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2,025 results for “AIS”
AI Ethics
<p>This repository contains datasets to support our paper titled <code>The Different Faces of AI Ethics Across the World: A Principle-Implementation Gap Analysis</code> submitted to IEEE Transactions on Artificial Intelligence.</p>
Dichotomies in AI life cycle
<p>Part of the publication "Assessing the Resource- and Energy Efficiency of AI-based Cyber-Physical Systems" @ KI 2022 (https://ki2022.gi.de/). The image shows possible, dichotomic influencing factors along the AI life cycle that can be considered when measuring and assessing the resource- and energy consumption of AI-based systems.</p>
Precision Health and AI: improving health for everyone - Arjun Panesar (DDM Health)
<p>This video is the twelfth talk from our two day Future Blood Testing: Challenges & Opportunities Event that took place on the 14/09/2022.</p> <p>Precision Health and AI: improving health for everyone - Arjun Panesar (DDM Health)</p> <p>Bio: Arjun Panesar is the founder of DDM Health, providers of clinically-validated digital health solutions to over 1.8 million people. Benefiting from almost two decades of experience in big data, AI and AI ethics, Arjun leads the development of evidence-based digital innovations that harness the power of machine learning to provide precision medicine to patients, health services, and governments. Arjun’s work has received international recognition featuring in the Forbes, New Scientist, BBC and The Times. Arjun is a best-selling author on the topics of healthcare and AI, authoring two editions of Machine Learning and AI in Healthcare, and contributing to Handbook of Global Health, a major reference work. Arjun is an advisor to the Information School, University of Sheffield, Fellow to the NHS Innovation Accelerator, visiting lecturer at University of Warwick Medical School, and was recognised by Imperial College as an Alumni Leader for his contribution and impact to society.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/13-14-09-2022/</p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: <a href="https://youtu.be/clPmdeLP5_E">https://youtu.be/clPmdeLP5_E</a></p>
Network Theme: The potential of machine learning and AI for blood based investigations - Professor Jeremy Frey (University of Southampton)
<p>This video is the sixth talk from our Future Blood Testing Network Plus Launch that took place on the 23/11/2021.</p> <p>Network Theme: The potential of machine learning and AI for blood based investigations - Professor Jeremy Frey (University of Southampton)</p> <p>Bio: Prof Jeremy Frey Professor of Physical Chemistry, Head of Computational Systems Chemistry, University of Southampton (UoS). He is PI of AI for Scientific Discovery Network+, and co_I on the Internet of Food Things Digital Economy Network+ and has had considerable involvement in the UK e-Science and Digital Economy programmes for many years (e.g., PI of the Digital Economy IT as a Utility Network+. He is a strong proponent of interdisciplinary research and the use of digital technology and ideas to enhance methods of scientific research & development. His own research involves activities across the physical land life sciences, from the application of novel mathematical analysis (e.g., Topological Data Analysis), laser spectroscopy and imagining techniques to chemical and biological problems, with the development of sensors and imagining systems such as the novel soft x-ray microscope. In parallel he works on the integration of these techniques with full provenance environment into laboratory systems using semantic web technologies.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/23-11-21-future-blood-testing-network-launch/</p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/eNORwfMy5cE</p>
Interpretable AI for drug response prediction
<p>This dataset contains input data needed to run the models evaluated in our study "Interpretable deep learning architectures for improving drug response prediction: myth or reality?". The data required to run each model is organized into sub-folders, with the folder name being the corresponding model name. The data in 'model_agnostic_data' is shared by all models. For details regarding each data file, please refer to the documentation ('input_data_doc') included. The code for this study is available at <a href="https://github.com/Emad-COMBINE-lab/InterpretableAI_for_DRP">https://github.com/Emad-COMBINE-lab/InterpretableAI_for_DRP</a></p> <p> </p>
