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47,155 results for “evaluations”
Pilot implementation, monitoring, co-evaluation and validation data_v1
<p>These data have been collected in the context of monitoring and assessing the pilot operation of the INVITE project (H2020 GA 763651) and its OI2lab platform during their 1st deployment round.</p>
Evaluating Robustness to Context-Sensitive Feature Perturbations of Different Granularities: Further Appendices
<p>Further appendices for <a href="https://arxiv.org/abs/2001.11055">Evaluating Robustness to Context-Sensitive Feature Perturbations of Different Granularities</a>.</p> <p>Includes:</p> <ol> <li>imagenet_examples_appendix.pdf, which contains many examples of context-sensitive ImageNet feature perturbations of different granularities.</li> <li>animations_appendix.zip, which contains animations showing the effect as the feature perturbations are gradually introduced to different images.</li> </ol> <p>Each appendix includes a much more detailed description of its contents.</p>
Apresentação para Sessão Oral - CBEB2020: Evaluation of the Motor Performance of People with Parkinson's Disease through the Autocorrelation Function Estimated from Sinusoidal Drawings
<p>The objective of this work was to use the autocorrelation function to assess the fine motor performance of a healthy control group (CG) and people with Parkinson's disease (PD), whose fluctuations caused by the disease compromise motor skills, performed mainly by the hands and fingers essential for daily activities such as dressing, carrying objects and taking care of personal hygiene. For this, accelerometer signals were collected while the volunteers drew a sinusoidal pattern. The correlogram was estimated and two features were calculated from it: (i) the normalized area under the curve (𝑨𝒖𝑪); (ii) the difference between matching peaks and valleys from the reference and estimated correlograms (𝒗𝒎𝒎). These features allowed for the discrimination between drawing patterns of CG (𝒗𝒎𝒎= 0.154±0.063, 𝑨𝒖𝑪 = 0.204±0.040) and PD (𝒗𝒎𝒎 = 0.239±0.085, 𝑨𝒖𝑪 = 0.179±0.033) groups. Following the verification of normality (Shapiro-Wilk, p > 0.05), the t-test was applied to confirm the significant differences between groups (p < 0.05). The Cohen’s d effect size (p < 0.05) was medium (0.730) for 𝑨𝒖𝑪 and large (1.252) for 𝒗𝒎𝒎. This further confirms the differences between the features extracted from the groups. Therefore, the features 𝑨𝒖𝑪 and 𝒗𝒎𝒎 estimated from the autocorrelation function are effective for the assessment of the motor performance of healthy individuals and people with PD. </p>
Apresentação para o Prêmio Iniciação Científica - CBEB2020: Evaluation of the Motor Performance of People with Parkinson's Disease through the Autocorrelation Function Estimated from Sinusoidal Drawings
<p>The objective of this work was to use the autocorrelation function to assess the fine motor performance of a healthy control group (CG) and people with Parkinson's disease (PD), whose fluctuations caused by the disease compromise motor skills, performed mainly by the hands and fingers essential for daily activities such as dressing, carrying objects and taking care of personal hygiene. For this, accelerometer signals were collected while the volunteers drew a sinusoidal pattern. The correlogram was estimated and two features were calculated from it: (i) the normalized area under the curve (𝑨𝒖𝑪); (ii) the difference between matching peaks and valleys from the reference and estimated correlograms (𝒗𝒎𝒎). These features allowed for the discrimination between drawing patterns of CG (𝒗𝒎𝒎= 0.154±0.063, 𝑨𝒖𝑪 = 0.204±0.040) and PD (𝒗𝒎𝒎 = 0.239±0.085, 𝑨𝒖𝑪 = 0.179±0.033) groups. Following the verification of normality (Shapiro-Wilk, p > 0.05), the t-test was applied to confirm the significant differences between groups (p < 0.05). The Cohen’s d effect size (p < 0.05) was medium (0.730) for 𝑨𝒖𝑪 and large (1.252) for 𝒗𝒎𝒎. This further confirms the differences between the features extracted from the groups. Therefore, the features 𝑨𝒖𝑪 and 𝒗𝒎𝒎 estimated from the autocorrelation function are effective for the assessment of the motor performance of healthy individuals and people with PD. </p>
