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25 results for “CEC”
S91| CECTOYS | Chemicals of Emerging Concern (CECs) in plastic toys
<p>This is the collection associated with list S91 CECTOYS, List of Chemicals of Emerging Concern (CECs) found in plastic toys on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p> </p> <p>A compiled list of 126 Chemicals of Emerging Concern (CECs) found in plastic toys described in Aurisano et. al DOI: 10.1016/j.envint.2020.106194. The list is categorized into four (as seen in CECTOYS_notes.txt) based on being included in regulatory lists of concern as well as reported hazard index (HI) and child cancer risk (CCR) based criteria.</p> <p>Structural identifiers and mapping to DTXSID provided by ECI.</p> <p> </p>
PVsyst Parameters Translated from the CEC Module Database
<p>The purpose of this dataset is to provide PVsyst model parameters for all modules in the CEC module database, <a href="https://github.com/NREL/SAM/blob/pysam-v5.1.0/deploy/libraries/CEC%20Modules.csv">taken from NREL's SAM</a>. The parameters were translated using a method described in <a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10893713"><em>Parameter Translation for Photovoltaic Single Diode Models</em></a>. Each module has a value for average normalized bias error for p_mp, v_mp, v_oc, i_mp, and i_sc. Of the 16,857 modules in the database, only 110 modules have p_mp NMBE larger than +/- 5%.</p> <p>This work was supported by the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) under the Solar Energy Technologies Office Award Number 38267. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy or the United States Government.</p> <p>Description of columns:</p> <table> <tbody> <tr> <td><strong>module_name</strong></td> <td><strong>alpha_sc</strong></td> <td><strong>gamma_ref</strong></td> <td><strong>mu_gamma</strong></td> <td><strong>I_L_ref</strong></td> <td><strong>I_o_ref</strong></td> <td><strong>R_sh_ref</strong></td> <td><strong>R_sh_0</strong></td> <td><strong>R_s</strong></td> <td><strong>cells_in_series</strong></td> </tr> <tr> <td>Module name in CEC database</td> <td>temperature coefficient of short circuit current <br>(A/C)</td> <td>diode ideality factor</td> <td>temperature coefficient of diode ideality factor (1/K)</td> <td>photocurrent at STC (A)</td> <td>diode reverse saturation current at STC (A)</td> <td>Shunt resistnace at STC (Ω)</td> <td>Shunt resistance at 0 irradiance (Ω)</td> <td>Series resistance at STC (Ω)</td> <td># of cells connected in series in module</td> </tr> </tbody> </table> <table> <tbody> <tr> <td><strong>pmp/vmp/vmp/isc/imp nmbe<br></strong></td> </tr> <tr> <td>average % error between value when calculated with original CEC parameters and new PVsyst parameters </td> </tr> </tbody> </table> <p>(SAND2024-15703O)</p>
S87 | CHLORINETPS | List of chlorination byproducts of 137 CECs and small disinfection byproducts
<p>This is the collection associated with list S87 CHLORINETPS of chlorination byproducts of 137 CECs and small disinfection byproducts on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>A list of chlorination byproducts of 137 contaminants of emerging concern (CECs) and small molecular weight disinfection byproducts from the CHLORINE_TPs database, described in Postigo et al<a href="https://doi.org/10.1016/j.teac.2021.e00148"> </a>DOI: <a href="https://doi.org/10.1016/j.teac.2021.e00148">10.1016/j.teac.2021.e00148</a>. 91% are amenable to LC-ESI-HRMS. </p>
Soil cation exchange capacity (CEC) from 9 Hillslope Project sites in Macon County, North Carolina, within the Upper Little Tennessee River Basin
Cation exchange capacity (CEC) of soil was analyzed as part of the hillslope plots in Macon County, North Carolina. There were 9 hillslope sites representing a gradient of development, including forested, valley agriculture, and mountain housing developments. There were 12 10 x 10-m plots at each site. A soil probe was used to collect soils from 3 depths at each plot: 0-10 cm, 10-30 cm, and 30 + cm. Soil was then dried, processed, and analyzed for CEC at the Coweeta Analytical Laboratory.
