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7,669 results for “2012”
Fish Counts and Lengths in South Bay and Hog Island Bay, Virginia 2012-2018
To study how seagrass restoration affects coastal fish communities over time, we sampled fishes at each site once or twice per year with beach seines (7.6 m wide × 1.8 m tall; 1.5 m deep pocket with 6.4 mm mesh) hauled along 25-m transects in the summer (May or June) and autumn (September or October) from 2012 through 2018. Researchers ceased sampling at the 4 initially unvegetated sites in South Bay after 2015, when these sites were colonized by seagrass, although seining occurred once more at these sites during the autumn of 2017. During each sampling event, we counted, measured (total length), and identified fish to the lowest possible taxon in the field prior to release. All seine hauls occurred during the day and within 3 hours of low tide for logistical reasons (n = 204). Due to methodological changes, after 2018 surveys are recorded in a different dataset VCR22364 "Abundance and Size of Seagrass-Associated Fishes in the Virginia Coastal Lagoons, 2019-xxxx" https://doi.org/10.6073/pasta/400c84b859e81e9a1e5212bccb37b759.
Atmospheric pressure on Hog Island, Phillips Creek Marsh and Oyster, VA 2012-2025
Barometric pressure is measured hourly at meteorological stations on the Atlantic Coast of the Delmarva Peninsula. Data is available as a long table that gives the average, minimum and maximum air pressure for each station at each time, or as a table that gives the average values for each of the stations, along with the median value across stations on each line.
North Sea Wave Database (NSWD) 2012-2017
<p>North Sea Wave Database (NSWD)<br> The dataset contains each year of spectral metocean condition for Significant Wave Height (HSIGN) and wave energy period (TMM10), in meters and seconds.<br> Each variable has a year timestap which the data corresponds too i.e 1980. The latitudes and longitudes of the dataset have resolution of 0.025 degrees at each direction. Latitude starting coordinate is 50 degrees and Longitude 0.</p> <p>For more information on the process that developed the dataset, the methodogies followed, calibration, valdiation and sensitivity analysis, <br> see: </p> <p>Lavidas, G., & Polinder, H. (2019). North Sea Wave Database (NSWD) and the Need for Reliable Resource Data: A 38 Year Database for Metocean and Wave Energy Assessments. Atmosphere, 10(9), <a href="https://doi.org/10.3390/atmos10090551">https://doi.org/10.3390/atmos10090551 </a></p> <p>Lavidas, G., & Polinder, H. (2019). Wind effects in the parametrisation of physical characteristics for a nearshore wave model. Proceedings of the 13th European Wave and Tidal Energy Conference 1-6 September 2019, Naples, Italy.</p> <p>The dataset was produced by Dr George Lavidas during the WAVe Resource for Electrical Production (WAVREP, which received funding from the European Union's Horizon 2020 research & innovation programme under the Marie Sklodowska-Curie grant agreement No 787344.</p> <p>The dataset is accompanied by two publication that (i) present the calibration-validation and production (ii) analysis of the dataset.</p> <p>The official CORDIS website is https://cordis.europa.eu/project/id/787344<br> A list of outcomes for the NSWD and the WAVREP project is found at the researcher's page:<br> <a href="https://www.researchgate.net/project/WAVe-Resource-for-Electrical-Production-WAVREP ">https://www.researchgate.net/project/WAVe-Resource-for-Electrical-Production-WAVREP </a></p> <p>It can also be found at the official CORDIS website<br> <a href="https://cordis.europa.eu/project/id/787344">https://cordis.europa.eu/project/id/787344</a></p> <p>Sharing and Access information<br> Creative Commons Attribution (CC BY-SA). <br> The Creative Commons Attribution license allows others remix, tweak, and build upon your work, as long as they credit you and license their new creations under the identical terms.</p>
Values of the reference prior for the Poi(s+b) model from JINST 7 (2012) P01012
