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506 results for “assessment model”
Assessing Heavy Metal Contamination in Agricultural Soils: A Predictive Model Integrating GIS Tools and Probability-Risk Matrix – Case Study: Guarda Region, Portugal
<p>In these files we can find the final risk map of heavy metal contamination for the guarding area in Portugal obtained according to the methodology explained in the paper "Assessing Heavy Metal Contamination in Agricultural Soils: A Predictive Model Instegrating GIS Tools and Probability-Risk Matrix - Case Study: Guarda Region (Portugal)</p> <p>Final Risk Equal.tiff: GeoTiff with a pixel size of 30m. EPSG:3763 - ETRS89 / Portugal TM06</p> <p>Also attached is the symbolisation for the image in .qml (Quantum GIS Layer Style File) format.</p> <p>A file called RISK RECLASS is also available, where you can find the risk classification maps for each of the studied factors: </p> <ul> <li>Proximity to roads</li> <li>Proximity to industrial areas</li> <li>Ph</li> <li>Soil organic content</li> <li>Slope</li> <li>Soil texture</li> <li>Mining extraction areas </li> <li>Drainage</li> </ul> <p>finally a DATABASE file where the data of the 360 points for the calculation of the risk maps can be found. </p>
Fig. 1 in Assessing The Abundance Of Caucasian Salamander, Mertensiella Caucasica (Caudata, Salamandridae), With N-Mixture Model In Northeastern Anatolia
Fig. 1. General view of study area.
Quantitative assessment of the reservoir-induced and urbanization-induced impact on multivariate flood risk via a nonstationary vine Copula model
<p>Here we show the results of the characteristics of the floods at Huayuankou, Lanzhou and Toudaoguai gauges selected by AMS and POT mentod, respectively. Besides that, the inormation about the reservoirs and the imprevious layer in the control catchment of each station is also uploaded.</p>
Scupa: Single-cell unified polarization assessment of immune cells using the single-cell foundation model
<p>This dataset contains the processed scRNA-seq data and code for single-cell unified polarization assessment. Please refer to the article "Scupa: Single-cell unified polarization assessment of immune cells using the single-cell foundation model" for the detailed data and method description.</p> <p>*.rds: the processed scRNA-seq datasets with Universal Cell Embeddings saved as a Seurat object. The Universal Cell Embeddings are saved in the assay 'uce'.</p> <ul> <li>immune_dict_uce.rds: Immune Dictionary.</li> <li>ifnb_treatment_uce.rds: human PBMCs treated with IFN-beta.</li> <li>macrophage_stimulation_uce.rds: human macrophages treated with one or two of IFN-beta, IFN-gamma, TNF-alpha and IL-4.</li> <li>il2_treatment_uce.rds: mouse spleen CD8+ T cells treated with IL-2 or anti-PD-L1.</li> <li>pan_cancer_myeloid_uce.rds: tumor-infiltrating myeloid cells across seven cancer types.</li> </ul> <p>notebooks.zip: Jupyter notebooks containing code for training models and applications to multiple datasets.</p>
Assessment of the sea surface temperature diurnal cycle in CNRM-CM6-1 based on its 1D coupled configuration - model outputs
<p>These tar file are associated with an article submitted to Geoscientific Model Development under identification number gmd-2021-413 (https://www.geoscientific-model-development.net): Assessment of the sea surface temperature diurnal cycle in CNRM-CM6-1 based on its 1D coupled configuration<br> By A. Voldoire, R. Roehrig, H. Giordani, R. Waldman, Y. Zhang, S. Xie, MN Bouin</p> <p>3 files correspond to code components that can be distributed freely</p> <p>- surfex.tgz for the surfex v8.0 distributed under a Cecill-C License</p> <p>- oasis-mct-3.0.tgz for oasis-mct3.0 distributed under a GNU General Public License</p> <p>- nemo_v3.6.tgz for the nemo, distibuted under a Cecill-C License</p> <p>These three components are mainly fortran codes.</p> <p>The last file "<a href="https://zenodo.org/api/files/e94922e7-22eb-445b-acc3-03e11ca1af6b/CNRM-CM6-1D_published_experiments.tgz?versionId=2460ec63-89f5-415f-9613-1954f048b238">CNRM-CM6-1D_published_experiments.tgz </a>" contains all model outputs that have been used in this article. These model outputs are in netcdf format and organized by experiment.</p>
The Effect of Stromal Vascular Fraction (SVF) & Scaffolds Application on Fracture Healing with Bone Defect as Assessed Through Osteocalcin and Bone Morphogenetic Protein-2 (BMP-2) Biomarker Examination: Experimental Study on Murine Model
<p>This data is the raw data for the manuscript with titled The Effect of Stromal Vascular Fraction (SVF) & Scaffolds Application on Fracture Healing with Bone Defect as Assessed Through Osteocalcin and Bone Morphogenetic Protein-2 (BMP-2) Biomarker Examination: Experimental Study on Murine Model.</p>
Fig.1 in Associations Between Habitat Quality And Body Size In The Carpathian-Podolian Land Snail Vestia Turgida: Species Distribution Model Selection And Assessment Of Performance
Fig.1. Sampling sites for Vestia turgida in Ukraine (photo by O. Baidashnikov).
