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306 results for “prototypes”
Decomissioned Site: Ichawanochaway Creek (D03 ICHA) Legacy and Prototype Aquatic invertebrates (repackaging of occurrences published by the NEON Biorepository Data Portal)
This collection contains legacy and prototype NEON aquatic invertebrates from the decommissioned Ichawanochaway Creek site in Baker County, Georgia. These samples and specimens were collected using NEON protocol NEON.DP1.20120 and are summarized in NEON Prototype dataset 1599af29-fad4-4721-9b09-f8324b672d50. This data is available here.
University of Leicester TROPOMI Stable Water Vapour Isotopologue (H2O-ISO) Prototype Product (Vesrion 1.0.0)
<p>This repository holds the prototype level 2 TROPOMI stable isotopologue product for June 2019 used in the study:</p> <p>Thurnherr, I., Sodemann, H., Trent, T., Werner, M., and Boesch, H., 2024. Evaluating TROPOMI δD column retrievals with in situ airborne measurements using expanded collocation criteria, Earth Space Science, in review</p> <p>For further details, please refer to the project website (https://s5pinnovationh2o-iso.le.ac.uk/), which contains the Algorithm Theoretical Baseline Document (ATBD) and Product User Guide (PUG). The final prototype product (version 1.0.2) is freely available from T. Trent (University of Leicester) upon request.</p>
A corpus-based study of the acquisition of the English progressive by L1 Chinese learners: From prototypical activities to marked statives
<p>This article investigates how EFL learners’ progressive markings are influenced by the lexical aspect of verbs, modality (spoken vs. written), and proficiency levels, focusing on the controversial issue of stative verbs in progressives in L2 acquisition. Spoken (SECCL) and written (WECCL) corpus data from two proficiency levels of Chinese EFL learners and comparison data from native English speakers (COCA) were analyzed. The results suggest that in both learner and native data the progressive -<em>ing</em> is strongly associated with activity verbs, stative verbs being least likely to be inflected with the progressive<em>,</em> as predicted by the Aspect Hypothesis (Andersen and Shirai 1994, 1996). However, inconsistently with the Aspect Hypothesis, this association strengthens with higher proficiency levels. Learners’ use of stative verbs in the progressive and the overextended use of stative progressives was also found to be related to spoken vs. written mode of production and proficiency levels, with learners retreating from overextension as their proficiency increases. A usage-based account of the findings is proposed.</p>
TERMINUS WP6: Prototype device for sorting and separation. Reprocessing of materials. TASK 6.2: Performances of packaging in use.
<p>Astrid E. Delorme, Tanja Radusin, Petri Myllytie, Vincent Verney and Haroutioun Askanian: Enhancement of Gas Barrier Properties and Durability of Poly(butylene succinate-co-butylene adipate)-Based Nanocomposites for Food Packaging Applications. Nanomaterials 2022, 12, 978. https://doi.org/10.3390/ nano12060978</p> <p> </p> <p><strong>Abstract</strong></p> <p>Poly(butylene succinate-co-butylene adipate), PBSA, based materials are receiving growing attention in the packaging industry for their promising biodegradability. However, poor gas barrier properties and low durability of biodegradable polymers, such as PBSA, have limited their wide-spread use in food packaging applications. Here we report a scalable solution to improve gas barrier properties and stabilize PBSA against photo-aging, with minimal modifications to the biodegradable polymer backbone by using a commercially available and biocompatible layered double hydroxide, LDH, filler. We investigate and compare the mechanical, gas barrier and photoaging properties of PBSA and PBSA-LDH nanocomposite films produced on pilot scale. An increase in rigidity in the nanocomposite was observed upon addition of LDH fillers to neat PBSA, which direct the application of neat PBSA and PBSA-LDH nanocomposite to different food packaging applications. The addition of LDH fillers into neat PBSA improves the oxygen and water vapour barriers for the PBSA based nanocomposites, which increases the attractiveness of PBSA material in food packaging applications. Through changes in the viscoelastic behaviour, we observe an improved photo-durability of photoaged PBSA-LDH nanocomposites compared to neat PBSA. It is clear from our studies that the presence of LDH enhances the lifetime durability and modulates the photodegradation rate of the elaborated biocomposites.