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2,516 results for “Seri”
Measured and modelled significant wave height time series at the Bothnian Sea Wave buoy in the Baltic Sea
<p>Significant wave height data at the location of FMI's wave buoy in the Bothnian Sea, Baltic Sea (61 degrees 8' N, 20 degrees 14' E). Contains 2011-2019 wave buoy observations, 1965-2005 SWAN modelled data (Björkqvist et al. 2018), and 1979-2013 WAM modelled data (Tuomi et al. 2019).</p>
GLC_FCS30: Global land-cover product with fine classification system at 30 m using time-series Landsat imagery
<p>A novel global 30-m land-cover product with a fine classification system for the year 2015 (GLC_FCS30-2015). The product was produced by combining time-series of Landsat imagery and high-quality training data from the GSPECLib (Global Spatial Temporal Spectra Library) on the Google Earth Engine computing platform. First, the global training data from the GSPECLib were developed by applying a series of rigorous filters to the MCD43A4 NBAR and CCI_LC land-cover products. Secondly, a local adaptive random forest model was built for each 5°×5° geographical tile by using the multi-temporal Landsat spectral and textures features of the corresponding training data, and the GLC_FCS30-2015 land-cover product containing 30 land-cover types was generated for each tile.</p>
Time series of electricity output for large grid connected photovoltaic installations in Chile
<p>These data sets accompany the paper "Simulation of multi-annual time series of solar photovoltaic power: is the ERA5-land reanalysis the next big step?". They include capacity factors (values 0 to 1) in hourly temporal resolution of 103 large PV installations in Chile derived from official sources as well as simulated capacity factors using PV_LIB with ERA5-land and MERRA-2 reanalysis data. The data covers the period 2014-2018 and simulations were performed assuming either a "fixed" system with orientation towards north and inclination equal to the latitude (i.e. optimal inclination) or a horizontal single axis “tracking” system with backtracking. Furthermore, accuracy indicators (Pearson’s correlation, mean bias error and root mean square error) are provided, comparing the simulated time series with capacity factors derived from official sources. Capacity factors were calculated for the 103 installations and both alternative configurations (“fixed” and “tracking”) but only a subset of 23 installations has reference data of sufficient quality to allow for a validation (for a more detailed description, see the paper). Indicators were also calculated for all installations and configurations but should be only compared for installations inside a particular configuration: there are 14 systems classified as “tracking” and 9 as “fixed”. Further details are available in the paper and the entire Python and R code is available on github at <a href="https://github.com/inwe-boku/PV_from_era5">https://github.com/inwe-boku/PV_from_era5</a>. </p>
Scanning electron diffraction tilt series data of an aluminium-steel interface region
<p>This dataset contains scanning electron diffraction (SED) data used in the publication entitled "<strong>Microstructural and mechanical characterisation of a second generation hybrid metal extrusion & bonding aluminium-steel butt joint</strong>". The data denoted “SED_HYB_...” were recorded from an aluminium-steel interface region that includes aluminium and steel grains, an interfacial Al-Fe-Si layer, and dispersoids and some oxide particles located within the aluminium region. The nanoscale interfacial intermetallic phase layer is polycrystalline, and to increase the probability of recording data from intermetallic phase crystals oriented close to zone axes, the data were recorded in a tilt series covering 30 degrees, in steps of 1 degree. The file names give the goniometer x-tilt values in degrees, e.g. " SED_HYB_TX-150.hdf5" denotes an x-tilt of -15.0 degrees. SED data recorded from an Au cross-grating specimen, named "SED_AuX.hdf5", and from a MoO3 specimen, named "SED_MoO3.hdf5", are also included for calibration purposes.</p>
Magneto-telluric data from the Los Humeros geothermal field in Mexico: time series and edi-files
<p>The data are from the Los Humeros geothermal field in Mexico.</p> <p>The dataset is composed of time series of magneto-telluric data and edi-files obtained from the time series. A file containing the location of the soundings and calibration files are also in the dataset.</p> <p>The Metronix equipment was used to acquire the data.</p> <p>The data were gathered under the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 727550, and by the Mexican Energy Sustainability Fund CONACYT-SENER, Project 2015-04-268074.</p>
