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12,237 results for “2019”
Survey of pond habitats and aquatic diversity of Madison, Wisconsin from May to Aug 2019 and 2020 Data Set
1) Urbanization may lead to changes in local richness (alpha diversity) or in community composition (beta diversity), although the direction of change can be challenging to predict. For instance, introduced species may offset the loss of native specialist taxa, leading to no change in alpha diversity in urban areas, but decreased beta diversity (i.e., more homogenous community structure). Alternatively, because urban areas can have low connectivity and high environmental heterogeneity between sites, they may support distinct communities from one another over small geographic distances. 2) Wetlands and ponds provide critical ecosystem services and support diverse communities, making them important systems in which to understand consequences of urbanization. To determine how urban development shapes pond community structure, we surveyed 68 ponds around Madison, Wisconsin, USA, which were classified as urban, greenspace, or rural based on surrounding land use. We evaluated the influence of local abiotic factors, presence of nonnative fishes, and landscape characteristics on alpha diversity of aquatic plants, macroinvertebrates, and vertebrates. We also analyzed whether surrounding land cover was associated with changes in community composition and/or the presence of specific taxa. 3) We found a 23% decrease in mean richness (alpha diversity) from rural to urban pond sites, and a 15% decrease in richness from rural to urban greenspace pond sites. Among landscape factors, observed pond richness was negatively correlated with adjacent developed land and mowed lawns, as well as greater distances to other waterbodies. Among pond level factors, habitat complexity was associated with increased richness, while the presence of invasive fish was associated with decreased richness. 4) Beta diversity was relatively high for all ponds due to turnover in composition between sites. Urban ponds supported more introduced species, lacked a subset of native species found in rural ponds,
Cascade Project at North Temperate Lakes LTER: Weekly dissolved methane profiles (2018, 2019, and 2024)
Weekly profiles of dissolved methane concentrations, dissolved oxygen, temperature, and light were measured during three summers (2018, 2019, and 2024) in two north temperate lakes. One of the lakes was experimentally enriched with nitrogen and phosphorus during two summers (2019 and 2024), and darkened using a blue dye in one summer (2024). The other lake was an unmanipulated reference. Using this series of whole-lake enrichment and shading experiments across three years with varying ice phenology, we assessed how eutrophication and ice cover affect within-year methane storage.
Shrub influence on soil moisture, nutrients, temperature and species composition, 2019 - 2020.
Shrubification, the expansion and densification of shrubs, is occurring in arctic and alpine zones across the globe (Myers-Smith et al., 2011). This alteration is primarily driven by warming temperatures (Elmendorf et al., 2012b, 2012a), and can have major consequences for the existing vegetation (Anthelme et al., 2007; Pajunen et al., 2011; Venn et al., 2014) and for nutrient pools (Sturm et al., 2005; DeMarco et al., 2014) due to the abiotic and biotic effects of shrubs. Shrubs accumulate snow which insulates the ground during the winter and provides more moisture later in the season (Liston et al., 2002). During the summer, shrubs provide shade and wind protection. Additionally, shrubs can increase the soil nitrogen (N) pool through their high input of plant material into the soil (DeMarco et al., 2014). These small-scale climatic and soil alterations have important consequences for plant community dynamics in the arctic and alpine. References: Anthelme, F., Villaret, J.-C., and Brun, J.-J., 2007: Shrub encroachment in the Alps gives rise to the convergence of sub-alpine communities on a regional scale. Journal of Vegetation Science, 18(3):355–362. DeMarco, J., Mack, M. C., and Bret-Harte, M. S., 2014: Effects of arctic shrub expansion on biophysical vs . biogeochemical drivers of litter decomposition. Ecological Society of America, 95(7):1861–1875. Elmendorf, S. C., Henry, G. H. R., Hollister, R. D., Björk, R. G., Bjorkman, A. D., Callaghan, T. V., Collier, L. S., Cooper, E. J., Cornelissen, J. H. C., Day, T. A., Fosaa, A. M., Gould, W. A., Grétarsdóttir, J., Harte, J., Hermanutz, L., Hik, D. S., Hofgaard, A., Jarrad, F., Jónsdóttir, I. S., Keuper, F., Klanderud, K., Klein, J. A., Koh, S., Kudo, G., Lang, S. I., Loewen, V., May, J. L., Mercado, J., Michelsen, A., Molau, U., Myers-Smith, I. H., Oberbauer, S. F., Pieper, S., Post, E., Rixen, C., Robinson, C. H., Schmidt, N. M., Shaver, G. R., Stenström, A., Tolvanen, A., Totland, Ø., Troxler, T., Wahren, C. H
Pollinator visitation and floral resource production in Black Sand plots, 2019.
