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.
65
datasets available to search
ShareScore release 0.7.1
Dataset results
65 results for “Subsidence”
Land Subsidence in Iran Estimated from a Nationwide InSAR Analysis of Sentinel-1 Observations 2014-2020
<p><strong>Overview</strong></p> <p>This dataset is a supplementary material to the paper "Haghighi and Motagh, 2024. Uncovering the Impacts of Depleting Aquifers: A Remote Sensing Analysis of Land Subsidence in Iran, Science Advances". It provides detailed insights into land subsidence across Iran, derived from Sentinel-1 InSAR observations. This dataset is intended for use by researchers, policymakers, and practitioners interested in land subsidence, groundwater depletion, and related fields.</p> <p><strong>Dataset Contents</strong></p> <ol> <li><em>Iran_subsidence_rate_2014-2020_Sentinel-1_InSAR_desc_v1.0.0.tif</em><br>Annual rate of land subsidence in Iran over the six-year period, projected from satellite Line of Sight to vertical.</li> <li><em>Iran_subsidence_rate_2014-2020_Sentinel-1_InSAR_desc_v1.0.0.jpg</em><br>Subsidence map of Iran visualized as jpg</li> <li><em>Iran_subsidence_seasonal_amplitude_2014-2020_Sentinel-1_InSAR_desc_v1.0.0.tif</em><br>Amplitude of seasonal ground deformation, projected from satellite Line of Sight to vertical.</li> <li><em>Iran_subsidence_mask_2014-2020_Sentinel-1_InSAR_desc_v1.0.0.tif</em><br>Land subsidence mask, based on the annual rate of land subsidence.</li> </ol> <p><strong>Methodology</strong></p> <p>The data were derived using Interferometric Synthetic Aperture Radar (InSAR) analysis of Sentinel-1 satellite imagery. The original SAR data includes more than 6000 scenes of Sentinel-1 images collected across 10 descending tracks between 2014 and 2020. The details can be found in the original paper.</p> <p><strong>Acknowledgements</strong></p> <p>We acknowledge the European Space Agency (ESA) for providing the Sentinel-1 satellite data used in this analysis.</p> <p><strong>License</strong></p> <p>This dataset is shared under CC BY 4.0 license, which allows for reuse and distribution, provided that the original authors and source are credited.</p> <p><strong>Citation</strong></p> <p>Please cite the following if you use this dataset:</p> <ol> <li>Haghighi and Motagh, 2024. Uncovering the Impacts of Depleting Aquifers: A Remote Sensing Analysis of Land Subsidence in Iran, Science Advances.</li> <li>Haghighi and Motagh, 2024. Land Subsidence in Iran Estimated from a Nationwide InSAR Analysis of Sentinel-1 Observations 2014-2020. Zenodo. doi:10.5281/zenodo.10815578</li> <li>The dataset contains modified Copernicus Sentinel data 2014-2020, processed by ESA.</li> </ol> <p><strong>Contact</strong></p> <p>Please contact Mahmud Haghighi for inquiries related to this dataset.<br>https://www.ipi.uni-hannover.de/en/haghighi</p>
Data and code to accompany the manuscript "Ground subsidence and heave over permafrost: hourly time series reveal inter-annual, seasonal and shorter-term movement caused by freezing, thawing and water movement"
<p>Data and code to accompany the manuscript "Ground subsidence and heave over permafrost: hourly time series reveal inter-annual, seasonal and shorter-term movement caused by freezing, thawing and water movement" submitted to The Cryosphere.</p>
Monitoring long-term peat subsidence with subsidence platens in Zegveld, The Netherlands
<p><span>Peat oxidation in peat meadow areas is causing greenhouse gas emissions as well as land subsidence. Due to yearly fluctuations in soil surface level, long-term monitoring is needed to determine long-term net subsidence rates. In the experimental peat-meadow farm at Zegveld (NL) subsidence platens were installed in 1970 in a field with low ditchwater level, and in 1973 in a field with high ditchwater level. Platens were installed at 7 different depths, allowing to investigate where in the peat profile subsidence occurs. Elevation of platens as well as soil surface has been measured with surveyor’s levelling each year at the end of winter, so that a long timeseries up to 2023 is available. Analysis showed that surface level in the field with high ditchwater level subsided by 23 cm in 50 years (4.6 mm/yr), while in the field with low ditchwater level this was 31 cm in 53 years (5.8 mm/yr). Results also showed that in the field with low ditch water level, most subsidence due to permanent shrinkage and peat oxidation occurred between 40 and 100 cm depth, while for the other field this was between 20 and 40 cm depth. Finally, in 2023 subsidence was still observed under continuously saturated conditions at 140 cm depth. Presumably, in the aerated part of the profile peat oxidation and the associated earthification process is the main cause of subsidence, while the observed subsidence in the saturated soil at 140 cm depth must be due to other processes, such as consolidation and creep.</span></p>
