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1,855 results for “Framework”
Deep Gradient Reinforcement learning for Music Improvisation in cloud computing framework
<p><span>The improvised music is further rendered in the MIDI format. The Bach Chorales dataset with six different attributes relevant to musical compositions is employed in implementing the present research. The model was set up in a containerised cloud environment and controlled for smooth load distribution. Five different parameters, such as pitch frequency (PF), standard pitch delay (SPD), average distance between peaks (ADP), note duration gradient (NDG) and pitch class gradient (PCG) are leveraged to assess the quality of the improvised music.</span></p>
Realtime Data Collection and Analysis Framework for Collaboration and Co-presence in a Virtual Reality Environment
<p>Title: Realtime Data Collection and Analysis Framework for Collaboration and Co-presence in a Virtual Reality Environment</p> <p>Abstract:</p> <p>As VR technologies continue to evolve and gain popularity, one of their most notable features is connecting with the virtual presence of a person who is not physically present. Understanding the dynamics of user interaction within these environments is crucial, as they can be utilized in various ways, including collaboration, communication, social interactions, or games and entertainment. This paper presents a method for measuring collaboration and co-presence factors of users by developing a real-time data collection and analysis framework. The designed framework focuses on different collaboration and co-presence scenarios and evaluates a comprehensive system for monitoring and analyzing user interactions in VR, employing both physiological sensors and subjective feedback to assess the sense of presence, co-presence, and collaboration quality. Through an extensive literature review, the paper studies how various factors, including avatar realism and communication modalities, influence user engagement and interaction efficacy. The experiment framework’s capability to integrate qualitative and quantitative data provides a deeper understanding of the immersive experience and its impact on collaborative tasks. The results highlight the importance of design choices in VR environments and their implications for human-computer interaction, user performance, and satisfaction. The findings offer practical guidance for developing more effective VR systems for collaborative work and social interaction.</p> <p>Data Description:</p> <p>1. User Interaction Logs:</p> <p><span> </span>- Data Type: Quantitative</p> <p><span> </span>- Description: Timestamped logs of user actions and interactions within the VR environment, including movement data, interaction with objects, and communication instances.</p> <p><span> </span>- Format: CSV</p> <p><span> </span>- Variables: User ID, Timestamp, Action Type, Object Interacted, Coordinates, Duration</p> <p>2. Physiological Sensor Data:</p> <p><span> </span>- Data Type: Quantitative</p> <p><span> </span>- Description: Real-time physiological data collected from users during VR sessions, including heart rate, skin conductance, and EEG data.</p> <p><span> </span>- Format: CSV,</p> <p><span> </span>- Variables: User ID, Timestamp, Heart Rate, Skin Conductance, EEG Channels</p> <p>3. Avatar Realism and Communication Modalities Data:</p> <p><span> </span>- Data Type: Quantitative</p> <p><span> </span>- Description: Data evaluating the impact of avatar realism and communication methods (e.g., voice chat, text chat) on user engagement and interaction efficacy.</p> <p><span> </span>- Format: CSV, Text</p> <p><span> </span>- Variables: User ID, Avatar Type, Communication Modality, Engagement Score, Interaction Quality Feedback</p> <p>4. Collaboration and Co-presence Metrics:</p> <p><span> </span>- Data Type: Quantitative</p> <p><span> </span>- Description: Calculated metrics for collaboration efficiency and co-presence, derived from interaction logs and physiological data.</p> <p><span> </span>- Format: CSV</p> <p><span> </span>- Variables: User ID, Collaboration Efficiency Score, Co-presence Score, Task Performance</p> <p> </p> <p> </p>
dataset of prompts for development of the optimization guiding framework if-act
Open the record for dataset details and reuse information.
