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5,737 results for “standards”
Precipitation measurements from historic and current standard, storage and recording rain gauges at the Andrews Experimental Forest, 1951 to present
Andrews Forest precipitation has been measured continuously using various rain gage types since 1951. Most of these rain gages are standard (non-recording) gages with 7.5 or 8 inch orifices or large capacity storage gages intended for sites with limited access collected irregularly over longer intervals. Recording rain gages have also been established to collect higher temporal resolutions (e.g., 5 minute or 15 minute) and also used as a means of parsing (“prorating”) these periodic interval measurements from these standard and storage gages into daily totals. This data set includes an inventory of all rain gages that have operated within the Andrews as well as one site in the nearby Wildcat RNA and one in the town of Blue River. The inventory includes information regarding the date range of operation, gage location, type of gage, the rain network within which it was established, general availability of data and descriptive notes. A second table includes all of the raw measurement data for these non-recording gages over every interval where data were taken, and additionally includes the corresponding recording gage and its measurement total used to prorate data into a daily record. A third table includes the prorated daily data for all of these standard and storage gages as well as the true daily totals for two recording rain gages. A fourth table includes high temporal resolution for one early recording gage at Forks and the Mack Creek recording gage. Note that while precipitation data associated with the 6 benchmark stations are included in this rain gage inventory (Entity 1), the daily and high temporal resolution data for these sites were available through a separate meteorological data set, database code MS001, until 2025. In 2025, the benchmark station data was migrated here and will be combined with the Forks and Mack Creek data.
Liquid Chromatography - Tandem Mass Spectrometry (LC-MS/MS) and Gas Chromatography - Mass Spectrometry (GC-MS) Reference Libraries from Global Natural Products Social Molecular Networking (GNPS) and National Institute of Standards and Technology (NIST) WebBook Processed for Spectral Library Matching
<div>In order to obtain a high-quality LC-MS/MS reference database for spectral library matching, we selected 22 high-quality GNPS tandem mass spectrometry databases generated under the positive ion mode. Further preprocessing similar to Huber et al involving mass-to-charge (m/z) and intensity filtering yields the database found in the file LCMS_GNPS_reference_library.csv which contains 14,705 electrospray ionization (ESI) mass spectra, each of which corresponds to a unique compound. The NIST WebBook database was used to construct GC-MS database contained in the file GCMS_NIST_WebBook.csv. This database contains 23,721 electron ionization (EI) mass spectra, each of which corresponds to a unique non-hyphenated Chemical Abstract Service (CAS) Registry Number.</div> <div> </div> <div>Both LC-MS/MS and GC-MS databases are organized into three columns: one for the identifier, one for the m/z values, and one for the intensity values. For example, if spectrum A has 20 ion fragments, then there will be 20 rows corresponding to spectrum A in the corresponding database with the identifier A repeated 20 times with the corresponding m/z and intensity values.</div>
Multi-organ Abdominal CT Reference Standard Segmentations
