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171 results for “time sampling”
SBC LTER: pH time series: Water-sample pH and CO2 system chemistry, ongoing since 2011
Data are a time-series of pH and carbonate chemistry in manually-collected sea water samples from sixteen near-shore locations and 20 locations along the Santa Barbara Channel, California, USA, and intended to benchmark data from moored pH instruments. Data collection began in June 2011 and is ongoing. Time-series for pH sensors deployed in the field (e.g., SeaFET) are available elsewhere. Discrete seawater samples were collected by hand or using a Niskin bottle. In the laboratory, samples were analyzed for pH (total scale) and total alkalinity. Two parameters were used to calculate the full suite of carbonate chemistry parameters using the CO2calc algorithms. Program outputs are included for laboratory conditions as input, and adjusted for situ temperature. See Methods for more information, e.g., Lunden, et al. Data were collected by a consortium of research groups with the intent to leverage their collective assets and expertise. These groups (and sponsors) include Santa Barbara Coastal LTER (NSF), OMEGAS Project (NSF), Passow Lab (Calif. State Water Board), Hofmann Lab (NSF, CINMS, US NPS).
A NICER View of the Massive Pulsar PSR J0740+6620 Informed by Radio Timing and XMM-Newton Spectroscopy: Nested Samples for Millisecond Pulsar Parameter Estimation
<p>Posterior sample files associated with the preprint "A <em>NICER</em> View of the Massive Pulsar PSR J0740+6620 Informed by Radio-Timing and <em>XMM-Newton</em> Spectroscopy" by Riley et al. (2021; <a href="https://arxiv.org/abs/2105.06980">arXiv:2105.06980 [astro-ph.HE]</a>; submitted to ApJL).</p> <p>Also included are: the data products; the numeric model files including the telescope calibration products; model modules in the Python language using the X-PSI framework; and Jupyter analysis notebooks.</p> <p>Please refer to the README for detailed information.</p> <p> </p> <p> </p>
CCE LTER process cruise, in the California Current region, event log records including date, time, position and activity for use in post-cruise data integration based on co-sampling indexes. From 2006 to 2019 CCE LTER used a locally developed event logging system. During P2107, CCE LTER started to utilize the R2R Event Logger on UNOL ships, 2006 - 2024 (ongoing).
The event logger program developed and maintained by the California Cooperative Oceanic Fisheries Investigations, SIO, program is used aboard CCE LTER process cruises to create indexes with temporal, spatial and activity information for post-cruise data integration. The event log is configured aboard the ship for the recording of sampling events by both ship crew personnel on the bridge, and research personnel in the lab. The event log is processed post-cruise to correct for various errors.
PIE LTER time series of nutrient grab samples from Ipswich River and Parker River watershed catchments, Masachusetts, with frequency ranging from weekly to monthly between 2001 and 2019.
Time series of nutrient grab samples collected by hand (i.e. not with the Sigma autosampler) with frequency ranging from weekly to monthly between 2001 and 2016. Sites include three headwater catchments of contrasting land use (forest, urban, wetland), and the mouth of the two main watersheds draining to the Plum Island Estuary (Ipswich and Parker R.). An additional time series was collected for a site in the Upper Ipswich at North Reading and at Fish Br. in Boxford – this sampling was ended in 2002. All Samples were analysed for NO3, NO2, NH4, PO4, TDN and DOC. Si was analyzed until 2002. Particulates, anions and TSS have been analyzed since 2006.