Associated dataset for "Instrumental Evaluation of Sensor Self-Noise in Binaural Rendering of Spherical Microphone Array Signals"
<p>The conducted instrumental evaluation utilizes the Real-Time Spherical Microphone Renderer (<a href="https://github.com/AppliedAcousticsChalmers/ReTiSAR">ReTiSAR</a>) for binaural reproduction in Python. The at that time employed code state should be used in order to exactly reproduce the rendering results in this data set. The frozen code state for this data set is available at:<br> <a href="https://github.com/AppliedAcousticsChalmers/ReTiSAR/releases/tag/v2020.FA">https://github.com/AppliedAcousticsChalmers/ReTiSAR/releases/tag/v2020.FA</a></p> <p>Download the rendering pipeline and follow the setup instructions! Use the here included Conda environment file when setting up the Python environment. In this way, you will obtain exactly the same Python setup as utilized in the instrumental evaluation in the publication:</p> <pre><code class="language-bash">conda env create --file ReTiSAR_environment_freeze.yml</code></pre> <pre><code class="language-bash">source activate ReTiSAR_FA_freeze</code></pre> <p>Directory "SMA sampling grids":</p> <ul> <li>Visualization of spatial arrangement (like Figure 4) for all investigated spherical microphone array rendering configurations (Table 1)</li> </ul> <p>Shell script "record_snr.sh":</p> <ul> <li>Record the input and output signals of the rendering pipeline for sound field (target / wanted) and self-noise (unwanted) components for all configurations at multiple head orientations</li> <li>All captured signals are contained in the "SNR" directory</li> </ul> <p>Matlab script "calculate_snr.m":</p> <ul> <li>Visualize the raw captured input and output signals (like Figure 1 for all configurations)</li> <li>Visualize the resulting signal-to-noise ratio (like Figure 2 for all configurations)</li> <li>Visualize the comparison of the resulting signal-to-noise ratio of all configurations (Figure 3, also for the resulting SNR from signals with A-weighting)</li> <li>All generated plots are contained in the "SNR" directory</li> </ul> <p>Shell script "record_noise.sh":</p> <ul> <li>Record the calibration and noise signals of the mh acoustic Eigenmike 32 spherical microphone array in the anechoic chamber at Chalmers University of Technology (Appendix)</li> <li>All captured signals are contained in the "EM32 measurements" directory</li> <li>Pictures of the measurement setup are contained in the "Pictures" subdirectory</li> </ul> <p>Matlab script "calculate_EM32_noise_levels.m":</p> <ul> <li>Determine the resulting target signal sensitivity and equivalent input noise levels for the investigated pre-amplification gains (Table 2)</li> <li>Visualize the statistical distribution of the individual raw and weighted SMA channels (like Figure 6 for all configurations)</li> <li>Visualize the spatial distribution of the individual raw and weighted SMA channels for all configurations</li> <li>Visualize the smoothed and averaged magnitude spectra of the individual raw and weighted SMA channels (like Figure 5 for all configurations)</li> </ul>
Dataset for pilot evaluation of an enzymatic assay for rapid measurement of antiretroviral drug concentrations
<p>Liquid chromatography tandem mass spectrometry (LC-MS/MS) data, and REverSe TRanscrIptase Chain Termination (RESTRICT) assay data for pilot evaluation study of enzymatic RESTRICT assay for rapid measurement of antiretroviral drug concentrations among people receiving oral pre-exposure prophylaxis (PrEP).</p>
Data of socio-technical evaluation of long-term spent nuclear fuel management options at San Onofre, California
<p>Data file supporting the socio-technical evaluation of long-term spent nuclear fuel management options at San Onofre, California.</p>
Mixed-stock analysis using Rapture genotyping to evaluate stock-specific exploitation of a walleye population despite weak genetic structure
