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4,182 results for “universities”
Hacettepe University Event (HUE) Dataset - Indoor - Part 1
<p>Low-light environments pose significant challenges for image enhancement methods. To address these challenges, in this work, we introduce the HUE dataset, a comprehensive collection of high-resolution event and frame sequences captured in diverse and challenging low-light conditions. Our dataset includes 106 sequences, encompassing indoor, cityscape, twilight, night, driving, and controlled scenarios, each carefully recorded to address various illumination levels and dynamic ranges. Utilizing a hybrid RGB and event camera setup. we collect a dataset that combines high-resolution event data with complementary frame data. We employ both qualitative and quantitative evaluations using no-reference metrics to assess state-of-the-art low-light enhancement and event-based image reconstruction methods. Additionally, we evaluate these methods on a downstream object detection task. Our findings reveal that while event-based methods perform well in specific metrics, they may produce false positives in practical applications. This dataset and our comprehensive analysis provide valuable insights for future research in low-light vision and hybrid camera systems. </p>
Hacettepe University Event (HUE) Dataset - City - Part 2
<p>Low-light environments pose significant challenges for image enhancement methods. To address these challenges, in this work, we introduce the HUE dataset, a comprehensive collection of high-resolution event and frame sequences captured in diverse and challenging low-light conditions. Our dataset includes 106 sequences, encompassing indoor, cityscape, twilight, night, driving, and controlled scenarios, each carefully recorded to address various illumination levels and dynamic ranges. Utilizing a hybrid RGB and event camera setup. we collect a dataset that combines high-resolution event data with complementary frame data. We employ both qualitative and quantitative evaluations using no-reference metrics to assess state-of-the-art low-light enhancement and event-based image reconstruction methods. Additionally, we evaluate these methods on a downstream object detection task. Our findings reveal that while event-based methods perform well in specific metrics, they may produce false positives in practical applications. This dataset and our comprehensive analysis provide valuable insights for future research in low-light vision and hybrid camera systems. </p>
Hacettepe University Event (HUE) Dataset - Twilight - Part 1
<p>Low-light environments pose significant challenges for image enhancement methods. To address these challenges, in this work, we introduce the HUE dataset, a comprehensive collection of high-resolution event and frame sequences captured in diverse and challenging low-light conditions. Our dataset includes 106 sequences, encompassing indoor, cityscape, twilight, night, driving, and controlled scenarios, each carefully recorded to address various illumination levels and dynamic ranges. Utilizing a hybrid RGB and event camera setup. we collect a dataset that combines high-resolution event data with complementary frame data. We employ both qualitative and quantitative evaluations using no-reference metrics to assess state-of-the-art low-light enhancement and event-based image reconstruction methods. Additionally, we evaluate these methods on a downstream object detection task. Our findings reveal that while event-based methods perform well in specific metrics, they may produce false positives in practical applications. This dataset and our comprehensive analysis provide valuable insights for future research in low-light vision and hybrid camera systems. </p>
Hacettepe University Event (HUE) Dataset - Driving - Part 2
