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47,155 results for “evaluations”
Material for manuscript submitted to Earth and Space Science "Evaluation of a mesoscale coupled ocean-atmosphere configuration for tropical cyclone forecasting in the South West Indian Ocean basin"
<p>Configuration files for AROME Indian Ocean, NEMO and OASIS which are necessary to reproduce the results in the publication :</p> <p>Corale, L; Malardel S. , Bielli S. and M-N Bouin (2022) Evaluation of a mesoscale coupled ocean-atmosphere configuration for tropical cyclone forecasting in the South West Indian Ocean basin. <em>Earth and Space Science.</em></p>
Sopley Evaluation Trench 30th Nov 2019
Evaluation trench of a possible long barrow being dug by Avon Valley Archaeological Society (AVAS) and the Christchurch Antiquarians (TCA) Source: Objaverse 1.0 / Sketchfab
Ultrasound evaluation of the airway in the ED: a feasibility study.
<p>INTRODUCTION Recognition of the difficult airway is a critical element of emergency practice. Mallampati score (MS) and body mass index (BMI) are not always predictive and they may be unavailable in critically ill patients. Ultrasonography provides high-resolution images that are rapidly obtainable, mobile, and non-invasive. Studies have shown correlation of ultrasound measurements with difficult laryngoscopy, however none have been performed in the Emergency Department (ED) using a consistent scanning protocol.</p> <p>OBJECTIVES This study seeks to determine the feasibility of ultrasound measurements of the upper airway performed in the ED by emergency physicians, the interrater reliability of such measurements, and their relationship with Mallampati score and BMI.</p> <p>METHODS A convenience sample of volunteer ED patients with no known airway issues, aged >18 years, had images taken of their airway using a standardized ultrasound scanning protocol by 2 EM ultrasound fellowship trained physicians. Measurements consisted of tongue base, tongue base-to-skin, epiglottic width and thickness, and pre-epiglottic space.. Mean and standard deviation (SD) was used to summarize measurements. Inter-rater reliability was assessed by intraclass correlation coefficients (ICCs). Analysis of variance with linear contrasts was used to compare measurements with Mallampati scores and linear regression with BMI.</p> <p>RESULTS Of 39 participants, 50% were female, 50% white, 42% black, with median age 32.5 years (range 19-90) and BMI 26.0 (range 19-47). Mean ± SD for each measurement (mm) were as follows: tongue base (44.6 ± 5.1), tongue base-to-skin (60.9 ± 5.3), epiglottic width (15.0 ± 2.8) and thickness (2.0 ± 0.37), and pre-epiglottic space (11.4 ± 2.4). ICCs ranged from 0.76-0.88 for all measurements except epiglottis thickness (ICC=0.57). Tongue base and tongue base-to-skin thickness were found to increase with increasing Mallampati score (p=.04, .01), whereas only tongue-to-skin thickness was loosely correlated with BMI (r=.38).</p> <p>CONCLUSION A standardized ultrasound scanning protocol demonstrates that the airway can be measured by emergency sonologists with good inter-operator reliability in all but epiglottic thickness. The measurements correlate with Mallampati score but not with BMI. Future investigation might focus on ultrasound evaluation of the airway in patients receiving airway management to determine whether ultrasound can predict challenging or abnormal airway anatomy prior to laryngoscopy.</p>
Similarity-Based Trust Model Evaluation Results
<p>The dataset contains the results of simulation described in the paper. The simulation consisted of 1000 cloud providers, and 30 cloud customers. The metrics extracted from the simulation are the number of active contracts within the system and the mean of the experiences reported after these contracts .</p> <p>Type of data: raw data ( data collected by the Multi-Agent Simulation Toolkit Repast Simphony. )</p> <p>Data format: CSV</p> <p>Source: simulation results</p> <p>Number of samples: 2*8 populations</p> <p>Size per sample: ~ 5000 instances</p> <p>Total size of samples: ~ 10000 instances</p> <p> </p>
Additional datasets and code accompanying the article "Evaluation of in silico algorithms for use with ACMG/AMP clinical variant interpretation guidelines"
<p> </p> <p> </p> <p>Datasets and code accompanying the manuscript titled “Evaluation of in silico algorithms for use with ACMG/AMP clinical variant interpretation guidelines” by Rajarshi Ghosh, Ninak Oak and Sharon E. Plon.</p>
Evaluation of comparative efficacy of Babool Vati versus Tablet Metformin in the Management of Type 2 Diabetes Mellitus (Prameha)- a randomized controlled trial protocol
Open the record for dataset details and reuse information.
