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.
16
datasets available to search
ShareScore release 0.9.0
Dataset results
16 results for “2014-2020”
Observed phenological indicators and environmental drivers at global change experiments at the Jornada Basin LTER site, 2014-2020
This dataset contains plant phenological data extracted from phenocams installed at a global exchange experiment involving Chihuahuan desert plant communities at the Jornada Basin LTER site in southern New Mexico, U.S.A. Cycles of plant growth, termed phenology, are tightly linked to environmental controls, and our overarching objective in this study is to determine if temperature or precipitation are relatively more important for determining shrub and grass greenup date (start of season) and senescence date (end of season). At these camera locations, we experimentally manipulated incoming precipitation at the Jornada Basin LTER for over a decade and recorded plant leaf phenology at the daily scale for seven years using phenocams. The data here are derived from raw "phenocam" camera data collected at two ongoing studies at the Jornada Basin LTER site, one studying ecosystem responses to long term changes in water and nitrogen availability, and one studying plant productivity and partitioning responses to water availability and herbivory (studies 349 and 456, respectively). Phenocams at the sites have collected images since 2014, and basic color and greenness data extracted from those images are available in a companion dataset on EDI (knb-lter-jrn.210574001). This dataset includes the derived annual and quarterly phenological indices and greenness indices for each plot monitored by phenocams, and temperature and precipitation variables aggregated to the same frequency. The dataset also includes R code and input files used to generate these derived data. See Currier and Sala 2022 for more details. This study is ongoing.
Meteorological data collected on Toolik Lake during the ice free season for 2014-2020, Arctic LTER, Toolik Research Station, Alaska
File describing the meteorological conditions on Toolik Lake (named the Toolik Lake Climate station), adjacent to the Toolik Field Research Station (68 38'N, 149 36'W). This is a floating climate station and should not be confused with the Toolik Field Station Climate site (TFS Climate Station or Met Station) which is a terrestrial station (located on land). Note that this land station has been called the "Toolik Main Climate Station", and the station on the lake is located where the main lake sampling site is located so it has also been called the Toolik Lake Main Climate Station. Measurements include air temperature, relative humidity, wind speed and direction, and radiation. Note: There are no discharge data for 2013 because of equipment malfunction.
Daily phenocam image data and derived timeseries for global change experiments at the Jornada Basin LTER site, 2014-2020
This dataset contains daily data extracted from phenocams installed at a global exchange experiment involving Chihuahuan desert plant communities at the Jornada Basin LTER site in southern New Mexico, U.S.A. Cycles of plant growth, termed phenology, are tightly linked to environmental controls, and our overarching objective in this study is to determine if temperature or precipitation are relatively more important for determining shrub and grass greenup date (start of season) and senescence date (end of season). At these camera locations, we experimentally manipulated incoming precipitation at the Jornada Basin LTER for over a decade and recorded plant leaf phenology at the daily scale for seven years using phenocams. The data included here comes from phenocams installed in two ongoing studies at the Jornada Basin LTER site, one studying ecosystem responses to long term changes in water and nitrogen availability, and one studying plant productivity and partitioning responses to water availability and herbivory (studies 349 and 456, respectively). Phenocams at the sites have collected images since 2014, and this dataset includes color values extracted from shrub and grass regions in these images. Further analyses, including daily values of calculated greenness (green chromatic coordinate), precipitation, and temperature, for all the plots included in the study are in EDI dataset knb-lter-jrn.210574002. This study is ongoing.