<p>Plain text table with the values of the reference prior π(s) for the Poi(s+b) model used in the statistical inference about counting experiments, as explained in JINST 7 (2012) P01012, doi:10.1088/1748-0221/7/01/P01012, http://arxiv.org/abs/1108.4270. The values are useful to find approximate expressions which are quicker to compute than the original prior, as explained in http://arxiv.org/abs/1407.5893 (where this dataset is referred to).</p> <p>Each line is a sequence of spaces-separated values, and the file can be considered a table. The first line starts with two strings "shape" and "rate" which represent the titles of the corresponding columns in the data table. They refer to the shape and rate parameters defining the background prior. Next, N signal values starting from s=0 to s=70 are reported. They are the values at which π(s) is computed for any subsequent line.</p> <p>Starting from the second line, the format is always the same. The first two values are the shape and rate parameters defining the background prior used to compute π(s) in this line. Next, the N values π(s=0), ..., π(s=70) are reported. As π(0) = 1, the third column is constant (it might be useful to debug the data reading).</p> <p>As explained in http://arxiv.org/abs/1108.4270, simple functional forms may be used to fit the N points (s, π(s)). As the shape and rate parameters from the user's application may be different from those reported in this table, the following procedure shall give a very good approximation to π(s). In the (log(shape), log(rate)) parameters space, locate the neighboring points to the user's background parameter values (in log-log scale). Then interpolate each of the π(s) values to obtain a set of N values (a linear interpolation in log-log scale shall be sufficient). Finally, fit these interpolated values to find the reference prior for the user's application.</p>
2012_2024_VIIRS_Fourier Processed_1k_ER
<h4>Overview:</h4> <p>This is a set of images produced by Temporal Fourier Analysis (TFA) of VIIRS data <strong>(New version updated until 2024)</strong>:</p> <p>NDVI: Normalised Difference Vegetation Index</p> <p>EVI: Enhanced Vegetation Index</p> <p>MIR: Middle Infra-Red</p> <p>DLST: Day-time Land Surface Temperature</p> <p>NLST: Night-time Land Surface Temperature</p> <p>The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for the European and North African extent.<br>This series of VIIRS data, processed according to Scharlemann et al (2008), has been updated to include imagery from 2012 to 2024. </p> <h4>Process:</h4> <p>Image values were extracted from VIRRS imagery from 2012 to 2024. The day and night land temperature came from the 8-day VNP21A2 data, whilst the vegetation indices and Middle Infra Red values were extracted from the VNP13A2 16-day datasets. Each parameter extract dataset was then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other outputs recorded the time series's mean, minimum, and maximum and the error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (<a href="https://doi.org/10.1371/journal.pone.0001408">https://doi.org/10.1371/journal.pone.0001408</a>)<br>Sea pixels were masked with a VIIRS land/sea layer, and the images were projected from sinusoidal to geographic. The E4warning study region was a subset of global images. Idrisi rasters were converted to GeoTIFF format in order to give data users more flexibility.</p> <p>This new VIIRS Dataset is used as an update and continuation of our MODIS TFA product and can be utilised similarly. </p> <p> </p> <p>Projection + EPSG code:<br>Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p>Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716<br><br><br><strong>File names:</strong></p> <p><br>The er at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the E4warning study area and is in geographic projection. 24 refers to the year timeline of 2012-2024.<br><br>The next two characters identify the channel:<br>03 - middle infra-red<br>07 - daytime land surface temperature<br>08 - nighttime land surface temperature<br>14 - NDVI: Normalised Difference Vegetation Index<br>15 - EVI: Enhanced Vegetation Index<br><br>The last two characters of each file name denote the output from the Fourier processing:<br>a0 - mean<br>mn - minimum<br>mx - maximum<br>a1 - amplitude of annual cycle<br>a2 - amplitude of bi-annual cycle<br>a3 - amplitude of tri-annual cycle<br>p1 - phase of annual cycle<br>p2 - phase of bi-annual cycle<br>p3 - phase of tri-annual cycle<br>d1 - variance in annual cycle<br>d2 - variance in bi-annual cycle<br>d3 - variance in tri-annual cycle<br>da - combined variance in annual, bi-annual, and tri-annual cycles<br>vr - variance in raw data<br><br>Parameter Fourier Variable Image values are<br>MIR (03) A0, A1, A2, A3, Min, Max, Vr Reflectance values * 10000<br>LST (07 day,08 night) A0, A1, A2, A3, Min, Max, Vr (Degrees Centigrade+273)*50<br>NDVI (14) and EVI (15) A0, A1, A2, A3, Index Value * 1000<br>NDVI (14) and EVI (15) A0, Min, Max, Index Value * 1000 + 10000<br>NDVI (14) and EVI (15) VR Value * 10000<br>ALL D1,D2,D3,Da Percentages<br>ALL E1,E2,E3 Percentages<br>ALL P1,P2.P3 Months*100. (Jan=100)</p> <h4>Global Files can be accessed <a title="VIIRS_TFA_1224" href="https://drive.google.com/drive/folders/117aWsu-Dy83Q4yRBCxWcTqnug-pRy8za?usp=sharing" target="_blank" rel="noopener">here</a>.</h4>