<p>Low-light environments pose significant challenges for image enhancement methods. To address these challenges, in this work, we introduce the HUE dataset, a comprehensive collection of high-resolution event and frame sequences captured in diverse and challenging low-light conditions. Our dataset includes 106 sequences, encompassing indoor, cityscape, twilight, night, driving, and controlled scenarios, each carefully recorded to address various illumination levels and dynamic ranges. Utilizing a hybrid RGB and event camera setup. we collect a dataset that combines high-resolution event data with complementary frame data. We employ both qualitative and quantitative evaluations using no-reference metrics to assess state-of-the-art low-light enhancement and event-based image reconstruction methods. Additionally, we evaluate these methods ona a downstream object detection task. Our findings reveal that while event-based methods perform well in specific metrics, they may produce false positives in practical applications. This dataset and our comprehensive analysis provide valuable insights for future research in low-light vision and hybrid camera systems. </p>
Hacettepe University Event (HUE) Dataset - Night
<p>Low-light environments pose significant challenges for image enhancement methods. To address these challenges, in this work, we introduce the HUE dataset, a comprehensive collection of high-resolution event and frame sequences captured in diverse and challenging low-light conditions. Our dataset includes 106 sequences, encompassing indoor, cityscape, twilight, night, driving, and controlled scenarios, each carefully recorded to address various illumination levels and dynamic ranges. Utilizing a hybrid RGB and event camera setup. we collect a dataset that combines high-resolution event data with complementary frame data. We employ both qualitative and quantitative evaluations using no-reference metrics to assess state-of-the-art low-light enhancement and event-based image reconstruction methods. Additionally, we evaluate these methods on a downstream object detection task. Our findings reveal that while event-based methods perform well in specific metrics, they may produce false positives in practical applications. This dataset and our comprehensive analysis provide valuable insights for future research in low-light vision and hybrid camera systems. </p>
Hacettepe University Event (HUE) Dataset - Twilight - Part 4
<p>Low-light environments pose significant challenges for image enhancement methods. To address these challenges, in this work, we introduce the HUE dataset, a comprehensive collection of high-resolution event and frame sequences captured in diverse and challenging low-light conditions. Our dataset includes 106 sequences, encompassing indoor, cityscape, twilight, night, driving, and controlled scenarios, each carefully recorded to address various illumination levels and dynamic ranges. Utilizing a hybrid RGB and event camera setup. we collect a dataset that combines high-resolution event data with complementary frame data. We employ both qualitative and quantitative evaluations using no-reference metrics to assess state-of-the-art low-light enhancement and event-based image reconstruction methods. Additionally, we evaluate these methods on a downstream object detection task. Our findings reveal that while event-based methods perform well in specific metrics, they may produce false positives in practical applications. This dataset and our comprehensive analysis provide valuable insights for future research in low-light vision and hybrid camera systems. </p>
Hacettepe University Event (HUE) Dataset - Twilight - Part 3
<p>Low-light environments pose significant challenges for image enhancement methods. To address these challenges, in this work, we introduce the HUE dataset, a comprehensive collection of high-resolution event and frame sequences captured in diverse and challenging low-light conditions. Our dataset includes 106 sequences, encompassing indoor, cityscape, twilight, night, driving, and controlled scenarios, each carefully recorded to address various illumination levels and dynamic ranges. Utilizing a hybrid RGB and event camera setup. we collect a dataset that combines high-resolution event data with complementary frame data. We employ both qualitative and quantitative evaluations using no-reference metrics to assess state-of-the-art low-light enhancement and event-based image reconstruction methods. Additionally, we evaluate these methods on a downstream object detection task. Our findings reveal that while event-based methods perform well in specific metrics, they may produce false positives in practical applications. This dataset and our comprehensive analysis provide valuable insights for future research in low-light vision and hybrid camera systems. </p>
