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3,672 results for “temporal”
Supplementary Material 4 (Spatio-temporal metabolic rifts in urban construction material circularity)
<p>This video map shows the relative changes in environmental impacts at different locations per year for 2017-2050.</p>
Supplementary Material 1-3 (Spatio-temporal metabolic rifts in urban construction material circularity)
<p>Dataset 1 contains life cycle impact factors used for each stage of the urban metabolism for all spatial levels.</p> <p>Dataset 2 contains the transport distances and modes from supplier locations for all spatial levels.</p> <p>Dataset 3 contains material flow and life cycle impact assessment results for each year (2017-2050) and all spatial levels.</p>
Maps of the detailed spatially and temporally attributed emission for area of Legerova and Sokolska (TURBAN-D18)
<h3>Basic information</h3> <p>This dataset contains six folders with maps of input data for simulations published in project TURBAN as result D17 (see <a href="../records/10982836">https://zenodo.org/records/10982836</a>). Each folder contains air quality inputs for the so-called Legerova domain, an area in the city of Prague, Czech Republic, centred around the traffic-heavy streets Legerova and Sokolská. All times are in UTC (local time in winter, CET, is UTC +01:00, summer time, CEST, is UTC +02:00). In total 6 episodes in 2022 and 2023 were selected:</p> <ol> <li>s1 2022-07-17 00:00:00 - 2022-07-20 00:00:00</li> <li>s2: 2022-08-02 00:00:00 - 2022-08-05 00:00:00</li> <li>s3: 2022-09-22 00:00:00 - 2022-09-25 00:00:00</li> <li>s4: 2022-12-08 00:00:00 - 2022-12-11 00:00:00</li> <li>s5: 2023-01-27 00:00:00 - 2023-01-30 00:00:00</li> <li>s6: 2023-02-13 00:00:00 - 2023-02-16 00:00:00</li> </ol> <p>For more detailed description of the experiments see the <strong>TURBAN</strong> project website at <a href="https://www.project-turban.eu/">https://www.project-turban.eu/</a>.</p> <h3>General organisation, variables and file nomenclature</h3> <p>Each selected epizode (s1-s6) has three subfolders; input files in ASCII (<em>output-ascii</em>) or GeoTiff (<em>output-gis</em>) formats that can be viewed in many GIS applications. In the third subfolder are maps in the PNG format (<em>output-png</em>).</p> <p>Each subfolder includes 4 subfolders with emissions summarized in all layers above ground. Variable <em>vsrc_PM10</em> is the concentration of volume source emissions (VSRC) of the PM10, <em>vsrc_PM25</em> is the concentration of PM2.5, <em>vsrc_NO</em> is the concentration of NO and <em>vsrc_NO2</em> is the concentration of NO2.</p> <p>Each file (PRJ, TIF, ASC or PNG) has the same nomenclature. An example (vsrc_NO_abs-01h_20220717_1200-1300.png) could be parsed as: variable name (vsrc_NO), processed input (abs-01h), date (20220717) and period (1200-1300). So, the result is a map with emission fluxes of NO between 12:00 and 13:00 UTC 24 Jul 2019.</p> <h3>Emissions (see section 2.4.3 in Resler et al., 2024)</h3> <p>The data were processed from datasets published by CHMI, data collected by the Municipality of Prague and its organizations, data obtained by the researcher (ATEM) while providing expert studies in the past, and results of previous research projects. The input data of the used emission sources can be divided into two basic groups: emission from local heating and transport sources.</p> <p>Emissions for local heating were determined by calculations based on data from CHMI and the Czech Statistical Office (CZSO). Emissions from the transport sources were modeled using the MEFA transportation emission model which is recommended for the use in the Czech Republic by the Ministry of Environment of the Czech Republic. The model takes into account factors such as road gradient, the number of vehicles on the road, the flow of traffic, the composition of car types, and the emission characteristics of the individual car types. The emission calculation is based on data from the traffic census provided by the Prague Technical Administration of Roads (TSK Praha) and on data from the census of the composition of the transportation fleet in Prague built in the MEFA emission model. The data are based on regular surveys of the fleet composition carried out in Prague (Karel et al., 2021). The dust resuspension was computed according to the methodology published by the Ministry of Environment (Karel et. al., 2015). This methodology is based on US EPA methodology AP-42 (EPA, 2011) and was adjusted for the conditions of the Czech Republic. For the garages and parking lots, the results of the project TH03030496 (Karel et al., 2020) were used and for the bus stations, publicly available data about transportation were gathered from the Prague Public Transit Company (DPP).