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7,669 results for “2012”
Hubbard Brook Experimental Forest: 1 meter LiDAR-derived and Hydro-enforced Digital Elevation Models, 2012
This data package contains a 1 m LiDAR-derived digital elevation model (DEM) and a 1 m hydro-enforced DEM across Hubbard Brook EF. The LiDAR was collected during leaf-off and snow-free conditions by Photo Science, Inc. in April 2012 for the White Mountain National Forest (WMNF). These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Perennial grass tiller and stolon counts in plots with experimentally altered precipitation variability at the Jornada Basin LTER site, 2012-2014
This dataset contains perennial grass tiller and stolon counts collected starting in 2012 for a long-term precipitation variability manipulation experiment at the Jornada Basin LTER site in southern New Mexico, U.S.A. The study was designed to assess the effect of interannual variability in precipitation on average aboveground net primary productivity (ANPP) in Chihuahuan Desert grasslands. The study began in 2009, has five annual precipitation treatments, and contains 50 plots (10 per treatment). This experiment uses precipitation shelters and irrigation treatments to manipulate water inputs to 2.5 x 2.5 meter plots in a desert grassland. There are high, low, and ambient (control) precipitation variability treatments. Ambient plots receive natural precipitation each year, while variability treatments alternate between 20% and 180% (high variability), or 50% and 150% (low variability) of ambient precipitation each year. Perennial grass tiller and stolon counts were made annually in each plot from 2012-2014. This is an ongoing study and the dataset will be updated as needed.
Perennial grass tiller and stolon density in plots with experimentally altered precipitation and nutrient inputs at the Jornada Basin LTER site, 2012-2014
This dataset contains perennial grass tiller and stolon counts collected starting in 2012 for a long-term precipitation and nutrient manipulation experiment at the Jornada Basin LTER site in southern New Mexico, U.S.A. This experiment uses precipitation shelters and irrigation treatments to manipulate water inputs, and fertilization treatments to alter nitrogen input to 2.5 x 2.5 meter plots in a desert grassland. Tillers and stolons of perennial grasses were counted in each plot in 2012, 2013 and 2014. This is an ongoing study and the dataset will be updated as needed.
MCR LTER: Coral Reef: Water Column: Offshore Ocean Acidification: Water Profiles, CTD, and Chemistry from 2005 to 2012
This data package contains water chemistry measurements taken 2 to 4 times per year at a station 5 km offshore of the north shore of Moorea, French Polynesia. Measurements include standard CTD parameters, phosphate, silicate, total alkalinity (TA) and total dissolved inorganic carbon (DIC). Sampling began in August, 2005. All water samples were collected with Niskin Bottles. This data includes excerpts from CTD data were collected with a SBE19-Plus Seacat Profiler. CTD and bottle samples were taken on separate casts at each station. (For full CTD data refer to knb-lter-mcr.10.) All other parameters were calculated from temperature, pressure, nutrients, TA and DIC with CO2Sys programs available at: http://cdiac.ornl.gov/oceans/co2rprt.html (Lewis E. and D. Wallace Program Developed for CO2 System Calculations) using constants K1, K2 from Mehrbach et al, 1973 refit by Dickson and Millero, 1987, Dickson KHSO4, and the Seawater pH scale (mol/kg-SW). If users wish to use different constants and scales, they will need to recalculate using the emperically collected data (TA and DIC).
MCR LTER: Coral Reef: Porites growth, respiration, and photophysiology data in support of Edmunds 2012 Marine Biology, v159, 2149-2160
These data were generated from a one-time experiment in support of a coral ecophysiology manuscript; published in Marine Biology. Edmunds (2012) Marine Biology 159: 2149-2160 doi:10.1007/s00227-012-2001-y The hypothesis that was tested stated that high pCO2 (76.6 Pa and 87.2 Pa vs. 42.9 Pa) has no effect on the metabolism of juvenile massive Porites spp. after 11 days at 28 °C and 545 lmol quanta m-2 s-1. The response was assessed as aerobic dark respiration, skeletal weight (i.e., calcification), biomass, and chlorophyll fluorescence. Corals were collected from the shallow (3–4 m) back reef of Moorea, French Polynesia (17°28.6140 S, 149°48.9170 W), and experiments conducted during April and May 2011. An increase in pCO2 to 76.6 Pa had no effect on any dependent variable, but 87.2 Pa pCO2 reduced area-normalized (but not biomass-normalized) respiration 36 %, as well as maximum photochemical efficiency (Fv/Fm) of open RCIIs and effective photochemical efficiency of RCIIs in actinic light (DF/Fm0 ); neither biomass, calcification, nor the energy expenditure coincident with calcification (J g-1) was effected. These results do not support the hypothesis that high pCO2 reduces coral calcification through increased metabolic costs and, instead, suggest that high pCO2 causes metabolic depression and photochemical impairment similar to that associated with bleaching. Evidence of a pCO2 threshold between 76.6 and 87.2 Pa for inhibitory effects on respiration and photochemistry deserves further attention as it might signal the presence of unpredictable effects of rising pCO2. This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 16-37396 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2
MCR LTER: Coral Reef Resilience: Algae, Coral, and Sediment data from Grazing Intensity Experiment, 2010 - 2012
These data describe the percent cover of benthic space holders (primarily algae), the biomass of algae and sediment, and the number of corals recruiting on 15 cm X 15 cm terra cota tiles experimentally manipulated on the forereef on the north shore of Moorea. The experiment was established to test whether and how different levels of grazing influence benthic community development. Five treatments were initially established to create a gradient in grazing pressure with a sixth treatment established shortly thereafter. Each treatment was replicated ten times using a randomized block design. Each cage initially contained four tiles, and one tile from each cage has been photographed and destructively sampled for biomass at regular intervals. In addition to the original tiles deployed in July 2010, tiles were subsequently deployed in March 2011, August 2011, and March 2012 to test whether community development varies among seasons. This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 16-37396 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2018). This work represents a contribution of the Moorea Coral Reef (MCR) LTER Site.