Hacettepe University Event (HUE) Dataset - Indoor - Part 3
<p>Low-light environments pose significant challenges for image enhancement methods. To address these challenges, in this work, we introduce the HUE dataset, a comprehensive collection of high-resolution event and frame sequences captured in diverse and challenging low-light conditions. Our dataset includes 106 sequences, encompassing indoor, cityscape, twilight, night, driving, and controlled scenarios, each carefully recorded to address various illumination levels and dynamic ranges. Utilizing a hybrid RGB and event camera setup. we collect a dataset that combines high-resolution event data with complementary frame data. We employ both qualitative and quantitative evaluations using no-reference metrics to assess state-of-the-art low-light enhancement and event-based image reconstruction methods. Additionally, we evaluate these methods on a downstream object detection task. Our findings reveal that while event-based methods perform well in specific metrics, they may produce false positives in practical applications. This dataset and our comprehensive analysis provide valuable insights for future research in low-light vision and hybrid camera systems. </p>
Advancing Large Language Models through Story Energy, Universal Harmony Energy, and SA-UUH-UPP
<p><span>In this groundbreaking exploration of AI, we unveil how Story Energy, Universal Harmony Energy, and the SA-UUH-UPP framework could revolutionize large language models (LLMs). Discover how these advanced concepts push AI beyond current boundaries, enabling deeper contextual understanding, energy-efficient models, and steps toward self-awareness. Whether you’re an AI researcher, developer, or enthusiast, this video provides insights that could redefine the future of AI. Watch now to dive into the next frontier of artificial intelligence!</span></p>
BMBF KüNO BluEs project: Work package 4.3 by University of Rostock
<p>Research data from the BMBF KüNO BluEs project: Work package 4.3 by the University of Rostock, grant number 03F0864B</p>
Acid blood gases including fetal hemoglobin from newborns, 2019-2023, General University Hospital in Prague
<p>This anonymized data is from the e-health database of the General University Hospital. It has blood gases that include the parameter of fetal hemoglobin percentage.</p> <p>The variables are as follows</p> <p>birthweight - birthweight</p> <p>EGA - gestational age in decimals</p> <p>day_of_life - postnatal day of life when the sample was taken</p> <p>blood_type - source of blood sample (a - arterial, c - capillary, v - venous)</p> <p>nr_of_preceding_trf - the amount of transfusions the patient has received preceding this blood sample</p> <p>hb - hemoglobin (g/l)</p> <p>F-hbf - fetal hemoglobin percentage</p> <p>pco2 - partial pressure of carbon dioxide (kPa)</p> <p>po2 - partial pressure of oxygen (kPa)</p> <p>so2 - oxygen saturation (ratio of oxyhemoglobin to total)</p> <p>pH - pH</p> <p>PO2 - partial pressure of oxygen (mmHg)</p> <p>estPaO2 - estimated partial pressure of oxygen at the given oxygen saturation according to the Severinghaus equation (mmHg)</p> <p>PaO2-extPO2 - difference between actual measured and estimated partial pressure of oxygen at the given oxygen saturation</p> <p>PCO2 - partial pressure of carbon dioxide (mmHg)</p>
University of Manitoba Breast Microwave Imaging Dataset (UM-BMID)
<p><strong>ABSTRACT </strong></p> <p>Microwave-based breast cancer detection is a growing field that has been investigated as a potential novel method for breast cancer detection. Breast microwave sensing (BMS) systems use low-powered, non-ionizing microwave signals to interrogate the breast tissues. While some BMS systems have been evaluated in clinical trials, many challenges remain before these systems can be used as a viable clinical option, and breast phantoms (breast models) allow for rigorous and controlled experimental investigations. This dataset, the University of Manitoba Breast Microwave Imaging Dataset (UM-BMID), contains S-parameter measurements from experimental scans of MRI-derived breast phantoms, obtained with a pre-clinical breast microwave sensing system operating over 1-8 GHz. The dataset consists of measurements from over 1250 scans of a diverse array of phantoms. The phantom array consists of phantoms of various sizes and breast densities. The .stl files used to produce the 3D-printed phantoms are also included in the dataset. We hope that this dataset can serve as a resource for researchers in breast microwave sensing to evaluate signal processing, image reconstruction, and tumour detection methods.