</p> <p>The disaggregation of the annual emissions into hourly intervals was then performed according to the type of source. For combustion sources distribution of emissions to days was done according to natural gas supply profiles for category DOM4 were used (OTE, 2024) and complemented by daily profiles for SNAP 2 (van der Gon, 2011). For transport sources, the census data from TSK Praha was utilized for all streets where it was available. For Legerova and Sokolská streets, hourly traffic intensity data were obtained and used directly for the selected episodes. For streets that were not covered by regular traffic surveys, the spatial and temporal distribution of the traffic intensities were based on analysis and evaluation of the relevant studies for the particular area (e.g. urban planning studies, Environmental Impact Assessment (EIA), etc.) and combined with information like street type, location, traffic regime, and pavement type. This approach allowed us to specify the distribution of the transportation intensities on smaller streets. For the detailed modeling of emissions from rail transport (diesel locomotives), the data of train rides were obtained from the Railway Administration (SŽ) and emission factors from the EMEP/EEA Air Pollutant Emission Inventory Guidebook 2019 (EEA, 2019) were used. Emissions from river ships were obtained from the CHMI national database and spatially distributed to the area of the river.</p> <p>Spatial transformation of the line and point emission into the corresponding areas was done with the utilization of the surrogates representing corresponding areas (e.g. areas of the street traffic lines and parking places for traffic emission and areas of the building roofs for local heating sources). This not only ensured the reasonable spatial distribution of the emission in the street canyon but also decreased the gradients of the emission field and with this proneness of the model to numerical inaccuracy of the micro-scale model. The processing of the emission sources into hourly emission flows was done in the emission model FUME recently extended for processing of the PALM emission (Belda et al., 2024).</p> <h3>Acknowledgements</h3> <p>The PALM simulations, and pre- and postprocessing were performed partially on the HPC infrastructure of the Institute of Computer Science of the Czech Academy of Sciences (ICS), supported by the long-term strategic development financing of the ICS (RVO:67985807) and partially on the IT4I HPC infrastructure supported by the Ministry of Education, Youth and Sports of the Czech Republic through the e-INFRA CZ (ID:90254). The work was performed within the project TURBAN (TO01000219; TURBAN – Turbulent-resolving urban modelling of air quality and thermal comfort) supported by Norway Grants and Technology Agency of the Czech Republic.</p> <h3>Literature</h3> <p>Note that some sources are available only in Czech language.</p> <p>Belda, M., et al. (2024) FUME 2.0 – Flexible Universal processor for Modeling Emissions, EGUsphere [preprint]. <a href="https://doi.org/10.5194/egusphere-2023-2740">https://doi.org/10.5194/egusphere-2023-2740</a></p> <p>Karel, J., et al. (2020) Projekt TH03030496 - Zmapování a emisní bilance neevidovaných zdrojů emisí znečišťujících látek na území městských aglomerací. Mapa neevidovaných zdrojů emisí znečišťujících látek na území aglomerace CZ01 Praha. Partially available at: <a href="https://www.atem.cz/neevidovane_zdroje.php">https://www.atem.cz/neevidovane_zdroje.php</a></p> <p>Karel, J., et al. (2015) Metodika pro výpočet emisí částic pocházejících z resuspenze ze silniční dopravy, CENEST, s. r. o., Prague. Available at: <a href="https://www.mzp.cz/C1257458002F0DC7/cz/doprava/$FILE/OOO-resuspenze_metodika-20190708.pdf">https://www.mzp.cz/C1257458002F0DC7/cz/doprava/$FILE/OOO-resuspenze_metodika-20190708.pdf</a></p> <p>Karel J., et. al. (2021) Zpráva o dynamické skladbě vozového parku na území hlavního města Prahy v roce 2020, Prague 2021. Available upon request from the Environmental Protection Division of the Prague Municipality.