2012_2014_VIIRS_Fourier Processed_1k_ER
<h4>Overview:</h4> <p>This is a set of images produced by Temporal Fourier Analysis (TFA) of VIIRS data:</p> <p>NDVI: Normalised Difference Vegetation Index</p> <p>EVI: Enhanced Vegetation Index</p> <p>MIR: Middle Infra-Red</p> <p>DLST: Day-time Land Surface Temperature</p> <p>NLST: Night-time Land Surface Temperature</p> <p>The imagery summarises some key environmental indicators, incorporating seasonal dynamics, for The European and North African extent.<br>This series of VIIRS data processed according to Scharlemann et al (2008), has been updated to include imagery from 2012 to 2014, and was produced to compare with next three years' time series,</p> <h4>Process:</h4> <p>Image values were extracted from VIRRS imagery from 2012 to 2014. The day and night land temperature came from the 8 day VNP21A2 data whilst the vegetation indices and Middle Infra Red values were extracted from the VNP13A2 16-day datasets. Each parameter extract dataset was then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other output recorded the time series's mean, minimum, and maximum, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (<a href="https://doi.org/10.1371/journal.pone.0001408">https://doi.org/10.1371/journal.pone.0001408</a>)<br>Sea pixels were masked with a VIIRS land/sea layer and the images were projected from sinusoidal to geographic. The E4warning study region was a subset of global images. Idrisi rasters were converted to GeoTIFF format in order to give data users more flexibility.</p> <p>This new VIIRS Dataset is used as an update and continuation of our MODIS TFA product and can be utilised in the same way. </p> <p> </p> <p>Projection + EPSG code:<br>Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p><br><br><br><strong>File names:</strong></p> <p><br>The er at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the E4warning study area and is in geographic projection. 14 refers to the year timeline of 2012-2014.<br><br>The next two characters identify the channel:<br>03 - middle infra-red<br>07 - daytime land surface temperature<br>08 - nighttime land surface temperature<br>14 - NDVI: Normalised Difference Vegetation Index<br>15 - EVI: Enhanced Vegetation Index<br><br>The last two characters of each file name denote the output from Fourier processing:<br>a0 - mean<br>mn - minimum<br>mx - maximum<br>a1 - amplitude of annual cycle<br>a2 - amplitude of bi-annual cycle<br>a3 - amplitude of tri-annual cycle<br>p1 - phase of annual cycle<br>p2 - phase of bi-annual cycle<br>p3 - phase of tri-annual cycle<br>d1 - variance in annual cycle<br>d2 - variance in bi-annual cycle<br>d3 - variance in tri-annual cycle<br>da - combined variance in annual, bi-annual, and tri-annual cycles<br>vr - variance in raw data<br><br>Parameter Fourier Variable Image values are<br>MIR (03) A0, A1, A2, A3, Min, Max, Vr Reflectance values * 10000<br>LST (07 day,08 night) A0, A1, A2, A3, Min, Max, Vr (Degrees Centigrade+273)*50<br>NDVI (14) and EVI (15) A0, A1, A2, A3, Index Value * 1000<br>NDVI (14) and EVI (15) A0, Min, Max, Index Value * 1000 + 10000<br>NDVI (14) and EVI (15) VR Value * 10000<br>ALL D1,D2,D3,Da Percentages<br>ALL E1,E2,E3 Percentages<br>ALL P1,P2.P3 Months*100. (Jan=100)</p> <h4> </h4>
Raman spectra dataset of hydrous glasses of Le Losq et al., 2012, Am. Min 97:779-790
<p>This dataset contains Raman spectra of hydrous glasses used in the publication of Le Losq et al. (2012) to implement a chemical-independent method to quantify water content of glasses with Raman spectroscopy.</p> <p>Spectra are unprocessed. They were acquired with a T64000 Jobin-Yvon triple grating Raman spectrometer equipped with a confocal system, a 1024 CCD detector cooled by liquid nitrogen and an Olympus microscope. The optimal spatial resolution allowed by the confocal system is 1–2 μm<sup>2</sup> with a 100× Olympus objective. The spectral resolution of the spectrometer is 0.7 cm<sup>–1</sup>. A Coherent laser 70-C5 Ar+, having a wavelength of 514.532 nm, is used for the excitation line.</p> <p>The file dataliste.csv contains a list of the spectra together with the sample name and water contents in wt%. See Tables 1 and 2 in Le Losq et al. (2012) for corresponding sample chemical composition and errors on water concentrations, as well as supplementary information for a table containing the regions of interest for background fitting.</p> <p>Reference</p> <p>Le Losq, C., Neuville, D.R., Moretti, R., Roux, J., 2012. Determination of water content in silicate glasses using Raman spectrometry: Implications for the study of explosive volcanism. American Mineralogist 97, 779–790. <a href="https://doi.org/10.2138/am.2012.3831">https://doi.org/10.2138/am.2012.3831</a></p>
