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.
156
datasets available to search
ShareScore release 0.9.0
Dataset results
156 results for “long-term dataset”
Long-Term Lithium Abundance Signatures following Planetary Engulfment [Dataset]
<p>MESA inlist files and chemical network used for the published article Sevilla et. al (2022).</p> <p>The inlist files given are for a 1 solar mass model. To generate the other models in the paper with a different stellar mass, the initial mass in the pre-ZAMS inlist was changed.</p> <p>We first used prezams.inlist to generate the model star, with mixing processes besides convection. This model saved after this run would be used for all other model runs for the same stellar mass.</p> <p>For a model run without planetary engulfment, we used one inlist to evolve the star through the main-sequence, and then obtained the desired data from the resulting profile and history files.</p> <p>For model runs with planetary engulfment, we first ran the model using an accretion inlist to simulate the engulfment process by turning on accretion, and then we used a main-sequence (MS) inlist to evolve the star through the main-sequence to produce the desired data.</p> <p>The corresponding inlists for each model run are as follows:</p> <p>10 Earth mass engulfment with included all mixing processes considered in our paper, which were convection, overshoot, thermohaline mixing, elemental diffusion, and a min_D_mix coefficient: 10earth_accretion.inlist for accretion, and 10earth_postaccretion.inlist for MS.</p> <p>10 Earth mass engulfment with all mixing processes except thermohaline mixing: 10earth_accretion_nothermohaline.inlist for accretion, and 10earth_postaccretion_nothermohaline.inlist for MS.</p> <p>10 Earth mass engulfment with all mixing processes except elemental diffusion: 10earth_accretion_nodiffusion.inlist for accretion, and 10earth_postaccretion_nodiffusion.inlist for MS.</p> <p>1 Earth mass engulfment with all mixing processes: 1earth_accretion.inlist for accretion, and 1earth_postaccretion.inlist for MS.</p> <p>100 Earth mass engulfment with all mixing processes: 100earth_accretion.inlist for accretion, and 100earth_postaccretion.inlist for MS.</p> <p>10 Earth mass engulfment with all mixing processes occurring 1 gyr after ZAMS: We first ran the model with evolve_1gyr.inlist to save a model of the star 1 gyr after ZAMS. Then, we used 1gyr_accretion.inlist for accretion, and 1gyr_postaccretion.inlist for MS.</p> <p>No planetary engulfment: No accretion inlist was used; we immediately ran no_accrete.inlist as the MS inlist.</p> <p> </p> <p>We also uploaded the chemical network used for our model as the file accrete.net.</p>
CASM: A long-term Consistent Artificial-intelligence based Soil Moisture dataset based on machine learning and remote sensing
<p>Paper to cite: Skulovich, O., Gentine, P. A Long-term Consistent Artificial Intelligence and Remote Sensing-based Soil Moisture Dataset. <em>Sci Data</em> 10, 154 (2023). https://doi.org/10.1038/s41597-023-02053-x</p> <p> </p> <p>The Consistent Artificial Intelligence (AI)-based Soil Moisture (CASM) dataset is a global, consistent, and long-term, remote sensing soil moisture (SM) dataset created using machine learning. It is based on the NASA Soil Moisture Active Passive (SMAP) satellite mission SM data as a target and is aimed at extrapolating SMAP-like quality SM data back in time with previous satellite microwave platforms. Machine learning approach, such as neural network (NN) has the advantage of being both nonlinear, and state-dependent, and naturally imposing a global distribution matching between the source and the target data. Utilizing this, the new CASM dataset was created using high-quality SMAP SM as a target and Soil Moisture and Ocean Salinity (SMOS) or Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E/2) brightness temperature as a source, which allowed extrapolating SM data 13 years back from before SMAP mission launch. CASM represents SM in the top soil layer, defined on a global 25 km EASE-2 grid and covers 2002-2020 with a 3-day temporal resolution. The resulting dataset exhibits excellent spatial and temporal homogeneity, without compromising the interannual variability, and is in excellent agreement with the SMAP data (with a mean correlation of 0.97 between the SMAP and CASM SM for the period when the two overlap). Moreover, the input and target datasets were divided into seasonal cycle and residuals, with the NN trained on the residuals. This approach ensures that the high performance does not