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Dataset results
156 results for “long-term dataset”
(07)-Ratke2020A-DS0004 – Tribolium castaneum AGOC{Zen1'#O(LA)-mEmerald} #1 subline long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(07)-Ratke2020A-DS0004 – <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-DS0003 – Drosophila melanogaster w[*]; P{w[+mC]=His2Av-EGFP.C}2/SM6a line long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(07)-Ratke2020A-DS0003 – <em>Drosophila melanogaster</em> w[*]; P{w[+mC]=His2Av-EGFP.C}2/SM6a line long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
(07)-Ratke2020A-DS0001 – Drosophila melanogaster y[1] w[67c23]; P{w[+mC]=Ubi-GFP.nls}ID-2; P{Ubi-GFP.nls}ID-3 line long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(07)-Ratke2020A-DS0001 – <em>Drosophila melanogaster</em> y[1] w[67c23]; P{w[+mC]=Ubi-GFP.nls}ID-2; P{Ubi-GFP.nls}ID-3 line long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
(07)-Ratke2020A-DS0002 – Drosophila melanogaster w[*]; P{w[+mC]=Tub84B-EGFP.NLS}3 long-term live imaging dataset acquired with light sheet fluorescence microscopy
<p>(07)-Ratke2020A-DS0002 <em>–</em> <em>Drosophila melanogaste</em>r y[1] w[67c23]; P{w[+mC]=Ubi-GFP.nls}ID-2; P{Ubi-GFP.nls}ID-3 (Bloomington <em>Drosophila</em> Stock Center #29724) long-term live imaging dataset acquired with light sheet fluorescence microscopy</p>
(08)-Strobl2021A-DS0003 – Tribolium castaneum Gruul #1 hybrid line long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(08)-Strobl2021A-DS0003 – <em>Tribolium castaneum</em> Gruul #1 hybrid line long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
(07)-Ratke2020A-DS0006 – Tribolium castaneum AGOC{Zen1'#O(LA)-mEmerald} #3 subline long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(07)-Ratke2020A-DS0006 – <em>Tribolium castaneum</em> AGOC{Zen1'#O(LA)-mEmerald} #3 subline long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
Long-Term Demographic Trends in Prehistoric Italy: climate impacts and regionalised socio-ecological trajectories (dataset and R script)
<p>The present digital archive is the outcome of the paper: <strong>Palmisano, A., Bevan, A., Kabelindde, A., Roberts, N., and Shennan, S., 2021. <a href="https://doi.org/10.1007/s10963-021-09159-3">Long-Term Demographic Trends in Prehistoric Italy: climate impacts and regionalised socio-ecological trajectories</a>. <em>Journal of World Prehistory, 34 (3)</em>, </strong>381-432<strong>.</strong></p> <p>The dataset included here provides a collection of <strong>4,010</strong> radiocarbon dates from <strong>947</strong> archaeological sites for a period spanning between 11,000 and 1500 BP. In addition, the digital archive related to this paper provides reproducible analyses in the form of one script written in R statistical computing language.</p> <p>List of versions:</p> <ul> <li><strong>1.0.</strong> 4 August 2021 - First public release of the dataset on Zenodo. </li> </ul>
A long-term monthly surface water storage dataset for the Congo basin from 1992 to 2015
<p><strong>1. Summary</strong></p> <p>The Congo basin’s Surface Water Storage (SWS) datasets are generated by Benjamin Kitambo, Fabrice Papa, Adrien Paris, Raphael M. Tshimanga, Frederic Frappart, Stephane Calmant, Omid Elmi, Ayan Santos Fleischmann, Melanie Becker, Mohammad J. Tourian, Rômulo A. Jucá Oliveira, Sly Wongchuig in the article entitled "A long-term monthly surface water storage dataset for the Congo basin from 1992 to 2015", <strong><em>Earth System Science Data (submitted)</em></strong>.</p> <p>The dataset was generated using two methods, one based on a multi-satellite approach and one on a hypsometric curve approach. The multi-satellite approach consists of the combination of surface water extent (SWE) from the Global Inundation Extent from Multi-satellite (GIEMS-2) and satellite-derived surface water height (SWH) from radar altimetry (long-term series ERS-2_ENV_SRL) on the same period of availability for the two datasets, here 1995-2015. The hypsometric curve approach consists of the combination of SWE from GIEMS-2 dataset and hypsometric curves obtained from various digital elevation models (DEMs) (i.e., ASTER, ALOS, MERIT, and FABDEM). Both methods estimate monthly spatio-temporal variations of SWS changes across the entire Congo River basin.</p> <p><strong>2. Name Description</strong></p> <ul> <li>HYPSO_XX: hypsometric curve providing the surface water extent area-elevation relationship from XX (where XX stands for ASTER, ALOS, MERIT, and FABDEM DEMs).