<p>DenseVNet Multi-organ Segmentation on Abdominal CT</p> <p>This dataset includes the multi-organ abdominal CT reference segmentations publicly released in conjunction with the IEEE Transactions on Medical Imaging paper "Automatic Multi-organ Segmentation on Abdominal CT with Dense V-networks" <a href="#1">[1]</a>.</p> <p>The data comprises reference segmentations for 90 abdominal CT images delineating multiple organs: the spleen, left kidney, gallbladder, esophagus, liver, stomach, pancreas and duodenum.</p> <p>The abdominal CT images and some of the reference segmentations were drawn from two data sets: <a href="http://doi.org/10.7937/K9/TCIA.2016.tNB1kqBU">The Cancer Image Archive (TCIA) Pancreas-CT data set</a> [<a href="#2">2</a>-<a href="#4">4</a>] and the <a href="https://doi.org/10.7303/syn3193805">Beyond the Cranial Vault (BTCV) Abdomen data set</a> [<a href="#5">5</a>-<a href="#6">6</a>]. The Pancreas-CT data set comprises abdominal CT acquired at the National Institutes of Health Clinical Center from pre-nephrectomy healthy kidney donors or patients with neither major abdominal pathologies nor pancreatic cancer lesions. Segmentations of the pancreas are included with this data set; images were manually labeled slice-by-slice by a medical student, and verified/modified by an experienced radiologist. The BTCV data set comprises abdominal CT acquired at the Vanderbilt University Medical Center from metastatic liver cancer patients or post-operative ventral hernia patients. Segmentations of the spleen, right and left kidney, gallbladder, esophagus, liver, stomach, aorta, inferior vena cava, portal vein and splenic vein, pancreas, right adrenal gland, left adrenal gland are included in this data set; images were manually labeled by two experienced undergraduate students, and verified by a radiologist on a volumetric basis using the MIPAV software.</p> <p>Segmentations that were not present in the original data sets were performed interactively using Matlab 2015b and ITK-SNAP 3.2 by an image research fellow under the supervision of a board-certified radiologist with 8 years of experience in gastrointestinal CT and MRI image interpretation. Segmentations that were present in the original data sets were edited to ensure a consistent segmentation protocol across the data set.</p> <p>Terms of use</p> <p>The terms of use of this data set include the terms of use of both the <a href="http://doi.org/10.7937/K9/TCIA.2016.tNB1kqBU">TCIA Pancreas-CT data set</a> (see tabs for data links and terms of use) and the <a href="https://doi.org/10.7303/syn3193805">Beyond the Cranial Vault (BTCV) Abdomen data set</a> (<a href="https://doi.org/10.7303/syn3193805">terms of use</a>; after <a href="https://www.synapse.org/#!Synapse:syn3193805/wiki/217753">registration</a>, you can <a href="https://www.synapse.org/#!Synapse:syn3376386">access the data</a>). If you use these reference segmentations, please cite the above manuscript and the references below. Because these data include manual segmentations of images from the Beyond the Cranial Vault challenge test data, they may not be used to develop submissions for the challenge.</p> <p>References</p> <p>[1] Gibson E, Giganti F, Hu Y, Bonmati E, Bandula S, Gurusamy K, Davidson B, Pereira SP, Clarkson MJ, Barratt DC. Automatic multi-organ segmentation on abdominal CT with dense v-networks. IEEE Transactions on Medical Imaging, 2018.