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1,118 results for “Time series”
Nutrient and stoichiometric time series measurements of decomposing coarse detritus in freshwaters worldwide from literature published between 1976-2020.
This data publication is a database of published estimates of nitrogen, phosphorus, and carbon content of decomposing coarse detritus though time in freshwater ecosystems worldwide. Nutrient content measurements are paired with estimates of detrital mass loss within decomposition time series (i.e., defined cohorts of decomposing material through time) with the goal of understanding patterns and drivers of temporal elemental dynamics of freshwater detritus. A systematic literature search for aquatic decomposition experiments conducted on 29 April 2020 generated 580 records (after trimming for duplicates and obvious relevance). From this literature pool, we extracted 810 decomposition time series and associated environmental data (e.g., temperature, water quality, detrital characteristics). Time series included in this synthesis include a range of detritus types (including terrestrial and aquatic plant material, carcasses, dung, and veneers), ecosystems (including streams, lakes, rivers, and wetlands), and settings (including natural ecosystems, field mesocosms, and laboratory microcosms).
LAGOS-NE v.1.054.1 Lake water clarity time series (1987-2011), climate, and geophysical data for 601 lakes across a 17-state region of the United States
Time series of median summer water clarity (secchi) values from 601 unique lakes in the Midwest and Northeast United States. Water clarity observations were derived from the Lake Multi-Scaled Geospatial and Temporal Database LAGOS-NELIMNO version 1.054.1. These data were used to assess long-term changes in water clarity from 1987-2011, and the potential drivers of those trends (Lottig et al. in press). Summer open water period was used to approximate the stratified period in the study lakes, which was defined as June 15 to September 15. Over the 25-year time period, each lake had to have at least a single summer water clarity observation for 22 of 25 years. The median number of secchi measurements that were used to derive a single annual median value for each lake was approximately 9. Of the over 14,000 annual estimates of water clarity that we generated, only two percent of those annual values were generated from a single observation and median number of observations for each lake over the 25-year study period was 223. Each unique lake with water clarity data also has supporting geophysical data, including climate, land use, hydrology, and topography derived at multiple spatial scales. Lake-specific characteristics, such as depth and area, are also reported. The geospatial data came from LAGOS-NEGEO version 1.03 except for the annual climate data which was aggregated at the HUC8 spatial scale from monthly PRISM data. For more specific information on how LAGOS-NE was created, see Soranno et al. 2015. Citations: Lottig, N.R., P-N. Tan, T. Wager, K.S. Cheruvelil, P.A. Soranno, E.H. Stanley, C.E Scott, C.A. Stow, and S. Yuan. in press. Macroscale patterns of synchrony identify complex relationships among spatial and temporal ecosystem drivers. Ecosphere Soranno P.A., Bissell E.G., Cheruvelil K.S., Christel S.T., Collins S.M., Fergus C.E., Filstrup C.T., Lapierre J.-F., Lottig N.R., Oliver S.K., Scott C.E., Smith N.J., Stopyak S., Yuan S., Bremigan M.T., Downing J.A., G
Decomposition of Fine Woody Roots: a Time Series Approach, 1995 to 2006
We examined the effects of species, initial substrate quality, and site differences on woody root decomposition and its nitrogen dynamics in Sitka spruce (Picea sitchensis), Douglas-fir (Pseudotsuga menziesii), and ponderosa pine (Pinus ponderosa) dominated forests in Oregon, U.S.A. using a time series approach. Roots of fourteen species and five size classes were placed in the field to incubate and then collected at pre-planned intervals to determine mass loss and nitrogen content.
Final chlorophyll and temperature measurements at 10m depth, offshore of Dana Point, California as part of an Ocean Institute time series, 2006 - 2025.
Time series of chlorophyll and temperature at 10m offshore of Dana Point, California. Measurements made as part of education/outreach programs involving students and teachers in oceanographic sampling and analytic sensitivity to time series. Cruises are conducted twice, monthly (or other) where sampling is performed by students assisted by technicians and supervisors.