Data for "High-Throughput Computational Evaluation of Low Symmetry Pd2L4 Cages to Aid in System Design"
<p>In the following subdirectories are the input and output of Gaussian calculations + structures from screening for this publication:</p> <p>chemrxiv: <a href="https://chemrxiv.org/engage/chemrxiv/article-details/60c758e0ee301c7eadc7b7df">10.26434/chemrxiv.14604294</a></p> <p>Published: <a href="https://onlinelibrary.wiley.com/doi/10.1002/anie.202106721">10.1002/anie.202106721</a></p> <p>Previously uploaded in 10.5281/zenodo.8432296 and <a href="https://github.com/andrewtarzia/citable_data" rel="noopener noreferrer">https://github.com/andrewtarzia/citable_data</a></p> <p>Software repository: <a href="https://github.com/andrewtarzia/unsymm_match">github.com/andrewtarzia/unsymm_match</a></p> <p>screening_structures directory:</p> <ul> <li>contains the structures from xTB optimisation that were used in ranking in structures.tar.gz as `.mol` files</li> <li>all_cage_results.txt contains their properties for ranking</li> </ul> <p>single_point_dft directory:</p> <ul> <li>contains the structures and output of single point DFT calculations</li> <li>during the revision process, we confirmed (based on reviewer suggestions) our DFT validation results using ORCA 4.2.1 with PBE0 and B97-3c in the gas phase. These results were consistent with our previous ones, so were not added to the manuscript. But are useful for future work! <ul> <li>These results are in the s_orca directory.</li> </ul> </li> </ul> <p>free_energy_calculations directory:</p> <ul> <li>during the revision process, it was suggested to calculate the free energies using the xTB method (low-cost) and compare that to the total energies we use.</li> <li>the script `run_gfn2_free_energy.py` in the unsymm_match code repository does this for top candidate ligands using the stko.XTB class. <ul> <li>for each structure, the free energy is output to a .fey file.</li> </ul> </li> </ul>
Do Large Language Models Have a Personality? A Psychometric Evaluation with Implications for Clinical Medicine and Mental Health AI Dataset
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DMP: Real estate evaluations_Melbourne vs Allegheny County
<p>This is the DMP in PDF format for the Real Estate Evaluation project. The input and output files can be found in the connected GitHub repository. The files can be created by running the Juypter Notebooks that are available in the linked repository. Details on the DMP can be found in the PDF document (this also includes the DOI of the repository connected to the project). </p>
European eVALuation sites (EVAL): A European sampling for regional satellite product intercomparison
<p>For the quality assessment of satellite-based products it is necessary to perform product intercomparisons at global and continental scales. In the context of the Copernicus Land Monitoring Service High-Resolution Vegetation and Productivity Parameters (HR-VPP) project, a network of European eVALuation (EVAL) sites was defined to perform product intercomparisons over a representative sampling in terms of land cover and geographical area. EVAL is thus a spatial sampling of 3800 evaluation sites over the 39 countries of the European Economic Area (EEA39) [1]. The main selection criterium is the homogeneity, in terms of land cover, over areas of 1 km x 1 km (more than 70% of the area corresponds to the same land cover class) to be useful for the evaluation of decametric and hectometric (300 – 500 m) resolution products. </p> <p>The selection was based on surface albedo validation sites (SAVS1.0) [2], and European long-term ecosystem research (eLTER) sites [3]. Those sites were first screened to be homogeneous in land cover type, and then visually inspected using Google Earth to select homogeneous sites. Initially, only 597 sites (158 SAVS1.0 and 439 eLTER) were selected. Then, the network was complemented with Land use and land cover survey (LUCAS) samples [4] to get a better representativeness of European land cover types and geographical distribution. 10 additional LUCAS sites were selected for each 1 x 1 degree according to the same homogeneity criteria. Thereafter, random rejection of sites was conducted to keep the distribution of EVAL sites similar to that of the EEA-39 area (see distribution in <em>Figure_1</em>). In the last step, under sampled regions (e.g., Turkey or Iceland) were filled through visually identification of additional homogeneous sites.