Atlantic sea scallop energy budget data on the Northeast U.S. Shelf, monthly in 2010 and 2012
This dataset includes monthly Atlantic sea scallop energy budget data from Georges Bank to the Mid-Atlantic Bight based on Scope For Growth (SFG) model results in 2010 and 2012. Results were supported in part by Northeast U.S. Shelf Long-Term Ecological Research (NES-LTER). For more details please see: Zang, Z., et al. (2022) Modeling Atlantic sea scallop (Placopecten magellanicus) scope for growth on the Northeast U.S. Shelf. Fisheries Oceanography, https://doi.org/10.1111/fog.12577.
WSC - Temperature and relative humidity data from 150 locations in and around Madison, Wisconsin from 2012 - 2021
To study the urban heat island and other local climatic processes in Madison, Wisconsin, in March 2012, 135 HOBO U23 Pro v2 temperature/relative humidity sensors in RS1 solar shields (Onset Computing) were attached to streetlight and utility poles in and around Madison, Wisconsin. Additional locations were added in 2012 and 2013 for a total of 150 locations. The sensors were installed at a height of 3.5 meters, and they automatically record instantaneous temperature and relative humidity every 15 minutes. This dataset includes all temperature/humidity measurements and a separate file with the coordinates of each measurement location.
Ground water well elevation for Niwot Ridge Saddle, 2012 - 2018.
Water level of Saddle Ground Water Wells. Ground Water Well locations, depths, and design were determined by Niwot Ridge LTER lead researchers, Mark Williams and Nel Caine, to monitor groundwater chemistry and water levels. Well locations were selected based on proximity to the headwater region of the Saddle stream channel. Ground Water Wells are located along an east-west transect at the Saddle site, each pair consisting of a deep well to a depth between 6.3 and 8.8 m, and a shallow well to a depth of 1.5 m. Ground surface elevations at the Saddle wells range from about 3522 m at the eastern wells to 3532 m at the western wells. The transect is roughly 170 m from east to west, has an average slope of 0.06, and is perpendicular to the Saddle stream. Saddle pair 3 was installed very close to the channel. Saddle pairs 2 and 4 were installed on opposite sides of the channel. Saddle pair 1 was installed furthest from the channel (King, 2012). Ground water wells were installed in October 2005 by Bandimere Geothermal Drilling Systems. Wells are cased with 2-inch nominal pipe size, Schedule 40 polyvinyl chloride (PVC), flush-threaded pipe. Well screens were constructed from 0.020-inch continuous slot PVC and installed in 5-ft (1.52 m) intervals. All of the wells have 1.52 m screens at the bottom of the well. The bottoms of the wells were capped with a PVC flush-threaded point cap. The annular space around each screen and pipe was backfilled with #10-20 silica sand to act as a filter (King, 2012). The Niwot Ridge LTER monitoring of these Ground Water Wells has varied over the years since the wells were installed. Prior to 2014, all wells were sampled weekly in summer and monthly in winter for chemistry, and alternating wells were monitored for water level with pressure transducers and a weighted tape measure. From 2015 through present, Saddle Deep 3 (SD3) and Saddle Deep 4 (SD4)- those located closest to the Saddle Stream headwaters- are being monitored for water level, wat
SBC LTER: Pore water constituents and residence times (Radon activity) from Santa Barbara beaches, 2012-2013
Constituents of beach pore water and parameters for calculating residence times are reported for two beaches in the Santa Barbara area, Isla Vista Beach and East Campus Beach, from July 2012 to June 2013. This dataset reports beach pore water concentrations of ammonium and nitrate, total dissolved Nitrogen and Carbon, particulate Nitrogen and Carbon, Radon, salinity, conductance, Oxygen and water temperature. Residence time ("Tau") can be calculated from Radon-222 activities in nearshore seawater, in pore water and at equilibrium, which are presented in a second table (also available in published paper). Results from these data were reported in: Goodridge, B. M. and J. M. Melack. 2014. Temporal evolution and variability of dissolved inorganic nitrogen in beach pore water revealed using radon residence times. Environmental Science and Technology, 48: 14211-14218. DOI:10.1021/es504017j
Records of moored SeaFET pH, SeaBird CTD and oxygen at Anacapa, Santa Cruz and San Miguel Islands, California from 2012-2015
