Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
2,025
datasets available to search
ShareScore release 0.9.0
Dataset results
2,025 results for “AIS”
Dataset: Spectral AI, Inc. (MDAI) 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.
Nanoparticle Size Estimation by Scanning Transmission Electron Microscopy and Generative AI
<p>The "raw" directories contain unaltered simulated and experimental data. The train and val directories contain normalized data used to train the models of the manuscript. The dataframes directory contains all information about the atomic models. Exp info contains info about the raw experimental data (excluding the gas-cell data). </p>
High-resolution AI image dataset for diagnosing oral submucous fibrosis and squamous cell carcinoma
<p>This deposition contains only training dataset of ORCHID database. The validation and test dataset related to the same study can be found at DOI: <strong>10.5281/zenodo.12646943.</strong></p>
Fig. 1 in Th E Ha B Itat O F M As K S N Ai L Is O Gn O M O Sto M A Isognomostomos Schröter In Latvia
Fig. 1. The distributional range of Isognomostoma isognomostomos (based on Kerney et al.1983); study area and locations of study sites.
Fig. 2 in Th E Ha B Itat O F M As K S N Ai L Is O Gn O M O Sto M A Isognomostomos Schröter In Latvia
Fig. 2. Ordination diagram (CCA analysis. Eigenwalues: Axes 1 - 0.236, Axes 2 - 0.124, Axes 3 - 0.144) of the distribution of snail species recorded on the study sites (RAV-1, RAV-2, RAV-3), related to the study sites ecological factors. Ecological factors: conif - conifers, dwood - fallen deadwood, litter - coverage of litter layer, Picabi - Picea abies, Ulmgla - Ulmus glabra.
Exploring New Possibilities of Life with Generative AI
<h2><a name="_Toc171508708"></a>ReLife - Prologue</h2> <p><strong>Abstract:</strong> ReLife is an innovative academic research project that aims to explore the frontiers of artificial intelligence, neuroscience, and virtual reality to offer a new chance at life for individuals in critical situations. The project's objective is to allow users to revisit a crucial point in their past and experience an alternative life generated by AI, providing a new perspective on existence.</p> <p>Advancements in artificial intelligence and virtual reality technologies have opened up new possibilities for applications in areas once thought unimaginable. The ReLife project emerges with the vision of utilizing these technologies to create an alternative life experience for people who, due to critical situations, seek a new opportunity to live.</p> <p>The core concept of ReLife is to allow a user to choose a crucial point in their past and make a different decision without perceiving they are in a simulation. From this new choice, generative AI creates an alternative life path, which the user lives in an immersive and interactive virtual reality environment.</p> <p><strong>Tech Objectives:</strong></p> <ul> <li><strong>Data Collection:</strong> Capture and securely store users' personal data, histories, and memories efficiently.</li> <li><strong>Infrastructure and Support:</strong> Develop the technical infrastructure necessary to support the continuous operation of ReLife.</li> <li><strong>Ethical and Legal Aspects:</strong> Establish ethical guidelines and legal compliance policies for consciousness transfer and data usage.</li> <li><strong>Security and Privacy:</strong> Ensure the protection of users' data against unauthorized access.</li> <li><strong>Practical Applications:</strong> Identify practical use cases and develop case studies to demonstrate the benefits of ReLife.</li> <li><strong>User Interface:</strong> Create intuitive and user-friendly interfaces to facilitate user interaction with the system.</li> <li><strong>Consciousness Transfer:</strong> Research and develop technologies for consciousness transfer and neural mapping.</li> <li><strong>Simulation and Virtual Reality:</strong> Create realistic and immersive virtual environments where users can live their new lives.</li> <li><strong>AI Modeling:</strong> Develop AI algorithms to generate alternative life scenarios based on users' decisions.</li> </ul> <p><strong>Conclusion:</strong> ReLife represents a significant step in exploring how technology can transform our lives, offering new opportunities and hope to those who need it most. Through a multidisciplinary approach, the project aims not only to advance technical knowledge but also to address the complex ethical and social issues associated with these innovations.</p> <h2><a name="_Toc171508709"></a>Introduction</h2> <h3><a name="_Toc171508710"></a>Context and Motivation</h3> <p>The ReLife project emerges at a time when advancements in artificial intelligence and virtual reality are challenging the limits of what is possible. The motivation behind this project is twofold: to stimulate the study and evolution of Artificial Intelligence, and to offer a new chance for those who, due to some unfortunate reason or choice, have had their lives drastically affected.