Nearly three centuries of Lava Flow Subsidence at1 Timanfaya, Lanzarote
<p>The following datasets are available for each InSAR dataset - Envisat and Sentinel-1, covering the island of Lanzarote between 2004/01/17 -2010/01/30 and 2016/09/14-2020/06/25 respectively:</p> <p><br> Line of Sight (LOS) velocities<br> LOS velocity standard deviations<br> LOS displacement time series<br> Vertical velocities<br> Vertical velocity standard deviations <br> East-West velocities<br> East-West velocity standard deviations</p> <p>The velocities and standard deviations are all geocoded (latitude/longitude in decimal degrees) geotiff files in mm/yr at 90 m resolution for Envisat and 30 m resolution for Sentinel-1. Negative values in the LOS data represent movement away from the satellite; in the vertical data represent subsidence; and in the East-West data represent westward movement. The velocities were calculated from the displacement time series as a least squares inversion and the standard deviations were calculated within LiCSBAS using a percentile bootstrap method. </p> <p>The Sentinel-1 time series is given as a 3D matrix (lon x lat x dates) within a .h5 file where each slice is the cumulative displacement (mm) in a lon lat grid format for each epoch date. The latitude and longitude are then given as separate vectors within the .h5 file.</p> <p>The Envisat time series is given as a .dat file where the first two columns are longitude and latitude. The remaining columns are the cumulative displacement (mm) for each epoch date.</p> <p>The epoch dates for both are given as separate text files and are given in the format YYYYMMDD.</p>
Datasets-A globally robust relationship between water table decline, subsidence rate and carbon release from peatlands
<p>Supplementary Data A shows meta-data for in-situ and laboratory measurements of soil respiration or its components soil heterotrophic respiration and autotrophic respiration, as well as associated environmental variables from global pristine peatlands and water table decline peatlands, respectively.</p> <p> </p> <p>Supplementary Data B shows the relationships between peatland subsidence rates and drainage years for different land uses in different climate zones, relationships between proportion of peatland subsidence rates due to oxidation and drainage years for different land uses in different climate zones, the estimated peat subsidence rates and peat subsidence rates due to oxidation, the synthesized soil organic carbon content and soil bulk density at the layer of 0-30 cm from pristine peatlands, and the in-situ measured annual soil heterotrophic respiration rates for validating the robustness of the developed emipirical models of this study. </p>
Data from: Detecting the effects of rapid tectonically-induced subsidence on Mayotte Island since 2018 on beach and reef morphology, and implications for coastal vulnerability to marine flooding
<p>This dataset contains data from the monitoring morphological evolution of beaches and coral reefs in Mayotte island. Mayotte, part of the coral reef-fringed Comoro archipelago in the SW Indian Ocean, experienced in 2018 and 2019 an intense seismic crisis. The repeated earthquake activity since May 2018 has been associated with deformation of the surface of Mayotte, resulting in land subsidence.</p> <p>The earlier 2006-2008 profiles were realized using a Leica TC 407® total station, and referenced to local IGN 50 benchmarks. The more recent 2019, 2020, and 2021 surveys were carried out using a GNSS differential Trimble R8S® system. Given the rapid subsidence that has affected Mayotte, the benchmarks used in this study, like others in Mayotte, need to be recalibrated by the IGN (French Institut Géographique National) and SHOM. This has still not yet been done, as the final outcome of the vertical island movements is still not clear.</p>
