Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
119
datasets available to search
ShareScore release 0.9.0
Dataset results
119 results for “time series analysis”
Integrated time-series analysis and high-content CRISPR screening delineate the dynamics of macrophage immune regulation [CROP-seq KO150]
GEO Series GSE263761. Mus musculus. 27 samples. Type: Expression profiling by high throughput sequencing; Other.
Analysis of population heterogeneity in CHO cells by genome-wide DNA methylation analysis and by multi-modal single-cell sequencing [time series]
GEO Series GSE273684. Cricetulus griseus. 21 samples. Type: Methylation 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.
Time series analysis of glucose-lactose diauxie: involvement of stringent response
GEO Series GSE7265. Escherichia coli. 136 samples. Type: Expression profiling by array.
Time series integrative analysis of RNA-Seq and miRNA expression data reveals key biologic pathways during keloid formation [RNA-seq]
GEO Series GSE113619. Homo sapiens. 64 samples. Type: Expression profiling by high throughput sequencing.
Analysis of time series gene expression and DNA methylation reveals the molecular features of myocardial infarction progression (MeDIP-seq)
GEO Series GSE206280. Mus musculus. 18 samples. Type: Methylation profiling by high throughput sequencing.
Integrated time-series analysis and high-content CRISPR screening delineate the dynamics of macrophage immune regulation [CROP-seq KO15]
GEO Series GSE263760. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing; Other.
Transthyrectin, a novel prognostic marker of POCD revealed by time-series RNA sequencing analysis
GEO Series GSE276942. Mus musculus. 17 samples. Type: Expression profiling by high throughput sequencing.
Time series scRNAseq analysis in mouse and human informs optimization of rapid astrocyte differentiation protocols
GEO Series GSE211140. Homo sapiens; Mus musculus. 45 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Time-series proteomic analysis of body fluid extracellular vesicles in amyotrophic lateral sclerosis
GEO Series GSE242978. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Post-infectious time-series DNA methylation analysis in gastric epithelial cells
GEO Series GSE89269. Homo sapiens. 22 samples. Type: Methylation profiling by genome tiling array.
A systematic analysis of the time series gene expression in TGF-beta induced EMT by Next-generation sequencing
GEO Series GSE69667. Homo sapiens. 16 samples. Type: Expression profiling by high throughput sequencing.
Time-series transcriptomic analysis of lung fibroblasts in bleomycin- and silica-induced pulmonary fibrosis
GEO Series GSE110540. Mus musculus. 21 samples. Type: Expression profiling by high throughput sequencing.
Multi-time series RNA-Seq analysis of Enterobacter lignolyticus SCF1during growth in lignin-amended medium
GEO Series GSE96828. [Enterobacter] lignolyticus SCF1. 18 samples. Type: Expression profiling by high throughput sequencing.
Analysis of time series gene expression and DNA methylation reveals the molecular features of myocardial infarction progression
GEO Series GSE206282. Mus musculus. 36 samples. Type: Expression profiling by high throughput sequencing; Methylation profiling by high throughput sequencing.
Time series transcriptome and DNA methylome analysis of immature T-cells and reMAIT cells
GEO Series GSE88938. Homo sapiens. 26 samples. Type: Methylation profiling by genome tiling array; Non-coding RNA profiling by array; Expression profiling by array.
Discovery of progenitor signatures by time series synexpression analysis during Drosophila cell immortalization
GEO Series GSE73354. Drosophila melanogaster. 50 samples. Type: Expression profiling by high throughput sequencing; Expression profiling by array.
Data from: Trends in malaria in Odisha, India—an analysis of the 2003-2013 time-series data from the National Vector Borne Disease Control Program
Background: Although Odisha is the largest contributor to the malaria burden in India, no systematic study has examined its malaria trends. Hence, the spatio-temporal trends in malaria in Odisha were assessed against the backdrop of the various anti-malaria strategies implemented in the state. Methods: Using the district-wise malaria incidence and blood examination data (2003-2013) from the National Vector Borne Disease Control Program, blood examination-adjusted time-trends in malaria incidence were estimated and predicted for 2003-2013 and 2014-2016, respectively. An interrupted time series analysis using segmented regression was conducted to compare the disease trends between the pre (2003-2007) and post-intensification (2009-2013) periods. Key-informant interviews of state stakeholders were used to collect the information on the various anti-malaria strategies adopted in the state. Results: The state annual malaria incidence declined from 10.82/1000 to 5.28/1000 during 2003-2013 (adjusted annual decline: -0.54/1000, 95% CI: -0.78 to -0.30). However, the annual blood examination rate remained almost unchanged from 11.25% to 11.77%. The key-informants revealed that intensification of anti-malaria activities in 2008 led to a more rapid decline in malaria incidence during 2009-2013 as compared to that in 2003-2007 [adjusted decline: -0.83 (-1.30 to -0.37) and -0.27 (-0.41 to -0.13), respectively]. There was a significant difference in the two temporal slopes, i.e., -0.054 (-0.10 to -0.002, p=0.04) per 1000 population per month, between these two periods, indicating almost a 200% greater decline in the post-intensification period. Although, the seven southern high-burden districts registered the highest decline, they continued to remain in that zone, thereby, making the achievement of malaria elimination (incidence <1/1000) unlikely by 2017. Conclusion: The anti-malaria strategies in Odisha, especially their intensification since 2008, have helped improve its malaria situation in recent years. These successful measures need to be sustained and perhaps intensified further for eliminating malaria from Odisha.