</p> <p>A total of 3800 sites were finally selected. The CORINE Land Cover 2018 (CLC2018) [5] was used to assign the land cover class to each site to allow the analysis per cover type. An aggregation of the CLC land cover classes was performed to reduce the number of classes to 8 main classes: Broad-leaved forest (BLF), Needle-leaf forest (NLF), Mixed Forest (MF), Crops, Grass, Shrubs, Agroforestry and Sparse. <em>Figure_2</em> shows the distribution of the 3800 EVAL sites per aggregated land cover type as compared to the actual distribution over the whole EEA-39. </p> <p> The xlsx file provided in this dataset contains two spreadsheets with the following information:</p> <ul> <li>‘EVAL_sites’: ‘Site label’, ‘Latitude’, ‘Longitude’, ‘CLC 2018’,’ Aggregated Land Cover’, and ‘Environmental zone’ for each location. </li> <li> ‘Legend’: information about the legends used in the dataset (CLC2018, aggregated land cover and environmental zone) and the number of EVAL samples for each class.</li> </ul> <p> </p> <p><strong><u>References:</u></strong></p> <p>[1] <a href="https://sdi.eea.europa.eu/catalogue/srv/api/records/8526ff78-b000-42e1-8360-a2fb3a51e4ac">https://sdi.eea.europa.eu/catalogue/srv/api/records/8526ff78-b000-42e1-8360-a2fb3a51e4ac</a></p> <p>[2] Loew, A., Bennartz, R., Fell, F., Lattanzio, A., Doutriaux-Boucher, M., Schulz, J., 2016. A database of global reference sites to support validation of satellite surface albedo datasets (SAVS 1.0). Earth Syst. Sci. Data 8, 425–438. https://doi.org/10.5194/essd-8-425-2016</p> <p>[3] <a href="https://www.lter-europe.net/">https://www.lter-europe.net/</a></p> <p>[4] <a href="https://ec.europa.eu/eurostat/statistics-explained/index.php?title=LUCAS_-_Land_use_and_land_cover_survey">https://ec.europa.eu/eurostat/statistics-explained/index.php?title=LUCAS_-_Land_use_and_land_cover_survey</a></p> <p>[5] <a href="https://doi.org/10.2909/960998c1-1870-4e82-8051-6485205ebbac" target="_blank" rel="noopener">https://doi.org/10.2909/960998c1-1870-4e82-8051-6485205ebbac</a></p> <p> </p>
Evaluating Software Modelling Recommendations: Towards Systematic Guidelines for Modelling
<p>The dataset contains four supporting documents for the paper title, "Evaluating Software Modelling Recommendations: Towards Systematic Guidelines for Modelling". </p> <p>The first documment is a pdf file containing the student survey questions.</p> <p>The second docummnet is an excel sheet containing the discussion of a focus group study. </p> <p>The third docummet is an excel sheet containing interview transcripts of 4 of the participants of this study (who agreed to publish their statements). We had to modify the transcript and omit a few discussions as it involves iedntification of names. <br><br><br>The final documment is a grading rubric used in one of the courses for grading. </p>
Evaluation_of_different_feeding_frequencies_in_RAS-based_juvenile_pikeperch-DATA
<p>Data file for a study examining various feeding strategies for juvenile pikeperch (<em>Sander lucioperca</em>) in a Recirculating Aquaculture System (RAS) over a period of 112 days. It compared five different feeding intervals (2 hours, 4 hours, 6 hours, 8 hours, and 12 hours) using an automatic feeder Imetronic® (Pessac, France). The results showed that shorter feeding intervals led to higher growth heterogeneity and slightly increased fin damage. However, all tested feeding frequencies provided sufficient nutrients for proper pikeperch growth and welfare. The 8-hour feeding interval was identified as optimal, supporting better conditions for fish (Fulton's coefficient), specific growth rate, survival rate, lower fin damage compared to other tested frequencies, and optimal levels of biochemical parameters in blood plasma.</p> <p> </p> <p>ACCESS<br>it is a condition of the CENAKVA RI financing provider to record accesses to open data. Data can be requested at the following email: <strong>dubova@frov.jcu.cz</strong></p> <p>or visit the CENAKVA RI website<br>https://www.frov.jcu.cz/en/faculty/faculty-parts/south-bohemian-research-centre-for-aquaculture-and-biodiversity-of-hydrocenoses-cenakva/large-research-infrastructure-cenakva</p>
Rendered Stimuli for "Spatial Analysis and Synthesis Methods: Subjective and Objective Evaluations Using Various Microphone Arrays in the Auralization of a Critical Listening Room"
