Assessment of the AI readiness of 193 governments across the world.

As of 2023, the strategy domain is the one with the largest number of companies claiming to be either a pacesetter or a chaser in terms of artificial intelligence (AI) readiness. The data domain held the largest number of laggards in the technology.

Description:

The dataset ‘DMSP Particle Precipitation AI-ready Data’ accompanies the manuscript “Next generation particle precipitation: Mesoscale prediction through machine learning (a case study and framework for progress)” submitted to AGU Space Weather Journal and used to produce new machine learning models of particle precipitation from the magnetosphere to the ionosphere. Note that we have attempted to make these data ready to be used in artificial intelligence/machine learning explorations following a community definition of ‘AI-ready’ provided at https://github.com/rmcgranaghan/data_science_tools_and_resources/wiki/Curated-Reference%7CChallenge-Data-Sets

The purpose of publishing these data is two-fold:

To allow reuse of the data that led to the manuscript and extension, rather than reinvention, of the research produced there; and

To be an ‘AI-ready’ challenge data set to which the artificial intelligence/machine learning community can apply novel methods.

These data were compiled, curated, and explored by: Ryan McGranaghan, Enrico Camporeale, Kristina Lynch, Jack Ziegler, Téo Bloch, Mathew Owens, Jesper Gjerloev, Spencer Hatch, Binzheng Zhang, and Susan Skone

Pipeline for creation:

The steps to create the data were (Note that we do not provide intermediate datasets):

Access NASA-provided DMSP data at https://cdaweb.gsfc.nasa.gov/pub/data/dmsp/

Read CDF files for given satellite (e.g., F-16)

Collect the following variables at one-second cadence: SC_AACGM_LAT, SC_AACGM_LTIME, ELE_TOTAL_ENERGY_FLUX, ELE_TOTAL_ENERGY_FLUX_STD, ELE_AVG_ENERGY, ELE_AVG_ENERGY_STD, ID_SC

Sub-sample the variables to one-minute cadence and eliminate any rows for which ELE_TOTAL_ENERGY_FLUX is NaN

Combine all individual satellites into single yearly files

For each yearly file, use nasaomnireader to obtain solar wind and geomagnetic index data programmatically and timehist2 to calculate the time histories of each parameter. Collate with the DMSP observations and remove rows for which any solar wind or geomagnetic index data are missing.

For each row, calculate cyclical time variables (e.g., local time -> sin(LT) and cos(LT))

Merge all years

How to use:

The Github repository https://github.com/rmcgranaghan/precipNet is provided to detail the use of these data and to provide Jupyter notebooks to facilitate getting started. The code is implemented in Python 3 and is licensed under the GNU General Public License v3.0

Citation:

For anyone using these data, please cite each of the following papers:

McGranaghan, R. M., Ziegler, J., Bloch, T., Hatch, S., Camporeale, E., Lynch, K., et al. (2021). Toward a next generation particle precipitation model: Mesoscale prediction through machine learning (a case study and framework for progress). Space Weather, 19, e2020SW002684. https://doi.org/10.1029/2020SW002684

McGranaghan, R. (2019), Eight lessons I learned leading a scientific “design sprint”, Eos, 100, https://doi.org/10.1029/2019EO136427. Published on 11 November 2019.

For questions or comments please contact Ryan McGranaghan (ryan.mcgranaghan@gmail.com)

In 2023, Singapore was the most AI-ready country in the Asia-Pacific region, scoring **** out of 100 points in the AI readiness index. In contrast, the Philippines had an AI readiness index score of **** in the same year, indicating a lower overall AI readiness level.