</p> <p>[2] Roth HR, Farag A, Turkbey EB, Lu L, Liu J, and Summers RM. (2016). Data From Pancreas-CT. The Cancer Imaging Archive. <a href="http://doi.org/10.7937/K9/TCIA.2016.tNB1kqBU">http://doi.org/10.7937/K9/TCIA.2016.tNB1kqBU</a></p> <p>[3] Roth HR, Lu L, Farag A, Shin H-C, Liu J, Turkbey EB, Summers RM. DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation. N. Navab et al. (Eds.): MICCAI 2015, Part I, LNCS 9349, pp. 556–564, 2015. <a href="http://arxiv.org/pdf/1506.06448.pdf">http://arxiv.org/pdf/1506.06448.pdf</a></p> <p>[4] Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. <a href="http://doi.org/10.1007/s10278-013-9622-7">http://doi.org/10.1007/s10278-013-9622-7</a></p> <p>[5] Xu Z, Lee CP, Heinrich MP, Modat M, Rueckert D, Ourselin S, Abramson RG, and Landman BA, "Evaluation of six registration methods for the human abdomen on clinically acquired CT," IEEE Trans. Biomed. Eng., vol. 63, no. 8, pp. 1563–1572, 2016.<a href="http://doi.org/10.1109/TBME.2016.2574816">http://doi.org/10.1109/TBME.2016.2574816</a></p> <p>[6] Landman BA, Xu Z, Igelsias JE, Styner M, Langerak TR, and Klein A, "MICCAI multi-atlas labeling beyond the cranial vault - workshop and challenge," 2015, <a href="https://doi.org/10.7303/syn3193805">https://doi.org/10.7303/syn3193805</a></p> <p>File format Labels are in NIfTI format with the following label definitions. Labels marked with * are only available in the BTCV data set.</p> <ol> <li>spleen</li> <li>right kidney*</li> <li>left kidney</li> <li>gallbladder</li> <li>esophagus</li> <li>liver</li> <li>stomach</li> <li>aorta*</li> <li>inferior vena cava*</li> <li>portal vein and splenic vein*</li> <li>pancreas</li> <li>right adrenal gland*</li> <li>left adrenal gland*</li> <li>duodenum</li> </ol> <p>Subjects included in the dataset</p> <p>The data comprises segmentation volumes for 90 cases, and the cropping coordinates (cropping.csv) used in the manuscript. The abdominal CT can be obtained from the links above. The reference standard segmentations may be incomplete outside of the specified cropping region. The cases are listed by their subject identifiers in their original data set:</p> <p> </p> <p><span class="math-tex">\(\begin{bmatrix} 1 & TCIA & Pancreas-CT & 0002\\ 2 & TCIA & Pancreas-CT & 0003\\ 3 & TCIA & Pancreas-CT & 0004\\ 4 & TCIA & Pancreas-CT & 0005\\ 5 & TCIA & Pancreas-CT & 0006\\ 6 & TCIA & Pancreas-CT & 0007\\ 7 & TCIA & Pancreas-CT & 0008\\ 8 & TCIA & Pancreas-CT & 0009\\ 9 & TCIA & Pancreas-CT & 0010\\ 10 & TCIA & Pancreas-CT & 0011\\ 11 & TCIA & Pancreas-CT & 0012\\ 12 & TCIA & Pancreas-CT & 0013\\ 13 & TCIA & Pancreas-CT & 0014\\ 14 & TCIA & Pancreas-CT & 0016\\ 15 & TCIA & Pancreas-CT & 0017\\ 16 & TCIA & Pancreas-CT & 0018\\ 17 & TCIA & Pancreas-CT & 0019\\ 18 & TCIA & Pancreas-CT & 0020\\ 19 & TCIA & Pancreas-CT & 0021\\ 20 & TCIA & Pancreas-CT & 0022\\ 21 & TCIA & Pancreas-CT & 0024\\ 22 & TCIA & Pancreas-CT & 0025\\ 23 & TCIA & Pancreas-CT & 0026\\ 24 & TCIA & Pancreas-CT & 0027\\ 25 & TCIA & Pancreas-CT & 0028\\ 26 & TCIA & Pancreas-CT & 0029\\ 27 & TCIA & Pancreas-CT & 0030\\ 28 & TCIA & Pancreas-CT & 0031\\ 29 & TCIA & Pancreas-CT & 0032\\ 30 & TCIA & Pancreas-CT & 0033\\ 31 & TCIA & Pancreas-CT & 0034\\ 32 & TCIA & Pancreas-CT & 0035\\ 33 & TCIA & Pancreas-CT & 0038\\ 34 & TCIA & Pancreas-CT & 0039\\ 35 & TCIA & Pancreas-CT & 0040\\ 36 & TCIA & Pancreas-CT & 0041\\ 37 & TCIA & Pancreas-CT & 0042\\ 38 & TCIA & Pancreas-CT & 0043\\ 39 & TCIA & Pancreas-CT & 0044\\ 40 & TCIA & Pancreas-CT & 0045\\ 41 & TCIA & Pancreas-CT & 0046\\ 42 & TCIA & Pancreas-CT & 0047\\ 43 & TCIA & Pancreas-CT & 0048\\ 44 & Synapse & BeyondTheCranialVault & 0001\\ 45 & Synapse & BeyondTheCranialVault & 0002\\ 46 & Synapse & BeyondTheCranialVault & 0003\\ 47 & Synapse & BeyondTheCranialVault & 0004\\ 48 & Synapse & BeyondTheCranialVault & 0005\\ 49 & Synapse & BeyondTheCranialVault & 0006\\ 50 & Synapse & BeyondTheCranialVault & 0007\\ 51 & Synapse & BeyondTheCranialVault & 0008\\ 52 & Synapse & BeyondTheCranialVault & 0009\\ 53 & Synapse & BeyondTheCranialVault & 0010\\ 54 & Synapse & BeyondTheCranialVault & 0021\\ 55 & Synapse & BeyondTheCranialVault & 0022\\ 56 & Synapse & BeyondTheCranialVault & 0023\\ 57 & Synapse & BeyondTheCranialVault & 0024\\ 58 & Synapse & BeyondTheCranialVault & 0025\\ 59 & Synapse & BeyondTheCranialVault & 0026\\ 60 & Synapse & BeyondTheCranialVault & 0027\\ 61 & Synapse & BeyondTheCranialVault & 0028\\ 62 & Synapse & BeyondTheCranialVault & 0029\\ 63 & Synapse & BeyondTheCranialVault & 0030\\ 64 & Synapse & BeyondTheCranialVault & 0031\\ 65 & Synapse & BeyondTheCranialVault & 0032\\ 66 & Synapse & BeyondTheCranialVault & 0033\\ 67 & Synapse & BeyondTheCranialVault & 0034\\ 68 & Synapse & BeyondTheCranialVault & 0035\\ 69 & Synapse & BeyondTheCranialVault & 0036\\ 70 & Synapse & BeyondTheCranialVault & 0037\\ 71 & Synapse & BeyondTheCranialVault & 0038\\ 72 & Synapse & BeyondTheCranialVault & 0039\\ 73 & Synapse & BeyondTheCranialVault & 0040\\ 74 & Synapse & BeyondTheCranialVault & 0061\\ 75 & Synapse & BeyondTheCranialVault & 0062\\ 76 & Synapse & BeyondTheCranialVault & 0063\\ 77 & Synapse & BeyondTheCranialVault & 0064\\ 78 & Synapse & BeyondTheCranialVault & 0065\\ 79 & Synapse & BeyondTheCranialVault & 0066\\ 80 & Synapse & BeyondTheCranialVault & 0067\\ 81 & Synapse & BeyondTheCranialVault & 0068\\ 82 & Synapse & BeyondTheCranialVault & 0069\\ 83 & Synapse & BeyondTheCranialVault & 0070\\ 84 & Synapse & BeyondTheCranialVault & 0074\\ 85 & Synapse & BeyondTheCranialVault & 0075\\ 86 & Synapse & BeyondTheCranialVault & 0076\\ 87 & Synapse & BeyondTheCranialVault & 0077\\ 88 & Synapse & BeyondTheCranialVault & 0078\\ 89 & Synapse & BeyondTheCranialVault & 0079\\ 90 & Synapse & BeyondTheCranialVault & 0080\\ \end{bmatrix}\)</span></p>
S42 | HDXNOEX | Hydrogen Deuterium Exchange (HDX) Standard Set
<p>This is the collection associated with list S42 HDXNOEX on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/?q=suspect-list-exchange">https://www.norman-network.com/?q=suspect-list-exchange</a></p> <p>S42</p> <p>HDXNOEX</p> <p><strong>Hydrogen Deuterium Exchange (HDX) Standard Set</strong></p> <p>HDXNOEX <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/120219Update/HDXNOEX_14022019.xlsx">XLSX</a>, <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/120219Update/HDXNOEX_14022019.csv">CSV</a> (14/02/2019)<br> CompTox <a href="https://comptox.epa.gov/dashboard/chemical_lists/hdxnoex">HDXNOEX List</a><br> CompTox <a href="https://comptox.epa.gov/dashboard/chemical_lists/hdxexch">HDXEXCH List</a></p> <p>HDXNOEX <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/120219Update/HDXNOEX_InChIKeys_14022019.txt">InChIKeys</a> (14/02/2019)</p> <p>Environmental standard set used to investigate hydrogen deuterium exchange in small molecule HRMS (Ruttkies et al. accepted). <a href="https://comptox.epa.gov/dashboard/chemical_lists/hdxexch">HDXEXCH</a> list also contains observed deuterated species. </p>
Standardized NEON organismal data (neonDivData)
To standardize NEON organismal data for major taxonomic groups, we first systematically reviewed NEON’s documentations for each taxonomic group. We then discussed as a group and with NEON staff to decide how to wrangle and standardize NEON organismal data. See Li et al. 2022 for more details. All R code to process NEON data products can be obtained through the R package ‘ecocomDP’. Once the data are in ecocomDP format, we further processed them to convert them into long data frames with code on Github (https://github.com/daijiang/neonDivData/tree/master/data-raw), which is also archived here.