mask a simple seasonal cycle matching but rather exemplifies the skill targeted at predicting extremes; with the NN achieving a correlation of 0.75 on the test data for the residuals. Comparison to 367 global in-situ SM monitoring sites shows a SMAP-like median correlation of 0.66 between station SM and CASM SM from the corresponding grid cell. Additionally, the SM product uncertainty was assessed, and both aleatoric and epistemic uncertainties were estimated and included in the dataset. Mean epistemic uncertainty, related to the NN model structure, ranges from 0.007 m<sup>3</sup>/m<sup>3</sup> to 0.014 m<sup>3</sup>/m<sup>3</sup> and on average is close to a desired SM product stability threshold of 0.01 m<sup>3</sup>/m<sup>3</sup> per year. Aleatoric uncertainty, defined as input noise propagated through the system, depends on the introduced level of noise. With 10% noise applied to the residuals, the resulting mean standard deviation of the model outputs rises from 0.005 to 0.007 m<sup>3</sup>/m<sup>3</sup>. </p>
Long-term demographic trends and spatio-temporal distribution of past human activity in Central Europe: Comparison of archaeological and palaeoecological proxies (datasets and R scripts)
<p>This digital archive is an outcome of the paper Kolář J., Macek M., Tkáč P., Novák D. & V.Abraham: Long-term demographic trends and spatio-temporal distribution of past human activity in Central Europe: Comparison of archaeological and palaeoecological proxies. Quaternary Science Reviews, 2022</p>
Dataset: BlackRock Long-Term U.S. Equity ETF (BELT) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Vanguard Long-Term Treasury Index Fund ETF Shares (VGLT) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Vanguard Long-Term Corporate Bond Index Fund ETF Shares (VCLT) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Datasets and code for Rapljenović et al. 2024: Adsorption of trace metals onto different plastics during long-term exposure in an estuarine environment: influence of time, stratified water column, and specific surface area
<p>This repository contains datasets and R code to reproduce results from Rapljenović et al. 2024: Adsorption of trace metals onto different plastics during long-term exposure in an estuarine environment: influence of time, stratified water column, and specific surface area.</p>
(05)-Strobl2018A-DS0001 – Tribolium castaneum AGOC{Zen1'#O(LA)-mEmerald} #2 subline long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(05)-Strobl2018A-DS0001 – <em>Tribolium castaneum</em> AGOC{Zen1'#O(LA)-mEmerald} #2 subline long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
(05)-Strobl2018A-DS0003 – Tribolium castaneum AGOC{ARP5'#O(LA)-mEmerald} #2 subline long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(05)-Strobl2018A-DS0003 – <em>Tribolium castaneum</em> AGOC{ARP5'#O(LA)-mEmerald} #2 subline long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
(05)-Strobl2018A-DS0002 – Tribolium castaneum AGOC{ARP5'#O(LA)-mEmerald} #1 subline long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(05)-Strobl2018A-DS0002 – <em>Tribolium castaneum</em> AGOC{ARP5'#O(LA)-mEmerald} #1 subline long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
Dataset: Long-Term Resistance of Gradient Anchorage for Prestressed CFRP Strips in Structural Concrete Retrofitting
<p>This dataset presents experimental research results on the long-term behaviour of a non-mechanical prestressed CFRP anchorage for concrete retrofitting.<br> The following data is included:<br> - Force-slip of lap-shear tests.<br> - Slip profiles (at last stage) of prestress force releasing and lap-shear tests.</p> <p> </p> <p>Further details on the experimental setup, as well as representation of datasets can be found in the following works.<br> Please cite the following works, if any part of the dataset is used within your research.</p> <p>https://doi.org/10.1016/j.compositesb.2017.11.062<br> https://doi.org/10.3390/polym10060565<br> Harmanci, Yunus Emre. Long-Term Resistance of Gradient Anchorage for Prestressed CFRP Strips in Structural Concrete Retrofitting. Diss. ETH Zurich, 2018.</p>
A Long-Term Micrometeorological and Hydrological Dataset Across an Elevation Gradient in Sagehen Creek, Sierra Nevada, California