Extreme weather events, including fires, heatwaves, and droughts, have significant impacts on earth, environmental, and energy systems. Mechanistic and predictive understanding, as well as probabilistic risk assessment of these extreme weather events, are crucial for detecting, planning for, and responding to these extremes. Records of extreme weather events provide an important data source for understanding present and future extremes, but the existing data needs preprocessing before it can be used for analysis. Moreover, there are many nonstandard metrics defining the levels of severity or impacts of extremes. In this study, we compile a comprehensive benchmark data inventory of extreme weather events, including fires, heatwaves, and droughts. The dataset covers the period from 2001 to 2020 with a daily temporal resolution and a spatial resolution of 0.5°×0.5° (~55km×55km) over the continental United States (CONUS), and a spatial resolution of 1km × 1km over the Pacific Northwest (PNW) region, together with the co-located and relevant meteorological variables. By exploring and summarizing the spatial and temporal patterns of these extremes in various forms of marginal, conditional, and joint probability distributions, we gain a better understanding of the characteristics of climate extremes. The resulting AI/ML-ready data products can be readily applied to ML-based research, fostering and encouraging AI/ML research in the field of extreme weather. This study can contribute significantly to the advancement of extreme weather research, aiding researchers, policymakers, and practitioners in developing improved preparedness and response strategies to protect communities and ecosystems from the adverse impacts of extreme weather events. Usage Notes We presented a long term (2001-2020) and comprehensive data inventory of historical extreme events with daily temporal resolution covering the separate spatial extents of CONUS (0.5°×0.5°) and PNW(1km×1km) for various applications and studies. The dataset with 0.5°×0.5° resolution for CONUS can be used to help build more accurate climate models for the entire CONUS, which can help in understanding long-term climate trends, including changes in the frequency and intensity of extreme events, predicting future extreme events as well as understanding the implications of extreme events on society and the environment. The data can also be applied for risk accessment of the extremes. For example, ML/AI models can be developed to predict wildfire risk or forecast HWs by analyzing historical weather data, and past fires or heateave , allowing for early warnings and risk mitigation strategies. Using this dataset, AI-driven risk assessment models can also be built to identify vulnerable energy and utilities infrastructure, imrpove grid resilience and suggest adaptations to withstand extreme weather events. The high-resolution 1km×1km dataset ove PNW are advantageous for real-time, localized and detailed applications. It can enhance the accuracy of early warning systems for extreme weather events, helping authorities and communities prepare for and respond to disasters more effectively. For example, ML models can be developed to provide localized HW predictions for specific neighborhoods or cities, enabling residents and local emergency services to take targeted actions; the assessment of drought severity in specific communities or watersheds within the PNW can help local authorities manage water resources more effectively.

This file contains the full version of the survey questionnaire used to assess researchers’ readiness to use artificial intelligence (AI) tools in various phases of academic work. The questionnaire includes items related to editorial, conceptual, analytical, and interpretative tasks, as well as contextual and institutional conditions influencing AI adoption in research practices.File format: DOCX (Microsoft Word)Language: English and PolishUse:Self-administered survey (online or paper-based)For research and educational purposesAnonymity and ethics:The questionnaire does not collect personal identifiers. It was designed to ensure respondent anonymity while maintaining methodological transparency.

According to the government artificial intelligence (AI) readiness index rankings, the United States (U.S.) is the highest-ranked country on the worldwide index in 2024, with an index score of *****. This means that the U.S. is considered the country best situated in the world to implement AI within public services, from healthcare to education to transportation. Other noteworthy countries with high indexes were Singapore, the Republic of Korea, and France coming in at second, third, and fourth respectively. China ranks **** on the index as it only measures AI readiness instead of AI implementation. Compared to many other countries that may score more highly for AI readiness, China is advanced in implementing AI capabilities to public services as it has made this a top government priority.

Multidisciplinary data generation project which aims to create and share multimodal dataset optimized for artificial intelligence research in type 2 diabetes. At each release of the AI-READI dataset, two sets will be made available: public access and controlled access set. The public set will be stripped of Protected Health Information (PHI) as well as information related to the sex and race/ethnicity of the participants.