Standard Lengths and Mean Weights for Prey-base Fishes from Taylor River and Joe Bay Sites, Everglades National Park (FCE), South Florida from January 2000 to April 2004
Prey-base fishes. The small demersal fishes of the coastal wetlands are a keystone element in this ecosystem. They are the primary and secondary consumers of the plants mentioned above and they are the primary food resource for myriad piscine (e.g. game species of fish), reptilian (e.g. juvenile crocodiles) and avian (e.g. wading birds) predators. The community dynamics of these fishes are dictated by hydrologic and hydrographic parameters so they also respond predictably to water management practices. Because they are a bottle-neck in the food web, their abundance and availability dictate the success of higher trophic levels. Fish are sampled in June, September and monthly from November through April at five locations. A 9m2 drop trap designed specifically for this habitat are used to quantify fish use. Nine traps are used at each site.
Precipitation data from a standard can rain gauge at the LTER weather station, Jornada Basin, southern New Mexico, USA, 1992-ongoing
This data package contains precipitation measurements collected from a "dipstick" rain gauge (NOAA standard can type) at the LTER Weather Station in the Jornada Basin, southern New Mexico, USA. The primary purpose of this data set is to validate the LTER Weather Station tipping bucket rain gauge data. The dipstick rain gauge (DSRG) data is measured at least weekly during scheduled maintenance trips to the LTER Weather Station to maintain the evaporation pan water levels. During the summer months this may be twice a week. Additionally, DSRG data is collected after any rain event that requires the collection of the Wetfall/Dryfall precipitation buckets which are located about 10 meters from the DSRG. This is usually any amount greater than 0.02 inches. DSRG data is also collected after very small events when personnel are in the vicinity. Rain gauge records at this gauge began in 1992 and the study is ongoing.
Standard meteorology and ancillary data from the Tromble Weir experimental watershed tower at the Jornada Basin LTER site, 2010-ongoing
This data package contains 30-minute standard meteorology and ancillary data collected at an eddy covariance tower in the Tromble Weir Watershed area of the Jornada Basin in southern New Mexico, USA. These data are used to quantify the water and energy balances in a small experimental watershed, including observational studies to calculate groundwater recharge as a water balance residual, to build relations between soil moisture state and ET flux, and to quantify land-atmosphere interactions and improve our understanding of the eddy covariance method. Additionally, they have been used in modeling studies as a validation of model performance. This file presents the ancillary data collected at the eddy covariance tower, excluding the 20Hz flux measurements that are archived at AmeriFlux (site US-Jo2; http://ameriflux.lbl.gov/sites/siteinfo/US-Jo2). This includes soil moisture and temperature at 4 depths, air temperature and pressure, humidity and vapor pressure, ground heat flux, incoming and outgoing longwave and shortwave radiation, photosynthetically active radiation for a subset of the study period, and surface soil temperature. Instrument descriptions and detailed procedures are found in the references listed in the Methods section of this package. This is an ongoing dataset that will be updated annually.
