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997 results for “AWARENESS”
Haplotype-aware reference genome reveals hidden somatic mutations of sweet orange
<p><strong>Filename: </strong>ASE_in_five_fruit_development.txt</p> <p><strong>Description: </strong>Based on our haplotype sequences, we confirmed biallelic genes showed significant expression difference between two alleles in at least one fruit developmental stage. We collected the RNA-seq data from fruit of Newhall navel orange at five developmental stages (90, 120, 150, 180 and 210 days after bloom). RNA-seq data from previous project GSE108930 in NCBI database.</p> <p> </p> <p><strong>Filename: </strong>Biallelic_genes_haplogenomes.tsv</p> <p><strong>Description: </strong>The biallelic genes were identified using the Genespace program.</p> <p> </p> <p><strong>Filename: </strong>Haplogenomes_CENH3_chip_peaks.bw</p> <p><strong>Description: </strong>The CENH3 sequences were collected from BankIt ID 2305947. These reads (including the input library as a control) were aligned to the two assembled haplotypes using Bowtie2 (v2.5.1) with default parameters. MACS2 (v2.2.7.1) with the additional parameters “-f BAM -ghs -B -q 0.01” was used to perform peak calling. The peaks generated from CENH3 chip-seq.</p> <p> </p> <p><strong>Filename: </strong>Haplogenomes_Control_chip_peaks.bw</p> <p><strong>Description:</strong> The peaks generated from Control chip-seq.</p> <p> </p> <p><strong>Filename: </strong>Haplotype_based_79accessions_somatic_variations.vcf</p> <p><strong>Description: </strong>The small somatic variations generated based on the haplotype-based method. The derived somatic mutations were identified based on nine samples from the outgroup (Earlier Clade I).</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_CuteSV.vcf</p> <p><strong>Description: </strong>The HiFi reads were mapped to haplotype A. We called SVs using the CuteSV program.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_gene_function_annotation.tsv</p> <p><strong>Description: </strong>The gene annotations of haplotype A.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_gene_model.gff3</p> <p><strong>Description:</strong> The gene structure model of haplotype A.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_genome.fa</p> <p><strong>Description:</strong> The genome sequences of haplotype A.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_PEPPER_OUTPUT.zip</p> <p><strong>Description: </strong>The small variations of sweet orange using the haplotype A as the reference genome.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_TEs_annotation.gff3</p> <p><strong>Description:</strong> The TE annotations of haplotype A.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeB_gene_function_annotation.tsv</p> <p><strong>Description:</strong> The gene annotations of haplotype B.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeB_gene_model.gff3</p> <p><strong>Description:</strong> The gene structure model of haplotype B.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeB_genome.fa</p> <p><strong>Filename: </strong>ASE_in_five_fruit_development.txt</p> <p><strong>Description: </strong>Based on our haplotype sequences, we confirmed biallelic genes showed significant expression difference between two alleles in at least one fruit developmental stage. We collected the RNA-seq data from fruit of Newhall navel orange at five developmental stages (90, 120, 150, 180 and 210 days after bloom). RNA-seq data from previous project GSE108930 in NCBI database.</p> <p> </p> <p><strong>Filename: </strong>Biallelic_genes_haplogenomes.tsv</p> <p><strong>Description: </strong>The biallelic genes were identified using the Genespace program.</p> <p> </p> <p><strong>Filename: </strong>Haplogenomes_CENH3_chip_peaks.bw</p> <p><strong>Description: </strong>The CENH3 sequences were collected from BankIt ID 2305947. These reads (including the input library as a control) were aligned to the two assembled haplotypes using Bowtie2 (v2.5.1) with default parameters. MACS2 (v2.2.7.1) with the additional parameters “-f BAM -ghs -B -q 0.01” was used to perform peak calling. The peaks generated from CENH3 chip-seq.</p> <p> </p> <p><strong>Filename: </strong>Haplogenomes_Control_chip_peaks.bw</p> <p><strong>Description:</strong> The peaks generated from Control chip-seq.</p> <p> </p> <p><strong>Filename: </strong>Haplotype_based_79accessions_somatic_variations.vcf</p> <p><strong>Description: </strong>The small somatic variations generated based on the haplotype-based method. The derived somatic mutations were identified based on nine samples from the outgroup (Earlier Clade I).