Anthropogenic climate change is altering interactions among numerous species, including plants and pollinators. Plant-pollinator interactions, crucial for the persistence of most plant and many insect species, are threatened by climate change-driven phenological shifts. Phenological mismatches between plants and their pollinators may affect pollination services, and simulations indicated that these mismatches may reduce floral resources available to up to 50 percent of insect pollinator species. Although alpine plants rely heavily on vegetative reproduction, seedling recruitment and seed dispersal are likely to be important drivers of alpine community structure. Similarly, advanced flowering may expose plants to increased risk of frost damage and shifted soil moisture regimes; phenologically advanced plants will experience these environmental factors differently, which may alter their floral resource production. Some species of alpine plants on the Niwot Ridge have displayed advanced phenology under treatments of advanced snowmelt (Forrester, unpublished data). However, little is understood about how these differences in distribution and phenology affect floral resources, pollinator community composition, and plant fecundity. Here we strive to examine how changes in the timing of flowering and number of flowers produced by plants, driven by experimental changes to climatic conditions at individual sites impact pollinator communities. In summer 2019, we found that plots with advanced phenology experienced peaks in pollinator visitation rates and pollinator diversity earlier than plots with unmanipulated snowmelt. We expect this to be because of the advanced floral phenology of certain key species in these plots. We did not find evidence that plants with advanced phenology produce fewer floral resources.
Shrub effects on seeding establishment, 2019 - 2020.
Plant communities in mountain ecosystems are particularly vulnerable due to amplified rates of warming (Pepin and Lundquist, 2008; Pepin et al., 2015) and declines in snowpack (Fyfe et al., 2017). As a result of these global change drivers, there is a widespread pattern of biotic shifts in the alpine (Bueno de Mesquita et al., 2018). However, it is unclear how important these biotic shifts will be in mediating the response of other species to global change. For example, the uphill movement and expansion of structure-forming species (e.g. trees, shrubs, cushion plants), can create small-scale variations in the conditions experienced by species (microclimates) through their effects on snow, wind, solar radiation, and soil properties (Holtmeier and Broll, 1992; Seastedt and Adams, 2001; Pistón et al., 2018; Chen et al., 2019). One such expanding structure-forming group of species are shrubs—the expansion and densification of shrubs is occurring in arctic and alpine zones across the globe (Myers-Smith et al., 2011; Formica et al., 2014; Elmendorf et al., 2012b, 2012a).Shrub microclimatic effects can alter fine-scale variability across the landscape, which may have important consequences for plant communities through increased heterogeneity and modified exposure. For instance, the microclimatic effects shrubs in the alpine may facilitate the establishment of subalpine species that need to migrate uphill to track their climatic niche (Akhalkatsi et al., 2006; Martínez, 2012; Cranston and Hermanutz, 2013). References Akhalkatsi, M., Abdaladze, O., Nakhutsrishvili, G., and Smith, W. K., 2006: Facilitation of seedling microsites by Rhododendron caucasicum extends the Betula litwinowii alpine treeline, Caucasus Mountains, Republic of Georgia. Arctic, Antarctic, and Alpine Research, 38(4):481–488. Bueno de Mesquita, C. P., Tillmann, L. S., Bernard, C. D., Rosemond, K. C., Molotch, N. P., and Suding, K. N., 2018: Topographic heterogeneity explains patterns of vegetation respons
Spatial distribution of snow depth for the Green Lakes Valley, 1997 - 2019
Climate warming represents an abiotic driver for change in alpine ecosystems, potentially altering the seasonal snowpack and thus water availability into the surrounding landscape. Future changes in snow accumulation and snowmelt distribution may have profound impacts on the flora and fauna of alpine ecosystems. In this regard, recent research has leveraged multi-year estimates of the spatial distribution of snow water equivalent (SWE) toward understanding alpine ecosystem function. The purpose of this project is to investigate the spatial variability of maximum snow depth at Niwot Ridge on an inter-annual basis.