</p> <p>In the era of science fiction, the idea of revisiting the past and making different decisions seemed unattainable. However, with current technological developments, we are approaching this possibility. ReLife aims to extend human consciousness through technology, breaking the physical barriers that limit our existence. This project not only challenges the status quo but also explores new frontiers of human knowledge, making previously unimaginable heights a reality.</p> <p>Through a multidisciplinary approach, encompassing neuroscience, artificial intelligence, and virtual reality, ReLife aims to create an immersive and realistic alternative life experience. This not only offers new perspectives of existence but also contributes to scientific and technological research, promoting significant advancement in the field of AI and its applications.</p> <h2><a name="_Toc171508711"></a>Assumptions of the ReLife Project</h2> <ol> <li><strong>Brain in Good Condition</strong>:</li> <ul> <li><strong>Description</strong>: The user must have a brain in good condition, without severe brain injuries, to allow effective and accurate cognitive mapping.</li> <li><strong>Justification</strong>: Brain injuries can hinder neural mapping and consciousness transfer, compromising the quality of the simulation.</li> </ul> <li><strong>Technological Advancements</strong>:</li> <ul> <li><strong>Description</strong>: Significant advancements in neuroscience, neural mapping technology, and generative AI are assumed.</li> <li><strong>Justification</strong>: Advanced technologies are essential for data collection, AI modeling, and creating realistic simulations.</li> </ul> <li><strong>Support Infrastructure</strong>:</li> <ul> <li><strong>Description</strong>: A robust infrastructure is necessary to support the storage and processing of large volumes of data.</li> <li><strong>Justification</strong>: Ensuring the system functions efficiently and securely.</li> </ul> <li><strong>Ethical and Legal Aspects</strong>:</li> <ul> <li><strong>Description</strong>: Compliance with all ethical and legal standards related to consciousness transfer and the use of personal data.</li> <li><strong>Justification</strong>: Protecting users' rights and ensuring regulatory compliance.</li> </ul> <li><strong>Security and Privacy</strong>:</li> <ul> <li><strong>Description</strong>: Implementation of robust security and privacy measures to protect users' data.</li> <li><strong>Justification</strong>: Ensuring the integrity and confidentiality of users' information.</li> </ul> <li><strong>Immersive Environment and Simulation</strong>:</li> <ul> <li><strong>Description</strong>: Creation of realistic and immersive virtual environments where users can live their new lives without realizing they are in a simulation.</li> <li><strong>Justification</strong>: Ensuring that the simulations are realistic and coherent with users' daily experiences, providing complete immersion.</li> </ul> </ol> <p>For more details, download the attached document</p>
Mutation-guided Metamorphic Testing of Optimality in AI Planning
<p>Experimental results presented in the article <em>Mutation-guided Metamorphic Testing of Optimality in AI Planning</em>, both the figures and the raw data.</p> <p> </p> <p>The framework itself is hosted on GitHub (see the link below).</p> <p>In order to use the data with the framework, unzip the files of the archive <em>results.zip</em> inside the folder <em>results/</em>.</p>
Artificial Intelligence and the Future of Smart Cities-Figure 9. AI influence on the environment
<p>The participants consider that AI development will increase the energy consumption and e- waste (M=3.60, SD=1.03), but will improve also the level of citizens’ information on the environmental changes (M=3.60, SD=.49). The contribution to CO2 emissions is on the fourth places (M=3.40, SD=.49), followed by the attracting of the community members to environmental actions (M=3.00, SD=.64). In the analysis of the statically significant differences by gender, female participants scored significantly higher (M=4.00, SD=.64) than male participants (M=3.63, SD=.77) in the case of the information of citizens on the environmental changes (M=3.80, SD=.75 vs. M=3.72, SD=.75) and the attracting of community members to environmental actions (M=3.00, SD=.90 vs. M=2.63, SD=.88). The analysis on age category, the results revealed that the 26-30 age group scored the highest at both questions (Figure 9).</p>
Artificial Intelligence and the Future of Smart Cities-Figure 4. Influence of AI in the development of smart cities by respondents age
