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.
1,118
datasets available to search
ShareScore release 0.9.0
Dataset results
1,118 results for “Time series”
Time series of Escherichia coli BL21(DE3) after temperature upshift from 37°C to 42°C, tiling arrays
GEO Series GSE148034. Escherichia coli BL21(DE3). 9 samples. Type: Expression profiling by genome tiling array.
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.
DIISCO: A Bayesian framework for inferring dynamic intercellular interactions from time-series single-cell data
GEO Series GSE255888. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing.
Drosophila ananassae embryonic development RAMPAGE time series
GEO Series GSE89298. Drosophila ananassae. 22 samples. Type: Expression profiling by high throughput sequencing.
Expression of time series data of spawning with light/dark conditions for mature adult Ciona intestinalis
GEO Series GSE31902. Ciona intestinalis. 24 samples. Type: Expression profiling by array.
Photoperiodic time-series mRNASeq assays of the sheep pars tuberalis
GEO Series GSE144677. Ovis aries. 60 samples. Type: Expression profiling by high throughput sequencing.
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.
Drosophila erecta embryonic development RAMPAGE time series
GEO Series GSE89304. Drosophila erecta. 23 samples. Type: Expression profiling by high throughput sequencing.
Seasonal time series with random collective outliers
<p>Dataset containing collections of time series ECG 1[1], ECG 2[1] and Sunspot[2].</p> <p>Each collection comprises 10 time series, where each time series has exactly one collective outlier.</p> <p>[1] https://www.cs.ucr.edu/~eamonn/discords</p> <p>[2] Andrews, D.F., Herzberg, A.M.: Data: a collection of problems from many fieldsfor the student and research worker. Springer Science & Business Media (2012)</p>
Artifact for Counterfactual Explanations for Machine Learning on Multivariate HPC Time Series Data
<p>This includes the data sets used in the SC'20 submission "Counterfactual Explanations for Machine Learning on Multivariate HPC Time Series Data".</p>
SPEI and SSI time-series datasets for the Indus Basin of Pakistan
<p>The file contains 2 datasets namely:<br> 1-Standard Precipitation and Evapotranspiration Index (SPEI) for four catchments (Chenab,Jhelum, Indus and Kabul) of the Indus Basin<br> 2-Standard Streamflow Index (SSI) for four catchments of the Indus Basin.</p> <p>SPEI:<br> The Standard Precipitation and Evapotranspiration Index (SPEI) (Vicente-Serrano et al., 2010a) is the indicator used <br> for quantification and monitoring of meteorological droughts.<br> SPEI is calculated for the four catchments (Chenab,Jhelum, Indus and Kabul) at each catchment level using gridded climate data from the CRU data set (Harris et al., 2020). <br> For catchment level SPEI calculations, gridded precipitation and Potential Evapotranspiration values extracted from CRU dataset are averaged over the catchment. A map(study_area) delineating the four catchments is provided.</p> <p>SSI:<br> The Standard Streamflow Index (SSI) (Nalbantis and Tsakiris, 2009; Modarres, 2007) is used (also called the Streamflow<br> Drought Index and the Streamflow Runoff Index) to quantify and analyze hydrological droughts.SSIs are computed at catchment outlets as shown in the attached map (study_area). Streamflow station names are Marala(for Chenab),Mangla (Jhelum), Tarbela(for Indus) and Nowshera(for Kabul).</p> <p><br> Units: Both the datasets are dimensionless </p> <p><br> References:</p> <p>Harris, I. C. and Jones, P. D.: CRU TS4.03: Climatic Research Unit (CRU) Time-Series (TS) version 4.03 of high-resolution gridded data of month-by-month variation in climate (Jan. 1901- Dec. 2018), https://doi.org/10.5285/10d3e3640f004c578403419aac167d82, university of East Anglia Climatic Research Unit; Centre for Environmental Data Analysis, 2020.</p> <p>Modarres, R.: Streamflow drought time series forecasting, Stochastic Environmental Research and Risk Assessment, 21, 223–233, https://doi.org/10.1007/s00477-006-0058-1, 2007.</p> <p>Nalbantis, I. and Tsakiris, G.: Assessment of hydrological drought revisited, Water Resources Management, 23, 881–897,<br> https://doi.org/10.1007/s11269-008-9305-1, 2009.</p> <p>Vicente-Serrano, S. M., Beguería, S., and López-Moreno, J. I.: A Multiscalar Drought Index Sensitive to GlobalWarming: The Standardized Precipitation Evapotranspiration Index, Journal of Climate, 23, 1696–1718, https://doi.org/10.1175/2009JCLI2909.1, https://doi.org/10.1175/2009JCLI2909.1, 2010a.</p>
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.