Kivalina subsidence observations
<p>Updated on 2023-10-4 to correct scaling error. The original estimates were a factor of two too large.</p> <p>Corrected version. The original estimates (v1.0) were a factor of two too large due to a coding error.</p> <p>Thaw-season subsidence in [m] near Kivalina, Northwestern Alaska, derived from Sentinel-1 observations.</p> <p>For each year yyyy from 2017-2019:</p> <ul> <li>yyyy_de_dl.tif contains the early-mid-season (10 June - 10 August) and late-season (10 August - 10 September) subsidence estimated from the spline reconstruction</li> <li>yyyy_timeseries.tif contains the regularly sampled, unconstrained subsidence</li> </ul> <p>The dates are:</p> <p>2017: ['20170604T173314', '20170616T173315', '20170628T173316', '20170710T173316', '20170722T173317', '20170803T173318', '20170815T173318', '20170827T173319', '20170908T173319']<br>2018: ['20180611T173321', '20180623T173322', '20180717T173323', '20180729T173324', '20180810T173325', '20180822T173325', '20180903T173326']<br>2019: ['20190606T173327', '20190618T173327', '20190630T173328', '20190712T173329', '20190724T173330', '20190805T173330', '20190817T173331', '20190829T173332', '20190910T173332']</p>
Subsidence of Beijing (China) mapped by Copernicus Sentinel-1 time series interferometry
<p><strong>RESULTS DESCRIPTION</strong></p> <p>Recent reports from scientific and mainstream media have indicated that the city of Beijing, together with its surroundings, is subsiding at fast and alarming rate as result of the overexploitation of groundwater. The depletion of groundwater causes underlying soil to compact, creating a phenomenon called subsidence. The Beijing region has been experiencing this phenomenon since 1935, but in last years the rate of sinking has significantly increased.</p> <p>A team of researchers, within ESA sponsored, SEOM InSARap project performed an interferometric analysis of Copernicus Sentinel-1 data which confirms the reported findings also with current data. While the results speak for themselves, we can just once more reiterate on the usefulness of the Copernicus Programme, in this case for deformation monitoring applications.</p> <p><strong>ANALYSIS SUMMARY</strong></p> <ul> <li>Data overview: <ul> <li>Sentinel-1 IW</li> <li>Track 47 descending</li> <li>Observation window December 2014 - June 2016</li> <li>Data download via Scientific Data Hub</li> </ul> </li> <li>Processing overview: <ul> <li>Time series analysis performed with Small Baseline Subset (SBAS) methodology</li> <li>Interferometric combinations of up to 96 days used</li> </ul> </li> </ul> <p><em>More information and context available at insarap.org</em></p> <p><em>Terms and Conditions:</em> All Sentinel-1 results that are available for download are Derived Works of Copernicus data (2014-2016), subject to the "<em>TERMS AND CONDITIONS FOR THE USE AND DISTRIBUTION OF SENTINEL DATA AND SERVICE INFORMATION</em>".</p> <p><em>Acknowledgments: </em> ESA SEOM InSARap project - Sentinel-1 InSAR Performance Study with TOPS Data, contract number 4000110680/14/I-BG-InSARap</p>
Data for "Unveiling the Global Extent of Land Subsidence: The sinking crisis"
<p>File ds01. Global dataset of subsidence rates.</p><p>File ds02. Zonal statistics and feature importance.</p><p>File ds03. Global raster map of subsidence rates.</p>
Data from: Inter- and intrapopulation resource use variation of marine subsidized western fence lizards
<p>Marine resource subsidies can alter consumer dynamics of recipient populations in coastal systems. The response to these subsidies by generalist consumers is often not uniform, resulting in inter- and intrapopulation diet variation and niche diversification that may be intensified across heterogeneous landscapes.</p> <p>We sampled western fence lizards, <em>Sceloporus occidentalis</em>, from Puget Sound beaches and from coastal and inland forest habitats, as well as the lizards' marine and terrestrial prey items to quantify marine and terrestrial resource use with stable isotope analysis (SIA) and mixing models.</p> <p>Isotopic results reveal beach lizards had higher average δ<sup>13</sup>C and δ<sup>15</sup>N values compared to coastal and inland forest lizards, exhibiting a strong mixing line between marine and terrestrial prey items. Across five beach sites, lizard populations received 20 to 51% of their diet from marine resources, on average, with individual lizards ranging between 7% and 86% marine diet.