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2,516 results for “Seri”
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
The dataset from a submitted journal entitled "Characterization of the Mamasa earthquake source in West Sulawesi based on the earthquake relocation data, gravity data, and coulomb stress change of Palu earthquake series"Dataset for paper
<p>This dataset consists of four files, namely:<br> 1. Coulomb Stress Input file. This data is input data for Coulomb 3.3 software<br> 2. Double Couple Percentage. This table is used for the Spatio-temporal Compensated Linear Vector Dipole (CLVD) analysis<br> 3. Gravity data. This data consists of coordinates, altitude, and Complete Bouguer Anomaly.<br> 4. Residual comparison of before and after the relocation. This table is to ensure that our relocation is successful</p>
PLATE XXI. Fig. 1 in New genera and species from the Belly River Series (mid-Cretaceous)
PLATE XXI. Fig. 1. Stegoceras validus, Lambe, prenasal bone; side view. Natural size. Page 63. Fig. 2. The same, inferior aspect. Fig. 3. Superior view of another specimen; natural size. Fig. 4. The same; side view. Fig. 5. The same; inferior view. Fig. 6. Stereocephalus tutus, Lambe, symmetrical plate, superior aspect; one-half the natural size. (Pro. visionally associated with S. tutus). Figs. 7 and S. Similar keeled plates one-half the natural size. Fig. 9. Shield of Acipenser albertensis, Lambe, viewed from above. Natural size. Page 29 .
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>
Expansion of UBTR012574 Chemical Series Pt. II
<p>Docking analogues of <a href="https://zenodo.org/record/3620775#.XoxqAohKjIV">UBTR012574</a> against the USP5 zinc finger ubiquitin-binding domain (ZnF-UBD) to prioritize compound synthesis.</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>
Science Communication Webinar Series: Taking Risks in Science Communication
<p>Blue-Action are coordinating a series of webinars on climate science communication in 2020. These are aimed at early-career researchers, but anyone who wishes to engage people with their research at any stage will find them valuable.</p> <p>This webinar was delivered on 18th September 2020 by <a href="https://www.linkedin.com/in/mcoath/?originalSubdomain=uk">Martin Coath</a>, who is a Senior Affiliated Researcher at the University of Lapland. Martin is an experienced science communicator, and here explains how sometimes you need to take risks to make an impact. </p>
Series arcing faults voltage and current signatures in a DC low power network.
<p>The dataset contains series arc faults voltage and current signatures in a DC low power network.</p> <p>The first apparatus performs a series arc fault by opening contacts (ReadMe_test 1). After breaking, copper electrodes are maintained at a constant gap. The measurements are performed with direct current and resisve/Inductive loads.</p> <p>The second apparatus performs contact breaking under chosen voltage and current supply condions (ReadMe_test2). After breaking, electrodes are maintained at a constant gap during electrical measurement. The measurements are performed with direct current and resisve loads by separating electrodes.<br> </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>
Dataset and Jupyter worksheet interpreting the (results from) small- and wide-angle scattering data from a series of boehmite/epoxy nanocomposites. Accompanies the publication "Competition of nanoparticle-induced mobilization and immobilization effects on segmental dynamics of an epoxy-based nanocomposite"
<p>Dataset and Jupyter worksheet interpreting the (results from) small- and wide-angle scattering data from a series of boehmite/epoxy nanocomposites. Accompanies the publication "Competition of nanoparticle-induced mobilization and immobilization effects on segmental dynamics of an epoxy-based nanocomposite", by Paulina Szymoniak, Brian R. Pauw, Xintong Qu, and Andreas Schönhals.</p> <p>Datasets are in three-column ascii (processed and azimuthally averaged data) from a Xenocs NanoInXider SW instrument. Monte-Carlo analyses were performed using McSAS 1.3.1, other analyses are in the Python 3.7 worksheet. Graphics and result tables are output by the worksheet. </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>
Data for Wang and Kent, GRL, 2021, "RESET: A method to monitor thermoremanent alteration in Thellier-series paleointensity experiments"
<p>This zip file contains data presented in the GRL paper “RESET: A method to monitor thermoremanent alteration in Thellier-series paleointensity experiments” by Wang and Kent, 2021. </p> <p>Folder “Figure2e” contains a subfolder that contains all the raw hysteresis measurements (with heated temperatures in degree Celsius in file names) and an Excel spreadsheet that summarizes the hysteresis parameters, which are used in plotting the Day diagram as shown in Figure 2e.</p> <p>Folder “Figure2f-s” contains raw FORC data of specimens GA79.5y and GA84.6y measured after each heating treatment (with heated temperatures in degree Celsius in file names) that are used in plotting the FORC diagrams as shown in Figure 2f to 2s.</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.