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1,291 results for “Artificial intelligence”
Sensing Performance of Artificially Intelligent Nanopores Developed by Integrating Solid-State Nanopores with Machine Learning Methods
<p>Ionic current-time data obtained from measuring nanoparticles with the diameters of 90, 100, 150, 200, 220, 270, and 300 nm, using nanopores with a diameter of 300nm.</p> <p>Test_100nm_1 means a test data of nanoparticles with a diameter of 100 nm.</p> <p>Train_100nm_1 means a training data of nanoparticles with a diameter of 100 nm.</p>
Radiomics and Artificial Intelligence Analysis with Textural Metrics Extracted by Contrast-Enhanced Mammography and Dynamic Contrast Magnetic Resonance Imaging to detect breast malignant Lesions
<p>We uploade the dataset of the manuscript "Radiomics and Artificial Intelligence Analysis with Textural Metrics Extracted by Contrast-Enhanced Mammography and Dynamic Contrast Magnetic Resonance Imaging to detect breast malignant Lesions" by Current Oncology.</p> <p> </p>
Classroom: Python based Artificial Intelligence
<p>Dataset for deep learning training course</p> <p> </p> <p>1) Heart Segmentation of ventricles</p> <p>2) ImageNet</p> <p>3) CIFAR10</p> <p> </p> <p>Npz, tfrecords dataset type</p> <p> </p>
Supplementary data for "Mapping global dynamics of benchmark creation and saturation in artificial intelligence"
<p>Supplementary data for the article "Mapping global dynamics of benchmark creation and saturation in artificial intelligence"</p>
Artificial Intelligence in Space - CIMON assisting astronauts on the International Space Station
<p><b>Abstract</b></p><p class="dhik-abstract-content">The talk introduces Cimon (Crew Interactive MObile CompanioN) and its missions in the International Space Station. It is a collaboration among Deutsches Zentrum für Luft- und Raumfahrt (DLR), European Space Agency, HSLU-BIOTESC (Biotechnology Space Support Center), LMU München, Airbus, and IBM. </p><p></p><p><b>Weitere Beiträge aus dem DHIK-Forum 2022 auf Zenodo:</b></p><p class="dhik-session-list"></p><ul><li>Session #1: Viktor Sigrist: Internationalisierung - Partnerschaften für den Ausbau von Forschung und Entwicklung (DOI:<a href="https://zenodo.org/record/7123701">10.5281/zenodo.7123701</a>)</li><li>Session #2: Dieter Leonhard: DHIK- Strategien der internationalen Zusammenarbeit in Forschung und Lehre (DOI:<a href="https://zenodo.org/record/7123456">10.5281/zenodo.7123456</a>)</li><li>Session #3: Stephen Wittkopf: Wissens- und Innovationstransfer - Interdisziplinäre Zusammenarbeit mit Unternehmen und Institutionen (DOI:<a href="https://zenodo.org/record/7025707">10.5281/zenodo.7025707</a>)</li><li>Session #4: Xiao Feng: CDHAW - Chinesisch-Deutsche Hochschule für Angewandte Wissenschaften (DOI:<a href="https://zenodo.org/record/7123458">10.5281/zenodo.7123458</a>)</li><li>Session #5: Antonio Pita und Isabel Kreiner: Academy-Industry-Collaboration - Outreach Strategy (DOI:<a href="https://zenodo.org/record/7123460">10.5281/zenodo.7123460</a>)</li><li>Session #6: Martin Sternberg: Promotionsrecht – aktueller Stand an deutschen Hochschulen für angewandte Wissenschaften (DOI:<a href="https://zenodo.org/record/7123757">10.5281/zenodo.7123757</a>)</li><li>Session #7: Adrian Derungs: Duo mit Innovationskraft - Zusammenspiel von Forschung und Wirtschaft in der Zentralschweiz (DOI:<a href="https://zenodo.org/record/7123767">10.5281/zenodo.7123767</a>)</li><li>Session #8: Theres Paulsen: Transdisziplinäre Forschung - komplexe gesellschaftliche Herausforderungen erfordern diverse Ansätze (DOI:<a href="https://zenodo.org/record/7123769">10.5281/zenodo.7123769</a>)</li><li>Session #9: Jörg Schneider: International research collaboration - New funding opportunities for universities of applied sciences (DOI:<a href="https://zenodo.org/record/7123771">10.5281/zenodo.7123771</a>)</li><li>Session #10: Cornelia Spycher und Matthew Whellens: Horizon Europe - overview of funding opportunities for your research