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1,855 results for “Framework”
Data from: Integrated survey methodologies provide process-driven framework for marine renewable energy environmental impacts
<p>Data from: Integrated survey methodologies provide process-driven framework for marine renewable energy environmental impacts</p>
Auto-scaling dataset based on the gym-hpa framework
<p>The implemented gym-hpa is a custom <a href="https://gym.openai.com/">OpenAi Gym</a> environment for the training of Reinforcement Learning (RL) agents for auto-scaling research in the Kubernetes (K8s) platform.</p> <p>Two environments exist based on the <a href="https://github.com/bitnami/charts/tree/master/bitnami/redis-cluster">Redis Cluster</a> and <a href="https://github.com/GoogleCloudPlatform/microservices-demo">Online Boutique</a> applications.</p> <p>Two collected datasets are shared here. The code has been released here: https://github.com/jpedro1992/gym-hpa</p> <p>Related Publication: Santos, J. et al. "gym-hpa: Efficient auto-scaling via reinforcement learning for complex microservice-based applications in Kubernetes." <em>NOMS2023, the IEEE/IFIP Network Operations and Management Symposium</em>. 2023.</p>
Data from "Benchmark Generation Framework with Customizable Distortions for Image Classifier Robustness"
<p>This repository contains the data from the paper, "Benchmark Generation Framework with Customizable Distortions for Image Classifier Robustness." </p> <p>Relevant URLs:</p> <p>https://hewlettpackard.github.io/trust-ml/</p> <p>https://github.com/HewlettPackard/trust-ml/</p> <p> </p> <p>Abstract:</p> <p>We present a novel framework for generating adversarial benchmarks to evaluate the robustness of image classification models. The RLAB framework allows users to customize the types of distortions to be optimally applied to images, which helps address the specific distortions relevant to their deployment. The benchmark can generate datasets at various distortion levels to assess the robustness of different image classifiers. Our results show that the adversarial samples generated by our framework with any of the image classification models, like ResNet-50, Inception-V3, and VGG-16, are effective and transferable to other models causing them to fail. These failures happen even when these models are adversarially retrained using state-of-the-art techniques, demonstrating the generalizability of our adversarial samples. Our framework also allows the creation of adversarial samples for non-ground truth classes at different levels of intensity, enabling tunable benchmarks for the evaluation of false positives. We achieve competitive performance in terms of net $L_2$ distortion compared to state-of-the-art benchmark techniques on CIFAR-10 and ImageNet; however, we demonstrate our framework achieves such results with simple distortions like Gaussian noise without introducing unnatural artifacts or color bleeds. This is made possible by a model-based reinforcement learning (RL) agent and a technique that reduces a deep tree search of the image for model sensitivity to perturbations, to a one-level analysis and action. The flexibility of choosing distortions and setting classification probability thresholds for multiple classes makes our framework suitable for algorithmic audits.</p>
Framework of clonal mutations concurrent with WT1 mutations in adults with acute myeloid leukemia (Alliance)
<p>Mutations affecting the tumor suppressor gene, WT1 transcription factor (<em>WT1</em>) are relatively common in adults with acute myeloid leukemia (AML) and have been reported to associate with a poor prognosis. However, there is limited data describing additional mutations co-occurring with <em>WT1</em> as well as clonal architecture and complexity. We performed targeted DNA sequencing on 96 pretreatment samples from adult patients with <em>de novo</em> AML who harbored <em>WT1</em> mutations and subsequently performed single-cell DNA sequencing on a subset of these samples. <em>FLT3</em>-ITD mutations were the most common co-occurring mutations detected in 47% of <em>WT1</em>-mutated patients (<em>WT1<sup>mut</sup></em>/<em>FLT3</em>-ITD). Among <em>WT1<sup>mut</sup></em>/<em>FLT3</em>-ITD patients, <em>NPM1</em> mutations were observed in 58% of patients. In contrast, patients with <em>WT1</em>-mutated AML but no <em>FLT3</em>-ITD (<em>WT1<sup>mut</sup></em>/no <em>FLT3</em>-ITD) had significantly more frequent mutations in alternative signal activating pathways (<em>FLT3</em>-TKD: 25% vs 11%; and NRAS: 37% vs 11%) compared with <em>WT1<sup>mut</sup></em>/<em>FLT3</em>-ITD patients, and, notably, fewer mutations in <em>NPM1</em> (16% vs 58%). Given