Magneto-telluric data from the Acoculco area in Mexico: time series and edi-files
<p>The dataset is composed of time series of magneto-telluric data and edi-files obtained from the time series.</p> <p>The Metronix equipment was used to acquire the data.</p> <p>The data were gathered under the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 727550, and by the Mexican Energy Sustainability Fund CONACYT-SENER, Project 2015-04-268074.</p>
LAI_TS_Val: LAI time-series validation datasets in the 1-km pixel grid at global scale from 2001 to 2011
<p>Leaf area index (LAI), which is defined as one half of the total green leaf area per unit ground surface area, is a critical structural variable for quantifying the exchange processes of energy and matter between the land surface and atmosphere, it is thus identified as a key parameter in most terrestrial ecosystem models. To acquire long-term LAI records at the global scale, several remote sensing LAI products have been generated from various satellite sensors. However, assessing the uncertainties associated with these LAI products through comparisons with independent ground-truth measurements is pivotal for an effective application of products. Many sites from global networks have collected and provided invaluable ground LAI measurements covering a wide range of biome types and spatial variabilities. These site-based LAI measurements have been obtained about 30 years (1990-now). However, the spatial scale mismatch between site and pixel observations restricts the utilization of LAI measurements for product time-series validation. This datasets were generated from site-based LAI measurements of FLUXET and Chinese Ecosystem Research Network (CERN), using the proposed GUGM (Grading and Upscaling of Ground Measurements) method to resolve the scale-mismatch issue between site and sensor observations and maximize the utility of time-series of site-based LAI measurements, which can achieve the goal of product time-series validation. This GUGM approach first ingests both high-resolution images and site-based LAI measurements to capture the spatiotemporal variability in the product pixel grid. Then, a strategy was employed to grade the spatial representativeness of LAI measurements in the product pixel grid. For those LAI measurements which cannot be directly used in the validation of products, a strategy was adopted to calculate the spatial upscaling coefficient based on site-based LAI measurements and aggregated high-resolution reference maps to derive reliable LAI time-series validation datasets. The GUGM method has been applied to the site-based LAI measurements to generate global time-series LAI validation datasets from 2001 to 2011 in the 1 km pixel grid. The datasets include 28 sites which are mainly located in North America and Asia, providing 924 validation data in total. Among these sites, 16 sites with 508 (55.0%) validation data were obtained for forest, while 11 sites with 341 (36.9%) validation data and one site with 75 (8.1%) were obtained for crops and grasses, respectively. This datasets were saved in two formats: *.xls and *.kmz and each format was zipped for 63 KB and 31 KB, respectively.</p>
Observed runoff time series from a green roof field campaign in Hannover-Herrenhausen
<p>This dataset includes a csv file comprising observed runoff time series from a green roof field campaign, which was conducted by Prof. Dr.-Ing. Hans-Joachim Liesecke. The csv file provides runoff from 11 green roof variants (10 was excluded, since it has a different design). Rows include daily runoff totals (collected each morning, excluding weekends). Please refer to this article, which describes the dataset in more detail: </p> <p><strong>Iffland, R., Förster, K., Westerholt, D., Pesci, M. H., & Lösken, G. Robust vegetation parameterization for green roofs in EPA SWMM. Hydrology. </strong><a href="https://doi.org/10.3390/hydrology8010012">https://doi.org/10.3390/hydrology8010012</a></p> <p>The field campaign involved a total of 11 superstructures in triple repetition. In the csv file, each column represents average values computed out of three independent measurements (in mm*d<sup>-1</sup>)</p> <p>The individual test plots were 2 m x 2 m with a slope of 2 % and a drainage opening in the middle of the lowest point of the slope. The outflowing water was collected in non-weighable lysimeters (rain barrels) that were read and emptied at 8 A.M. every day. On weekends, readings were taken the following workday.</p> <p> </p>