Standard body length of Euphausia superba collected with a 2-m, 700-um net towed from surface to 120 m, collected aboard Palmer LTER annual cruises off the coast of the Western Antarctic Peninsula, 2009 - 2024.
Antarctic krill, Euphausia superba, are a critical food-web link between phytoplankton primary production and higher trophic levels, such as whales, penguins, and seals. Krill standard length was measured from LTER zooplankton tows along the western Antarctic Peninsula. Annual cruises take place between late December to early February, except for the NBP21-13 cruise, which was November and December. Length data provides estimates of age-class abundance and recruitment. Climate-induced changes in krill recruitment are an important consideration in the management and modelling of krill populations.
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.
Relations in the Biographical Dictionary of Republican China - Standardized output
<p>This dataset contains the data on relations in the BDRC. It is based on the raw data output to be found in this collection. This file retained only the person-to-person relations. It served as a reference file to create the edge and node lists used for SNA under Cytoscape. All the corresponding networks are available as interactive networks in the <a href="http://public.ndexbio.org/#/group/4ea8024e-094c-11eb-948d-0ac135e8bacf?searchType=All&searchString=bdrc&searchTermExpansion=false">ENP-China Group</a> on the NDEx platform.</p>
A Standardized Review of Bat Names Across Multiple Taxonomic Authorities
<p>The Bat Eco-Interactions Working Group, in collaboration with GBatNet and the international Bat Taxonomy Group, developed the <strong>Bat Taxonomic Alignment (BTA)</strong> to reconcile taxonomic discrepancies across currently recognized bat species. As knowledge of bat population structure and evolutionary history advances, taxonomic boundaries and species names are frequently revised. To address these changes, the BTA integrates data from ten leading taxonomic authorities and consolidates relationships, synonyms, and historic combinations for over <strong>1,480 valid bat species</strong> across <strong>1,680 taxonomic treatments</strong>.</p> <p>This open-access, searchable tool provides a time-calibrated inventory of Chiroptera taxonomy, documenting valid names, alternative names, subspecies, and synonymies. By aligning these classifications, the BTA enables users to identify unharmonized binomials and trace nomenclatural changes over time. It promotes taxonomic clarity critical for research, biodiversity assessments, and conservation planning, where misidentified or misaligned taxa can lead to gaps in knowledge, resource misallocation, or overlooked species. The BTA thus represents a foundational advancement in bat biodiversity informatics, emphasizing transparency, data provenance, and interoperability across digital taxonomic frameworks.</p> <p> </p>
Chemical sensors for fire detection and nuisance rejection under EN-5420 standard conditions and reduced-scale chamber
<p>The dataset was acquired using a gas sensor array placed in the celling of a validated standard fire room (240 m3) located in Minimax Company. The dataset includes measurements of three different campaigns that were performed over 15 months. The dataset includes standard EN-54 smoldering fires and non-standard smoldering fires (such as plastic fires; PVC, cables Fire). In order to generate scenarios that may result in false-positive alarms when gas sensors are used, different nuisance experiments were also performed (such as cleaners, and air fresheners). Additionally, an additional measurement campaign was performed in a small chamber. The small-scale experiments dataset includes scale-down replicates of the fire and nuisances experiments performed in the standard fire room (EN-54 smoldering fire experiments, non-standard fires, and nuisance experiments).</p> <p>Citation request: Ana Solórzano et al, Early fire detection based on gas sensor arrays: Multivariate calibration and validation, Sensors and Actuators B: Chemical, 2021, <a href="https://doi.org/10.1016/j.snb.2021.130961">https://doi.org/10.1016/j.snb.2021.130961</a>.</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>
Respectable Standards of Living: The Alternative Lens of Maintenance Costs, Britain 1270-1860
<p>Data set and code book. Replication materials for paper accepted in Economic History Review, April 2024.</p> <p>Abstract </p> <p><span>This paper argues that in all societies there is considerable agreement about what goods and services are needed to provide a decent living, and that this standard can be measured by the expense involved in maintaining people of good standing.<span> </span><span> </span>Maintenance costs include two components of living costs that are neglected in conventional approaches.<span> </span>First, in contrast to the usual focus on a fixed basket of commodities, maintenance costs capture changes in the composition and quality of the goods required for a respectable lifestyle.<span> </span>Second, unlike the conventional accounting they include the costs of the household services required to turn the basket commodities into livings. Ignored in the conventional methodology, the inclusion of these costs represents a core innovation. More than 4600 observations, drawn mainly from primary sources, trace levels and trends in maintenance costs for Britain, 1270-1860. <span> </span>These can be compared with established cost of living indicators to offer a complementary perspective on real consumption that accommodates aspirational goods and the input of household labour.<span> </span>The struggle to support families at respectable standards emerges as driving industriousness and motivating prudence among a class that played a major role in economic development.<span> </span><span> </span></span></p> <p> </p>
Dataset of report "A.2.2.6: Validation of the fitness of purpose of the performance assessment protocol developed in A2.1.4 by demonstrating its applicability for 2 terpenes using TD-GC/MS/FID and the static standards produced in A1.1.2."