<h2>Rendered Stimuli from the Subjective Evaluation</h2> <p>This archive (<code>Stimuli.zip</code>) contains the rendered stimuli used in the subjective evaluation of various spatial analysis and synthesis methods, as described in the paper "Spatial Analysis and Synthesis Methods: Subjective and Objective Evaluations Using Various Microphone Arrays in the Auralization of a Critical Listening Room" by Alan Pawlak, Hyunkook Lee, Aki Mäkivirta, and Thomas Lund.</p> <p>The stimuli are provided to improve the reproducibility of the study and to allow readers to listen to the same audio samples used in the subjective evaluation.</p> <h2>File Naming Convention:</h2> <p><code>SYSTEM_PROGRAMMEMATERIAL_AZIMUTH_ELEVATION_-26LUFS.wav</code></p> <p>- <code>SYSTEM</code>: The spatial analysis and synthesis method used (e.g., BSDM-6OM1-Omni, HO-SIRR, SDM-em32, etc.)<br>- <code>PROGRAMMEMATERIAL</code>: The anechoic audio sample used (Bongo, Speech, Orchestra)<br>- <code>AZIMUTH</code>: The azimuth angle of the sound source (e.g., 0, 30, 45, 90, 135)<br>- <code>ELEVATION</code>: The elevation angle of the sound source (e.g., 0, 45)</p> <h2>Audio File Specifications:</h2> <p>- Format: WAV<br>- Sample Rate: 48 kHz<br>- Bit Depth: 32-bit<br>- Loudness Normalization: -26 LUFS</p> <p>To use these stimuli, simply load the desired WAV file into your audio playback software.</p> <p>For more information about the study, please refer to the full paper.</p> <p>Pawlak, A., Lee, H., Mäkivirta, A. and Lund, T., 2024. Spatial Analysis and Synthesis Methods: Subjective and Objective Evaluations Using Various Microphone Arrays in the Auralization of a Critical Listening Room.</p>
The basic scheme of the multi-target evaluation (MTE) framework
<p>The following supporting information can be downloaded at: https://www.mdpi.com/article/... /supplementary_MTE.mp4. Video S1: MTE. <span>The basic scheme of the multi-target evaluation (MTE) framework is shown in this video for a specific TR<sub>3</sub> size, using the HPA for mapping SAR<sub>10g,max</sub>. For MTE, hypothetical TRs (borderlines marked in black and green) are moved across the entire brain slice for the evaluation of the quality of targeted RF heating instead of using the conventional approach of defining a limited number of specific but arbitrary TRs. EMF simulations were performed for each TR position across a 26x27 rectangular grid with 702 points (step size = 7mm). For evaluation, the metric success score was used, which represents the percentage of the number of locations with </span>SAR<sub>10g<span>,max</span></sub><span>>40 w/kg over the total number of locations. </span>Video S2-S9: APA/HPA_TR1-TR4_MTE</p>
An evaluation of cloud-precipitation structures in mixed-phase stratocumuli over the southern ocean in kilometer-scale ICON simulations during CAPRICORN
<p>The repository contains ICON simulated outputs (Cntrl_ICON.nc.gz, No_Gr_ICON.nc.gz, No_Ice_ICON.nc.gz, 100m_Vert_ICON.nc.gz, and 50m_Vert_ICON.nc.gz) and two km mean and ensemble forward simulated results (Cntrl_PAMTRA.gz, No_Gr_PAMTRA.gz, No_Ice_PAMTRA.gz, 100m_Vert_PAMTRA.gz, and 50m_Vert_PAMTRA.gz) for the two day period (00:00 UTC on March 26, 2016 to 00:00 UTC on March 28, 2016). The prefix in the filenames corresponds to the experiments described in the manuscript.</p>
Experiment resources for "Quality of Binaural Rendering From Baffled Microphone Arrays Evaluated Without an Explicit Reference"
<div>This data set contains the following resources to reproduce the listening experiment and statistical analysis of the referenced manuscript:</div> <div> <ul> <li>The <em>binaural room impulse responses</em> (<strong>BRIR</strong>s) of all listening conditions presented in the perceptual experiment.</li> <li>The tools to create the listening test infrastructure, including <em>Pure Data</em> (<strong>Pd</strong>) patches and configuration files for the <em>SoundScape Renderer</em> (<strong>SSR</strong>) and <em>graphical user interface</em> (<strong>GUI</strong>).</li> <li>The raw response data as gathered from the experiment subjects.</li> <li>The R and Stan scripts to perform the statistical analysis and generate the resulting plots and tables.</li> </ul> </div> <div> <p> </p> <p>The archive contains the following components described below.</p> <p>Directory "dependencies/":</p> <ul> <li>Matlab, R, Stan, and Pd functions that are utilized in the code and experimental setup</li> <li>Additional dependencies of available open-source projects may be required for certain code functions. If so, the source and setup process for the necessary dependencies are documented in the file header.