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Dataset results
21 results for “generative artificial intelligence”
Generative artificial intelligence predicts human performance
<p>Research data for a study that used generative artificial intelligence (i.e., ChatGPT with the GPT-4 and the Google Gemini 2.0 Flash models) to predict human performance in a language-based memory task. In particular, we studied the effects of context on the relatedness and memorability of garden-path sentences. </p>
Dataset: Themes Generative Artificial Intelligence ETF (WISE) 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.
Data from an empirical study on generative artificial intelligence painting
<p>This is an empirical study on generative artificial intelligence painting, including research methods, basic data of 17 subjects and their AI art painting, and quantitative data after their data analysis.</p>
Supplementary data for: "Towards automatic generation of control structures for Process Flow Diagrams (PFDs) with Artificial Intelligence"
<p>Uploaded on 20. February 2023</p> <p>This is the supplementary data for the publication</p> <p>"Towards automatic generation of control structures for Process Flow Diagrams (PFDs) with Artificial Intelligence" (2023) by Edwin Hirtreiter, Lukas Schulze Balhorn, Artur M. Schweidtmann</p> <p>Corresponding author: A. M. Schweidtmann, E-mail: a.schweidtmann@tudelft.nl<br> Delft University of Technology, Department of Chemical Engineering, Process Intelligence Group, Van der Maasweg 9, 2629 HZ Delft, The Netherlands</p> <p>The folder contains json files with the training (train), test (test), and augmented training (train_augm) data files. The json files contain syntetically generated SFILES. </p> <p>The pre-print of the manuscript is accessible at https://doi.org/10.48550/arXiv.2211.05583</p>
Mimicking Clinical Trials with Synthetic Acute Myeloid Leukemia Patients Using Generative Artificial Intelligence
<p>We used two different methodologies of generative artificial intelligence, CTAB-GAN+ and normalizing flows (NFlow), to synthesize patient data based on 1606 patients with acute myeloid leukemia that were treated within four multicenter clinical trials. The resulting data set consists of 1606 synthetic patients for each of the models.</p> <p>This dataset is associated with our publication "Mimicking clinical trials with synthetic acute myeloid leukemia patients using generative artificial intelligence" by Eckardt et al., npj Digital Medicine, 2024 (<a href="https://doi.org/10.1038/s41746-024-01076-x" target="_new">https://doi.org/10.1038/s41746-024-01076-x</a>). If you use this dataset, please cite our paper.</p> <p> </p> <p><strong>Data Dictionary</strong></p> <table> <tbody><tr> <th>NAME</th> <th>LABEL</th> <th>TYPE</th> <th>CODELIST</th> </tr> </tbody><tbody> <tr> <td>AGE</td> <td>age</td> <td>num</td> <td>in years</td> </tr> <tr> <td>AMLSTAT</td> <td>AML status</td> <td>char</td> <td>de novo, sAML, tAML</td> </tr> <tr> <td>ASXL1</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>ATRX</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>BCOR</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>BCORL1</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>BRAF</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>CALR</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>CBL</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>CBLB</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>CDKN2A</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>CEBPA</td> <td>CEBPA mutation</td> <td>char</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>CGCX</td> <td>complex cytogenetic karyotype</td> <td>char</td> <td>0 'No', 1 'Yes'</td> </tr> <tr> <td>CGNK</td> <td>cytogenetic normal karyotype</td> <td>char</td> <td>0 'No', 1 'Yes'</td> </tr> <tr> <td>CR1</td> <td>first complete remission</td> <td>char</td> <td>0 = 'not achieved', 1 = 'achieved'</td> </tr> <tr> <td>CSF3R</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>CUX1</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>DNMT3A</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>EFSSTAT</td> <td>status variable for EFSTM</td> <td>num</td> <td>0 'censored' 1 'event'</td> </tr> <tr> <td>EFSTM</td> <td>event free survival time</td> <td>num</td> <td>in months</td> </tr> <tr> <td>ETV6</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>EXAML</td> <td>extramedullary AML</td> <td>char</td> <td>0 'No', 1 'Yes'</td> </tr> <tr> <td>EZH2</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>FBXW7</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>FLT3I</td> <td>FLT3-ITD mutation status</td> <td>char</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>FLT3T</td> <td>FLT3-TKD mutation status</td> <td>char</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>GATA2</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>GNAS</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>HB</td> <td>hemoglobin</td> <td>num</td> <td>in