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327 results for “Time-Series”
Time-series of 5 minute water temperatures averages from Toolik Lake, Toolik Field Station, Alaska, Summer 2002.
Time-series of temperatures were measured using self-contained temperature loggers on taut-line moorings with a subsurface float 1 m below the air-water. Theses are the 5 minute averages of 10 second measuremsents.
Time-series of 5 minute water temperatures averages from Toolik Lake, Toolik Field Station, Alaska, Summer 2003.
Time-series of temperatures were measured using self-contained temperature loggers on taut-line moorings with a subsurface float 1 m below the air-water. Theses are the 5 minute averages of 10 second measuremsents.
Time-series of 5 minute water temperatures averages from Toolik Lake, Toolik Field Station, Alaska, Summer 2005.
Time-series of temperatures were measured using self-contained temperature loggers on taut-line moorings with a subsurface float 1 m below the air-water. Theses are the 5 minute averages of 10 second measuremsents.
Time-series measurements of size fractionated primary production (>3 micron) in the subtropical North Pacific Ocean
<p>Filter size-fractionated (>3 μm) particulate 14C-based rates of primary production were measured at six discrete depths (5, 25, 45, 75, 100, and 125 m) throughout the euphotic zone. Seawater samples from each depth were subsampled into triplicate 30-mL polycarbonate centrifuge tubes from a pre-dawn cast, inoculated with 70 µL of NaH14CO3-, then incubated over the full photoperiod (~12-14 hours) on a floating in situ array at the corresponding depths where the water was collected. At the end of the incubation period (after sundown), 25 mL of each sample was vacuum-filtered first onto a 25-mm diameter 3-μm pore size polycarbonate membrane.</p>
Time-series measurements of size fractionated primary production (14C-assimilation) in the subtropical North Pacific Ocean
<p>Filter size-fractionated (>0.2-3 μm) particulate 14C-based rates of primary production were measured at six discrete depths (5, 25, 45, 75, 100, and 125 m) throughout the euphotic zone. Seawater samples from each depth were subsampled into triplicate 30-mL polycarbonate centrifuge tubes from a pre-dawn cast, inoculated with 70 µL of NaH14CO3-, then incubated over the full photoperiod (~12-14 hours) on a floating in situ array at the corresponding depths where the water was collected. At the end of the incubation period (after sundown), 25 mL of each sample was vacuum-filtered first onto a 25-mm diameter 3-μm pore size polycarbonate membrane, then the filtrate was vacuum-filtered onto a 25-mm diameter 0.2-μm pore size polycarbonate membrane filter. The rates reported here are for the >0.2-3 μm size fraction.</p>
Time-series rates of dissolved organic carbon production in the subtropical North Pacific Ocean
<p>Over a 3-year period (April 2010-April 2013), we measured 14C-DOC production from vertical profiles used for determination of 14C-particle production, utilizing 0.2 um filtrates. Seawater for these experiments 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 14C-bicarbonate. One hundred milliliters from one replicate per depth was vacuum filtered through a 0.2 mm polycarbonate filter and the filtrate served as a time zero blank. 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 (<50 mm Hg) onto 0.2 mm polycarbonate filters. These 0.2 mm filtrates were stored frozen (-20oC) until subsequent processing for determination of 14C-DOC productivity. Samples were processed as follows: 100 ml of the 14C-PC filtrates were thawed, poured into 500 ml polyethylene separatory funnels, and acidified by the addition of 500 µl of 2 M sulfuric acid (H2SO4). Samples were vigorously bubbled with air in a fume hood to remove 14CO2. A 70 ml subsample was removed from each separatory funnel and poured into a 100 ml glass serum bottle containing 1 ml of 2 M sodium hydroxide (NaOH) and 10 ml of 0.37 M potassium persulfate (K2S2O8) in 1 M NaOH. Bottles were sealed with rubber stoppers, crimp sealed with an aluminum cap, and autoclaved at 126°C for 200 minutes; oxidizing 14C-DOC to 14C-DIC in an alkaline solution. Once cooled to room temperature, samples were uncapped and resealed using rubber sleeve stoppers holding plastic center wells containing ~2 x 2 cm pieces of fluted chromatographic filter paper (Whatman 2) soaked with 0.2 ml of β-phenylethylamine. A syringe was used to inject 4 ml of 9 N H2SO4 into the solution, converting the 14C-labeled dissolved inorganic carbon (hereafter 14C-DIC) to 14CO2. Samples were stored undisturbed at room temperature, passively trapping the 14CO2 on the β-phenylethylamine soaked wick. After at least 100 hours, rubber sleeve stoppers were removed and center wells and wicks were placed in scintillation vials, followed by the addition of 10 ml of Ultima Gold LLT scintillation cocktail. Samples were subsequently counted on a Perkin Elmer Tri-Carb 2800TR liquid scintillation counter. Rates of 14C-DOC production were computed for each cruise as the mean of the triplicate bottles from each depth minus the average 14C-activity of the time zero (blank) samples.</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: 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>
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: 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.
