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.
1,118
datasets available to search
ShareScore release 0.9.0
Dataset results
1,118 results for “Time series”
Monitoring clearcutting and subsequent rapid recovery in Mediterranean coppice forests with Landsat time series
<p>This dataset considers information from clear-cut coppice forest occurred between 1999 and 2015 in three different area of Tuscany Region (Italy). </p> <p>The dataset consists in one shape file and two csv files.</p> <p>The shape file contains in 2371 spatial polygons of clear-cut occurred in the three area between 1999 and 2015. The polygons were generated using visual interpretation of the Landsat Time Series and high resolution regional ortomosaic. Photo interpreters delineated the spatial extent of each clear-cut and recorded the year of harvest, adopting a minimum mapping unit of 0.1 hectares. The database associated with shape file report information related with area extent of clear-cut, year of clear-cut and the forest types that is associated with the clear-cut derived by the Tucant Regional Forest Inventory (RFI) data that give information on dominant species. </p> <p>The .csv files reported the temporal characteristic of clear-cut based on Landsat Time Series data and based on Airborne Laser Scanner (ALS) Canopy Height Model (CHM), in order to describe the temporal trend of clear-cut based on remote sensing data.</p>
Time series measurements of 14C-based primary production (>0.2 um) in the subtropical North Pacific Ocean
<p>Over a 3-year period (April 2010-April 2013), we measured primary production from vertical profiles at near-monthly time scales in the subtropical North Pacific Ocean. Production measurements were based on <sup>14</sup>C-assimilation into >0.2 um plankton biomass. Seawater for the productivity measurements was collected from predawn CTD hydrocasts into acid-cleaned 500-ml polycarbonate bottles. A total of four replicate 500 ml bottles were subsampled per depth and each bottle was spiked with ~1.85 MBq <sup>14</sup>C-bicarbonate. One hundred milliliters from one replicate per depth was immediately vacuum filtered through a polycarbonate filter as a time zero blank. These filters were placed in 20 ml glass scintillation vials and stored at -20oC until shore-based laboratory processing. The remaining three bottles were hung on a free-drifting array, deployed before dawn, and incubated at their initial collection depths throughout the photoperiod (typically 11-13 hours). After sunset the array was recovered, and 100 ml subsamples of all bottles were filtered under gentle vacuum onto 0.2 um polycarbonate filters that were then placed in scintillation vials and frozen. The total radioactivity added to each sample bottle was determined by subsampling 250 µl aliquots into scintillation vials containing 500 µl of β-phenylethylamine. At the shore-based laboratory, filters were acidified, passively vented, and the resulting radioactivity was determined using liquid scintillation counting.</p>
Time series measurements of nitrogen fixation in the subtropical North Pacific
<p>Rates of N<sub>2</sub> fixation were measured using the <sup>15</sup>N<sub>2</sub> isotopic tracer technique. Sampling occurred during near-monthly Hawaii Ocean Time-series cruises. Whole seawater samples from six discrete depths (5, 25, 45, 75, 100, and 125 m) were subsampled into acid-washed 4.3 L polycarbonate bottles. Between June 2005 and May 2012, 3 mL of <sup>15</sup>N<sub>2</sub> gas was injected into each bottle. Beginning in August 2012, the <sup>15</sup>N<sub>2</sub> gas was first dissolved into seawater and 100 mL of the resulting <sup>15</sup>N<sub>2</sub>-enriched water was added to 4.3 L polycarbonate sampling bottles. The resulting atom % enrichment of stocks of <sup>15</sup>N<sub>2</sub>-enriched seawater was measured using a membrane inlet mass spectrometer. Incubation bottles amended with the <sup>15</sup>N<sub>2</sub> tracer were attached to a free-drifting array and incubated at the discrete depths from which samples had been collected. The array was deployed before dawn and samples were incubated at in situ light and temperature for 24 h. After recovery of the array, the entire volume from each bottle was filtered onto a pre-combusted glass microfiber filter (Whatman 25 mm GF/F) and filters were placed onto pre-combusted pieces of foil in Petri dishes and stored frozen at -20°C. Filters were dried for 24 h at 60°C, pelleted, and the total mass of N and its isotopic signature on each filter were analyzed on an elemental analyzer-isotope ratio mass spectrometer (Carlo-Erba EA NC2500 coupled with ThermoFinnigan Delta S). </p>