We introduce a new AI-ready computational pathology dataset containing restained and co-registered digitized images from eight head-and-neck squamous cell carcinoma patients. Specifically, the same tumor sections were stained with the expensive multiplex immunofluorescence (mIF) assay first and then restained with cheaper multiplex immunohistochemistry (mIHC). This is a first public dataset that demonstrates the equivalence of these two staining methods which in turn allows several use cases; due to the equivalence, our cheaper mIHC staining protocol can offset the need for expensive mIF staining/scanning which requires highly skilled lab technicians. As opposed to subjective and error-prone immune cell annotations from individual pathologists (disagreement > 50%) to drive SOTA deep learning approaches, this dataset provides objective immune and tumor cell annotations via mIF/mIHC restaining for more reproducible and accurate characterization of tumor immune microenvironment (e.g. for immunotherapy). We demonstrate the effectiveness of this dataset in three use cases: (1) IHC quantification of CD3/CD8 tumor-infiltrating lymphocytes via style transfer, (2) virtual translation of cheap mIHC stains to more expensive mIF stains, and (3) virtual tumor/immune cellular phenotyping on standard hematoxylin images. The code for stain translation is available at https://github.com/nadeemlab/DeepLIIF and the code for performing interactive deep learning whole-cell/nuclear segmentation is available at https://github.com/nadeemlab/impartial. After scanning the full images, nine regions of interest (ROIs) from each slide/Case were chosen by an experienced pathologist on both mIF and mIHC images: three in the tumor core (T), three at the tumor margin (M),and three outside in the adjacent stroma (S) area. These individual ROIs were further subdivided into four 512x512 patches with indices [0_0], [0_1], [1_0], [1_1]. The final notation for each file is Case[patient_id]_[T/M/S][1/2/3]_[ROI_index]_[Marker_name]. More details can be found in the paper.

The U.S. government scores the highest on AI vision with a score of 100 out of 100 according to a 2020 government artificial intelligence (AI) readiness index ranking. Its overall index score reached *****, making it the highest-ranked country worldwide, which means that the U.S. is considered the country best situated in the world to implement AI within public services. Apart from AI vision, various other categories such as governance and ethics, infrastructure, data availability, and data representativeness are also taken into account.

The study examines variables to assess teachers' preparedness for integrating AI into South African schools. The dataset on the Excel sheet consists of 42 columns. The first ten columns comprise demographic variables such as Gender, Years of Teaching Experience (TE), Age Group, Specialisation (SPE), School Type (ST), School Location (SL), School Description (SD), Level of Technology Usage for Teaching and Learning (LTUTL), Undergone Training/Workshop/Seminar on AI Integration into Teaching and Learning Before (TRAIN), and if Yes, Have You Used Any AI Tools to Teach Before (TEACHAI). Columns 11 to 42 contain constructs measuring teachers' preparedness for integrating AI into the school system. These variables are measured on a scale of 1 = strongly disagree to 6 = strongly agree.

AI Ethics (AE): This variable captures teachers' perspectives on incorporating discussions about AI ethics into the curriculum.

Attitude Towards Using AI (AT): This variable reflects teachers' beliefs about the benefits of using AI in their teaching practices. It includes their expectations of having a positive experience with AI, improving their teaching experience, and enhancing their participation in critical discussions through AI applications.

Technology Integration (TI): This variable measures teachers' comfort in integrating AI tools and technologies into lesson plans. It also assesses their belief that AI enhances the learning experience for students, their proactive efforts to learn about new AI tools, and the importance they place on technology integration for effective AI education.

Social Influence (SI): This variable examines the impact of colleagues, administrative support, peer discussions, and parental expectations on teachers' preparedness to incorporate AI into their teaching practices.

Technological Pedagogical Content Knowledge (TPACK): This variable assesses teachers' ability to use technology to facilitate AI learning. It includes their capability to select appropriate technology for teaching specific AI content, and bring real-life examples into lessons.

AI Professional Development (AIPD): This variable evaluates the impact of professional development training on teachers' ability to teach AI effectively. It includes the adequacy of these programs, teachers' proactive pursuit of further professional development opportunities, and schools' provision of such opportunities.

AI Teaching Preparedness (AITP): This variable measures teachers' feelings of preparedness to teach AI. It includes their belief that their teaching methods are engaging, their confidence in adapting AI content for different student needs, and their proactive efforts to improve their teaching skills for AI education.

Perceived Self-Efficacy to Teaching AI (PSE): This variable captures teachers' confidence in their ability to teach AI concepts, address challenges in teaching AI, and create innovative AI-related teaching materials.

Additional file 2: Supplementary Table 1. Dataset of MAIRS-MS questionnaire with “then-” and “post-” items. Note: Item VP01_01 is a pseudonym generated to enable comparisons between the self-assessment of AI readiness and the evaluation results of the blended learning course. All items ending in “01” (e.g., “CO10_01”) represent ratings after attending the course, while all items ending in “02” (e.g., “CO10_02”) reflect retrospective ratings before attending the course. Supplementary Table 2. Dataset of evaluation results. Note: Item VP01_01 is a pseudonym generated to enable comparisons between the self-assessment of AI readiness and the evaluation results of the blended learning course.