the difference in NRAS mutation frequency in <em>WT1<sup>mut</sup></em>/<em>FLT3</em>-ITD compared to <em>WT1<sup>mut</sup></em>/no <em>FLT3</em>-ITD patients, we then compared the variant allele frequencies (VAF) of <em>NRAS</em> mutations in the presence or absence of FLT3 mutations. There was a trend for higher NRAS VAF in <em>WT1</em>-mutated patients with concurrent <em>NRAS</em> mutations but no <em>FLT3</em> mutations (<em>FLT3</em>-WT), suggesting this may be due to WT1 being the founding mutation with two or more distinct sub-clones. To further delineate this observation, we performed single-cell DNA sequencing on seven samples with co-occurring <em>WT1</em> and <em>NRAS</em> mutations with or without <em>FLT3</em> mutations. Single-cell sequencing revealed that <em>WT1</em> mutations were more frequently observed in the founder clone (6 out of 7 samples), with or without <em>NPM1</em> mutations. Five of the seven samples included in the single-cell sequencing harbored both a <em>FLT3</em> (ITD or TKD) and an <em>NRAS</em> mutation. The analysis shows these mutations arose in mutually exclusive sub-clones from a <em>WT1</em> mutated founder clone in all 5 cases. These data, to our knowledge, represent the largest series of adults with <em>WT1</em>-mutated AML, and for the first time describe clonal architecture and the combination of mutations co-occurring in single cells. These data provide a deeper understanding of the genomic complexity and biology of <em>WT1</em>-mutated AML and may provide insight into the future development of novel therapeutic strategies.</p>
Image bank to use with the Resource Flow Mapping tool when implementing the Clean and Green framework
<p>This image bank consists of a set of 116 images that can be used together with the Resource Flow mapping tool and other sensitization tools when implementing the Clean and Green framework as part of rural sanitation programs. The image bank consists of a wide range of images including everything from images depicting the use of various types of ecological sanitation toilets to the use of excreta-derived products in agriculture. All the images have a white background, and hence can be used in a variety of media and contexts. Each image has a descriptive file name. The images can be downloaded individually, or collectively by downloading the "Images.zip" file. The image bank was developed as part of the Clean and Green framework project at the Stockholm Environment Institute, in collaboration with WaterAid Burkina Faso. Learn more: https://www.sei.org/projects-and-tools/projects/clean-and-green/ </p> <p> </p>
On the de novo emergence of ecological interactions during evolutionary diversification: A conceptual framework and experimental test
<p><span>Ecological interactions are crucial to the structure and function of biological communities, but we lack a causal understanding of the forces shaping their emergence during evolutionary diversification. Here we provide a conceptual framework linking different modes of diversification (e.g. ecological diversification), which depend on environmental characteristics, to the evolution of particular interactions (e.g. resource partitioning) in asexual lineages. We tested the framework by examining the net interactions in communities of <em>Pseudomonas aeruginosa</em> produced via experimental evolution in nutritionally simple (SIM) or complex (COM) environments by contrasting the productivity and competitive fitness of whole evolved communities relative to their component isolates. As expected, we found that nutritional complexity drove the evolution of communities with net positive interactions whereas SIM communities had similar performance as their component isolates. A follow-up experiment revealed that high fitness in two COM communities was driven by rare variants (frequency <0.1%) that antagonized PA14, the ancestral strain and common competitor used in fitness assays. Our study suggests that the evolution of <em>de novo</em> ecological interactions in asexual lineages is predictable at a broad scale from environmental conditions. Further, our work demonstrates that rare variants can disproportionately impact the function of relatively simple microbial communities.</span></p>
Enhancing Microservice Reusability in MSA through the Reusable Microservices Framework: Development, Validation, and Evaluation
<p>The data of the paper: Enhancing Microservice Reusability in MSA through the Reusable Microservices Framework: Development, Validation, and Evaluation</p>
Supplementary figures for 'Domino: A new framework for the automated identification of weather event precursors, demonstrated for European extreme rainfall.'