Validation of Time Series Technique for Prediction of Conformational States of Amino Acids
<p>Validation of Time Series Technique for Prediction of Conformational States of Amino Acids</p> <p>- a project for fulfillment of M.Sc (Master of Science) in Bioinformatics.</p>
A Subset of CyberShake Ground Motion Time Series for Response History Analysis
<p>A subset of CyberShake numerically simulated ground motions that were selected and vetted for use in engineering response history analyses.</p> <p>v1.0.1: update readme file</p>
Analysis data for ""Integration of time-series meta-omics data reveals how microbial ecosystems respond to disturbance""
<p>Analysis data for the manuscript: "Integration of meta-omics data reveals how microbial ecosystems respond to disturbance"</p> <p>Files used with the repository: https://git-r3lab.uni.lu/malte.herold/laots_niche_ecology_analysis/</p> <p>The archive was split into multiple parts for uploading to zenodo which need to be joined in order to extract the files:</p> <pre><code class="language-bash">cat resultsdir_laots.tar.gz.part_* > resultsdir_laots.tar.gz tar xvfz resultsdir_laots.tar.gz</code></pre> <p>Version 2 contains additional files generated in the revision.</p> <p> </p>
Data from: Dynamics of deep soil carbon - insights from 14C time series across a climatic gradient
Quantitative constraints on soil organic matter (SOM) dynamics are essential for comprehensive understanding of the terrestrial carbon cycle. Deep soil carbon is of particular interest, as it represents large stocks and its turnover times remain highly uncertain. In this study, SOM dynamics in both the top and deep soil across a climatic (average temperature ~1-9 °C) gradient are determined using time-series (~20 years) 14C data from bulk soil and water-extractable organic carbon (WEOC). Analytical measurements reveal enrichment of bomb-derived radiocarbon in the deep soil layers on the bulk level during the last two decades. The WEOC pool is strongly enriched in bomb-derived carbon, indicating that it is a dynamic pool. Turnover time estimates of both the bulk and WEOC pool show that the latter cycles up to a magnitude faster than the former. The presence of bomb-derived carbon in the deep soil, as well as the rapidly turning WEOC pool across the climatic gradient implies that there likely is a dynamic component of carbon in the deep soil. Precipitation and bedrock type appear to exert a stronger influence on soil C turnover time and stocks as compared to temperature.
Data from: The value of time-series data for conservation planning
<ol> <li>Protected areas (PAs) are increasingly being used worldwide for the conservation and management of wildlife. Systematic conservation planning (SCP) aims at ensuring biodiversity persistence while minimizing the threats faced by the species and/or the economic costs related to protection. To account for spatio-temporal interactions between species and human threats, conservation planning for mobile wildlife requires time-series data derived from monitoring of species and human threats, a process that is costly and technically challenging. Therefore, assessments of the monitoring period needed to ensure sufficient data input in the design of efficient, adequate and representative networks of PAs are crucial.</li> <li>We demonstrated the value of time-series data in conservation planning by implementing SCP and data from different monitoring periods to identify priority conservation areas for highly mobile marine megafauna accounting for their main threat: commercial fishing. Two analyses of ten reserve-design scenarios each, replicated as many times as the data composing each scenario permitted were run in Marxan. The best solutions of the planning scenarios were statistically compared using the Cohen`s Kappa test. We also assessed differences in spatial similarity among and within scenarios using the Wilcoxon non-parametric test and a non-metric multidimensional scaling analysis. Finally, we compared the necessary cost and the area selected for each scenario.</li> <li>Our study highlights the importance of time-series ecological and socioeconomic data for the robust selection of priority conservation areas. The results revealed different thresholds of the minimum temporal data required to design efficient networks of PAs for highly mobile species, demonstrating that the incorporation of data covering longer periods to the scenarios produce a more robust selection of priority conservation areas. Conservation plans using data covering less than three years were missing important priority areas.</li> <li> <i>Synthesis and applications. </i>We provide a method for estimating the minimum number of years of monitoring required to design efficient networks of protected areas that ensure the persistence of highly mobile species such as cetaceans and seabirds. This method can be used within an adaptive management framework to evaluate whether a network of PAs performs as planned, and to test whether management strategies should be altered or adjusted in response to local and global changes.</li> </ol>
