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113
datasets available to search
ShareScore release 0.9.0
Dataset results
113 results for “Temporal resolution”
Temporal resolution of gene derepression and proteome changes upon PROTAC-mediated degradation of BCL11A protein in erythroid cells
GEO Series GSE194348. Homo sapiens. 139 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing; Methylation profiling by high throughput sequencing.
Dissecting the onset of random X-chromosome inactivation at high temporal and allelic resolution
GEO Series GSE151009. Mus musculus. 105 samples. Type: Expression profiling by high throughput sequencing.
Dissection of the 4D chromatin structure of the alpha-globin locus through in vivo erythroid differentiation with extreme spatial and temporal resolution [CITE-seq]
GEO Series GSE137476. Mus musculus. 2 samples. Type: Expression profiling by high throughput sequencing; Other.
High Resolution Repli-Seq defines the temporal choreography of initiation, elongation and termination of replication in mammalian cells
GEO Series GSE137764. Mus musculus; Homo sapiens. 85 samples. Type: Other.
High-Resolution temporal profiling of developing pancreas uncovers the mechanisms of endocrine cell fate determination [MATQ-seq]
GEO Series GSE100620. Mus musculus. 27 samples. Type: Expression profiling by high throughput sequencing.
High-Resolution temporal profiling of developing pancreas uncovers the mechanisms of endocrine cell fate determination [Drop-seq]
GEO Series GSE100619. Mus musculus. 2 samples. Type: Expression profiling by high throughput sequencing.
Temporal resolution of gene derepression and proteome changes upon PROTAC-mediated degradation of BCL11A protein in erythroid cells [Bisulfite-seq]
GEO Series GSE194344. Homo sapiens. 20 samples. Type: Methylation profiling by high throughput sequencing.
High temporal resolution RNA-seq time series of serum-starved T98G human glioblastoma cells responding to serum stimulation
GEO Series GSE138662. Homo sapiens. 41 samples. Type: Expression profiling by high throughput sequencing.
Spatial and temporal heterogeneity of mouse and human microglia at single-cell resolution
GEO Series GSE124335. Homo sapiens. 32 samples. Type: Expression profiling by high throughput sequencing.
A high-resolution temporal transcriptomic dataset of porcine wound healing
GEO Series GSE305817. Sus scrofa domesticus. 150 samples. Type: Expression profiling by high throughput sequencing.
Arabidopsis defense against Botrytis cinerea: chronology and regulation deciphered by high-resolution temporal transcriptomic analysis (time series)
GEO Series GSE29642. Arabidopsis thaliana. 48 samples. Type: Expression profiling by array.
Arabidopsis defense against Botrytis cinerea: chronology and regulation deciphered by high-resolution temporal transcriptomic analysis
GEO Series GSE39598. Arabidopsis thaliana. 48 samples. Type: Expression profiling by array.
High temporal resolution of mRNA expression patterns during the early macrophage response to LPS [RNA_Seq]
GEO Series GSE52320. Mus musculus. 8 samples. Type: Expression profiling by high throughput sequencing.
Floral transition trajectories dissected by single meristem transcriptomes at high temporal resolution.
GEO Series GSE166929. Solanum lycopersicum. 6 samples. Type: Expression profiling by high throughput sequencing.
Temporal resolution of gene derepression and proteome changes upon PROTAC-mediated degradation of BCL11A protein in erythroid cells [DNA methylation]
GEO Series GSE194340. Homo sapiens. 24 samples. Type: Methylation profiling by high throughput sequencing.
Microscopy and flow cytometry raw data for: A genetically encoded biosensor to monitor dynamic changes of c-di-GMP with high temporal resolution
<p><br>This repository contains the microscopy and flow cytometry raw data for the following publication:</p> <p><strong>A genetically encoded biosensor to monitor dynamic changes of c-di-GMP with high temporal resolution</strong></p> <p>Andreas Kaczmarczyk, Simon van Vliet, Roman Peter Jakob, Raphael Dias Teixeira, Inga Scheidat, Alberto Reinders, Alexander Klotz, Timm Maier, Urs Jenal</p> <p>Biozentrum, University of Basel, 4056 Basel, Switzerland</p> <p>Correspondence to: urs.jenal[at]unibas.ch, andreas.kaczmarczyk[at]unibas.ch</p>
Spatially and temporally probing distinctive glycerophospholipid alterations in Alzheimer's disease mouse brain via high-resolution ion mobility-enabled sn-position resolved lipidomics
<p><span>High-resolution IM-MS-based four-dimensional lipidomics strategy for glycerophospholipid analysis.</span></p>
M3CropSeg: A Multi-platform, Multi-temporal, and Multi-resolution Remote Sensing Dataset for Crop Semantic to Instance and Dynamic Segmentation.
<p>Earth observation (EO) provides various multi-platform, multi-temporal, and multiresolution<br> remote sensing imagery for dynamic monitoring of planet Earth, with<br> a wide variety of uses. Crop monitoring is a typical application, which involves<br> timely gathering of the information of crop types, boundaries, and dynamic changes<br> during the whole crop growth period. However, most of the existing datasets and<br> benchmarks focus on medium-resolution (≥ 10 m) classification of the main crop<br> type by using satellite image time series (SITS), where the individual boundaries<br> (parcels) and the dynamic changes of the crop cannot be obtained, due to the<br> limited spatial resolution and the lack of multi-season annotation. In this paper,<br> a multi-platform, multi-temporal, and multi-resolution (M3) remote sensing crop<br> segmentation dataset (M3CropSeg) is introduced for very high resolution (VHR, 1<br> m) crop semantic segmentation to instance segmentation and dynamic segmentation.<br> Specifically, M3CropSeg contains 16311 pairs of airborne VHR (1 m) and SITS<br> (10 m) images, with 45 crop types and 101k instance annotations, covering a<br> 26,000 km<sup>2</sup> area of California in the U.S. M3CropSeg has various challenges,<br> including M3 data fusion, class imbalance, fine-grained classification, and multilabel<br> classification. Three tracks are designed for M3CropSeg, i.e., M3 semantic<br> segmentation, M3 instance segmentation, and M3 dynamic segmentation, to obtain<br> high-resolution pixel-level, parcel-level, and multi-season crop types, respectively.<br> The corresponding benchmarks are also provided to address the above challenges,<br> along with a variety of experimental analyses.</p>
Linking Tau PET to Medial Temporal Lobe Subregions With High Resolution MRI
ClinicalTrials.gov study NCT04251130. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Interest of Using Deep Learning Algorithm for Otosclerosis Detection on Temporal Bone High Resolution CT
ClinicalTrials.gov study NCT05987215. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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.