Sample of facial mask N95 and FFP2 pricing on retail webs and time evolution per country
<p>We've gathered - for a Data Science educational project - the pricing of several face mask for breathing protection in a given period of time.</p> <p>Countries : Spain', 'USA', 'France', 'UK', 'Germany', 'Italy', 'Netherlands', 'Australia'</p> <p> </p> <p>'asin' type: STRING "Código de identifícación único de product equivalente de AMAZON"</p> <p>'description' type: STRING 'Texto descriptivo del producto'</p> <p>'dateTime' type: TIMESTAMP 'Cadena de carateres que contiene fecha y hora GMT'</p> <p>'date' Type: DATETIME ' Formato diferente de la misma fecha / hora de captura '</p> <p>'country' type: STRING 'Pais al que pertenece a distribución del producto 'Valores posibles: '</p>
Dataset for evaluation of unrealistic optimism in time of pandemic COVID-19 on a Polish sample
<p>This dataset contains the data used in a project called "Unrealistic optimism in the eye of the storm. Positive bias towards the consequences of COVID-19 during the second and third waves of the pandemic. ". The project concerns the occurrence of a cognitive bias - unrealistic optimism - with regard to contracting the coronavirus. The following information are attached to the dataset: codebooks with variables' names; analysis codes to replicate our results; supplementary materials with the description of procedures. </p>
Whole-genome genotype data for French Large White pigs from two distinct sampling times
<p>Genotype data at plink binary format for 36 pigs from the french Large White breed: 13 animals from the female line born in 2014 and 2015, 13 animals from the male line born between 2012 and 2016, and 10 animals from a common ancestral line, born in 1977. These genotypes were obtained from individual whole genome sequencing (WGS) data, whiwh are available at https://www.ebi.ac.uk/ena under the accession number PRJEB51909.</p> <p>Two different genotype datasets were obtained from the raw WGS:</p> <p>1) snp20_auto_cr (.bed/bim/fam): High quality autosomal SNPs, called by 3 different software, with a call rate of at least 90%</p> <p>2) all10_auto (.bed/bim/fam): All SNPs or indels called by at least one of 3 different software.</p> <p>More details about these datasets and their use can be found in the following study:</p> <p>Boitard et al (under revision): Whole-genome sequencing of cryo-preserved resources from French Large White pigs at two distinct sampling times reveals strong signatures of convergent and divergent selection between the dam and sire lines.</p>
Catchment attributes and hydro-meteorological time series for large-sample studies across hydrologic Switzerland (CAMELS-CH)
<p>CAMELS-CH (Catchment Attributes and MEteorology for large-sample Studies - Switzerland) is a large-sample hydro-meteorological data set for hydrological Switzerland in Central Europe that covers 331 basins within Switzerland and neighboring countries (Austria, France, Germany and Italy). CAMELS-CH comprises dynamic hydro-meteorological variables and static catchment attributes.</p> <p>The data set covers 40 years of data between 1st January 1981 and 31st December 2020 for each catchment: daily time series of stream flow and water levels, of meteorological data such as precipitation and air temperature and of daily snow water equivalent data. Additionally, CAMELS-CH encompasses annual time series of land cover change and glacier evolution per catchment. The static catchment attributes comprise the following categories: location and topography, climate, hydrology, soil, hydrogeology, geology, land use, human impact and glaciers.</p> <p>The corresponding manuscript is published at the journal "Earth System Science Data" (ESSD) and available <a href="https://essd.copernicus.org/articles/15/5755/2023/">here</a>. The code used to generate the dataset is available on <a href="https://github.com/camels-ch">Github</a>.</p> <p>The data description file below contains a comprehensive list of all time series and attribute variables covered by the dataset and references to the original data sources. Further, this repository contains the "Caravan extension CH" for the "Caravan - A global community dataset for large-sample hydrology" <a href="../records/7944025">Caravan dataset</a> (see the <a href="https://github.com/kratzert/Caravan/discussions/10">list of extensions</a>). This extension has the same format like other Caravan parts and is based on the same data sources. Note that some features like the annual glacier time series, etc. are therefore only available in the original CAMELS-CH dataset.