When it comes to readiness for the adoption of artificial intelligence (AI) among big pharmaceutical companies, Swiss company Roche was at the top, as of August 2023. According to an index, calculated based on talent, innovation and execution, Roche had the highest score. In many cases, big pharma companies build up their AI readiness by acquisition of smaller, but extremely innovative and technology-driven companies.

In 2023, in an index to assess artificial intelligence (AI) readiness, Singapore scored **** out of a hundred for government readiness, and **** for business readiness. Government readiness is based on various indicators to assess the level to which public sector actors enable AI through funds and frameworks. Consumer readiness is defined by the way consumers perceive, understand and trust AI. Business readiness represents the level of equipment in the private sector to adopt AI.

The global Machine Learning as a Service (MLaaS) market is poised for exponential growth, with its valuation projected to reach a staggering $71.34 million by 2033. This surge is fueled by a robust compound annual growth rate (CAGR) of 34.10% during the forecast period from 2025 to 2033. The MLaaS market is driven by the burgeoning adoption of artificial intelligence (AI) and machine learning (ML) solutions across a diverse spectrum of industries, including IT and telecom, healthcare, automotive, and retail. This adoption is fueled by the increasing demand for data-driven insights, predictive analytics, and automated decision-making to optimize operations and enhance customer experiences. Key market trends include the rise of cloud-based MLaaS platforms, which offer greater accessibility and scalability to businesses of all sizes. Furthermore, advancements in ML algorithms and the availability of vast datasets are empowering businesses to build sophisticated ML models that can solve complex problems and uncover new opportunities. The market is further segmented by application, organization size, and end user, with notable players such as SAS Institute Inc., Google LLC, and Amazon Web Services Inc. driving innovation and competition. However, the growth of the MLaaS market may be hindered by factors such as data security and privacy concerns, as well as the need for skilled professionals to develop and deploy ML models. Recent developments include: February 2024: Jio Platform launched a new AI-driven platform called 'Jio Brain,' which will enable the integration of machine learning capabilities into telecom networks, enterprise networks, or IT environments without the need to transform the network completely., February 2024: Wipro Limited launched the Wipro Enterprise AI-Ready Platform (Wipro AI-Ready Platform), a new service enabling clients to build enterprise-level, fully integrated, and tailored AI environments. The Wipro enterprise AI-ready platform will empower clients with AI infrastructure and core software to consume AI and generic AI workloads. It will also provide code-based configurations to improve automation and dynamic resource management to adapt dynamically to changing workloads with predictive analytics, as well as help enterprise organizations reduce incidents and improve operational efficiency.. Key drivers for this market are: Increasing Adoption of IoT and Automation, Increasing Adoption of Cloud-based Services. Potential restraints include: Privacy and Data Security Concerns, Need for Skilled Professionals. Notable trends are: Increasing Adoption of IoT and Automation is Expected to Drive Growth.

Description This dataset is the June 2025 Data Release of Cell Maps for Artificial Intelligence (CM4AI; CM4AI.org), the Functional Genomics Grand Challenge in the NIH Bridge2AI program. This Beta release includes perturb-seq data in undifferentiated KOLF2.1J iPSCs; SEC-MS data in undifferentiated KOLF2.1J iPSCs, iPSC-derived NPCs, neurons, cardiomyocytes, and treated and untreated MDA-MB468 breast cancer cells; and IF images in MDA-MB-468 breast cancer cells in the presence and absence of chemotherapy (vorinostat and paclitaxel). External Data Links Access external data resources related to this dataset: Sequence Read Archive (SRA) Data: NCBI BioProject Mass Spectrometry Data (Human iPSCs): MassIVE Repository Mass Spectrometry Data (Human Cancer Cells): MassIVE Repository Data Governance & Ethics Human Subjects: No De-identified Samples: Yes FDA Regulated: No Data Governance Committee: Jillian Parker (jillianparker@health.ucsd.edu) Ethical Review: Vardit Ravitsky (ravitskyv@thehastingscenter.org) and Jean-Christophe Belisle-Pipon (jean-christophe_belisle-pipon@sfu.ca) Completeness These data are not yet in completed final form: Some datasets are under temporary pre-publication embargo Protein-protein interaction (SEC-MS), protein localization (IF imaging), and CRISPRi perturbSeq data interrogate sets of proteins which incompletely overlap Computed cell maps not included in this release Maintenance Plan Dataset will be regularly updated and augmented through the end of the project in November 2026 Updates on a quarterly basis Long term preservation in the University of Virginia Dataverse, supported by committed institutional funds Intended Use This dataset is intended for: AI-ready datasets to support research in functional genomics AI model training Cellular process analysis Cell architectural changes and interactions in presence of specific disease processes, treatment conditions, or genetic perturbations Limitations Researchers should be aware of inherent limitations: This is an interim release Does not contain predicted cell maps, which will be added in future releases The current release is most suitable for bioinformatics analysis of the individual datasets Requires domain expertise for meaningful analysis Prohibited Uses These laboratory data are not to be used in clinical decision-making or in any context involving patient care without appropriate regulatory oversight and approval Potential Sources of Bias Users should be aware of potential biases: Data in this release was derived from commercially available de-identified human cell lines Does not represent all biological variants which may be seen in the population at large