Plant heights from permanent plots in a Spartina alterniflora-dominated marsh, Nelson Island, Parker River National Wildlife Refuge, Plum Island Ecosystems LTER, MA (2019-2025).
Plant heights are measured during the growing season in permanent plots at a Spartina alterniflora-dominated salt marsh on Nelson Island, Parker River National Wildlife Refuge, within the Plum Island Ecosystems (PIE) LTER site. Plant heights are converted to plant weight using an algorithm to generate a non-destructive estimate of aboveground plant biomass.
VCR Fowling Point Salt Marsh Flux Tower, 2019-2023
This dataset contains eddy covariance CO2 and H2O fluxes, sensible and latent heat fluxes, and meteorological measurements collected in an intertidal salt marsh. The flux tower is part of the Virginia Coastal Reserve Long Term Ecological Research (VCR LTER) site on the Eastern Shore of Virginia, USA. The marsh is on the Atlantic Ocean side of the Delmarva Peninsula and faces shallow coastal lagoons backed by barrier islands. No major rivers drain into the area. The tower is located 2 km from the shoreline and 85 m from a major creek edge. The marsh is dominated by Spartina alterniflora (also referred to as Sporobolus alterniflorus), with an average height of 0.6 m. The semidiurnal tidal cycle inundates the marsh platform twice daily, with 10% of time points having water above the mud platform (indicated by the MHHW dataset variable). Fluxes were measured at 10Hz and averaged over 30 minutes. The CO2 and energy fluxes were gap-filled using the REddyProc software package (doi: 10.5194/bg-15-5015-2018). Using the nighttime approach, the gross primary production and ecosystem respiration fluxes were partitioned from the net ecosystem exchange flux. The research was conducted at the Virginia Coastal Reserve Long Term Ecological Research (VCR LTER) site on the Eastern Shore of Virginia, USA. The flux tower site (37-deg 24'39.85"N, 75-deg 50'0.53"W) a lagoonal salt marsh which is located near the area of Fowling point. The site is located at about 2.2 kilometers away from the mainland and 10.7 kilometers away from Hog Island, the nearest barrier island. The flux tower is situated at about 80 meters away from the creek edge, on the lagoonal salt marsh.
Surface Elevation (SET) Data for Brownsville Forest at the Virginia Coastal Reserve, 2019-2025
This dataset contains data from Surface Elevation Tables (SETs) located in the Brownsville Forest at the Virginia Coastal Reserve. These research plots were set up in 2019 as a part of a larger forest disturbance project. Marker Horizons and Shallow SETs were installed in 2020 but have not yet been measured. Tyler Messerschmidt has made all measurements of these SETs
Sediment organic matter and infauna at eight sites on the Virginia Coast, 2016 and 2019
To determine how oysters impact the spatial distribution of infauna and sediment composition through ecosystem engineering, we sampled 8 intertidal mud- flats adjacent to oyster reefs in coastal Virginia, USA. This work describes how local site characteristics, including distance to oysters, elevation, and hydrodynamics, influence infaunal community structure and sediment composition.