Data are pH (total scale, SeaFET), salinity, conductivity, temperature, depth (Seabird 37 CTD) and Oxygen (MicroCAT C-T-ODO) from instruments moored at three sites along the north shores of the northern Santa Barbara Channel Islands, California, USA. At Anacapa Island (ALC), in a marine reserve with kelp forest habitat; at Santa Cruz Island (PRZ), surrounded by a large shallow eelgrass bed (Zostera pacifica); and at San Miguel Island (SMN), in open water over a sandy bottom. pH sensors were deployed at all three sites in 2012, with CTD and Oxyten senosrs added to moorings at ALC and PRZ in May 2013. Benchmark samples for SeaFET sensors calibration were collected 1 to 8 times during each 2 - 3 month deployment via SCUBA, free diving or from a pier with a GO-FLOW (General Oceanics) bottle (see methods). Oxygen data were validated by Winkler titations, with concentrations reported in various units and as saturation. Data are presented in: Kapsenberg, L. and G. E. Hofmann 2016. Ocean pH time-series and drivers of variability along the northern Channel Islands, California, USA. Limnology and Oceanography. 61: 953-968. DOI: 101002/lno.10264.
Arctic Ocean state estimates for 2012 using the GECCO model
<p>The dataset contains the 2012 data of a 10-year ocean synthesis (2007-2016) obtained by assimilating available observations of sea ice and ocean parameters into the GECCO model. Data from, among others, several satellite programs such as AMSRE, SSMI, AMSR2, Envisat, Jason, Cryosat., AVHRR, and SMOS, and available moorings in the Davis Strait, the Bering Strait, the Fram Strait, the Barents Sea Opening, and by the Nansen and Amundsen Basins Observational System (NABOS), the North Pole Environmental Observatory (NPEO), and the Beaufort Gyre Exploration Project (BGEP) project. A detailed description can be found in Lyu et al., 2020.</p> <p>Guokun Lyu, Nuna Serra, Armin Koehl and Detlef Stammer, 2020. INTAROS Deliverable 6.4 Ice-ocean statistics and state estimation V1. https://intaros.nersc.no/sites/intaros.nersc.no/files/D6.4_INTAROS_Data_assimilation_v1.3.pdf </p>
ACS_Bayelva_class: 302 high-resolution snow cover maps covering the 2012-2017 snowmelt seasons in the Bayelva catchment (Svalbard, Norway)
<p>The ACS_Bayelva_class dataset contains 302 high-resolution binary snow cover images that were obtained by classifying orthrorectified photographs of a 1.77 km^2 area of interest in the Bayelva catchment. This latest version (2.0) of the dataset includes the orthorectified photographs that were used to classify the binary snow cover images. The catchment is close to Ny-Ålesund, the northernmost permanent civilian settlement in the world and a major hub for polar research, in the Norwegian high-Arctic Svalbard archipelago. The imagery has a (roughly) daily temporal resolution and a ground sampling distance (pixel spacing) of 0.5 m. The dataset spans 6 snowmelt seasons, covering the months May-August for the period 2012-2017. The orthophotos were obtained by processing oblique time-lapse photographs taken by a terrestrial automatic camera system (ACS) mounted at 562 m a.s.l. near the summit of Scheteligfjellet (719 m a.s.l.) a few kilometers west of Ny-Ålesund. The orthophotos were manually classified into binary snow cover images (0=no snow, 1=snow) by iteratively selecting a (visually) optimal threshold on the intensity in the blue-band for each image. More details are provided in the study of Aalstad et al. (2020) [a copy is available in this repository] where this dataset was created. The ACS was maintained by scientists from the group of Sebastian Westermann at the Section for Physical Geography and Hydrology in the Department of Geosciences at the University of Oslo, Oslo, Norway. </p>
Open Access journals charging publication fees for a) the ten countries with the highest output of Open Access journals and b) sociological journals (2012, 2014)
<p>The files represent data on Open Access journals charging publication fees for a) the ten countries with the highest output of Open Access journals and b) all sociological journals indexed in the Directory of Open Access Journals DOAJ. The data covers a time span of about 18 months, it was collected in November 2012 and June 2014.</p>
Still images from the KuramBio expedition 2012 (Stations 3-6, 8-11) obtained with the Ocean Floor Observation System