</li> </ul> </div> <div> <p>Directory "plots/4_Equalization/":</p> <ul> <li>Plots of all available headphone equalization filters as generated by the following Matlab scripts.</li> </ul> <p>Directory "plots/8_User_study/":</p> <ul> <li>Plots of the raw and analyzed experimental results as generated by the following Matlab and R scripts.</li> </ul> </div> <div> <p>Directory "resources/BRIR_auralization/":</p> <ul> <li>Audio files with static binaural auralizations of all listening conditions presented in the perceptual experiment as generated by the following Matlab scripts.</li> <li>The files in "Kemar_HRTF_sofa_N44_adjusted" do not include a headphone equalization.</li> <li>The files in "Kemar_HRTF_sofa_N44_adjusted+Sennheiser_HD650_lin" include the equalization for the <em>Sennheiser HD650</em> headphones employed in the listening experiment. The files are identical to the annotated <a href="http://www.ta.chalmers.se/research/audio-technology-group/audio-examples/jaes-2024a/" target="_blank" rel="noopener">listening examples</a> published for the manuscript.</li> </ul> <p>Directory "resources/BRIR_rendered/":</p> <ul> <li>BRIRs and rendering parameters of all listening conditions presented in the perceptual experiment as generated from the associated <a href="../doi/10.5281/zenodo.8206570" target="_blank" rel="noopener">data set</a> and <a href="https://github.com/HaHeho/baffled-arrays-to-binaural/releases/tag/v2024.JAES" target="_blank" rel="noopener">rendering code</a>.</li> <li>Scene configuration files for the SSR with all listening conditions presented in the perceptual experiment as generated from the following Matlab scripts.</li> </ul> <p>Directory "resources/HPCF_KEMAR/":</p> <ul> <li>Impulse responses of equalization filters for various headphones on the G.R.A.S KEMAR acoustic dummy head as measured for this experiment.</li> </ul> <p>Directory "resources/User_study/":</p> <ul> <li>Various resources for the listening experiment.</li> <li>The files in "Exp1_analysis" include intermediate and final statistical analysis results as generated from the following R scripts.</li> <li>"Exp1_config.json" contains the configuration of the study GUI with conditions presented in the listening experiment.</li> <li>"Exp1_Introduction.pdf" contains the instructions presented to the subjects at the start of the listening experiment.</li> <li>"Exp1_Part2_data_strings.xls" contains all subjects' raw perceptual response data gathered from the listening experiment.</li> <li>"Questionnaire.pdf" contains the questionnaire given to the subjects at the end of the listening experiment.</li> </ul> <p>Matlab script "x4_Gather_Headphone_Compensations.m":</p> <ul> <li>Generate plots of measured headphone compensation filters. Furthermore, the generated minimum phase filters and filters to yield a linear phase response from the headphones are extracted as separate WAV files.</li> </ul> </div> <div> <p>Matlab script "x6_Gather_SSR_Configurations.m":</p> <ul> <li>Collect several specified pre-rendered binaural room impulse response sets into an ASD file. The SSR can load this scene to present all gathered configurations in direct comparison with head tracking.</li> </ul> </div> <div> <p>Readme file "x6a_Normalize_SSR_Loudnesses.txt":</p> </div> <div> <div> <div> <ul> <li>Ideally, the rendering script would implement a measure to provide a reliable estimation of the binaural loudness of the rendered configuration. This could be used to normalize all stimuli levels. However, such a measure is currently not available or implemented.</li> <li>Therefore, tuning the stimuli loudness for the user study was performed beforehand by ear. The adjusted playback levels are set in a modified SSR configuration file for the listening experiment.</li> </ul> </div> <div> <p>Matlab script "x6_Gather_SSR_Configurations.m":</p> <ul> <li>Perform convolution of (rendered) binaural room impulse responses with a source audio signal. This is done for a specified selection of static head orientations and a continuous rotation over all horizontal head orientations.</li> <li>The resulting auralizations are published as <a href="http://www.ta.chalmers.se/research/audio-technology-group/audio-examples/jaes-2024a/">supplementary materials</a> to the manuscript.