</p> <p>EPA (2011) Compilation of Air Pollutant Emission Factors, Volume I, AP-42. Section 13.2.1. Paved roads. EPA Research Triangle Park, US, 2003, updated 2011. Available at: <a href="https://www.epa.gov/air-emissions-factors-and-quantification/ap-42-compilation-air-emissions-factors-stationary-sources">https://www.epa.gov/air-emissions-factors-and-quantification/ap-42-compilation-air-emissions-factors-stationary-sources</a></p> <p>van der Gon, H.D., et al. (2011) Description of Current Temporal Emission Patterns and Sensitivity of Predicted AQ for Temporal Emission Patterns. EU FP7 MACC Deliverable Report D_D-EMIS_1.3. Available at: <a href="https://atmosphere.copernicus.eu/sites/default/files/2019-07/MACC_TNO_del_1_3_v2.pdf">https://atmosphere.copernicus.eu/sites/default/files/2019-07/MACC_TNO_del_1_3_v2.pdf</a></p> <p>EEA (2019) European Environment Agency, EMEP/EEA air pollutant emission inventory guidebook 2019 – Technical guidance to prepare national emission inventories, Publications Office. Available at: <a href="https://data.europa.eu/doi/10.2800/293657">https://data.europa.eu/doi/10.2800/293657</a></p> <p>OTE (2024) Gas Load Profiles - temperature and recalculated TDD. Available at: <a href="https://www.ote-cr.cz/en/statistics/gas-load-profiles/normalized-lp?set_language=en">https://www.ote-cr.cz/en/statistics/gas-load-profiles/normalized-lp?set_language=en</a></p> <p> </p> <p> </p>
Dataset related to the publication "Temporal dynamics and environmental controls of carbon dioxide and methane fluxes measured by the eddy covariance method over a boreal river"
<h2>Summary</h2> <p>Dataset related to the publication "Temporal dynamics and environmental controls of carbon dioxide and methane fluxes measured by the eddy covariance method over a boreal river" by Aki Vähä, Timo Vesala, Sofya Guseva, Anders Lindroth, Andreas Lorke, Sally MacIntyre, and Ivan Mammarella (2024), published in Biogeosciences.</p> <h2>Materials and Methods</h2> <h3>Measurement site</h3> <p>The experiment was conducted on a floating platform on the River Kitinen in northern Finland. The measurements took place from 1 June to 2 October, 2018.</p> <p>The River Kitinen is 235 km long and has a catchment area of 7672 km2. The catchment area consists mostly of managed boreal forest with Scots pine (Pinus sylvestris) and Norway spruce (Picea abies) as the main tree species, wetlands of which a large portion is drained, small streams and rivers, some low mountains and a few small settlements. The experiment site (67.37◦ N, 26.62◦ E, 173 m above sea level) was located next to the Finnish Meteorological Institute’s research and weather station in Tähtelä. At the experiment location the river is 180 m wide and forms a straight section extending approximately 600 m upstream and 1000 m downstream from the site. The direction of the river at the site is roughly north-northwest–south-southeast and it flows towards the south. The mean annual discharge, measured at the closest power plant downstream, is 103 m3 s−1. The maximum depth at the site is 7 m. The River Kitinen’s Strahler stream order at the site is 5. The floating platform was located about 70 m from the eastern river bank where the water depth was 4.5 m.</p> <h3>Eddy covariance</h3> <p>The eddy covariance system measuring water-atmosphere turbulent fluxes was mounted on a mast on the southern side of the platform. This installation consisted of an ultrasonic anemometer (uSonic-3 Scientific, METEK Meteorologische Messtechnik GmbH, Elmshorn, Germany) for measuring the wind speed in three Cartesian coordinates and the sonic temperature, an enclosed-path gas analyser (LI-7200RS, LI-COR Biosciences, Inc., Lincoln, Nebraska, USA) for measuring carbon dioxide and water vapour mole fractions, and a closed-path gas analyser (G1301-f, Picarro, Inc., Santa Clara, California, USA) for measuring methane and water vapour mole fractions. The centre of the sonic anemometer was 1.82 m above the water surface. An inclinometer (DOG2 micro-electro-mechanical system, Measurement Specialties, Inc., Hampton, Virginia, USA) was used for measuring the pitch and roll of the platform. Eddy covariance fluxes were calculated using the EddyUH software (Mammarella et al. 2016), following the state of art methodologies (Sabbatini et al. 2018, Nemitz et al. 2018).