<p>Mixed-stock analyses using genetic markers have informed fisheries management in cases where strong genetic differentiation occurs among local spawning populations, yet many fisheries are supported by multiple spawning stocks that are weakly differentiated. Freshwater fisheries exemplify this problem, with many harvested populations supported by multiple stocks of young evolutionary age and that are isolated across small spatial scales. As a result, attempts to conduct genetic mixed-stock analyses of inland fisheries have often been unsuccessful. Advances in genomic sequencing now offer the ability to discriminate among populations with weak population structure, by providing the necessary resolution to conduct mixed-stock assignment among previously indistinguishable stocks. We demonstrate the use of genomic data to conduct a mixed-stock analysis of Lake Erie's commercial and recreational walleye (<em>Sander vitreus</em>) fisheries and estimate the relative harvest of weakly differentiated stocks. We used RAD-capture (Rapture) to sequence and genotype individuals at 12,081 loci that had been previously determined to be capable of discriminating between western and eastern basin stocks (mean pairwise F<sub>ST</sub> = 0.001) with 95% reassignment accuracy. An outcome not possible in the past with microsatellite markers. Genetic assignment of 1,075 fish harvested from recreational and commercial fisheries in the eastern basin indicated that western basin stocks supported the majority of the harvest during peak harvest (July – September). Composition of harvest changed seasonally, with eastern basin fish comprising much of the early season harvest (May – June). Clear spatial structure in stock-specific harvest existed; more easterly sites contained more individuals of east basin origin than did westerly sites. Our study provides important stock contribution estimates for Lake Erie fishery management and demonstrates the power of genomic data to facilitate mixed-stock analysis in exploited fish populations with weak population structure or limited existing genetic resources.</p>
Molecular geometries and energies from quantum mechanical calculations and small molecule force field evaluations.
<p>Force fields are used in a wide variety of contexts for classical molecular simulation, including studies on protein-ligand binding, membrane permeation, and thermophysical property prediction.<br> The quality of these studies relies on the quality of the force fields used to represent the systems. <br> Focusing on small molecules of fewer than 50 heavy atoms, this data compares nine force fields: GAFF, GAFF2, MMFF94, MMFF94S, OPLS3e, SMIRNOFF99Frosst, and the Open Force Field Parsley, versions 1.0, 1.1, and 1.2.<br> On a dataset comprising 22,675 molecular structures of 3,271 molecules, we analyzed force field-optimized geometries and conformer energies compared to reference quantum mechanical (QM) data.<br> <br> The data was created using scripts of the <a href="https://github.com/MobleyLab/benchmarkff/commit/fa45247aa9867f504c02eb6f62d8459a94a0a936">benchmarkff github repository</a>.</p> <p>A corresponding manuscript is submitted, a preprint is available on ChemRxiv:<br> <a href="https://doi.org/10.26434/chemrxiv.12551867.v2 ">Lim, Victoria T.; Hahn, David F.; Tresadern, Gary; Bayly, Christopher I.; Mobley, David (2020): Benchmark Assessment of Molecular Geometries and Energies from Small Molecule Force Fields. ChemRxiv. Preprint</a></p> <p>Read below or the file README.md for further information and description of the content:</p> <pre><code class="language-markdown"># README Version: 04 Nov 2020 For Python scripts that are NOT found in these directories, please check the [BenchmarkFF Github repo](https://github.com/MobleyLab/benchmarkff/tree/master/tools). ## Procedure 1. Prep OPLS3e file for analysis: standardize format by OpenEye in case of differences and convert from kJ/mol to kcal/mol. ``` cd prep python convert_extension.py -i opls3e_minimized.sd -o opls3e.sdf ``` 2. Remove mols that couldn't parameterize by ALL FFs. ``` python get_by_tag.py -i