Dataset of Sentinel-1 surface soil moisture time series at 1 km resolution over Southern Italy
<p>The dataset consists of a time series of the Sentinel-1 (S-1) surface soil moisture (SSM) product at 1 km spatial resolution validated in Balenzano et al. (2021 a) over the Southern Italy. The specifications of the S-1 SSM product are provided in Balenzano et al. (2021 b). The SSM time series was obtained in correspondence of the ascending (RON A146) S-1 Interferometric Wide swath (IW) acquisition dates from January 2015 to December 2018 with a temporal gap between consecutive of 6 days (when both S-1A and S-1B data are available) or 12 days. On each date (183 in total), two co-registered layers are provided: mean SSM [m3/m3] and its standard deviation [m3/m3], which provides the SSM uncertainty. The retrieval algorithm is a time series short term change detection (STCD) that is implemented in the “Soil MOisture retrieval from multi-temporal SAR data” (SMOSAR) code (Balenzano et al. 2013).</p>
DELANA - An eyetracking dataset from facilitating a series of laptop-based lessons
<p>This dataset contains eye-tracking data from a two subjects (an expert and a novice teachers), facilitating three collaborative learning lessons (2 for the expert, 1 for the novice) in a classroom with laptops and a projector, with real master-level students. These sessions were recorded during a course on the topic of digital education and learning analytics at [EPFL](http://epfl.ch).</p> <p>This dataset has been used in several scientific works, such as the [CSCL 2015](http://isls.org/cscl2015/) conference paper "The Burden of Facilitating Collaboration: Towards Estimation of Teacher Orchestration Load using Eye-tracking Measures", by Luis P. Prieto, Kshitij Sharma, Yun Wen & Pierre Dillenbourg. The analysis and usage of this dataset is available publicly at https://github.com/chili-epfl/cscl2015-eyetracking-orchestration</p>
1988-2009 time-series of land-use/land-cover maps for the Mar Menor / Campo de Cartagena watershed by means of supervised classification of Landsat images.
<p>Serie de mapas de usos y coberturas de la cuenca del Mar Menor (SE España): 2009, 2000, 1997 y 1998. Así como el documento completo de tesis en las que se generaron y analizaron.</p> <p>Time-series of land-use / land-cover maps of Mar Menor watershed (SE Spain): 2009, 2000, 1997 y 1998. As well as the complete thesis document in which they were generated and analyzed.</p>
Modelled time series of CO2 in the vicinity of a seep in the North Sea.
<p>The Bergen Ocean Model (BOM) is used to simulate dispersion of CO2 leaking from nine different locations in the North Sea, focusing on temporal and spatial variability of the CO2 concentration. </p> <p>For details see: Ali, A., Frøysa, H. G., Avlesen, H., & Alendal, G. (2016). Simulating spatial and temporal varying CO 2signals from sources at the seafloor to help designing risk-based monitoring programs. <em>Journal of Geophysical Research-Oceans</em>, <em>121</em>(1), 745–757. http://doi.org/10.1002/2015JC011198.</p> <p>The data files contains time series of excess CO2 concentration, velocity components and time, and position in longitude and latitude. One file for each grid position, a total of 53x51 grid points with the seep in location (I,j)=(28,27). </p>
Multi-year temperature time series from cinder cones on the Mauna Kea summit plateau, Hawaii
<p>Multi-year time series from data loggers in several cinder cones on the Mauna Kea summit plateau, Hawaii. The variable measured is primarily ground temperature, but several time series for air temperature, relative humidity, and light intensity have also been obtained. </p> <p>Study sites, time period:<br>Puu Wekiu (Summit Cone Crater), 2012-2025<br>Puu Hau Kea (Goodrich Cone), 2012-2025<br>Puu Waiau, 2013-2016<br>Puu Pohaku (Douglas Cone), 2014-2016<br>Puu Makanaka, 2016</p> <p>The raw data are downloads from HOBO data loggers. The reduced data are merged time series without time gaps and corrected for known issues. A list of data loggers with time periods and sensor depths is provided in loggers_summary_mk.csv. For file formats and further information, see README.TXT. For context see references.</p>
Synthetic mobile service traffic time series