</li> <li>HYPSO_CORR_XX: corrected hypsometric curve providing the surface water extent area-elevation relationship from XX (where XX stands for ASTER, ALOS, MERIT, and FABDEM DEMs).</li> <li>AREA_STOR_XX: hypsometric curve providing the surface water extent area-storage relationship from XX (where XX stands for ASTER, ALOS, MERIT, and FABDEM DEMs).</li> <li>SWS_XX: monthly surface water storage variations from XX (where XX stands for ASTER, ALOS, MERIT, FABDEM DEMs, and Multi-satellite approach).</li> </ul> <p><strong>3. File Description </strong></p> <p>The SWS estimates from the multi-satellite approach (1995-2015), as well as the hypsometric curves providing the surface water extent area-elevation relationship from the four DEMs (before and after the corrections), the surface water extent area-storage relationship, along with the four SWS estimates (1992-2005).</p> <p>The dataset is gridded on equal-area of 0.25° spatial resolution at the equator, each pixel covers almost 773 km². The reference point for calculating the volume variation is the minimum of surface water extent for each pixel.</p> <p>The files are organized in matrix:</p> <ul> <li><strong>First column</strong> represents the latitude in degree.</li> <li><strong>Second column</strong> represents the longitude in degree.</li> <li><strong>From the third column</strong>: data. In case of hypsometric curve, the data represents elevation in meter on 101 columns representing the increment of 1% flooding in each 773 km<sup>2</sup> pixel from GIEMS-2. In case of SWS data (in km³), there are 288 (respectively 252) columns representing each month of the period over 1992-2015 (respectively 1995-2015) from hypsometric curve approach (respectively multi-satellite approach).</li> </ul>
Techno-economic dataset for long-term energy systems modelling in Viet Nam
<p>Techno-economic data and assumptions for long-term energy systems modelling in Viet Nam. This includes data on electricity generation and consumption, electricity imports and exports, fuel prices, emissions, refineries, power transmission and distribution, electricity generation technologies, and renewable energy potential and reserves for the years 2015 to 2050.</p>
Dataset for: 'Patterns in the Plankton – Spatial distribution and long-term variability of copepods on the Agulhas Bank'
<p>This dataset contains environmental data (in situ temperature and chlorophyll <em>a</em>) and integrated biomass (mg C m<sup>-2</sup>) data for a number of copepod taxa, as well as total copepod biomass and abundance, on the Agulhas Bank, South Africa, as predicted by a Generalized Additive Model (GAM), during late austral spring (October-December) from 1988 to 2011. Mean environmental and copepod biomass parameters for each area and year are also provided. Relevant information on sampling and statistical analysis of spatial distributions has been extracted from the paper. Please see paper for full details and figures, including supplementary data; <a href="https://doi.org/10.1016/j.dsr2.2023.105265">https://doi.org/10.1016/j.dsr2.2023.105265</a>. Please see the Word document Huggett_et_al_2023_README.docx for a list of the data files and descriptions of the contents.</p>
General relativistic precession and the long-term stability of the solar system: SimulationArchive dataset
<p>We share the data for 1280 long-term solar system simulations used in <a href="https://doi.org/10.1093/mnras/stad719">Brown & Rein (2023)</a>. The simulations are zipped together consecutively in groups of 16 and saved in the <code>REBOUND</code> (3.18.1) SimulationArchive format. Please see the companion paper and <a href="https://doi.org/10.5281/zenodo.7753656">code</a> for details.</p> <p><strong>Abstract</strong></p> <p>The long-term evolution of the solar system is chaotic. In some cases, chaotic diffusion caused by an overlap of secular resonances can increase the eccentricity of planets when they enter into a linear secular resonance, driving the system to instability. Previous work has shown that including general relativistic contributions to the planets' precession frequency is crucial when modelling the solar system. It reduces the probability that the solar system destabilizes within 5 Gyr by a factor of 60. We run 1280 additional <em>N</em>-body simulations of the solar system spanning 12.5 Gyr where we allow the general relativistic precession rate to vary with time. We develop a simple, unified, Fokker-Planck advection-diffusion model that can reproduce the instability time of Mercury with, without, and with time-varying general relativistic precession. We show that while ignoring general relativistic precession does move Mercury's precession frequency closer to a resonance with Jupiter, this alone does not explain the increased instability rate. It is necessary that there is also a significant increase in the rate of diffusion. We find that the system responds smoothly to a change in the precession frequency: There is no critical general relativistic precession frequency below which the solar system becomes significantly more unstable. Our results show that the long-term evolution of the solar system is well described with an advection-diffusion model. </p>