</p> <p> </p> <h2>Updates:</h2> <p>- Update version 0.9: affects "Caravan_extension_CH" - In version 1.5 of the Caravan dataset, Penman-Monteith PET was added as an additional time series feature. Additional to the new time series feature, also all pet-related climate indices were recomputed using the new Penman-Monteith PET. For consistency, the old ERA5-Land potential_evaporation time series and climate indices were kept, but renamed for a better identification of the differences. </p> <p>- Update version 0.8: resolving projection issue for shapefiles in "Caravan_extension_CH" using EPSG:4326 (WGS84); updating readme file of "camels_ch" regarding the <a href="../communities/dischma/">Dischma</a> catchment</p> <p>- Update version 0.7: update corresponding to the revision of the manuscript at "Earth System Science Data" (ESSD)</p> <ul> <li>dataset file delimiters have been changed to commas from semicolons</li> <li>the "time_series" folder was renamed to "timeseries"</li> <li>in the simulation-based data, there was an error in the previous aggregation of precipitation and evapotranspiration. The corresponding time series, affected hydrologic signatures and climatic indices were corrected</li> <li>the order of simulation-based variables in the timeseries files was changed to resemble the order shown in the tables of the corresponding publication in ESSD</li> <li>blank values that were masked by "NA" are now consistently indicated by "NaN"</li> <li>the readme file has been extended</li> </ul> <p>- Update version 0.6: updating links to related material (all links and references are available in the preprint/manuscript) and abstract</p> <p>- Update version 0.5: adding the "camels_ch_data_description.pdf" file</p> <p>- Update version 0.4: update of several static attributes in "Caravan_extension_CH" following a general update in Caravan and all its extensions + adopting the geographic coordinate system to Caravan-standard EPSG:4326</p> <p>- Update version 0.3: renaming single files/entries in "Caravan_extension_CH" to start with "camelsch" as unique Caravan extension identifier</p> <p>- Update version 0.2: CH extension to <a href="../records/7944025">Caravan</a> added</p>
Imaging of Organic Samples with Megaelectron Volt Time-of-Flight Secondary Ion Mass Spectrometry Capillary Microprobe
<p>Time-of-flight Secondary Ion Mass Spectrometry (TOF SIMS) with MeV primary ions offers a fine balance between secondary ion yield for molecules in the mass range from 100 to 1000 Da and beam spot size, both of which are critical for imaging applications of organic samples. Using conically shaped glass capillaries with an exit diameter of a few micrometers, a high energy heavy primary beam can be collimated to less than 10 μm. In this work, imaging capabilities of such a setup are presented for some organic samples (leucine-evaporated mesh, fly wing section, ink deposited on paper). Lateral resolution measurement and molecular distributions of selected mass peaks are shown. The negative influence of the beam halo, an unavoidable characteristic of primary beam collimation with a conical capillary, is also discussed. A new start trigger for TOF measurements based on the detection of secondary electrons released by the primary ion is presented. This method is applicable for a continuous primary ion beam, and for thick targets that are not transparent to the primary ion beam. The solution preserves the good mass resolution of the thin target setup, where the detection of primary ions with a PIN diode is used for a start trigger, reduces the background, and enables a wide range of samples to be analyzed.</p>
Time to Update the Split-Sample Approach in Hydrological Model Calibration v1.1
<p><strong>Time to Update the Split-Sample Approach in Hydrological Model Calibration</strong></p> <p>Hongren Shen<sup>1</sup>, Bryan A. Tolson<sup>1</sup>, Juliane Mai<sup>1</sup></p> <p><sup>1</sup>Department of Civil and Environmental Engineering, University of Waterloo, Waterloo, Ontario, Canada</p> <p>Corresponding author: Hongren Shen (hongren.shen@uwaterloo.ca)</p> <p><strong>Abstract</strong></p> <p>Model calibration and validation are critical in hydrological model robustness assessment. Unfortunately, the commonly-used split-sample test (SST) framework for data splitting requires modelers to make subjective decisions without clear guidelines. This large-sample SST assessment study empirically assesses how different data splitting methods influence post-validation model testing period performance, thereby identifying optimal data splitting methods under different conditions. This study investigates the performance of two lumped conceptual hydrological models calibrated and tested in 463 catchments across the United States using 50 different data splitting schemes. These schemes are established regarding the data availability, length and data recentness of the continuous calibration sub-periods (CSPs). A full-period CSP is also included in the experiment, which skips model validation. The assessment approach is novel