MCR LTER: Coral Reef: pH Time Series from Bottom-mounted SeaFET on the Fringing Reef, January-February 2011
Bottom-mounted instrumentation (SeaFET, Seabird thermistors) sampled for 6 weeks on the fringing reef of Moorea Island, French Polynesia at site LTER Fringe 1. Sampling began in January 2011. The instruments were secured to a cement piling at 3.3 meters depth and 0.7 meters above the sandy bottom. The SeaFET recorded voltages from a thermistor and pH electrodes at a 10-minute sampling interval. Discrete seawater samples were collected using a Niskin bottle during the deployment; pH, salinity, and total alkalinity of this sample were measured to calculate seawater pH (total scale) from raw SeaFET data as well as other carbonate chemistry parameters. The Seabird thermistors provided measures of seawater temperature at 10-minute sampling intervals. These data are published in Rivest, E.B. and G.E. Hofmann. 2014. Responses of the metabolism of the larvae of Pocillopora damicornis to ocean acidification and warming. PLoS ONE DOI:10.1371/journal.pone.0096172
PIE LTER time series of methane, CO2 and N2O ebullition measurements at four headwater streams in Massachusetts and New Hampshire.
Methane ebullition was monitored at four headwater streams during 2018 and 2019. Stationary bubble traps were deployed from approximately May through October. CC and SB were monitored in 2018 and 2019, while DB and CB were only monitored in 2019. 12 traps were deployed at CC, SB, and DB, and 9 traps were deployed at CB. The concentration measured in the emitted gas was multiplied by the volume measured in a trap to calculated the total methane flux via ebullition. The traps were visited at least once weekly. The mean, median, minimum, and maximum rate of ebullition across all traps at a site over a two week period are listed here. Relevant publications: Robison, A.L. (2021) Carbon emissions from streams and river: Integrating methane emission pathways and storm carbon dioxide emissions into stream and river carbon balances. Doctoral Dissertation. University of New Hampshire. Robison, A.L., W.M. Wollheim, B. Turek, C. Bova, C Snay, & R.K. Varner (in review). Spatial and temporal heterogeneity of methane ebullition in lowland headwater streams. Limnology and Oceanography.
SBC LTER: OCEAN: Time series of sediment temperatures by depth
This data package includes the results of efforts to resolve the flushing rates of pore waters in sediments adjacent to Mohawk Reef (34, 23.251 N, 119, 43.685 W). Tidbit temperature sensors were attached to a fiberglass pole at set intervals. The pole was then buried in the sediments and left to log temperature at four depths below the seafloor (5, 15, 30, 45 cm) and two above the seafloor (10 cm, 50 cm) every five minutes for three weeks.
Annual and monthly time series of estimated kelp spore dispersal times among ROMS cells in southern California, 1996 – 2006
These data describe the estimated dispersal duration of spores of giant kelp, Macrocystis pyrifera, among connectivity cells in a high-resolution, three-dimensional, spatiotemporally-explicit ocean circulation model (Regional Oceanic Modeling System, ROMS) in southern California, USA, for an 11-year period from the beginning of 1996 to the end of 2006. Asymmetrical and dynamic estimates of giant kelp spore dispersal durations connecting source and destination ROMS cells were estimated on monthly and annual timescales using minimum mean transit times.