</p> <h3>Auxiliary measurements</h3> <p>Ambient air temperature and relative humidity were measured with a Rotronic HC2-S3C03 probe (Rotronic AG, Bassersdorf, Germany), mounted inside a Young model 41003 (R. M. Young Company, Traverse City, Michigan, USA) multi-plate radiation shield on the platform’s north-eastern corner. Air temperature and relative humidity were available only after 15th of June. Before that, the sonic temperature and humidity calculated from χH2O, measured with the LI-7200RS, were used instead. Atmospheric pressure and precipitation were measured at the Tähtelä weather station. Photosynthetically active radiation (PAR) in water was measured with two LI-192 sensors (LI-COR Biosciences, Inc., Lincoln, Nebraska, USA) and one LI-193 sensor (LI-COR). The sensors were hanging from wires at 0.3 m, 0.65 m and 1.0 m depths on a beam on the southern side of the platform. Measurements of water side CO2 partial pressure (pCO2) were done by using an off-axis integrated cavity output spectrometer (Ultraportable Greenhouse Gas Analyzer – UGGA), Los Gatos Research, Inc., Santa Clara, California, USA) that was connected to the headspace of an equilibrator consisting of a floating Plexiglas chamber.</p> <p>A water temperature chain was set up 100 m upstream of the platform. It consisted of five temperature loggers of the type RBR Solo (RBR Ltd. Ottawa, Ontario, Canada). The loggers were placed on a taut line mooring at depths of 0.35 m, 1.35 m, 2.35 m, 3.35 m and 4.35 m (6 June to 17 June) and 0.07 m, 1.05 m, 2.05 m, 3.05 m and 4.05 m (17 June onwards). The topmost measurement was used as the surface temperature. The water flow velocity was measured with a acoustic Doppler velocimeter (Nortek Vector, Nortek AS, Rud, Norway) which was installed on a beam on the north-western corner of the platform, facing down (Guseva et al., 2021). The depth of the measurements was 0.4 m below the surface.</p>
Code and data to "Statistical learning and topkriging improve spatio-temporal low-flow estimation"
<p>This data and software supports the manuscript "Statistical learning and topkriging improve spatio-temporal low-flow estimation" (https:://doi.org/<span>10.1029/2024WR038329</span>).</p> <p>The dataset consists of:</p> <ul> <li>all produced predictions of the models (data/predictions.RDS and data/predictions_csv/*)</li> <li>observational data (data/observations.csv)</li> <li>additional catchment data (data/catchment_data.csv) used for presenting the figures</li> <li>state boundaries of Austria as a shape file (data/boundaries.*)</li> <li>partial predictions of a model-based boosting approach (data/partial_predictions.csv)</li> <li>Example output of number of EOF, due to long computational time (data/number_eofs.RDS)</li> <li>IDs of near natural catchments (data/ids_low_flow.csv)</li> </ul> <p>Additionally, the code is provided to:</p> <ul> <li>Compute the number of EOFs (functions/number_eofs.R)</li> <li>Produce all the figures and tables in the paper (scripts/plotting_results.R)</li> </ul>
Multi-temporal digital terrain models of the NBS experiment in OAL-Austria
<p>Multi-temporal digital terrain models of the NBS experiment in OAL-Austria with a spatial resolution of 10cm, derived from 3D point clouds acquired with a terrestrial laser scanner (Riegl-VZ2000i); Projection: EPSG 31254</p> <p>The TLS-monitoring is intended for assessing the stability of the embankment at the NBS field demonstrator in OAL-Austria. The digital terrain models were acquired after applying the ground classification filter proposed by Axelsson (2000)</p>
Mimicking CA3 Temporal Dynamics Controls Limbic Ictogenesis
<p>This dataset includes the microelectrode array recordings from the paper Mimicking CA3 Temporal Dynamics Controls Limbic Ictogenesis.</p>
Data for: From pattern to process? Dual travelling waves, with contrasting propagation speeds, best describe a self-organised spatio-temporal pattern in population growth of a cyclic rodent
<p>Centroid data used for the analysis in Roos et al. Eco Lett.</p> <p>Transects, up to 99 m in length (dependent on the field's length), were surveyed in linear stable landscape features (field, track or ditch margins) to estimate vole abundance from November 2011 until September 2017. Each transect was divided into 3 m sections (33 in total) and the presence or absence of one or more signs of vole activity (i.e., latrines by burrows, fresh vegetation clippings, and recent burrow excavations) in each section was noted. The proportion of sections with signs of vole presence per transect was then used as the abundance index. The number of surveys carried out at any time varied adaptively with the perceived risk of an outbreak (according to changes in estimated abundance in previous monitoring surveys).