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Dataset results
49 results for “requirements analysis”
Integrative gene regulatory analysis reveals transcriptional mechanisms required in mature human hepatocytes and during in vitro hepatocyte differentiation (RNA-Seq)
GEO Series GSE182603. Homo sapiens. 11 samples. Type: Expression profiling by high throughput sequencing.
Gene regulatory network analysis predicts cooperating transcription factor regulons required for FLT3-ITD+ AML growth [shRNA-seq]
GEO Series GSE236774. Homo sapiens. 22 samples. Type: Other.
Gene regulatory network analysis predicts cooperating transcription factor regulons required for FLT3-ITD+ AML growth [scRNA-seq]
GEO Series GSE236773. Homo sapiens. 2 samples. Type: Expression profiling by high throughput sequencing.
Transcriptional analysis of developing Aspergillus fumigatus biofilms reveals metabolic shifts required for biofilm maintenance
GEO Series GSE298422. Aspergillus fumigatus. 12 samples. Type: Expression profiling by high throughput sequencing.
FoxA1 and FoxA2 are required for gastric differentiation in NKX2-1-negative lung adenocarcinoma [single cell analysis]
GEO Series GSE115900. Mus musculus. 134 samples. Type: Expression profiling by high throughput sequencing.
Integrative epigenomic and transcriptomic analysis reveals the requirement of JUNB for hematopoietic fate induction
GEO Series GSE168372. Homo sapiens. 41 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Gene regulatory network analysis predicts cooperating transcription factor regulons required for FLT3-ITD+ AML growth [RNA-seq]
GEO Series GSE236772. Homo sapiens. 27 samples. Type: Expression profiling by high throughput sequencing.
FoxA1 and FoxA2 are required for gastric differentiation in NKX2-1-negative lung adenocarcinoma [total RNA-seq analysis]
GEO Series GSE115899. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
Data analysis on the design requirements of the app for communication bridging as part of Co-design Activity 1
<p>This dataset includes the analyses of the participants' statements and drawing toward the design requirements of the app for communication bridging.</p>
Automatic Requirements Testability Analysis Dataset
<p>This repository contains the data and results from the paper “<a href="https://link.springer.com/article/10.1007/s00521-024-09730-x" target="_blank" rel="noopener">Requirement testability measurement based on requirement smells</a>”. We announce a public dataset of software requirements along with their quality issues to facilitate the development and evaluation of software requirement quality tools. The Automatic Requirement Testability Analyzer (ARTA) is a web application to measure requirement testability based on the requirement smells. It facilitates the requirement engineering quality assurance process and allows requirement engineers to manage the requirements of their projects.</p> <p>ARTA dataset is a collection of requirements along with their smells and degree of testability of each requirement. It has been used in the initial evaluation of ARTA testability measurement algorithms. To the best of our knowledge, there is no public dataset for developing and evaluating the requirements smell detector tools.</p> <p><strong>Dataset Structure</strong></p> <p>ARTA dataset contains 4752 software requirements extracted from 24 project documentation (in English). The ARTA dataset (release 1.0.0) is available as a collection of Microsoft Excel files. The <em>Datasets</em> directory contains five Excel files, described as follows:</p> <ol> <li><strong>DS1.xlsx:</strong> This file contains 985 requirements from 6 projects and nine types of smells manually labeled for each requirement.</li> <li><strong>DS2.xlsx:</strong> This file contains 1092 requirements from 8 projects and nine types of smells manually labeled for each requirement.</li> <li><strong>DS3.xlsx:</strong> This file contains 1522 requirements from 6 projects and nine types of smells manually labeled for each requirement.</li> <li><strong>DS4.xlsx:</strong> This file contains 1153 requirements from 4 projects and nine types of smells manually labeled for each requirement.</li> <li><strong>SmellyWordsDictionary.xlsx:</strong> This file contains 1000 most frequent words in computer science domains, which is sorted based on their similarity. Four types of requirement smells are detected based on the words in the smelly words dictionary. For each word, the type of smell has been determined manually by experts.</li> </ol> <p>The <em>DS1_Evaluation </em>directory contains the result of different smell detection and testability measurement algorithms, described as follows:</p> <ol> <li><strong>001_dataset1kv1.xlsx (primary dataset):</strong> The same DS1 in the Datasets directory. This file contains 985 requirements from 6 projects and nine types of smells manually labeled for each requirement.