Global AI, ML, Data salaries
<p><strong>Dataset de Salarios en el Ámbito de Inteligencia Artificial y Ciencia de Datos</strong></p><p>Este conjunto de datos recopila información salarial de diversos puestos de trabajo vinculados a inteligencia artificial y a la ciencia de datos en todo el mundo. Es una adaptación del conjunto original <strong>salaries</strong> que pertenece a <strong>ai-jobs.net</strong>.</p><p>Se ha ampliado con nuevas variables para ofrecer una visión más completa de las condiciones laborales. Las variables originales son:</p><ul><li><strong>work_year</strong>: Año en que se pagó el salario (<i>Categórica</i>)</li><li><strong>experience_level</strong>: Nivel de experiencia (EN: Junior, MI: Medio, SE: Senior, EX: Director/Ejecutivo) (<i>Categórica</i>)</li><li><strong>employment_type</strong>: Tipo de contrato (PT: Tiempo parcial, FT: Tiempo completo, CT: Contrato de servicio, FL: Freelance) (<i>Categórica</i>)</li><li><strong>job_title</strong>: Puesto de trabajo (<i>Categórica</i>)</li><li><strong>salary</strong>: Salario bruto anual (<i>Cuantitativa</i>)</li><li><strong>salary_currency</strong>: Divisa del salario bruto (<i>Categórica</i>)</li><li><strong>salary_in_usd</strong>: Salario en USD (<i>Cuantitativa</i>)</li><li><strong>employee_residence</strong>: País de residencia del empleado (ISO3166) (<i>Categórica</i>)</li><li><strong>remote_ratio</strong>: Cantidad de teletrabajo (0: Presencial, 50: Híbrido, 100: Remoto) (<i>Categórica</i>)</li><li><strong>company_location</strong>: País de la empresa (ISO3166) (<i>Categórica</i>)</li><li><strong>company_size</strong>: Tamaño de la empresa (S: Menos de 50 empleados, M: Entre 50 y 250 empleados, L: Más de 250 empleados) (<i>Categórica</i>)</li></ul><p>Las nuevas variables incluidas son:</p><ul><li><strong>company_continent</strong>: Continente donde se encuentra la empresa (<i>Categórica</i>)</li><li><strong>employee_continent</strong>: Continente donde reside el empleado (<i>Categórica</i>)</li><li><strong>company_continent_region</strong>: Región continental donde está ubicada la empresa (<i>Categórica</i>)</li><li><strong>employee_continent_region</strong>: Región continental donde reside el empleado (<i>Categórica</i>)</li><li><strong>salary_rounded_in_eur</strong>: Salario en euros redondeado (calculado con la media de cada año) (<i>Cuantitativa</i>)</li><li><strong>salary_k_in_eur</strong>: Salario en miles de euros (<i>Cuantitativa</i>)</li><li><strong>salary_type</strong>: Tipo de salario (VL: Very Low, L: Low, M: Medium, H: High, VH: Very High) (<i>Categórica</i>)</li><li><strong>same_country</strong>: ¿Residencia y lugar de trabajo en el mismo país? (<i>Categórica</i>)</li></ul>
The Why and What of AI Deployment and Innovation in Companies – Results and Learnings from a Systematic Literature Research
<p>These appendices are made available online as a critical part of the research paper accepted for publication at ISPIM 2024 in Tallinn on 10-12June 2024.</p> <p><strong>Abstract:</strong> Despite vast and growing investments in Artificial Intelligence, only a tiny fraction of companies report achieving tangible results. To fill this gap and establish a baseline for companies, we conducted Systematic Literature Research on the status of AI deployment in companies. We analyzed and synthesized 99 peer-reviewed articles for objectives, approaches, results, and learnings. This research confirms that AI is still at the pre-deployment stage in companies. Developing the capability to deploy AI is more challenging and takes more time, concerted effort, innovation, and learning than generally thought. Through the results and the conceptual framework, this research increases understanding of AI deployment and emphasizes the importance of the pre-deployment stages from theoretical and practical viewpoints. The Sensing stage of the dynamic capabilities theory aligns well with the developed pre-deployment stages. More real-life case research is needed, and existing theories verified to deploy AI knowledgeably for business and stakeholder benefits. </p>
TWIGMA: A dataset of AI-Generated Images with Metadata From Twitter
