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357 results for “Exports”
Stormwater Nitrogen in Arizona (SNAZ): runoff and stormwater-mediated export from urbanized catchments within the greater Phoenix metropolitan area, Arizona, USA (2010-2012)
Urbanization alters dramatically watershed ecosystem processes. Land-use change and anthropogenic activities contribute to increased inputs of nutrients and other materials, while changes to land cover alter hydrology and the corresponding movement of materials. These changes have ramifications for both watershed processes and downstream systems. The impacts of urbanization on aquatic systems are well-studied, and frequently encapsulated in the ‘urban stream syndrome’ (Walsh et al. 2005) that describes, among others, increased nutrient loading and stream flashiness. However, there is some evidence that aridland cities behave differently (Grimm et al. 2004, 2005), and the complex dynamics among catchment characteristics, storm attributes, and runoff in highly urbanized settings of the arid Southwest remains poorly understood. To enhance our understanding of stormwater dynamics and watershed functioning in aridland, urban environments, the Central Arizona–Phoenix Long-Term Ecological Research (CAP LTER) program began monitoring stormwater runoff at the outflow of the Indian Bend Wash (IBW) in 2008. The IBW is a tributary to the Salt River in central Arizona, and is a major drainage within the greater Phoenix metropolitan area, encompassing much of the City of Scottsdale. A model of soft engineering, the IBW as it runs through much of the City of Scottsdale is comprised largely of a series of artificial lakes, parks, paths, golf courses, ball fields, and other non-structural elements designed with the dual roles of providing outdoor amenities to the City residents while serving as an effective flood water conveyance feature. A unique biogeochemistry of this novel system is detailed by Roach et al. (2008), and Roach and Grimm (2011). Data and expertise garnered by stormwater monitoring near the outflow of the IBW helped pave the way for a more expansive stormwater research effort facilitated by a leveraged grant from the National Science Foundation (DEB-0918457, NSF Eco
Haloferax volcanii theoretical proteome exported from HaloLex
<p>The theoretical proteome is derived from the annotated Haloferax volcanii DS2 genome (Hartmann et al, 2010).</p> <p>This proteome represents a Gold Standard Protein based annotation (Pfeiffer and Oesterhelt, 2015).</p> <p>Proteome data from Haloferax volcanii were comprehensively analyzed in a community-based effort (Schulze et al, 2020)</p> <p>Various proteome studies are based on versions of this theoretical proteome. Different versions, which are cited in proteome papers, are made available in this series of Zenodo uploads.</p>
Biomass Exports in Europe by Country
<p>Biomass exports in thousand tons, and tons per capita for European countries.</p> <p>Our dataset has a 10.4% larger congruent dataset (to be used in various supervised or unsupervised learning models, such as machine learning) than the original Eurostat dataset after imputation, backcasting, forecasting. It has overall 18% more observations after processing than the dataset at source. </p>
Market Power / Import demand elasticity faced by an exporter at 6-digit HS level from Solleder (2020)
