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2,025 results for “AIS”
SME employee concerns regarding AI, their current knowledge regarding AI, their willingness to adopt it and learn about it.
<p><strong>Title</strong>: SME Employee Perspectives on AI Adoption</p> <p><strong>Abstract</strong>: This dataset comprises encoded interview responses, raw survey data, and cleaned survey data collected from employees of small and medium-sized enterprises (SMEs), as well as a codebook that acts as metadata storage. The focus of the data is on employee concerns, current knowledge, and willingness to adopt and learn about artificial intelligence (AI) technologies. This dataset is intended to support research into the factors influencing AI integration in SME environments and to assess the readiness of employees to engage with these technologies.</p> <p><strong>Data Collection Methods</strong>:</p> <ul> <li><strong>Interviews</strong>: Semi-structured interviews were conducted with a selection of employees from various departments within SMEs. Responses have been anonymized and encoded to protect participant privacy.</li> <li><strong>Surveys</strong>: Two sets of survey data are included: <ul> <li><strong>Raw Survey Data</strong>: Contains all original responses, including demographic information and unprocessed answers to questions regarding AI knowledge and perceptions.</li> <li><strong>Cleaned Survey Data</strong>: This dataset has been processed to remove incomplete responses and normalize the data for analysis.</li> </ul> </li> </ul> <p><strong>Key Variables</strong>:</p> <ul> <li><strong>Employee Concerns</strong>: Qualitative data on personal and professional concerns regarding AI, such as job security, privacy, and trust in technology.</li> <li><strong>Knowledge of AI</strong>: Employee self-assessments and objectively measured knowledge levels regarding AI technologies and their applications.</li> <li><strong>Willingness to Adopt AI</strong>: Measures of openness to integrating AI into their work processes and willingness to participate in AI-related training and development.</li> </ul>
Supplemental Material: A Classification Study on Testing and Verification of AI-based Systems
<p>This archive contains the whole set of documents that have been considered in the classification study reported in the manuscript titled: "A Classification Study on Testing and Verification of AI-based Systems".</p>
Continuous Development of AI: a Practitioners' Survey
<p>The upload contains a raw dataset with the survey questions and a data analysis file as an appendix of the paper "Continuous Development of AI: a Practitioners Survey".</p>
Predicting the Future of AI with AI
<p>This dataset augments the paper "Predicting the Future of AI with AI: High-quality link prediction in an exponentially growing knowledge network" by Mario Krenn, Lorenzo Buffoni, Bruno Coutinho, Sagi Eppel, Jacob Gates Foster, Andrew Gritsevskiy, Harlin Lee, Yichao Lu, Joao P. Moutinho, Nima Sanjabi, Rishi Sonthalia, Ngoc Mai Tran, Francisco Valente, Yangxinyu Xie, Rose Yu, Michael Kopp.</p> <p>GitHub: https://github.com/artificial-scientist-lab/FutureOfAIviaAI</p>
[HECAT] [ETAPAS] [TECHETHOS] Policy Launch: Public Service AI /// Trust – Values – Accuracy
<p><strong>Public Service AI /// Trust – Values – Accuracy</strong></p> <p><strong>Policy Launch 26.05.23 11:00 CET Zoom</strong></p> <p><strong>YouTube Link to POLICY LAUNCH Recording: </strong><a href="https://youtu.be/OqkqUbA-6PI">https://youtu.be/OqkqUbA-6PI</a></p> <p><strong>Policy 1 – HECAT Project </strong> <a href="https://zenodo.org/record/7921614">“Algorithm Profiling in Public Employment Services; Reporting Standards”</a></p> <p><strong>Policy 2 – ETAPAS Project </strong> <a href="https://zenodo.org/record/7634617#.ZFIf7ezMKt_">“ETAPAS AND ALTAI – Two European Trustworthy AI Assessment Methodologies”</a></p> <p><strong>Policy 3 – TechEthos Project </strong><a href="https://www.techethos.eu/wp-content/uploads/2023/03/TECHETHOS_Policy-Brief_XR-General-purpose-AI_final.pdf">“XR and General Purpose AI: From Values and Principals to Norms and Standards”</a></p>
Investigating the Use of AI-Generated Exercises for Beginner and Intermediate Programming Courses: A ChatGPT Case Study