Fig. 5 in Associations Between Habitat Quality And Body Size In The Carpathian-Podolian Land Snail Vestia Turgida: Species Distribution Model Selection And Assessment Of Performance
Fig. 5. Partial dependence plot for terrain roughness index (tri).
Fig. 7 in Associations Between Habitat Quality And Body Size In The Carpathian-Podolian Land Snail Vestia Turgida: Species Distribution Model Selection And Assessment Of Performance
Fig. 7. Partial dependence plot for silt content (SLT).
Fig. 6 in Associations Between Habitat Quality And Body Size In The Carpathian-Podolian Land Snail Vestia Turgida: Species Distribution Model Selection And Assessment Of Performance
Fig. 6. Partial dependence plot for pH water (phh2o).
Optimization of cancer risk assessment model for PM2.5-bound PAHs Application in Shanxi of China SM
<p><span>To optimize the models for cancer risk assessment of PM<sub>2.5</sub></span><span>-bound polycyclic aromatic hydrocarbons (PAHs), six models of three types utilized with high frequency in recent years, including the USEPA recommendation (Model I), inhalation carcinogen unit risk (Models </span><span>II</span><span>A–IID), and three exposure pathways (inhalation, dermal, and oral) (Model </span><span>III), were selected to calculate potential cancer risk using the benzo[a]pyrene toxicity equivalent method. The results indicated no significant differences in the risk values between Models IIA and III, and no significant differences were found among Models </span><span>IIB</span><span>, </span><span>IIC</span><span>, and </span><span>IID</span><span>. </span><span>However, there were significant differences between Models I and </span><span>II</span><span>, and between Models I and </span><span>III. </span><span>Furthermore, the optimization of Model I indicated that the population exposure parameters for each country should be used and age differences should not be disregarded. However, no significant differences were observed with respect to gender.</span><span> In conclusion, Model I was superior to the other models; when age differences were not considered, Model IID (the USEPA's integrated risk information system) was preferred to replace Models </span><span>IIB</span><span> and </span><span>IIC</span><span>. Models IIA and </span><span>III may overestimate the risk of carcinogens, </span><span>and their parameters need to be reoptimized to accurately assess cancer risks.</span></p>
Documenting And Assessing Open Innovation: Co-creation Of An Open Data Model For Surgical Training (Additional materials, tables 2 & 3)
<p>Challenge competitions have recently resurged for promoting open innovation in areas where markets fail to provide incentives, such as the Sustainable Development Goals (SDGs). Challenges call for the general public to contribute novel solutions to a well-defined problem, in exchange for prizes, credentials and the promise of further development of selected solutions. The aim of this paper is to report on the development of an open and collaborative data model to document and evaluate innovations in the context of a challenge competition, while also being compatible with the work of other open source communities to validate and improve them. By reusing open documentation standards and embedding them into a semantic collaborative platform, the model aimed to be flexible enough to respond to the evaluation needs of the project organisers and self-assessment for participants. We expect our experience provides insights on the potential of semantic, collaborative platforms and standards for increasing the impact of innovations towards the SDGs.</p> <p>The developer team defined the goal and scope of the ontology in collaboration with the GSTC organisers. This was done by agreeing on scenarios where the ontology will be used and establishing competency questions that the ontology has to be able to respond to. Table 2 describes the four motivating scenarios, including actors involved, requirements, sequence of actions and main problems identified. Table 3 details the competency questions for each scenario.</p>
Assessment of the stream invertebrate β-diversity along an elevation gradient using a bidimensional null model analysis