Data from: Soil solution in Swiss forest stands: a 20 year's time series
<p>Soil solution chemistry is influenced by atmospheric deposition of air pollutants, exchange processes with the soil matrix and soil-rhizosphere-plant interactions. In this study we present the results of the long-term Intercantonal Forest Observation Program in Switzerland with soil solution measurements since 1998 on a current total of 47 plots. The forest sites comprise two major forest types of Switzerland including a wide range of ecological gradients such as different nitrogen (N) deposition and soil conditions. The long-term data set of 20 years of soil solution measurements revealed an ongoing, but site-specific soil acidification. In strongly acidified soils (soil pH below 4.2), acidification indicators changed only slowly over the measured period, possibly due to high buffering capacity of the aluminum buffer (pH 4.2 – 3.8). In contrast, in less acidified sites we observed an increasing acidification rate over time, reflected, for example, by the continuous decrease in the ratio of base cations to aluminum (BC/Al ratio). Nowadays, the main driver of soil acidification is the high rate of N deposition, causing cation losses and hampering sustainable nutrient balances for tree nutrition. Mean nitrate leaching rates for the years 2005-2017 were 9.4 kg N ha<sup>‑1</sup> yr<sup>‑1</sup>, ranging from 0.04 to 53 kg N ha<sup>‑1</sup> yr<sup>‑1</sup>. Three plots with high N input had a remarkable low nitrate leaching. Both N deposition and nitrate leaching have decreased since 2000. However, the latter trend may be partly explained due to increased drought in recent years. Nonetheless, those high N depositions are still affecting the majority of the forest sites. Taken together, this study gives evidence of anthropogenic soil acidification in Swiss forest stands. The underlying long-term measurements of soil solution provides important information on nutrient leaching losses and the impact climate change effects such as droughts.. Furthermore, this study improves the understanding of forest management and tree mortality regarding varying nitrate leaching rates.</p>
Dataset for "Recursive Input and State Estimation: A General Framework for Learning from Time Series with Missing Data"
<p>Dataset for "Recursive Input and State Estimation: A General Framework for Learning from Time Series with Missing Data"</p> <p> </p> <p>Missing values in the blood glucose datasets are represented with -2.</p>
Analysis and Figures from "Causal network inference from gene transcriptional time-series response to glucocorticoids"
<p>Gene regulatory network inference is essential to uncover complex relationships among gene pathways and inform downstream experiments, ultimately enabling regulatory network re-engineering. Network inference from transcriptional time-series data requires accurate, interpretable, and efficient determination of causal relationships among thousands of genes. Here, we develop Bootstrap Elastic net regression from Time Series (BETS), a statistical framework based on Granger causality for the recovery of a directed gene network from transcriptional time-series data. BETS uses elastic net regression and stability selection from bootstrapped samples to infer causal relationships among genes. BETS is highly parallelized, enabling efficient analysis of large transcriptional data sets. We show competitive accuracy on a community benchmark, the DREAM4 100-gene network inference challenge, where BETS is one of the fastest among methods of similar performance and additionally infers whether the causal effects are activating or inhibitory. We apply BETS to transcriptional time-series data of 2,768 differentially-expressed genes from A549 cells exposed to glucocorticoids over a period of 12 hours. We identify a network of 2,768 genes and 31,945 directed edges (FDR <= 0.2). We validate inferred causal network edges using two external data sources: overexpression experiments on the same glucocorticoid system, and genetic variants associated with inferred edges in primary lung tissue in the Genotype-Tissue Expression (GTEx) v6 project. BETS is available as an open source software package at https://github.com/lujonathanh/BETS</p> <p>This upload documents the analysis and figure files that support each numerical claim of the manuscript. Full Progeny.xlsx lists out the relevant code and files for each numerical claim of the manuscript, assuming the home folder of port-from-della</p>