and innovation (DOI:<a href="https://zenodo.org/record/7123773">10.5281/zenodo.7123773</a>)</li><li>Session #11: Janique Siffert: Eureka Eurostars - erfolgreiche Förderung für internationale Innovationsprojekte (DOI:<a href="https://zenodo.org/record/7123777">10.5281/zenodo.7123777</a>)</li><li>Session #12: Ludger Fischer: Energy Lab - ein Netzwerk für innovative Lösungen im Energiebereich (DOI:<a href="https://zenodo.org/record/7123779">10.5281/zenodo.7123779</a>)</li><li>Session #13: Jörg Worlitschek: Thermal energy storage - heating the north, cooling the south (DOI:<a href="https://zenodo.org/record/7123781">10.5281/zenodo.7123781</a>)</li><li>Session #14: Jonas Mühlethaler: Neues DC Microgrid-Konzept – netzunabhängige Elektrifizierung in Entwicklungsländern (DOI:<a href="https://zenodo.org/record/7123783">10.5281/zenodo.7123783</a>)</li><li>Session #15: Tommy Claussen: Dekarbonisierung des Gebäudesektors - digitale Transformation in der Gebäudetechnik und im Gebäudemanagement (DOI:<a href="https://zenodo.org/record/7123785">10.5281/zenodo.7123785</a>)</li><li>Session #16: Christoph Imboden: Flexibility solutions - making the power grid fit for the future (DOI:<a href="https://zenodo.org/record/7123787">10.5281/zenodo.7123787</a>)</li><li>Session #17: Uwe Schulz: Spielerisches Sarnetz - Simulationen für die fossile Unabhängigkeit einer Ortschaft (DOI:<a href="https://zenodo.org/record/7123790">10.5281/zenodo.7123790</a>)</li><li>Session #18: Jana Koehler: Künstliche Intelligenz – Erfolg durch Erwünschtheit, Machbarkeit und Wirtschaftlichkeit (DOI:<a href="https://zenodo.org/record/7123792">10.5281/zenodo.7123792</a>)</li><li>Session #19: Rolf Kamps: KI in der Prävention - Befragungsmethoden und Schulungen trainieren, Krankheitserreger erkennen (DOI:<a href="https://zenodo.org/record/7123794">10.5281/zenodo.7123794</a>)</li><li><b>Session #20: Gwendolyne Pascua: Artificial Intelligence in Space - CIMON assisting astronauts on the International Space Station (<a href="#collapseTwo">Video</a>)</b></li><li>Session #21: Tobias Matter et.al.: Augmented Reality Soundscapes - mit maschinellem Lernen Klangkulissen von zukünftigen Bauvorhaben generieren (DOI:<a href="https://zenodo.org/record/7123798">10.5281/zenodo.7123798</a>)</li><li>Session #22: Angela Nicoara: Internet of Things - transforming businesses, people's lives and driving growth in the coming years (DOI:<a href="https://zenodo.org/record/7123800">10.5281/zenodo.7123800</a>)</li><li>Session #23: Adrian Koller: Feldrobotik - unermüdliche und zunehmend intelligentere Hilfe in der Landwirtschaft (DOI:<a href="https://zenodo.org/record/7123802">10.5281/zenodo.7123802</a>)</li><li>Session #24: Widar von Arx et.al.: Realisierung der Verkehrswende - Einfluss der Preispolitik in der Mobilität (DOI:<a href="https://zenodo.org/record/7124000">10.5281/zenodo.7124000</a>)</li><li>Session #25: Andreas Liebrich: Tourismusdateninfrastruktur - Was die Schweiz von Europa lernen kann (DOI:<a href="https://zenodo.org/record/7123806">10.5281/zenodo.7123806</a>)</li><li>Session #26: Frank Pöhlau und Stefan May: Find life on Mars - Schülerprojekte zur mobilien Robotik (DOI:<a href="https://zenodo.org/record/7123808">10.5281/zenodo.7123808</a>)</li><li>Session #27: Jiayun Shen: Open Innovation - Innovationsmanagement bei der Schweizerischen Post (DOI:<a href="https://zenodo.org/record/7123810">10.5281/zenodo.7123810</a>)</li><li>Session #28: Tobias Specker: Interkulturelles Management – innovative Konzepte zum Ausbau der China-Kompetenzen an Hochschulen (DOI:<a href="https://zenodo.org/record/7123812">10.5281/zenodo.7123812</a>)</li><li>Session #29: Elena Algorri: Swimming robots - exploring the unterwater from the surface (DOI:<a href="https://zenodo.org/record/7123814">10.5281/zenodo.7123814</a>)</li><li>Session #30: Sergio Camacho: Robotics and Digital Systems Engineering at the Tec de Monterrey (DOI:<a href="https://zenodo.org/record/7123816">10.5281/zenodo.7123816</a>)</li><li>Session #31: Thomas Dorn: Industrie 4.0 - Forschungskooperationen mit der CDHAW und der Tongji Universität Shanghai (DOI:<a href="https://zenodo.org/record/7123818">10.5281/zenodo.7123818</a>)</li><li>Session #32: Walter Reichert et.al.: Kollaboration und Unterstützung - Mobile Robotik und Exoskelette in der flexiblen Produktion (DOI:<a href="https://zenodo.org/record/7123820">10.5281/zenodo.7123820</a>)</li><li>Session #33: Louis Palmer: Solar Butterfly - climate pioneer world tour supported by HSLU (DOI:<a href="https://zenodo.org/record/7123822">10.5281/zenodo.7123822</a>)</li></ul><p></p>
