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7,195 results for “SET”
The predicted values of HiSIF-DTA on the Davis, KIBA, and Human test sets
<p>We propose a novel DTA prediction method (<strong>HiSIF-DTA</strong>) that enables hierarchical fusion of protein semantic information to enrich protein representations and enhance DTA prediction performance. Simultaneously, we have designed two different backbones for semantic information fusion: <strong><em>Top-Down</em></strong> (TDNet) and <strong><em>Bottom-Up</em></strong> (BUNet). To validate the superiority of the model, we have trained and tested it on DTA datasets (davis and kiba) as well as the CPI dataset (Human). These files contain the predicted values of all models used in HiSIF-DTA on the test sets of these three datasets.</p> <p><strong>**Please note that these predicted values are specific to the models in HiSIF-DTA and the respective test datasets mentioned. **</strong></p>
Small educational data sets
<p>Small open data sets, with cut-down size and simplified labels, for educational purposes</p>
DISPLACE Corpus 2023: Dev Set
<p>The DISPLACE challenge entailed a first of kind task to perform speaker and language diarization on the same data, as the data contains multi-speaker social conversations in multi-lingual code-mixed speech. In multi-lingual communities, social conversations frequently involve code-mixed and code-switched speech. In such cases, various speech processing systems need to perform the speaker and language segmentation before any downstream task. The current speaker diarization systems are not equipped to handle multi-lingual conversations, while the language recognition systems may not be able to handle the same talker speaking in multiple languages within the same recording. With this motivation, the DISPLACE challenge addressed Speaker Diarization (SD) in multi-lingual settings and Language Diarization (LD) in multi-speaker settings, using the same underlying dataset. For this challenge, a natural multi-lingual, multi-speaker conversational dataset was distributed. </p> <p>More details about the dataset can be found at </p> <p>https://www.isca-speech.org/archive/pdfs/interspeech_2023/baghel23_interspeech.pdf <br> </p>
Data set side effect and satisfaction subject
<p>Data set side effect and satisfaction subject </p>
Data set for Exploring Machine Learning-Based Methods for anomalies detection: Evidence from cryptocurrencies
<p><strong>Exploring Machine Learning-Based Methods for anomalies detection: Evidence from cryptocurrencies</strong></p>
Skootsky et al., 2023 - Final Data Set
Open the record for dataset details and reuse information.
Micro-CT tomographic data set of 38 mummy labels from the BNU in Strasbourg (1/2)
<p><strong>Summary</strong></p> <p>This submission contains a tomographic dataset of 38 mummy labels from the BNU in Strasbourg used to perceive the anatomical identification possibilities of the woods used for mummy labels and to carry out ring width measurements. The data will be made available as part of [Blondel et al., 2024].</p> <p><strong>Apparatus</strong></p> <p>The dataset is acquired using the EasyTom 150/160 X-ray tomograph (RX Solutions). This tomograph is equipped with a sealed X-ray generator with a compact tube and an interchangeable-plane sensor fitted with a CsI scintillator. The CT scanner parameters for the session carried out on the mummy labels were set at 90 Kv with an intensity of 195 mA for an acquisition resolution varying between 11 and 42 µm with 2016 projections (that is about 20 images on average per projection) with a frame rate of 12,5 and a temperature of 28°C. Each image was then reconstructed by filtered retroprojection using the XAct software (RX Solutions).</p> <p><strong>Information on placing mummy labels in the tomograph</strong></p> <p>The installation of the mummy labels was the same for all the different labels, some of which varied in size. They were attached to a plastic clamping vice-type support covered in expanded foam to prevent the labels from being marked during clamping, before being placed on the tomograph's rotating platform.