Micro X-ray CT time-series (4D dataset)
<p>The CGLS reconstruction from a time-series with 21 tomograms each collected with 91 projections. For the reconstruction the Savu Python package was used (https://doi.org/10.5281/zenodo.32840). We acknowledge Diamond Light Source for the time on I13-2 under proposal mt9396.</p>
Data from: Using time series analysis to characterize evolutionary and plastic responses to environmental change: a case study of a shift toward earlier migration date in sockeye salmon
Environmental change can shift the phenotype of an organism through either evolutionary or nongenetic processes. Despite abundant evidence of phenotypic change in response to recent climate change, we typically lack sufficient genetic data to identify the role of evolution. We present a method of using phenotypic data to characterize the hypothesized role of natural selection and environmentally driven phenotypic shifts (plasticity). We modeled historical selection and environmental predictors of interannual variation in mean population phenotype using a multivariate state-space model framework. Through model comparisons, we assessed the extent to which an estimated selection differential explained observed variation better than environmental factors alone. We applied the method to a 60-year trend toward earlier migration in Columbia River sockeye salmon Oncorhynchus nerka, producing estimates of annual selection differentials, average realized heritability, and relative cumulative effects of selection and plasticity. We found that an evolutionary response to thermal selection was capable of explaining up to two-thirds of the phenotypic trend. Adaptive plastic responses to June river flow explain most of the remainder. This method is applicable to other populations with time series data if selection differentials are available or can be reconstructed. This method thus augments our toolbox for predicting responses to environmental change.
Data from: Spatiotemporal dynamic of surface water bodies using Landsat time-series data from 1999 to 2011
Detailed information on the spatiotemporal dynamic in surface water bodies is important for quantifying the effects of a drying climate, increased water abstraction and rapid urbanization on wetlands. The Swan Coastal Plain (SCP) with over 1500 wetlands is a global biodiversity hotspot located in the southwest of Western Australia, where more than 70% of the wetlands have been lost since European settlement. SCP is located in an area affected by recent climate change that also experiences rapid urban development and ground water abstraction. Landsat TM and ETM+ imagery from 1999 to 2011 has been used to automatically derive a spatially and temporally explicit time-series of surface water body extent on the SCP. A mapping method based on the Landsat data and a decision tree classification algorithm is described. Two generic classifiers were derived for the Landsat 5 and Landsat 7 data. Several landscape metrics were computed to summarize the intra and interannual patterns of surface water dynamic. Top of the atmosphere (TOA) reflectance of band 5 followed by TOA reflectance of bands 4 and 3 were the explanatory variables most important for mapping surface water bodies. Accuracy assessment yielded an overall classification accuracy of 96%, with 89% producer's accuracy and 93% user's accuracy of surface water bodies. The number, mean size, and total area of water bodies showed high seasonal variability with highest numbers in winter and lowest numbers in summer. The number of water bodies in winter increased until 2005 after which a decline can be noted. The lowest numbers occurred in 2010 which coincided with one of the years with the lowest rainfall in the area. Understanding the spatiotemporal dynamic of surface water bodies on the SCP constitutes the basis for understanding the effect of rainfall, water abstraction and urban development on water bodies in a spatially explicit way.
