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37,310 results for “6”
Bryophyte Cover Summary Data for 83 Locations of 6-163 Years Old Black Spruce, Alaska Paper Birch, and Aspen Stands Across Interior Alaska. Sampled in 2008-2010 and 2013-2015.
This dataset contains the summarized bryophyte (percent cover) data obtained from point frame measurements, as used in Jean et al. 2017 Canadian Journal of Forest Research. ?Samples of all encountered unknown species were collected for identification in the lab. Bryophyte nomenclature followed Anderson et al. (1990).
Longitudinal Stream Chemistry at the Hubbard Brook Experimental Forest, Watershed 6, 1982 - ongoing
Longitudinal variation in streamwater chemistry has been measured in two watersheds (W1 and W6) at the Hubbard Brook Experimental Forest. This dataset includes samples that have been collected approximately monthly in Watershed 6 at six sampling locations (1982 - present) along an elevational gradient. Samples are analyzed for pH, ANC, anions, cations, nitrogen, dissolved inorganic and organic carbon. 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.
Mass and Chemistry of Organic Horizons and Surface Mineral Soils on Watershed 6 at the Hubbard Brook Experimental Forest, 1976 - present
The forest floor of Watershed 6 was first sampled in 1969-70. These data include forest floor thickness, soil mass, organic matter content, and major-element composition for samples collected since 1976. Watershed 6 has been resampled at intervals varying from one to ten years. Sampling at five to ten year intervals is expected to continue. 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.
Hubbard Brook Experimental Forest: Watershed 6 Temporal Canopy Leaf Chemistry, 1992 - ongoing
Overstory foliage is collected in late summer from a reference forest to the west of Watershed 6 (also referred to as Bear Brook Watershed). Concentrations of C, N, P, K, Ca, Mn, Mg, and the natural abundance of N and C isotopes (delta-15N and delta-13C) in foliage are measured. These measurements, in combination with litterfall estimates of foliar biomass, allow us to estimate the pool of nutrients in foliage. They also allow us to estimate nutrient retranslocation, using measurements of leaf litterfall chemistry. Long-term measurements continue with the aim of detecting disturbances in nutrient cycling and trends in foliar chemistry over long time scales. 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.
Hubbard Brook Experimental Forest: Watershed 6 Tree Inventory, 1965 - ongoing
The watershed is forested by typical northern hardwood species (sugar maple, beech and yellow birch) on the lower 90% of its area and by a montane boreal transition forest of red spruce, balsam fir and white birch on the highest 10%. Forest inventory surveys were initiated in 1965, repeated in 1977, and repeated at 5 year intervals after that. This data set includes all inventories from 1965 to 2022 (11 surveys). The inventory consists of a total inventory of all trees ≥10 cm diameter-at-breast-height (dbh) (over 11,000 individual stems overtime) on the whole of the watershed (13.23 ha, 549−792 m in elevation), as measured in each of the 208 grid cells (= plots; 25 m x 25 m, 625 m2). Trees ≥2 to <10 cm dbh were subsampled using a 3 meter wide strip along one edge of each 25 m x 25 m plot. While the specifics of the inventory design varied between watersheds and over time, the core measurements were consistent. Differences between exact inventory methods over time are detailed in the Methods. The surveys include 6000 – 7000 live trees and another 2000-3000 dead standing trees. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES) and funded largely through the Long-term Ecological Research (LTER) program through NSF since 1988. 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.
Hubbard Brook Experimental Forest: Chemistry of freely-draining soil solutions in Watershed 6, 1984 - ongoing
Tension-free lysimeters were installed in three soil horizons at three locations adjacent to Hubbard Brook Experiment Forest Watershed 6. At each location lysimeters were placed beneath the Oa horizon and within the upper and lower B horizon. Soil solution samples were collected approximately monthly and analyzed for pH, acid neutralizing capacity (ANC), base cations, soluble anions, trace metals, nitrogen and carbon. 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.