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>
Underwater noise from ships during 2014-2020
<p>These data accompany the manuscript "Underwater noise emissions from ships during 2014-2020" submitted for publication in Environmental Pollution. These data consist of daily emissions of underwater noise emissions from global shipping, reported as noise energy in Joules, in three frequencies (63, 125 and 2000 Hz). The data is provided in zip compressed gridded binary netcdf3 data format using Climate & Forecast conventions. </p>
Underwater noise from ships during 2014-2020
<p>These data accompany the manuscript "Underwater noise emissions from ships during 2014-2020" submitted for publication in Environmental Pollution. These data consist of daily emissions of underwater noise emissions from global shipping, reported as noise energy in Joules, in three frequencies (63, 125 and 2000 Hz). The data is provided in zip compressed gridded binary netcdf3 data format using Climate & Forecast conventions. <br> </p>
Surface elevation (2012-2020) and ice thickness (2014-2020) datasets measured at Artesonraju Glacier, Cordillera Blanca, Perú
<p>We present a set of data of interpreted ice thickness and ice surface elevation of Artesonraju glacier.<br> The ice thickness was obtained by means of Ground Penetrating Radar (GPR) which was measured by Instituto Nacional de Investigación en Glaciares y Ecosistemas de Montaña (INAIGEM) and Autoridad Nacional de Agua (ANA). The years of ice thickness records include 2013, 2014, 2015, 2017, 2018, and 2020. On the other hand, the surface elevation points were obtained by means of automated total stations and mass balance stakes, integrally measured by ANA. The years of measurements are 2012, 2014, 2015, 2017, 2018, 2019, and 2020.<br> The results from GPR data show a maximum depth of 235±18 m and a decreasing mean depth of ranging from 134±18 m in 2013 to 110±18 m in 2020. Additionally, we estimate a mean ice thickness change rate of 4.2±3.2 m yr<sup>-1</sup> between 2012 and 2020 with GPR data alone, which is in agreement with the elevation change in the same period. The latter was estimated with the more accurate surface elevation data, yielding a change rate of -3.2±0.2 m yr<sup>-1</sup>, and hence, confirming a negative glacier mass balance. The datasets can be valuable for further analysis when combined with other data types, and as input for glacier dynamics modeling, ice volume estimations, and GLOF risk assessment.</p>
Fire Events with Water Balance Shift for Western US during 2014-2020
<p>This dataset comprises fire events in the Western US during 2014-2020, with respective fire characteristics, centroid locations, and variables necessary to calculate water balance shift during pre- and post-fire years, as described in the manuscript (Ahmad et al., 2023)</p>
Wildfire, prescribed burn, and agricultural burn smoke PM2.5 estimates for CA, WA, and OR 2014-2020
<p>Wildfires, prescribed burns, and agricultural burns all impact ambient air quality across the Western U.S.; however, little is known about how communities across the region are differentially exposed to smoke from each of these fire types. To address this gap, we quantify smoke exposure stemming from wildfire, prescribed, and agricultural burns across Washington, Oregon, and California from 2014-2020 using a fire type-specific biomass burning emissions inventory and the GEOS-Chem chemical transport model. We examine fire type-specific PM<sub>2.5</sub> concentration by race/ethnicity, socioeconomic status, and in relation to the Center for Disease Control's Social Vulnerability Index. Overall, population average PM<sub>2.5</sub> concentrations are greater from wildfires than from prescribed and agricultural burns. While we found limited evidence of exposure disparities among sub-groups across the full study area, we did observe disproportionately higher exposures to wildfire-specific PM<sub>2.5</sub> exposures among Native communities in all three states and, in California, higher agricultural burn-specific PM<sub>2.5</sub> exposures among lower socioeconomic groups. We also identified, for all three states, areas of significant spatial clustering of smoke exposures from all fire types and increased social vulnerability. These results provide a first look at the differential contributions of smoke from wildfires, prescribed burns, and agricultural burns to PM<sub>2.5</sub> exposures among demographic subgroups, which can be used to inform more tailored exposure reduction strategies across sources.</p>
Anthropogenic carbon monoxide emissions during 2014-2020 in China constrained by in-situ observations
<p><strong>The description of the NetCDF files (12×200×350):</strong></p> <ol> <li> <p>The first dimension represents the months, the second represents latitude, and the third represents longitude.</p> </li> <li> <p>The latitude ranges from 15.1°N to 54.9°N, and the longitude ranges from 66.1°E to 135.9°E, with a uniform grid spacing of 0.2° for both.</p> </li> </ol> <p><strong>The units for all files are as follows:</strong></p> <table style="border-collapse: collapse; width: 100%;"><colgroup><col style="width: 33.2913%;"><col style="width: 33.2913%;"><col style="width: 33.2913%;"></colgroup> <tbody> <tr> <td> <p>File</p> </td> <td> <p>Format</p> </td> <td> <p>Unit</p> </td> </tr> <tr> <td> <p>All emission data.zip</p> </td> <td> <p>netcdf</p> </td> <td> <p>kg·m<sup>-2</sup>·s<sup>-1</sup></p> </td> </tr> <tr> <td> <p>Emissions in seven regions.csv</p> </td> <td> <p>csv</p> </td> <td> <p>10<sup>3</sup> kt</p> </td> </tr> <tr> <td> <p>Simulated CO concentrations.zip</p> </td> <td> <p>txt</p> </td> <td> <p>μg·m<sup>-3</sup> </p> </td> </tr> </tbody> </table>