Oyster recruitment to standardized ceramic tiles on the Virginia Coast in 2018, 2019, and 2021
This dataset contains measurements of Eastern oyster (Crassostrea virginica) recruitment to standardized ceramic tiles deployed across intertidal oyster reef sites in the Virginia Coast Reserve. Recruitment is defined as the number of macroscopic oyster recruits (less than or equal to 25 mm shell height) per square centimeter of tile surface, capturing settlement and early post-settlement survival. Data were collected in 2018, 2019, and 2021 across 9-16 reef sites per year, including both natural and restored reefs. The dataset supports research on spatial and environmental drivers of oyster recruitment and has been validated against natural reef substrate data for comparability.
Living Lab Station data Jun-Oct 2019, Sutherland Memorial Park, Guildford
<p>The data collected before and after hedge in Sutherland Memorial Park, Guildford using Living lab station during Jun-Oct 2019. </p>
Depth of the sea floor along the 5-minute resolution cruise track of the Antarctic Circumnavigation Expedition (ACE) derived from GEBCO 2019 bathymetry data.
<p><strong>Dataset abstract</strong></p> <p>Depth of the seabed along the five-minute averaged cruise track (Landwehr et al., 2020; DOI: 10.5281/zenodo.3752691) of the Antarctic Circumnavigation Expedition (ACE) was calculated from the the General Bathymetric Chart of the Oceans (GEBCO; GEBCO Compilation Group, 2019) 2019 gridded 30-arc second bathymetry data. The nearest gridded value from the bathymetry dataset was used to find the depth at the averaged position.</p> <p>Provided within this dataset is the average position of the vessel during a five-minute time period (where the time given is the middle time of the averaging interval).</p> <p><strong>Dataset contents</strong></p> <ul> <li>cruise_track_gebco2019_depth_5min.csv, data file, comma-separated values</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata, text</li> <li>ace_cruise_track_gebco2019_depth_5min_change_log.txt, metadata, text</li> </ul> <p><strong>Change log</strong></p> <p><strong>v1.1</strong> - Added additional data coverage and therefore track coverage from 2016-11-17 - 2016-11-22 inclusive. Updated README.txt with information about data coverage. Added change_log file.</p> <p><strong>v1.0</strong> - Initial release of depth along cruise track data set.</p> <p><strong>Dataset license</strong></p> <p>This GEBCO sea floor depth along the cruise track dataset from ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Italia - Popolazione residente al 1 Gennaio 2019
<p>Dati ufficiali ISTAT più recenti sulla popolazione residente nei Comuni italiani derivanti dalle indagini effettuate presso gli Uffici di Anagrafe aggiornati al 1 Gennaio 2019</p>
Datos Scielo USTA ( hasta 2019)
<p>Consolidado de los datos de las publicaciones de autores con filiación USTA (Colombia) en Scielo. Parte del servicio de vigilancia tecnológica del Observatorio de Cienciometría de la Universidad Santo Tomás.</p> <p>Ecuación de búsqueda</p> <p>(OO=universidad santo tomas AND CU=COLOMBIA) OR (OO=saint thomas aquinas univer sity AND CU=COLOMBIA) OR (OO=saint thomas university AND CU=COLOMBIA) OR (OO=santo tomás university AND CU=COLOMBIA) OR (OO=santo tomas university AND CU=COLOMBIA) OR (OO= Univ Santo Tomas AND CU=COLOMBIA) NOT ( (OO=Univ Santo Tomas AND CU=Philippines) OR (OO=Univ Santo Tomas Hosp AND CU=Philippines) OR (OO=Univ Santo Tomas AND CU=Chile))</p> <p>Refinado por: [excluyendo] AÑOS DE PUBLICACIÓN: ( 2020 )</p> <p>Período de tiempo: Todos los años. Índices: SCIELO.</p>
LMD Inconel 718 Thin walls 2019-12-05