Local optima network metrics from the IEEE CEC 2024 paper "Information flow and Laplacian dynamics on local optima networks"
<p>Local optima network metrics from the IEEE CEC 2024 paper "Information flow and Laplacian dynamics on local optima networks". </p> <p>There are two CSV files: one for each of the two iterated local search confgurations used to construct the networks (low or high). In each file, a row contains information about one QAPLIB instance. Easch row contains all the metrics computed for the associated LON and also algorithm performance data on the instance. </p>
Dataset of chemicals of emerging concern (CECs) in surface water in the Swedish west coast (Stenungsund)
<p>This repository encompasses the environmental concentrations of emerging chemical contaminants (CECs) present in surface water samples obtained from the west coast of Sweden, specifically Stenungsund.</p>
Dataset on concentrations of CECs / PMTs /vPvM chemicals in Northern Portugal - Galicia (NW Spain) surface waters and wastewaters
<p>Dataset of concentrations of different contaminants of emerging concern (CECs), including persistent mobile and toxic (PMT) and very persistent and very mobile (vPvM) chemicals, in raw and treated wastewater, inland and coastal water samples from Galicia (NW Spain) and the North of Portugal.</p> <p>Data is provided in MS Excel (xlsx) and CSV formats and contains chemicals data, concentrations, location of the samples (coordinates) and sampling date. Location of the wastewater samples is not provided ought to confidentiality agreement.</p> <p>Further details are provided in the associated publication:</p> <p><strong><a href="http://doi.org/10.1016/j.scitotenv.2023.163737"><em>R. Montes et al. Occurrence of persistent and mobile chemicals and other contaminants of emerging concern in Spanish and Portuguese wastewater treatment plants, transnational river basins and coastal water. Science of the Total Environment 2023, 885, 163737. DOI: </em>10.1016/j.scitotenv.2023.163737</a></strong></p> <p><strong>If you use this data, please cite this ZENODO deposit (DOI: <a href="https://doi.org/10.5281/zenodo.6603302">10.5281/zenodo.6603302</a>) and the associated publication mentioned above (DOI: <a href="http://doi.org/10.1016/j.scitotenv.2023.163737">10.1016/j.scitotenv.2023.163737</a>)</strong></p>
Renewable Generation Profiles and Weather Correlated Loads Used in CEC EPC-19-056 Final Report
<p>This repository contains 8760 hour time series data for load and renewable generation utilized in the <br> CEC EPC-19-056 Final Report: Assessing the Value of LDES in California. The details of how these profiles were constructed can be found in Appendix B of the report.</p> <p>The load balancing authorities and renewable resources modeled in this study are consistent with those found in the 2019-20 CPUC IRP Inputs and Assumptions, which can be found here: https://www.cpuc.ca.gov/-/media/cpuc-website/divisions/energy-division/documents/integrated-resource-plan-and-long-term-procurement-plan-irp-ltpp/2019-2020-irp-events-and-materials/inputs--assumptions-2019-2020-cpuc-irp_20191106.pdf</p>
Soil Cation Exchange Capacity by the summation method CEC, Ca, Mg, K: Biodiversity II: Effects of Plant Biodiversity on Population and Ecosystem Processes
Biodiversity II (E120) is designed to determine how the number of plant species affects the dynamics of ecological processes at the population, community, and ecosystem levels. By experimentally manipulating the number of species and the kinds of species, the amount of plant growth and the change from year to year, that result can be examined. Plots are large (9m x 9m actively maintained) and well-replicated, allowing responses of plant pathogens, insect herbivores, seed predators, soil parameters, invasive plant species and other variables to also be studied. Plots were seeded in May 1994 to have 1, 2, 4, 8, or 16 species, with roughly 30 replicates of each diversity level. The species composition of each plot was chosen by random draw from a pool of 18 grassland perennials that included four warm-season (C4) grasses, four cool-season (C3) grasses, four legumes, four non-legume forbs, and two woody species. All species occur in monoculture allowing comparison of responses of each species in monoculture to combinations of these same species. The experiment was established in 1994 by the lead investigators David Tilman, Peter Reich, Johannes Knops, and David Wedin. Experiment 120 is similar to Experiment 123, but it uses larger plots to provide a large capacity for long-term subexperiments.