SBC LTER: Ocean: Time-series: Mid-water SeaFET pH and CO2 system chemistry with surface and bottom Dissolved Oxygen at Arroyo Quemado Reef(ARQ), 2012-2017
Calibrated pH (Total scale, SeaFET sensor) an disoolved oxygen (miniDOT)data were collected from Arroyo Quemado Reef in the Santa Barbara Channel (site ID: ARQ). pH data are accompanied by in situ temperature and associated carbonate chemistry parameters. The SeaFET instrument is located about 4 meters from the surface, with other moored instruments. Associated carbonate chemistry parameters were calculated with the CO2calc programs from USGS, and include: partial pressure and fugosity of CO2, concentrations of bicarbonate, carbonate and hyrdroxide ion, Omega (saturation state) of calcite and aragonite. Dissolved oxygen sensors (miniDOT, PME) were added in 2014, and are mounted near the ocean surface and near the seafloor, and also report temperature. All data have been interpolated to a 20 minute time interval for compatibility with other SBC LTER moored instrument data. Data coverage is 2012-07-30 to 2017-03-10.All data from this site have been concatenated with the pH data from the other sites and merged into one data package: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-sbc&identifier=6005
SBC LTER: Ocean: Time-series: Mid-water SeaFET pH and CO2 system chemistry with surface and bottom Dissolved Oxygen at Mohawk Reef(MKO), 2012 - 2017
Calibrated pH (Total scale, SeaFET sensor) an disoolved oxygen (miniDOT)data were collected from Mohawk Reef in the Santa Barbara Channel (site ID: MKO). pH data are accompanied by in situ temperature and associated carbonate chemistry parameters. The SeaFET instrument is located about 4 meters from the surface, with other moored instruments. Associated carbonate chemistry parameters were calculated with the CO2calc programs from USGS, and include: partial pressure and fugosity of CO2, concentrations of bicarbonate, carbonate and hyrdroxide ion, Omega (saturation state) of calcite and aragonite. Dissolved oxygen sensors (miniDOT, PME) were added in 2014, and are mounted near the ocean surface and near the seafloor, and also report temperature. All data have been interpolated to a 20 minute time interval for compatibility with other SBC LTER moored instrument data. Data coverage is 2012-01-11 to 2017-12-19.The update of this dataset was terminated in 2019. All data from this site have been concatenated with the pH data from the other sites and merged into one data package: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-sbc&identifier=6005
SBC LTER: Ocean: Time-series: Mid-water SeaFET pH and CO2 system chemistry with surface and bottom Dissolved Oxygen at Santa Barbara Harbor/Stearns Wharf(SBH), 2012-2017
Calibrated pH (Total scale, SeaFET sensor) an disoolved oxygen (miniDOT)data were collected from Santa Barbara Harbor/Stearns Wharf in the Santa Barbara Channel (site ID: SBH). pH data are accompanied by in situ temperature and associated carbonate chemistry parameters. The SeaFET instrument is located about 4 meters from the surface, with other moored instruments. Associated carbonate chemistry parameters were calculated with the CO2calc programs from USGS, and include: partial pressure and fugosity of CO2, concentrations of bicarbonate, carbonate and hyrdroxide ion, Omega (saturation state) of calcite and aragonite. Dissolved oxygen sensors (miniDOT, PME) were added in 2014, and are mounted near the ocean surface and near the seafloor, and also report temperature. All data have been interpolated to a 20 minute time interval for compatibility with other SBC LTER moored instrument data. Data coverage is 2012-09-15 to 2016-09-14. The update of this dataset was terminated in 2019. All data from this site have been concatenated with the pH data from the other sites and merged into one data package: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-sbc&identifier=6005
Applying time series analyses on continuous accelerometry data – Dataset
<p>Data and analysis script accompanying the study:</p> <p>Applying time series analyses on continuous accelerometry data – a clinical example in older adults with and without cognitive impairment</p>
Woody Cover Mapping in the Kruger National Park using Sentinel-1 time series and LiDAR data