MIRCA-BC-USMX: Irrigated and planted fractions over the continental United States and Mexico for years 1992, 2002, and 2012
<p>The MIRCA-BC-USMX project contains a spatially explicit mean annual cycle of monthly planted and irrigated fractions at 0.0625 degree (6 km) spatial resolution over the continental United States and Mexico for years 1992, 2002, and 2012.</p> <p>These fractions were generated by (1) reconciling the MIRCA2000 Global Monthly Irrigated and Rainfed Crop Areas dataset (Portmann et al., 2010) with the cropland and pasture classes of year 2001 of the harmonized NLCD_INEGI land cover dataset (Bohn and Vivoni, 2019b); (2) bias-correcting the irrigated and planted fractions to match state-by-state total irrigated and planted areas from government records in the United States (USDA, 2016) and Mexico (SADER, 2014; SAGARPA, 2016).</p> <p>These fractions have been added to land surface parameter files for the Variable Infiltration Capacity (VIC) model (Liang et al., 1994) version 5.1 (Hamman et al., 2018), extended to include the irrigation module of Haddeland et al. (2006), available on <a href="https://github.com/tbohn/VIC/tree/feature/irrig.imperv.deep_esoil">GitHub</a>. The parameter files were taken from the MOD-LSP project, available on <a href="https://zenodo.org/record/2612560">Zenodo</a> (Bohn and Vivoni, 2019a). The VIC 5 image driver requires a "domain" file to accompany the parameter file. This domain file is also necessary for disaggregating the daily gridded meteorological forcings to hourly for input to VIC via the disaggregating tool <a href="https://github.com/UW-Hydro/MetSim">MetSim</a> (Bennett et al., 2018). We have provided a domain file compatible with the meteorological forcings of Livneh et al (2015) and the MIRCA-BC-USMX parameters, on <a href="https://zenodo.org/record/2564019">Zenodo</a> (Bohn et al., 2019a,b).</p> <p>Contents:</p> <ul> <li>Input Files <ul> <li>county_codes.csv - table mapping the numerical codes for counties with the county names used by the US Census Bureau and USDA. This was created by parsing this information from US Census tables from years 1990, 2000, and 2010 and USDA tables from years 1992, 2002, and 2012 and manually reconciling discrepancies across years. Thus the names may not match county names in the original files exactly from year to year, but rather represent my own naming convention. However these discrepancies were rare.</li> <li>mun_us.0.01_deg.asc.tgz and mun_mx.0.0.01_deg.asc.tgz - gzipped tar archives containing mun_us.0.01_deg.asc and mun_mx.0.01_deg.asc, which are ascii-format ESRI grid files created by rasterizing publicly available shapefiles of US and Mexican counties/municipios. These have 0.01 degree (1 km) spatial resolution and pixels have numerical values equal to the codes in county_codes.csv.</li> </ul> </li> <li>Output Files <ul> <li>fplant_firr_bc.$LCYEAR.nc, where $LCYEAR is one of ("s1992","2001", or "2011") - NetCDF-format files at 0.0625 degree (6 km) resolution containing 12 monthly maps each of bias-corrected "fplant" (planted area fraction) and "firr" (irrigated area fraction) for a specific historical year.The value of $LCYEAR indicates the snapshot of the NLCD_INEGI harmonized land cover classification with which fplant and firr were reconciled (so that these area fractions would not exceed the total agricultural/pastoral area given by NLCD_INEGI). Values of fplant and firr were bias corrected so that state-wide total areas matched government records from USDA (USDA, 2014) and SAGARPA (SADER, 2014; SAGARPA, 2016). For $LCYEAR = ("s1992", "2001", "2011"), the agricultural census year used in the bias correction was (1992, 2002, 2012).</li> <li>fplant_firr_bc.2011.mun_mx.nc - same as fplant_firr_bc.2011.nc, but bias-corrected at the municipio level in Mexico. County-level bias correction was not possible in the US due to lack of sufficient resolution USDA records. Similarly, municipio-level records were not available in Mexico prior to year 2003.