opls3e.sdf -s "SMILES QCArchive" -list trim3.txt -o trim3_full_opls3e.sdf ``` 3. Run analysis. ``` conda activate parsley # calc ddE, RMSD, and TFD distributions python compare_ffs.py -i match.in -t 'SMILES QCArchive' --plot > metrics.out # match_minima, only in 01_analysis_all and 02_analysis_all_smaller_cutoff python match_minima.py -i match.in --plot --cutoff 1.0 --readpickle # look at specific subsets, only in 01_analysis_all python color_by_moiety.py -i match.in -p metrics.pickle -s N-N.dat azetidine.dat octahydrotetracene.dat -o scatter_tfd_3_ # look at outliers,only in 01_analysis_all and 02_analysis_all_smaller_cutoff python tailed_parameters.py -i refdata_trim_overlap_full_openff_unconstrained-1.2.0.sdf -f <offxml file> --metric 'TFD' --cutoff 0.12 --tag "TFD to trim_overlap_full_qcarchive.sdf" --tag_smiles "SMILES QCArchive" > output_tfd.dat ``` ## Brief description of contents * High level: ``` . ├── 00_prep │ ├── convert_extension.py │ ├── opls3e_minimized.sd OPLS3e minimized structures from Schrodinger Maestro │ ├── opls3e.sdf standardized through OpenEye tools │ ├── opt_openff*.sdf OpenFF minimized conformations ├── 01_analysis_all compare all ffs (qm, GAFF(2), MMFF94(S), Smirnoff, OpenFF-X.X, OPLS3e) ├── 02_analysis_all_smaller_cutoff compare all ffs (qm, GAFF(2), MMFF94(S), Smirnoff, OpenFF-X.X, OPLS3e) with a smaller cutoff of .3 for match_minima ├── 03_analysis_latest_ffs compare only the latest versions of ffs (qm, GAFF2, MMFF94S, OpenFF-1.2, OPLS3e) ├── 04_analysis_openff_only compare only OpenFF ffs (qm, Smirnoff, OpenFF-X.X) └── README.md ``` * Inside an output directory: ``` YY_analysis_* various output files of above mentioned scripts, some are listed and described below: ├── bar*.png parameter coverage bar plots ├── ddE.dat relative energies data ├── fig_density_*.png scatter plots of ddE vs (RMSD or TFD) for each force field ├── match.in input file for compare_ffs.py ├── metrics.out output file for compare_ffs.py ├── metrics.pickle pickle file for compare_ffs.py -- you can read this into compare_ffs instead of rerunning the full analysis ├── refdata_*.sdf output SDF files with stored RMSD / TFD scores with reference to QM for each structure ├── relene_*.dat relative energies of matched conformers ├── ridge_dde.png compared energies plot ├── ridge_rmsd.svg compared rmsds plot ├── ridge_tfd.svg compared tfds plot ├── fig_scatter_*.png scatter plots of ddE vs (RMSD or TFD). these are noisy; I don't use these ├── trim3_*.sdf input SDF files for compare_ffs.py listed in match.in file ├── violin*.* violin plot showing ddE distributions ``` </code></pre>
Do children learn from their mistakes? A registered report evaluating error-based theories of language acquisition
<p>Error-based theories of language acquisition suggest that children, like adults, continuously make and evaluate predictions in order to reach an adult-like state of language use. However, while these theories have become extremely influential, their central claim - that unpredictable input leads to higher rates of lasting change in linguistic representations – has scarcely been tested. We designed a prime surprisal-based intervention study to assess this claim.</p> <p>As predicted, both 5- to 6-year-old children (n=72) and adults (n=72) showed a pre- to post-test shift towards producing the dative syntactic structure they were exposed to in surprising sentences. The effect was significant in both age groups together, and in the child group separately when participants with ceiling performance in the pre-test were excluded. Secondary predictions were not upheld: there were no verb-based learning effects and there was only reliable evidence for immediate prime surprisal effects in the adult, but not in the child group. To our knowledge this is the first published study demonstrating enhanced learning rates for the same syntactic structure when it appeared in surprising as opposed to predictable contexts, thus providing crucial support for error-based theories of language acquisition.</p>
Benchmark data used in the evaluation of ChiRA tool-suite.