</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_CuteSV.vcf</p> <p><strong>Description: </strong>The HiFi reads were mapped to haplotype A. We called SVs using the CuteSV program.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_gene_function_annotation.tsv</p> <p><strong>Description: </strong>The gene annotations of haplotype A.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_gene_model.gff3</p> <p><strong>Description:</strong> The gene structure model of haplotype A.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_genome.fa</p> <p><strong>Description:</strong> The genome sequences of haplotype A.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_PEPPER_OUTPUT.zip</p> <p><strong>Description: </strong>The small variations of sweet orange using the haplotype A as the reference genome.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_TEs_annotation.gff3</p> <p><strong>Description:</strong> The TE annotations of haplotype A.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeB_gene_function_annotation.tsv</p> <p><strong>Description:</strong> The gene annotations of haplotype B.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeB_gene_model.gff3</p> <p><strong>Description:</strong> The gene structure model of haplotype B.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeB_genome.fa</p> <p><strong>Description:</strong> The genome sequences of haplotype B.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeB_TEs_annotation.gff3</p> <p><strong>Description:</strong> The TE annotations of haplotype B.</p> <p> </p> <p><strong>Filename: </strong>Single_reference_87accessions_somatic_variations.vcf</p> <p><strong>Description:</strong> The small somatic variations generated based on the single reference genome (Haplotype A).</p> <p> </p> <p><strong>Filename: </strong>Somatic_material_RNA_seq_matrix.txt</p> <p><strong>Description: </strong>The expression matrix of BT_3 and BT_5 (a set of somatic mutation material).</p> <p> </p> <p><strong>Filename: </strong>ASE_in_five_fruit_development.txt</p> <p><strong>Description: </strong>Based on our haplotype sequences, we confirmed biallelic genes showed significant expression difference between two alleles in at least one fruit developmental stage. We collected the RNA-seq data from fruit of Newhall navel orange at five developmental stages (90, 120, 150, 180 and 210 days after bloom). RNA-seq data from previous project GSE108930 in NCBI database.</p> <p> </p> <p><strong>Filename: </strong>Biallelic_genes_haplogenomes.tsv</p> <p><strong>Description: </strong>The biallelic genes were identified using the Genespace program.</p> <p> </p> <p><strong>Filename: </strong>Haplogenomes_CENH3_chip_peaks.bw</p> <p><strong>Description: </strong>The CENH3 sequences were collected from BankIt ID 2305947. These reads (including the input library as a control) were aligned to the two assembled haplotypes using Bowtie2 (v2.5.1) with default parameters. MACS2 (v2.2.7.1) with the additional parameters “-f BAM -ghs -B -q 0.01” was used to perform peak calling. The peaks generated from CENH3 chip-seq.</p> <p> </p> <p><strong>Filename: </strong>Haplogenomes_Control_chip_peaks.bw</p> <p><strong>Description:</strong> The peaks generated from Control chip-seq.</p> <p> </p> <p><strong>Filename: </strong>Haplotype_based_79accessions_somatic_variations.vcf</p> <p><strong>Description: </strong>The small somatic variations generated based on the haplotype-based method. The derived somatic mutations were identified based on nine samples from the outgroup (Earlier Clade I).</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_CuteSV.vcf</p> <p><strong>Description: </strong>The HiFi reads were mapped to haplotype A. We called SVs using the CuteSV program.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_gene_function_annotation.tsv</p> <p><strong>Description: </strong>The gene annotations of haplotype A.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_gene_model.gff3</p> <p><strong>Description:</strong> The gene structure model of haplotype A.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_genome.fa</p> <p><strong>Description:</strong> The genome sequences of haplotype A.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_PEPPER_OUTPUT.zip</p> <p><strong>Description: </strong>The small variations of sweet orange using the haplotype A as the reference genome.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_TEs_annotation.gff3</p> <p><strong>Description:</strong> The TE annotations of haplotype A.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeB_gene_function_annotation.tsv</p> <p><strong>Description:</strong> The gene annotations of haplotype B.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeB_gene_model.gff3</p> <p><strong>Description:</strong> The gene structure model of haplotype B.