<p><strong>Update May 2024: Fixed a data type issue with "id" column that prevented twitter ids from rendering correctly.</strong></p> <p>Recent progress in generative artificial intelligence (gen-AI) has enabled the generation of photo-realistic and artistically-inspiring photos at a single click, catering to millions of users online. To explore how people use gen-AI models such as DALLE and StableDiffusion, it is critical to understand the themes, contents, and variations present in the AI-generated photos. In this work, we introduce TWIGMA (TWItter Generative-ai images with MetadatA), a comprehensive dataset encompassing 800,000 gen-AI images collected from Jan 2021 to March 2023 on Twitter, with associated metadata (e.g., tweet text, creation date, number of likes).</p> <p>Through a comparative analysis of TWIGMA with natural images and human artwork, we find that gen-AI images possess distinctive characteristics and exhibit, on average, lower variability when compared to their non-gen-AI counterparts. Additionally, we find that the similarity between a gen-AI image and human images (i) is correlated with the number of likes; and (ii) can be used to identify human images that served as inspiration for the gen-AI creations. Finally, we observe a longitudinal shift in the themes of AI-generated images on Twitter, with users increasingly sharing artistically sophisticated content such as intricate human portraits, whereas their interest in simple subjects such as natural scenes and animals has decreased. Our analyses and findings underscore the significance of TWIGMA as a unique data resource for studying AI-generated images.</p> <p>Note that in accordance with the privacy and control policy of Twitter, <strong>NO raw content from Twitter is included</strong> in this dataset and users could and need to retrieve the original Twitter content used for analysis using the Twitter id. In addition, users who want to access Twitter data should consult and follow rules and regulations closely at the official Twitter developer policy at https://developer.twitter.com/en/developer-terms/policy. </p> <p> </p> <p> </p>
RU-AI: A Large Multimodal Dataset for Machine Generated Content Detection
<p>This repository contains all the collected and aligned data for RU-AI dataset. It is constructed based on three large publicly available datasets: Flickr8K, COCO, and Places205, by adding their corresponding machine-generated pairs from five different generative models in each modality. </p>
Reading Journal of eScience Librarianship: Responsible AI in Libraries and Archives
A special issue of Journal of eScience Librarianship was brought to my attention. The issue was on the topic of responsible AI in libraries and archives. I did a bit of distant reading against the issue, and outlined here are some of my take-aways. In short, AI is something to consider in Library Land, but not without some forethought.
AI course names from undergraduate programs
<p><span>We collected 750 course names covered by 29 universities’ undergraduate AI edu</span><span>cation programs, providing a comprehensive dataset for analysis on the AI curriculum.</span></p>
AI Tool Use and Adoption in Software Development by Individuals and Organizations: A Grounded Theory Study
<div> <p>This data represents four artifacts from our research in studying what impacts AI adoption and use in SE. It includes our interview questions, the codebook with example quotes, survey questions, and table with the code to category generation.</p> <p>This page includes supplementary materials associated with our paper entitled "<span>AI Tool Use and Adoption in Software Development by </span><span>Individuals and Organizations: A Grounded Theory Study</span>".</p> </div>
Dataset: Healthcare AI Acquisition Corp. (HAIAW) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Healthcare AI Acquisition Corp. (HAIAU) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Healthcare AI Acquisition Corp. (HAIA) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Guardforce AI Co., Limited (GFAIW) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Guardforce AI Co., Limited (GFAI) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Bullfrog AI Holdings, Inc. (BFRGW) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Bullfrog AI Holdings, Inc. (BFRG) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: AI Transportation Acquisition Corp (AITR) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.