</p> <p> </p> <p><strong>Dataset</strong></p> <p>This dataset contains all the rheological, UV-Vis, DSC, TGA, IR Tensile testing, Gas Barrier and DSC raw data used to generate graphs and discussions in the article “Enhancement of gas barrier properties and durability of Poly(butylene succinate-co-butylene adipate)-based nanocom-posites for food packaging applications” doi . Data are available in a compressed .zip file with 1 folder (Packaging-performances-PBSA-LDH_v01_TER_WP6_D6-2.zip) containing 7 .xlsx files containing all the rheological, IR, UV-Vis, DSC, TGA, Tensile testing, Gas Barrier raw data, 4 .pdf files one describing the experimental methods and materials, a second .pdf file outlining the metadata and information (this document) and two are the material data sheets for SOBACID®911 and PBSA used in the study, and one .zip file containing the SEM images in .TIF and .JPG formats.</p> <p> </p> <ul> <li>The <em>DSC-data-Packaging-performances-PBSA-LDH_v01_TER_WP6_D6-2.xlsx</em> file contains the data of the heating and cooling steps of the DSC experiments performed on PBSA and PBSA-LDH nanocomposite films.</li> <li>The <em>Gas-Barrier-data-Packaging-performances-PBSA-LDH_v01_TER_WP6_D6-2.xlsx</em> file contains the Oxygen Transmission Rates (OTR) and Water Vapour Transmission Rates (WVTR) of PBSA and PBSA-LDH nanocomposite films.</li> <li>The <em>IR-data-Packaging-performances-PBSA-LDH_v01_TER_WP6_D6-2.xlsx</em> file contains the FT-IR data used to create the IR-spectra of aged and unaged PBSA and PBSA-LDH nanocomposite films.</li> <li>The <em>Rheology-data-Packaging-performances-PBSA-LDH_v01_TER_WP6_D6-2.xlsx</em> file contains the melt rheology data of unaged and aged PBSA and PBSA-LDH nanocomposite films used for Cole-Cole plots and extrapolation of zero shear viscosity.</li> <li>The <em>Tensile-data-Packaging-performances-PBSA-LDH_v01_TER_WP6_D6-2.xlsx</em> file contains the tensile testing data of PBSA and PBSA-LDH nanocomposite films.</li> <li>The <em>TGA-data-Packaging-performances-PBSA-LDH_v01_TER_WP6_D6-2.xlsx</em> file contains the TGA data of PBSA and PBSA-LDH nanocomposite films.</li> <li>The<em> UV-data-Packaging-performances-PBSA-LDH_v01_TER_WP6_D6-2.xlsx</em> file contains the UV-Vis absorption data of aged and unaged PBSA and PBSA-LDH nanocomposite films.</li> <li>The <em>PBSA-Packaging-performances-PBSA-LDH_v01_TER_WP6_D6-2.pdf</em> is the product sheet of PBSA used in this study.</li> <li>The <em>SORBACID-911-Packaging-performances-PBSA-LDH_v01_TER_WP6_D6-2.pdf</em> is the product sheet of SORBACID<sup>®</sup> 911 used in this study.</li> <li>The <em>SEM-images-Packaging-performances-PBSA-LDH_v01_TER_WP6_D6-2.zip</em> file contains for SEM images (.jpg and .tif) corresponding to neat PBSA film and PBSA-LDH nanocomposite films with 2 wt%, 5 wt% and 8 wt% LDH loadings.</li> <li>The <em>Materials_and_experimental_method-D6-2-Packaging-performances-PBSA-LDH.pdf </em>file details the experimental method and conditions for the data acquisition presented in the Laccase-thermostability-DES_v1.0_TER_WP4_D4-2.xlsx. Guidance is also provided on how to use the data to calculate the laccase activity and thermostability.</li> <li>The <em>Metadata_information-D6-2- Packaging-performances-PBSA-LDH.pdf</em> file includes more detailed metadata information for the datasets represented in here.</li> </ul>
Probabilistic forecasts of the daily maximum of the Kp index produced by the SERENADE prototype model and three empirical models for the period 2010-2018