mmol/l</td> </tr> <tr> <td>HRAS</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>IDH1</td> <td>IDH1 mutation status</td> <td>char</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>IDH2</td> <td>IDH2 mutation status</td> <td>char</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>IKZF1</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>JAK2</td> <td>Jak2 Mutation</td> <td>char</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>KDM6A</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>KIT</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>KRAS</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>MPL</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>MYD88</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>NOTCH1</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>NPM1</td> <td>NPM1 mutation status</td> <td>char</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>NRAS</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>OSSTAT</td> <td>status variable for OSTM</td> <td>num</td> <td>0 'censored' 1 'event'</td> </tr> <tr> <td>OSTM</td> <td>overall survival time</td> <td>num</td> <td>in months</td> </tr> <tr> <td>PDGFRA</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>PHF6</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>PLT</td> <td>platelet count</td> <td>num</td> <td>in 10⁶/l</td> </tr> <tr> <td>PTEN</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>PTPN11</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>RAD21</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>RUNX1</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>SETBP1</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>SEX</td> <td>sex</td> <td>char</td> <td>f 'female', m 'male'</td> </tr> <tr> <td>SF3B1</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>SMC1A</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>SMC3</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>SRSF2</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>STAG2</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>SUBJID</td> <td>subject identifier</td> <td>char</td> <td> </td> </tr> <tr> <td>TET2</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>TP53</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>U2AF1</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>WBC</td> <td>white blood count</td> <td>num</td> <td>in 10⁶/l</td> </tr> <tr> <td>WT1</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>ZRSR2</td> <td>mutation indicator, NGS</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>inv16_t16.16</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>t8.21</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>t.6.9..p23.q34.</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>inv.3..q21.q26.2.</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>minus.5</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>del.5q.</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>t.9.22..q34.q11.</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>minus.7</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>minus.17</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>t.v.11..v.q23.</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>abn.17p.</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>t.9.11..p21.23.q23.</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>t.3.5.</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>t.6.11.</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>t.10.11.</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>t.11.19..q23.p13.</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>del.7q.</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>del.9q.</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>trisomy 8</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>trisomy 21</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>minus.Y</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> <tr> <td>minus.X</td> <td>mutation indicator, cytogenetics</td> <td>num</td> <td>0 = 'no mutation', 1 = 'mutation'</td> </tr> </tbody> </table>
The Metadata of "A Rapid Investigation of Artificial Intelligence Generated Content Footprints in Scholarly Publications"
<p>This resource contains all the raw data associated with the article "A Rapid Investigation of Artificial Intelligence Generated Content Footprints in Scholarly Publications." This raw data underpins the conclusions presented in the article.</p>
Recursiveness in Multimodal Generative Artificial Intelligence