<p>In recent years, artificial intelligence (AI) has been increasingly used in education and supports teachers in creating educational material and students in their learning progress. AI- driven learning support has recently been further strengthened by the release of ChatGPT, in which users can retrieve expla- nations for various concepts in a few minutes through chat. However, to what extent the use of AI models, such as ChatGPT, is suitable for the creation of didactically and content-wise good exercises for programming courses is not yet known. Therefore, in this paper, we investigate the use of AI-generated exercises for beginner and intermediate programming courses in higher education using ChatGPT. We created 12 exercise sheets with ChatGPT for a beginner to intermediate programming course focusing on the objects-first approach. We report our process, prompts, and experience using ChatGPT for this task and outline good practices we identified. The generated exercises are assessed and revised, primarily using ChatGPT, until they met the requirements of the programming course. We assessed the quality of these exercises by using them in our course as external teaching assignment at the University of Education Ludwigsburg and let the students evaluate them. Results indicate the quality of the generated exercises and the time-saving for creating them using ChatGPT. However, our experience showed that while it is fast to generate a good version of an exercise, almost every exercise requires minor manual changes to improve its quality.</p>
Illustrations for AI for multiple long-term conditions: Research Support Facility
<p>Illustrations created by <a href="http://www.scriberia.co.uk/">Scriberia</a> as part of <a href="https://www.turing.ac.uk/research/research-projects/ai-multiple-long-term-conditions-research-support-facility">AI for multiple long-term conditions: Research Support Facility.</a> The archived website link is available <a href="https://web.archive.org/web/20250212145350/https://www.turing.ac.uk/research/research-projects/ai-for-multiple-long-term-conditions-research-support-facility?__cf_chl_rt_tk=XKaDrLiiU8hmzZk2rKaWDBVpnO8exVqC4EHijqlWTIQ-1739372030-1.0.1.1-hmHFksRkdOQcBH9PsOgRVdNAatQdr3i4hFOqtyPcyZU">here</a>.</p> <p>The AIM RSF is funded by the NIHR Artificial Intelligence for Multiple Long-Term Conditions (AIM) programme (NIHR202647).</p> <p>When using any of the images, please credit them with</p> <p><em>"This image was created by <a href="http://www.scriberia.co.uk/">Scriberia</a> for AI for multiple long-term conditions: Research Support Facility and is used under a CC-BY licence."</em><br><br>We have created Alternative texts (Alt text) for all images stored in PDF and text format.</p>
Dataset of the authorisation protocol of the AI-SPRINT project
<p>Dataset concerning the authorisation protocol of the AI-SPRINT European project which was created for performance evaluation purpose</p>
Hidden and total fishing activity hotspots in the Mediterranean Sea between 2017 and 2022 estimated from AIS data
<p>Hidden and total fishing activity hotspots in the Mediterranean Sea between 2017 and 2022 estimated from AIS data</p>
Data for paper "The Two Faces of AI in Green Mobile Computing: A Literature Review"
<p>This is the data associated with the literature review presented in the paper “The Two Faces of AI in Green Mobile Computing:<br> A Literature Review” accepted at SEAA 2023.</p>
Balancing AI Advancements: Preserving Human Creativity Amidst Growing Artificial Ingenuity
<p>a recent empirical study in which tasks designed according to the principles of the grandomastery framework, specifically created to foster and evaluate skills in narrative and abstract thinking, were assigned to Chat GPT, Bing, and Google Bard. This experiment serves as a clear example of the abilities demonstrated by artificial intelligence (AI). By harnessing AI's skill in storytelling and introducing randomness through keywords and a predetermined thematic structure, the resulting outcomes were highly captivating. The visual representations of completed tasks related to comprehensive cognitive processes, organized within the grandomastery approach, effectively and eloquently depict AI's recent evolutionary path. This advancement spans from basic manipulation of data to the intricate art of crafting narratives that deeply resonate with audiences.</p>
Test Dataset of AI Altered Images for Photogrammetric Reconstruction
<p>As a part of a test of the capability of using AI altered images in photogrammetry, I generated these image sets using the Prisma AI app. All of these datasets will result in a model in Agisoft Metashape. Prisma AI acts as a filter, changing an input image to the style of another artwork. </p> <p>Original images were collected using a Samsung S22 camera on the 1x zoom rear lens. The subject is a rock decoration in the Utrecht University Botanical Garden.