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Dataset results
63 results for “feature type”
Inflammatory Type 2 cDCs Acquire Features of cDC1s and Macrophages to Orchestrate Immunity to Respiratory Virus Infection
GEO Series GSE149619. Mus musculus. 28 samples. Type: Expression profiling by high throughput sequencing; Expression profiling by array.
Dissection of thousands of cell type-specific enhancers identifies dinucleotide repeat motifs as general enhancer features
GEO Series GSE49809. Drosophila melanogaster. 3 samples. Type: Expression profiling by high throughput sequencing; Other.
PMP22 antisense oligonucleotides reverse clinical, pathological, and molecular features of Charcot-Marie-Tooth disease type 1A
GEO Series GSE103799. Mus musculus. 43 samples. Type: Expression profiling by high throughput sequencing.
Data from: Variation in three community features across habitat types and scales within a 15-ha subtropical evergreen-deciduous broadleaved mixed forest dynamics plot in China
The evergreen and deciduous broadleaved mixed forests (EDBMFs) belong to one of the ecosystems most sensitive to environmental change, however, little is known about the environmental determinants for their plant diversity and forest structure at different habitat types and spatial scales. Here, we used data from a 15-ha (300×500 m) forest dynamic plot (FDP) of an old-growth EDBMF to examine the patterns and determinants of the three community features (stem abundance, rarefied species richness and basal area) in three habitat types (ridge, hillside and foothill) and at three spatial scales (20×20 m, 50×50 m and 100×100 m). We found that the three community features significantly changed with habitat type, but only one of them (rarefied richness) changed with scale. Among spatial scales, the principle environmental factors that widely affected community features were pH, SOM and TP, while these effects only taken place at certain habitat. Variations in the three community features explained by soil conditions were generally greater than those explained by topographical conditions. With changes in habitat type, the proportion of variations explained by environmental conditions was 31-53%, 8-25%, and 18-26% for abundance, rarefied richness and basal area (BA), respectively. With increasing spatial scale, the variations explained by environmental conditions were 44-75% for abundance, 28-95% for rarefied richness, and 18-86% for BA. Our study demonstrated that environmental factors had great impacts on the plant diversity and forest structure in the EDBMFs, especially the soil factors such as pH. In addition, the importance of the environmental determinants on these community features was highly related to the spatial scale.
Sex disparities in clinical features and burden of narcolepsy type 1
<p><span>To investigate potential sex-related differences in patients with <span>narcolepsy type 1 (NT1), we carried out an analysis of baseline data from 93 women and 89 men with NT1 who participated in the TElemedicine for NARcolepsy (TENAR) trial. The following data were considered: sociodemographics, diagnostic (disease history, polysomnography, orexin, HLA) and clinical features including sleepiness (Epworth Sleepiness Scale, ESS), cataplexy and other narcolepsy symptoms, disease severity (Narcolepsy Severity Scale, NSS), pharmacological treatment, depressive symptoms (Beck Depression Inventory, BDI), and self-reported relevance of 8 narcolepsy-related issues. We found that, compared with men, significantly more women reported automatic behaviors (55.4% vs. 40%) and had higher ESS (median 10 vs. 9) and BDI scores (median 10.5 vs. 5), and there was a trend for a higher NSS total score in women (median 19 vs. 18, p=0.057). More women than men were officially recognized as having a disability (38% vs. 22.5%) and considered 5/8 narcolepsy-related issues investigated as a relevant problem. More severe sleepiness and a greater narcolepsy-related burden in women could mirror sex differences present in the general population, or may be related to suboptimal management of NT1 or to more severe depressive symptoms in women. </span>Future studies and guidelines should address these aspects.<span> </span></span></p>
FEATURES OF OPTICAL FIBER SENSORS DIFFERENT FROM OTHER TYPES OF SENSOR SYSTEMS
Open the record for dataset details and reuse information.