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeB_genome.fa</p> <p><strong>Description:</strong> The genome sequences of haplotype B.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeB_TEs_annotation.gff3</p> <p><strong>Description:</strong> The TE annotations of haplotype B.</p> <p> </p> <p><strong>Filename: </strong>Single_reference_87accessions_somatic_variations.vcf</p> <p><strong>Description:</strong> The small somatic variations generated based on the single reference genome (Haplotype A).</p> <p> </p> <p><strong>Filename: </strong>Somatic_material_RNA_seq_matrix.txt</p> <p><strong>Description: </strong>The expression matrix of BT_3 and BT_5 (a set of somatic mutation material).</p> <p> </p> <p><strong>Filename: </strong>ASE_in_five_fruit_development.txt</p> <p><strong>Description: </strong>Based on our haplotype sequences, we confirmed biallelic genes showed significant expression difference between two alleles in at least one fruit developmental stage. We collected the RNA-seq data from fruit of Newhall navel orange at five developmental stages (90, 120, 150, 180 and 210 days after bloom). RNA-seq data from previous project GSE108930 in NCBI database.</p> <p> </p> <p><strong>Filename: </strong>Biallelic_genes_haplogenomes.tsv</p> <p><strong>Description: </strong>The biallelic genes were identified using the Genespace program.</p> <p> </p> <p><strong>Filename: </strong>Haplogenomes_CENH3_chip_peaks.bw</p> <p><strong>Description: </strong>The CENH3 sequences were collected from BankIt ID 2305947. These reads (including the input library as a control) were aligned to the two assembled haplotypes using Bowtie2 (v2.5.1) with default parameters. MACS2 (v2.2.7.1) with the additional parameters “-f BAM -ghs -B -q 0.01” was used to perform peak calling. The peaks generated from CENH3 chip-seq.</p> <p> </p> <p><strong>Filename: </strong>Haplogenomes_Control_chip_peaks.bw</p> <p><strong>Description:</strong> The peaks generated from Control chip-seq.</p> <p> </p> <p><strong>Filename: </strong>Haplotype_based_79accessions_somatic_variations.vcf</p> <p><strong>Description: </strong>The small somatic variations generated based on the haplotype-based method. The derived somatic mutations were identified based on nine samples from the outgroup (Earlier Clade I).</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_CuteSV.vcf</p> <p><strong>Description: </strong>The HiFi reads were mapped to haplotype A. We called SVs using the CuteSV program.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_gene_function_annotation.tsv</p> <p><strong>Description: </strong>The gene annotations of haplotype A.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_gene_model.gff3</p> <p><strong>Description:</strong> The gene structure model of haplotype A.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_genome.fa</p> <p><strong>Description:</strong> The genome sequences of haplotype A.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_PEPPER_OUTPUT.zip</p> <p><strong>Description: </strong>The small variations of sweet orange using the haplotype A as the reference genome.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_TEs_annotation.gff3</p> <p><strong>Description:</strong> The TE annotations of haplotype A.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeB_gene_function_annotation.tsv</p> <p><strong>Description:</strong> The gene annotations of haplotype B.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeB_gene_model.gff3</p> <p><strong>Description:</strong> The gene structure model of haplotype B.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeB_genome.fa</p> <p><strong>Description:</strong> The genome sequences of haplotype B.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeB_TEs_annotation.gff3</p> <p><strong>Description:</strong> The TE annotations of haplotype B.</p> <p> </p> <p><strong>Filename: </strong>Single_reference_87accessions_somatic_variations.vcf</p> <p><strong>Description:</strong> The small somatic variations generated based on the single reference genome (Haplotype A).</p> <p> </p> <p><strong>Filename: </strong>Somatic_material_RNA_seq_matrix.txt</p> <p><strong>Description: </strong>The expression matrix of BT_3 and BT_5 (a set of somatic mutation material).</p> <p><strong>Filename: </strong>ASE_in_five_fruit_development.txt</p> <p><strong>Description: </strong>Based on our haplotype sequences, we confirmed biallelic genes showed significant expression difference between two alleles in at least one fruit developmental stage. We collected the RNA-seq data from fruit of Newhall navel orange at five developmental stages (90, 120, 150, 180 and 210 days after bloom). RNA-seq data from previous project GSE108930 in NCBI database.