<p><strong>Description</strong></p> <p>This dataset contains the market power of exporters at the country level for more than 4000 6-digit HS codes (HS 1992 / H0) from Solleder (2020). Market power is proxied by the inverse of the import demand elasticity faced by the exporting country. Elasticities are estimated following the method developed by Kee et al. (2008). For more information, please refer to Solleder (2020).</p> <p>The <em>dta </em>file can be opened with STATA 14 or above. The <em>csv</em> file is a comma-separated value file. The separator is ',', and the first row is variable names. The content is the same in both files. Variables are:</p> <ul> <li><em>exporter</em>: ISO 3166 3-character country codes, string; </li> <li><em>commoditycode</em>: product 6-digit HS codes in HS revision 1992 (H0), string;</li> <li><em>epsilon</em>: import demand elasticity faced by the exporter, numeric;</li> <li><em>epsilon_se</em>: standard error of <em>epsilon</em>, numeric;</li> <li><em>marketpower</em>: market power, inverse of the absolute value of the import demand elasticity faced by the exporter, numeric.</li> </ul> <p> </p> <p><strong>Reference</strong></p> <div> <div>Kee H.L., A. Nicita, M. Olarreaga 2008 'Import demand elasticities and trade distortions' Rev. Econ. Stat., 90 (4), pp. 666-682</div> <div> </div> <div>Solleder J.M. 2020 'Market power and export taxes' European Economic Review, Volume 125, 103425, ISSN 0014-2921, <a href="https://doi.org/10.1016/j.euroecorev.2020.103425">https://doi.org/10.1016/j.euroecorev.2020.103425</a>.</div> </div> <p> </p>
Natrialba magadii theoretical proteome exported from HaloLex
<p>The theoretical proteome is derived from the annotated Natrialba magadii ATCC 43099 genome (Siddaramappa et al, 2012).</p> <p>This proteome represents a Gold Standard Protein based annotation (Pfeiffer and Oesterhelt, 2015).</p> <p>This proteome was exported from HaloLex (Pfeiffer et al, 2008)</p> <p>A proteome study is based on this version of the theoretical proteome (Cerletti et al, 2018).</p>
Academic Family Tree Data Export
<p>The Academic Family Tree is a live, crowdsourced project, that documents academic mentoring relationships across many fields. Data are updated continually. This snapshot was taken on 2024-10-18.</p> <p>We welcome inquiries about this dataset and are interested in learning about any new results you uncover. Contact: <a href="mailto:davids@ohsu.edu?subject=Academic%20Family%20Tree%20%2F%20Zenodo%20dataset">davids@ohsu.edu</a>.</p> <p>This dataset contains key tables from the Academic Family Tree, including information on names/institutions of academic mentoring relationships and semi-automated links of authors to publications and US grants (NSF, NIH only). A subset of publication and grant links have been validated by human users. To save space, only unique identifiers are included for publications (PMID, DOI) and grants (federal project number), without other metadata (author, title, journal, principle investigator, etc). These identifiers should be adequate to link to other databases. Also note, author-publication links are broken into several separate files. These files should be concatenated into a single table to generate a complete dataset. Some additional information is available here: <a href="https://academictree.org/export.php">https://academictree.org/export.php</a>.</p> <p>These data are associated with Liénard, J.F., Achakulvisut, T., Acuna, D.E. <em>et al.</em> Intellectual synthesis in mentorship determines success in academic careers. <em>Nature Communications</em> <strong>9, </strong>4840 (2018) (<a href="https://doi.org/10.1038/s41467-018-07034-y">https://doi.org/10.1038/s41467-018-07034-y</a>). Please cite this publication in work that uses this dataset.</p> <p>Funded by NSF Award 1933675.</p>
StreamFRE: Downstream leaf litter exports in Prieta and lateral and vertical leaf litter inputs.
StreamFRE: Downstream leaf litter exports in Prieta and lateral and vertical leaf litter inputs. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
SBC LTER: Beach: Kelp export to select sandy beaches, Isla Vista, 2005-2006
The composition, cover and wet biomass of macroalgal wrack accumulated in the intertidal zone measured on selected sandy beaches of the mainland coast of the Santa Barbara Channel.