<p>This data repository presents a workflow to derive woody cover information for the Kruger National Park, South Africa, from freely available Sentinel-1 C-Band time series and LiDAR data (modified from Smit et al. 2016) using machine learning (MLR and Ranger in R). The methodology is described in following publication:</p> <p><em>Urban, M., K. Heckel, C. Berger, P. Schratz, I.P.J. Smit, T. Strydom, J. Baade & C. Schmullius (2020): Woody Cover Mapping in the Savanna Ecosystem of the Kruger National Park Using Sentinel-1 C-Band Time Series Data. Koedoe.</em></p> <p>In order to derive woody cover percentage information, download all files into one folder and run the R-Files consecutively from 01_ to 04_. Follow the instruction within each of the R-Files, which are written as comments in the programming code.</p> <p>The data repository consist of the following files:</p> <p><strong>R-Files:</strong></p> <p>1. Script 1: 01_MLR_tune_spatial_final</p> <p>2. Script 2: 02_MLR_cross_validation_spatial_final</p> <p>3. Script 3: 03_MLR_RANGER_train_final</p> <p>4. Script 4: 04_MLR_prediction_woody_cover_final</p> <p> </p> <p><strong>Training dataset - ENVI FILE (layerstack of Sentinel-1 VH and VV backscatter between 2016 and 2017 and the woody cover reference derived from the LiDAR data) :</strong></p> <p>1. S1_A_VH_VV_16_17_lidar</p> <p> </p> <p><strong>Data for prediction - ENVI FILES (3 example regions in the Kruger National Park):</strong></p> <p>1. S1_A_VH_VV_16_17_subset_example_Letaba_Rest_Camp</p> <p>2. S1_A_VH_VV_16_17_subset_example_Lower_Sabie</p> <p>3. S1_A_VH_VV_16_17_subset_example_Pafuri</p> <p> </p> <p><strong>Final woody cover maps of the Kruger National Park:</strong></p> <p>1. xx_woody_cover_map_final.rar (contains final maps in 10m, 30m, 50m and 100m spatial resolution as .tif and a QGIS project)</p> <p> </p> <p><em>References:</em></p> <p>Smit, I.P.J., Asner, G.P., Govender, N., Vaughn, N.R. & Wilgen, B.W. van, 2016, ‘An examination of the potential efficacy of high-intensity fires for reversing woody encroachment in savannas’, <em>Journal of Applied Ecology</em>, 53(5), 1623–1633.</p>
Pre-eruption InSAR time-series at Kīlauea (Hawai`i, USA): COSMO-SkyMed Descending 2018
<p>InSAR time-series data for Kīlauea (Hawai`i, USA), between Jan 2010 and Sep 2011 . Data were obtained by processing COSMO-SkyMed descending SAR data (track 165). Data were processed using the JPL-developed InSAR Scientific Computing Environment (<code>ISCE</code>) open-source software package, and further time-series analysis was performed using the <code>MintPy</code> software toolbox (<a href="https://github.com/insarlab/MintPy">Miami INsar Time-series software in PYthon</a>), developed at the University of Miami. </p> <p>The following file is available in Hierarchical Data Format:</p> <p><code>geo_timeseries_tropHgt_demErr_cskDT165.h5</code>: Descending Track timeseries file. Dates available:</p> <p><code>['timeseries-20101001', 'timeseries-20101009', 'timeseries-20101017', 'timeseries-20101025', 'timeseries-20101102', 'timeseries-20101110', 'timeseries-20101118', 'timeseries-20101126', 'timeseries-20101204', 'timeseries-20101212', 'timeseries-20101220', 'timeseries-20110129', 'timeseries-20110206', 'timeseries-20110214', 'timeseries-20110222', 'timeseries-20110302', 'timeseries-20110303', 'timeseries-20110310', 'timeseries-20110318', 'timeseries-20110319', 'timeseries-20110322', 'timeseries-20110326', 'timeseries-20110403', 'timeseries-20110404', 'timeseries-20110407', 'timeseries-20110411', 'timeseries-20110419', 'timeseries-20110420', 'timeseries-20110423', 'timeseries-20110505', 'timeseries-20110506', 'timeseries-20110509', 'timeseries-20110513', 'timeseries-20110521', 'timeseries-20110522', 'timeseries-20110525', 'timeseries-20110529', 'timeseries-20110606', 'timeseries-20110607', 'timeseries-20110614', 'timeseries-20110622', 'timeseries-20110630', 'timeseries-20110708', 'timeseries-20110709', 'timeseries-20110716', 'timeseries-20110724', 'timeseries-20110725', 'timeseries-20110801', 'timeseries-20110809', 'timeseries-20110810', 'timeseries-20110817', 'timeseries-20110825', 'timeseries-20110826', 'timeseries-20110902', 'timeseries-20110910', 'timeseries-20110918']</code></p> <p> </p> <p>These data are supplemental to: Farquharson, J. I. and Amelung, F. [2020], "<em>Extreme rainfall triggered the 2018 rift eruption at Kīlauea Volcano.</em>" <a href="https://doi.org/10.1038/s41586-020-2172-5">https://doi.org/10.1038/s41586-020-2172-5</a></p>