Experimental volumetric hydrogen uptake determination at 77 K of commercially available metal-organic framework materials
<p>File: carbon-08-00005-with-cover.pdf</p> <p>This file contains the published paper (PDF) with a cover page.</p> <p> </p> <p>File: carbon-1535765-supplementary.pdf</p> <p>This file (PDF) contains supporting data on the characterization of MOFs and hydrogen adsorption measurements.</p>
Raw data files for the "The Mechanism of Rapid and Green Metal–Organic Framework Synthesis by In Situ Spectroscopy and Diffraction" manuscript
<p>All files are organized by figures in the main text of the original publication: https://doi.org/10.1021/acs.chemmater.4c00879, in the form of .csv files.</p>
PlasEval: a framework for comparing and evaluating plasmid binning tools
<p>PlasEval is a tool aimed at evaluating the accuracy and at comparing methods for the problem of <strong>plasmid binning</strong>. It has two modes: <strong>evaluate</strong> and <strong>compare</strong>.</p> <p>In the <strong>evaluation</strong> mode of PlasEval, a given set of <em>predicted plasmid bins</em> resulting from a plasmid binning method is evaluated against a <em>ground truth</em> set of plasmid bins, yielding three statistics, the <em>precision</em>, the <em>recall</em> and the <em>F1-score</em>.</p> <p>In the <strong>comparison</strong> mode of PlasEval, given two sets of <em>predicted plasmid bins</em> (either resulting from two plasmid binning tools or from a plasmid binning tool and a ground truth), PlasEval computes a <em>dissimilarity measure</em> that indicates how much both sets of predicted plasmid bins are in agreement.</p> <p>The data shared consists of output files of PlasBin-flow as well as those of 3 other plasmid binning methods, namely, HyAsP, MOB-recon and gplas for 53 test samples. Details about each sample have been provided in the file <strong>Ecoli_samples.csv</strong>. It also includes the short read assembly files (fasta as well as gfa) and the hybrid assembly files. The mappings of short-read contigs to hybrid contigs for all 53 samples have been provided in the <strong>BLAST_output </strong>folder. The plasmid bins, including those representing the ground truth, used as input for PlasEval have been provided in the <strong>PlasEval_input</strong> folder.</p> <p> </p>
A machine learning framework for extracting information from biological pathway images in the literature
<p><strong>466 target chemicals_selected chemicals:</strong></p> <p>Target chemicals satisfying the criteria for biochemical reactions not covered by MetaNetX and KEGG.</p> <p> </p> <p><strong>466 target chemicals_statistics:</strong><br>Numbers of MetaCyc reactions, papers, and pathway images collected for 466 target chemicals from the bio-based chemicals map (Jang et al., Trends in Biotechnology, 2023).</p> <p> </p> <p><strong>arrow detection_bounding box labels:</strong><br>Bounding box labels for 6,471 images in the training and validation datasets and 100 images in the test dataset. The corresponding images are available in "arrow detection_training and validation datasets.zip".</p> <p> </p> <p><strong>arrow detection_test dataset:</strong></p> <p>Test dataset for arrow detection using Faster R-CNN model. A total of 100 images have been prepared from 89 papers searched through PubMed Central (PMC).</p> <p> </p> <p><strong>arrow detection_training and validation datasets:</strong></p> <p>Training and validation datasets for arrow detection using Faster R-CNN model. A total of 6,471 images have been prepared, including 2,332 images from five different sources and 4,139 augmented images.</p> <p> </p> <p><strong>EBPI outputs:</strong></p> <p>Reaction information extracted using EBPI from 49,846 biological pathway images across 466 target chemicals.</p> <p> </p> <p><strong>text classification_training, validation and test datasets:</strong></p> <p>Dataset for text classification using BioBERT. A total of 59,370 terms have been prepared, including 15,101 “gene” terms, 21,417 “protein” terms, and 22,852 “others” terms by combining the data from MetaCyc and the PaddleOCR results from the papers.</p>
Input event files for the 2014 Pileup Workshop framework
<p>This dataset contains event files for the 2014 workshop on <a href="https://indico.cern.ch/event/306155/">Mitigation of Pileup Effects at the LHC</a>. Code for using the event files is hosted on <a href="https://github.com/PileupWorkshop/2014PileupWorkshop">github</a>.</p>
A Power-Aware, Self-Adaptive Macro Data Flow Framework
