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.
66
datasets available to search
ShareScore release 0.7.1
Dataset results
66 results for “Timeseries”
Erebus GNSS timeseries with respect to stable Antarctica
<p>GNSS timeseries for Ross Island / Erebus volcano in an Antarctica stable reference frame. Files contain fields for</p> <p>dec year | dE (mm) | dN (mm) | dU (mm) | sigE (mm) | sigN (mm) | sigU (mm) | Ren | Reu | Rnu | seconds past J2000 | year | month | day | hour | min | second | solution path<br> <br> Details on processing and corrections are explained in</p> <p>Grapenthin, R., P. Kyle, R.C. Aster, M. Angarita, T. Wilson, J. Chaput, Deformation at the open-vent Erebus volcano, Antarctica, from more than 20 years of GNSS Observations, (under consideration for) JVGR.</p> <p> </p> <p> </p>
Mid-Atlantic Satellite Derived Shorelines, Transects, Trends, Reference Shorelines, Timeseries Data
<p>Geographic scope: Delmarva Peninsula, New Jersey Shore, Long Island (ocean side)</p> <p>Temporal range: 1984-2022 (variable timespacing)</p> <p>Satellites: L5, L7, L8, S2</p> <p>The shorelines and timeseries data were extracted with code and models available at <a href="https://github.com/mlundine/Shoreline_Extraction_GAN">https://github.com/mlundine/Shoreline_Extraction_GAN</a>.</p> <p>Each region's folder contains the extracted shorelines, the transects (200 m longshore spacing), the transects scaled to the linear trends, the reference shoreline, the csvs for each transect's cross-shore position timeseries (Region_#.csv) and the linear fit (Site_#_linear_trend.csv). The cross-shore positions here have not been corrected to tides or wave data. Feel free to experiment with corrections and/or timeseries analysis tools.</p> <p> </p> <p> </p>
Basic-ECVs for Case Studies (yearly timeseries)
<p>Yearly timeseries (.csv) of basic-ecvs spatially averaged over Case Studies for different climate scenarios (historical, SSP1-2.6, SSP2-4.5, SSP5-8.5) and time horizons (1985-2014, 2015-2100). Data are created by RethinkAction project using statistical downscaling method from CMIP6 simulations.</p> <p>We acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP6. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the data and providing access, and the multiple funding agencies who support CMIP6 and ESGF.</p> <p>Moreover, we acknowledge the Copernicus Climate Change Service (C3S) Climate Data Store (CDS) to provide access to CMIP6, CERRA, ERA5 and ERA5-Land data:</p> <ul> <li>Copernicus Climate Change Service, Climate Data Store, (2021): CMIP6 climate projections. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.c866074c.</li> <li>Schimanke S., Ridal M., Le Moigne P., Berggren L., Undén P., Randriamampianina R., Andrea U., Bazile E., Bertelsen A., Brousseau P., Dahlgren P., Edvinsson L., El Said A., Glinton M., Hopsch S., Isaksson L., Mladek R., Olsson E., Verrelle A., Wang Z.Q., (2021): CERRA sub-daily regional reanalysis data for Europe on single levels from 1984 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.622a565a</li> <li>Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D.,Thépaut, J-N. (2023): ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24381/cds.adbb2d47</li> <li>Muñoz Sabater, J. (2019): ERA5-Land hourly data from 1950 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: 10.24381/cds.e2161bac</li> </ul> <p>Acknowledgement also to:</p> <ul> <li>DRAAC, 2023, Regional climate data provided by the Regional Ditectorate for the Environment and Climate Change of the Regional Autonomous Government of Azores (<a href="https://web.archive.org/web/20250325064114/https://urldefense.com/v3/__https:/portal.azores.gov.pt/en/web/draac__;!!D9dNQwwGXtA!TAB9_FXZEEA4K_6AkmoIqX-krFMSiGcKRY--rOpV9psI98vjxa-sLAZQYR1s0G1fFmBrENoHHpZCdvP0s67vzss$" target="_blank" rel="noopener">https://portal.azores.gov.pt/en/web/draac</a>)</li> <li>SRAA\CCIAM, 