A dataset of the crackdown on cross-border wildlife crimes in China, 2014-2020
<p class="MsoNormal"><span>Wildlife crimes </span><span>that involve</span><span> </span><span>s</span><span>muggling threaten national security and biodiversity, cause regional conflicts, and hinder economic development, especially in developing countries </span><span>with</span><span> </span><span>abundant</span><span> </span><span>wildlife</span><span> </span><span>resources</span><span>. </span><span>Over the past few decades</span><span>, significant headway has been made in combating </span><span>wildlife smuggling and the related illegal domestic trade</span><span> in China.</span><span> </span><span>Previous studies on </span><span>the </span><span>wildlife smuggling trade were mostly based on customs punishment and confiscation</span><span> data</span><span>. </span><span>From </span><span>the China Judgments Online </span><span>website, w</span><span>e retrieved</span><span> </span><span>cases related to cross-border wildlife and wildlife products</span><span> smuggling</span><span> <span>from 2014 </span></span><span>to </span><span>2020. </span><span>A total of</span><span> <span>5</span></span><span>10</span><span> <span>available cases and</span></span><span> 927</span><span> <span>records for more than </span></span><span>1</span><span>1</span><span>0</span><span> <span>species</span></span><span> </span><span>were</span><span> registered</span><span>. We thoroughly studied each judgment and ruling file to extract information on cases, defendants, species</span><span>,</span><span> <span>sentences</span></span><span>, and origins and destinations of wildlife and wildlife products</span><span>. Furthermore</span><span>, </span><span>frequency of </span><span><span>origin-destination</span></span><span> </span><span><span>place</span></span><span> occurrences </span><span><span>and</span></span><span> </span><span>spatial patterns of cross-border wildlife crime in China were shown in this data paper. </span><span>The main </span><span>purpose </span><span>of our dataset is to make these wildlife and wildlife products trade data accessible for researchers to develop </span><span>conservation</span><span> </span><span>studies. We expect that this dataset will be valuable for network analysis of regional or global wildlife trafficking, which has </span><span>attracted</span><span> </span><span>global</span><span> </span><span>attention</span><span>. There are no copyright restrictions on the data</span><span>;</span><span> </span><span>we ask that researchers</span><span> <span>please cite this paper and the associated dataset when using the data in publications.</span></span></p>
A dataset of the crackdown on cross-border wildlife crimes in China, 2014-2020
Open the record for dataset details and reuse information.
Wildfire, prescribed burn, and agricultural burn smoke PM2.5 estimates for CA, WA, and OR 2014-2020
Open the record for dataset details and reuse information.
STILNOVO project (Sub.measure 16.2 RDP Tuscany Region 2014-2020)
<p>Dataset of pasture production and quality and milk production and quality of a rotational grazing trial carried out in 2018 in south Tuscany.</p>
Youth Panel (2014-2020) by KEIS
<p>Youth Panel (2014-2020) provided by Korea Employment Information Service </p>
Geophone plate data from the Sulden/Solda basin (South Tyrol, Italy) 2014-2020
<p>This dataset contains the post-processing of 7 years of seismic data collected through geophone plates installed at the monitoring station of Stilfserbrücke - Ponte Stelvio (South Tyrol, Italy) in the framework of the AQUASED project (<a href="http://www.mountain-eering.com/wp/mountain-eering/i-nostri-servizi/ricerca-e-sviluppo/studio-aquased/">http://www.mountain-eering.com/wp/mountain-eering/i-nostri-servizi/ricerca-e-sviluppo/studio-aquased/</a>) and analyzed in the paper “Bedload fluxes in a glacier-fed river at multiple temporal scales” by Coviello et al. (2022). The monitoring station is located in the Sulden/Solda basin (130 km<sup>2</sup>). Eight geophone plates are equipped with 10-Hz vertical geophones, which continuously measure the vibration induced by the impact of bedload at a sampling frequency of 5 kHz. The 8 geophone plates are installed along the cross section and extends for 4 m, corresponding to half of the channel width (8 m) from the mid-channel (ch 1) to the right bank (ch 8), and are fixed on a check dam with a cantilevered metal structure. From the raw seismic data, the following signal metrics have been extracted and constitute the dataset: the impulses (Imp/min) calculated with four different thresholds (0.01, 0.04, 0.16, 0.32 V), the maximum amplitude (V), the main frequency (Hz) and the integral of the squared amplitude (V<sup>2</sup>) over 1-min time window. This dataset provides the above-mentioned signal metrics for the period 2014 to 2020, separated per plate (from DB_aquased_ch1.dat to DB_aquased_ch8.dat).</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.