</p> <p><strong>Issues relating to the data collected</strong></p> <p>The data collected for this study were carried out to perceive the possibilities of anatomical identification from tomographic images in the transverse plane. The tangential and radial planes were not of sufficiently high resolution due to the dimensions of the mummy labels, see details in [Blondel et al., 2024]. The other objective was to use tomographic imagery to facilitate the acquisition of ring widths in the transverse plane of mummy labels. The mummy labels were not tomographed in their entirety. Only the central part, a few centimetres high, was tomographed to maximise resolution. The number of projections and the resolution per label are specified in table form in [Blondel et al., 2024], as they vary according to the width and thickness of the mummy labels. All raw tomography image data (i.e. without corrections) are available in .tif format. The post-processing steps are described in the methodology of [Blondel et al., 2024].</p> <p><strong>List of Contents</strong></p> <p>The content of the submission is divided into 38 data sets corresponding to the 38 mummy labels. Each set is labelled with the inventory number of the BNU mummy label and its resolution. Each set contains:<br>- All the images of the transverse plane in .tif format, the number of projections of which varies from one label to another depending on the resolution of the acquisitions, see details in [Blondel et al., 2024].<br>- The .xls file containing a summary of the scanner metadata for each of the mummy labels.<br>- The three images processed in the transverse plane for each label, including those used to measure ring width for the 7 labels for which ring width measurement was possible, as presented in [Blondel et al., 2024].<br>- Colour photographs of the front and back of each tomographed mummy label including those on which ring width measurements were taken on their surface, unless otherwise stated<a title="" href="#_ftn1" name="_ftnref1">[1]</a>. All these photographs are marked: Coll._et_photogr._BNU_Strasbourg_OpenLicence, accompanied by the inventory number.</p> <p><strong>Acknowledgments</strong></p> <p>We would also like to thank engineers Damien Favier and Antoine Egele from the Charles Sadron Institute for their work on the tomographic acquisitions carried out on the 38 mummy labels.</p> <div><br> <div> <p><a title="" href="#_ftnref1" name="_ftn1">[1]</a> The photographs of the front and back of mummy label HO255 are not available, as they are currently being studied.</p> </div> </div>
Detecting Chirality-Induced Spin Selectivity in Randomly Oriented Radical Pairs Photogenerated by Hole Transfer. Open data set
<p>Data supporting the original figures 2 through 7 of the related publication.</p>
Extension of Vervoort et al. 2019 simulation set for Trudgill et al. 2024
<p>cGENIE simulations that expand the Vervoort et al. 2019 simulation set (additional CIE duration and selected simulations re-run with neritic rather than pelagic carbonate production)</p>
Study of Cabozantinib as Monotherapy or in Combination With Nivolumab in Patients With Advanced or Metastatic Renal Cell Carcinoma Under Real-life Clinical Setting in 1st Line Treatment.
ClinicalTrials.gov study NCT03647878. IPD Sharing: YES. Countries: 2. Publications: 0.
Endoscopic Unroofing for Small Gastric SETs
ClinicalTrials.gov study NCT02587923. IPD Sharing: Not stated. Countries: 0. Publications: 7.
Evaluating Whether Treating Elevated Blood Pressure in the Inpatient Setting Impacts Patient Outcomes
ClinicalTrials.gov study NCT07208669. IPD Sharing: YES. Countries: 1. Publications: 0.
Investigator Initiated Trial Using Dry Eye Surveys to Evaluate and Better Understand FDA Approved Acoltremon Treatment for Dry Eye Disease in Real World Treatment Settings (DESERT)
ClinicalTrials.gov study NCT07281950. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Analgesics in the Pre-hospital Setting: Implications on Hemorrhage Tolerance - Morphine
ClinicalTrials.gov study NCT04138615. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Functionality and Accuracy of the smART System in Real-Life ICU Settings
ClinicalTrials.gov study NCT03198988. IPD Sharing: Not stated. Countries: 1. Publications: 0.
SET-to-Meet; Pilot Testing of a Nurse-led Intervention
ClinicalTrials.gov study NCT03904524. IPD Sharing: YES. Countries: 1. Publications: 0.
Comparison of the Functional Walking Outcomes of Two Settings of a Commercially Available AFO in Adult Stroke Patients
ClinicalTrials.gov study NCT02122783. IPD Sharing: NO. Countries: 1. Publications: 0.
Optimization of Glycemic Control in the Hospital and Critical Care Setting
ClinicalTrials.gov study NCT04015388. IPD Sharing: NO. Countries: 0. Publications: 10.
Brief Suicide Intervention for Youth in Juvenile Detention Settings
ClinicalTrials.gov study NCT05225103. IPD Sharing: NO. Countries: 1. Publications: 0.
Delivery of Pharmacogenetic Testing in a Community Pharmacy Setting
ClinicalTrials.gov study NCT02937545. IPD Sharing: NO. 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.