</p> <p>The response variable typically used in all models is proportional growth rate (r_{t,i}, where is the abundance index for site at time (Royama 1992; Berryman 2002). A benefit of using r_{t,i}, rather than ln(N_{t,i}), is that any multiplicative effects of site quality are cancelled out, provided they are constant over time. To calculate r_{t,i}, vole abundance indices are required at the same location in successive time periods (i.e., N_{t,i} and N_{t+1,i}). Given that exact transect locations were rarely reused in successive months, and all transect measurements took place throughout the year rather than discrete seasons, the data had to be aggregated to consistent locations and times to allow growth rate to be calculated. As such, transects were temporally aggregated into a respective yearly quarter (e.g., January to March 2014). Transects were spatially aggregated by sequentially selecting an unassigned transect as a reference point for the ith centroid and assigning all unassigned transects within a 5 km radius to the ith centroid, and repeating until all transects had been allocated (see Figure 2 for a summary of the number of transects assigned to each centroid, centroid locations, and time series of growth rate of each centroid). Once complete, the mean Julian day, X and Y UTM (Universal Transverse Mercator) and the mean index was calculated for all transects assigned to each centroid for each time period. Where a centroid had successive values of N_{t,i} and N_{t+1,i} available, the corresponding proportional growth rate was calculated.</p> <p>A constant of 3.03 was added to N_{t,i} to avoid zero entries (3.03 was the lowest non-zero value of <em>N</em> observed). The final dataset consisted of 3,751 observations.</p>
Data archive for Anaerobic methane oxidation in a coastal oxygen minimum zone: spatial and temporal dynamics
<p>Data collected during annual sampling campaigns to the coastal oxygen minimum zone of Golfo Dulce, carried out in January-February 2018, 2019 and 2020. Methods and results are presented and discussed in Steinsdóttir et al. 2022. Anaerobic methane oxidation in a coastal oxygen minimum zone: spatial and temporal dynamics. Environmental Microbiology, in press, doi: 10.1111/1462-2920.16003</p> <p>The content of files is as follows:</p> <p>nutrient_and_methane_concentrations.csv - Concentrations of methane, nitrite, nitrate, and ammonium.</p> <p>methane_oxidation_rates.csv - Rates of anaerobic methane oxidation.</p> <p>kinetics_of_anaerobic_methane_oxidation.csv - Kinetics of anaerobic methane oxidation, carried out in 2019.</p> <p>methylococcales.fa - Methylococcales 16S rRNA amplicon sequences</p> <p>methanofastidiosa.fa - Methanofastidiosa 16S rRNA amplicon sequences</p>
Intrinsic Temporal Behavior of Titanium Oxide Memristors for Neuromorphic Systems: Raw dataset
<p><strong>Methodology</strong></p> <p>Experiments were conducted on in-house fabricated Pt/TiO<sub>2</sub>/Au structure with thickness of 15/25/20<em>nm</em> respectively. The fabricated structures were characterized using a 16 x 16 probe card mounted on a Cascade Microtech Summit 12000 Prober controlled via a ARC ONE DC I-V control system.</p> <p>Stimulation was conducted over five repeating cycles to demonstrate the behaviour of the resistance over time. Potentiation and depression were triggered by pulses of opposite polarity. The bias could potentially be altered to achieve a desired starting resistance.</p> <p>A total of five repeating cycles were conducted. Each experiment was conducted on a pristine device with 4V programming pulse width of 10ms and inter-pulse of 100ms. A total of 100 pulses were applied per direction. Each identical programming pulse was followed by a 0.1V non-invasive reading pulse for a fixed duration.</p> <p>Attached csv file include resistive values for three devices along with their corresponding pulse train and time.</p>
Remote sensing of river discharge (RSQ) estimates derived from multi-temporal Landsat width observations and BAM/geoBAM discharge inversion algorithms
<p><strong>This repository provides three data files in CSV format:</strong><br> 1. Gauge name, lat/lon information<br> 2. Gauge name, date, and multi-temporal river width extracted from Landsat<br> 3. Gauge name, date, and BAM/geoBAM estimates of river discharge with monthly Q priors</p> <p>Note: the multi-temporal river width data were extracted from Landsat imageries using RivWidthCloud, where the river centerline/orthogonal line definition and the cross-section sampling strategies were made prior to, and different from Feng et al. (2022). So the width values may be different from Feng et al. (2022) at some locations due to these differences. The discharge estimates were derived from BAM/geoBAM algorithms with width-only observations. More details of the technical workflow and the inner workings of BAM/geoBAM were provided in the literature below and papers therein.</p> <p> </p> <p><strong>Reference:</strong></p> <p>Lin, P., D. Feng, C.J. Gleason, M. Pan, C.B. Brinkerhoff, X. Yang, H.E. Beck, R. Frasson (2023). Inversion of river discharge from remotely sensed river widths: a critical assessment at three-thousand global river gauges. <em>RSE</em>.</p> <p> </p> <p>Updated: 2022/6/17, 2023/1/19</p> <p> </p>