<p>This dataset contains synthetic mobile service traffic time series used in the paper titled "kaNSaaS: Combining Deep Learning and Optimization for Practical Overbooking of Network Slices", presented at ACM MobiHoc 2023 in Washington, USA. It is composed of 20 time series representing the fluctuations of demands for diverse services categorized under 5G types, including enhanced Mobile Broadband (eMBB), ultra-Reliable Low Latency Communication (uRLLC), and massive Machine Type Communication (mMTC). The time series cover a period of XXX days, and were shown to yield similar properties as those observed in real-world traffic collected in a production mobile network.</p>
ICOPS Workshop Series - FAIR data
<p><strong>This is the sixth workshop in the International Committee on Open Phytolith Science (ICOPS) workshop series on Open Research Skills. </strong></p> <p>In this workshop we had multiple speakers:</p> <ul> <li> <p>Introduction to FAIR and FAIR Phytoliths project results - Emma Karoune - slides in the main presentation</p> </li> <li> <p>FAIR Data principles and Imaging Resources - Jean-Marie Burel - slides attached as pdf.</p> </li> <li> <p>Sobre la nomenclatura fitolitica en Argentina y el uso del ICPN 2.0 - Maria-Gabriela Musaubach - slides in the main presentation</p> </li> </ul> <p>Youtube video of the workshop:</p> <p>Emma Karoune <a href="https://www.youtube.com/watch?v=VsphfgppcWg&list=PLSOpdKfRN6mwBRRvcXC4u2h7nMvAlgNvU&index=2&ab_channel=ICOPSopenphytoliths">FAIR Data Workshop_1.Emma.Introduction to FAIR and FAIR Phytoliths Project results - YouTube</a></p> <p>Jean-Marie Burel <a href="https://www.youtube.com/watch?v=yNcLApDHxFY&list=PLSOpdKfRN6mwBRRvcXC4u2h7nMvAlgNvU&index=1&ab_channel=ICOPSopenphytoliths">FAIR Data workshop_2.Jean Marie. FAIR Principles and imaging resources - YouTube</a></p> <p>Maria-Gabriela Musaubach <a href="https://www.youtube.com/watch?v=UZizjXCvnbM&list=PLSOpdKfRN6mwBRRvcXC4u2h7nMvAlgNvU&index=4&ab_channel=ICOPSopenphytoliths">FAIR Data Workshop_3.Gabi. Sobre la nomenclatura fitolítica en Argentina, el uso del ICPN 2.0. - YouTube</a></p> <p>Round table <a href="https://www.youtube.com/watch?v=--OO8upPhhM&list=PLSOpdKfRN6mwBRRvcXC4u2h7nMvAlgNvU&index=3&ab_channel=ICOPSopenphytoliths">FAIR Data Workshop_4.Round table on the FAIR Data session - YouTube</a></p>
ICOPS Workshop Series - Standard Vocabularies and Ontologies
<p><strong>This is the fifth workshop in the International Committee on Open Phytolith Science (ICOPS) workshop series on Open Research Skills. </strong></p><p>In this workshop we had multiple speakers:</p><ul><li>Introduction - Henriette Harmse - slides in the main presentation</li><li>Case study and demo of image database - Frances Wong - slides attached as pdf.</li><li>Phytolith standarised nomenclature ICPN 2.0 - Luc Vrydaghs [*presentation not included*]</li><li>Phytolith ontology - Celine Kerfant and Zach Dunseth - slides in the main presentation</li></ul><p>Youtube video of the workshop:</p><p><a href="https://youtu.be/qZaWkJlVXvE">https://youtu.be/qZaWkJlVXvE</a></p>
Evolution of Surface Tension and Hansen Parameters of Homologous Series of Imidazolium-Based Ionic Liquids
<p>This is the raw data for the manuscript:</p> <p>Evolution of Surface Tension and Hansen Parameters of Homologous Series of Imidazolium-Based Ionic Liquids</p> <p> </p> <p>All data are sorted according to their appearance in the figures of the main manuscript. All data is stored as .xslx format, the content of the colums can be found in the headlines and is following the nomenclature of the manuscript.</p> <p>doi journal article: <a title="DOI URL" href="https://doi.org/10.1021/acs.langmuir.4c00094">https://doi.org/10.1021/acs.langmuir.4c00094</a></p>
Perodiogram NDVI Time Series From AVHRR
<p>Vegetation seasonality assessment through remote sensing data is crucial to understand ecosystem responses to climatic variations and human activities at large-scales. Whereas the study of the timing of phenological events showed significant advances, their recurrence patterns at different periodicities has not been widely study, especially at global scale. In this work, we describe vegetation oscillations by a novel quantitative approach based on the spectral analysis of Normalized Difference Vegetation Index (NDVI) time series. A new set of global periodicity indicators permitted to identify different seasonal patterns regarding the intra-annual cycles (the number, amplitude, and stability) and to evaluate the existence of pluri-annual cycles, even in those regions with noisy or low NDVI. Most of vegetated land surface (93.18%) showed one intra-annual cycle whereas double and triple cycles were found in 5.58% of the land surface, mainly in tropical and arid regions along with agricultural areas. In only 1.24% of the pixels, the seasonality was not statistically significant. The highest values of amplitude and stability were found at high latitudes in the northern hemisphere whereas lowest values corresponded to tropical and arid regions, with the latter showing more pluri-annual cycles. The indicator maps compiled in this work provide highly relevant and practical information to advance in assessing global vegetation dynamics in the context of global change.</p>