in multiple ways including how model building decisions are framed as a decision tree problem and viewing the model building process as a formal testing period classification problem, aiming to accurately predict model success/failure in the testing period. Results span different climate and catchment conditions across a 35-year period with available data, making conclusions quite generalizable. Calibrating to older data and then validating models on newer data produces inferior model testing period performance in every single analysis conducted and should be avoided. Calibrating to the full available data and skipping model validation entirely is the most robust split-sample decision. Experimental findings remain consistent no matter how model building factors (i.e., catchments, model types, data availability, and testing periods) are varied. Results strongly support revising the traditional split-sample approach in hydrological modeling.</p> <p><strong>Version updates</strong></p> <p><strong>v1.1 Updated on May 19, 2022.</strong> We added hydrographs for each catchment.</p> <p><strong>There are <em>8 parts</em> of the zipped file attached in v1.1. You should download all of them and <em>unzip all those eight parts together</em>.</strong></p> <p>In this update, we added two zipped files in each gauge subfolder:</p> <p> (1) GR4J_Hydrographs.zip and</p> <p> (2) HMETS_Hydrographs.zip</p> <p>Each of the zip files contains 50 CSV files. These CSV files are named with keywords of model name, gauge ID, and the calibration sub-period (CSP) identifier.</p> <p>Each hydrograph CSV file contains four key columns:</p> <p> (1) Date time (note that the hour column is less significant since this is daily data);</p> <p> (2) Precipitation in mm that is the aggregated basin mean precipitation;</p> <p> (3) Simulated streamflow in m3/s and the column is named as "subXXX", where XXX is the ID of the catchment, specified in the CAMELS_463_gauge_info.txt file; and</p> <p> (4) Observed streamflow in m3/s and the column is named as "subXXX(observed)".</p> <p>Note that these hydrograph CSV files reported period-ending time-averaged flows. They were directly produced by the Raven hydrological modeling framework. More information about the format of the hydrograph CSV files can be redirected to the <a href="http://raven.uwaterloo.ca/">Raven webpage</a>.</p> <p><strong>v1.0 First version published on Jan 29, 2022.</strong></p> <p><strong>Data description</strong></p> <p>This data was used in the paper entitled "Time to Update the Split-Sample Approach in Hydrological Model Calibration" by Shen et al. (2022).</p> <p>Catchment, meteorological forcing and streamflow data are provided for hydrological modeling use. Specifically, the forcing and streamflow data are archived in the Raven hydrological modeling required format. The GR4J and HMETS model building results in the paper, i.e., reference KGE and KGE metrics in calibration, validation and testing periods, are provided for replication of the split-sample assessment performed in the paper.</p> <p><strong>Data content</strong></p> <p>The data folder contains a <strong>gauge info file (<em>CAMELS_463_gauge_info.txt</em>)</strong>, which<em> </em>reports basic information of each catchment, and <strong>463 subfolders</strong>, each having four files for a catchment, including:</p> <p> (1) <strong>Raven_Daymet_forcing.rvt</strong>, which contains Daymet meteorological forcing (i.e., daily precipitation in mm/d, minimum and maximum air temperature in deg_C, shortwave in MJ/m2/day, and day length in day) from Jan 1st 1980 to Dec 31 2014 in a Raven hydrological modeling required format.</p> <p> (2) <strong>Raven_USGS_streamflow.rvt</strong>, which contains daily discharge data (in m3/s) from Jan 1st 1980 to Dec 31 2014 in a Raven hydrological modeling required format.</p> <p> (3) <strong>GR4J_metrics.txt</strong>, which contains reference KGE and GR4J-based KGE metrics in calibration, validation and testing periods.</p> <p> (4) <strong>HMETS_metrics.txt</strong>, which contains reference KGE and HMETS-based KGE metrics in calibration, validation and testing periods.</p> <p><strong>Data collection and processing methods</strong></p> <p> <strong>Data source</strong></p> <ul> <li> Catchment information and the Daymet meteorological forcing are retrieved from the CAMELS data set, which can be found <a href="https://ral.ucar.edu/solutions/products/camels">here</a>.</li> <li> The USGS streamflow data are collected from the U.S. Geological Survey's (USGS) National Water Information System (NWIS), which can be found <a href="https://waterdata.usgs.gov/nwis/sw">here</a>.</li> <li>The GR4J and HMETS performance metrics (i.e., reference KGE and KGE) are produced in the study by Shen et al. (2022).