Data from: Biodiversity-ecosystem functioning relationships in long-term time series and palaeoecological records: deep sea as a test bed
The link between biodiversity and ecosystem functioning (BEF) over long temporal scales is poorly understood. Here, we investigate biological monitoring and palaeoecological records on decadal, centennial and millennial time scales from a BEF framework, by using deep-sea, soft-sediment environments as a test bed. Results generally show positive BEF relationships, in agreement with BEF studies based on present-day spatial analyses and short-term manipulative experiments. However, the deep-sea BEF relationship is much noisier across longer time scales compared with modern observational studies. We also demonstrate with palaeoecological time-series data that a larger species pool does not enhance ecosystem stability through time, whereas abundance, as an indicator of higher ecosystem functioning, may enhance ecosystem stability. These results suggest that BEF relationships are potentially timescale-dependent. Environmental impacts on biodiversity and ecosystem functioning may be much stronger than biodiversity impacts on ecosystem functioning at long, decadal–millennial, time scales. Longer time-scale perspectives, including palaeoecological and ecosystem monitoring data, are critical for predicting future BEF relationships on a rapidly changing planet.
Multilevel modeling of time-series cross-sectional data reveals the dynamic interaction between ecological threats and democratic development
<p>What is the relationship between environment and democracy? The framework of cultural evolution suggests that societal development is an adaptation to ecological threats. Pertinent theories assume that democracy emerges as societies adapt to ecological factors such as higher economic wealth, lower pathogen threats, less demanding climates, and fewer natural disasters. However, previous research confused within-country processes with between-country processes and erroneously interpreted between-country findings as if they generalize to within-country mechanisms. In this article, we analyze a time-series cross-sectional dataset to study the dynamic relationship between environment and democracy (1949-2016), accounting for previous misconceptions in levels of analysis. By separating within-country processes from between-country processes, we find that the relationship between environment and democracy not only differs by countries but also depends on the level of analysis. Economic wealth predicts increasing levels of democracy in between-country comparisons, but within-country comparisons show that democracy declines as countries become wealthier over time. This relationship is only prevalent among historically wealthy countries but not among historically poor countries, whose wealth also increased over time. By contrast, pathogen prevalence predicts lower levels of democracy in both between-country and within-country comparisons. Our longitudinal analyses identifying temporal precedence reveal that not only reductions in pathogen prevalence drive future democracy, but also democracy reduces future pathogen prevalence and increases future wealth. These nuanced results contrast with previous analyses using narrow, cross-sectional data. As a whole, our findings illuminate the dynamic process by which environment and democracy shape each other.</p>
GNSS uplift time series and ice surface elevation changes
<p class="MsoNoSpacing">We use Global Navigation Satellite System (GNSS) stations attached to bedrock to measure elastic displacements of the solid Earth caused by dynamic thinning near the glacier terminus. When we compare our results with discharge, we find a time lag between glacier speedup/slowdown and onset of dynamic thinning/thickening. Our results show that dynamic thinning/thickening on Jakobshavn Isbræ occurs 0.87 ± 0.07 years before speedup/slowdown. This implies that using GNSS time series we are able to predict speedup/slowdown of Jakobshavn Isbræ by up to 10.4 months. For Kangerlussuaq Glacier the lag between thinning/thickening and speedup/slowdown is 0.37 ± 0.17 years (4.4 months).</p>
Data from: What explains rare and conspicuous colours in a snail? A test of time-series data against models of drift, migration or selection
It is intriguing that conspicuous colour morphs of a prey species may be maintained at low frequencies alongside cryptic morphs. Negative frequency-dependent selection by predators using search images ('apostatic selection') is often suggested without rejecting alternative explanations. Using a maximum likelihood approach we fitted predictions from models of genetic drift, migration, constant selection, heterozygote advantage or negative frequency-dependent selection to time-series data of colour frequencies in isolated populations of a marine snail (Littorina saxatilis), re-established with perturbed colour morph frequencies and followed for >20 generations. Snails of conspicuous colours (white, red, banded) are naturally rare in the study area (usually <10%) but frequencies were manipulated to levels of ~50% (one colour per population) in 8 populations at the start of the experiment in 1992. In 2013, frequencies had declined to ~15–45%. Drift alone could not explain these changes. Migration could not be rejected in any population, but required rates much higher than those recorded. Directional selection was rejected in three populations in favour of balancing selection. Heterozygote advantage and negative frequency-dependent selection could not be distinguished statistically, although overall the results favoured the latter. Populations varied idiosyncratically as mild or variable colour selection (3–11%) interacted with demographic stochasticity, and the overall conclusion was that multiple mechanisms may contribute to maintaining the polymorphisms.