</p> <p> </p> <p><strong>Filename: </strong>Biallelic_genes_haplogenomes.tsv</p> <p><strong>Description: </strong>The biallelic genes were identified using the Genespace program.</p> <p> </p> <p><strong>Filename: </strong>Haplogenomes_CENH3_chip_peaks.bw</p> <p><strong>Description: </strong>The CENH3 sequences were collected from BankIt ID 2305947. These reads (including the input library as a control) were aligned to the two assembled haplotypes using Bowtie2 (v2.5.1) with default parameters. MACS2 (v2.2.7.1) with the additional parameters “-f BAM -ghs -B -q 0.01” was used to perform peak calling. The peaks generated from CENH3 chip-seq.</p> <p> </p> <p><strong>Filename: </strong>Haplogenomes_Control_chip_peaks.bw</p> <p><strong>Description:</strong> The peaks generated from Control chip-seq.</p> <p> </p> <p><strong>Filename: </strong>Haplotype_based_79accessions_somatic_variations.vcf</p> <p><strong>Description: </strong>The small somatic variations generated based on the haplotype-based method. The derived somatic mutations were identified based on nine samples from the outgroup (Earlier Clade I).</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_CuteSV.vcf</p> <p><strong>Description: </strong>The HiFi reads were mapped to haplotype A. We called SVs using the CuteSV program.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_gene_function_annotation.tsv</p> <p><strong>Description: </strong>The gene annotations of haplotype A.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_gene_model.gff3</p> <p><strong>Description:</strong> The gene structure model of haplotype A.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_genome.fa</p> <p><strong>Description:</strong> The genome sequences of haplotype A.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_PEPPER_OUTPUT.zip</p> <p><strong>Description: </strong>The small variations of sweet orange using the haplotype A as the reference genome.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeA_TEs_annotation.gff3</p> <p><strong>Description:</strong> The TE annotations of haplotype A.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeB_gene_function_annotation.tsv</p> <p><strong>Description:</strong> The gene annotations of haplotype B.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeB_gene_model.gff3</p> <p><strong>Description:</strong> The gene structure model of haplotype B.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeB_genome.fa</p> <p><strong>Description:</strong> The genome sequences of haplotype B.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeB_TEs_annotation.gff3</p> <p><strong>Description:</strong> The TE annotations of haplotype B.</p> <p> </p> <p><strong>Filename: </strong>Single_reference_87accessions_somatic_variations.vcf</p> <p><strong>Description:</strong> The small somatic variations generated based on the single reference genome (Haplotype A).</p> <p> </p> <p><strong>Filename: </strong>Somatic_material_RNA_seq_matrix.txt</p> <p><strong>Description: </strong>The expression matrix of BT_3 and BT_5 (a set of somatic mutation material).</p> <p><strong>Description:</strong> The genome sequences of haplotype B.</p> <p> </p> <p><strong>Filename: </strong>HaplotypeB_TEs_annotation.gff3</p> <p><strong>Description:</strong> The TE annotations of haplotype B.</p> <p> </p> <p><strong>Filename: </strong>Single_reference_87accessions_somatic_variations.vcf</p> <p><strong>Description:</strong> The small somatic variations generated based on the single reference genome (Haplotype A).</p> <p> </p> <p><strong>Filename: </strong>Somatic_material_RNA_seq_matrix.txt</p> <p><strong>Description: </strong>The expression matrix of BT_3 and BT_5 (a set of somatic mutation material).</p>
Quality Assurance Awareness in Open Source Software Projects on GitHub Analysis Dataset
<p>Dataset for the paper "Quality Assurance Awareness in Open Source Software Projects on GitHub", submitted to the 23rd IEEE International Working Conference on Source Code Analysis and Manipulation (SCAM), 2023.</p>
The Awareness Assessment Model (Case Study Validation)
<p>Dataset of the case study validation of the paper "The Awareness Assessment Model: Measuring Awareness and Collaboration Support Over Participant's Perspective"</p>
The Awareness Assessment Model (expert panel evaluation)
<p>The dataset of the expert panel evaluation, described in the paper "The Awareness Assessment Model: Measuring Awareness and Collaboration Support Over Participant's Perspective"</p>
Evaluation of knowledge and awareness of pediatric oral health among school teachers of Hazaribag before and after oral health education.