<p>Respondents were asked to indicate how they evaluate the influence of AI in the development of intelligent cities. In order to fully understand the concept of smart cities, the definition of smart city given by Caragliu (2009) was given to the respondents. It is presented in section 2. On question 6 two-way analysis was used to determine the difference by age and gender. There was no statistically significant interaction between groups as determined by two-way ANOVA F (3, 106) = 8.675, p value>0.05 (p=.387). The assumption of homogeneity of variance was tested using the Brown-Forsythe Test. There were statistically significant differences by gender (F=2.169, p<0.05) and by age (F=30.885, p<0.05). More than 9 in ten (almost 94%) consider AI to be important (50%) or very important (43.8%) while just a few (6.2%) recall a moderate importance for smart cities development. Female participants scored significantly higher (M=4.60, SD=.49) than male participants (M=4.27, SD=.62) on question 6 „Generally speaking, how do you assess the influence of AI in the development of intelligent cities”. At the same question: the 41-50 age group scored the highest score followed by the 18-25 age group (M=4.40, SD=.49). The 26-30 age group scored lower than the 18-25 age group (M=4.37, SD=.48) and significantly higher than the 31-40 age group (Figure 4).</p>
Artificial Intelligence and the Future of Smart Cities-Figure 5. Smart features as the main beneficiaries of AI in terms of the respondent's age (statistically significant differences only for 7.1 and 7.3)
<p>The majority of the respondents who found the smart features to be the main beneficiaries of AI facilities were ranging between 31-40 years old and +41 age old, followed by the 18-25 age group (M=3.80, SD =0.75), 26-30 (MD=4.0, SD =.75) (Figure 5).</p>
Artificial Intelligence and the Future of Smart Cities-Figure 8. Influence of AI on individual safety by respondents age
<p>On question 11 respondents were asked to consider the influence of AI on individual safety using a 5 points Likert Scale ranging from 1 being “totally unimportant” and 5 being “very important”. The analysis of variance was used to determine the differences by age group and by gender. The analysis shows that no statistical interaction was found between age and gender F=3.081, p=0.08. We found statistically significant differences by gender F=7.639, p<0.05 and age F=6.318, p=.001). Female participants scored significantly higher (M=4.40, SD=.81) than male participants (M=4.00, SD=.60) on question 11 about the influence of AI on individual safety. At the same question: the 41-50 age group scored the highest (M=4.50, SD=.51), followed by the 18-25 age group (M=4.40, SD=.81); the 26-30 age group scored lower than the 18-25 age group and significantly higher than the 31-40 age group (M=3.87, SD=.60) (Figure 8).</p>
Artificial Intelligence and the Future of Smart Cities-Figure 7. Q.9.Which of the following job functions will AI impact the most over the next 10 years? (Statistically significant differences by age for 9.3, 9.4, 9.5 and 9.6)
<p>The analysis reveals that people perceive that AI will have a greater impact over the next 10 years on marketing (for example, intelligent customer targeting, planning and executing marketing campaigns) scored significantly higher (M=4.37, SD=.69) than on finance (for example, robotic financial advisors, automated corporate financial analysis) (M=3.87, SD=.60) (Figure 7). For the same question customer services scored significantly higher (M=3.75, SD=.83) than health (e.g. consultation and diagnosis, surgery) (M=3.25, SD=.83). For the same question, the analyses by gender reveals that the majority of female participants scored significantly higher (M=3.40, SD=.81) than male participants (M=3.18, SD=.57) and those aged in the second group.</p>
Artificial Intelligence and the Future of Smart Cities-Figure 6. Importance of AI for respondents business or industry by respondent's age
<p>On question 8 respondents have to indicate on a scale of 1 to 5 (1 being “not important” and 5 being “critical for survival”), how important they think the AI will be for their business or industry (or for the one they are preparing for) in the next 10 years? No statistical interaction was found between age and gender F (3, 108) = .174, p>0.05). There were statistically significant differences by gender F (1, 108) = 50.261, p<0.05 and age, F (3, 108) = 9.298, p<0.05. Female participants scored significantly higher (M=4.60, SD=.49) than male participants (M=3.36, SD=.88) on question 8 about the importance of AI for the fields of activity of the participants (or for those they are preparing for) in the next 10 years. At the same question: the 26-30 age group scored the highest followed by the 18-25 age group (M=3.80, SD=1.18); the 31-40 age group scored lower than the 18-25 age group (M=3.50, SD=.87); the 31-40 age group scored also lower than the 18-25 age group (M=3.50, SD=.51) (Figure 6).</p>
Çatalhöyük Image Metadata with AI
<p>This dataset contains predictions made with various methods about the content of images from the Çatalhöyük Archaeological Project. It also contains a selection of metadata from the original image repository.</p>
Multi-Source Distributed System Data for AI-powered Analytics