</li> <li>params.USMX.NLCD_INEGI.$LCYEAR.$YEAR1_$YEAR2.with_irrig.nc - VIC 5 image driver-compliant input parameter files into which fplant and firr of the given $LCYEAR have been inserted. $YEAR1 and $YEAR2 indicate the first and last years of MODIS data used to estimate the annual cycle of monthly LAI, fcanopy, and albedo (independent of the values of fplant and firr).</li> <li>params.USMX.NLCD_INEGI.2011.$YEAR1_$YEAR2.with_irrig.mun_mx.nc - same as params.USMX.NLCD_INEGI.2011.$YEAR1_$YEAR2.with_irrig.nc but with fplant and firr bias-corrected at the municipio level in Mexico.</li> </ul> </li> </ul> <p>These parameters were created with scripts archived on <a href="https://github.com/tbohn/MIRCA-BC-USMX/releases/tag/v1.1">GitHub</a> (Bohn, 2019).</p>
Health of the Nation. National NCD risk factor survey, Barbados (2012-13)
<p><strong>Executive Summary</strong></p> <div> <div>The Caribbean is experiencing increasing levels of illness and death from non-communicable disease (NCD) causes. Regional leaders pledged to combat this epidemic through increased surveillance and intervention, implementing healthcare policies and programmes across our countries. In Barbados, one of the Ministry of Health (MoH)’s initiatives has been the Health of the Nation (HotN) Survey, to provide information on the prevalence and social determinants of risk factors for lifestyle-related NCD. This will allow identification of potential targets for future interventions to improve prevention and control of these diseases in the Barbadian population. In this comprehensive, cross-sectional survey, data were collected for 1234 participants aged at least 25 years (response rate: 55%) on demographics, behavioural risk factors, medical history, place of treatment and costs incurred, blood pressure and anthropometry, and biochemical measures. The survey sample under-represented young adults (particularly men) and over-represented the elderly (particularly women), so a weighting scheme was utilised to balance the sample distribution for age and sex with that of the Barbados 2010 Census. Prevalence of each risk factor was estimated overall, for each sex separately, and stratified by three broad age-groups.</div> <br> <div>Main findings show that Barbadian adults are at high risk from NCDs due to high prevalence of biological and behavioural risk factors. Most alarming is that two in every three adults in our population (and three-quarters of women) are overweight and/or obese. In addition, more than one in three adults in Barbados (more than one in two of those aged at least 45 years) are hypertensive, and one in five have diabetes (almost one in two of those aged 65 years or older). At least one in three of those with known hypertension or diabetes who were receiving treatment had sub-optimal control. </div> <br> <div>Daily tobacco use was reported by one in 10 men, vs one in 50 women. Harmful alcohol use followed a similar pattern, i.e. was mainly reported by young men, with excessive weekly alcohol consumption over the past 30 days reported by roughly the same proportions of men and women reporting daily tobacco use. One in three men aged 25–44 years reported binge drinking in the past 30 days. Core survey results show that Barbadian residents have low fruit and vegetable consumption, while half of the sample reported low levels of physical activity. About one in four adults had healthcare insurance (one in three of those who were employed). More in-depth information on diet, physical activity and cost/insurance will be provided from the relevant survey sub-studies at a later date. Urgent action is required to address the low levels of healthy behavioural risk and high levels of biological risk present in the Barbadian adult population. Community and civil society involvement could help support healthier behaviours. A multi-sectoral approach is required to combat NCD risk on all levels, with creation of national guidelines to supplement an appropriate regulatory framework within an enabling environment.</div> </div>
CMS DoubleMuParked dataset from 2012 in simple little endian binary format
<p>The Muon.bin file contains in a binary data little-endian format the dataset from Ref [1]. This dataset contains about 60 millon data events from the CMS detector taken in 2012 during Run B and C.</p> <p>The file format is described in the companion file Muon.txt.</p> <p>[1] Wunsch, Stefan; (2019). DoubleMuParked dataset from 2012 in NanoAOD format reduced on muons. CERN Open Data Portal. DOI:<a href="http://doi.org/10.7483/OPENDATA.CMS.LVG5.QT81">10.7483/OPENDATA.CMS.LVG5.QT81</a></p>