<p>Supplementary dynamics and skill plots for the paper 'Domino: A new framework for the automated identification of weather event precursors, demonstrated for European extreme rainfall', submitted to QJRMS.</p>
Data from: Smart self-propelled particles: A framework to investigate the cognitive bases of movement
<p>We present a framework specifically developed to develop theories of spatial decision-making and to fully understand the rational of decisions embedded in an environment (and therefore the underlying evolutionary processes). This is achieved by the means of cognitive agents, moving thanks to artificial neural networks controlling movements and whose parameters are optimised with a genetic algorithms. Specifically, we investigate a simple task in which single agents need to learn to explore their square arena without leaving its boundaries. We show that agents evolve by developing increasingly optimal strategies to solve a spatially-embedded learning task while not having an initial arbitrary model of movements. The process allows the agents to learn how to move (i.e. by avoiding the arena walls) in order to make increasingly optimal decisions (improving their exploration of the arena).</p> <p>Our dataset is made of 4 sets of simulations: parameters of reference, a survival objective function, introducing a turning penalty and with a slower speed (see details of parameters below). Each set of simulations is made of 60 trials with different initial conditions, each of which has been simulated with 20,000 agents for 150 generations. For each generation, we record:</p> <ul> <li>the score of the 20,000 agents across four runs with different and random initial conditions</li> <li>the score of the 20,000 agents from the same controlled initial condition (files with _com suffix)</li> <li>metadata with parameters used in this simulation</li> <li>parameters (weights and biases) of the artificial neural network of the best agent (i.e. with highest score) of each generation</li> <li>parameters (weights and biases) of the artificial neural network of the best agent (i.e. with highest score in the controlled initial condition run) of each generation (files with _com suffix)</li> </ul>
Towards a practical framework to "ethics by design" data sharing and machine learning applications
<p>Responsible AI and data-driven applications can only be developed when teams integrate the ethical principles directly into the development process. An important prerequisite is the involvement of a diverse group of stakeholders who build and are affected by AI and data systems. We present a practical framework that helps teams build trustworthy AI systems and data strategies by combining expertise and training from philosophy, law, machine learning and design.</p>
DeepCV: A Deep Learning Framework for Blind Search of Collective Variables in Expanded Configurational Space
<p>We present <em>Deep learning for Collective Variables</em> (DeepCV), a computer code that provides an efficient and customizable implementation of the deep autoencoder neural network (DAENN) algorithm that has been developed in our group for computing collective variables (CVs) and can be used with enhanced sampling methods to reconstruct free energy surfaces of chemical reactions. DeepCV can be used to conveniently calculate molecular features, train models, generate CVs, validate rare events from sampling, and analyze a trajectory for chemical reactions of interest. We use DeepCV in an example study of the conformational transition of cyclohexene, where metadynamics simulations are performed using DAENN-generated CVs. The results show that the adopted CVs give free energies in line with those obtained by previously developed CVs and experimental results. DeepCV is open-source software written in Python/C++ object-oriented languages, based on the TensorFlow framework and distributed free of charge for noncommercial purposes, which can be incorporated into general molecular dynamics software. DeepCV also comes with several additional tools, i.e., an application program interface (API), documentation, and tutorials.</p>
Dataset - Removal of diclofenac by adsorption process studied in free-base porphyrins Zr-metal organic frameworks (Zr-MOFs)
<p>Dataset for the paper entitled "Removal of diclofenac by adsorption process studied in free-base porphyrins Zr-metal organic frameworks (Zr-MOFs) "</p>
Photovoice: definition, theoretical framework and steps
<p>In this video Aloe Cubero (URV), PhD Fellow at the Pedagogy Department, introduces photovoice methodology together with a guide for its implementation in classroom settings. Presentation elaborated by Aloe Cubero (URV) and Rocío Garrido (US) in the framework of the Project Mainstreaming Sexual and Gender-Related Violence sensibilities into university courses through Photovoice experiences (2020 INDOV00003).</p> <p>Project link: http://www.innovaciondocentegenero.eu/photovoice/</p>