<p><strong>Abstract:</strong></p> <p>In recent years there has been an increased interest in Artificial Intelligence for IT Operations (AIOps). This field utilizes monitoring data from IT systems, big data platforms, and machine learning to automate various operations and maintenance (O&M) tasks for distributed systems.<br> The major contributions have been materialized in the form of novel algorithms.<br> Typically, researchers took the challenge of exploring one specific type of observability data sources, such as application logs, metrics, and distributed traces, to create new algorithms.<br> Nonetheless, due to the low signal-to-noise ratio of monitoring data, there is a consensus that only the analysis of multi-source monitoring data will enable the development of useful algorithms that have better performance. <br> Unfortunately, existing datasets usually contain only a single source of data, often logs or metrics. This limits the possibilities for greater advances in AIOps research.<br> Thus, we generated high-quality multi-source data composed of distributed traces, application logs, and metrics from a complex distributed system. This paper provides detailed descriptions of the experiment, statistics of the data, and identifies how such data can be analyzed to support O&M tasks such as anomaly detection, root cause analysis, and remediation.</p> <p><strong>General Information:</strong></p> <p>This repository contains the simple scripts for data statistics, and link to the multi-source distributed system dataset.</p> <p>You may find details of this dataset from the original paper:</p> <p><em>Sasho Nedelkoski, Jasmin Bogatinovski, Ajay Kumar Mandapati, Soeren Becker, Jorge Cardoso, Odej Kao, "Multi-Source Distributed System Data for AI-powered Analytics". </em></p> <p><strong>If you use the data, implementation, or any details of the paper, please cite!</strong></p> <p> </p> <p>BIBTEX:</p> <p>_________________________________________</p> <pre>@inproceedings{nedelkoski2020multi, title={Multi-source Distributed System Data for AI-Powered Analytics}, author={Nedelkoski, Sasho and Bogatinovski, Jasmin and Mandapati, Ajay Kumar and Becker, Soeren and Cardoso, Jorge and Kao, Odej}, booktitle={European Conference on Service-Oriented and Cloud Computing}, pages={161--176}, year={2020}, organization={Springer} } </pre> <p>___________________________</p> <p>The multi-source/multimodal dataset is composed of distributed traces, application logs, and metrics produced from running a complex distributed system (Openstack). In addition, we also provide the workload and fault scripts together with the Rally report which can serve as ground truth. We provide two datasets, which differ on how the workload is executed. The <em><strong>sequential_data</strong> </em>is generated via executing workload of sequential user requests. The <strong><em>concurrent_data </em></strong>is generated via executing workload of concurrent user requests.</p> <p>The raw logs in both datasets contain the same files. If the user wants the logs filetered by time with respect to the two datasets, should refer to the timestamps at the metrics (they provide the time window). <strong>In addition, we suggest to use the provided aggregated time ranged logs for both datasets in CSV format.</strong></p> <p><strong><strong>Important:</strong> The logs and the metrics are synchronized with respect time and they are both recorded on CEST (central european standard time). The traces are on UTC (Coordinated Universal Time -2 hours). They should be synchronized if the user develops multimodal methods. Please read the IMPORTANT_experiment_start_end.txt file before working with the data.</strong></p> <p>Our GitHub repository with the code for the workloads and scripts for basic analysis can be found at: <a href="https://github.com/SashoNedelkoski/multi-source-observability-dataset/">https://github.com/SashoNedelkoski/multi-source-observability-dataset/</a></p>
Toward multimodal information and AI interaction: a quasi-experiment with ChatGPT
<p>The development of argumentative text and information comprehension (CoI) skills related to the critical reconstruction of meaning (CT) is crucial in undergraduate education. Especially now in the era of social media and AI-mediated information. Generative AI aids in information creation, but its unconscious use can complicate complex information navigation. Argument maps (AM), commonly used for analyzing analog and static texts, can help visualize, understand, and rework multimodal and dynamic arguments and information.</p> <p>Stemming from the Vygotskian idea, our study used a design-based research approach on the use of AMs and ChatGPT as socio-technical artifacts to stimulate and support the understanding of information (CoI) and thus the development of critical thinking (CT). The workshop introduced the multimodal element through a 3-group quasi-experiment. The first group dealt with fully analog texts, the second group used maps with multimodal textual modes, and the third group only interacted with ChatGPT. The research focused on comparing the three groups and focusing on the two experimental groups (experimental macro-focus). </p> <p>The research had three main objectives: 1) to test whether AMs improved students' CoI enhancement and critical processing (CT); 2) to determine whether interaction with ChatGPT supported information reprocessing and critical construction of opinions and assessment tools; and 3) to determine whether interaction with ChatGPT alone, without AMs, still fostered greater integration of information and viewpoints.