NMNH Images: NMNH images from GBIF export
Public records of accessioned specimens and observations curated by the National Museum of Natural History, Smithsonian Institution. These data are from the Departments of Botany, Entomology, Invertebrate Zoology and Vertebrate Zoology (Amphibians & Reptiles, Birds, Fishes, and Mammals) and include more than 270,000 primary type specimen records. <p></p>https://collections.nmnh.si.edu/ipt/resource?r=nmnh_extant_dwc-a<p></p>
Chirila export of languages in Malaspinas et al sample
<p>Linguistic cognate dataset that underlies the language tree associated with Malaspinas, Anna-Sapfo, et al. "A genomic history of Aboriginal Australia." <em>Nature</em> 538.7624 (2016): 207.</p>
Exported Definitions, References, Document Structure from EU Legislations
<p>A sample corpus of definitions, references, and document structures extracted from the EUR-LEX corpus of EU Legislations.</p>
[MetFrag] MoNA Export LC-MS-MS Spectra for MetFrag
<p>This is an updated version of the LC-MS/MS MoNA library for use in <a href="https://ipb-halle.github.io/MetFrag/">MetFrag</a>. </p> <p>Once you download this file, you can use it in <a href="https://github.com/ipb-halle/MetFragRelaunched/releases/latest">MetFrag Command Line</a> with the following command: </p> <pre><code>OfflineSpectralDatabaseFile = ~/MoNA-export-LC-MS-MS_Spectra-20241014-0.005.mb</code></pre> <p>Thanks to Bego for thoroughly testing this file and to Christoph for his tips throughout the years!</p>
FT-IR spectra – exported as text
<p>The codenames in the filenames denote the following compounds:</p> <p>1Br: 1-butylquininium bromide</p> <p>2Br: 1-octylquininium bromide</p> <p>3Br: 1-dodectylquininium bromide</p> <p>4Br: 1-(2-hydroxyethyl)quininium bromide</p> <p>1Asp: 1-butylquininium L-asparaginate</p> <p>2Asp: 1-octylquininium L-asparaginate</p> <p>3Asp: 1-dodectylquininium L-asparaginate</p> <p>4Asp: 1-(2-hydroxyethyl)quininium L-asparaginate</p> <p>1Ala: 1-butylquininium L-alaninate</p> <p>2Ala: 1-octylquininium L-alaninate</p> <p>3Ala: 1-dodectylquininium L-alaninate</p> <p>4Ala: 1-(2-hydroxyethyl)quininium L-alaninate</p>
COALMOD-World 2.0 data, results, figures for: Stranded Assets in the Coal Export Industry? The Case of the Australian Galilee Basin
<p>This dataset contains all COALMOD-World 2.0 data for Hauenstein et al. (2023): New coal mines in the Australian Galilee Basin are not economically viable and are prone to become stranded assets (doi.org/10.1016/j.oneear.2023.07.005).</p> <p>With the input data files and the GAMS scenario file the model (https://github.com/chauenstein/COALMOD-World_v2.0) can be run to reproduce the model results.</p> <p>Furthermore, the output.zip folder contains the results file, the R code to compile the figures, and PDFs of the figures.</p>
Chinese Engineers Relational Database (CERD) Bi-monthly Export
<p><strong>This is a bi-monthly export.</strong></p> <p>CERD is a database of engineers from the Chinese Republican period (1912–1949). Based on various digitised historical sources, it is a prosopographic catalogue of individuals, their education and their employment, and the institutions connected with it. Most biographical events have geographical information attached to them. The data can be used freely by researchers to answer individual research questions.</p> <p><strong>Citation recommendation:</strong></p> <p>Pelzer, Thorben, et al., eds. (2021–2023). Chinese Engineers Relational Database (CERD) (Version 1.7.0). Zenodo. http://doi.org/10.5281/zenodo.4075601.</p> <p><strong>Changelog:</strong></p> <p>1.7.0 (February 2023): Approx. 17,600 individuals, two additional sources<br> 1.6.0 (August 2022): Approx. 17,400 entries, completed sources, minor corrections, mergers, translations<br> 1.5.0 (June 2022): Approx. 17,300 entries, added additional memberships<br> 1.4.0 (April 2022): Approx. 16,800 entries, added additional sources, schooling<br> 1.3.0 (February 2022): Approx. 16,500 entries, added documentation, additional memberships<br> 1.2.0 (December 2021): Approx. 16,300 entries, added frequent CSV exports, source annotations, 5 missing <em>minglu</em> pages, additional association memberships<br> 1.1.0 (October 2021): Approx. 15,700 entries, added selected association memberships<br> 1.0.0 (August 2021): Approx. 15,400 entries [complete <em>gongchengren minglu</em> dataset milestone]<br> 0.5.0 (June 2021): Approx. 13,000 entries<br> 0.4.0 (April 2021): Approx. 10,500 entries<br> 0.3.0 (February 2021): Approx. 7,500 entries, as well as various corrections, mergers, translations<br> 0.2.0 (December 2020): Approx. 5,000 entries, as well as various corrections, mergers, translations<br> 0.1.0 (October 2020): Early version with approx. 3,000 entries</p> <p><strong>Online Access:</strong></p> <p>Via Heurist: <a href="https://home.uni-leipzig.de/cerd/">https://home.uni-leipzig.de/cerd/</a></p>