Unit-cell-thick zeolitic imidazolate framework films for membrane application
<p>raw data for the manuscript with title of Unit-cell-thick zeolitic imidazolate framework films for membrane application</p>
An Interpretable Framework to Characterize Compound Treatments on Filamentous Fungi using Cell Painting and Deep Metric Learning
<p>This deposit contains:</p> <ul> <li>{train,test,val}.csv: Meta-data files</li> <li>images.tar.gz: Archive of images</li> <li>checkpoint.pth.tar: Model weights</li> <li>code.tar.gz: Code necessary to reproduce our experiments.</li> </ul>
Synthetic control of correlated disorder in UiO-66 frameworks
<p>Raw data associated to the Figures 2, 3, 4, 5 and 7 in the main text.</p>
Covalent Organic Framework Thin-Film Photodetectors from Solution-Processable Porous Nanospheres
<p>This repository contains the source data for the manuscript <strong>"Covalent Organic Framework Thin-Film Photodetectors from Solution-Processable Porous Nanospheres" </strong></p> <p>The statistical model of 50 layers, 4-6 layers for TpDPP, TpTta, and TpTab.</p> <p>A code for building the statistical structure for layered materials by stacking layers following the Maxwell-Boltzmann energy distribution of their stacking modes. Full Changelog: <a href="https://github.com/Ying-Ying-Zhang/COFpiler/commits/v1.1">https://github.com/Ying-Ying-Zhang/COFpiler/commits/v1.1</a></p>
Replication Package - From Research to Practice: A Survey of XAI Process Frameworks
<p>Replication package for the ICSE NIER Submission "From Research to Practice: A Survey of XAI Process Frameworks." Provides details about the methods and data used in our analysis. For more information, please take a look at the README.</p>
A comparative benchmarking and evaluation framework for heterogeneous network-based drug repositioning methods
<p>This repository contains all the datasets used for benchmarking drug repositioning methods based on heterogeneous networks, including drug-drug similarity, disease-disease similarity, and drug-disease associations.</p> <p>The repository consists of:</p> <ol> <li>Name or keyword information for drugs and diseases information contained in the dataset</li> <li>Inputs needed for different drug repositioning methods: multiple drug similarity matrices,multiple disease similarity matrices, and drug-disease associations matrices.</li> </ol> <p> </p> <p> </p> <blockquote> <p><strong>Please cite us :</strong><br>Yinghong Li, Yinqi Yang, Zhuohao Tong, Yu Wang, Qin Mi, Mingze Bai, Guizhao Liang, Bo Li, Kunxian Shu, A comparative benchmarking and evaluation framework for heterogeneous network-based drug repositioning methods, <em>Briefings in Bioinformatics</em>, Volume 25, Issue 3, May 2024, bbae172, <a href="https://doi.org/10.1093/bib/bbae172">https://doi.org/10.1093/bib/bbae172</a></p> </blockquote>
A Comparison framework for deep learning RFI detection algorithms in radio astronomy
<p>These are the datasets used for the study titled: A Comparison framework for deep learning RFI detection algorithms in radio astronomy. These files are made publicly available as an additional resource to the submission of the author's Masters degree at Stellenbosch University. The detection is done in the field of radio astronomy. Each dataset consists of images/spectrograms/waterfall plots for baselines, and the corresponding binary mask for each image. The datasets can be used to train machine learning models, or for the case of this study, supervised fully convolutional neural networks.</p> <p>The LOFAR datasets consists of real observations and was slightly modified from https://zenodo.org/record/6724065. See this resource regarding the observational parameters used to retrieve the data from the LOFAR Long Term Archive.The HERA dataset consists of simulated observations generated with hera_sim (https://readthedocs.org/projects/hera-sim/). The 28 March dataset consists of accurate pixel-perfect binary masks for each image. The 20 July dataset is identical to the first, except the binary masks are generated with AOFlagger. All three datasets have a test set stored with pixel-perfected simulation masks (HERA) or expert hand labeled masks (LOFAR).</p> <p>The csv file contains the results of all trained models and and has fields for: model class, #filters, #FLOPS, #weights, preprocessing methods, train, validation and test accuracy scores as well as list of (threshold, FPR, TPR) values to generate receiver operating characteristic curves. See https://github.com/CharlDuToit/RFI-NLN to visualize the results, to train new models.</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.