</p> <p><strong>Inspiration:</strong></p> <p>This dataset uploaded to U-BRITE for "AI against CANCER DATA SCIENCE HACKATHON"</p> <p>https://cancer.ubrite.org/hackathon-2021/</p> <p><strong>Acknowledgements</strong></p> <p>Tyson Reimer, Jordan Krenkevich, Stephen Pistorius, June 16, 2021, "University of Manitoba Breast Microwave Imaging Dataset (UM-BMID)", IEEE Dataport, doi: https://dx.doi.org/10.21227/1y0z-8t98.</p> <p>https://ieee-dataport.org/open-access/university-manitoba-breast-microwave-imaging-dataset-um-bmid</p> <p><strong>U-BRITE last update date:</strong> 07/21/2021</p>
Physical, psychological, academic, and sexual abuse of university students in five Latin American countries: characteristics and association with student satisfaction
<p><strong>Background:</strong> A high rate of student abuse has been reported in various studies. This study aimed to characterize the abuse of university students in five Latin American countries and its association with student satisfaction.</p> <p><strong>Methods:</strong> An analytical transversal study was conducted, in which 2,141 undergraduate students from Paraguay, Ecuador, Colombia, Venezuela, and Panama were surveyed. A validated questionnaire was applied to evaluate four types of abuse (Cronbach's Alpha: 0.90): psychological, physical, academic, and sexual. In addition, students’ perception of satisfaction of four items in the university environment (Cronbach's Alpha: 0.85) was also analyzed.</p> <p><strong>Results:</strong> It was found that the older students were, the more dissatisfied they were with “logistics” (aPR: 1.02; 95% CI: 1.00-1.04; p-value; 0.014) and the top-third segment of those who perceived “sexual abuse” were more dissatisfied with the teaching provided by their universities (aPR: 1.23; 95% CI: 1.00-1.52; p-value: 0.048). Moreover, those who were most dissatisfied with the agreements were part of the top-third segment of those who perceived sexual abuse (aPR: 1.16; 95% CI: 1.03-1.32; p-value: 0.016) and those in the country of Panama were less dissatisfied with “agreements” (RPa: 0.70; 95% CI: 0.66-0.073; p-value<0.001). The countries of Colombia (aPR: 0.08; IC95%: 0.01-0.59; p-value: 0.014) and Panama (aPR: 0.57; 95% CI: 0.52-0.64; p-value<0.001) were less dissatisfied with investigation programs.</p> <p><strong>Conclusions: </strong>The perception of ‘sexual abuse’ was related with dissatisfaction with teaching and agreements.</p>
WiFi RSS measurements in Tampere University multi-building campus, 2017
<p>This package contains two Wi-Fi RSSI Datasets, namely TIE1 and SAH1.</p> <ul> <li>TIE1 was collected in the Tietotalo building from Tampere University during the period of August-December 2017 and it is composed of four files: <ul> <li>TIE1_training_rss.csv - Radio map or reference dataset as a matrix with 10633 reference samples (rows) and 613 APs (columns) </li> <li>TIE1_training_coordinates.csv - Ground truth location of the 10633 reference samples (rows) as X,Y,Z and floor being the coordinates in a local system</li> <li>TIE1_test_rss.csv - Operational fingerprints as a matrix with 50 evaluation samples (rows) and 613 APs (columns) </li> <li>TIE1_test_coordinates.csv - Ground truth location of the 50 evaluation samples (rows) as X,Y,Z and floor being the coordinates in a local system</li> </ul> </li> <li>SAH1 was collected in the Sahkotalo building from Tampere University during the period of October-December 2017 and it is composed of four files: <ul> <li>SAH1_training_rss.csv - Radio map or reference dataset as a matrix with 9291 reference samples (rows) and 775 APs (columns) </li> <li>SAH1_training_coordinates.csv - Ground truth location of the 9291 reference samples (rows) as X,Y,Z and floor being the coordinates in a local system</li> <li>SAH1_test_rss.csv - Operational fingerprints as a matrix with 156 evaluation samples (rows) and 775 APs (columns) </li> <li>SAH1_test_coordinates.csv - Ground truth location of the 156 evaluation samples (rows) as X,Y,Z and floor being the coordinates in a local system</li> </ul> </li> </ul> <p>Additional considerations:</p> <ul> <li>All the RSSI values are stored are they were registered with a regular smartphone. In order to generate the fingerprint vectors, the non-detected APs where filled with the default bogus value +100dBm. i.e., the value +100dBm in the training and test rss files indicates that the AP was not detected. Generally, this default bogus value has been replaced with a low RSSI value in experiments, such as -100dBm, -110dBm, -150dBm or -200dBm.