CEC EPC-19-056 Long-Duration Energy Storage Modeling Dataset
<p>Modeling dataset to study the value of long-duration energy storage (LDES) as part of the California bulk electricity system. Based on California Public Utilities Commission (CPUC) Integrated Resource Planning (IRP) proceeding 2019-2020 cycle data.</p>
S93 | CECMOUTHING | Chemicals of Emerging Concern (CECs) in children's mouthing exposure
<p>This is the collection associated with list S93 CECMOUTHING | Chemicals of Emerging Concern (CECs) in children's mouthing exposure on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p> </p> <p>A dataset of 60 Chemicals of Concern (CECs) and their chemical migration rate into saliva due to mouthing exposure in children studied in Aurisano et. al DOI:<a href="https://www.nature.com/articles/s41370-021-00354-0">10.1038/s41370-021-00354-0</a>.<br> This dataset was used to develop a regression model for predicting migration rates for manifold chemical-material combinations to support high-throughput screening.<br> </p> <p>Structural identifiers and mapping to DTXSID provided by ECI.</p>
STE CECs: Coastal Radon Time-series v1.0
<p><strong>Description: </strong>Radon in coastal water time-series data and accompanying water and meteorological parameters collected from 27 different locations globally. These data were used to train and validate two deep learning models.</p> <p>Data for each study site were provided by authors - please see the "References" sheet in the document for the original source and citation of the data.</p> <p>Associated code can be found here: 10.5281/zenodo.7581389</p> <p>Associated publication accepted (in press) in <em>Water Resources Research.</em></p> <p><strong>Column names in the "data" sheet are as follows:</strong></p> <p>datetime = date and time of measurement (local time, MM/DD/YY HH:MM)</p> <p>Location = name of location of measurement (data = string data)</p> <p>Aquifer_type = categorical aquifer type (data = string data, options: rocky, sandy, or muddy).</p> <p>depth_m = depth of water column (m). Measured with CTD probe or similar.</p> <p>ctdtemp_C = water temperature (degrees Celsius) at point of radon measurement. Measured with CTD probe or similar.</p> <p>ctdsal = water salinity (unitless) at point of radon in water measurement. Measured with CTD probe or similar.</p> <p>windsp_ms = wind speed, 10 m above sea level (units = m/s). Data from wunderground.com from closest weather station for all sites except for Kīholo Bay, HI, USA (data sourced from RAWS USA Climate Archive, Puu Waawaa station: https://raws.dri.edu/cgi-bin/rawMAIN.pl?hiHPUW) and FSUCML, FL, USA (data sourced from FAWN Carrabell Station: https://fawn.ifas.ufl.edu/data/reports/)</p> <p>airtemp_C = air temperature (units = degrees Celsius). Data from wunderground.com from closest weather station for all sites except for Kīholo Bay, HI, USA (data sourced from RAWS USA Climate Archive, Puu Waawaa station: https://raws.dri.edu/cgi-bin/rawMAIN.pl?hiHPUW) and FSUCML, FL, USA (data sourced from FAWN Carrabell Station: https://fawn.ifas.ufl.edu/data/reports/)</p> <p>Rn_Bqm3 = radon in water (units = Bq/m^3). Measured with Durridge RAD7 or similar radon-in-air detector or underwater gamma spectrometer (e.g., Dulai et al., 2016: https://doi.org/10.1007/s10967-015-4580-9).</p>
CECS, Survey of wildlands professionals and the general public in California, 2022
<p><span>Natural resource managers are increasingly required to make tradeoffs between the economical, ecological, and social outcomes of different management actions, in many cases making the ability to ensure public acceptance an important part of the effective management of natural resources. However, in comparison to our knowledge of the drivers of the economic, and ecological aspects of natural-resource management, our understanding of the drivers of social acceptance remains limited. </span></p> <p><span>Relying on survey data collected in California, we examine what factors drive a belief among the general public in the capacity of managers to limit the occurrence of natural-resource events related to wildfires, water shortages, and utility failures. These results are also compared with a sample consisting of natural-resource professionals. Our results show that the general public had a more positive view of management