Fluorescence image time series for pair correlation function analysis
<p>Data files for the tutorial "<a href="https://github.com/cgohlke/ipcf.ipynb">Pair Correlation Function Analysis of Fluorescence Fluctuations in Big Image Time Series using Python</a>", presented at the Big Data Image Processing & Analysis BigDIPA workshops 2016, 2017, and 2018.<br><br>The <strong>Simulation_Channel.bin</strong> file contains the result of a simulation of fluorescent particles diffusing on a 64x64 grid. The grid contains a diagonal, 300 nm wide channel, which restricts free diffusion. The file was produced using the Globals for Images, SimFCS software. The array shape is (32000, 64, 64), the data type is uint16.<br><br>The <strong>Simulation_Channel.ipcf.bin</strong> file contains the expected results of an ipCF analysis with 32 points at radius 6, and 32 bins. The array shape is (52, 52, 32, 30), the data type is float32.<br><br>The <strong>nih3t3-egfp_2.zip</strong> dataset contains a time series of SPIM images of a biological cell. It consists 35,000 TIFF files of 1024x512 16-bit grayscale samples each, 34.5 GB total.</p>
Video results of the study "Dynamic Region of Interest Generation for Maritime Horizon Line Detection using Time Series Analysis"
<p># Video Results of the Study "Dynamic Region of Interest Generation for Maritime Horizon Line Detection using Time Series Analysis"</p> <p>This repository contains the result videos from the study titled *"Dynamic Region of Interest Generation for Maritime Horizon Line Detection using Time Series Analysis"*. These videos showcase the performance of the algorithm on two datasets: the **Singapore Maritime Dataset (Onboard Segment)** and the **Buoy Dataset**. Please note that the original datasets are not included in this repository but are publicly available elsewhere.</p> <p>## Contents</p> <p>### 1. Singapore Maritime Dataset (Onboard Segment)<br>The videos labeled with **MVI** belong to the Singapore Maritime Dataset. These result videos demonstrate the performance of the horizon line detection algorithm on onboard maritime footage. For example:<br>- **MVI_0792_VIS_OB.avi** - Original input video (available in the Singapore Maritime Dataset)<br>- **MVI_0792_VIS_OB_R.avi** - The output video showing the results of the algorithm</p> <p>### 2. Buoy Dataset<br>The videos labeled with **buoyGT** belong to the Buoy Dataset. These results show the performance of the algorithm near maritime buoys. For example:<br>- **buoyGT_2_5_3_0.avi** - Original input video (available in the Buoy Dataset)<br>- **buoyGT_2_5_3_0_R.avi** - The output video showing the results of the algorithm</p> <p>## Video Naming Convention<br>Each result video follows a consistent naming convention related to the original input videos:<br>- **Original Video Filename**: [dataset]_[video details].avi<br>- **Result Video Filename**: [original video filename]_R.avi</p> <p>For instance:<br>- **buoyGT_2_5_3_0.avi** corresponds to **buoyGT_2_5_3_0_R.avi**<br>- **MVI_0792_VIS_OB.avi** corresponds to **MVI_0792_VIS_OB_R.avi**</p> <p>The **_R** suffix in the result videos indicates that the video contains the output of the horizon line detection algorithm.</p> <p>## How to Use<br>- To view the results for a specific video, locate the original input video in the corresponding public dataset and find the matching result video in this repository.<br>- For example, the result for the video "buoyGT_2_5_3_0.avi" can be found as "buoyGT_2_5_3_0_R.avi".</p> <p>## Datasets<br>The original datasets used in this study are not included in this repository. They are publicly available from the following sources:<br>1. **Singapore Maritime Dataset** (Onboard Segment)<br>2. **Buoy Dataset**</p> <p>Please refer to the respective dataset repositories for the original video files.</p> <p>## Citation<br>If you use these videos or the method presented in this study in your work, please cite the following:</p> <p>*Dynamic Region of Interest Generation for Maritime Horizon Line Detection using Time Series Analysis*.</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.