</p> <p>The hillslope of the transition zone between marine and terrestrial environments at beach sites was positively associated with marine-based diets, as beach sites with the steepest slopes had the highest percent marine diets. Additionally, within-beach variation in transition zone slope was positively correlated with the isotopic niche space of beach lizard populations. Together, these results demonstrate that the physiography of transitional landscapes can mediate resource flow between environments, and variable habitat topography promotes niche diversification within a lizard population.</p> <p>Marine resource subsidization of Puget Sound beach <em>S. occidentalis </em>populations may facilitate occupation of the northwesternmost edge of the species range. </p> <p>Human impacts to the physiography of Puget Sound beaches, such as shoreline armoring, may influence the quantity of driftwood habitat available to support the unique ecology of beach-dwelling <em>S. occidentalis</em>. This highlights the importance of shoreline restoration, and conservation of intact beach habitat.</p>
Dataset related to Balazs et al. The dynamics of forearc – back-arc basin subsidence: numerical models and observations from Mediterranean subduction zones
<p>Additional model data to publication by Balazs et al. The dynamics of forearc – back-arc basin subsidence: numerical models and observations from Mediterranean subduction zones</p>
Failed despots and the equitable distribution of fitness in a subsidized species
<p>Territorial species are often predicted to adhere to an ideal despotic distribution and under-match local food resources, meaning that individuals in high-quality habitat achieve higher fitness than those in low-quality habitat. However, conditions such as high density, territory compression, and frequent territorial disputes in high-quality habitat are expected to cause habitat quality to decline as population density increases and, instead, promote resource matching. We studied a highly human-subsidized and under-matched population of Steller's jays (Cyanocitta stelleri) to determine how under-matching is maintained despite high densities, compressed territories, and frequent agonistic behaviors, which should promote resource matching. We examined the distribution of fitness among individuals in high-quality, subsidized habitat, by categorizing jays into dominance classes and characterizing individual consumption of human food, body condition, fecundity, and core area size and spatial distribution. Individuals of all dominance classes consumed similar amounts of human food and had similar body condition and fecundity. However, the most dominant individuals maintained smaller core areas that had greater overlap with subsidized habitat than those of subordinates. Thus, we found that 1) jays attain high densities in subsidized areas because dominant individuals do not exclude subordinates from human food subsidies and 2) jay densities do not reach the level necessary to facilitate resource matching because dominant individuals monopolize space in subsidized areas. Our results suggest that human-modified landscapes may decouple dominance from fitness and that incomplete exclusion of subordinates may be a common mechanism underpinning high densities and creating source populations of synanthropic species in subsidized environments.</p>
Database of subsidence in major coastal cities around the world
<p>This database aims to be an open-source, accurate, peer-reviewed and comprehensive database of coastal cities currently experiencing land subsidence and its main and secondary causes. It aims to facilitate future research regarding subsidence in both priorly identified at-risk areas and in areas where the potential impact of subsidence is still unknown.</p> <p>The selection of the cities is based on the following papers:</p> <p>· Nicholls, R. J. (2008). The Exposure of Port Cities to Flooding: A Comparative Global Analysis.</p> <p>· Hallegatte, S., Green, C., Nicholls, R. J., & Corfee-Morlot, J. (2013). Future flood losses in major coastal cities. Nature climate change, 3(9), 802-806.</p> <p>· Solari, L., Del Soldato, M., Bianchini, S., Ciampalini, A., Ezquerro, P., Montalti, R., ... & Moretti, S. (2018). From ERS 1/2 to Sentinel-1: subsidence monitoring in Italy in the last two decades. Frontiers in Earth Science, 6, 149.</p>