</li> </ul> <p><strong> Forcing data processing</strong></p> <ul> <li>A quality assessment procedure was performed. For example, daily maximum air temperature should be larger than the daily minimum air temperature; otherwise, these two values will be swapped.</li> <li>Units are converted to Raven-required ones. Precipitation: mm/day, unchanged; daily minimum/maximum air temperature: deg_C, unchanged; shortwave: W/m2 to MJ/m2/day; day length: seconds to days.</li> <li>Data for a catchment is archived in a RVT (ASCII-based) file, in which the second line specifies the start time of the forcing series, the time step (= 1 day), and the total time steps in the series (= 12784), respectively; the third and the fourth lines specify the forcing variables and their corresponding units, respectively.</li> <li>More details of Raven formatted forcing files can be found in the Raven manual (<a href="http://raven.uwaterloo.ca/">here</a>).</li> </ul> <p><strong> Streamflow data processing</strong></p> <ul> <li>Units are converted to Raven-required ones. Daily discharge originally in cfs is converted to m3/s.</li> <li>Missing data are replaced with -1.2345 as Raven requires. Those missing time steps will not be counted in performance metrics calculation.</li> <li>Streamflow series is archived in a RVT (ASCII-based) file, which is open with eight commented lines specifying relevant gauge and streamflow data information, such as gauge name, gauge ID, USGS reported catchment area, calculated catchment area (based on the catchment shapefiles in CAMELS dataset), streamflow data range, data time step, and missing data periods. The first line after the commented lines in the streamflow RVT files specifies data type (default is HYDROGRAPH), subbasin ID (i.e., SubID), and discharge unit (m3/s), respectively. And the next line specifies the start of the streamflow data, time step (=1 day), and the total time steps in the series(= 12784), respectively.</li> </ul> <p><strong>GR4J and HMETS metrics </strong></p> <p>The GR4J and HMETS metrics files consists of reference KGE and KGE in model calibration, validation, and testing periods, which are derived in the massive split-sample test experiment performed in the paper.</p> <ul> <li>Columns in these metrics files are gauge ID, calibration sub-period (CSP) identifier, KGE in calibration, validation, testing1, testing2, and testing3, respectively.</li> <li>We proposed 50 different CSPs in the experiment. "CSP_identifier" is a unique name of each CSP. e.g., CSP identifier "CSP-3A_1990" stands for the model is built in Jan 1st 1990, calibrated in the first 3-year sample (1981-1983), calibrated in the rest years during the period of 1980 to 1989. Note that 1980 is always used for spin-up.</li> <li>We defined three testing periods (independent to calibration and validation periods) for each CSP, which are the first 3 years from model build year inclusive, the first 5 years from model build year inclusive, and the full years from model build year inclusive. e.g., "testing1", "testing2", and "testing3" for CSP-3A_1990 are 1990-1992, 1990-1994, and 1990-2014, respectively.</li> <li>Reference flow is the interannual mean daily flow based on a specific period, which is derived for a one-year period and then repeated in each year in the calculation period. <ul> <li>For calibration, its reference flow is based on spin-up + calibration periods.</li> <li>For validation, its reference flow is based on spin-up + calibration periods.</li> <li>For testing, its reference flow is based on spin-up +calibration + validation periods.</li> </ul> </li> <li>Reference KGE is calculated based on the reference flow and observed streamflow in a specific calculation period (e.g., calibration).<strong> Reference KGE is computed using the KGE equation with substituting the "simulated" flow for "reference" flow in the period for calculation</strong>. Note that the reference KGEs for the three different testing periods corresponds to the same historical period, but are different, because each testing period spans in a different time period and covers different series of observed flow.</li> </ul> <p><strong>More details of the split-sample test experiment and modeling results analysis can be referred to the paper by Shen et al. (2022).</strong></p> <p><strong>Citation</strong></p> <p><strong>Journal Publication</strong></p> <p>This study:</p> <p>Shen, H., Tolson, B. A., & Mai, J.