</li> <li><strong>002_dataset1kv1_smell_frequency_with_testability.xlsx:</strong> This file contains the number of words of each requirement, the number of smelly words, number of smelly words according to the type of smell, degree of cleanness, and testability of each requirement, which has been computed based on “001_dataset1kv1.xlsx”. The requirements are in the same order in “001_dataset1kv1.xlsx”.</li> <li><strong>003_dataset1kv1_ARTA_result.xlsx: </strong>This file contains the result of automatically detected smells by <em>ARTA</em>, our proposed smell detector tool. The requirements are in the same order in “001_dataset1kv1.xlsx”.</li> <li><strong>004_dataset1kv1_ARTA_result_smell_frequency_with_testability.xlsx:</strong> This file contains the number of words of each requirement, the number of smelly words, number of smelly words according to the type of smell, degree of cleanness, and testability of each requirement, which has been computed based on “003_dataset1kv1_ARTA_result.xlsx”. The requirements are in the same order in “001_dataset1kv1.xlsx”.</li> <li><strong>005_dataset1kv1_Smella_result.xlsx:</strong> This file contains the result of automatically detected smells by <em>Smella</em>, another smell detector tool. The requirements are in the same order in “001_dataset1kv1.xlsx”.</li> <li><strong>006_dataset1kv1_Smella_result_smell_frequency_with_testability.xlsx:</strong> This file contains the number of words of each requirement, the number of smelly words, number of smelly words according to the type of smell, degree of cleanness, and testability of each requirement, which has been computed based on “005_dataset1kv1_Smella_result.xlsx”. The requirements are in the same order in “001_dataset1kv1.xlsx”.</li> </ol> <p> </p>
The dataset of the paper titled "Human Value Requirements in AI Systems: Empirical Analysis of Amazon Alexa"
<p>This package includes the dataset (user reviews) of Amazon Alexa and the corresponding labels of human values used in this paper which was submitted to EmpiRE 2023. It also includes the details of the results with some visualizations.</p>
MRI in Multiple Myeloma: Analysis of the Axial Skeleton Versus Whole Body. What is the Required Analysis?
ClinicalTrials.gov study NCT03618212. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Oral & Faecal Microbiota Analysis in Patients With Rectal Cancer Requiring Pre-operative Therapy Before Surgery, & Correlation With Response
ClinicalTrials.gov study NCT04601727. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Global analysis of gene expression reveals mRNA superinduction is required for the inducible immune response to a bacterial pathogen
GEO Series GSE89184. Mus musculus. 41 samples. Type: Expression profiling by high throughput sequencing; Other.
Gene regulatory network analysis predicts cooperating transcription factor regulons required for FLT3-ITD+ AML growth.
GEO Series GSE236775. Homo sapiens. 67 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by high throughput sequencing; Other.
Differential gene expression is not required for facultative sex allocation: a transcriptome analysis of brain tissue in the parasitoid wasp Nasonia vitripennis.
GEO Series GSE105796. Nasonia vitripennis. 23 samples. Type: Expression profiling by high throughput sequencing.
Expression analysis suggests that DNMT3L is required for oocyte de novo DNA methylation only in Muridae and Cricetidae rodents
GEO Series GSE236457. Heterocephalus glaber; Cavia porcellus; Spalacopus cyanus. 7 samples. Type: Expression profiling by high throughput sequencing.
Integrative gene regulatory analysis reveals transcriptional mechanisms required in mature human hepatocytes and during in vitro hepatocyte differentiation (scRNA-Seq)
GEO Series GSE182604. Homo sapiens. 18 samples. Type: Expression profiling by high throughput sequencing.
Analysis of NS2-dependent effects on influenza PB1 segment extends replication requirements beyond the canonical promoter
GEO Series GSE276697. Influenza A virus (A/WSN/1933(H1N1)). 30 samples. Type: Other.
Transcriptome analysis of Drosophila melanogaster third instar larval ring glands points to novel functions and uncovers a cytochrome p450 required for development
GEO Series GSE76304. Drosophila melanogaster. 6 samples. Type: Expression profiling by high throughput sequencing; Third-party reanalysis.
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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)
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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.