<p>Repository with still images obtained from the videos of the KuramBio 2012 expedition in 5 second intervals. Still images belong to deep-sea stations where the Ocean Floor Observation System (OFOS) was deployed and enough video survey was obtained (i.e., Stations 3-6, 8-11). The Station 7 survey length was enough but has no HD video. Thus, images were not retrieved since the quality does not allow accurate lebensspuren or benthic fauna identification. </p><p>The OFOS was lowered into the water at the CTD position. The first 300 meters lowering was conducted with 0.5 m/sec, and then the speed was increased to 0.8 m/sec while the ship was kept in position. At 500 meters above ground the speed was reduced to 0.5 m/sec, and further reduced to 0.3 m/sec at 200 meters above ground. As soon as visual contact with the bottom was established, the winch was stopped. The ship started moving with 0.5 knots in the appropriate direction, which was chosen depending on current and wind situation at the according station. The OFOS was kept in an appropriate distance to the seafloor, enabling the scientists to watch the macrofaunal organisms. The approximate size of the observed animals could be calculated with the help of two laser pointers having a distance of 10 cm between each other (see Fig. 1-3). Generally, the survey lasted slightly over one hour, then the ship was stopped and heaving of the OFOS started. This was first conducted at 0.5 m/sec and accelerated to 1.0 m/sec. For more information see the cruise report (RV Sonne cruise SO223; doi:10.1016/j.dsr2.2014.11.001).</p>
Phlorest phylogeny derived from Bowern & Atkinson 2012 'Computational phylogenetics and the internal structure of Pama-Nyungan'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Bowern C & Atkinson QD. 2012. Computational phylogenetics and the internal structure of Pama-Nyungan. Language, 88(4), 817-845.</p> </blockquote>
Phlorest phylogeny derived from Bouckaert et al. 2012 'Mapping the Origins and Expansion of the Indo-European Language Family'
<p>Cite the source of the dataset as:</p> <blockquote> <p>Bouckaert RR, Lemey P, Dunn M, Greenhill SJ, Alekseyenko AV, Drummond AJ, Gray RD, Suchard MA & Atkinson QD. 2012. Mapping the Origins and Expansion of the Indo-European Language Family. Science, 337(6097), 957-960.</p> </blockquote>
Phlorest phylogeny derived from De Filippo et al. 2012 'Bringing together linguistic and genetic evidence to test the Bantu expansion'
<p>Cite the source of the dataset as:</p> <blockquote> <p>De Filippo, C., Bostoen, K., Stoneking, M., & Pakendorf, B. (2012). Bringing together linguistic and genetic evidence to test the Bantu expansion. Proceedings of the Royal Society B: Biological Sciences, 279(1741), 3256–3263. doi:10.1098/rspb.2012.0318</p> </blockquote>
Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor (2012): BSF, minNDTI, and NOS
<h2><strong>Data information</strong></h2> <p>This dataset provides annual aggregated predictors BSF, minNDTI, and NOS for the year 2012. They are calculated as follows:</p> <ul> <li><strong>minNDTI (minimum NDTI)</strong> is determined as the minimum value of the six NDTI values over a year (<a href="https://www.sciencedirect.com/science/article/pii/S0034425711003439?casa_token=5P1XeaRMuT4AAAAA:8VyzOkBJb_gbpap0uzjuT6YoYhylvqc_vY2-AOX2KCsZGrwQw1jySGd4Ox9JFz55TPQmF231IQ">Zheng et al., 2012</a>).</li> <li><strong>BSF (bare soil fraction)</strong> is calculated by dividing the number of pixels classified as bare surface within a year's time series (identified by the criterion of NDVI values below 0.35) by the total number of pixels analyzed in that year (<a href="https://www.mdpi.com/2072-4292/11/18/2121">Castaldi et al., 2019</a>).</li> <li><strong>NOS (number of seasons)</strong> indicates the frequency of cropping cycles within a year, calculated by counting NDVI peaks throughout the year (<a href="https://www.mdpi.com/2072-4292/6/3/2473">Li et al., 2014</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0034425720304685?casa_token=u4YFBsBotd4AAAAA:TgMiw5E7HNA9-oSF-oszmaRLzLPQJJmV-I13lvdkog1ibfEIZoPxJ3jA4F8Jcu5eXpzVBOyjew">Liu et al., 2020</a>). </li> </ul> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor P50 (2012): Reflectances bands, NDVI and NDWI
<h2><strong>Data information</strong></h2> <p>This dataset provides the P50 (median) values of corresponding predictors for the year 2012. The median values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI.</p> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</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.