2017. Programa Regional de Alterações Climáticas (PRAC), Secretaria Regional do Ambiente e Ação Climática (SRAA) of the Governo dos Açores, Climate Change Impacts, Adaptation and Modelling (CCIAM) of the Faculdade de Ciências da Universidade de Lisboa (FCUL), <a href="https://web.archive.org/web/20250325064114/https://urldefense.com/v3/__https://snig.dgterritorio.gov.pt/rndg/srv/por/catalog.search*/metadata/8804acd9-9d0f-40fb-bc2e-e4dff8c2b4b1__;Iw!!D9dNQwwGXtA!SmILhnS0zICNt1ZcxuQ0VPP7VxtFeSEdLt4chAw8y5tsdlWqsgkyt9kESGhRu-00ZQqakzH36tvV-_DcQI7jGo-lOg$" target="_blank" rel="noopener">https://snig.dgterritorio.gov.pt/rndg/srv/por/catalog.search#/metadata/8804acd9-9d0f-40fb-bc2e-e4dff8c2b4b1</a></li> </ul>
Real and Psuedosynthetic timeseries used in "Characterizing High Rate GNSS Velocity Noise for Synthesizing a GNSS Strong Motion Learning Catalog"
<p><strong>5Hz GNSS Velocity Data for the submitted work: </strong>"Characterizing High Rate GNSS Velocity Noise for Synthesizing a GNSS Strong Motion Learning Catalog" Dittmann et al (202?)</p> <p><strong>Datasets included:</strong></p> <ol> <li>Pseudosynthetic timeseries, ambient timeseries and training featuresets generated for Dittmann, et al (202?) </li> <li>Real GNSS 5Hz validation featuresets from <a href="https://doi.org/10.1029/2022JB024854">Dittmann, et al. (2022) </a></li> </ol> <p>Timeseries and Featuresets are stored in <a href="https://parquet.apache.org/">Apache Parquet</a> format.</p> <p><strong>Getting Started:</strong><br> Notebook demos for reading using <a href="https://docs.conda.io/en/latest/">conda</a>+ jupyterlab (easiest).<br> In a terminal:</p> <ol> <li>Unzip untar (mac/linux tar -xvf psuedo_synth_gnssvel.tar.gz)</li> <li> <pre><code>conda env create -f environment.yml conda activate pgv23_zenodo jupyter lab</code></pre> <p> </p> </li> <li>Open the notebook “reading_data.ipynb”</li> </ol> <p> </p> <p><strong>Data References:</strong></p> <p><a href="https://ngawest2.berkeley.edu/">NGA-West 2 (NGAW2) Ground Motion Database </a></p> <p><a href="https://github.com/crowellbw/SNIVEL">SNIVEL</a> GNSS Velocity Processing</p> <p> </p>
Italian Retweets Timeseries
<p>This dataset contains temporal data of about 5,121,132 retweets from 47,947 users took from the italian twittersphere published between 18/06/2018 and 01/07/2018. </p> <p>Refer to the paper below for more details.</p> <p>Mazza, M., Cresci, S., Avvenuti, M., Quattrociocchi, W., & Tesconi, M. (2019). RTbust: Exploiting Temporal Patterns for Botnet Detection on Twitter. <em>arXiv preprint arXiv:1902.04506</em>.</p>
Timeseries of Arctic-Boreal Lake Area Derived from CubeSat Imagery, 2017
This dataset provides near-daily lake area timeseries for 85,358 lakes across four study areas in Northern Canada and Alaska, USA, between May 1 and October 1, 2017. These lake area estimates were produced using digital images from newly developed Planet Labs CubeSats, small satellites with a 4-band (blue, green, red, near-infrared) camera payload. In constellation, CubeSats collected imagery at very high spatial (3-5m) and temporal (near-daily) resolution. From the imagery, each lake's mean, minimum, and maximum areas and seasonal dynamism were derived. The dataset covers four Arctic-Boreal regions: the Yukon Flats Basin (YFB) in eastern interior Alaska, and the Mackenzie River Valley (MRV), Canadian Shield Transect (CST), and Hudson Bay Lowland (HBL) in Canada.
An integrative multiomics framework for identification of therapeutic targets in pulmonary fibrosis [TimeSeries]
GEO Series GSE213709. Mus musculus. 25 samples. Type: Expression profiling by high throughput sequencing.