<p><strong>Aim:</strong> β-Diversity, commonly defined as the compositional variation among localities that links local diversity (α‐diversity) and regional diversity (γ‐diversity), can arise from two different ecological phenomena, namely the spatial species turnover (i.e. species replacement) and the nestedness of assemblages (i.e. species loss). However, any assessment that does not account for stochasticity in community assembly could be biased and misinform conservation management. In this study, we aimed to provide a better understanding of the overall ecological phenomena underlying stream β-diversity along elevation gradients, and at contributing to the rich debate on null model approaches to identify non-random patterns in the distribution of taxa.</p> <p><strong>Location:</strong> Swiss Alpine region.</p> <p><strong>Methods:</strong> Based on presence-absence data of 78 stream invertebrate families from 309 sites, we analyzed the effect size of non-random spatial distribution of stream invertebrates on the β-diversity and its two components (i.e. turnover and nestedness). We used a modelling framework that allows exploring the complete range of existing algorithm sused in null model analysis, and to assess how distribution patterns vary according to an array of possible ecological assumptions.</p> <p><strong>Results:</strong> Overall, the turnover of stream invertebrates and the nestedness of assemblages were significantly lower and higher, respectively, than the ones expected by chance. This pattern increased with elevation, and the consistent trend observed along the altitudinal gradient, even in the most conservative analysis, strengthened our findings.</p> <p><strong>Main conclusions:</strong> Our study suggests that deterministic distribution of stream invertebrates in the Swiss Alpine region is significantly driven by differential dispersal capacity and environmental stress gradients. As long as the ecological assumptions for constructing the null models and their implications are acknowledged, we believe that they still represent useful tools to measure the effect size of non-random spatial distribution of taxa on β-diversity.</p>
Fig. 4 in Long Term (1985-2018) Changes Of The Habitat Suitability Of European Souslik Assessed By Maxent Modelling Based On Landsat Satellite Imagery - A Case Study From A Mountain Landscape Of Central Bulgaria
Fig. 4. Response curves, representing the dependence of predicted suitability both on the
Fig. 1 in Long Term (1985-2018) Changes Of The Habitat Suitability Of European Souslik Assessed By Maxent Modelling Based On Landsat Satellite Imagery - A Case Study From A Mountain Landscape Of Central Bulgaria
Fig. 1. Satellite view of the study area. White circles represent the location of the colonies
Robustness assessment via simulation-based fault injection of the implementation level models of the LEON3, MC8051, and PIC microcontrollers in presence of stuck-at, bit-flip, pulse, and delay fault models
<p>This dataset package contains the results of fault injection experiments for RTL and implementation-level models 3 microprocessors (LEON3, MC8051, PIC).</p> <p><strong> 1. Package contents:</strong><br> - raw traces of fault injection experiments: *.lst files<br> - analysis results grouped by fault models: *.html files in the /REPORT subfolders<br> - summary for each targeted HDL model and fault model: index.html in each design folder</p> <p>To facilitate the navigation through the contents, it is organized as a tree of *.html pages. The root page is 'index.html' in the archive root.<br> Additionally, to observe the raw traces for each experiment in the convenient form, the *.lst files are processed on the fly by custom python-script, returning the interactive *.html page. Each observation trace is a table, where: <br> - each row represents an observation vector, comprising {simulation time stamp}, {flags}, {internal state}, {outputs}.<br> - each cell is highlighted:<br> a) green if matches with reference trace (fault-free simulation),<br> b) red if mismatches with reference trace, denoting error for internals / failure for outputs,<br> c) violet in case of vector whose timestamp was not in reference (unexpected transition).<br> These highlighting options can be customized by modifying the linked *.css files.</p> <p> <br> <strong> 2. Installation</strong><br> 2.1 Ensure to have Web-Server installed (Apache preferable). For instance, XAMPP: https://www.apachefriends.org/index.html</p> <p>2.2 Ensure to have python ver. 2.x installed. Type in terminal (cmd console in Windows): “python --version” – if the output looks like > Python 2.x.x – python is installed. <br> Otherwise install the relevant 2.x.x distribution: https://www.python.org/<br> Add python installation path to environment path variable.<br> <br> 2.2 Ensure that Web-server is configured to execute CGI scripts, particularly python-scripts:<br> In the 'httpd.conf' file (XAMMP control panel – button config in front of apache module):<br> – search for line Options Indexes FollowSymLinks and add ExecCGI, so the resulting line looks like this: <br> Options Indexes FollowSymLinks ExecCGI<br> – search for #AddHandler cgi-script .cgi, uncomment (remove #), and append “.py” to this line, so the resulting line:<br> AddHandler cgi-script .cgi .pl .asp .py </p> <p>2.3 Unpack the *.zip package into the folder on the Web Server. For instance 'Web-server root folder'/ExperimentalResults.