Amazonian manatee critical habitat revealed by artificial intelligence-based passive acoustic techniques
<p>These files are part of the publication under the same name. The pkl file contains manatee vocalizations mixed with background sounds that have been used to train a CNN. The resulting model is available in the .tflite file. The Python file can browse the vocalizations that have been made available, visualize the human labels, and if Tensorflow is available classify the samples, providing the classifier output for the manatee class. The corresponding paper should be referred to for thresholds and performance details that were used for field data.</p>
Clinical Dataset for Artificial Intelligence-Driven Predictive Modeling for Home Discharge in Neurological and Orthopedic Conditions
<p><span>In recent years, the fusion of the medical and computer science domains has gained significant traction in the scientific research landscape. Progress in both fields has enabled the generation of a vast amount of data used for making predictions and identifying interesting clusters and pathways. The Machine Learning model's application in the medical domain is one of the most compelling and challenging topics to explore, bridging the gap between Artificial Intelligence (AI) and healthcare. The combination of AI and medical information offers the possibility to create tools that can benefit both healthcare providers and physicians. This enables the enhancement of rehabilitation therapy and patient care. In the rehabilitation context, this work provides an alternative perspective: prediction of patients’ home discharge upon completing the rehabilitation protocol. Demographic and clinical data were collected from electronic Medical Record. </span></p> <p><span>The original analysis dataset includes clinical and demographic data of adults admitted to the neurology and orthopedic departments of a rehabilitation hospital in Italy from January 2015 to August 2022. The completion of patients’ ADR-r form</span><span> resulted in the collection of data for 10520 individuals, whose information is distributed across 120 initial features. We anonymized dataset rows. Clinical data, including the primary reason for rehabilitation, any associated medical conditions, impairments, and admission/discharge mBi scores were collected. </span></p> <p><span>A legend file is enclosed to explain variable labels.</span></p>
Replication Package: How Artificial-Intelligence Incidents Can Jeopardize Safety and Security
<p><strong>Welcome to the public repository for the additional content of the paper "SoK: How Artificial-Intelligence Incidents Can Jeopardize Safety and Security", accepted at the ARES 2024.</strong></p> <p>This repository provides additional information to the conducted study, including the following file:</p> <ul> <li>analysis_sheet_ARES2024.csv: sheet containing security- and safety-related data regarding the harmonized datasets AIAAIC, AIID, AVID and Where In the World Is AI?, including 240 incidents classified according to the extraction criteria given in the paper.</li> </ul>
Using explainable artificial intelligence as a diagnostic tool for exploration of hydrologic connectivity at watershed scale
<p><strong><span>Using explainable artificial intelligence as a diagnostic tool for exploration of hydrologic connectivity at watershed scale</span></strong></p>