<p>This study was done to assess the knowledge of school teachers regarding child dental health. The number of subjects participated in research were more than 150. . A self‑administered, 30‑item questionnaire was designed in English language on clinical pediatric oral health. After receiving google forms, all the participants participated in oral health education webinar program using a web based online education training protocol on ZOOM platform. Post training assessment of the oral health knowledge scores was done using the same online semi‑structured self‑administered questionnaire. The inadequacy in knowledge of school teachers was evident and they need to be trained in varied areas of pediatric oral health.</p>
Landmark-Aware Visual Navigation (LAVN) Dataset
<p>We present a \textbf{L}andmark-\textbf{A}ware \textbf{V}isual \textbf{N}avigation (LAVN) dataset to allow for supervised learning of human-centric exploration policies. We collect RGB observation and human point-click pairs as a human annotator explores virtual and real world environments with the goal of full coverage exploration of the space. These human point-clicks serve as direct supervision for waypoint prediction when learning to explore in environments. The human annotators also provide distinct landmark examples along each trajectory, which we intuit will simplify the task of map or graph building and localization. </p>
Quality-aware Analysis and Optimisation of Virtual Network Function
<p># SPLC'22 Quality-aware Analysis and Optimisation of Virtual Network Functions</p><p> </p><p>DATA: Quality-aware Analysis and Optimisation of Virtual Network Functions</p><p> </p><p>This repository contains the models, operations and results empirically used in [Quality-aware Analysis and Optimisation of Virtual Network Functions](https://doi.org/10.1145/3546932.3547007) at SPLC 2022.</p><p>Due to copyright issues, it does not contain the tools (i.e., automated reasoners), although their official sites are provided.</p><p> </p><p>It is licensed under the [MIT license](https://github.com/danieljmg/SPLC22/blob/main/LICENSE).</p><p> </p><p> </p><p>## SPLC'22 Models, Categorical Operations and Datasets</p><p> </p><p>This data-set contains:</p><p> </p><p>1. The 5 SPL categories in CQL alongside the 11 tested operations.</p><p>2. The 5 SPL Clafer models.</p><p>3. The 5 SPL XMLs (for the AAFM Python Framework).</p><p>4. The 5 SPL XMLs (for SATIBEA).</p><p>5. The previous models are enriched with quality attributes measurements at feature and configuration levels.</p><p>6. A Microsoft Excel file with the scalability results obtained.</p><p> </p><p> </p><p>## Automated Reasoners</p><p> </p><p>- CQL IDE: https://github.com/CategoricalData/CQL</p><p>- Clafermoo: http://t3-necsis.cs.uwaterloo.ca:8092/</p><p>- AAFM Python Framework: https://pypi.org/project/famapy/</p><p>- SATIBEA: https://github.com/jmguo/SMTIBEA</p><p> </p><p> </p><p>## Requirements</p><p> </p><p>The data-set has been generated using Java JDK 18.0.2 for CQL IDE, Clafermoo, and SATIBEA, and Python 3.9.13 x86_64 for AAFM Python Framework.</p><p> </p><p>## Authors</p><p> </p><p>1. **[Daniel-Jesus Munoz](https://github.com/danieljmg)**: [ITIS Software](https://www.uma.es/institutos-uma/info/118460/instituto-de-tecnologias-e-ingenieria-del-software/), [CAOSD](http://caosd.lcc.uma.es/), Dpt. LCC, Universidad de Málaga, Andalucía Tech, Spain</p><p>2. **Mónica Pinto**: [ITIS Software](https://www.uma.es/institutos-uma/info/118460/instituto-de-tecnologias-e-ingenieria-del-software/), [CAOSD](http://caosd.lcc.uma.es/), Dpt. LCC, Universidad de Málaga, Andalucía Tech, Spain</p><p>3. **Lidia Fuentes**: [ITIS Software](https://www.uma.es/institutos-uma/info/118460/instituto-de-tecnologias-e-ingenieria-del-software/), [CAOSD](http://caosd.lcc.uma.es/), Dpt. LCC, Universidad de Málaga, Andalucía Tech, Spain</p>
Evaluation Study of the Online High School Media Aware Program
ClinicalTrials.gov study NCT04035694. IPD Sharing: NO. Countries: 1. Publications: 1.