RNA-Sequencing of Streptomyces coelicolor A3(2) (M145) in timeseries
GEO Series GSE132487. Streptomyces coelicolor A3(2). 27 samples. Type: Expression profiling by high throughput sequencing.
Timeseries RNA-Seq analysis of low phosphorus-induced adventitious root formation in Populus ussuriensis
GEO Series GSE174726. Populus ussuriensis. 20 samples. Type: Expression profiling by high throughput sequencing.
p300 catalytic inhibition selectively targets IRF4 oncogenic activity in multiple myeloma (MM1S_KB528_timeseries)
GEO Series GSE274841. Homo sapiens. 15 samples. Type: Expression profiling by high throughput sequencing.
Identification and Correction of Time-Series Transcriptomic Anomalies[K562_timeseries]
GEO Series GSE270332. Homo sapiens. 24 samples. Type: Expression profiling by high throughput sequencing.
Identification and Correction of Time-Series Transcriptomic Anomalies[Scerevisiae_38_5C_timeseries]
GEO Series GSE270331. Saccharomyces cerevisiae. 30 samples. Type: Expression profiling by high throughput sequencing.
RNA-Sequenzing of Streptomyces coelicolor A3(2) (M1152) in timeseries
GEO Series GSE132488. Streptomyces coelicolor A3(2). 27 samples. Type: Expression profiling by high throughput sequencing.
p300 catalytic inhibition selectively targets IRF4 oncogenic activity in multiple myeloma (RPMI8226_KB528_timeseries)
GEO Series GSE274845. Homo sapiens. 15 samples. Type: Expression profiling by high throughput sequencing.
p300 catalytic inhibition selectively targets IRF4 oncogenic activity in multiple myeloma (PBMC_KB528_timeseries)
GEO Series GSE274843. Homo sapiens. 15 samples. Type: Expression profiling by high throughput sequencing.
1 km Monthly Minimum Temperature Dataset for China from 1952 to 2019 (ChinaClim_timeseries)
<p>ChinaClim_timeseries is a monthly temperatures and precipitation dataset in China for the period of 1952-2019 of 1km spatial resolution, the data was generated by superimposing monthly anomaly surface and baseline climatology surface (ChinaClim_baseline) based on climatologically aided interpolation (CAI). The scale factor of the data is 0.1.</p>
MEDUSA Campi Flegrei cGPS Weekly Timeseries (2016-2019)
<p>Weekly positions time series of the 4 cGPS stations on buoys ( MEDUSA marine research infrastructure) in Campi Flegrei caldera marine sector (Gulf of Pozzuoli) from January 2016 to December 2019.</p> <p>A full description of MEDUSA cGPS time series analysis is reported in:<br> - De Martino P, Guardato S, Donnarumma GP, Dolce M, Trombetti T, Chierici F, Macedonio G, Beranzoli L and Iannaccone G (2020). Four Years of Continuous Seafloor Displacement Measurements in the Campi Flegrei Caldera. Front. Earth Sci. 8:615178. doi: 10.3389/feart.2020.615178.<br> - De Martino P, Dolce M, Brandi G, Scarpato G, Tammaro U (2021). The Ground Deformation History of the Neapolitan Volcanic Area (Campi Flegrei Caldera, Somma–Vesuvius Volcano, and Ischia Island) from 20 Years of Continuous GPS Observations (2000–2019). Remote Sensing. 13(14):2725. doi:10.3390/rs13142725.</p> <p>Please cite these when using the dataset.</p>
Timeseries analysis of gene expression in Adrb2+/+ and Adrb2-/- Clone4-transgenic CD8+ T cells responding to Vesicular Stomatitis Virus expressing hemagglutinin from influenza A PR/8.
GEO Series GSE102478. Mus musculus. 30 samples. Type: Expression profiling by high throughput sequencing.
Timeseries of small RNA and mRNA expression during zebrafish heart regeneration
GEO Series GSE106884. Danio rerio. 42 samples. Type: Expression profiling by high throughput sequencing.
Metabolic switching in Streptomyces coelicolor Timeseries 1
GEO Series GSE18489. Streptomyces coelicolor A3(2); Streptomyces coelicolor. 55 samples. Type: Expression profiling by array.
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.