FAIRmat Tutorial 3: Introduction to the Artificial Intelligence Toolkit
<p>This tutorial is dedicated to the NOMAD artificial-intelligence (AI) toolkit, the platform for running (jupyter) notebooks to analyse with AI tools the data contained in the NOMAD Archive. </p> <p>We will cover, in an interactive, hands-on fashion, the several aspects of the AI-toolkit: the query over the NOMAD Archive via the NOMAD API, the basic notebooks for learning AI methods, and the advanced notebooks, where the workflow of relevant publications, in which AI is applied to materials science, can be interactively reproduced and further explored. Furthermore, we will introduce the local AI-toolkit app that allows to run a local version of the notebooks, e.g., to combine own data with the NOMAD Archive data. </p> <p>At the end of the first day, few tutorial notebooks will be suggested to be perused by the participants before the second day starts. In the second day, break-out rooms will be organized, and in each room one of the selected tutorial notebooks will be discussed.</p> <p> </p> <p><strong>Disclaimer:</strong> NOMAD is being continuously developed based on input and feedback from the scientific community. Hence the features, services or interface may have changed since the time of recording of this video. For up-to-date information please consult our latest tutorials and the NOMAD documentation <a href="https://nomad-lab.eu/prod/v1/docs/">https://nomad-lab.eu/prod/v1/docs/</a></p>
Spanish Language Benchmark for Artificial Intelligence Models (TELEIA)
<h1>TELEIA Datasets</h1> <p>The dataset contains test questions to evaluate LLMs in Spanish</p> <ul> <li>TELEIA_Cervantes_AVE.csv: test questions on vocabulary and grammatical structures, following the format of the Cervantes AVE exam. The column "question" contains a sentence with a gap that must be filled in. The columns "option_a", "option_b", "option_c", "option_d" and "correct_answer" (A,B,C or D) represent the different possibilities for the gap and the correct one.</li> <li>TELEIA_PCE.csv: test on morphology and semantics resembling the style of the PCE exam, consisting of short questions or sentences to be completed. It is composed of the following columns: "question", "option_a", "option_b", "option_c" and "correct_answer" (A, B or C).</li> <li>TELEIA_SIELE.csv: different texts with questions related to them, based on the reading comprehension task of the SIELE exam. It is composed of the following columns: "question", "option_a", "option_b", "option_c" and "correct_answer" (A, B or C). </li> </ul>
Outputs from policy sprint "How does artificial intelligence contribute to sustainability in Switzerland?"
<p>This dataset contains English-language output from a policy sprint on the question "How does artificial intelligence contribute to sustainability in Switzerland?" by the Swiss organization Expedition Zukunft, documented at https://www.expeditionzukunft.ch/sprint-ki.</p> <p>The information included in this dataset was made publicly available by the organization at https://www.expeditionzukunft.ch/sprint-ki. This dataset is simply a transfer of this information on the output of the policy sprint in tabular form, for ease of consumption and analysis.</p> <p>sprint_outputs.csv contains the raw textual data from the website on the outputs of the policy sprint process.<br>metadata.csv contains an explanation of all columns in sprint_outputs.csv.</p> <p>The dataset accompanies an analysis of the policy sprint published in (preprint) form at <a href="https://doi.org/10.31235/osf.io/xykej">https://doi.org/10.31235/osf.io/xykej</a>.<br>When citing this dataset, please credit the organisation Expedition Zukunft (https://www.expeditionzukunft.ch) as the creators and curators of the original raw data source.</p>
Data for publication: Pixelated High-Q Metasurfaces for in Situ Biospectroscopy and Artificial Intelligence-Enabled Classification of Lipid Membrane Photoswitching Dynamics
Open the record for dataset details and reuse information.