Awareness Enhancing Interventions
ClinicalTrials.gov study NCT04683510. IPD Sharing: YES. Countries: 1. Publications: 42.
Culturally Aware AET Non-Initiation Intervention
ClinicalTrials.gov study NCT05465408. IPD Sharing: YES. Countries: 1. Publications: 1.
Towards Green Cartography & Visualization: An automated, semantically-enriched method of generating energy-aware color schemes for digital maps and visualizations
<p>Towards Green Cartography & Visualization: An automated, semantically-enriched method of generating energy-aware color schemes for digital maps and visualizations</p>
Reproducibility Package for TACAS'21 Paper Resilient Capacity-Aware Routing
<p>This package contains all the necessary information for the reproduction of the experimental results in the paper "Resilient Capacity-Aware Routing" accepted for TACAS'21. In particular we provide all the python scripts that we used, their dependencies and the shell scripts for running the experiments or its subset.</p>
Experimental Data for the Paper 'Rotation-Aware Representation Learning for Remote Sensing Image Retrieval'
<p><strong>Experimental Data for the Paper 'Rotation-Aware Representation Learning for Remote Sensing Image Retrieval'</strong></p> <p>In this repository, we provide the implementation of the algorithms developed in the paper 'Rotation-Aware Representation Learning for Remote Sensing Image Retrieval' along with the experimental results.<br> The goal is to provide the elements needed to validate and reproduce our research work as well as all the tools needed to reach the same conclusions as we did.<br> The licences valid for the elements of this repository are discussed under point "2. Licenses" below.</p> <p><em><strong>1. Structure</strong></em></p> <p>The repository contains the following items:</p> <ol> <li>"data" - the results from our experiments</li> <li>"lib" - some external functions used in the experiments</li> <li>"make_data" - the training and test data</li> <li>"fmt-vgg.py" - the FMT-RAN model</li> <li>"stn.py" - the STN module of ST-RAN</li> <li>"st_ran.py" - the ST-RAN model</li> <li>"README" - this text here.</li> <li>"LICENSE" - the <a href="https://mit-license.org/">MIT License</a></li> </ol> <p><strong><em>2. License</em></strong></p> <p>The following licenses apply for the files and folders:</p> <ul> <li>The files "stn.py" and "spatial_transformer_tutorial.py" in the folder "lib" are from the GitHub repository <a href="https://github.com/GHamrouni/stn-tuto">https://github.com/GHamrouni/stn-tuto</a> and therefore are under the copyright of its repository owner Ghassen Hamrouni.</li> <li>All other files are under the <a href="https://mit-license.org/">MIT License</a>.</li> </ul> <p>The <a href="https://mit-license.org/">MIT License</a> is included here as file "LICENSE".</p> <p><em><strong>3. Contact</strong></em></p> <p>1. Dr. <a href="http://iao.hfuu.edu.cn/146">Zhize WU</a>, <a href="mailto:wuzz@hfuu.edu.cn">wuzz@hfuu.edu.cn</a><br> 2. Dr. <a href="http://iao.hfuu.edu.cn/5">Thomas WEISE</a>, <a href="mailto:tweise@hfuu.edu.cn">tweise@hfuu.edu.cn</a>, <a href="http://mailto:tweise@ustc.edu.cn">tweise@ustc.edu.cn</a></p> <p><a href="http://iao.hfuu.edu.cn">Institute of Applied Optimization</a>, <br> School of Artificial Intelligence and Big Data, <br> Hefei University, South Campus 2, Jinxiu Dadao 99, <br> Hefei Economic and Technological Development Area, <br> Shushan District, Hefei 230601, Anhui, China</p>
Pythia Generated Jet Images for Location Aware Generative Adversarial Network Training