<p>Dataset of report "A.2.2.6: Validation of the fitness of purpose of the performance assessment protocol developed in A2.1.4 by demonstrating its applicability for 2 terpenes using TD-GC/MS/FID and the static standards produced in A1.1.2."</p>
VSR Databases used in article "Standardization of noisy volcano-seismic waveforms as a key step towards station-independent, robust automatic recognition"
<p>This dataset contains required volcano-seismic waveform DBs (<em>dec.95M.16c</em> and <em>dec.09U.4c</em>) used in the article:</p> <p>"<em>Standardization of noisy volcano-seismic waveforms as a key step towards station-independent, robust automatic recognition</em>",</p> <p>published in the Seismological Research Letters (<a href="https://doi.org/10.1785/0220180334">https://doi.org/10.1785/0220180334</a>). The authors want to thank everyone at the Instituto Andaluz of Geofísica (<a href="http://iagpds.ugr.es">http://iagpds.ugr.es</a>), precisely to Prof. Jesús Ibáñez and Dr. Javier Almendros, IPs of several research projects which </p> <p>have made possible the monitoring of Deception Island since early 1990s.</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement No.[749249] (VULCAN.ears).</p>
Clay content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution
<p>Clay content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. Based on machine learning predictions from global compilation of soil profiles and samples. Processing steps are described in detail <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil">here</a></strong>. Antarctica is not included.</p> <p>To access and visualize 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>clay.wfraction = variable: sand weight fraction,</li> <li>usda.3a1a1a = 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>v0.2 = version number: 0.2,</li> </ul>
Soil pH in H2O at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution
<p>Soil pH in H2O in × 10 at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. Processing steps are described in detail <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil">here</a></strong>. Antarctica is not included.</p> <p>To access and visualize 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>ph.h2o = variable: soil pH in H2O,</li> <li>usda.4c1a2a = 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>v0.2 = version number: 0.2,</li> </ul>
Soil organic carbon content in x 5 g / kg at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution
<p>Soil organic carbon content in × 5 g / kg (to convert to % divide by 2) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. The maps are provided using Byte type to significantly reduce file size. Predicted from a global compilation of soil points. Also available for download: soil organic stock maps in in kg / m<sup>2</sup> (<a href="https://doi.org/10.5281/zenodo.1475453">https://doi.org/10.5281/zenodo.1475453</a>) and bulk density maps in kg / m<sup>3</sup> (<a href="https://doi.org/10.5281/zenodo.1475970">https://doi.org/10.5281/zenodo.1475970</a>). Processing steps are described in detail <strong><a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil">here</a></strong>. Antarctica is not included.</p> <p>To access and visualize 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>organic.carbon = variable: soil organic carbon content in x 5 g / kg,</li> <li>usda.6a1c = 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>v0.2 = version number: 0.2,</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.