</li> </ul> <p>Shell script "x7_Start_Study_GUI.sh":</p> <ul> <li>Initialize all required components to perform the perceptual user study, including: <ul> <li>SSR to perform the real-time rendering of the BRIRs with head tracking</li> <li>SSR to extract head-tracking data (in case a Polhemus tracker is used)</li> <li>Pd to extract head-tracking data (in case a Supperware tracker is used)</li> <li>Pd to perform real-time convolution to apply headphone compensation</li> <li>Pd to receive OSC messages from the study GUI</li> <li>Pd to trigger audio file playback from received OSC messages</li> <li>Pd to translate OSC messages into FUDI messages for the SSR</li> <li>The GUI to be used by the participants and implement the study procedure</li> </ul> </li> <li>Some static configuration variables can be adjusted, whereas other parameters are chosen during script execution.</li> </ul> <p>Matlab script "x8_Gather_Study_Data.m":</p> <ul> <li>Transform the raw result data from the questionnaire (*.xls) and the study GUI (*.json) into a compact format (*.xls) that can be loaded to plot the raw data and imported by software for the subsequent statistical analysis.</li> <li>Note that this contains the responses from all subjects, whereas responses from the investigators must be excluded from the statistical analysis (which is implemented in the analysis scripts).</li> </ul> <p>Matlab script "x8a_Plot_Study_Data.m":</p> <ul> <li>Generate a set of violin plots to visualize the initial distribution of the raw perceptual data. The data is split by specified attributes and plotted separately for visual inspection.</li> <li>The data may also be transformed into ranks for a first distribution inspection. Note that implementing the ranking method, notably how ties are resolved, may differ from the technique employed in the statistical analysis.</li> <li>Note that this contains the responses from all subjects, whereas responses from the investigators must be excluded from the statistical analysis (which is implemented in the analysis scripts).</li> </ul> <p>R script "x8b_Analyze_Exp1_Data.R":</p> <ul> <li>Perform the statistical analysis by transforming the observed subject ratings into a predicted distribution of ranks using a hierarchical generalized linear regression model.</li> <li>Executing the statistical model may take some time due to the Bayesian framework employing Markov-chain Monte Carlo simulations.</li> <li>Data is exported at various intermediate steps to be loaded and visualized by the following R script.</li> </ul> <p>R markdown script "x8c_Plot_Exp1_Results.Rmd":</p> <ul> <li>Generate various plots and data tables of the observed data and the predicted results to visualize the distribution and influence of different analysis parameters.</li> <li>Some of the resulting plots were used in the manuscript.</li> <li>"x8c_Plot_Exp1_Results.html" conveniently summarizes all plots and data tables generated by "knitting" the R markdown script.</li> </ul> </div> </div> </div>
Raw data for publication: Weathering of Wood Modified with Acetic Anhydride – Physical, Chemical, and Aesthetical Evaluation
<p>Color.xlsx</p> <p>This file contains CIE Lab* color coordinates measured on the surface of wood samples.</p> <p>Weather data.xlsx</p> <p>This file contains daily local weather conditions in San Michelle, Italy during the natural weathering test.</p> <p>Gloss.xlsx</p> <p>This file contains the gloss value measured on the surface of wood samples.</p> <p>contact angle and surface energy.xlsx</p> <p> This file contains dynamic contact angle and surface energy measured on the surface of wood samples.</p> <p>TGA.xlsx</p> <p>This file contains the Thermogravimetric (TG) and derivative thermogravimetric (DTG) data of the wood samples.</p> <p>Surface roughness.xlsx</p> <p>This file contains the roughness value of wood samples.</p>
Evaluation of in vitro genotoxicity tests
<p>In human health risk assessment of chemicals and pharmaceuticals, assessment of genetic toxicity for regulatory purposes usually starts with a standard battery of in vitro genotoxicity tests. This battery is needed to cover the different genetic toxicity endpoints. The tests included in the battery (partly) differ in biology, due to which resulting data may seem contradictory, thereby complicating accurate interpretation of the findings. This could be overcome through application of mathematical modelling. One of the advantages of mathematical modelling is that strengths and weaknesses of each test are taken into account. Furthermore, the generated predictions are objective and inform on the associated uncertainties. This approach was explored by the working group “Predictivity of In Vitro Genotoxicity