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>
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.
Time-series global 30 m wetland maps from 2000 to 2022
<p><strong>Basic information</strong></p><p>A novel global 30 m wetland annual dataset with fine classification system is generated, covering the period of 2000-2022 and containing 8 wetland subcategories (permanent water, swamp, marsh, flooded flats, saline, mangrove forest, salt marshes, and tidal flats). </p><p><strong>Notes:</strong></p><p>The GWL_FCS30 annual maps are divided into 961 5°×5° geographical tiles, and each tile contains 23 bands which denotes the tidal flat maps in 2000, 2001, 2002,..., 2021, 2022.</p><p><strong>Usage Policy:</strong></p><p>If you plan to use our data in <strong>a scientific analysis paper</strong>, we strongly recommend contacting us in advance to seek opinions, and consider our contributions in the acknowledgments or as co-authors.</p><p><strong>Citations:</strong></p><p>Zhang, X., Liu, L., Zhao, T., Chen, X., Lin, S., Wang, J., Mi, J., and Liu, W.: GWL_FCS30: a global 30 m wetland map with a fine classification system using multi-sourced and time-series remote sensing imagery in 2020, Earth Syst. Sci. Data, 15, 265–293, <a href="https://doi.org/10.5194/essd-15-265-2023">https://doi.org/10.5194/essd-15-265-2023</a>, 2023</p>
R code and supplementary data for : "A framework for mapping conservation agricultural fields using time-series optical and radar imagery"
<p>Source code and cover crop maps for the paper "A framework for mapping conservation cropland using optical and radar time series imagery." (Zhou et al., 2025)</p> <p>https://doi.org/10.1016/j.rse.2025.114858</p> <p> </p> <p>The entire workflow consists of these steps:</p> <p>1. Obtain satellite data from Google Earth Engine platform. script path: (<a href="https://code.earthengine.google.com/?scriptPath=users%2Fyuez9466%2FCApractice%3ANDVI">https://code.earthengine.google.com/?scriptPath=users%2Fyuez9466%2FCApractice%3ANDVI</a>). You need to obtain the NDVI, NBR2, Sentinel-1 Radar dataset and Precipitation data for your research area and seltected time interval. Download .csv data from Google Cloud, then convert the format of the data for following calculations.(see 1_import_transfer_data.R)</p> <p>2. Obtain the annual crop types in your study area, either through agricultural census data or remote sensing predictions (not mentioned in this paper), calculate organic carbon input based on the crop types. Extracting seasons based on time-series NDVI values using phenofit package. (see 2_NDVI_Smooth_Divide_seasons.R)</p> <p>3. Calculating the length of the cover crop growing season and periods of bare soil, also get the nessasary covariates for tillage model meanwhile. (see 3_CC_BS_length_add_Tillage.R)</p> <p>4. Build a tillage model. (see 4_Build_Tillage_model)</p> <p>Build your own conservation agriculture fields model.</p>
Time-series of groundwater recharge, Tiber Riber Basin, Italy from 801 CE to the present day
<p>Groundwater, essential for water availability, sanitation, and achieving Sustainable Development Goals, is shaped by climate dynamics and complex hydrogeological structures. Here, we provide a time-series of groundwater recharge from 801 CE to the present day in the Tiber River Basin, Italy, using historical records and hydrological modelling. Groundwater drought occurred in 36% of the Medieval Climatic Anomaly (801-1249) years, 12% of the Little Ice Age (1250-1849) years, and 26% of the Modern Warming Period (1850-2020) years. Importantly, a predominant warm phase of the Atlantic Multidecadal Oscillation, aligned with solar maxima, coincided with prolonged dry spells during both the medieval and modern periods, inducing a reduction in recharge rates due to hydrological memory effects. This study enhances understanding of climate-water interactions, offering a comprehensive view of groundwater dynamics in central Mediterranean and highlighting the importance of the past for sustainable future strategies. Leveraging this understanding can address water scarcity and enhance basin resilience.</p>
Time-series analysis of rhenium(I) organometallic covalent binding to a model protein for drug development: Raw Diffraction Images (38 week soak). Zenodo
<p>The synchrotron raw diffraction images obtained at 38 weeks and wavelength 0.976 Å, illustrates the covalent coordination of the rhenium(I) tricarbonyl fragment to the His and Asp amino acid residues as well as movement along the solvent channels as described in the publication titled "Time-series analysis of rhenium(I) organometallic covalent binding to a model protein for drug development", written by Jacobs, Helliwell & Brink,<em> IUCrJ</em>, 2024, https://doi.org/10.1107/S2052252524002598.</p> <p>The raw diffraction images for the DLS data sets are made available at the Zenodo research data archive, as specified in the publication.</p>