Cancer type and survival prediction based on transcriptomic feature map
<p>Upload all source data of our paper "Cancer type and survival prediction based on transcriptomic feature map"</p> <p># Zendo Project</p> <p> </p> <p>This project encompasses a series of files related to the integration of cancer survival data, feature extraction, and the augmentation of data using Generative Adversarial Networks (GANs). The project aims to improve the accuracy of survival predictions and support cancer research through in-depth analysis and processing of cancer-related data.</p> <p> </p> <p>## Project Overview</p> <p> </p> <p>This repository includes HDF5 files of cancer survival data, intermediate files for feature extraction, and data files augmented using GAN networks. Among them, `Data Integration.ipynb` is a Jupyter notebook used for slicing and merging `feat_part1.h5` to `feat_part6.h5`, while files starting with four letters are data files for specific cancer types, and files starting with `GAN` are data that have been augmented by GAN networks.</p> <p> </p> <p>## File Descriptions</p> <p> </p> <p>### Data Files</p> <p> </p> <p>- BLCA.h5: Survival data for bladder cancer.</p> <p>- BRCA.h5: Survival data for breast cancer.</p> <p>- HNSC.h5: Survival data for head and neck cancer.</p> <p>- KIRC.h5: Survival data for kidney clear cell carcinoma.</p> <p>- LIHC.h5: Survival data for liver hepatocellular carcinoma.</p> <p>- LUAD.h5: Survival data for lung adenocarcinoma.</p> <p>- LUSC.h5: Survival data for lung squamous cell carcinoma.</p> <p>- OVxx.h5: Survival data for ovarian cancer.</p> <p>- SKCM.h5: Survival data for skin cutaneous melanoma.</p> <p>- STAD.h5: Survival data for stomach adenocarcinoma.</p> <p> </p> <p>### Feature Extraction Files</p> <p> </p> <p>- feat_part1.h5 to feat_part6.h5: Intermediate data files for feature extraction, used to store partial feature data during the processing.</p> <p> </p> <p>### Feature Map Files</p> <p> </p> <p>- fmap_BLCA.h5: Feature map data for bladder cancer.</p> <p>- fmap_BRCA.h5: Feature map data for breast cancer.</p> <p>- fmap_HNSC.h5: Feature map data for head and neck cancer.</p> <p>- fmap_KIRC.h5: Feature map data for kidney clear cell carcinoma.</p> <p>- fmap_LIHC.h5: Feature map data for liver hepatocellular carcinoma.</p> <p>- fmap_LUAD.h5: Feature map data for lung adenocarcinoma.</p> <p>- fmap_LUSC.h5: Feature map data for lung squamous cell carcinoma.</p> <p>- fmap_OVxx.h5: Feature map data for ovarian cancer.</p> <p>- fmap_SKCM.h5: Feature map data for skin cutaneous melanoma.</p> <p>- fmap_STAD.h5: Feature map data for stomach adenocarcinoma.</p> <p> </p> <p>### GAN Augmented Data Files</p> <p> </p> <p>- GAN500sur_BLCA.h5: Augmented survival data for bladder cancer.</p> <p>- GAN500sur_BRCA.h5: Augmented survival data for breast cancer.</p> <p>- GAN500sur_HNSC.h5: Augmented survival data for head and neck cancer.</p> <p>- GAN500sur_KIRC.h5: Augmented survival data for kidney clear cell carcinoma.</p> <p>- GAN500sur_LIHC.h5: Augmented survival data for liver hepatocellular carcinoma.</p> <p>- GAN500sur_LUAD.h5: Augmented survival data for lung adenocarcinoma.</p> <p>- GAN500sur_LUSC.h5: Augmented survival data for lung squamous cell carcinoma.</p> <p>- GAN500sur_OVxx.h5: Augmented survival data for ovarian cancer.</p> <p>- GAN500sur_SKCM.h5: Augmented survival data for skin cutaneous melanoma.</p> <p>- GAN500sur_STAD.h5: Augmented survival data for stomach adenocarcinoma.</p> <p> </p> <p>### Jupyter Notebook</p> <p> </p> <p>- Data Integration.ipynb: The code for slicing and merging `feat_part1.h5` to `feat_part6.h5`, a core tool in the feature integration process.</p> <p> </p> <p>## Usage Guide</p> <p> </p> <p>1. Data Preparation: Place the original data files in the project directory.</p> <p>2. Feature Extraction: Run the `Data Integration.ipynb` notebook to merge the intermediate feature extraction files.</p> <p>3. Data Augmentation: Use GAN networks to augment survival data, generating new data files.</p> <p>4. Analysis Results: Use the augmented data for survival prediction analysis.</p> <p> </p> <p>## Contributing and Collaboration</p> <p> </p> <p>We welcome contributions and collaboration in any form. If you have any questions or suggestions about the project, please contact us through GitHub Issues.</p> <p> </p> <p> </p>