Long time-series (1980-2020) high-resolution (1km) and multi-depth soil organic carbon dataset in China
<p>unit: kg C m-2 (soil oganic carbon density)</p><p>0100: denote 0-100 cm</p><p>020: denote 0-20 cm</p><p>Example 1980: 1980-1984 (five years mean soc)</p><p> </p><p> </p><p> </p>
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>
Data for the preprint of "Layer-by-layer unsupervised clustering of statistically relevant fluctuations in noisy time-series data of complex dynamical systems"
<p>README: description of the files. </p> <p>This Zenodo repository contains all the data and original code necessary to reproduce the results of the paper https://doi.org/10.48550/arXiv.2402.07786. The code (continuously mantained and updated) is available open-source as a Python package at https://pypi.org/project/onion-clustering/ and on GitHub (https://github.com/matteobecchi/timeseries_analysis). </p> <p>The repository contains the folders "Fig1", "Fig2" etc, which contain the corresponding Datasets, together with the code to reproduce the figures. Additionally, the folder "FigS1 contains code and data for FigS1. </p> <p>The repository also contains the Supplementary Movies S1 to S4, in .mp4 format. </p>
Carotenoid and Apocarotenoid profiles across a 24h time series of stress in three streptophytes
<p>Raw stress metabolite time series (and calibrations) data generated by RP-C<sub>30</sub>-HPLC-UV-Vis (carotenoids and chlorophylls) and HS-SPME-GC-MS (volatile apocarotenoids)</p>
SITS-Former: A pre-trained spatio-spectral-temporal representation model for Sentinel-2 time series classifcation
<p>This is the unlabeled dataset we introduced in the presented paper '<strong>SITS-Former: A pre-trained spatio-spectral-temporal representation model for Sentinel-2 time series classifcation</strong>'. This dataset can be used to pre-train a specified deep learning model (such as SITS-Former, CNN-Transformer, ConvLSTM. etc) for patch-based Sentinel-2 time series classification. </p> <p>In this dataset, each sample corresponds to an unlabeled image patch time series, which is stored as a separate numpy file named '<em>unlabeled_XXX.npz</em>'. You can use '<em>np.load</em>' to open a saved '<em>.npz</em>' file and get two arrays (querid by "ts" and "doy") from the returned dictionary. The code will be released at <em>https://github.com/linlei1214/SITS-Former</em> soon.</p>
Methylamonium-Free Lead Halide thin film degradation GIWAXS time series.