(2022). Time to update the split-sample approach in hydrological model calibration. Water Resources Research, 58, e2021WR031523. <a href="https://doi.org/10.1029/2021WR031523">https://doi.org/10.1029/2021WR031523</a></p> <p>Original CAMELS dataset:</p> <p>A. J. Newman, M. P. Clark, K. Sampson, A. Wood, L. E. Hay, A. Bock, R. J. Viger, D. Blodgett, L. Brekke, J. R. Arnold, T. Hopson, and Q. Duan (2015). Development of a large-sample watershed-scale hydrometeorological dataset for the contiguous USA: dataset characteristics and assessment of regional variability in hydrologic model performance. Hydrol. Earth Syst. Sci., 19, 209-223, <a href="http://doi.org/10.5194/hess-19-209-2015">http://doi.org/10.5194/hess-19-209-2015</a></p> <p><strong>Data Publication</strong></p> <p>This study:</p> <p>H. Shen, B. A. Tolson, and J. Mai (2022). Time to Update the Split-Sample Approach in Hydrological Model Calibration. Zenodo. <a href="http://doi.org/10.5281/zenodo.5915374">http://doi.org/10.5281/zenodo.5915374</a></p> <p>Original CAMELS dataset:</p> <p>A. Newman; K. Sampson; M. P. Clark; A. Bock; R. J. Viger; D. Blodgett, 2014. A large-sample watershed-scale hydrometeorological dataset for the contiguous USA. Boulder, CO: UCAR/NCAR.<a href="http:// https://dx.doi.org/10.5065/D6MW2F4D"> https://dx.doi.org/10.5065/D6MW2F4D</a></p>
Samples of solar flares classes, active regions and time of occurrence
<p>This dataset contains samples of solar flares measurements of classes X, M, C and B.</p> <p>For clarification, the flares are classified as follows:</p> <ul> <li>Class X: flares <span class="math-tex">\(>10^{-4} watts/m^{2}\)</span></li> <li>Class M: <span class="math-tex">\(10^{-5} watts/m^{2} <\)</span>flares <span class="math-tex">\(<10^{-4} watts/m^{2}\)</span></li> <li>Class C: <span class="math-tex">\(10^{-6} watts/m^{2}<\)</span>flares<span class="math-tex">\(<10^{-5} watts/m^{2}\)</span></li> <li>Class B: <span class="math-tex">\(10^{-7} watts/m^{2}<\)</span>flares<span class="math-tex">\(<10^{-6} watts/m^{2}\)</span></li> </ul> <p>This dataset was assembled with data from https://www.spaceweatherlive.com/en/solar-activity/top-50-solar-flares</p> <p>The date (yyyy-mm-dd hh:mm:ss) the authors assembled the data is 2017-11-14 13:48:37</p> <p>The original data source is the National Oceanic & Atmospheric Administration (NOAA), U.S. Departement of Commerce.</p> <p>Data description:</p> <ul> <li><strong>Class</strong>: Class of the flare: X, M, C or B</li> <li><strong>Date</strong>: Date of occurence in yyyy-mm-dd format.</li> <li><strong>AR</strong>: Active Region ID attributed by NOAA.</li> <li><strong>Begin</strong>: time the flare begins in hh:mm:ss format.</li> <li><strong>Max</strong>: time the flare reaches its max value in hh:mm:ss format.</li> <li><strong>End</strong>: time the flare vanishes in hh:mm:ss format.</li> </ul> <p>The dataset has 2,256 tuples divided as follows:</p> <ul> <li>171 tuples with X class flares data (7.58%).</li> <li>572 tuples with M class flares data (25.35%).</li> <li>767 tuples with C class flares data (34%).</li> <li>746 tuples with B class flares data (33.07%).</li> </ul> <p>The data collected refer to the period between August 25, 1996 and May 29, 2017.</p>
Assessing First Time Usability of a Hand Augmentation Device in a Large Sample of Diverse Users
<p>This upload provides additional information and data related to the paper "<span>Assessing First Time Usability of a Hand Augmentation Device in a Large Sample of Diverse Users</span>" (Clode et al., 2024).</p> <p>The study investigated users' first-time usage of the Third Thumb at "The Royal Society Summer Science Exhibition 2022", a large public engagement event held from July 6th to July 10th, 2022, at The Royal Society in London, England. The exhibition attracted more than 6,000 visitors.</p> <p>The Third Thumb is a supernumerary robotic finger. During the event, nearly 600 participants practiced using the Third Thumb in motor tasks aimed at different aspects of motor control. We examined how various demographic factors affected performance.</p>
Historical time-series reconstruction benchmark dataset of Landsat bi-monthly aggregates from GLAD ARD-2 at 30-m resolution with stratified sampling based on ESA CCI
<h2>Description</h2> <p>Historical time-series reconstruction benchmark dataset presented here is designed for evaluating and comparing the performance of time series reconstruction methods in the context of land cover change detection. The dataset is based on the European Space Agency Climate Change Initiative (ESA CCI) land cover dataset, which has been aggregated into 18 classes to facilitate analysis. The dataset includes information on land cover dynamics from 2000 to 2020, focusing on identifying and characterizing changes in land cover over time.</p> <h3><strong>Data Collection and Processing:</strong></h3> <p>The dataset is derived from the ESA CCI land cover dataset, which provides information on land cover classes at a global scale. The original dataset, containing 37 land cover classes, was aggregated into 18 classes based on similarity. Pixels with stable land cover over the study period and pixels with one or multiple land cover changes were identified and grouped into strata for sampling purposes.