Historical (1979 - 2020) data for anthropogenic inputs to a catchment and riverine mainstem exports for carbon, nitrogen, and phosphorus
<p>We estimated the difference in Net Anthropogenic Nitrogen and Phosphorus Inputs (NANI-NAPI) at the finest scale possible (the municipality) in the <em>Rivière du Nord</em> watershed (Québec, Canada) between 1981 and 2016. The dataset here reports the delta between those two years for each municipality in the watershed.</p> <p>Three sites along the mainstem of <em>Rivière du Nord </em>have been sampled ~bi-monthly from ~1979 - 2020 for dissolved organic carbon (DOC), total nitrogen (TN), and total phosphorus (TP), from which we estimated annual riverine export at each site. We also include annual precipitation (as the sum of rain and snow), and NANI-NAPI interpolated for each sub-watershed for 1981, 1986, 1991, 1996, 2001, 2006, 2011, and 2016.</p> <p> </p> <p> </p>
SI2: How circular is an extractive economy? South Africa's export orientation results in low circularity and insufficient societal stocks for service-provisioning
<p>Supporting information SI2 for the manuscript under review:</p> <p>How circular is an extractive economy? South Africa’s export orientation results in low circularity and insufficient societal stocks for service-provisioning </p> <p> </p> <p>It provides the data used and the basic mass balanced calculation for a circularity assessment.</p>
Exported particulate carbon and nitrogen measurements from 4-day sediment trap deployments in the CCE region, 2007 - 2019 (ongoing).
Sediment traps are used to measure particulate export flux of organic matter from the euphotic zone at various depths (up to 100), so to better understand what flows through the water column regarding food chains in the CCE. Tubes are filled with dense seawater to create a density gradient, and are attached to a wire connected to a drifting float for up to 4 days. Samples from the recovered array of the trap material (fecal matter and other sinking particles - zooplankton removed) are filtered onto precombusted filters for analyses of particulate carbon and nitrogen flux rates for each cycle in the CCE Process cruises (since 2007, ongoing)
Wordpress blog export, posts from 2006--18 July, 2015.
<p>An XML Wordpress export of the 440 blog posts and associated comments by Henry Rzepa up to July 18, 2015.</p>
Arctic Rivers Dissolved Organic Carbon River Export Analysis
<p>This repository has data for the estimation of dissolved organic carbon and colored dissolved organic carbon in the 6 Great Arctic Rivers. The data has been derived from the arcticgreatrivers.org repository for use in the USGS LOADEST model https://water.usgs.gov/software/loadest/ to predict river mass load as a function of measured discharge. The *_discharge.dat files contain the river discharge data from arcticgreatrivers.org and each *.tar directory with the river's name contain the output file from the LOADEST model with 100 model runs each for each parameter defined below. The netcdf file ArcticRivers_CarbonTrends.nc contains all of the LOADEST model prediction ensembles and mean/total seasonal values used in the trend analysis.</p> <p>DOC=Dissolved organic carbon (mg/L)</p> <p>CDOC=Colored dissolved organic carbon (mg/L)</p> <p>S1=CDOM absorption spectral slope between 275-295 nm (1/nm)</p> <p>S2=CDOM absorption spectral slope between 350-400 nm (1/nm)</p> <p>a300 = CDOM absorption at 300 nm (1/m)</p> <p>There is also a file River_CDOM_PUB.mat that is a MATLAB data structure with the data used to construct the LOADEST model input files.</p> <p>Dr. J. Blake Clark should be contacted at bclark@umbc.edu with any specific questions.</p>
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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