Characterization of Irreversible Land Subsidence in the Yazd-Ardakan Plain, Iran from 2003 to 2020 InSAR Time Series
<p>This repository contains the data used in <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JB022258">Mirzadeh et al., 2021</a>. It includes two InSAR time-series datasets from Envisat and Sentinel-1 satellite in both ascending and descending orbits, acquired over Yazd-Ardakan Plain, Iran, as well as, the population density information and weather data for this study area.</p> <p>Dataset 1: Envisat ascending track 99 and descending track 20</p> <ul> <li>Date: 06 Sep 2004 - 12 Jul 2010 (17 ascending acquisitions) + 26 Mar 2003 - 23 Oct 2010 (23 descending acquisitions)</li> <li>Processor: ISCE/stripmapStack + MintPy</li> <li>Displacement time-series (in HDF-EOS5 format): timeseries_LODcor_ERA5_ramp_demErr.h5</li> <li>Mean LOS Velocity (in HDF-EOS5 format): velocity.h5</li> <li>Mask Temporal Coherence (in HDF-EOS5 format): maskTempCoh.h5</li> <li>Geometry (in HDF-EOS5 format): geometryRadar.h5</li> </ul> <p>Dataset 2: Sentinel-1 ascending track 130 and descending track 64</p> <ul> <li>Date: 14 Oct 2014 - 28 Mar 2020 (129 ascending acquisitions) + 10 Oct 2014 - 24 Mar 2020 (119 descending acquisitions)</li> <li>Processor: ISCE/topsStack + MintPy</li> <li>Displacement time-series (in HDF-EOS5 format): timeseries_ERA5_ramp_demErr.h5</li> <li>Mean LOS Velocity (in HDF-EOS5 format): velocity.h5</li> <li>Mask Temporal Coherence (in HDF-EOS5 format): maskTempCoh.h5</li> <li>Geometry (in HDF-EOS5 format): geometryRadar.h5</li> </ul> <p>The time series and Mean LOS Velocity (MVL) products can be georeferenced and resampled using the makTempCoh and geometryRadar products, and the MintPy commands/functions.</p>
Data observed at Kawajima land subsidence observatory (Japan) and ensemble model
<p>This data set contains all data necessary to reproduce the results of the manuscript submitted by Akitaya & Aichi. The data set includes hydraulic head and land subsidence data observed at the Kawajima land subsidence observatory (Japan), and an ensemble model parameter set constructed using the evolutionary multimodal algorithm, ensemble simulation results, and predictive uncertainty analysis results obtained through ensemble model output statistics. Please refer to the "readme.txt" file provided in each individual folder for detailed instructions on how to read the data.</p>
Localized uplift, widespread subsidence and implications for sea level rise in the NYC Metropolitan Area
<p>Regional relative sea level rise is exacerbating flooding hazards in the coastal zone. In addition to changes in the ocean, vertical land motion (VLM) is a driver of spatial variation in sea-level change that can either diminish or enhance flood risk. Here we apply state-of-the-art Interferometric Synthetic Aperture Radar (InSAR) and global navigation satellite system (GNSS) time-series analysis to estimate velocities and corresponding uncertainties at 30 m resolution in the New York City Metropolitan Area, revealing VLM with unprecedented detail. We find broad subsidence of 1.6 mm/yr consistent with glacial isostatic adjustment to the melting of the former ice sheets, and previously undocumented hotspots of both subsidence and uplift that can be physically explained in some locations. Our results inform ongoing efforts to adapt to sea-level rise and reveal points of VLM that motivate both future scientific investigations into surface geology and assessments of engineering projects.</p>
Health Outcomes of Tai Chi in Subsidized Senior Housing
ClinicalTrials.gov study NCT02346136. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Innovative Public-private Partnership to Target Subsidized Antimalarials in the Retail Sector (Aim 2)
ClinicalTrials.gov study NCT02461628. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Localized uplift, widespread subsidence and implications for sea level rise in the NYC Metropolitan Area
Open the record for dataset details and reuse information.
Data from: Inter- and intrapopulation resource use variation of marine subsidized western fence lizards
Open the record for dataset details and reuse information.
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.