</p> <p>Sampling points were selected using a stratified sampling design, ensuring representation across different land cover classes and change scenarios. Approximately 2600 points were selected from each stratum, resulting in a total of 51,978 sampling points. The selected points were uniformly distributed along the strata, with spatial variations accounted for.</p> <p>Bimonthly time series data were extracted for each sampling point from 1997 to 2022, capturing temporal dynamics in land cover. Artificial gaps were introduced into the time series data to simulate real-world data loss, allowing for the evaluation of time series reconstruction methods under varying gap densities.</p> <p>The time series values were extracted from Landsat GLAD imagery using the specified spectral bands, including blue, green, red, NIR, SWIR1, SWIR2, and thermal bands. Additionally, a clear quality band was also extracted.</p> <h3>Data Details</h3> <ul> <li><strong>Time Period:</strong> 1997-01-01 to 2022-12-31</li> <li><strong>Type of Data: </strong>R data frame / Geopackage points.</li> <li><strong>Collection/Derivation:</strong> Derived from Landsat ARD v2, processed with Scikit-map.</li> <li><strong>Coordinate Reference System:</strong> EPSG:4326</li> <li><strong>Bounding Box:</strong> All the globe</li> <li><strong>File Format:</strong> RDS</li> </ul> <p> </p> <h3><strong>Reclassified Classes of ESA CCI Land Cover Dataset</strong></h3> <table> <tbody> <tr> <td> <div> <div> <p><strong>Aggregated Class Code</strong></p> </div> </div> </td> <td> <div> <div> <p><strong>Aggregated Class Label</strong></p> </div> </div> </td> <td> <div> <div> <p><strong>Original ESA CCI Classes</strong></p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>10</p> </div> </div> </td> <td> <div> <div> <p>Cropland rainfed</p> </div> </div> </td> <td> <div> <div> <p>10, 11, 12</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>30</p> </div> </div> </td> <td> <div> <div> <p>Mosaic cropland | natural vegetation</p> </div> </div> </td> <td> <div> <div> <p>30, 40</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>50</p> </div> </div> </td> <td> <div> <div> <p>Tree cover broadleaved evergreen</p> </div> </div> </td> <td> <div> <div> <p>50</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>60</p> </div> </div> </td> <td> <div> <div> <p>Tree cover broadleaved deciduous</p> </div> </div> </td> <td> <div> <div> <p>60, 61, 62</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>70</p> </div> </div> </td> <td> <div> <div> <p>Tree cover needleleaved evergreen</p> </div> </div> </td> <td> <div> <div> <p>70, 71, 72</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>80</p> </div> </div> </td> <td> <div> <div> <p>Tree cover needleleaved deciduous</p> </div> </div> </td> <td> <div> <div> <p>80, 81, 82</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>90</p> </div> </div> </td> <td> <div> <div> <p>Tree cover mixed leaf type</p> </div> </div> </td> <td> <div> <div> <p>90</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>100</p> </div> </div> </td> <td> <div> <div> <p>Mosaic tree and shrub | herbaceous cover</p> </div> </div> </td> <td> <div> <div> <p>100, 110</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>120</p> </div> </div> </td> <td> <div> <div> <p>Shrubland</p> </div> </div> </td> <td> <div> <div> <p>120, 121, 122</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>150</p> </div> </div> </td> <td> <div> <div> <p>Sparse vegetation</p> </div> </div> </td> <td> <div> <div> <p>150, 151, 152, 153</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>160</p> </div> </div> </td> <td> <div> <div> <p>Tree cover flooded</p> </div> </div> </td> <td> <div> <div> <p>160, 170</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>180</p> </div> </div> </td> <td> <div> <div> <p>Shrub or herbaceous cover flooded</p> </div> </div> </td> <td> <div> <div> <p>180</p> </div> </div> </td> </tr> <tr> <td> <div> <div> <p>200</p> </div> </div> </td> <td> <div> <div> <p>Bare areas</p> </div> </div> </td> <td> <div> <div> <p>200, 201, 202</p> </div> </div> </td> </tr> </tbody> </table> <p>In the table, each row represents a reclassified land cover class, identified by a unique code. The 'Original ESA CCI Classes' column lists the specific land cover classes from the European Space Agency Climate Change Initiative dataset that are grouped together to form each broader category. Note that land cover classes not listed in this table were retained in their original value and were not reclassified.</p> <h3><strong>File Format</strong></h3> <p>The dataset comprises observations spanning from January 1997 to November 2022, capturing data for 51,978 samples.</p> <ul> <li>blue.rds: Time series data for the blue spectral band.</li> <li>green.rds: Time series data for the green spectral band.</li> <li>red.rds: Time series data for the red spectral band.