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1,118
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ShareScore release 0.9.0
Dataset results
1,118 results for “Time series”
Time series of Balb/c spleen infected with Plasmodium yoelii non-lethal vs lethal strains
GEO Series GSE17603. Mus musculus. 12 samples. Type: Expression profiling by array.
Genome-wide time series analysis of gastrin induced gene expression in AR42J cells with and without protein synthesis
GEO Series GSE32869. Rattus norvegicus. 90 samples. Type: Expression profiling by array.
CIS (multiple sclerosis) (case-control) (time-series)
GEO Series GSE13732. Homo sapiens. 113 samples. Type: Expression profiling by array.
Time-series of IL-6 stimulated primary mouse hepatocytes
GEO Series GSE21031. Mus musculus. 12 samples. Type: Expression profiling by array.
High-time-resolution time-series transcriptome data of Pseudomonas aeruginosa PAO1 under two inverted oxygen-availability transitions
GEO Series GSE52445. Pseudomonas aeruginosa PAO1; Pseudomonas aeruginosa. 28 samples. Type: Expression profiling by array.
Time series of diabetes and exercise training induced expression changes in cardiac muscle of mice
GEO Series GSE4616. Mus musculus. 12 samples. Type: Expression profiling by array.
Time series trancriptional profiling of mouse liver after up to 13 weeks administration of Phenobarbital [mRNA]
GEO Series GSE80018. Mus musculus. 69 samples. Type: Expression profiling by array.
human peripheral blood derived monocytes, LPS stimulation time-series
GEO Series GSE5504. Homo sapiens. 10 samples. Type: Expression profiling by array.
CASSINI JUP UVIS SOLAR STELLAR BRIGHTNESS TIME SERIES 1.0
Photometric observations of stellar occultations by Saturnian rings, satellites, atmospheres, and Jovian atmosphere.
LP Moon MAG Level 4 Lunar Magnetic Field Time Series V1.0
LP magnetometer (MAG) Level 2 Data (CODMAC Level 4). Selected and filtered time series data at 5 second intervals. Approximate measurements of the lunar crustal magnetic field with external (solar wind and magnetospheric) fields removed or minimized.
Long-term prediction of nonlinear time series
This paper is about applying recurrent least squares support vector machines (LS-SVM) on three ESTSP08 competition datasets. Least squares support vector machines are used as nonlinear models in order to avoid local minima problems. Then prediction task is re-formulated as function approximation task. Recurrent LS-SVM uses nonlinear autoregressive exogenous (NARX) model to build nonlinear regressor, by estimating in each iteration the next output value, given the past output and input measurements.
Earth Radiation area average time series through Wide-field-of-view nonscanner abroad Earth Radiation Budget Satellite Edition 4.1
ERBE_S10N_WFOV_SF_ERBS_AreaAverageTimeSeries is the Earth Radiation Budget Experiment (ERBE) through Earth Radiation Budget Satellite (ERBS) area average time series through Wide-field-of-view nonscanner abroad Earth Radiation Budget Satellite Edition 4.1 data product. Understanding the mean and variability of the Earth’s radiation budget (ERB) at the Top-of-Atmosphere (TOA) and surface is a fundamental quantity governing climate variability and, for that reason, NASA has been making concerted efforts to observe the ERB since1984 through two projects: ERBE and Clouds and the Earth's Radiant Energy System (CERES), that span nearly 30 years to date. The ERBE MEaSUREs project uses knowledge gained in the last 10 years through CERES data analyses and applies the knowledge to existing data to develop long-term (nearly 30 years) consistent and calibrated data product (TOA irradiances at the same radiometric scale) from multiple missions (ERBS and CERES). This project proposes to produce level 3 surface irradiance products that are consistent with observed TOA irradiances in a framework of 1D radiative transfer theory. Based on these TOA and surface irradiance products, a data product will be developed which contains the contribution of atmospheric and cloud property variability to TOA and surface irradiance variability. All algorithms used in the process are based on existing CERES algorithms. All data sets produced by this project will be available from the Atmospheric Science Data Center.
SCALABLE TIME SERIES CHANGE DETECTION FOR BIOMASS MONITORING USING GAUSSIAN PROCESS
SCALABLE TIME SERIES CHANGE DETECTION FOR BIOMASS MONITORING USING GAUSSIAN PROCESS VARUN CHANDOLA* AND RANGA RAJU VATSAVAI* Abstract. Biomass monitoring, specifically, detecting changes in the biomass or vegetation of a geographical region, is vital for studying the carbon cycle of the system and has significant implications in the context of understanding climate change and its impacts. Recently, several time series change detection methods have been proposed to identify land cover changes in temporal profiles (time series) of vegetation collected using remote sensing instruments. In this paper, we adapt Gaussian process regression to detect changes in such time series in an online fashion. While Gaussian process (GP) has been widely used as a kernel based learning method for regression and classification, their applicability to massive spatio-temporal data sets, such as remote sensing data, has been limited owing to the high computational costs involved. In our previous work we proposed an efficient Toeplitz matrix based solution for scalable GP parameter estimation. In this paper we apply these solutions to a GP based change detection algorithm. The proposed change detection algorithm requires a memory footprint which is linear in the length of the input time series and runs in time which is quadratic to the length of the input time series. Experimental results show that both serial and parallel implementations of our proposed method achieve significant speedups over the serial implementation. Finally, we demonstrate the effectiveness of the proposed change detection method in identifying changes in Normalized Difference Vegetation Index (NDVI) data.
