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Dataset results
43 results for “AI tool”
Human-AI Collaboration: A tool to enable AI model generation with human-in-the-loop
<p>Human-AI collaboration enables domain experts to contribute their expertise with the goal of enhancing the knowledge learned by the AI models from the patterns in the data. This enables the integration of domain-specific knowledge to enrich the data for further improvement of the models through retraining. The human-AI collaboration is composed of multiple sub-components and interfaces that enables communication with external systems such as data sources, model repositories, machine configurations and decision support systems.</p> <p>Human-AI Collaboration component is developed using Python programming language. The frontend is developed using Streamlit1. The backend is developed using python and the API is implemented using FastAPI2. The choice of the programming language was made because of its wide usage and vast user base. The frameworks Streamlit and FastAPI are chosen because of the rich features for functionality and documentation as well as suitability for data analysis tasks. The applications are packaged as docker images for deployment. The application runs as a web application served by nginx for reverseproxying and users can access it via client applications such as web browsers or REST clients like Postman.</p>
Fighting COVID-19 with computational tools: an AI guided review of 17,000 studies - The CSCoV database.
<p>CSCoV (Computational Studies about COVID-19) is a dataset containing COVID-19 related studies extracted from PubMed, bioRxiv, medRxiv, and arXiv, together with article and author related metrics obtained from Semantic Scholar (plus page views from bioRxiv and medRxiv). Using machine learning, the articles are categorized in six topics (Pharmacology, Genomics, Epidemiology, Healthcare, Clinical Medicine, Clinical Imaging) and prioritized. The database is periodically updated.</p> <ul> <li>Publication: TBA</li> <li>Files included in this release: <ul> <li>cscov_09_2021.png: dataset statistics for the current CSCoV release.</li> <li>cscov_09_2021.tsv: CSCoV database.</li> <li>schema.json: metadata.</li> <li>cscov_09_2021.tar.gz: Doc2Vec and DeepWalk features used for the DL model</li> </ul> </li> <li> <p>Source code: <a href="https://github.com/SFB-KAUST/covid-review">https://github.com/SFB-KAUST/covid-review</a></p> </li> </ul>
Designing With: AI, ML and DV - Interactive Framework Tools Dataset
<p>This dataset presents a compilation of 182 AI tools, specifically designed for incorporation into design education. The collection was meticulously assembled through a process of rigorous mining of existing internet repositories and academic literature, resulting in a diverse array of AI tools that are applicable to a multitude of design methodologies.</p>
Evaluation Tools for Human-AI Interactions Involving Older Adults with Mild Cognitive Impairments
<h1>Abstract</h1> <p>As artificial intelligence (AI) systems have already proven useful in human lives generally, there is an opportunity for specialized human-AI interaction (HAI) systems to support and provide care for older adults with mild cognitive impairment (MCI). However, the integration of this technology in this population must be thoughtfully designed to accommodate specific needs and limitations. This includes careful measurement of both humans and systems. We developed an evolving dataset categorizing relevant measurement tools into five groups: cognitive ability, demographics & personality, activity level, state of mind, and perceptions of the AI system. Each instance of the tool being used in the literature cataloged in the dataset is qualified in terms of how likely we would recommend using it in the domain of HAI for older adults with MCI based on contextual factors and internal reliability measures. This dataset will serve as a valuable resource for future research, aiding in the identification of promising areas and trends in AI systems for older adults with MCI as well as providing essential tools for future studies.</p> <h1>Methodology</h1> <p>This dataset was not derived through a typical literature review or survey process, but rather followed a more flexible research method. To collect resources for the dataset, we searched numerous databases to identify studies and review types of publications in journals and conferences between the dates of 2000 to 2022. For the papers that contained extensive reviews of literature or cited original tools, we would further look into the citations of those papers, taking us beyond our limited date range. The tools used were categorized into five groups to broadly distinguish their usage in a study, measuring:</p> <ol> <li>Cognitive ability</li> <li>Demographics, personality, and experiences</li> <li>Activity