Raw sequencing data PhD Mixoplankton spatio-temporal diversity and its environmental drivers in the North Sea
<p>Raw sequencing data PhD Mixoplankton spatio-temporal diversity and its environmental drivers in the North Sea</p>
SEESAW quantification data for temporal gene expression across osteoblastogenesis (B6xCAST), n=9
<p>Osteoblast cells mature from a mesenchymal stem cell pool to become cells capable of forming bone matrix and mineralizing this matrix. The goal of this study was to characterize temporal changes in the transcriptome across osteoblast maturation, starting with committed mesenchymal stem cell/ early pre-osteoblast stage through to mature osteoblasts capable of matrix mineralization. Methods: Enriched populations of pre-osteoblast-like cells were obtained from neonatal calvaria from B6xCAST mice expressing CFP under the control of the Col3.6 promoter. These cells were placed into culture for 4 days, removed from culture and subjected to FACS sorting based on the presence/absence of CFP expression. Cells expressing CFP were returned to culture, subjected to an osteoblast differentiation cocktail and RNA was collected at 2, 4, 6, 8, 10, 12, 14, 16 and 18 days post differentiation. Methods II: mRNA profiles for each time point were generated by next generation RNA sequencing, using an Illumina HiSeq 2000. Three technical replicates per sample were sequenced. Overall design: Gene expression in calvarial osteoblasts from neonatal B6xCAST-Col3.6 CFP mice at 9 time points post differentiation.</p> <p>File description: the R data files (.rda) provide outputs of the scripts in the mikelove/osteoblast-quant GitHub repo (July 2022, commit 01d96490), having run the fishpond package function importAllelicCounts() followed by minimal filtering. The `_counts.rda` files contain SummarizedExperiment objects with estimated count, TPM abundance, and effective length, but do not contain inferential replicates (bootstrap counts), although the transcript-level allelic counts object contains bootstrap mean and variances for every isoform, sample, and allele. The other two `.rda` files are summarized to gene level.</p> <p>The `_quant_dirs.tgz` files contain all the Salmon quantification data including bootstraps for the 9 time points. They are grouped into sets of three for convenience. The `CAST_EiJ.diploid.fa.gz` file provides the transcript sequences that were used for Salmon quantification.</p> <p>The `B6xCAST_discordant_global_AI.csv` file contains the same information as presented in Table S1 of Wu et al (2022). These are TSS-level results for 134 genes showing significant and discordant patterns within gene.</p> <p>The other 6 CSV files provide global and dynamic AI testing results at three levels of resolution: gene level, isoform level (txp), and TSS level where TSS within 50bp are combined into a single TSS-group. The significance cutoff is a q-value of 0.05. The code used for generating these results is provided in the GitHub repo: FennecFish/osteoblast-test.</p>
Data set for the publication entitled "Azithromycin alters spatial and temporal dynamics of airway microbiota in idiopathic pulmonary fibrosis"
<p>Set of files containing data used for microbiota analysis by 16S rRNA amplicon sequencing.</p> <p>The study cohort included patients with idiopathic pulmonary fibrosis from four centres in Switzerland, treated with azithromycin or placebo, sampled sequentially by oropharyngeal swab.</p> <p>This work is available in medRxiv and has been submitted</p>
High temporal and spatial resolution emission inventory for maritime shipping emissions on the North Sea and Baltic Sea (2015)