Semantic Segmentation of Time Series Imagery Using Deep Convolutional Neural Networks: A Case Study of Sandbars in Grand Canyon
<p>This dataset contains imagery used to train and test Deep Convolutional Neural Networks for the purpose of binary semantic segmentation of a time series of oblique imagery capturing sandbar monitoring sites in The Grand Canyon. In addition the scripts needed for removing image distortion, registering, rectifying, and labeling imagery is present. </p>
Association between meteorological factors and the number of tuberculosis notifications: a time-series study in Hong Kong
<p> Using a 22-year consecutive surveillance data in Hong Kong, including monthly averages of meteorological factors, air pollution concentrations , total number of TB cases notified, to analyze the association of monthly average temperature and relative humidity with temporal dynamics of monthly total number of TB cases notified. </p>
Time Series used in the Forecasting Benchmark
<p>This data set contains the time series used in Libra (GitHub: <a href="https://github.com/DescartesResearch/ForecastBenchmark">https://github.com/DescartesResearch/ForecastBenchmark</a> ; CodeOcean: <a href="https://doi.org/10.24433/CO.3240518.v1">https://doi.org/10.24433/CO.3240518.v1</a>). Libra is a forecasting benchmark that automatically evaluates and ranks forecasting methods based on their performance in a diverse set of evaluation scenarios. The benchmark comprises four different use cases, each covering 100 heterogeneous time series taken from different domains.</p>
Observed and model postseismic time series at GPS sites due to the 2012 Craig and 2013 Haida Gwaii earthquakes
<p><strong>Files with the observed and model displacements, along with predicted model time series</strong>, which derive from the paper of '<em>Postseismic Deformation Due To the 2012 MW 7.8 Haida Gwaii and 2013 MW 7.5 Craig Earthquakes and Its Implications for regional rheological structure'</em>.</p> <p><strong>SITE.obs files:</strong> observed postseismic time series due to the 2012 Mw 7.8 Haida Gwaii and 2013 Mw 7.5 Craig earthquakes</p> <p><strong>SITE.mod files:</strong> Model postseismic displacements, along with predicted time series. Detailed explanations please see <strong>readme.txt</strong>.</p> <p><strong>GPS site names</strong> are the same with the study of Tian et al. (2021). </p> <p> </p>
Observed and model postseismic time series at GPS sites due to the 2012 Craig and 2013 Haida Gwaii earthquakes
<p><strong>Files with the observed and model displacements, along with predicted model time series</strong>, which derive from the paper of '<em>Postseismic Deformation Due To the 2012 MW 7.8 Haida Gwaii and 2013 MW 7.5 Craig Earthquakes and Its Implications for regional rheological structure' </em><strong>JGR: Soild Earth (2021), </strong><a href="https://doi.org/10.1029/2020JB020197">https://doi.org/10.1029/2020JB020197</a>.</p> <p><strong>SITE.obs files:</strong> observed postseismic time series due to the 2012 Mw 7.8 Haida Gwaii and 2013 Mw 7.5 Craig earthquakes</p> <p><strong>SITE.mod files:</strong> Model postseismic displacements, along with predicted time series. Detailed explanations please see <strong>readme.txt</strong>.</p> <p><strong>GPS site names</strong> are the same with the study of Tian et al. (2021). <a href="https://doi.org/10.1029/2020JB020197">https://doi.org/10.1029/2020JB020197</a>.</p>
A Tool for Uncertainty Quantification in Reconstructing Sparse Water Quality Time Series Data to Assess Risk Metrics for Watershed Health and TMDL Analysis
<p>The uploaded file contains the input and output data which can be used to reproduce the results in the research article 'Uncertainty Quantification in Reconstruction of Sparse Water Quality Time Series: Implications for Watershed Health and Risk-Based TMDL Assessment'. Please refer to the file '<a href="https://zenodo.org/api/files/31b59cce-8eb2-4ee7-93aa-61474c6f6359/dst_2019_SJRW_TP_TDS.zip?versionId=2af2b54d-d5fb-4720-919d-de2e827595e2">dst_2019_SJRW_TP_TDS.zip'</a> for updated files..</p>
ScienceDex guides
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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.