<p>Data collected at the NSLS-II CMS beamline, February 2024. The dataset is for a particular sample, 28c, which was composed of a glass substrate, and a FA(1-x)CsxPbI3 thin film. The FA and Cs content were approximately 0.98 and 0.02, respectively. Note that FA = formamidinium. </p> <p>The data archive contains a file '28c_in-situ_data.json' with details of each scan file and image file. The scan files are in simple column format, while the image files are png. The data represents 18 images, 9 at 0.1° incidence and 9 at 0.25° incidence with a 10 second data collection time with an X-ray area detector. The images comprise a time series during degradation in 85% relative humidity over a time interval of approximately 22511 seconds (about 6.25 hours). </p> <p>See the forthcoming article in the journal MRS Advances for additional details. The title of the article is:</p> <p><strong><span>Degradation processes in methylammonium-free lead-halide perovskite thin films in high-humidity conditions.</span></strong></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>
The supporting data for the paper "Synergistic Enhancement of LSTM Time Series Prediction via Companion Strategy and Decay Operator-Improved Aquila Optimization"
<p>数据生成程序</p> <p>该数据集是使用 The Investor's Exchange API 生成的,脚本会定期获取标准普尔 500 指数中所有公司的历史股价。详细说明和脚本可以在 GitHub 存储库中找到。该数据每5年更新一次,最近一次更新于2018年2月。</p> <p> </p> <p>数据处理方法和步骤</p> <p>数据处理的主要步骤包括:</p> <p> </p> <p>数据采集:使用 API 获取每只股票的历史数据,存储在.csv文件中。</p> <p>数据清理:删除重复条目,纠正格式错误,确保数据完整性。</p> <p>数据合并:将单个股票数据合并到一个大.csv文件中,以便于使用。</p> <p>数据验证:通过检查时间序列的连续性和完整性来验证数据的准确性。</p> <p>使用的设备和工具</p> <p>数据采集工具:Python 脚本</p> <p>数据处理工具:用于数据清洗和处理的 Pandas 库</p> <p>数据存储:CSV文件格式</p> <p>时间和地理范围</p> <p>时间范围:数据涵盖过去 5 年的历史股票价格,最新更新于 2018 年 2 月。</p> <p>地理范围:数据涵盖标准普尔500指数中的所有公司,主要是美国市场数据。</p> <p>时间和空间分辨率</p> <p>时间分辨率:每日数据,每个交易日一条记录。</p> <p>空间分辨率:无地理空间分辨率;数据按公司分组。</p> <p>表格数据</p> <p>条目总数:条目总数取决于标准普尔500指数中的公司数量和总交易日数。</p> <p>行标题和列标题:</p> <p>日期:交易日期格式为yy-mm-dd</p> <p>开盘价:开盘价(美元)</p> <p>最高价:当日最高价(美元)</p> <p>最低价:当日最低价格(美元)</p> <p>收盘价:收盘价(美元)</p> <p>交易量:成交股数</p> <p>名称:以股票代码的名义</p> <p>缺失数据</p> <p>数据集在某些交易日可能缺少数据,主要是由于非交易日(例如节假日)或API数据采集过程中的临时网络问题。这些缺失的数据通常不会影响整体分析结果。</p> <p> </p> <p>数据错误</p> <p>由于数据源是第三方 API,因此数据错误的可能性很低。如果发现错误,通常是由于 API 数据采集过程中的临时网络问题造成的。数据清理过程旨在最大限度地减少和纠正这些错误。</p> <p> </p> <p>数据文件说明</p> <p>数据文件类型:</p> <p>all_stocks_5yr.csv:包含所有股票的合并数据文件。</p> <p>individual_stocks_5yr文件夹:包含每个股票的单个.csv文件。</p> <p>文件内容和格式:文件采用 CSV 格式,每个文件包含日期、开盘价、最高价、最低价、收盘价、成交量和股票名称列。</p> <p>文件大小:文件大小取决于特定股票的交易数据量,通常从几MB到几十MB不等。</p> <p>文件格式说明</p> <p>数据以通用的 CSV 格式存储,可以使用 Excel、Notepad++ 或任何支持 CSV 文件的工具打开和查看。对于进一步的数据处理和分析,可以使用 Python Pandas 库。</p> <p> </p> <p>总结</p> <p>该数据集提供过去5年标准普尔500指数中所有公司的历史股价数据,包括开盘价、最高价、最低价、收盘价、交易量等详细信息。它适用于各种财务数据分析和建模应用。数据通过 API 获取并处理,以确保准确性和完整性。</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.