<p>This dataset contains the probabilistic outputs of SERENADE's first prototype model dedicated to the forecasting of the <span class="math-tex">\(\textit{Kp}_{\textrm{max, 24 h}}\)</span> index for forecasting horizons ranging between 2 and 7 days. The period covered is the one of the SDOML dataset, which is 2010-05 --- 2018-12. All data is contained in a single pickle file, that can be opened in Python, using the following code lines:</p> <p><span class="math-tex">\(\texttt{import pickle}\\ \texttt{with open(DATA_PATH+`/serenade_outputs.pkl', `rb') as f:}\\ ~~~~\texttt{dict_outputs = pickle.load(f)}\)</span></p> <p>The pickle file contains a dictionnary, which itself contains Pandas DataFrames. Each DataFrame corresponds to a forecasting horizon. The dictionnary's keys are the forecasting horizons stored as strings, that is:</p> <p><span class="math-tex">\(\texttt{dict_output.keys() = [`2',`3',`4',`5',`6',`7']}\)</span></p> <p>The DataFrames are indexed by datetime. They contain the observed (true) hourly values of the daily maximum of the Kp index, the forecasts provided by SERENADE and three baseline models (Climatology model, Persistence model and 27-day Recurrence model). The forecast values include the mean and the standard deviation of the forecast normal distributions. Missing moments are due to the absence of EUV images needed to provide the forecast at the given moment.</p> <p> </p>
Supplementary data for: Comparison of transcriptomic profiles between HFPO-DA and prototypical PPARa, PPARg, and cytotoxic agents in mouse, rat, and pooled human hepatocytes
<p>Like many per- or polyfluorinated alkyl substances (PFAS), toxicity studies with HFPO-DA (ammonium,2,3,3,3-tetrafluoro-2-(heptafluoropropoxy)-propanoate), a short-chain PFAS used in the manufacture of some types of fluorinated polymers, indicate that the liver is the primary target of toxicity in rodents following oral exposure. Although the current weight of evidence supports the PPARa mode of action (MOA) for liver effects in HFPO-DA-exposed mice, alternate MOAs have also been hypothesized including PPARg or cytotoxicity. To further evaluate the MOA for HFPO-DA in rodent liver, transcriptomic analyses were conducted on samples from primary mouse, rat and pooled human hepatocytes treated for 12, 24 or 72 hours with various concentrations of HFPO-DA, or agonists of PPARa (GW7647), PPARg (rosiglitazone), or cytotoxic agents (i.e., acetaminophen or d-galactosamine). Concordance analyses of enriched pathways across chemicals within each species demonstrated greatest concordance between HFPO-DA and PPARa agonist GW7647-treated hepatocytes compared to the other chemicals evaluated. These findings were supported by benchmark concentration modeling and predicted upstream regulator results. In addition, transcriptomic analyses across species demonstrated a greater transcriptomic response in rodent hepatocytes treated with HFPO-DA or agonists of PPARa or PPARg, indicating rodent hepatocytes are more sensitive to HFPO-DA or PPARa/g agonist treatment. These results are consistent with previously published transcriptomic analyses and further support that liver effects in HFPO-DA-exposed rodents are mediated through rodent-specific PPARa signaling mechanisms as part of the MOA for PPARa activator-induced rodent hepatocarcinogenesis. Thus, effects observed in mouse liver are not appropriate endpoints for toxicity value development for HFPO-DA in human health risk assessment.</p>
Po delta to Gulf of Trieste: Microbiological connectivity study and field testing of a Video-CTD probe prototype (JERICO-S3 PoGo project)
<p>ADCP and CTD data from field campaigns at S1-GB site (Po delta, Italy) performed in the scope of the JERICO-S3 PoGo project. This is raw data as collected by both instruments. The CTD prototype is under development at National Institute of Biology, Marine Biology Station Piran and hasn't been fully calibrated. The new recalibrated values have been added to mini-CTD-recalibrated archive.<br>The project report is available in this repository and at: https://www.jerico-ri.eu/ta/call-program/third-call/</p>
Figure 8. Prototype Front-End-Generative Learning Objects Instantiated with Random Numbers Based Expressions
<p>We modeled classes for each AGLO section: scenario, theory, questions, and<br> feedbacks. We modeled domain specific classes for the learned concepts like trees and graphs<br> having generative methods controllable through parameters.<br> Figure 8 depicts our prototype front-end.</p>
Figure 2. Screenshot of the Google Earth web-site's prototype on the visualization of heat/cold waves-Early Warning of Heat/Cold Waves as a Smart City Subsystem: A Retrospective Case Study of Non-anticipative Analog Methodology-