</p> <p>Results can be seen on Sketchfab: https://sketchfab.com/gspeed0689/collections/prisma-photogrammetry-2e334a36c6e942798370d594de5e6f0c </p> <p>Included datasets:</p> <ul> <li>true-color.zip - The original images from the Samsung S22</li> <li>aviator-lowres - Processed images at normal (low resolution) quality with the filter labelled Aviator</li> <li>bubblegum-lowres - Processed images at normal (low resolution) quality with the filter labelled Bubblegum</li> <li>gothic-lowres - Processed images at normal (low resolution) quality with the filter labelled Gothic</li> <li>gothic-hd.zip - Processed images at HD (paid subscription required) quality with the filter labelled Gothic</li> <li>true-color.fbx - 3D model created with Agisoft Metashape using the parameters listed below and the images from true-color.zip</li> <li>aviator-lowres.fbx - 3D model created with Agisoft Metashape using the parameters listed below and the images from aviator-lowres.zip</li> <li>bubblegum-lowres.fbx - 3D model created with Agisoft Metashape using the parameters listed below and the images from bubblegum-lowres.zip</li> <li>gothic-lowres.fbx - 3D model created with Agisoft Metashape using the parameters listed below and the images from gothic-lowres.zip</li> <li>gothic-hd.fbx - 3D model created with Agisoft Metashape using the parameters listed below and the images from gothic-hd.zip</li> </ul> <p>Processing parameters in Agisoft Metashape v 1.7:</p> <ul> <li>Alignment defaults</li> <li>Dense cloud creation at "High" quality</li> <li>Mesh defaults</li> </ul>
AI-ENGAGEMENT's Gantt
<p>AI-ENGAGEMENT’s Gantt</p>
AIS heatmap: North Sea and Dutch Inland Waterways for the months January, April, July, October in 2019
<p>This dataset contains information on vessel movements in the North Sea and Dutch Inland Waterways for the months January, April, July, and October in 2019. It provides a heatmap representation of vessel traffic density during these specific months, which can be useful for various maritime and environmental analyses.</p> <p>1. File Formats</p> <p>The dataset is provided in the following file formats:</p> <ul> <li>NetCDF : The primary data files are available in netcdf format. For each grid cell the variables sog (Speed Over Ground) and count (Number of AIS messages) are available</li> <li>GeoTIFF (Georeferenced Tagged Image File Format): Heatmap images are provided in GeoTIFF format, suitable for geographic visualization.</li> </ul> <p>The dataset is split into tiles. Each tile conforms to the <a href="https://wiki.openstreetmap.org/wiki/Tiles">OSM tiling</a> naming scheme.</p> <p>2. Variables </p> <p>The dataset includes the following key variables:</p> <ul> <li><strong>Speed Over Ground (SOG)</strong>: The average vessel's speed over the ground for all the messages.</li> <li><strong>Count</strong>: The number of AIS messages received in this location</li> </ul> <p>3. Data Collection Method </p> <p>The AIS data used in this dataset was collected from AIS transponders on vessels operating in the North Sea and Dutch Inland Waterways. These transponders transmit information such as vessel position, speed, and identification. The dataset aggregates this information to create heatmap images for analysis. We did this on all the messages. Some ships emit more messages than others. Ships emit messages at higher frequency when sailing than when stationary. </p> <p>4. Source of Original Data</p> <p>The original AIS data used to create this dataset was sourced from the AIS archive from Rijkswaterstaat. This dataset was analysed for the purpose of a <a href="https://ais-scrolly.netlify.app/">storymap</a>.</p> <p> </p> <p> </p>
Hybrid AI trustworthiness characteristics mind map
<p>Mind map that considers the majority of AI criteria to be assessed for trustworthiness purpose on Hybrid AI robotic systems. We labelled the terms that have been emphasized, as requirements or European values to be respected, by references such as ALTAI and IEEE Ethically Aligned Design.</p>
FlashNet: AI framework for lightning forecasts
<p>This repository contains the code and a subset of the data used in:<br> "AI vs fully-deterministic algorithms: unraveling the dilemma for lightning prediction in the medium-range forecast horizon" by Mattia Cavaiola, Federico Cassola, Davide Secchetti, Francesco Ferrari, and Andrea Mazzino</p>
CONFIDENCE-AI Financial Education for Caregivers (CONFIDENCE-AI)
ClinicalTrials.gov study NCT06134180. IPD Sharing: YES. Countries: 1. Publications: 3.
Validity of an AI-based Program to Identify Foods and Estimate Food Portion Size
ClinicalTrials.gov study NCT05343585. IPD Sharing: NO. Countries: 1. Publications: 1.
Detection of Colonic Polyps Via a Large Scale Artificial Intelligence (AI) System
ClinicalTrials.gov study NCT04693078. IPD Sharing: NO. Countries: 1. Publications: 1.
A Phase III Study of BKM120 With Fulvestrant in Patients With HR+,HER2-, AI Treated, Locally Advanced or Metastatic Breast Cancer Who Progressed on or After mTORi
ClinicalTrials.gov study NCT01633060. IPD Sharing: UNDECIDED. Countries: 22. Publications: 1.
ScienceDex guides
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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.