<p>We compile and release ~55 years of daily and ~20 years of hourly Micrometeorological and hydrological data from Sagehen Creek a 28 km〖^2〗 watershed with observation sites spanning 1771 to 2670 m. A USGS gauging station measures streamflow at the catchment outlet. There are three Snow Telemetry (SNOTEL) stations: Independence Camp(2128 m), Independence Creek (1962 m) and Independence Lake (2541 m) that measure hourly precipitation, temperature, soil moisture (at 5, 20, and 50 cm), as well as daily snow water equivalent (SWE) and snow depth. A new method was used to estimate hourly precipitation data using quality controlled daily totals. A NOAA cooperative observer (COOP) station measures daily precipitation, temperature, SWE, and snow depth from 1953-1997 and then measures hourly precipitation, temperature, SWE, and snow depth, relative humidity, and solar radiation from 2001 through 2017 2001-present. There are an additional three towers with data beginning in 2009 measuring snow depth, SWE, solar radiation, barometric pressure, precipitation, relative humidity, and temperature: Tower 1 (1934m), Tower 3 (2114 m), and Tower 4 (2350 m). Wind speed, temperature, and relative humidity measured at 7.6 and 30.5 m at each site. Data from all stations were checked for poor QA/QC and substantial and sophisticated gap-filling techniques were deployed. This dataset holds potential for improving understanding of orographic processes and their implications for streamflow generation in a groundwater-dominated watershed.</p>
(06)-He2019A-DS0003 – Tribolium castaneum AGOC #6 subline × foxQ2-5' line long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(06)-He2019A-DS0003 – <em>Tribolium castaneum</em> AGOC #6 subline × foxQ2-5' line long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
(06)-He2019A-DS0002 – Tribolium castaneum foxQ2-5' line × AGOC #6 subline long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(06)-He2019A-DS0002 – <em>Tribolium castaneum</em> foxQ2-5' line × AGOC #6 subline long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
Long-term Performance and Life Cycle Assessment of Energy Piles in three Different Climatic Conditions_Dataset
<p>In this file it is possible to find the dataset linked to the related pubblication. In the file each spreadsheet corresponf to a picture of the paper.</p>
European Long-term Irrigation Area Datasets version 1.0
<p>These files accompany the manuscript titled "<strong><em>Climate-Driven Interannual Variability in Subnational Irrigation Areas Across Europe</em></strong>" in the journal <strong><em>Communications Earth & Environment</em>.</strong></p> <p>We have developed the <strong>European Long-term Irrigation Area Dataset (ELIAD)</strong>, which offers annual subnational data on total irrigated and irrigable areas for 32 European regions from 1990 to 2020. For most countries, the data is at the NUTS2 level, except for the UK, Germany, and Ireland, where it is at the NUTS1 level.</p> <p>Supplementary A: Contains the supplementary figures and tables referenced in the manuscript.<br>Supplementary B: Provides detailed information on the irrigation reference periods for EU farm structure surveys and agricultural censuses.<br>Supplementary C: Describes the methodology used to generate the ELIAD dataset.<br>Supplementary D: Includes the ELIAD dataset itself.<br>Supplementary E: Details the remote sensing products used for comparison with ELIAD in the manuscript.<br>Supplementary F: Contains the data directly used in the figures and tables presented in the manuscript.</p>
(09)-Pereyra2021A-DS0001--DS0003 – Three Tribolium castaneum long-term live imaging datasets of embryonic development acquired with light sheet fluorescence microscopy
<p>(09)-Pereyra2021A-DS0001--DS0003 – Three <em>Tribolium castaneum</em> long-term live imaging datasets of embryonic development acquired with light sheet fluorescence microscopy</p>
(08)-Strobl2021A-DS0002 – Tribolium castaneum ACOS{ATub'H2B-mRuby} #1 subline long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(08)-Strobl2021A-DS0002 – <em>Tribolium castaneum</em> ACOS{ATub'H2B-mRuby} #1 subline long-term live imaging data of embryonic development acquired with light sheet fluorescence microscopy</p>
(08)-Strobl2021A-DS0001 – Tribolium castaneum AGOC{Zen1'#O(LA)-mEmerald} #1 subline long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(08)-Strobl2021A-DS0001 – <em>Tribolium castaneum</em> AGOC{Zen1'#O(LA)-mEmerald} #1 subline long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
(07)-Ratke2020A-DS0005 – Tribolium castaneum AGOC{Zen1'#O(LA)-mEmerald} #2 subline long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(07)-Ratke2020A-DS0005 – <em>Tribolium castaneum</em> AGOC{Zen1'#O(LA)-mEmerald} #2 subline long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</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.