Global Landside Clustering of Aquaculture Ponds Distribution Acquired from Dense Time-Series Sentinel-2 Images by Google Earth Engine
<p>This dataset reveals the global distribution pattern of landside clustering aquaculture ponds (LCAP) from a spatial perspective for the first time. It was derived from 4,015,054 tiles of the 10-m Sentinel-2 time-series images collected throughout 2020. The total area of global LCAP was estimated at 55,337.03 km2. Accuracy verification revealed that the Omission Error and Commission Error of the data is 7.51% and 16.69% respectively. We provide this dataset in <em>ESRI</em> <em>shapefile </em>format (.zip), which can be opened by <em>ArcGIS. </em>We invite you to download and utilize this dataset and recommend citing the following two references.</p>
Time-series transcriptome analysis identified differentially expressed genes in broiler chicken infected with mixed Eimeria species
<p>Coccidiosis caused by the <em>Eimeria</em> species is a highly problematic disease in the chicken industry. Here, we used RNA sequencing to observe the time-dependent host responses of <em>Eimeria</em>-infected chickens to examine the genes and biological functions associated with immunity to the parasite. Transcriptome analysis was performed at three time points: 4, 7, and 21 days post-infection (dpi). Based on the changes in gene expression patterns, we defined three groups of genes that showed differential expression. This enabled us to capture evidence of endoplasmic reticulum stress at the initial stage of <em>Eimeria</em> infection. Furthermore, we found that innate immune responses against the parasite were activated at the first exposure; they then showed gradual normalization. Although the cytokine-cytokine receptor interaction pathway was significantly operative at 4 dpi, its downregulation led to an anti-inflammatory effect. Additionally, the construction of gene co-expression networks enabled identification of immunoregulation hub genes and critical pattern recognition receptors after <em>Eimeria</em> infection. Our results provide a detailed understanding of the host-pathogen interaction between chicken and <em>Eimeria</em>. The clusters of genes defined in this study can be utilized to improve chickens for coccidiosis control.</p>
Time-series covering up to four decades reveals major changes and drivers of marine growth and proportion of repeat spawners in an Atlantic salmon population
<p><span>1. </span><span>Wild Atlantic salmon populations have declined in many regions and are affected by diverse natural and anthropogenic factors. To facilitate management guidelines, precise knowledge of mechanisms driving population changes in demographics and life history traits is needed. </span></p> <p><span>2. </span><span>Our analyses were conducted on a) age and growth data from scales of salmon caught by angling in the river Etneelva, Norway, covering smolt year classes from 1980 to 2018, b) extensive sampling of the whole spawning run in the fish trap from 2013 onwards, and c) time series of sea surface temperature, zooplankton biomass and salmon lice infestation intensity. </span></p> <p><span>3. </span><span>Marine growth during the first year at sea displayed a distinct stepwise decline across the four decades. Simultaneously, the population shifted from predominantly 1SW to 2SW salmon, and the proportion of repeat spawners increased from 3-7%. The latter observation most evident in females, and likely due to decreased marine exploitation. Female repeat spawners tended to be less catchable than males by anglers. </span></p> <p><span>4. </span><span>Depending on the time-period analysed, marine growth rate during the first year at sea was both positively and negatively associated with sea surface temperature. Zooplankton biomass was positively associated with growth while salmon lice infestation intensity was negatively associated with growth. </span></p> <p><span>Collectively these results are likely to be linked with both changes in oceanic conditions and harvest regimes. Our conflicting results regarding the influence of sea surface temperature on marine growth is likely to be caused by long-term increases in temperature which may have triggered (or coincided with) ecosystem shifts creating generally poorer growth conditions over time, but within shorter data sets warmer years gave generally higher growth. We encourage management authorities to expand the use of permanently monitored reference rivers with complete trapping facilities, like the river Etneelva, generating valuable long-term data for future analyses.</span></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.