<p>Description of dataset 10.5281/zenodo.3981107</p> <p>Deposition of Inconel 718 thin walls with process parameters:<br> - Nominal power = 200 (W)<br> - Nominal velocity = 300 (mm/min)<br> - Powder flux = 0.0825 (g/s)<br> - Nr. layers per wall: 1, 2, 4, 8, 16<br> - Nr. nozzles = 4<br> - Argon carrier flux = 4 (l/min)<br> - Argon shielding gas flux = 15 (l/min)<br> - Substrate temperature = Ambient<br> - Nr. repetitions: 2 (of whole experiment)</p> <p>The dataset is constituted by:<br> - Melt pool images, in file Experiment_2019_12_5__15_58_3.zip (subfolder Deposition_2019_12_05__16_00_17 for rep1, subfolder Deposition_2019_12_05__16_14_23 for rep2), acquired at 200fps with 850 nm narrow band filter, 1ms exposure time. 400x400 px size<br> - trackData_20191205_rep1.csv (or [...]_rep2.csv) containing:<br> - t: timestamp in ms. Synchronized imageData_20191008.csv<br> - Xpos: laser spot X position in workspace<br> - Ypos: laser spot Y position in workspace<br> - Zpos: laser spot Z position in workspace<br> - G1: binary signal indicating active deposition (G1=1) or not<br> - D: track width measured at [Xpos(t), Ypos(t), Zpos(t)]<br> - H: track heigth measured at [Xpos(t), Ypos(t), Zpos(t)]<br> - A: track section area measured at [Xpos(t), Ypos(t), Zpos(t)]<br> - sdres: roughness index of section profile (std. deviation w.r.t. smoothed profile)<br> - Vnom: laser spot translational speed in m/s (computed from Xpos, Ypos, Zpos and t data)<br> - Pnom: nominal power<br> - V: Vnom in mm/min<br> - imageData_20191205_rep1.csv (or [...]_rep2.csv) containing:<br> - t: timestamp in ms. Synchronized with trackData_20191205_rep1.csv (some frames may have been lost)<br> - I_mean: mean image intensity (only on red channel)<br> - I_mean_crop: mean image intensity computed on central cropped image area (180x180 pixels)<br> - M_I_mean: I_mean after application of 8-sample moving average<br> - M_I_mean_crop: I_mean_crop after application of 8-sample moving average<br> - fileName: associated image file name<br> - beamON: laserON signal obtained from thresholding on images (background noise = off, minimal intensity level = on)</p>
A long term hourly eddy covariance dataset of consistently processed CO2 and H2O Fluxes from the Tibetan Alpine Steppe at Nam Co (2005 - 2019)
<p>The data set contains nearly 15 years of eddy covariance data from an alpine steppe ecosystem on the central Tibetan Plateau. The data was processed following standardized quality control methods to allow for comparability between the different years of our record and with other data sets. To ensure meaningful estimates of ecosystem atmosphere exchange, careful application of the following correction procedures and analyses was necessary: (1) Due to the remote location, continuous maintenance of the eddy covariance (EC) system was not always possible, so that cleaning and calibration of the sensors was performed irregularly. Furthermore, the high proportion of bare soil and high wind speeds led to accumulation of dirt in the measurement path of the infrared gas analyzer (IRGA). The installation of the sensor in such a challenging environment resulted in a considerable drift in CO2 and H2O gas density measurements. If not accounted for, this concentration bias may distort the estimation of the carbon uptake. We applied a modified drift correction procedure following Fratini et al. (2014) which, instead of a linear interpolation between calibration dates, uses the CO2 concentration measurements from the Mt. Waliguan atmospheric observatory as reference time series. (2) We applied rigorous quality filtering of the calculated fluxes to retain only fluxes which represent actual physical processes. (3) During the long measurement period, there were several buildings constructed in the near vicinity of the EC system. We