Moored current and temperature measurements in the Southern Adriatic Sea at mooring site BB and FF, March 2012-June 2020
<p>This data set includes n.4 files (NetCDF format) containing observational data and related metadata from two mooring sites, sites BB and FF, located in the Southern Adriatic Sea from the period from March 2012 to June 2020. The stand-alone moorings are equipped with an ADCP-RDI system which measures currents along the last 100 meters of the water column and a CTD probe located approximatively 10 m above the bottom. Moorings were configured and maintained for continuous long-term monitoring following the approach of the CIESM Hydrochanges Program (www.ciesm.org/marine/programs/hydrochanges.html). The moorings are currently operational as from 2021 they have joined the southern Adriatic submarine observatory of EMSO-ERIC European Consortium. The data are described in data paper Paladini et al., (In prep): Deep water hydrodynamic observations of two moorings sites on the continental slope of the Southern Adriatic Sea (Mediterranean Sea). </p>
Time series of high-frequency sensors measuring water temperature and dissolved oxygen at discrete depths in Falling Creek Reservoir, Virginia, USA in 2012-2018
We measured water temperature and dissolved oxygen at multiple depths in Falling Creek Reservoir (Vinton, Virginia, USA) with high-frequency (10 to 15-minute) sensors for different durations during 2012 to 2018. Falling Creek Reservoir is owned and managed by the Western Virginia Water Authority as a primary drinking water source for Roanoke, Virginia. All measurements were collected at discrete depths at the deepest site of the reservoir adjacent to the dam. The sensors consisted of: 1) InsiteIG dissolved oxygen and water temperature sensors (Model 20 dissolved oxygen sensor) at both 1 m (November 2015 - December 2018) and 8 m (September 2012 - December 2018) and 2) HOBO (HOBO Pendant Temperature/Light 64K Data Logger) water temperature loggers deployed at 1, 2, 3, 4, 5, 6, 7, 8, and 9.3 m depths (September 2015 - January 2018).
A scrubbed subset of near-surface, soil, and air temperature data acquired across multiple locations on the San Joaquin Experimental Range, California, 2012-2017
These temperature records were collected as part of a larger study relating microclimates to tree seedling survival in southern California mountains. These temperature records are for studies at the San Joaquin Experimental Range (Lat 37.083, Long -119.716, elevation 210-520 m, www.fs.fed.us/psw/ef/san_joaquin/). Temperature sensors were located at 23 sites across the landscape. Sites were selected to sample topographic variation in surface and air temperatures within a narrow range of elevations on northeast to southwest-facing slopes, ridges, and valleys. To characterize surface temperature variation within a site, 21 sensors were arranged in an identical pattern around and in six, 5x5 m experimental gardens. An additional 18 sensors were placed along three transects over the landscape running E-W. They were placed strategically to sample topographic inflection points (hill tops and valley bottoms) as well as north and south facing slopes. Temperatures were recorded on a 10 or 20-minute interval, depending on the sensor, using HOBO (Onset, www.onsetcomp.com) devices.
New Hampshire Soil Sensor Network: Snow depth (2012-2022)
The goal of the New Hampshire Soil Sensor Network is to examine spatial and temporal changes in soil properties and processes as the climate changes. Data collected can also calibrate and validate models that examine how ecosystems may respond to changing climate and land use. To determine how soil processes are affected by climate change and land management, this soil sensor network measures snow depth, air temperature, soil temperature, soil volumetric water content, and soil electrical conductivity, as well as soil CO2 fluxes. This data package includes data from snow depth sensors. Data were collected at the following sites: BRT = Bartlett Experimental Forest, Bartlett, NH; BDF = Burley-Demmerit Farm, Lee, NH; DCF = Dowst Cate Forest, Deerfield, NH; HUB = Hubbard Brook Experimental Forest, Woodstock, NH; SBM = Saddleback Mountain, Deerfield, NH; THF = Thompson Farm, Durham, NH; and Trout Pond Brook, Strafford, NH.