<p>The dataset contains images extracted from the <a href="https://cocodataset.org/#home">COCO</a> dataset that have been tested in the Recursive Modality Changes process: a caption is extracted from an image and used to generate a new image. The process is repeated in a loop. For the extraction of the description was used GPT-4o and for the generation of the images DALL-E3. A second experiment with a subset of the previous images have been done with Flux.1 and Phi-3.5 </p> <p> </p> <div> <div> </div> <h2>Description</h2> </div> <div> <p>The dataset contains experiments of applying the RMC of length 40 generations from images that contains elements of the following categories: apples, elephants, fire-hydrants, persons, toilets, and trains. In total, there are 10 RMC loops per category (40*10*6 = 2,400 images) and the comparison between the images and descriptions using the metrics <a href="https://richzhang.github.io/PerceptualSimilarity/">LPIPS VGG</a>, TF-IDF, <a href="https://www.tensorflow.org/text/api_docs/python/text/BertTokenizer">BERT tokenizer</a>, <a href="https://arxiv.org/abs/2201.12086">BLIP</a>.</p> <ul> <li>1_coco_dataset: information from COCO images of each category</li> <li>2_categories: loops of each category <ul> <li>results_{category} <ul> <li>experiments <ul> <li>results_dall-e-3_hd_{style}_{category} -> hd (high definition), style (vivid or natural) -> all the experiments of that {style} and {category} <ul> <li>results_all.xlsx -> similarity with metrics LPIPS, TF-IDF, BLIP, BERT aggregated</li> <li>experiments <ul> <li>{date}_{style}_{coco_id} -> each experiment from a coco image <ul> <li>imgs -> images</li> <li>imgs_resized -> resized images</li> <li>experiment.json -> json with data of the experiment (description, number of generations, etc.)</li> <li>results.xlsx -> metrics of each individual loop</li> </ul> </li> </ul> </li> </ul> </li> </ul> </li> <li>images_all.xlsx -> summary of all the images generated and their descriptions</li> <li>inter-experiments_results.xlsx -> aggregated results of inter-trajectory experiments of this {category}</li> <li>intra-experiments_results.xlsx -> aggregated results of intra-trajectory experiments of this {category}</li> </ul> </li> </ul> </li> <li>3_combined results: combined results of all categories <ul> <li>rsults_hd_labels_{style} -> all the results from images generated with the same style (vivid or natural) <ul> <li>images_all.xlsx -> summary of all images</li> <li>inter-exeriments_results_all.xlsx</li> <li>intra-experiments_results_all.xlsx</li> <li>results_all_labels.xlsx -> summary of results per {category}</li> </ul> </li> </ul> </li> <li>4_different_styles -> experiments comparing styles (natural and vivid)</li> </ul> <h2>Paper</h2> <ul> <li>Paper: </li> <li>Cite:</li> </ul> <p><code>@misc{</code><br><code>}</code></p> </div>
Detailed dataset and code generation for Artificial intelligence-based modelling of compressive strength of slurry infiltrated fiber concrete
Open the record for dataset details and reuse information.
Optimization of Imagery Rescripting Research Using Generative Artificial Intelligence
ClinicalTrials.gov study NCT07189715. IPD Sharing: YES. Countries: 1. Publications: 9.
Artificial Intelligence-Generated Written Communication for Families of Intensive Care Unit Patients
ClinicalTrials.gov study NCT06969196. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.
The Use of Artificial Intelligence Generated Contours in Radiation Planning of the Prostate Brachytherapy
ClinicalTrials.gov study NCT06964412. IPD Sharing: Not stated. Countries: 0. Publications: 8.
Ghost in the machine or monkey with a typewriter - generation of Christmas BMJ titles using artificial intelligence: an observational study
<p>Datasets and analysis scripts used in this paper</p>
Generative Artificial Intelligence Tools and Technologies
<p>Generative Artificial Intelligence tools and technologies mind map</p>
Simulating Psychotherapeutic Sessions With Generative Artificial Intelligence
ClinicalTrials.gov study NCT06813066. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Using Generative Artificial Intelligence (AI) to Create Patient-Friendly Discharge Summaries
ClinicalTrials.gov study NCT06711458. IPD Sharing: NO. Countries: 1. Publications: 0.
Generation of an Artificial Intelligence Algorithm Based on the Analysis of Melanoma Peri-scar Dermatoheliosis, as a Predictive Factor of Response to Anti-PD-1
ClinicalTrials.gov study NCT05856565. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Generative Artificial Intelligence Intervention and Individual Psychological Counseling on Emotional Distress in Young Adults
ClinicalTrials.gov study NCT06992180. IPD Sharing: YES. Countries: 0. Publications: 0.
Efficacy of Artificial Intelligence-Generated Music Therapy in Reducing Dental Anxiety Among Adults Undergoing Impacted Third Molar Extraction
ClinicalTrials.gov study NCT06998979. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Comparative Analysis of Artificial Intelligence-Generated and Studio-Based Reformer Pilates Interventions for Forward Head Posture
ClinicalTrials.gov study NCT07205835. IPD Sharing: NO. Countries: 1. Publications: 0.
Generative Artificial Intelligence Nurse Staffing Study
ClinicalTrials.gov study NCT06978790. IPD Sharing: NO. Countries: 0. 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.