investigated the influence of these obstacles on the turbulent flow regime to identify fluxes with uncertain land cover contribution and exclude them from subsequent computations. (4) We calculated the de-facto standard correction for instrument surface heating during cold conditions (hereafter called sensor self heating correction) following Burba et al. (2008) and a revision of the original method following Frank and Massman (2020). (5) Subsequently, we applied the traditional and widely used gap filling procedure following Reichstein et al. (2005) to provide a more complete overview of the annual net ecosystem CO2 exchange. (6) We estimated the flux uncertainty by calculating the random flux error (RE) following Finkelstein and Sims (2001) and by using the standard deviation of the fluxes used for gap filling (NEE_fsd) as a measure for spatial and temporal variation.</p> <p>References:</p> <ol> <li>Burba, G. G., McDermitt, D. K., Grelle, A., Anderson, D., and XU, L. (2008). Addressing the influence of instrument surface heat exchange on the measurements of CO2 flux from open-path gas analyzers, Global Change Biology, 14, 1854-1876, <a href="https://doi.org/10.1111/j.1365-2486.2008.01606.x">https://doi.org/10.1111/j.1365-2486.2008.01606.x</a>.</li> <li>Finkelstein, P. L. and Sims, P. F. (2001). Sampling error in eddy correlation flux measurements, J. Geophys. Res. Atmos., 106, 3503–3509, doi:10.1029/2000JD900731.</li> <li>Frank, J. M. and Massman, W. J.: A new perspective on the open-path infrared gas analyzer self-heating correction, Agricultural and Forest Meteorology, 290, 107986, doi:10.1016/j.agrformet.2020.107986, 2020.</li> <li>Fratini, G., McDermitt, D. K., and Papale, D. (2004). Eddy-covariance flux errors due to biases in gas concentration measurements: origins, quantification and correction, Biogeosciences, 11, 1037-1051, <a href="https://doi.org/10.5194/bg-11-1037-2014">https://doi.org/10.5194/bg-11-1037-2014</a>.</li> <li>Reichstein, M., Falge, E., Baldocchi, D., Papale, D., Aubinet, M., Berbigier, P., Bernhofer, C., Buchmann, N., Gilmanov, T., Granier, A., Grunwald, T., Havrankova, K., Ilvesniemi, H., Janous, D., Knohl, A., Laurila, T., Lohila, A., Loustau, D., Matteucci, G., Meyers, T., Miglietta, F., Ourcival, J.-m., Pumpanen, J., Rambal, S., Rotenberg, E., Sanz, M., Tenhunen, J., Seufert, G., Vaccari, F., Vesala, T., Yakir, D., and valentini, R. (20050. On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm, Global Change Biology, 11, 1424-1439, <a href="https://doi.org/10.1111/j.1365-2486.2005.001002.x">https://doi.org/10.1111/j.1365-2486.2005.001002.x</a>.</li> </ol>
Vielle_2019_EEPS_DATASET
<p>This dataset contains the underlying data for the following publication: <strong>Vielle, M. (2020). Navigating various flexibility mechanisms under European burden-sharing. <em>Environmental Economics and Policy Studies, 22</em>(2), 267-313., 10.1007/s10018-019-00257-3. </strong>Full details of methods used to create the dataset and provided within this publication.</p>
MeteoSerbia1km: the first daily gridded meteorological dataset at a 1-km spatial resolution across Serbia for the 2000–2019 period
<p>MeteoSerbia1km is the first daily gridded meteorological dataset at a 1-km spatial resolution across Serbia for the 2000–2019 period. The dataset consists of five daily variables: maximum, minimum and mean temperature, mean sea level pressure, and total precipitation. Besides daily summaries, it contains monthly and annual summaries, daily, monthly, and annual long term means (LTM). Daily gridded data were interpolated using the Random Forest Spatial Interpolation methodology based on Random Forest and using nearest observations and distances to them as spatial covariates, together with environmental covariates.