level</li> <li>State of mind</li> <li>Perceptions of the AI system</li> </ol> <p>Subsequently, we conducted an examination of their Cronbach’s 𝛼 scores to assess internal reliability. We created tiers based on how likely we would be to recommend using each tool in the domain of human-AI (HAI) with older adults with MCI, as follows:</p> <ul> <li>Tier 1 included tools with Cronbach’s 𝛼 ≥ 0.7 when used with older adults with MCI in experimental settings interacting with AI</li> <li>Tier 2 included tools with Cronbach’s 𝛼 ≥ 0.7 when used with older adults, with or without MCI, in experimental settings with or without AI interaction</li> <li>Tier * included tools that satisfy the criteria for Tier 1, but, to the best of our knowledge, lack reported Cronbach’s 𝛼 scores</li> <li>Tier 3 included all remaining tools that do not meet the criteria for Tier 1, 2, or *</li> </ul> <p>It should be emphasized that a tool may be found in one or more tiers because multiple studies used the same tool yet resulted in varying reliability scores, contexts, etc.</p> <h1>Contribute</h1> <p>Readers are encouraged to reach out to Adam Norton (adam[underscore]norton[at]uml.edu) to recommend additional tools and entries to the dataset.</p> <h1>Publication</h1> <p>This dataset is published as a short contribution to the Human-Robot Interaction (HRI) 2024 conference. The corresponding paper citation is below:</p> <p>Daisy M. Kiyemba, Jasmin Marwad, Elizabeth J. Carter, and Adam Norton. <strong>Evaluation Tools for Human-AI Interactions Involving Older Adults with Mild Cognitive Impairments</strong>. In Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction (HRI ’24), March 11–14, 2024, Boulder, CO, USA. ACM, New York, NY, USA, 4 pages. <a href="https://doi.org/10.1145/3610977.3637474" target="_blank" rel="noopener">https://doi.org/10.1145/3610977.3637474</a></p> <h1>Acknowledgements</h1> <p>This work was supported by the National Science Foundation (IIS-2112633) as part of the AI-CARING Institute: <a href="https://ai-caring.org/" target="_blank" rel="noopener">https://ai-caring.org/</a></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>
Datasets, models and demos associated to "Celldetective: an AI-enhanced image analysis tool for unraveling dynamic cell interactions"
<p>This repository contains datasets, models and demos associated to <a href="https://github.com/remyeltorro/celldetective">Celldetective</a>, a software for single-cell analysis from multimodal time lapse microscopy images. </p> <h1>Demos</h1> <h2>Cell-cell interaction assay: ADCC</h2> <p>We imaged a co-culture of MCF-7 breast cancer cells (targets) and human primary NK cells (effectors), interacting in the presence of bispecific antibodies, to measure antibody dependent cellular cytotoxicity (ADCC). The nuclei of all cells are marked with the Hoechst nuclear stain, the dead nuclei with the propidium iodide nuclear stain, the cytoplasm of the NK cells with CFSE. The system in epifluorescence and brightfield at either 20 or 40X magnification. We provide a single position demo for the ADCC assay, as "demo_adcc.zip". After unzipping, the demo_adcc folder can be loaded in Celldetective for testing. </p> <h2>Cell-surface interaction assay: RICM</h2> <p>We imaged human primary NK cells engaging in spreading with a surface coated with a bispecific antibody similar to the one used in the ADCC assay (replacing the target cells with a flat surface). The system is imaged using the RICM technique. Images are normalized using a median estimate of the background, pooled from all the positions in a well and dividing the images by this estimate. Here, we provide a single position demo for the cell-surface interactiona assay imaged in RICM, as "demo_ricm.zip". As above, after unzipping, the experiment can be tested and processed in Celldetective.</p> <h1>Datasets</h1> <h2>Image annotations for segmentation</h2> <h3>Cell-cell interaction assay: ADCC</h3> <p>We generated two sets of annotations from images of a co-culture of MCF-7 breast cancer cells and human primary NK cells, interacting in the presence of bispecific antibodies, to measure antibody dependent cellular cytotoxicity (ADCC). Since there are two separate cell populations of interest, the targets (MCF-7) and effectors (NK cells), we curated two datasets. Each sample in a dataset consists of a multichannel image (up to five channels in the context of ADCC, among brightfield , Hoechst nuclear stain, PI nuclear stain, CFSE, LAMP1), the associated instance segmentation annotation for the population of interest and a json file summarizing the content of each channel and the spatial calibration of the image. These sample data are generated directly in Celldetective, using a custom napari plugin.</p> <ul> <li>db_mcf7_nuclei_w_lymphocytes: MCF-7 cell nuclei are annotated specifically on images where primary NK cells (or rarely primary T cells), and RBCs co-exist. The annotation exploits up to four channels simultaneously.