capacity than did the professionals sampled, also displaying greater levels of trust and belief in the efficiency of management, while being less concerned about potential future risks. </span></p> <p><span>We also found structural differences in attitude formation between the public and the natural-resource professionals. Personal experience with natural-resource events and perceived future risk drove beliefs about management capacity in both samples, while level of trust and belief in the efficiency of management only had statistically significant effects in the public sample. These findings suggest that natural-resource management in California is likely to enjoy high levels of social acceptance among the public, as long as interventions continue to be perceived as effective. Findings also highlight structural differences in attitude formation between the public and natural-resource professionals in California.</span></p>
CECS, Survey of wildlands professionals and the general public in California, 2022
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1996 Ring soil texture, pH and Cation Exchange Capacity (CEC):BioCON : Biodiversity, Elevated CO2, and N Enrichment
BioCON (Biodiversity, CO2, and Nitrogen) is an ecological experiment started in 1997 at the University of Minnesota's Cedar Creek Ecosystem Science Reserve. BioCON's goal is to explore the ways in which plant communities will respond to three environmental changes that are known to be occurring on a global scale: increasing nitrogen deposition, increasing atmospheric CO2, and decreasing biodiversity. Why Biodiversity, CO2, and Nitrogen? While there are many uncertainties in global change biology, there are also some well documented facts. Some of these are: 1. The amount of carbon dioxide (CO2) in the atmosphere is rising. Since the industrial revolution, the CO2 concentration in the atmosphere has increased from approximately 275 parts per million (ppm) to about 378 ppm today. This has been largely the result of fossil fuel burning. It is expected that CO2 levels will continue to rise, and that by the year 2050 these levels will be approximately 550 ppm. CO2 is the raw material for photosynthesis and is known to affect plant growth and development. 2. The amount of nitrogen moving through terrestrial ecosystems has increased in the recent past. While natural "background" levels of nitrogen fixation have remained constant, human additions to the system through fertilizer production and fossil fuel use have increased dramatically. Nitrogen is a key nutrient for plant growth and plays a critical role in plant community structure and composition in many environments. 3. Biodiversity levels are falling. While the research and data are not as complete as they are for CO2 and nitrogen, data indicate that the number of species globally, is being reduced. Perhaps more important for ecosystem function, diversity levels on local to regional scales have fallen due to land use change, biotic invasion and many other drivers. While much is known about how each of these factors affects ecosystem functioning, many questions remain. There is also little data on how these issues affe
CEC Count Changes to Support GvHD Diagnosis.
ClinicalTrials.gov study NCT02064972. IPD Sharing: YES. Countries: 1. Publications: 10.
Evaluating a Clinical Ethics Committee (CEC) Implementation Process
ClinicalTrials.gov study NCT05466292. IPD Sharing: NO. Countries: 1. Publications: 1.
Origin of CEC in Patients After Allo-HSCT
ClinicalTrials.gov study NCT04038827. IPD Sharing: YES. Countries: 1. Publications: 13.
Comparison of Non-Surgical Treatment Options for Chronic Exertional Compartment Syndrome (CECS)
ClinicalTrials.gov study NCT04409600. IPD Sharing: Not stated. Countries: 1. Publications: 0.
IceBridge Ku-Band Radar L1B Geolocated Radar Echo Strength Profiles, Version 1
This data set contains elevation and surface measurements over Greenland, the Arctic, and Antarctica, as well as flight path charts and echogram images acquired using the Center for Remote Sensing of Ice Sheets (CReSIS) Ku-Band Radar Altimeter.
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Allen Brain Atlas
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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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