</p> <p>Complete script in R and datasets used for modelling, tuning, validation, and prediction of daily meteorological variables are available <a href="https://github.com/AleksandarSekulic/MeteoSerbia1km">here</a>.</p> <p>If you discover a bug, artifact or inconsistency in the MeteoSerbia1km maps, or if you have a question please use <a href="https://github.com/AleksandarSekulic/MeteoSerbia1km/issues">this channel</a>.</p> <p>File naming convention of .zip files and containing MeteoSerbia1km files:</p> <ul> <li>Daily summaries per year: day_<em>yyyy</em>_<em>proj</em>.zip <ul> <li><em>var</em>_day_<em>yyyymmdd</em>_<em>proj</em>.tif</li> </ul> </li> <li>Monthly summaries: mon_<em>proj</em>.zip <ul> <li><em>var</em>_mon_<em>yyyymm</em>_<em>proj</em>.tif</li> </ul> </li> <li>Annual summaries: ann_<em>proj</em>.zip <ul> <li><em>var</em>_ann_<em>yyyy</em>_<em>proj</em>.tif</li> </ul> </li> <li>Daily, monthly and annual LTM: ltm_<em>proj</em>.zip <ul> <li>daily LTM: <em>var</em>_ltm_day_mmdd_<em>proj</em>.tif</li> <li>monthly LTM: <em>var</em>_ltm_mon_mm_<em>proj</em>.tif</li> <li>annual LTM: <em>var</em>_ltm_ann_<em>proj</em>.tif</li> </ul> </li> </ul> <p>where:</p> <ul> <li><em>var</em> is a daily meteorological variable name - tmax, tmin, tmean, slp, or prcp</li> <li><em>proj</em> is a dataset projection - wgs84 or utm34</li> </ul> <p>Units of the dataset values are</p> <ul> <li>temperature (Tmean, Tmax, and Tmin) - tenths of a degree in the Celsius scale (℃)</li> <li>SLP - tenths of a mbar</li> <li>PRCP - tenths of a mm</li> </ul> <p>All dataset values are stored as integers (INT32 data type) in order to reduce the size of the GeoTIFF files, i.e., temperature values should be divided by 10 to obtain degrees Celsius, and the same for SLP and PRCP to obtain millibars and millimeters.<br> </p>
Infrasound array data recorded in July-August, 2019, at Mt. Etna (Italy) during the VOSSIA field experiment
<p>We present infrasound data recorded by two infrasound arrays installed at Mt. Etna (Italy) within the framework of the VOSSIA (Volcanic emissions analysis through Seismic and Infrasound Advanced monitoring) project. VOSSIA was supported by the Trans-National Access component of the EUROVOLC project (European Network of Observatories and Research Infrastructures for Volcanology, EU Horizon 2020 Research Infrastructure Project grant No 731070).</p> <p>This data repository includes continuous raw waveforms recorded by two 6-element, small-aperture, infrasound arrays during July-August, 2019. The arrays, ENEA and ENCR, were installed on Mt. Etna in proximity of the summit Nord East and South East craters, respectively. ENEA was equipped with Chaparral M60 sensors (<a href="http://chaparralphysics.com/specs/specs_model60UHP.pdf">http://chaparralphysics.com/specs/specs_model60UHP.pdf</a>), while IST2018 microphones (<a href="https://doi.org/10.1016/j.jvolgeores.2019.106668">https://doi.org/10.1016/j.jvolgeores.2019.106668</a>) were installed at ENCR. Data at both arrays were recorded with a sampling frequency of 100 Hz and 24-bit resolution using DiGOS Datacube<sup>3</sup> digitizers (<a href="https://digos.eu/seismology-and-cubes/">https://digos.eu/seismology-and-cubes</a>).</p> <p>Waveform data are provided as day-long files in MSEED format (<a href="https://ds.iris.edu/ds/nodes/dmc/data/formats/">https://ds.iris.edu/ds/nodes/dmc/data/formats/</a>).</p> <p>We also provide metadata including:</p> <p>1) Station coordinates (.csv file station_coords.csv);</p> <p>2) Instrument_response.rar: Instrument response information in different formats (individual RESP files, SEED DATALESS, xlm DATALESS)</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.