Dataset for the paper High durability and stability of 2D nanofluidic devices for long-term single-molecule sensing
<p>Information regarding the Dataset, corresponding to the paper: “Thakur, M., Cai, N., Zhang, M. et al. High durability and stability of 2D nanofluidic devices for long-term single-molecule sensing. npj 2D Mater Appl 7, 11 (2023). https://doi.org/10.1038/s41699-023-00373-5”</p> <p>This folder contains the raw data and complete package of codes used to analyze, view, save, and plot data for the publication titled "High durability and stability of 2D nanofluidic devices for long-term single-molecule sensing". The code folder, "OpenNanopore-nanopore-tools", can be used to plot raw data which corresponds to the figures in the paper and supplementary information. </p>
Dataset for Modelling the long-term carbon storage potential from recalcitrant matter inputs in tropical arable croplands
<p>Soil organic carbon dynamics for Ecuadorian croplands on the period 2020-2070, under RCP4.5, for various carbon sequestration alternatives, relative to a business as usual situation. Results obtained from adapted version of RothC for carbon sequestration technologies.</p>
Dataset for the intervention effects and long-term changes in physical activity and cardiometabolic outcomes among children at risk of noncommunicable diseases in South Africa
<p>Dataset used to evaluate the short-term effects of the physical and health <em>KaziKidz</em> intervention on cardiometabolic risk factors and the long-term changes thereof among school-aged children at risk of NCDs from disadvantaged communities in South Africa.</p> <p>It encompasses anonymized, unique, identification numbers, anthropometric and clinical measures, such as blood pressure, blood sugar and blood lipids, and Actigraphy-measured physical activity levels. Assigned categories to each cardiovascular risk factor and the overall classification as at risk or not is available too.</p>
The Long-term, High-accuracy and Seamless Soil Moisture (LHS-SM) dataset over the Qinghai-Tibet Plateau: part 1 (2001-2010)
<p>Soil moisture (SM) is a vital variable in the water-energy cycle and characterizing its spatiotemporal dynamics is crucial for understanding the impacts of climate change. Although substantial efforts have been devoted to derive SM data at fine scale, there is still a research gap in obtaining the long-term, high-accuracy and high-resolution SM data over the Qinghai-Tibet Plateau (QTP) due to its complex topography. Therefore, this study generated the long-term, high-accuracy and seamless soil moisture (LHS-SM) dataset over the QTP during 2001-2020 using a two-step downscaling method. First the daily SM data from the Climate Change Initiative program of the European Space Agency (ESA CCI) was downscaled to 1km utilizing five machine learning approaches. Then a dynamic data merging method that considers the spatiotemporal nonstationary error was applied to derive the final LHS-SM data. Results indicated that LHS-SM data exhibited satisfying accuracy (mean R = 0.55, ubRMSE = 0.049 m³/m³) and certain improvement to the ESA CCI SM data both at station and network scales. The dataset can be used for various regional hydrology, meteorology, ecological analysis and modeling.</p>
Primary data for: "Remotely sensed localised primary production anomalies predict the burden and community structure of infection in long-term rodent datasets"
<p>Datasets</p>
SGS-LTER Ecosystem Stress Area - long-term density dataset following nutrient enrichment stress on the Central Plains Experimental Range in Nunn, Colorado, USA 1975-2011, ARS Study Number 3