American Residential Macrosystems - Leaf functional traits and raw data in five major metropolitan areas, 2012-2013
"We used leaf functional traits in residential yards and nearby natural areas to assess biotic ecological homogenization in five cities across the U.S. that span major ecological biomes and climatic regions: Baltimore, MD, Boston, MA, Los Angeles, CA, Miami, FL, and Minneapolis-St. Paul, MN."
Nyack Floodplain RiverNet surface water and groundwater dissolved oxygen, conductivity, water level, and temperature Northwest Montana, USA, 2012-2020
Water dissolved oxygen, conductivity, temperature, and level from ten locations in the Nyack Floodplain of the Middle Fork of the Flathead River in Northwest Montana, USA. Measurements are made hourly for the period of 2012 to 2020. Six sensor are placed in groundwater wells and four are placed in surface water. Data up to June 26h, 2019 have been cleaned to remove bad data and flag potentially anomalous observations.
Advanced Resolution Canopy FLOw (ARCFLO) experiment employing the SUbcanopy Sonic Anemometer Network (SUSAN) in WS01 of the HJ Andrews Experimental Forest, July-September 2012
This dataset was collected during one of the ARCFLO (Advanced Resolution Canopy FLOw) experiment series’ field campaigns. This field campaign was carried out in WS1 of the HJ Andrews Experimental forest during July-September 2012 by the biomicrometeorology group, PI Christoph Thomas. The ARCFLO experiment series spanned a wide range of topographic conditions (flat, sloped, mountainous) and canopy architectures (grassland, orchard, open forest, dense forest) and was carried out between 2011 and 2014. It was funded through the NSF Career Award in Physical and Dynamical Meteorology to PI Christoph Thomas. The main goal of this project was to develop a novel improved framework to describe the airflow and its transport under weak-wind conditions for a continuous variation of overstory density and stratification. The objective is to i) identify forcing mechanisms of submeso motions, ii) evaluate the impact of plant canopies of different overstory density on the wind, temperature, and humidity fields, and iii) improve predictors for mixing in plant canopies that incorporate the important physical mechanisms. Observations were be made with a unique combination of new and standard techniques including optical fiber measurement of temperature structure, acoustic remote sensing, ultrasonic anemometers, and laser-illuminated flow visualizations.
Kuparuk River Whole Stream Metabolism Arctic LTER, Toolik Field Station Alaska 2012-2017
The Kuparuk River has been the central research location on the impact of added phosphorus to arctic streams. Additions of phosphorus occred since 1983. Today, 4 specific reaches show certain characteristics based on the years that they recieved fertilization. Whole Stream Metabolism is a way to quantify primary production of this stream system. Calculations were done using dissolved oxygen, discharge, stage, light and temperature measured by sondes and other equipment strategically deployed in the field at locations to quantify each of the unique stream reaches.
2012 climate data for eddy flux platform on Toolik Lake, Alaska
Yearly file describing the metological conditions on Toolik Lake adjacent to the Toolik Field Research Station (68 38'N, 149 36'W). This location is a floating platform where eddy flux measurements have been made, and should not be confused with either the Toolik Field Station Climate site, which is a land-based station, or the Toolik Lake Climate Station that is lake-based but at a different location (approximately 300 m from the eddy platform). Note that the terrestrial station has been called the "Toolik Main Climate Station", and the station on the lake is located near the main Arctic long term ecological research lake sampling site, and has also been called the Toolik Lake Main Climate Station. Measurements at the eddy climate platform described here include air temperature, relative humidity, barometric pressure, wind direction, wind speed, radiation, and water temperature.
Arthropod biomass captured by sweepnet (weekly) and sweepnet biomass model predictions (daily) near Toolik Field Station, Alaska, summers 2012-2016
This data set contains information about the per sample sweepnet arthropod biomass captured (or modeled using GAM modelling approaches) near Toolik Field Station from 2012 to 2016 under National Science Foundation (NSF) Office of Polar Programs ARC 0908444 (to Laura Gough), ARC 0908602 (to Natalie Boelman), and ARC 0909133 (to John Wingfield). It is associated with publication DOI: 10.1111/jav.01712.
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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.