<p>Dataset containing 872666 jet images to train Location Aware Generative Adversarial Networks (LAGAN) for High Energy Physics. Results are published in [arXiv:1701.05927].</p> <p><strong>Format</strong>:<br> HDF5 file with the following fields:</p> <ul> <li>'image' : array of dim (872666, 25, 25), contains the pixel intensities of each 25x25 image</li> <li>'signal' : binary array to identify signal (1, i.e. W boson) vs background (0, i.e. QCD)</li> <li>'jet_eta': eta coordinate per jet</li> <li>'jet_phi': phi coordinate per jet</li> <li>'jet_mass': mass per jet</li> <li>'jet_pt': transverse momentum per jet</li> <li>'jet_delta_R': distance between leading and subleading subjets if 2 subjets present, else 0</li> <li>'tau_1', 'tau_2', 'tau_3': substructure variables per jet (a.k.a. n-subjettiness, where n=1, 2, 3)</li> <li>'tau_21': tau<sub>2</sub>/tau<sub>1</sub> per jet</li> <li>'tau_32': tau<sub>3</sub>/tau<sub>2</sub> per jet</li> </ul> <p><strong>Details</strong>:</p> <ul> <li>Simulated using Pythia 8.219 at √ s = 14 TeV</li> <li>Image pre-processing using method from in L. de Oliveira et al., Jet-Images -- Deep Learning Edition [arXiv:1511.05190]</li> <li>scikit-image==0.12.0 implementation of cubic spline rotation</li> <li>Finite calorimeter granularity simulated with 0.1×0.1 grid in η and φ, with η × φ ∈ [−1.25, 1.25] × [−1.25, 1.25]</li> <li>Jet clustering with anti-k<sub>t</sub> algorithm with a radius R = 1.0 using FastJet 3.2.1; constituent re-clustering into R = 0.3 k<sub>t</sub> subjets</li> <li>Intensity of pixel = p<sub>T</sub> of cell</li> <li>60 GeV < m<sup>jet</sup> < 100 GeV</li> <li>250 GeV < p<sub>T</sub><sup>jet</sup> < 300 GeV</li> <li>Sparse images (~10% NNZ)</li> </ul> <p>Full dataset description in [arXiv:1701.05927].</p>
Dataset and source code for ICSME2017 paper "Supervised vs Unsupervised Models: A Holistic Look at Effort-Aware Just-in-Time Defect Prediction"
<p>Dataset and source code for ICSME2017 paper “Supervised vs Unsupervised Models: A Holistic Look at Effort-Aware Just-in-Time Defect Prediction”</p> <p>There are four different models in the paper (i.e., EALR, LT, CBS and OneWay). Each model was implemented in a single Java file in the model package. To reproduce the experiment results of each model in the paper, just run the main method in the corresponding Java file. </p> <p> </p>
Replication data for Anterior Cruciate Ligament Injury: identifying information sources and risk factor awareness among the general population
<p>Creating awareness about a disorder is important for prevention from the perspective of public health. However, for sports injuries, like the anterior cruciate ligament (ACL) injury, there is no study which has investigated the awareness of risk factors for the injury and prevention methods among the general population, to the best of our knowledge. The sources of information among the population are also unclear. The purpose of present study was to identify these aspects of public awareness about the ACL injury.</p> <p>A questionnaire was randomly distributed among the general population registered with a web based questionnaire supplier, to recruit 900 participants who were aware about the ACL injury. The questionnaire consisted of two parts: Question 1 asked them about their sources of information regarding the ACL injury; Question 2 asked them about the risk factors for ACL injury. Multivariate logistic regression was used to determine the information sources that provide a good understanding of the risk factors.</p> <p>The leading source of information for ACL injury was television (57.0%). However, the results of logistic regression analysis revealed that television was not an effective medium to create awareness about the risk factors, among the general population. Instead “Lecture by a coach”, “Classroom session on Health”, and “Newspaper” were significantly effective in creating a good awareness of the risk factors (p < 0.001).</p>