<br> The Web-Server root can be configured in the ‘httpd.conf’ file in the DocumentRoot section, for instance: <br> DocumentRoot "F:/HTWEB"<br> <Directory "F:/HTWEB"><br> ...</p> <p>2.4 In the web-browser navigate to the root directory of extracted package:<br> http://localhost/ExperimentalResults/index.html</p> <p> </p> <p><strong>3. How to read the contents</strong><br> The root page contains links to different analysis reports, for each HDL design under study and considered fault models. <br> The pages on the first tree level, represent the summary for each injection campaign, describing the rate of failure modes, number of experiments, latencies, and supplementary info. <br> The pages on the second tree level are the detailed analysis reports for each experiment, detailing the fault target, parameters of injected fault, detected failure mode, number of errors, etc. <br> The cells of the first column are highlighted a) in green if injection did not cause the failure, b) in red otherwise. The links in this first column navigate to the detailed traces for each experiment. <br> The latter requires that Web-server is configured to execute the python-scripts (see section 2 - Installation); otherwise the raw traces (*.lst files in ./results folders) can be observed by any text editor (notepad++, etc.).<br> </p>
Parameters for the modelization of operator, worker, and bystander exposure to pesticides in market gardening areas in Burkina Faso using three international exposure assessment models.
<p>Parameters used for the modelization of operator, worker, and bystander exposure to pesticides in market gardening areas in Burkina Faso using the following exposure assessment models:</p> <p>EFSA 2014: EFSA, 2014. Guidance on the assessment of exposure of operators, workers, residents and bystanders in risk assessment for plant protection products. EFSA J. 12, 3874. doi:10.2903/j.efsa.2014.3874</p> <p>HAIR2010: Kruijne, R., Deneer, J.W., Lahr, J., Vlaming, J., 2011. HAIR2010 Documentation: calculating risk indicators related to agricultural use of pesticides within the EU 205.</p> <p>WHO 2011: WHO, 2011. Generic risk assessment model for indoor and outdoor space spraying - WHO Pesticide Evaluation Scheme 1–72.</p> <p><br> This work was realized in the framework of the research component "Pesticides" (1.2.4) of the 3E program funded by the Swiss Agency for Development and Cooperation (SDC).</p> <p><br> Details on algorithm adaptations, scenario definitions and model outputs are available in the PhD thesis of Dr. Edouard Lehmann available from 2018 at https://infoscience.epfl.ch/</p>
Data from: Socio-ecological drivers of long-term ecosystem carbon stock trend: An assessment with the LUCCA model of the French case
Open the record for dataset details and reuse information.
Assessing the ocean carbon sink: Assimilation of temperature and salinity into a global ocean biogeochemistry model
<p>Data underlying figures in manuscript draft.</p>
Data from: Too much of a good thing? Supplementing current species observations with fossil data to assess climate change vulnerability via ecological niche models
<p>Ecological niche models (ENMs) are a powerful tool in ecological research and conservation planning. Since ENMs provide probability maps of suitable areas under environmental change, they may assist in designing conservation actions and addressing conservation priorities. However, ENMs are usually implemented by learning the species climatic preferences from their current geographic distribution, which leaves them vulnerable to the issue of niche truncation issues, as if comes with non-climatic limits to the current species distribution posed by e.g. anthropic activities and settlements, and is bound to assume that species are at equilibrium with their environments. These problems might be alleviated by the inclusion of fossil occurrences, which refer to moments during species evolution when such limits were absent, and a larger fraction of the species fundamental niche was probably explored. Here, we combined current and fossil occurrence data for 38 medium-large mammal species of conservation concern to assess the influence of the fossil record on ENM predictions under future climate change scenarios. We found that ignoring or including fossil data yields consistent trends in terms of predicted range increase/decrease. Yet, although adding fossil data invariably results in increased niche width, estimates of range change magnitude improved for just one half only of the species. These results suggest that most species might be in non-equilibrium with their environment, and that the inclusion of fossil data may be crucial to the better understanding of species climatic requirements, hence for designing effective conservation strategies. </p>
ScienceDex guides
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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.