpacking the Government Open Data Management Platform Market Landscape

The market encompasses a range of solutions designed to facilitate the efficient and secure handling of data throughout its lifecycle. According to recent studies, organizations adopting data lifecycle management practices experience a 30% reduction in data processing costs and a 25% improvement in ROI. Performance benchmarking is crucial for ensuring optimal system scalability, with leading platforms delivering up to 50% faster query response times than traditional systems. Data anonymization techniques and data modeling methods enable compliance with data protection regulations, while open data standards streamline data access and sharing. Data lineage tracking and metadata management are essential for maintaining data quality and ensuring data interoperability. API integration strategies and data transformation methods enable seamless data enrichment processes and knowledge graph implementation. Data access control, data versioning, and data security protocols

To support the implementation of the Impact Assessment Act, Natural Resources Canada (NRCan) received funding over  five years (2018-23) for cumulative effects research to be conducted by three of NRCan’s science sectors – the Canada Centre for Mapping and Earth Observation within the Strategic Policy and Innovation Sector, the Canadian Forest Service, and the Geological Survey of Canada within the Lands and Minerals Sector – to conduct key earth observation, forest, and geoscience research. The overarching goal of this research is to inform regional assessment and related impact assessment processes, with a central focus on making the science and knowledge generated open and accessible to the public via the Open Science and Data Platform . Projects from all three sectors generated authoritative data on the status and trends of ecosystem parameters, as well as provided unique science and technical analysis, synthesis and advice on topics related to the cumulative effects of natural resource development. This Story Map synthesizes the cumulative effects science generated over the past five years. Through the Story Map platform, we hope to illustrate the national scale of this research program and the diversity of locations in Canada within which research has been conducted.

Content

CDC Science Clips is an online bibliographic digest featuring scientific articles and publications that are shared with the public health community each week, to enhance awareness of emerging scientific knowledge.

Context

This is a dataset hosted by the Centers for Disease Control and Prevention. The organization has an open data platform found here and they update their information according the amount of data that is brought in. Explore CDC Data using Kaggle and all of the data sources available through the CDC organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

This dataset is distributed under NA

Content

More details about each file are in the individual file descriptions.

Context

This is a dataset hosted by the City of New York. The city has an open data platform found here and they update their information according the amount of data that is brought in. Explore New York City using Kaggle and all of the data sources available through the City of New York organization page!

• Update Frequency: This dataset is updated annually.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

This dataset is distributed under the following licenses: Public Domain

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

Content

More details about each file are in the individual file descriptions.

Context

This is a dataset hosted by the City of New York. The city has an open data platform found here and they update their information according the amount of data that is brought in. Explore New York City using Kaggle and all of the data sources available through the City of New York organization page!

• Update Frequency: This dataset is updated annually.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

This dataset is distributed under the following licenses: Public Domain

Content

More details about each file are in the individual file descriptions.

Context

This is a dataset hosted by the State of New York. The state has an open data platform found here and they update their information according the amount of data that is brought in. Explore New York State using Kaggle and all of the data sources available through the State of New York organization page!

• Update Frequency: This dataset is updated weekly.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

This dataset is distributed under the following licenses: Public Domain

Raw data from a real-time Delphi exercise used to write the policy brief: Supporting Open Science Hardware in Academia: Policy Recommendations for Science Funders and University Managers

Access to the Real-Time Delphi platform was kindly provided by 4strat. See tab "meta - start here" for description of the dataset.

The NASA Life Sciences Portal (NLSP) is the data platform for accessing NASA's Life Sciences Data Archive (LSDA). The LSDA is an active archive that provides information and data from 1961 (Mercury Project) through current flight and flight analog studies (International Space Station, Shuttle, bed rest studies, etc.) involving human, plant and animal subjects. Alternative contact: Jessica Keune (jessica.a.keune@nasa.gov)

For more than a decade, open access book platforms have been distributing titles in order to maximise their impact. Each platform offers some form of usage data, showcasing the success of their offering. However, the numbers alone are not sufficient to convey how well a book is actually performing.

Our data set is consists of 18,014 books and chapters. The selected titles have been added to the OAPEN Library collection before 1 January 2022, and the usage data of twelve months (January to December 2022) has been captured. During that period, this collection of books and chapters has been downloaded more than 10 million times. Each title has been linked to one broad subject and the title’s language has been coded as either English, German or other languages.

The titles are rated using the TOANI score.

The acronym stands for Transparent Open Access Normalised Index. The transparency is based on the application of clear regulations, and by making all data used visible. The data is normalised, by using a common scale for the complete collection of an open access book platform. Additionally, there are only three possible values to score the titles: average, less than average and more than average. This index is set up to provide a clear and simple answer to the question whether an open access book has made an impact. It is not meant to give a sense of false accuracy; the complexities surrounding this issue cannot be measured in several decimal places.

The TOANI score is based on the following principles:

Select only titles that have been available for at least 12 months;

Use the usage data of the same 12 months period for the whole collection;

Each title is assigned one – high level – subject;

Each title is assigned one language;

All titles are grouped based on subject and language;

The groups should consists of at least 100 titles;

The following data must be made available for each title:

Platform

Total number of titles in the group

Subject

Language

Period used for the measurement

Minimum value, maximum value, median, first and third quartile of the platform’s usage data

Based on the previous, titles are classified as:

“Less than average” – First quartile; 25 % of the titles

“Average” – Second and third quartile; 50% of the titles

“More than average” – Fourth quartile; 25 % of the titles