CASSINI SATURN UVIS SOLAR STELLAR BRIGHTNESS TIME SERIES 1.1
Photometric observations of stellar occultations by Saturnian rings, satellites, atmospheres, and Jovian atmosphere.
SnowEx21 Time Series Snow Pits V001
The data set is a time-series of snow pit measurements obtained by the SnowEx community during the 2021 field campaign. etween November 2020 and May 2021 data from 247 snow pits were collected at 24 unique sites distributed over 4 states (CO, ID, MT, UT) throughout the Western United States. Five of the unique sites had a single visit to establish baseline conditions, while the remaining 19 sites had 3 or more repeat visits throughout the season, with a median visit count of 11.5. On a weekly interval, a snow pit was dug approximately 1 m away from the previous week’s snow pit. Available measured parameters are: snow depth, snow temperature, snow density, stratigraphy, grain size, manual wetness, liquid water content (LWC), and snow water equivalent (SWE). Also available are photos of the field notes and snow pit sites.
Ground-Based Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS) Earth Orientation Parameters Time Series Product from NASA CDDIS
Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS) Earth Orientation Parameters Time Series Product from the NASA Crustal Dynamics Data Information System (CDDIS). DORIS is a dual-frequency Doppler system consisting of a receiver flying aboard a satellite and a globally distributed network of ground beacons. The DORIS receiver on-board the orbiting satellite tracks the dual-frequency radio signals transmitted by the network of ground beacons and generates the DORIS data. A measurement is made of either the Doppler shift or absolute phase as the satellite’s orbit moves over the ground-based beacon. DORIS data records contain a time-tagged range-rate measurement with associated ancillary information. DORIS observations from a global network can be utilized for a variety of products. Analysis Centers (ACs) of the International DORIS Service (IDS) retrieve DORIS data on a regular basis to compute various DORIS products from data generated by the DORIS beacons supporting the IDS network, including the time series of Earth orientation parameters (EOPs). The IDS Analysis Center Coordinator combines these solutions to produce an official IDS EOP product. The EOP time series are available in text format.
Multivariate Time Series Search
Multivariate Time-Series (MTS) are ubiquitous, and are generated in areas as disparate as sensor recordings in aerospace systems, music and video streams, medical monitoring, and financial systems. Domain experts are often interested in searching for interesting multivariate patterns from these MTS databases which can contain up to several gigabytes of data. Surprisingly, research on MTS search is very limited. Most existing work only supports queries with the same length of data, or queries on a fixed set of variables. In this paper, we propose an efficient and flexible subsequence search framework for massive MTS databases, that, for the first time, enables querying on any subset of variables with arbitrary time delays between them. We propose two provably correct algorithms to solve this problem — (1) an R-tree Based Search (RBS) which uses Minimum Bounding Rectangles (MBR) to organize the subsequences, and (2) a List Based Search (LBS) algorithm which uses sorted lists for indexing. We demonstrate the performance of these algorithms using two large MTS databases from the aviation domain, each containing several millions of observations. Both these tests show that our algorithms have very high prune rates (>95%) thus needing actual disk access for only less than 5% of the observations. To the best of our knowledge, this is the first flexible MTS search algorithm capable of subsequence search on any subset of variables. Moreover, MTS subsequence search has never been attempted on datasets of the size we have used in this paper.
CASSINI SATURN UVIS SOLAR STELLAR BRIGHTNESS TIME SERIES 1.0
Photometric observations of stellar occultations by Saturnian rings, satellites, atmospheres, and Jovian atmosphere.
SnowEx20 Time Series Snow Pit Measurements V002
The data set is a time-series of snow pit measurements obtained by the SnowEx community during the 2020 campaign. Between October 2019 and May 2020, data were collected from 454 snow pits at 12 regional locations throughout California, Colorado, Idaho, New Mexico, and Utah, USA. At each of the locations, between 1 and 11 sites covering a range of conditions (terrains, snow depths, etc.) were chosen for weekly snow pit observations. Also available are photos of the field notes and snow pit sites.
Fast and Flexible Multivariate Time Series Subsequence Search
Multivariate Time-Series (MTS) are ubiquitous, and are generated in areas as disparate as sensor recordings in aerospace systems, music and video streams, medical monitoring, and financial systems. Domain experts are often interested in searching for interesting multivariate patterns from these MTS databases which can contain up to several gigabytes of data. Surprisingly, research on MTS search is very limited. Most existing work only supports queries with the same length of data, or queries on a fixed set of variables. In this paper, we propose an efficient and flexible subsequence search framework for massive MTS databases, that, for the first time, enables querying on any subset of variables with arbitrary time delays between them. We propose two provably correct algorithms to solve this problem — (1) an R-tree Based Search (RBS) which uses Minimum Bounding Rectangles (MBR) to organize the subsequences, and (2) a List Based Search (LBS) algorithm which uses sorted lists for indexing. We demonstrate the performance of these algorithms using two large MTS databases from the aviation domain, each containing several millions of observations. Both these tests show that our algorithms have very high prune rates (>95%) thus needing actual disk access for only less than 5% of the observations. To the best of our knowledge, this is the first flexible MTS search algorithm capable of subsequence search on any subset of variables. Moreover, MTS subsequence search has never been attempted on datasets of the size we have used in this paper.
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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)
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.