</li> <li>db_primary_NK_w_mcf7: human primary NK cells, with annotated cytoplasm (mostly from CFSE) but exploiting brightfield and Hoechst to segment out of focus or poorly labelled cells.</li> </ul> <p>These datasets are used to train several segmentation models to segment on one hand the MCF-7 nuclei and on the other hand the primary NK cells.</p> <h3>Cell-surface interaction assay: RICM</h3> <ul> <li>db_spreading_lymphocytes: we provide a dataset of primary NK cells (and occasionnaly mice T cells) imaged in RICM (with sometimes paired brightfield images). Cells are detected as soon as they start forming interferences on the image (hovering behavior). A pre-annotation was performed using a threshold based segmentation on the RICM modality. Manuel separation of cell-cell contacts and removal of false positive objects was performed by an expert annotator (using brightfield when available). RBCs are ignored in the annotations. </li> </ul> <h2>Single-cell signal annotations for classification and regression</h2> <h3>Cell-cell interaction assay: ADCC</h3> <p>We generated several signal classification/regression datasets with Celldetective to characterize the ADCC assay. Briefly, for a given event cells can be classified as "the event occured during the observation", "no event occured during the observation", "the event already occured prior to observation". If the event occurred during the observation, we can estimate when (the regression). Each single-cell is a dictionary with a collection of signals. The attribute "class" sets the class and "t0" the time of event (default is -1 for absence of event). </p> <ul> <li>db-si-NucPI: classification and regression of single-cells with respect to lysis events characterized by a strong PI increase upon lysis (also associated with decreasing nuclear area and sometimes a decreasing Hoechst)</li> <li>db-si-NucCondensation: classification and regression of single-cells with respect to nucleus shrinking events characterized by a decreasing nuclear area (UPDATE on 23/01/2024)</li> </ul> <h1>Models</h1> <h2>Segmentation models</h2> <h3>Generalist models</h3> <p>We integrated in Celldetective select published models for cellular segmentation from StarDist and Cellpose. We wraped the models with an input configuration to help Celldetective handle the normalization, rescaling and channel selection upon inference. </p> <ul> <li>Cellpose [1,2]: <em>cyto3</em>, <em>livecell</em>, <em>tissuenet</em>, <em>nuclei</em></li> <li>StarDist [3]: <em>versatile_fluo</em>, <em>versatile_he</em></li> </ul> <p>If you use any of these models your research, don't forget to cite the StarDist or Cellpose papers accordingly!</p> <h3>ADCC models</h3> <ul> <li>MCF-7 (in the presence of lymphocytes): <em>mcf7_nuc_multimodal, mcf7_nuc_stardist_transfer</em></li> <li>primary NKs (in the presence of MCF-7): <em>primNK_multimodal</em>, <em>primNK_SD</em>, <em>primNK_cfse</em></li> </ul> <h3>Spreading-assay models</h3> <ul> <li>Lymphocytes: <em>lymphocytes_ricm</em></li> </ul> <h2>Signal analysis models</h2> <p>We developed Deep Learning models that classify and regress the time of events from single-cell signals, applied to the ADCC assay.</p> <ul> <li> lysis detection: <em>lysis_H_PI</em>, <em>lysis_PI_area</em><em>. </em>Detect lysis events characterized at least by an increase of PI from one or more measurements (respectively PI+Hoechst and PI+nucleus area, trained on db-si-NucPI)</li> <li>nucleus shrinking detection:<em> NucCond</em>. Detect nucleus shrinking events from nuclear area signal (db-si-NucCondensation)</li> </ul> <h1>References</h1> <ol> <li>Stringer, C., Wang, T., Michaelos, M. & Pachitariu, M. Cellpose: a generalist algorithm for cellular segmentation. Nat Methods 18, 100–106 (2021).</li> <li>Pachitariu, M. & Stringer, C. Cellpose 2.0: how to train your own model. Nat Methods 19, 1634–1641 (2022).</li> <li>Schmidt, U., Weigert, M., Broaddus, C. & Myers, G. Cell Detection with Star-Convex Polygons. in Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 (eds. Frangi, A. F., Schnabel, J. A., Davatzikos, C., Alberola-López, C. & Fichtinger, G.) 265–273 (Springer International Publishing, Cham, 2018). doi:10.1007/978-3-030-00934-2_30.</li> </ol> <p> </p> <p> </p>
Supporting data for Emerging AI-based weather prediction models as downscaling tools
<p>Supporting data for "Emerging AI-based weather prediction models as downscaling tools" by Nikolay Koldunov, T. Rackow, Christian Lessig, S. Danilov, S. Cheedela, D. Sidorenko, Irina Sandu, Thomas Jung</p> <p><a href="https://t.co/PSUCUvh9lf" target="_blank" rel="noopener noreferrer nofollow"><span>https://</span>doi.org/10.48550/arXiv<span>.2406.17977</span></a></p>
Performance Model, Deliverable D2.4 Final Release and Evaluation of the AI-SPRINT Design Tools.