LABind: Identifying Protein Binding Ligand-Aware Sites via Learning Interactions Between Ligand and Protein
<p>This dataset contains the three datasets used in LABind. For each dataset, we have saved the corresponding FASTA sequence files, the associated labels (0 for non-binding and 1 for binding), and the corresponding PDB files.</p>
Stimuli from: AwarePrompt: Using Diffusion Models to Create Methods for Measuring Value-Aware AI Architectures
<p>Stimuli generated using a Diffusion Model from the paper "AwarePrompt: Using Diffusion Models to Create Methods for Measuring Value-Aware AI Architectures".</p> <p>Please cite as:<br><span>Ciupinska, K.; Marchesi, S.; Abbo, G. A.; Belpaeme, T. and Wykowska, A. (2024). <strong>AwarePrompt: Using Diffusion Models to Create Methods for Measuring Value-Aware AI Architectures</strong>. In <em>Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: AWAI</em>; ISBN 978-989-758-680-4; ISSN 2184-433X, SciTePress, pages 1436-1443. DOI: 10.5220/0012596400003636</span></p>
The Awareness Assessment Model repository
<p>Dataset of the paper "The Awareness Assessment Model: Measuring Awareness and Collaboration Support Over Participant's Perspective". This dataset contains the supplementary materials about:</p> <p>+ the systematic mapping study;</p> <p>+ the taxonomy elaboration;</p> <p>+ the awareness assessment process;</p> <p>+expert panel validation;</p> <p>+ the case study validation;</p> <p>+ R scripts and observations.csv</p> <p>For more information, please get in touch with us (marcio.mantau@gmail.com).</p>
Awareness of fifth metatarsal stress fractures among soccer coaches in Japan: A cross-sectional study
<p>Although a fifth metatarsal stress fracture is the most frequent stress fracture in soccer players, awareness of fifth metatarsal stress fractures among soccer coaches is unclear. Therefore, we performed an online survey of soccer coaches affiliated with the Japan Football Association to assess their awareness of fifth metatarsal stress fractures. A total of 150 soccer coaches were invited for an original online survey. Data on participants' age, sex, types of coaching licence, coaching category, types of training surface, awareness of fifth metatarsal stress fractures, and measures employed to prevent fifth metatarsal stress fractures were collected using the survey. Data from 117 coaches were analysed. Eighty-seven of the 117 coaches were aware of fifth metatarsal stress fractures; however, only 30% reported awareness of preventive and treatment measures for fifth metatarsal stress fractures. Licensed coaches (i.e., licensed higher than level C) were also more likely to be aware of fifth metatarsal stress fractures than unlicensed coaches were. Furthermore, although playing on artificial turf is an established risk factor for numerous sports injuries, soccer coaches who usually trained on artificial turf were more likely to be unaware of the risks associated with fifth metatarsal stress fractures than coaches who trained on other surfaces were (e.g., clay fields).<strong> </strong>Soccer coaches in the study population were generally aware of fifth metatarsal stress fractures; however, most were unaware of specific treatment or preventive training strategies for fifth metatarsal stress fractures. Additionally, coaches who practised on artificial turf were not well educated on fifth metatarsal stress fractures. Our findings suggest the need for increased awareness of fifth metatarsal stress fractures and improved education of soccer coaches regarding injury prevention strategies. </p>
ScienceDex guides
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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.