The Generative AI Market size was valued at USD 43.87 USD Billion in 2023 and is projected to reach USD 453.28 USD Billion by 2032, exhibiting a CAGR of 39.6 % during the forecast period. The market's expansion is driven by the increasing adoption of AI in various industries, the growing demand for personalized experiences, and the advancement of machine learning and deep learning technologies. Generative AI is a form of AI technology that come with the capability to generate content in several of forms such us that include text, images, audio data, and artificial data. In the latest trend of the use of generative AI, fingertip friendly interfaces that allow for the creation of top-quality text design, and videos in a brief time of only seconds have been the leading cause of the hype around it. The AI technology called Generative AI employs a variety of techniques that its development is still being improved. Fundamentally, AI foundation models are based on training on a wide spate of unlabelled data that can be used for many tasks; working primarily on specific areas where additional fine-tuning finds its place. Over-simplifying the process, huge amounts of maths and computer power get used to develop AI models. Nevertheless, at its core, it is the predictions amplified. Generative AI relies on deep learning models – sophisticated machine learning models that work as neural networks and learn and take decisions just the human minds do. Such models are based on the detection and emission of codes of complex relationships or patterns in huge information volumes and that data is used to respond to users' original speech requests or questions with native language replies or new content. Recent developments include: June 2023: Salesforce launched two generative artificial intelligence (AI) products for commerce experience and customized consumers –Commerce GPT and Marketing GPT. The Marketing GPT model leverages data from Salesforce's real-time data cloud platform to generate more innovative audience segments, personalized emails, and marketing strategies., June 2023: Accenture and Microsoft are teaming up to help companies primarily transform their businesses by harnessing the power of generative AI accelerated by the cloud. It helps customers find the right way to build and extend technology in their business responsibly., May 2023: SAP SE partnered with Microsoft to help customers solve their fundamental business challenges with the latest enterprise-ready innovations. This integration will enable new experiences to improve how businesses attract, retain and qualify their employees. , April 2023: Amazon Web Services, Inc. launched a global generative AI accelerator for startups. The company’s Generative AI Accelerator offers access to impactful AI tools and models, machine learning stack optimization, customized go-to-market strategies, and more., March 2023: Adobe and NVIDIA have partnered to join the growth of generative AI and additional advanced creative workflows. Adobe and NVIDIA will innovate advanced AI models with new generations aiming at tight integration into the applications that significant developers and marketers use. . Key drivers for this market are: Growing Necessity to Create a Virtual World in the Metaverse to Drive the Market. Potential restraints include: Risks Related to Data Breaches and Sensitive Information to Hinder Market Growth . Notable trends are: Rising Awareness about Conversational AI to Transform the Market Outlook .

This dataset features over 10,000 high-quality images of packages sourced from photographers worldwide. Designed to support AI and machine learning applications, it provides a diverse and richly annotated collection of package imagery.

Key Features: 1. Comprehensive Metadata The dataset includes full EXIF data, detailing camera settings such as aperture, ISO, shutter speed, and focal length. Additionally, each image is pre-annotated with object and scene detection metadata, making it ideal for tasks like classification, detection, and segmentation. Popularity metrics, derived from engagement on our proprietary platform, are also included.