<p><em><strong>Abstract: </strong>The dataflow programming model has been extensively used as an effective solution to implement efficient parallel programming frameworks. However, the amount of resources allocated to the runtime support is usually fixed once by the programmer or the runtime, and kept static during the entire execution. While there are cases where such a static choice may be appropriate, other scenarios may require to dynamically change the parallelism degree during the application execution. In this paper we propose an algorithm for multicore shared memory platforms, that dynamically selects the optimal number of cores to be used as well as their clock frequency according to either the workload pressure or to explicit user requirements. We implement the algorithm for both structured and unstructured parallel applications and we validate our proposal over three real applications, showing that it is able to save a significant amount of power, while not impairing the performance and not requiring additional effort from the application programmer.</em></p> <p>This dataset contains the raw data of the experiments and the scripts used to plot them.</p> <p> </p>
Nornir: A Customizable Framework for Autonomic and Power-Aware Applications
<p><strong><em>Abstract: </em></strong><em>A desirable characteristic of modern parallel applications is the ability to dynamically select the amount of resources to be used to meet requirements on performance or power consumption. In many cases, providing explicit guarantees on performance is of paramount importance. In streaming applications, this is related with the concept of elasticity, i.e. being able to allocate the proper amount of resources to match the current demand as closely as possible. Similarly, in other scenarios, it may be useful to limit the maximum power consumption of an application to do not exceed the power budget. In this paper we propose Nornir, a customizable C++ framework for autonomic and power-aware parallel applications on shared memory multicore machines. Nornir can be used by autonomic strategy designers to implement new algorithms and by application users to enforce requirements on applications.</em></p> <p>This dataset contains the raw data of the experiments and the scripts used to plot them.</p>
Dataset to reproduce the paper "A new framework to evaluate urban design using urban microclimatic modelling in future climatic conditions"
<p>This dataset has been generated with the paper " A new framework to evaluate urban design using urban microclimatic<br> modelling in future climatic conditions" (https://doi.org/10.3390/su10041134). A Python notebook is also included to conduct the analysis.</p> <ol> <li>Data analysis - Sustainability paper.ipynb : Python notebook</li> <li>Geneva_Eur11_TDY_2010_2039 : Climate file for the year 2039 obtained from RCA4</li> <li>Geneva_Eur11_TDY_2010_2039_cim : Climate file for the year 2039 obtained from RCA4-CIM</li> <li>Geneva_Eur11_TDY_2010_2039 : Climate file for the year 2069 obtained from RCA4</li> <li>Geneva_Eur11_TDY_2010_2069_cim : Climate file for the year 2069 obtained from RCA4-CIM</li> <li>Geneva_Eur11_TDY_2010_2099 : Climate file for the year 2099 obtained from RCA4</li> <li>Geneva_Eur11_TDY_2010_2099_cim : Climate file for the year 2099 obtained from RCA4-CIM</li> <li>heating_2039 : Heating demand from CitySim for the year 2039</li> <li>heating_2039_cim : Heating demand from CitySim-CIM for the year 2039</li> <li>heating_2069 : Heating demand from CitySim for the year 2069</li> <li>heating_2069_cim : Heating demand from CitySim-CIM for the year 2069</li> <li>heating_2099 : Heating demand from CitySim for the year 2099</li> <li>heating_2099_cim : Heating demand from CitySim-CIM for the year 2099</li> <li>heating_2099_minP : Heating demand from CitySim for the year 2099 with Minergie-P scenario</li> <li>heating_2099__minP_cim : Heating demand from CitySim-CIM for the year 2099 with Minergie-P scenario</li> <li>cooling_2039 : Cooling demand from CitySim for the year 2039</li> <li>cooling_2039_cim : Cooling demand from CitySim-CIM for the year 2039</li> <li>cooling_2069 : Cooling demand from CitySim for the year 2069</li> <li>cooling_2069_cim : Cooling demand from CitySim-CIM for the year 2069</li> <li>cooling_2099 : Cooling demand from CitySim for the year 2099</li> <li>cooling_2099_cim : Cooling demand from CitySim-CIM for the year 2099</li> <li>cooling_2099_minP : Cooling demand from CitySim for the year 2099 with Minergie-P scenario</li> <li>cooling_2099__minP_cim : Cooling demand from CitySim-CIM for the year 2099 with Minergie-P scenario</li> <li>temp_cim : simulated temperature from CIM using Meteonorm</li> <li>temp_meteonorm : temperature from Meteonorm</li> <li>u_cim : simulated wind speedfrom CIM using Meteonorm</li> <li>u_meteonorm : wind speed from Meteonorm</li> </ol> <p> </p>