<p>This repository includes the code and results of analysis for predicting unseen layers of the same network and predicting new unseen networks reported in Deliverable D2.4 Final Release and Evaluation of the AI-SPRINT Design Tools<strong>. </strong>The folder is organised as follows:</p> <p>- **pareto** contains the results of the profiling of the networks trained and validated on CIFAR-10</p> <p>- **pareto_onnx** contains the results of the profiling of the networks trained and validated on MNIST</p> <p>- The folders "**network_to_network**" contain the models trained using aMLLibrary for the analysis on unseen networks done using Eduard's data. </p> <p>- **notebook-B5.ipynb** contains the code to perform the analysis on B and C networks</p> <p>- **nextLayerPrediction.ipynb** contains the code to perform the prediction of unseen layers of the same network</p> <p>- **allLayerPrediction.ipynb** contains the code for the analysis on unseen networks</p>
Evaluating an AI Tool for Detecting Thyrotoxic States
ClinicalTrials.gov study NCT07017907. IPD Sharing: NO. Countries: 0. Publications: 5.
Exploring Generative AI Tools for Software Quality: Insights from a Rapid Multivocal Literature Review
Open the record for dataset details and reuse information.
Datasets for experiments in support of "HADA: an Automated Tool for Hardware Dimensioning of AI Applications"
<p>The zip archive contains three datasets used during the experimental phase of the paper:</p> <ul> <li><em>ANTICIPATE_trainDataset.csv</em>: used in order to train the ML models for the ANTICIPATE algorithm (Section 4.1);</li> <li><em>CONTINGENCY_trainDataset.csv</em>: used in order to train the ML models for the CONTINGENCY algorithm (Section 4.1);</li> <li><em>EmpiricalValidationSet.csv</em>: used for validating the EML optimization model (Section 4.2)</li> </ul>
The Impact of Generative AI-Powered Code Generation Tools on Software Engineer Hiring: Recruiters' Experiences, Perceptions, and Strategies
<p>This is the dataset for the paper: The Impact of Generative AI-Powered Code Generation Tools on Software Engineer Hiring: Recruiters' Experiences, Perceptions, and Strategies</p> <p> This paper was accepted for publication at the 58th Hawaii International Conference on System Sciences (HICSS) - Software Technology Track</p>
A Wearable AI Feedback Tool for Pediatric OCD
ClinicalTrials.gov study NCT05064527. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Developing Trustworthy Artificial Intelligence (AI)-Driven Tools to Predict Vascular Disease Risk and Progression
ClinicalTrials.gov study NCT06206369. IPD Sharing: Not stated. Countries: 6. Publications: 1.
Retrospective Case-Control Study for Developing an Artificial Intelligence (AI) Tool for Lesion Detection Using Magnetic Resonance Imaging (MRI) and Clinical Variables for Early Diagnosis of Axial Spo
ClinicalTrials.gov study NCT06591481. IPD Sharing: NO. Countries: 4. Publications: 8.
Using AI as a Diagnostic Decision Support Tool to Help the Diagnosis of Skin Disease in Primary Healthcare in Catalonia
ClinicalTrials.gov study NCT04562168. IPD Sharing: YES. Countries: 1. Publications: 13.
Supplemental package of a study on the implications of AI-based tools for the human aspects of software engineering
Open the record for dataset details and reuse information.
Safe use of AI Tools in a university environment
<p>The flow chart presents a simplified decision-making process as to whether the user should, or should not, use an AI tool, taking into account desired results, types of data, and GDPR compliance, among other relevant variables.</p>
THE ROLE OF AI TOOLS LIKE CHATGPT AND GEMINI IN CREATING CUSTOMIZED EDUCATIONAL PLANS FOR PARENTS AND BEHAVIOR SPECIALISTS
Open the record for dataset details and reuse information.
ACTIVATE: AI-driven Clinical-trial Trial-Information and Viability Assessment Tool for EHRs
ClinicalTrials.gov study NCT07232043. IPD Sharing: YES. Countries: 1. Publications: 0.
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.