</li> <li>The coordinates X,Y,Z are expressed in meters in a local coordinate system. Additionally, the sequential floor identifier is also provided.</li> </ul> <p>Please cite the source at Zenodo when using any of these two datasets:</p> <blockquote> <p>Lohan, E.S; Torres-Sospedra, J.; Gonzalez, A.; "WiFi RSS measurements in Tampere University multi-building campus, 2017", Zenodo, 2021.<br> https://zenodo.org/record/5174851<br> https://www.doi.org/10.5281/zenodo.5174851</p> </blockquote>
COVID19 vaccination choice among Iraqi students at Al-Zahraa University for women
<p>198 first-year students in the Department of Anesthesia at University of Al-Zahra participated in this cross-sectional questionnaire-based study. A Google Classroom event was attended by 198 students resulting in a response rate of (100 %). This research study was analytical in nature. The questionnaire was created by the researcher, and a few questions were adapted from other studies and incorporated into the questionnaire. Ethical approval was granted by the independent ethics committee of Al-Zahra University in Karbala-Iraq prior to the research. The period of research and data collection was between Feb. 2021 and August 2021. Questionnaires were distributed among the students using Google Classroom and were returned by the students after completion via the same method. The responses were retrieved as an excel file from the google form a questionnaire and imported into SPSS version.23 to be analyzed and frequencies and percentages determined. The questionnaire is related to COVID19 vaccination preference. The questionnaire is comprised of four tables with eight questions. Statistical tests used frequency and rates.</p>
Supporting data for 'Fast universal quantum gate above the fault-tolerance threshold in silicon'
<p>Data supporting for paper 'Fast universal quantum gate above the fault-tolerance threshold in silicon'.</p> <p>All the data are stored in the HDF5 format that can be conveniently loaded by the xarray Python package.</p>
Catalogues of voids as antihalos in the local Universe
<p>These archives contain the data products described in "Catalogues of voids as antihalos in the local Universe" by H. Desmond, M. Hutt, J. Devriendt and A. Slyz (2021). See the readme for further details.</p>
Pressure signals acquired in a pipe network at the Water Engineering Laboratory of the University of Perugia, Italy
<p>This dataset contains the pressure signals (i.e., the time-history of the pressure head, with t = 0 indicating the maneuver starting time) acquired in an HPDE network (with two 100 m x 100 m square loops) installed at the Water Engineering Laboratory of the University of Perugia, Italy. In this network, a short branch is installed. Tests refer to transients generated by the fast and total closure of a downstream end valve of such a branch. </p> <p> </p>
University students' adherence and vaccination attitude during the COVID-19 pandemic
<p>Data on university students' adherence and vaccination attitudes, accompanying the manuscript a manuscript (in press). </p> <p>Data collection took place between November 23rd and December 21st, 2020.</p> <p>The sample consists of German-speaking university students, recruited at Innsbruck Universities.</p> <p> </p> <p>Scale Sources: <br> Reactance - COSMO COVID-19 Snapshot Monitoring <br> Betsch, C., Korn, L., Felgendreff, L., Eitze, S., Schmid, P., Sprengholz, P., Wieler, L., Schmich, P., Stollorz, V., Ramharter, M., Bosnjak, M., Omer, S., Thaiss, H., Bock, F. de, & Rüden, U. von (2020). <em>Covid-19 Snapshot Monitoring (COSMO Germany) - Welle 27</em>. PsychArchives. https://doi.org/10.23668/PSYCHARCHIVES.4381</p>
Pictures taken during the ISUBII22 Stay at the Ilia State University
<p>Pictures taken during the ISUBII22 stay at the Ilia State University Tbilisi in October 2022.</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.