</li> <li>nir.rds: Time series data for the near-infrared (NIR) spectral band.</li> <li>swir1.rds: Time series data for the shortwave infrared 1 (SWIR1) spectral band.</li> <li>swir2.rds: Time series data for the shortwave infrared 2 (SWIR2) spectral band.</li> <li>thermal.rds: Time series data for the thermal infrared band.</li> <li>clear.rds: Time series data for the clear quality band, used for masking out cloudy observations.</li> </ul> <p>How open the files in R:</p> <p><code>blue <- readRDS("blue.rds")</code></p> <p>To open the files in Python, you need to the <code>pyreadr</code> library:</p> <p><code>import pyreadr</code><br><code>blue = pyreadr.read_r('blue.rds')</code></p> <p> </p>
Storage time and temperature affect plasma osmolality values in field-collected blood samples
<p>Data and code for a study assessing the effect of storage conditions on blood plasma samples, published in Comparative Biochemistry and Physiology in 2024.</p>
Stable Modeling on Resource Usage Parameters of MapReduce Application-Figure 8. Minimum sample time of statistical metrics of MapReduce applications
<p>Figure 8 presents the minimum sampling time distribution of statistic metrics which ensures the stable modeling. Overall, the minimum sampling time of statistic metrics is smaller than sampling time of estimated coefficients. For different applications, a time-consuming application like Terasort needs the largest sampling time to tend to be stable. The Pi application shows the smallest minimum sampling time to reach stability.</p>
Stable Modeling on Resource Usage Parameters of MapReduce Application-Figure 7. Minimum sampling time of MapReduce applications on various response variable
<p>Pi application needs the smallest sampling time. The remaining applications need the similar minimum sampling time. Overall, the stable regression models on memory usage as response show the least need for sampling time. The results show that various applications have different minimum sampling time to get stable. The application which performs more read/write operations shows larger sampling time need.</p>
Real-time tilt undersampling optimization during electron tomography of beam sensitive samples using golden ratio scanning and RECAST3D
<p>This dataset contains experimental data related: "Real-time tilt undersampling optimization during electron tomography of beam sensitive samples using golden ratio scanning and RECAST3D", T.Craig, A. Kadu, K.J. Batenburg and S. Bals, <em><strong>Nanoscale</strong></em>, 2023,<strong>15</strong>, 5391-5402 (2023).</p>
Data and software associated with the paper "Bayesian Inference of Joint Coalescence Times of Sampled Sequences"
<p>1. Data files and run logs produced for the paper "Bayesian Inference of Joint Coalescence Times of Sampled Sequences".</p> <p>2. Software script versions used in the above.</p>
Text-fig. 13. Scatter diagram of m1 length vs SDQ for pre-Eemian (time slice 5) Arvicola samples from different geographical provenances compared with M. savini-A. mosbachensis and Arvicola sapidus. Empty dotted ovals indicate the range of extant Arvicola ex gr. amphibius samples from Italy (cyan) and from the other European locations (green) Abbreviations: FR – France, GE – Germany, IT – Italy, SP – Spain. in Independent Water Vole (Mimomys Savini, Arvicola: Rodentia, Mammalia) Lineages In Italy And Central Europe
Text-fig. 13. Scatter diagram of m1 length vs SDQ for pre-Eemian (time slice 5) Arvicola samples from different geographical provenances compared with M. savini-A. mosbachensis and Arvicola sapidus. Empty dotted ovals indicate the range of extant Arvicola ex gr. amphibius samples from Italy (cyan) and from the other European locations (green) Abbreviations: FR – France, GE – Germany, IT – Italy, SP – Spain.
Text-fig. 11. Scatter diagram of m1 length vs SDQ for Würmian/Weichselian (time slice 3) Arvicola samples from different geographical provenances compared with M. savini-A. mosbachensis and extant Arvicola sapidus. Empty dotted ovals indicate the range of extant Arvicola ex gr. amphibius samples from Italy (cyan) and from the other European locations (green) Abbreviations: FR – France, GE – Germany, IT – Italy, SP – Spain. in Independent Water Vole (Mimomys Savini, Arvicola: Rodentia, Mammalia) Lineages In Italy And Central Europe
Text-fig. 11. Scatter diagram of m1 length vs SDQ for Würmian/Weichselian (time slice 3) Arvicola samples from different geographical provenances compared with M. savini-A. mosbachensis and extant Arvicola sapidus. Empty dotted ovals indicate the range of extant Arvicola ex gr. amphibius samples from Italy (cyan) and from the other European locations (green) Abbreviations: FR – France, GE – Germany, IT – Italy, SP – Spain.
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.