<p>A temporally and spatially highly resolved emission inventory for the North Sea and Baltic Sea for the year 2015, created with current emission factors and ship activity data. The emissions inventory is available as 396 csv files, one for each day in 2015 and December 2014, grouped as monthly archives. </p> <p><strong>Note that due to the underlying ship activity data and the geographic boundaries, the time index in the <em>Datetime </em>column in the <em>ship_emissions_YYYYMMDD.csv</em> files is not equidistant.</strong> For example, since vessels leave the geographic area and reenter later, no data is available for the time the vessel is not within the area.</p> <p>The underlying model source code is available on Github, with a release of the associated version on Zenodo: [](https://doi.org/10.5281/zenodo.6951672)</p> <p> </p>
Characterisation and calibration of low-cost PM sensors at high temporal resolution to reference grade performances - dataset
<p>This repository contains the data used for the analysis of the paper "Characterisation and calibration of PM sensors at high temporal resolution to reference grade performances" submitted to Heliyon and available as a pre-print:</p> <p>Bulot, Florentin M. J. and Ossont, Steven J. and Morris, Andrew and Basford, Philip J. and Easton, Natasha H. C. and Mitchell, Hazel L. and Foster, Gavin L. and Cox, Simon J. and Loxham, Matthew, Characterisation and Calibration of Low-Cost Pm Sensors at High Temporal Resolution to Reference-Grade Performance. Available at SSRN: <a href="https://ssrn.com/abstract=4360707">https://ssrn.com/abstract=4360707</a> or <a href="http://dx.doi.org/10.2139/ssrn.4360707">http://dx.doi.org/10.2139/ssrn.4360707</a></p> <p> </p> <p>The code used to conduct the data analysis is available at <a href="https://doi.org/10.5281/zenodo.7261417">https://doi.org/10.5281/zenodo.7261417</a></p> <p> </p> <p>.</p> <p> </p> <p>The files are available in .csv and in .rds (for R) formats. For details about the measurement equipment used<br> during this study, please refer to the methods section of the paper.</p> <p>Description of the files.</p> <p>202007_to_202107_nocs - contains the data from the low-cost sensors</p> <p>It contains the following headers:<br> - "sensor" - sensor id<br> - "site" - name of the air quality monitor hosting the sensor<br> - "median_PM1" - PM1 mass concentration (ug/m3)<br> - "median_PM10" - PM10 mass concentration (ug/m3)<br> - "median_PM25" - PM25 mass concentration (ug/m3)<br> - "median_PM4" - PM4 mass concentration (ug/m3) (only available for SPS30)<br> - "median_n05" - particle number concentration (SPS30) of particles between 0.3um and 0.5um<br> - "median_n1" - particle number concentration (SPS30) of particles between 0.3um and 1um<br> - "median_n10" - particle number concentration (SPS30) of particles between 0.3um and 10um<br> - "median_n25" - particle number concentration (SPS30) of particles between 0.3um and 2.5um<br> - "median_n4" - particle number concentration (SPS30) of particles between 0.3um and 4um<br> - "median_gr03um" - particle number concentration (PMS5003) of particles >0.3um<br> - "median_gr05um" - particle number concentration (PMS5003) of particles >0.5um<br> - "median_gr100um" - particle number concentration (PMS5003) of particles >10um<br> - "median_gr10um" - particle number concentration (PMS5003) of particles >1um<br> - "median_gr25um" - particle number concentration (PMS5003) of particles >2.5um<br> - "median_gr50um" - particle number concentration (PMS5003) of particles >5um<br> - "median_pm100_cf1" - PM10 mass concentration with cf1 calibration for PMS5003<br> - "median_pm10_cf1" - PM1 mass concentration with cf1 calibration for PMS5003<br> - "median_pm25_cf1" - PM25 mass concentration with cf1 calibration for PMS5003<br> - "date" - date, format "yyyy-mm-dd HH:MM:SS GMT" </p> <p> </p> <p>df_pm_2min - contains the PM mass concentration data from the Fidas 200S.</p> <p>It contains the following headers:<br> - "PM2.5" - PM2.5 mass concentration (ug/m3) Fidas 200S<br> - "PM10" - PM10 mass concentration (ug/m3) Fidas 200S<br> - "PMtot" - PM total mass concentration (ug/m3) Fidas 200S<br> - "PM1" - PM1 mass concentration (ug/m3) Fidas 200S<br> - "date" - date, format "yyyy-mm-dd HH:MM:SS GMT" </p> <p> </p> <p>df_weather_2min - contains the weather data from the Fidas 200S</p> <p>It contains the following headers:<br> - "rh" - relative humidity (%)<br> - "dew_point_temperature" - dew point temperature (Celsius)<br> - "air_pressure" - Air pressure (hPa)<br> - "temperature" - temperature (Celsius)<br> - "date" - date, format "yyyy-mm-dd HH:MM:SS GMT"</p>
Tracking Selection using Temporal Population Genomics Data