Series production data set for 5-axis CNC milling
<p>The data set described encompasses features extracted from the machine control of a five-axis milling machine across thirteen series of productions. Each production series entails a setup changeover to prepare the machine for another product type. Alongside timestamps and twenty features derived from Numerical Control (NC) variables, the data set includes labels denoting various production phases. These labels, up to 23 in total, are structured around a generalized milling process. Comprising thirteen .csv files, each corresponding to a series production, the dataset was gathered within a production company operating in the contract manufacturing sector. These components are tied to actual series orders within ongoing industrial production.</p> <p>The complete description of the data set is published here: <a href="https://www.mdpi.com/2306-5729/9/5/66" target="_blank" rel="noopener">https://doi.org/10.3390/data9050066</a></p>
The World Wide Lightning Location Network (WWLLN) Global Lightning Climatology (WGLC) and time series
<p><strong>The World Wide Lightning Location Network (WWLLN) Global Lightning Climatology (WGLC) and time series</strong></p> <p>This repository contains global lightning stroke density and stroke power calculated from georeferenced stroke count data from the World Wide Lightning Location Network <a href="http://wwlln.net">WWLLN</a>. The real-time raw stroke count data were reprocessed by WWLLN to remove artifacts and improve geolocation, which resulted in the "AE" georeferenced and timestamped stroke count data. These data were then gridded at 0.5 degree 5 arc-minute and hourly resolution, converted into density, and corrected for detection efficiency using the WWLLN global gridded detection efficiency maps. Mean, median, and standard deviation of stroke power are also provided at 30-minute resolution. The corrected hourly rasters were then aggregated into daily and monthly totals and into a multi-year monthly mean climatology. The data cover the period 2010-2024 and will be updated in the coming years.</p> <p>For a complete description of the data see:</p> <p>Kaplan, J. O., & Lau, K. H.-K. (2021). The WGLC global gridded lightning climatology and time series. <em>Earth System Science Data, 13</em>(7), 3219-3237. <a href="dx.doi.org/10.5194/essd-13-3219-2021">doi:10.5194/essd-13-3219-2021</a></p> <p>Kaplan, J. O., & Lau, K. H.-K. (2022). World Wide Lightning Location Network (WWLLN) Global Lightning Climatology (WGLC) and time series, 2022 update. <em>Earth System Science Data, 14</em>(12), 5665-5670. <a href="dx.doi.org/10.5194/essd-14-5665-2022">doi:10.5194/essd-14-5665-2022</a></p> <p>The data are stored in a <a href="https://www.unidata.ucar.edu/software/netcdf/">NetCDF</a> (version 4) files and have the following attributes:</p> <ul> <li>Spatial extent: Entire Earth</li> <li>Spatial reference system (SRS): Unprojected (geographic, WGS84)</li> <li>Spatial resolution: half-degree and 5 arc-minute</li> <li>Temporal extent: 2010-2024</li> <li>Temporal resolution: daily and monthly*1,2</li> </ul> <p><strong>Variables included in this release</strong></p> <ul> <li>Lightning density (strokes km-2 day-1)</li> <li>Lightning mean, median, and standard deviation of stroke power (MW, 30 arc-minute version only)</li> </ul> <p>For further details, see <a href="https://github.com/ARVE-Research/WGLC">https://github.com/ARVE-Research/WGLC</a></p> <p>1*5479 elements in the time dimension for daily data; 180 for monthly data; 12 for the climatology.</p> <p>2*Daily fields currently available at 30-minute resolution only.</p> <p><a href="../doi/10.5281/zenodo.4774528">The WWLLN Global Lightning Climatology and timeseries (WGLC) </a>© 2025 by Jed O. Kaplan is licensed under <a href="http://creativecommons.org/licenses/by-sa/4.0/?ref=chooser-v1">CC BY-SA 4.0</a></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.