Clinicopathological Features and Molecular Typing of Invasive Fibroma of Abdominal Wall
ClinicalTrials.gov study NCT06204198. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Clinical Features and Outcome in Patients With Osseomuscular Type of Wilson's Disease
ClinicalTrials.gov study NCT04965558. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Morphological Pancreatic Features in Diabetes Mellitus Type 2
ClinicalTrials.gov study NCT05762653. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Data from: Variation in three community features across habitat types and scales within a 15-ha subtropical evergreen-deciduous broadleaved mixed forest dynamics plot in China
Open the record for dataset details and reuse information.
SMARCA4-deficient rhabdoid tumours show intermediate molecular features between SMARCB1-deficient rhabdoid tumours and small cell carcinomas of the ovary, hypercalcaemic type.
GEO Series GSE175893. Homo sapiens. 53 samples. Type: Expression profiling by high throughput sequencing; Methylation profiling by genome tiling array; Third-party reanalysis.
Aboral cell types of Clytia and coral larvae have shared features and link taurine to the regulation of settlement [bulk_RNAseq]
GEO Series GSE288934. Pocillopora acuta; Astroides calycularis; Clytia hemisphaerica. 16 samples. Type: Expression profiling by high throughput sequencing.
Inflammatory Type 2 cDCs Acquire Features of cDC1s and Macrophages to Orchestrate Immunity to Respiratory Virus Infection (microarray)
GEO Series GSE149618. Mus musculus. 24 samples. Type: Expression profiling by array.
Adipocyte Precursor Cells from First Degree Relatives of type 2 diabetic patients feature changes of hsa-mir-23a-5p, -193a-5p, and -193b-5p and Insulin-Like Growth Factor 2 expression [RNA-Seq]
GEO Series GSE161989. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing.
Adipocyte Precursor Cells from First Degree Relatives of type 2 diabetic patients feature changes of hsa-mir-23a-5p, -193a-5p, and -193b-5p and Insulin-Like Growth Factor 2 expression [smallRNA-seq]
GEO Series GSE162133. Homo sapiens. 4 samples. Type: Non-coding RNA profiling by high throughput sequencing.
FIGURE 4 in New records of Neopetrosia carbonaria (Lamarck, 1814) from the Brazilian coast reveal new morphological features and spicule types
FIGURE 4. Megascleres and microscleres of Neopetrosia carbonaria. (A) Oxeas I, (B) details of the apex of oxeas I, (C) Oxeas II, (D'–D'') Toxas.
Adipocyte Precursor Cells from First Degree Relatives of type 2 diabetic patients feature changes of hsa-mir-23a-5p, -193a-5p, and -193b-5p and Insulin-Like Growth Factor 2 expression
GEO Series GSE162135. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing; Non-coding RNA profiling by high throughput sequencing.
Molecular and immunological interrogation of a live-attenuated enterotoxigenic Escherichia coli vaccine highlights features unique to wild type infection
GEO Series GSE134792. Escherichia coli; Homo sapiens. 250 samples. Type: Protein profiling by protein array.
YAP1 Activation Rescues Nagashima-type Palmoplantar Keratosis Pathological Features in Skin Organoid Disease Models
GEO Series GSE285879. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
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.