Leaderboard Spanish Language Benchmark for Artificial Intelligence Models (TELEIA)
<h1>TELEIA Datasets Leaderboard</h1> <p>These dataset contains the answers of different LLMs to the <a href="../doi/10.5281/zenodo.12571762">TELEIA (Spanish Language Benchmark for Artificial Intelligence Models)</a> dataset.<br><br><span>LLMs evaluated:</span></p> <ul> <li>Yi-6B-Chat</li> <li>Meta-Llama-3-8B-Instruct</li> <li>Llama-2-7b-chat-hf</li> <li>gemma-7b-it</li> <li>Mistral-7B-Instruct-v0.1</li> <li>occiglot-7b-es-en-instruct</li> <li>GPT3.5</li> <li>GPT4</li> </ul> <p><span>Files:</span></p> <ul> <li><em>TELEIA_Cervantes_AVE_results.xlsx: </em>vocabulary and grammatical structures, following the format of the Cervantes AVE exam</li> <li><em>TELEIA_PCE_results.xlsx: </em>test on morphology and semantics resembling the style of the PCE exam, consisting of short questions or sentences to be completed</li> <li><em>TELEIA_SIELE_results.xlsx: </em>different texts with questions related to them, based on the reading comprehension task of the SIELE exam</li> </ul> <p>Each .xlsx contains a sheet with the results of each model and the following columns:</p> <ul> <li><em>question: </em>question from TELEIA</li> <li><em>option_a:</em> possible answer from TELEIA </li> <li><em>option_b:</em> possible answer from TELEIA<em> </em></li> <li><em>option_c: </em>possible answer from TELEIA <em> </em></li> <li><em>option_d: </em>possible answer from TELEIA<em> </em></li> <li><em>correct_answer:</em> correct answer form TELEIA </li> <li><em>llm_question:</em> complete question made to the LLM </li> <li><em>tokens_in:</em> list of tokens that compound the question </li> <li><em>tokens_in_count:</em> number of tokens that compound the question </li> <li><em>llm_answer:</em> raw answer from the LLM </li> <li><em>llm_answer_filtered:</em> answer in format {A,B,C,D} from the LLM </li> <li><em>tokens_out :</em> list of tokens that compound the raw answer </li> <li><em>tokens_out_count:</em> number of tokens that compound the raw answer </li> <li><em>word_count :</em> number of words that compound the raw answer </li> </ul> <p> </p>
Guide for Artificial Intelligence Ethical Requirements Elicitation - RE4AI Ethical Guide - Supplementary Material
<p>This is the Supplementary Material provided for the work accepted on HICSS 55 - 2022.</p> <p>Title of work: Guide for Artificial Intelligence Ethical Requirements Elicitation - RE4AI Ethical Guide.</p> <p> </p> <p>Guide can be found here: <a href="https://josesiqueira.github.io/RE4AIEthicalGuide/index.html">https://josesiqueira.github.io/RE4AIEthicalGuide/index.html</a></p> <p>Guide source code can be found here: <a href="https://github.com/josesiqueira/RE4AIEthicalGuide">https://github.com/josesiqueira/RE4AIEthicalGuide</a></p> <p> </p> <p>This file presents 4 Tables that add significant information to the paper.</p> <p>Table 1 shows ethical principles present in ECCOLA method by Vakkuri et al. [1].</p> <p>Table 2 shows principles and their ethical issues presented in the work of Ryan and Stahl [2], as it is.</p> <p>Table 3 shows a standardisation of the principles in ECCOLA with the principles in Ryan and Stahl through a mapping.</p> <p>Table 4 presents a mapping of tools found in our previous study [3] with principles and ethical issues presented by Ryan and Stahl [2].</p> <p> </p> <p>References:</p> <p>[1] Vakkuri, K. Kemell, and P. Abrahamsson, “ECCOLA - a method for implementing ethically aligned AI systems,” CoRR, vol. abs/2004.08377, 2020.</p> <p>[2] M. Ryan and B. C. Stahl, “Artificial intelligence ethics guidelines for developers and users: clarifying their content and normative implications,” Journal of Information, Communication and Ethics in Society,2020.</p> <p>[3] J. A. Siqueira De Cerqueira, L. Dos Santos Althoff, P. Santos De Almeida, and E. Dias Canedo, “Ethical perspectives in ai: A two-folded exploratory study from literature and active development projects,” in Proceedings of the 54th Hawaii International Conference on System Sciences, p. 5240, 2021.</p>
Artificial Intelligence and COVID-19 using chest CT scan and chest X-ray images: Machine Learning and Deep Learning Approaches for Diagnosis and Treatment
<p>We uploaded the Table of included articles in the systematic review "Artificial Intelligence and COVID-19 using chest CT scan and chest X-ray images: Machine Learning and Deep Learning Approaches for Diagnosis and Treatment"</p>
Designing Data Governance Policies for the Inclusive use of Artificial Intelligence in Africa
<p>Discussion: Designing Data Governance Policies for the Inclusive use of Artificial Intelligence in Africa.<br> Panel: Towards an Inclusive Data Governance Policy for the Use of AI in Africa<br> <br> </p>