1. Unique Sourcing Capabilities The images are collected through a proprietary gamified platform for photographers. Competitions focused on package photography ensure fresh, relevant, and high-quality submissions. Custom datasets can be sourced on-demand within 72 hours, allowing for specific requirements such as packaging types (e.g., boxes, envelopes, branded parcels) or environmental settings (e.g., in transit, on doorsteps, in warehouses) to be met efficiently.

2. Global Diversity Photographs have been sourced from contributors in over 100 countries, ensuring a wide variety of packaging designs, shipping labels, languages, and handling conditions. The images cover diverse contexts, including retail shelves, delivery trucks, homes, and distribution centers, offering a comprehensive view of real-world packaging scenarios.

3. High-Quality Imagery The dataset includes images with resolutions ranging from standard to high-definition to meet the needs of various projects. Both professional and amateur photography styles are represented, offering a mix of artistic and functional perspectives suitable for a variety of applications.

4. Popularity Scores Each image is assigned a popularity score based on its performance in GuruShots competitions. This unique metric reflects how well the image resonates with a global audience, offering an additional layer of insight for AI models focused on user preferences or engagement trends.

5. AI-Ready Design This dataset is optimized for AI applications, making it ideal for training models in tasks such as package recognition, logistics automation, label detection, and condition analysis. It is compatible with a wide range of machine learning frameworks and workflows, ensuring seamless integration into your projects.

6. Licensing & Compliance The dataset complies fully with data privacy regulations and offers transparent licensing for both commercial and academic use.

Use Cases: 1. Training computer vision systems for package identification and tracking. 2. Enhancing logistics and supply chain AI models with real-world packaging visuals. 3. Supporting robotics and automation workflows in warehousing and delivery environments. 4. Developing datasets for augmented reality, retail shelf analysis, or smart delivery applications.

This dataset offers a comprehensive, diverse, and high-quality resource for training AI and ML models, tailored to deliver exceptional performance for your projects. Customizations are available to suit specific project needs. Contact us to learn more!

About this data An ultrasound dataset to use in the discovery of ultrasound features associated with pain and radiographic change in KOA is highly innovative and will be a major step forward for the field. These ultrasound images originate from the diverse and inclusive population-based Johnston County Health Study (JoCoHS). This dataset is designed to adhere to FAIR principles and was funded in part by an Administrative Supplement to Improve the AI/ML-Readiness of NIH-Supported Data (3R01AR077060-03S1). Working with this dataset WorkingWithTheDataset.ipynb Jupyter notebook If you are familiar with working with Jupyter notebooks, we recommend using the WorkingWithTheDataset.ipynb Jupyter notebook to retrieve, validate, and learn more about the dataset. You should downloading the latest WorkingWithTheDataset.ipynb file and uploading it to an online Jupyter environment such as https://colab.research.google.com or use the notebook in your Jupyter environment of choice. You will also need to download the CONFIGURATION_SETTINGS.template.md file from this dataset since the contents are used to configure the Jupyter notebook. Note: at the time of this writing, we do not recommend using Binder (mybinder.org) if you are interested in only reviewing the WorkingWithTheDataset.ipynb notebook. When Binder loads the dataset, it will download all files from this dataset, resulting in a long build time. However, if you plan to work with all files in the dataset then Binder might work for you. We do not offer support for this service or other Jupyter Lab environments. Metadata The DatasetMetadata.json file contains general information about the files and variables within this dataset. We use it as our validation metadata to verify the data we are importing into this Dataverse dataset. This file is also the most comprehensive with regards to the dataset metadata. Data collection in progress This dataset is not complete and will be updated regularly as additional data is collected.

The R4GenAiTool file contains the practical implementation of a diagnostic tool designed to assess organizational readiness for the adoption of Generative Artificial Intelligence (GAI) in the IT sector. The spreadsheet is organized into key categories such as Governance Structure, Organizational Structure, Ethical Considerations, Risk Management, and Security, among others. Each category is broken down into subcategories and specific assessment statements. Organizations evaluate their current state using a three-level scale: Fully Implemented, Partially Implemented, or Not Implemented. The tool consolidates the results by category and subcategory, and uses tables and charts to visualize the level of readiness. This Excel file serves as the operational artifact supporting the research project based on the Design Science Research (DSR) methodology.