Results from network experiments conducted on ULiège testbed with the Network Performance Framework (NPF) tool
<p>This dataset results from network experiments on a router configuration with replayed traffic. The network trace comes from a border router of our campus network. The router is used in both directions, i.e. both for reception and transmission of packets (to and from our campus network). Each experiment was done 3 times (for a total of 11466 experiments).</p> <p> </p>
Example Data for the SSJ Framework
<p>Example data supplementing the paper <strong>The SSJ Framework: Augmenting Social Interactions using Mobile Signal Processing and Live Feedback</strong>.</p>
A general framework for optimizing arterial spin labeling MRI experiments
<p>This repository contains the simulation data, preprocessed in vivo data, and analysis code used in the Magnetic Resonance in Medicine article titled "A general framework for optimizing arterial spin labeling MRI experiments" (<a href="https://doi.org/10.1002/mrm.27580">https://doi.org/10.1002/mrm.27580</a>). Please cite this article if you use this code or data in your work.</p>
An unsupervised deep learning framework with variational autoencoders for genome-wide DNA methylation analysis and biologic feature extraction applied to breast cancer
<p>Supplemental data for the paper titled "An unsupervised deep learning framework with variational autoencoders for genome-wide DNA methylation analysis and biologic feature extraction applied to breast cancer"</p>
Data and Source Codes used in "Development of a Global Quasi-3-D Multiscale Modeling Framework: I. Vector Vorticity Model on Cubed Sphere as Cloud-Resolving Component"
<p>Data and Source Codes used in the paper "Development of a Global Quasi-3-D Multiscale Modeling Framework: I. Vector Vorticity Model on Cubed Sphere as Cloud-Resolving Component"</p> <p>Advection Test (ADV): East-West A_TST (100km, Cube), C_TST (25km, Cube), E_TST (5km, Cube),</p> <p> North-South K_TST (100km, Cube), M_TST (25km, Cube), O_TST (5km, Cube) </p> <p>Barotropic Test (BAR): A_TST5 (100km, Cube), Y_TST4 (100km, RLL), C_TST3 (5km, Cube), C_TST1 (5km, RLL)</p> <p>Baroclinic Test (BCL): J_TST30 (100km, Cube), J_TST20 (100km, RLL)</p>
Data set associated with the paper "Implementation of the Vector Vorticity Dynamical Core on Cubed Sphere for Use in the Quasi-3-D Multiscale Modeling Framework"
<p>New data set associated with the revision of the paper "Development of a Global Quasi-3-D Multiscale Modeling Framework: <br> I. Vector Vorticity Model on Cubed Sphere as Cloud-Resolving Component"</p> <p>The title of the paper has been changed to "Implementation of the Vector Vorticity Dynamical Core on Cubed Sphere for Use in the Quasi-3-D Multiscale Modeling Framework"</p> <p>New simulated data set of the advection test is in the folder ADVEC_NEW; New simulated data set of the barotropic instability test is in the folder BARO_NEW; New simulated data set of the baroclinic instability test is in the folder BCL_NEW</p>
Dataset for the article "MiMiC: A Novel Framework for Multiscale Modeling in Computational Chemistry"
<p>This dataset contains additional material related to the article: "MiMiC: A Novel Framework for Multiscale Modeling in Computational Chemistry". The preprint is available at <a href="https://doi.org/10.26434/chemrxiv.7635986">https://doi.org/10.26434/chemrxiv.7635986</a>. Final article is available at <a href="https://doi.org/10.1021/acs.jctc.9b00093">https://doi.org/10.1021/acs.jctc.9b00093</a>.</p>
Supplementary Data for "Structure−Activity Relationships that Identify Metal−Organic Framework Catalysts for Methane Activation"
<p>DFT-optimized structures, energies, and computed physicochemical properties of metal-organic frameworks that correspond with work in "Structure−Activity Relationships that Identify Metal−Organic Framework Catalysts for Methane Activation" (DOI: <a href="https://pubs.acs.org/doi/10.1021/acscatal.8b05178">10.1021/acscatal.8b05178</a>).</p>
A narrative synthesis analysis of lifecourse domains within health service utilisation frameworks
<p>This paper is a scoping review which identifies existing frameworks, other than Anderson's Behavioural model, that look at Health Service Utilisation (HSU). In addition it investigates if these frameworks incorporate the use of a life course approach to understand HSU.</p> <p>The data included in this repository is:</p> <p>1. PRISMA flowchart</p> <p>2. PRISMA-ScR scoping review checklist</p> <p>3. Spreadsheet which contains all 70 frameworks (Table 6)</p> <p>4. References for the 70 Frameworks in Table6.</p> <p> </p> <p> </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.