</p> <p>Our preliminary analysis showed that AMs improved students' CoI and CT, especially when exposed to multimodal information. ChatGPT interaction increased critical reflection and awareness of AI's role in education. Students using only ChatGPT performed well in argumentative reworking, suggesting that interaction with the chatbot can be effective. However, integrating AMs and ChatGPT could provide optimal support for comprehension and critical thinking skills.</p> <p>This Zenodo record follows the full analysis process with R (https://cran.r-project.org/bin/windows/base/ ) and Nvivo (https://lumivero.com/products/nvivo/) composed of the following datasets, script and results:</p> <p>1. Comprehension of Text and AMs Results - Arg_Map.xlsx</p> <p>2. Critical Thinking level - CriThink.xlsx</p> <p>3. Descriptive and Inferential Statistics Comprehension and Critical Thinking - Preliminary Analysis.R</p> <p>4. Elaboration and Integration Opinion - Opi_G1.xlsx; Opi_G2.xlsx & Opi_G3.xlsx</p> <p>5. Descriptive and Inferential Statistics Opinion level - Preliminary Analysis_opi.R</p> <p>6. Sentiment Analysis - Sentiment Analysis.R</p> <p>7. Vocabulary Frequent words - Vocabulary.csv</p> <p>8. Codebook qualitative Analysis with Nvivo (Codebook.xlsx)</p> <p>9. Results Nvivo Analysis G1 & G2 - Codebook-ChatGPT_G1&G2.docx</p> <p> </p> <p>Any comments or improvements are welcome!</p>
AI for SDG Acceleration - NSG MasterClass in Cooperation with MGG-PRODIGEES
<p>The video contains the lecture by Dr Reevana Balmahoon on artificial intelligence as a tool for accelerating the achievement of the Sustainable Development Goals (SDGs). Her presentation was part of the joint conference “International Capacity Development for the Civil Service - The Sustainable Digitalisation Agenda”, May 5-8, 2024, Cape Town, South Africa. The conference was jointly organised by the German Institute of Development and Sustainability (IDOS) and the National School of Government of South Africa (The NSG) in the framework of the ‘Managing Global Governance (MGG) network and its PRODIGEES project on digitalisation towards sustainable development. Dr Balmahoon is an AI and extended reality research lead of the South African Council for Scientific and Industrial Research (CSIR). The lecture and ensuing discussion, with responses from Dr Sven Grimm (IDOS) and Serusha Govender (Chatham House), were part of the NSG Master Class Series.</p>
AI Platforms Overview for Higher Education
<p>This release includes a detailed table comparing various AI platforms. The table covers:</p> <ul> <li>Focus: Primary applications of each platform.</li> <li>Strengths: Key features and benefits.</li> <li>Uniqueness: Distinctive aspects.</li> <li>Limitations: Notable constraints.</li> </ul> <p>How to Use</p> <ol> <li>Download the attached file.</li> <li>Review the table for a comprehensive comparison.</li> </ol> <p>DOI<br>For more information, visit the DOI page: [10.5281/zenodo.13738758](https://doi.org/10.5281/zenodo.13738758)</p> <p>Contact<br>For any inquiries, contact [bsrinivasan@twu.edu](mailto:bsrinivasan@twu.edu).</p>
AI adoption indices by Oxford, CISCO and EDBI
<p>Details data on AI adoption indecses provided by:</p> <ul> <li>Oxford Insights Government AI Readiness Index (https://oxfordinsights.com/ai-readiness/ai-readiness-index/) - 2023</li> <li>Cisco AI Readiness Index (https://oxfordinsights.com/ai-readiness/ai-readiness-index/) - 2023</li> <li>Kearney Report: Racing toward the future: artificial intelligence in Southeast Asia (https://www.kearney.com/service/digital-analytics/article/-/insights/racing-toward-the-future-artificial-intelligence-in-southeast-asia) - 2020</li> </ul>
AgoraSpeech: A multi-annotated comprehensive dataset of political discourse through the lens of humans and AI
<blockquote> <p>Political discourse datasets are important for gaining political insights, analyzing communication strategies or social science phenomena. Although numerous political discourse corpora exist, comprehensive, high-quality, annotated datasets are scarce. This is largely due to the substantial manual effort, multidisciplinarity, and expertise required for the nuanced annotation of rhetorical strategies and ideological contexts. In this paper, we present <strong>AgoraSpeech</strong>, a meticulously curated, high-quality dataset of 171 political speeches from six parties during the Greek national elections in 2023. The dataset includes annotations (per paragraph) for six natural language processing (NLP) tasks: text classification, topic identification, sentiment analysis, named entity recognition, polarization and populism detection. A two-step annotation was employed, starting with ChatGPT-generated annotations and followed by exhaustive human-in-the-loop validation. The dataset was initially used in a case study to provide insights during the pre-election period. However, it has general applicability by serving as a rich source of information for political and social scientists, journalists, or data scientists, while it can be used for benchmarking and fine-tuning NLP and large language models (LLMs).</p> </blockquote>
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.