<p>A non-anticipative analog method consists of four main steps:<br> 1. Generation of the prediction rules.<br> 2. Analysis of the prediction rules. The rules with time slots, which are not concentrated at<br> the same frame, are excluded.<br> 3. Generation of possible extremes.<br> 4. Analysis of the generated possible extremes. The extremes with time slots, which do not<br> correspond to the time slots of the appropriate rules, are excluded.<br> The results of the heat/cold waves’ prediction from 2011 to 2014 at different locations<br> (places are selected randomly) are presented in Table 2.</p>
Prototyping 3D Virtual Learning Environments with X3D-based Content and Visualization Tools-Figure 10. Room model generated with Autodesk 123D Catch - the 3D model (screen capture from GLC Player)
<p>Structure from motion was used for rapid modeling of a small room with all its objects. Two files were generated, a Wavefront obj and mtl (corresponding to the texture). The 3D model was post-processed with MeshLab, during which several filters were applied to clean up the model. The mesh model was also connected with the scanned model, by choosing at least 4 connection points. The 2D and 3D results are shown in Figures 9, 10. A post-processing could also be performed using the Autodesk 123D Catch web application.</p>
Prototyping 3D Virtual Learning Environments with X3D-based Content and Visualization Tools-Figure 2. The model of the 3D virtual campus - details from the building interior (3D modeling by Marius Hodea)
<p>The processing workflow for 3D modeling and design for a 3DVLE represents a time- consuming stage in the overall pipeline production. One reason is that a range of technologies and tools are typically used. In (Cudworth 2014) a 3-week period is indicated for experienced users to perform the 3D modeling of a virtual space. In our case, a 3-month work was needed for designing a working model of a 3D virtual campus (see Figure 1 and Figure 2 for final results).</p>
Prototyping 3D Virtual Learning Environments with X3D-based Content and Visualization Tools-Figure 1. The model of the 3D virtual campus - an outdoor view (3D modeling by Marius Hodea)
<p>The processing workflow for 3D modeling and design for a 3DVLE represents a time- consuming stage in the overall pipeline production. One reason is that a range of technologies and tools are typically used. In (Cudworth 2014) a 3-week period is indicated for experienced users to perform the 3D modeling of a virtual space. In our case, a 3-month work was needed for designing a working model of a 3D virtual campus (see Figure 1 and Figure 2 for final results).</p>
Prototyping 3D Virtual Learning Environments with X3D-based Content and Visualization Tools-Figure 9. Room model generated with Autodesk 123D Catch - the 2D model
<p>Structure from motion was used for rapid modeling of a small room with all its objects. Two files were generated, a Wavefront obj and mtl (corresponding to the texture). The 3D model was post-processed with MeshLab, during which several filters were applied to clean up the model. The mesh model was also connected with the scanned model, by choosing at least 4 connection points. The 2D and 3D results are shown in Figures 9, 10. A post-processing could also be performed using the Autodesk 123D Catch web application.</p>
Prototyping 3D Virtual Learning Environments with X3D-based Content and Visualization Tools-Figure 8. The 3D model of the faculty building in a HTML page
<p>The pipeline processing was the following: a) the 3D model from Sketchup was saved as a Collada file; b) this file has been imported in MeshLab (MESHLAB 2017) and converted to VRML97 format (wrl); c) aopt utility was used to convert wrl files to X3D and HTML5 files. The model was visualized in the OpenSim virtual world setting using an external browser (Figure 8).</p>
Prototyping 3D Virtual Learning Environments with X3D-based Content and Visualization Tools-Figure 5. Iteration of the 3D modeling of the virtual faculty building (3D modeling by Marius Hodea)