Augmented emission maps: the 1461 cc 81 kW Euro 6 diesel engine: update 1
<p>In order to enable the sharing of data the emission data for vehicles is standardized. The data exchange format contains all data that is applicable for a specific engine taxonomy code.</p> <p>This specific data set refers to the 1461 cc 81 kW Euro 6 diesel engine that has been applied in the several Renault, Nissan and Mercedes-Benz models (Kadjar, Megane, Scenic, Talisman, Captur, Clio, Kangoo; Qashqai, Juke, Pulsar, NV200; Citan).</p> <p>The standardized emission map has a “.map.txt” extension and is also human readable. The files starts with metadata which contains information about:</p> <ul> <li>the engine taxonomy code,</li> <li>total driven kilometers over which the data was gathered,</li> <li>total time in hours over which the data was gathered,</li> <li>the number of vehicles which were tested to create the emission map,</li> <li>the DOI (Digital Object Identifier) reference,</li> <li>Which emission maps are available in the file.</li> </ul> <p>The DOI 10.5281/zenodo refers to a meta-data document that provides the full description of the standardized emission map.</p>
ChinaHighPM2.5: VIIRS 6 km Ground-level PM2.5 Dataset for China
<p>ChinaHighPM<sub>2.5</sub> is one of the series of long-term, full-coverage, high-resolution, and high-quality datasets of ground-level air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from the big data (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence by considering the spatiotemporal heterogeneity of air pollution.</p> <p>This is the VIIRS derived yearly 6 km ground-level PM<sub>2.5</sub> dataset in China from 2013 to 2018, and this dataset yields a high quality with a cross-validation coefficient of determination (CV-R<sup>2</sup>) reaching 0.88 and a root-mean-square error (RMSE) of 16.52 µg m<sup>-3</sup> on a daily basis.</p> <p>If you use the ChinaHighPM<sub>2.5</sub> dataset for related scientific research, please cite the corresponding reference (Wei et al., TGRS, 2022):</p> <ul> <li> <p>Wei, J., Li, Z., Sun, L., Xue, X., Ma, Z., Liu, L., Fan, T., and Cribb, M. <a href="https://weijing-rs.github.io/publications/Wei_et_al-TGRS-2022.pdf">Extending the EOS long-term PM<sub>2.5</sub> data records since 2013 in China: application to the VIIRS Deep Blue aerosol products</a>. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 2022, 60, 4100412. https://doi.org/10.1109/TGRS.2021.3050999</p> </li> </ul> <p><strong>More CHAP datasets of different air pollutants can be found at: <a href="https://weijing-rs.github.io/product.html">https://weijing-rs.github.io/product.html</a></strong></p>
Calix[6]arenes with halogen bond donor groups as selective and efficient anion transporters
<p>Dataset for the publication: <strong>Calix[6]arenes with halogen bond donor groups as selective and efficient anion transporters</strong> by A. Singh, A. Torres-Huerta, T. Vanderlinden, N. Renier, L. Martínez-Crespo, N. Tumanov, J. Wouters, K. Bartik, I. Jabin, H. Valkenier, <em>Chem. Commun.</em> <strong>2022</strong>, doi:10.1039/D2CC008472E,</p> <p>containing:</p> <ul> <li>A file with the structures of compounds <strong>1</strong>-<strong>5</strong> (PDF)</li> <li>NMR spectra for the characterisation of compounds <strong>1a</strong>, <strong>1b</strong>, <strong>1c</strong>, <strong>2</strong>, and <strong>3</strong> (Mestrenova files)</li> <li>NMR spectra for the titration experiments with compounds <strong>1</strong><strong>-5</strong> in different solvents (Mestrenova files)</li> <li>Concentrations of Host and Guests in the various titration experiments (Excel file)</li> <li>Transport data in the lucigenin assay (Excel file)</li> <li>Transport data in the HPTS assay (Excel file)</li> </ul> <p> </p> <p> </p> <div> </div>
blocking_6
This is the process of making a carafe Bontemps. The step depicted is called "blocking"(from the Mingei project).
gathering_6
This is the process of making a carafe Bontemps. The step depicted is called gathering(from the Mingei project).
beak_cutting_preparation_6
This is the process of making a carafe Bontemps. The step depicted is called "beak_cutting_preparation"(from the Mingei project).
cord_laying_6
This is the process of making a carafe Bontemps. The step depicted is called "cord_laying"(from the Mingei project).
cord_preparation_6
This is the process of making a carafe Bontemps. The step depicted is called "cord_preparation"(from the Mingei project).
Cervix_Refining_6
This is the process of making a carafe Bontemps. The step depicted is called "cervix_refining"(from the Mingei project).
handle_laying_prep_6
This is the process of making a carafe Bontemps. The step depicted is called "handle_laying_prep"(from the Mingei project).