This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. Water, nitrogen, and water-plus-nitrogen at levels beyond the range normally experience by shortgrass steppe communities were applied from 1971 through 1975, plant densities were sampled through 1977, and then sampling resumed in 1982, with sampling frequencies changing from annually to every other year. The initial sampling from 1970 to 1974 showed that the water and water plus nitrogen treatments had the strongest effect on plant community structure, both treatments increased biomass, and exotic weed species were noted on the water plus nitrogen treatment. Later sampling from 1982 to 1991 showed a ten-fold increase in exotic weed species on the water plus nitrogen plots as compared to the controls (Milchunas and Lauenroth 1995), a community change that has persisted on this site due to a chronic elevation of soil nitrogen caused by a plant tissue/soil organic matter feedback mechanism (Vinton and Burke 1995). In 1998, Six new treatments were superimposed on the historic study site. The six new treatments were: control, sugar, lignin, sawdust, lignin and sugar, and sawdust and sugar.In 2010, plots will be sampled every 5 years. Our objective in this study is to examine how plant communities change through time and explore implications of these changes for monitoring
SGS-LTER Ecosystem Stress Area - long-term point-frame (percent basal cover) dataset following nutrient enrichment stress on the Central Plains Experimental Range in Nunn, Colorado, USA 1982-2011, ARS Study Number 3
This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. Water, nitrogen, and water-plus-nitrogen at levels beyond the range normally experience by shortgrass steppe communities were applied from 1971 through 1975, plant densities were sampled through 1977, and then sampling resumed in 1982, with sampling frequencies changing from annually to every other year. The initial sampling from 1970 to 1974 showed that the water and water plus nitrogen treatments had the strongest effect on plant community structure, both treatments increased biomass, and exotic weed species were noted on the water plus nitrogen treatment. Later sampling from 1982 to 1991 showed a ten-fold increase in exotic weed species on the water plus nitrogen plots as compared to the controls (Milchunas and Lauenroth 1995), a community change that has persisted on this site due to a chronic elevation of soil nitrogen caused by a plant tissue/soil organic matter feedback mechanism (Vinton and Burke 1995). In 1998, Six new treatments were superimposed on the historic study site. The six new treatments were: control, sugar, lignin, sawdust, lignin and sugar, and sawdust and sugar.In 2010, plots will be sampled every 5 years. Our objective in this study is to examine how plant communities change through time and explore implications of these changes for monitoring
BACCHUS Long-Term Dataset
<h2>Agricultural multi-month dataset with overlapping paths for mapping and localisation algorithms for autonomous robots</h2><p>In March 2022, we started conducting the long-term data acquisition campaign at Ktima Gerovassiliou vineyard. This vineyard extends for more than 100ha located on the outskirts of Epanomi, Greece.</p><p>As shown in the image below (taken in March and June), agricultural environments present seasonal changes, repetitive structures, uneven terrain and different weather conditions, which make achieving long-term autonomy for robots a challenging problem.</p><p>Motivated by these challenging conditions and the lack of an agricultural dataset in the literature, we present the BLT dataset. Its primary objective is to push developments and evaluations of different mapping and localisation algorithms for long-term autonomous robots operating in agricultural fields. However, we believe that thanks to its temporal aspect, the dataset can also be used for phenotyping and crop mapping tasks.</p><p>Available from <a href="https://lcas.lincoln.ac.uk/wp/research/data-sets-software/blt/">https://lcas.lincoln.ac.uk/wp/research/data-sets-software/blt/</a></p><p> </p>
Dataset: Lodgepole pine Pinus contorta Douglas ex Loudon invasion in subarctic Iceland: evidence from a long-term study.
<p>This dataset supports the paper <em>Lodgepole Pine (Pinus contorta Douglas ex Loudon) Invasion in Subarctic Iceland: Evidence from a Long-Term Study</em>, published in <em>NeoBiota</em>. It includes the following files:</p> <ol> <li><strong>Point_data_distribution.csv</strong>: Contains distribution point data for <em>Pinus contorta</em> in Steinadalur, using the WGS 84 coordinate reference system (EPSG:4326).</li> <li><strong>Plot_data_pinus.csv</strong>: provides data on plant species richness within study plots.</li> <li><strong>Transects_data.csv</strong>: Includes information on the density of <em>P. contorta</em> along transects.</li> </ol>
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.