<p>For our research, several iterations and methods were employed for the 3D design of an online campus. Different virtual models of a faculty building (see Figures 4,5) were designed and finally a virtual model of a 3D campus comprising a simplified 3-story faculty building (Figure 6) was created. The objective was the optimization of the 3D model and the demonstration of the desired functionalities. For these purposes two 3D modeling and post-processing software were used, i.e. 3DSMax and Trimble Sketchup. The model of the building resulted in 5962 vertices and 4528 faces. The textures and illumination were applied using OpenSim’s in-world tools. Furniture objects (tables, chair, computer monitors) were taken from the Google 3D Warehouse, distributed and shared under Trimble General Model License.</p>
Prototyping 3D Virtual Learning Environments with X3D-based Content and Visualization Tools-Figure 12. Diagram of the proposed working methodology
<p>The chart below (Figure 12) summarizes the workflow recommended for the implementation of a prototype of a 3D online campus.</p>
Prototyping 3D Virtual Learning Environments with X3D-based Content and Visualization Tools-Figure 11. Online accessible repository of digital data on cultural heritage with X3D models (STARC Web Repository, 2017, © Copyright 2017, STARC, Cyprus Institute. Used with permission)
<p>Prototyping can also include the development of toolkits for automatic content generation simulator, but in the case of an architectural environment, the components are too complex to be automatically generated. Furniture elements or the learning artifacts (i.e. content created by learners) can be converted to be viewed in X3D compatible browsers or included in online galleries (Figure 11). After functional and 3D content prototyping, certain components of the virtual campus can be easily modified and adapted as needed.</p>
Prototyping 3D Virtual Learning Environments with X3D-based Content and Visualization Tools-Figure 3. The graphic stack of X3DOM (Havele 2011)
<p>The current release of X3DOM supports native implementations (iOS8, Chrome, and Firefox for Android), with fallback to WebGL API, and partially to X3D/SAI plugins (INSTANTREALITY 2017). X3DOM is above WebGL, OpenGL and DirectX, and subsequently has less complexity (in Figure 3 is shown the graphical stack). Integrated into the HTML DOM, X3DOM allows web programmers to continue their experience, based on known web technologies such as CSS, Java Script, JQuery or Ajax. Standard technologies can streamline a VR or AR application development, by hiding the low-level complex tasks, and allow the access to device sensors and video camera via high-level API functions. X3DOM supports embedded X3D-XML files references using inline nodes, i.e. an X3D- XML file can reference other X3D-XML files and build a hierarchy of assets (X3DOM 2017) which can be loaded in the background with a higher throughput.</p>
Prototyping 3D Virtual Learning Environments with X3D-based Content and Visualization Tools-Figure 6. The first implementation of the virtual campus (OpenSim import of the 3D model)
<p>For our research, several iterations and methods were employed for the 3D design of an online campus. Different virtual models of a faculty building (see Figures 4,5) were designed and finally a virtual model of a 3D campus comprising a simplified 3-story faculty building (Figure 6) was created. The objective was the optimization of the 3D model and the demonstration of the desired functionalities. For these purposes two 3D modeling and post-processing software were used, i.e. 3DSMax and Trimble Sketchup. The model of the building resulted in 5962 vertices and 4528 faces. The textures and illumination were applied using OpenSim’s in-world tools. Furniture objects (tables, chair, computer monitors) were taken from the Google 3D Warehouse, distributed and shared under Trimble General Model License.</p>
Prototyping 3D Virtual Learning Environments with X3D-based Content and Visualization Tools-Figure 7. The HTML source (partial) code, integrating the X3D model
<p>To integrate the model into a web page, a model conversion to X3D format and an X3DOM output under the form of an HTML5 encoded webpage (Figure 7) were needed. Instant Reality distribution provides a command line transcoding tool, named Avalon Optimizer (aopt), that was used to convert a VRML format (wrl extension) of the model to X3D. </p>
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.