IPBES Assessment of the diverse values and valuation of nature - Tables presented in Chapter 6
<p>These tables are an integral part of Chapter 6 of the Methodological assessment of the diverse values and valuation of nature of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. To see the full document visit the related links. </p>
Soil water content (volumetric %) for 33kPa and 1500kPa suctions predicted at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution
<p>Soil water content (volumetric) in percent for 33 kPa and 1500 kPa suctions predicted at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. Training points are based on a global compilation of soil profiles (<a href="https://ncsslabdatamart.sc.egov.usda.gov/">USDA NCSS</a>, <a href="https://www.isric.org/projects/africa-soil-profiles-database-afsp">AfSPDB</a>, <a href="https://data.isric.org/geonetwork/srv/eng/catalog.search#/metadata/a351682c-330a-4995-a5a1-57ad160e621c">ISRIC WISE</a>, <a href="http://egrpr.esoil.ru/">EGRPR</a>, <a href="https://esdac.jrc.ec.europa.eu/content/soil-profile-analytical-database-2">SPADE</a>, <a href="https://open.canada.ca/data/en/dataset/6457fad6-b6f5-47a3-9bd1-ad14aea4b9e0">CanNPDB</a>, <a href="https://data.nal.usda.gov/dataset/unsoda-20-unsaturated-soil-hydraulic-database-database-and-program-indirect-methods-estimating-unsaturated-hydraulic-properties">UNSODA</a>, <a href="https://doi.pangaea.de/10.1594/PANGAEA.885492">SWIG</a>, <a href="http://www.cprm.gov.br/en/Hydrology/Research-and-Innovation/HYBRAS-4208.html">HYBRAS</a> and <a href="http://dx.doi.org/10.4228/ZALF.2003.273">HydroS</a>). Data import steps are available <a href="https://gitlab.com/openlandmap/compiled-ess-point-data-sets/-/tree/master/themes/sol/SoilHydroDB"><strong>here</strong></a>. Spatial prediction steps are described in detail <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil/soil_water">here</a></strong>. Note: these are actually measured and mapped soil content values; no Pedo-Transfer-Functions have been used (except to fill-in the missing NCSS bulk densities). Available water capacity in mm (derived as a difference between field capacity and wilting point multiplied by layer thickness) per layer is available <strong><a href="https://doi.org/10.5281/zenodo.2629148">here</a></strong>. Antarctica is not included.</p> <p>To access and visualize some of the maps use: <a href="http://www.openlandmap.org/">OpenLandMap.org</a></p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>sol = theme: soil,</li> <li>watercontent.33kPa = water content (volumetric percent) under field capacity (33 kPa suction),</li> <li>usda.4b1c = determination method: laboratory method code,</li> <li>m = mean value,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b10..10cm = vertical reference: 10 cm depth below surface,</li> <li>1950..2017 = time reference: period 1950-2017,</li> <li>v0.1 = version number: 0.1,</li> </ul>
Regular triangulations of the Hypersimplex(3,6)
<p>This dataset contains the 42 489 025 regular triangulations of the Hypersimplex(3,6) computed with mptopcom. The triangulations were computed up to symmetry and the result contains one representative from every orbit. The dataset is structured as follows:</p> <ul> <li>`hypersimplex_3_6.dat` contains the input points and the group acting.</li> <li>`hypersimplex_3_6.result.*.xz` contains the output from mptopcom.</li> <li>`hypersimplex_3_6.job.auto` is the cluster script used to produce these triangulations.</li> <li>`hypersimplex_3_6.checkp.*` are the checkpoints between the different result files.</li> </ul> <p>Since the computation was too large, it had to split up into four parts.</p>
MOD17A2H version 6 Gross Primary Productivity (GPP) global mosaics at 500 m resolution and difference 2000-2017
<p>MOD17A2H version 6 Gross Primary Productivity (GPP) global mosaics at 500 m resolution and difference in GPP for the period 2000-2017. Changes in GPP could be used to estimate land degradation or similar. Derived using <a href="https://gitlab.com/openlandmap/global-layers/tree/master/input_layers/MOD17A2H">the data.table package and quantile function in R</a>. For more info about the MODIS LST product see: https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod17a2h_v006. Antartica is not included.</p> <p>If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels:</p> <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> <p>All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:</p> <ul> <li>veg = theme: vegetation,</li> <li>gpp = variable: gross primary productivity in kg C m<sup>2</sup>,</li> <li>mod17a2h.oct = determination method: MOD17A2H product, GPP values for October,</li> <li>d = median value / difference = difference between periods / u.975 = aggregation/statistics method: 97.5% probability upper quantile,</li> <li>500m = spatial resolution / block support: 500 m,</li> <li>s0..0cm = vertical reference: land surface,</li> <li>2000..2017 = time reference: from 2000 to 2017,</li> <li>v1.0 = version number: 1.0,</li> </ul>
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.