UTC STRIDE Project G2: Quantitatively Evaluate Work Zone Driver Behavior Using 2D Imaging, 3D LiDAR, and Artificial Intelligence in Support of Congestion Mitigation Model Calibration and Validation
<p>This repository contains the extracted traffic data used in the case study for UTC STRIDE project G2 "Quantitatively Evaluate Work Zone Driver Behavior Using 2D Imaging, 3D LiDAR, and Artificial Intelligence in Support of Congestion Mitigation Model Calibration and Validation". The traffic data was extracted using manual or AI-based methods (presented in the project final report) from two 30-minute videos with high and low traffic density. </p> <p>Below are the description of each data file:</p> <ul> <li><strong>high_density_both_lane_raw_AI_speed.csv</strong> <ul> <li><strong>Description: </strong>extracted individual vehicle speed data of the high traffic density video using the AI-based method.</li> <li><strong>Data Fields:</strong> <ul> <li>object_id: unique id for each detected and tracked vehicle</li> <li>frame_index: frame index of the video when other tracked vehicle exit the virtual speed loop.</li> <li>lane_id: lane id (1=outer lane, 2=inner lane)</li> <li>speed: average speed traveling through the virtual speed loop (kph)</li> </ul> </li> </ul> </li> <li><strong>high_density_inner_lane_traffic_AI_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>AI extracted count, and speed data on the inner lane of the high traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>time (in min): i th minute in the 30-minute video.</li> <li>Count: number of vehicles counted in that minute of video.</li> <li>Speed (KPH): average vehicle speed in that minute of video.</li> </ul> </li> </ul> </li> <li><strong>high_density_inner_lane_traffic_manual_count.csv</strong> <ul> <li><strong>Description: </strong>manually extracted individual vehicle speed, and headway data on the inner lane of the high traffic density data </li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li><strong>high_density_inner_lane_traffic_manual_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>manually extracted count, and speed data on the inner lane of the high traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> <li><strong>high_density_outer_lane_traffic_AI_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>AI extracted count, and speed data on the outer lane of the high traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>time (in min): i th minute in the 30-minute video.</li> <li>Count: number of vehicles counted in that minute of video.</li> <li>Speed (KPH): average vehicle speed in that minute of video.</li> </ul> </li> </ul> </li> <li><strong>high_density_outer_lane_traffic_manual_count.csv</strong> <ul> <li><strong>Description: </strong>manually extracted individual vehicle speed, and headway data on the outer lane of the high traffic density data </li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li><strong>high_density_outer_lane_traffic_manual_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>manually extracted count, and speed data on the outer lane of the high traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> <li><strong>low_density_inner_lane_traffic_manual_count.csv</strong> <ul> <li><strong>Description: </strong>manually extracted individual vehicle speed, and headway data on the inner lane of the low traffic density data </li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li><strong>low_density_inner_lane_traffic_manual_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>manually extracted count, and speed data on the inner lane of the low traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> <li><strong>low_density_outer_lane_traffic_manual_count.csv</strong> <ul> <li><strong>Description: </strong>manually extracted individual vehicle speed, and headway data on the outer lane of the low traffic density data </li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li><strong>low_density_outer_lane_traffic_manual_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>manually extracted count, and speed data on the outer lane of the low traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> </ul>
Interpretable Geotechnical Artificial Intelligence (XGeoT-AI) Application to Demystify Image Recognition of Soil Cracks [Datasets]
<p>Here is the test data for the paper "Interpretable Geoscience Artificial Intelligence (XGeoS-AI): Application to Demystify Image Recognition".</p>
Analysis and Prediction of Athlete's Anxiety State based on Artificial Intelligence
<p>This is the data set used for the implementation of Project Analysis and Prediction of Athlete's Anxiety State based on Artificial Intelligence</p>
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.