Testing”, formed in the context of the 8th International Workshop on Genotoxicity Testing (IWGT). We applied mathematical modelling to a database with (partly) publicly available in vitro and in vivo data for genotoxicity. The results indicate that a mammalian in vitro clastogenicity test and a mammalian cell gene mutation test together provide the strongest weight-of-evidence for predicting genotoxic potential. Remarkably, the Ames bacterial mutation test appeared to contribute very little to these predictions when used in combination with in vitro genetic toxicity tests using cells of mammalian origin. Further research comparing additional in vitro genotoxicity data with in vivo data will provide additional insights whether it is indeed time to reconsider the composition of the standard in vitro genotoxicity battery.</p>
Data from: Beyond the colony-forming-unit: Rapid bacterial evaluation in Osteomyelitis
<p>Examination of bacteria/host cell interactions is important for understanding the aetiology of many infectious diseases. The colony-forming-unit (CFU) has been the standard for quantifying bacterial burden for the past century, however, this suffers from low sensitivity and is dependent on bacterial culturability in vitro. Our data demonstrate the discrepancy between the CFU and bacterial genome copy number in an osteomyelitis-relevant co-culture system and we confirm diagnosis and quantify bacterial load in clinical bone specimens. This study provides insight into improving the quantification of bacterial burden in such cases.</p>
Evaluation of seed-dispersal services by ants at a temperate pasture: Results of direct observations in an ant suppression experiment
<p><em>Ant dispersal</em></p> <p>Ants disperse seeds of many plant species adapted to myrmecochory. While advantages of this ant–plant mutualism for myrmecochorous plants (myrmecochores) have been previously studied in temperate region mostly in forests, our study system was a pasture. Moreover, we used a unique combination of observing the effect of ant‐activity suppression on ant dispersal and comparison of the contribution of ant and unassisted dispersal to the distance from mother plant. We established plots without and with ant‐activity suppression (enclosures). We offered diaspores of a myrmecochorous (<em>Knautia arvensis</em>), and a non‐myrmecochorous (<em>Plantago lanceolata</em>) species in a choice test and followed ants carrying diaspores during days and nights (focus of previous studies was on diurnal dispersal). We measured frequency and distances of ant dispersal and compared them with unassisted dispersal recorded using sticky trap method. The dispersal frequency was lower in enclosures (3.16 times). Ants strongly preferred diaspores of the myrmecochore to non‐myrmecochore with 586 and 42 dispersal events, respectively (out of 6400 diaspores of each species offered). Ant dispersal resulted in more even and on average longer distances (maximum almost tenfold longer, 994 cm) in comparison to unassisted dispersal. Ant dispersal altered the distribution of distances of the myrmecochore from roughly symmetric for unassisted dispersal to positively skewed. Ants dispersed heavier diaspores farther. Ants dropped the majority of diaspores during the dispersal (which reduces clustering of seeds), while several (11%) were carried into anthills. Anthills are disturbed microsites presumably favorable for germination in competitive habitats. Ants provided non‐negligible dispersal services to myrmecochorous <em>K. arvensis</em> but also, to a lesser extent, of non‐myrmecochorous <em>P. lanceolata</em>.</p> <p><em>Unassisted dispersal</em></p> <p>A sticky trap of the same size as the experimental plots of the cafeteria experiment (3 m × 3 m) was used to record unassisted dispersal (i.e., without any biotic dispersal agent, mostly by gravity and additionally wind). We used linoleum covered with insect glue to trap the falling diaspores. Five full‐sized individuals (with majority of infructescences with mature seeds) of <em>K. arvensis</em> were fixed in a water container in the center of the plot for approximately 10 days. The procedure was repeated five times during July and August. The distances of trapped diaspores were measured in the same manner as in the case of ant dispersal, thus the dispersal by ants and unassisted dispersal can be compared.</p>
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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.