Forecasting hourly emergency department arrival using time series analysis
<p></p> Background/aims <p>The stochastic arrival of patients at hospital emergency departments complicates their management. More than 50% of a hospital's emergency department tends to operate beyond its normal capacity and eventually fails to deliver high-quality care. To address this concern, much research has been carried out using yearly, monthly and weekly time-series forecasting. This article discusses the use of hourly time-series forecasting to help improve emergency department management by predicting the arrival of future patients.</p> Methods <p>Emergency department admission data from January 2014 to August 2017 was retrieved from a hospital in Iowa. The auto-regressive integrated moving average (ARIMA), Holt–Winters, TBATS, and neural network methods were implemented and compared as forecasters of hourly patient arrivals.</p> Results <p>The auto-regressive integrated moving average (3,0,0) (2,1,0) was selected as the best fit model, with minimum Akaike information criterion and Schwartz Bayesian criterion. The model was stationary and qualified under the Box–Ljung correlation test and the Jarque–Bera test for normality. The mean error and root mean square error were selected as performance measures. A mean error of 1.001 and a root mean square error of 1.55 were obtained.</p> Conclusions <p>The auto-regressive integrated moving average can be used to provide hourly forecasts for emergency department arrivals and can be implemented as a decision support system to aid staff when scheduling and adjusting emergency department arrivals.</p> <p></p><p></p><p></p>
Water depth time series derived by FLOW-R2D model simulating the Tous dam break
<p>In this dataset, the water depth time series in 21 gauges of Sumacarcel town are derived by the FLOW-R2D model,<br> assuming 240 different combinations of three input paramaters:</p> <p>a) input flow to the computatioal domain (upstream boundaries) (Q)<br> b) the Manning coefficient of the computational domain (n)<br> c) effective slope (required at the upstream boundaries) (S)</p> <p>The sampling for the three parameters is made by Latin Hypercube technique, assuming for each parameter the following interval:</p> <p>a) 10000-20000 m^3/s<br> b) 0.03-0.21 s/m^(1/3)<br> c) 0.0001 - 0.02</p> <p>The dataset consists of the following:<br> <br> 1) input_data.csv file, in which the 240 combinations of the three input parameters is provided (Scenario 100 - 399)<br> 2) runs_tous folder, in which files 100.cv-399.csv. Water depth time series are recorded in 21 gauges.<br> The first column is time (in seconds), and the water dpeths are in meters.<br> <br> Papers relative to this dataset:</p> <p>1) Description of the case study:<br> Alcrudo F and Mulet J (2007). Description of the Tous Dam break case study (Spain).<br> Journal of Hydraulic Research, 45, 45-57.</p> <p>2) Simulation of tous dam break with FLOW-R2D:<br> Bellos V and Tsakiris G (2015). 2D flood modelling: the case of Tous dam break.<br> Proceedings of 36th World Congress of IAHR, the Hague, the Netherlands. (e-proceedings).</p> <p>3) Calibration of the case study of the FLOW-R2D paramteters:<br> Christelis V, Bellos, V, Tsakiris, G (2016). Employing surrogate modelling for the calibration of a 2D flood simulation model.<br> Proceedings of 4th European Congress of IAHR, “Sustainable Hydraulics in the era of global change”,<br> edited by S. Erpicum, B. Dewals, P. Archambeau, M. Pirotton, Liege, Belgium: 727-732.</p> <p>4) Presentation of the FLOW-R2D model:<br> Tsakiris G and Bellos, V (2014) A numerical model for two-dimensional flood routing in complex terrains.<br> Water Resources Management 28(5):1277-1291</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.