<p>This repository contains the implementation of a pipeline to run the simulations and to produce a reference table for the ABC-RF inference of demography and selection. In its new release, this repository contains the whole-genome polymorphism of contemporary and museum specimens of <em>Apis mellifera</em> feral populations analyzed by Cridland et al. (2018).</p>
A dataset for temporal analysis of files related to the JFK case
<p>This dataset contains the content of the subset of all files with a correct publication date from the 2017 release of files related to the JFK case (retrieved from https://www.archives.gov/research/jfk/2017-release). This content was extracted from the source PDF files using the R OCR libraries tesseract and pdftools.</p> <p>The code to derive the dataset is given as follows:</p> <p>### BEGIN R DATA PROCESSING SCRIPT</p> <p>library(tesseract)<br> library(pdftools)</p> <p>pdfs <- list.files("/home/STAFF/luczakma/RProjects/JFK/data/files/")</p> <p>meta <- read.csv2("/home/STAFF/luczakma/RProjects/JFK/data/jfkrelease-2017-dce65d0ec70a54d5744de17d280f3ad2.csv",header = T,sep = ',')</p> <p>meta$Doc.Date <- as.character(meta$Doc.Date)</p> <p>meta.clean <- meta[-which(meta$Doc.Date=="" | grepl("/0000",meta$Doc.Date)),]<br> for(i in 1:nrow(meta.clean)){<br> meta.clean$Doc.Date[i] <- gsub("00","01",meta.clean$Doc.Date[i])<br> <br> if(nchar(meta.clean$Doc.Date[i])<10){<br> meta.clean$Doc.Date[i]<-format(strptime(meta.clean$Doc.Date[i],format = "%d/%m/%y"),"%m/%d/%Y")<br> }<br> <br> }</p> <p>meta.clean$Doc.Date <- strptime(meta.clean$Doc.Date,format = "%m/%d/%Y")</p> <p>meta.clean <- meta.clean[order(meta.clean$Doc.Date),]</p> <p>docs <- data.frame(content=character(0),dpub=character(0),stringsAsFactors = F)<br> for(i in 1:nrow(meta.clean)){<br> #for(i in 1:3){<br> pdf_prop <- pdftools::pdf_info(paste0("/home/STAFF/luczakma/RProjects/JFK/data/files/",tolower(gsub("\\s+"," ",gsub(" ","",meta.clean$File.Name[i])))))<br> tmp_files <- c()<br> for(k in 1:pdf_prop$pages){<br> tmp_files <- c(tmp_files,paste0("/home/STAFF/luczakma/RProjects/JFK/data/tmp/",k))<br> }<br> <br> img_file <- pdftools::pdf_convert(paste0("/home/STAFF/luczakma/RProjects/JFK/data/files/",tolower(gsub("\\s+"," ",gsub(" ","",meta.clean$File.Name[i])))), format = 'tiff', pages = NULL, dpi = 700,filenames = tmp_files)<br> <br> txt <- ""<br> <br> for(j in 1:length(img_file)){<br> extract <- ocr(img_file[j], engine = tesseract("eng"))<br> #unlink(img_file)<br> txt <- paste(txt,extract,collapse = " ")<br> }<br> <br> docs <- rbind(docs,data.frame(content=iconv(tolower(gsub("\\s+"," ",gsub("[[:punct:]]|[\n]"," ",txt))),to="UTF-8"),dpub=format(meta.clean$Doc.Date[i],"%Y/%m/%d"),stringsAsFactors = F),stringsAsFactors = F)<br> }</p> <p>### END R DATA PROCESSING SCRIPT</p>
Peak Flow Event Durations in the Mississippi River Basin and Implications for Temporal Sampling of Rivers
<p><strong>Corresponding peer-reviewed publication</strong></p> <p>This repository corresponds to all the input and output files that were used in the study reported in:</p> <ul> <li>Cerbelaud, A., David, C. H., Biancamaria, S., Wade, J., Tom, M., Prata de Moraes Frasson, R., & Blumstein, D. (2024). Peak flow event durations in the Mississippi River basin and implications for temporal sampling of rivers. Geophysical Research Letters, 51, e2024GL109220. <a href="https://doi.org/10.1029/2024GL109220" target="_blank" rel="noopener">https://doi.org/10.1029/2024GL109220</a>.</li> </ul> <p>When making use of any of the output files of this dataset, please cite both the aforementioned article and the dataset herein. </p> <p><strong>Main goals of the publication</strong></p> <p>The corresponding work aims to quantify peak flow event durations at an hourly time scale and their impact on high-frequency river sampling requirements using sampling ratios. The analysis is performed over the Mississippi basin using hourly USGS gages over 2010-2022.</p> <p>The findings derived from these output files have direct implications for future satellite missions concerned with capturing high-frequency dynamics in rivers, including flood events.</p>
Data supporting 3D Super-resolution Optical Fluctuation Imaging with Temporal Focusing with two-photon excitation
<p>Data to support the publication combining temporal focusing two photon excitation with super-resolution optical fluctuation imaging.</p> <div>This research was funded by National Centre of Science, grant number: 2022/47/B/ST7/03465. For the purpose of Open Access, the author has applied a</div> <div>CC-BY public copyright licence to any author Accepted Manuscript (AAM) version arising from this submission</div>
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.