<p>The reads generated mimic CLASH experimental data. Each read is a fusion of a human hg38 miRBase mature miRNAs and some random TargetScan target sequences. The numbers 10, 12, 15, 18, and 20 in the file names represent the length of the chimeric arms. The are 1million reads in FASTA file. Files with "Insert" in their names contain a short 5 nt random sequence, whereas the "noInsert" files do not. TargetScanSites_merged.fa.fasta and hg38_MIR_mature.fa.fasta contain the reference sequences. The sequence identifiers in the FASTA file are in the following format:</p> <p>>hsa-miR-193b-5p_MIMAT0004767:1-20||chr7:114656010-114656173+:82-95</p> <p>where,</p> <ul> <li>hsa-miR-193b-5p_MIMAT0004767 is the sequence ID of the miRNA from which first chimeric arm of this read was derived from. This ID can be found in hg38_MIR_mature.fa.fasta.</li> <li>1-20 represents the 1-based start and end positions on hsa-miR-193b-5p_MIMAT0004767 representing the origin of the first chimeric arm.</li> <li>chr7:114656010-114656173+ is the sequence ID of the TargetScan target site from which the second chimeric arm of this read was derived from. This ID can be found in TargetScanSites_merged.fa.fasta</li> <li>82-95 represents the 1-based start and end positions on chr7:114656010-114656173+ representing the origin of the second chimeric arm.</li> </ul>
Data From: Evaluating the correlation between genome-wide diversity and the release of plastic phenotypic variation in experimental translocations to novel natural environments
<p>Phenotypic reaction norms are often shaped and constrained by selection and are important for allowing organisms to respond to environmental change. However, selection cannot constrain reaction norms for environmental conditions that populations have not experienced. This may allow cryptic neutral genetic variation for the reaction norm to accumulate such that a release of phenotypic variation occurs when it is exposed to novel conditions. Most genomic diversity behaves as if functionally neutral. Genome-wide diversity metrics may therefore correlate with levels of cryptic genetic variation and, as a result, could exhibit a positive relationship with a release of phenotypic variation in novel environments. To test this hypothesis, we conducted translocations of juvenile brook trout (Salvelinus fontinalis) from 12 populations to novel uninhabited ponds that represented a gradient of environmental conditions. We assessed reaction norms for morphological traits (body size and four morphometric relative warps) across pond environmental gradients and evaluated the effect of genome-wide heterozygosity on phenotypic variability. All traits displayed plastic reaction norms. Overall, we found some evidence that a release of phenotypic variation consistent with cryptic genetic variation can occur in novel environmental conditions. However, the extent to which this release was correlated with average genome-wide diversity was limited to only one of five morphological traits examined. Our results suggest that the link between genomic diversity and the accumulation of cryptic genetic variation in reaction norms may be limited. Similarly, reaction norms were constrained for many of the morphological traits examined. Past conditions may have constrained reaction norms in the putatively novel environments despite significant deviations from contemporary source population habitat. Additionally, as a generalist colonizing species brook trout may exhibit plastic phenotypes across a wide range of environmental conditions.</p>
Data-based evaluation of paleoclimate records from the terrestrial Indian region: opportunities and gaps
<p>We explored the accessibility to paleoclimate records from the terrestrial Indian region for a PAGES article. The table gives information on the location, archive, entity, proxy and repository/database where the record is hosted. </p>
Dataset accompanying paper submission for "Toward data-driven generation and evaluation of model structure for integrated representations of human behavior in water resources systems"
<p>This data set accompanies code archived at DOI: <a href="https://doi.org/10.5281/zenodo.3833186">10.5281/zenodo.3833186</a>, which was used in the experiments for the paper submission "Toward data-driven generation and evaluation of model structure for integrated representations of human behavior in water resources systems"</p>
Assemblies evaluated in the wtdbg2 paper
<p>Assemblies evaluated in the wtdbg2 paper</p>
Dataset related to article "Evaluation of cell metabolic adaptation in wound and tumour by fluorescence Lifetime imaging Microscopy"
<p>This record contains data related to article "Evaluation of cell metabolic adaptation in wound and tumour by fluorescence Lifetime imaging Microscopy"</p> <p>Abstract</p> <p>Acidic pH occurs in acute wounds progressing to healing as consequence of a cell metabolic adaptation in response to injury-induced tissue hypoperfusion. In tumours, high metabolic rate leads to acidosis affecting cancer progression. Acidic pH affects activities of remodelling cells in vitro. The pH measurement predicts healing in pathological wounds and success of surgical treatment of burns and chronic ulcers. However, current methods are limited to skin surface or based on detection of fluorescence intensity of specific sensitive probes that suffer of microenvironment factors. Herein, we ascertained relevance in vivo of cell metabolic adaptation in skin repair by interfering with anaerobic glycolysis. Moreover, a custom-designed skin imaging chamber, 2-Photon microscopy (2PM), fluorescence lifetime imaging (FLIM) and data mapping analyses were used to correlate maps of glycolytic activity in vivo as measurement of NADH intrinsic lifetime with areas of hypoxia and acidification in models of skin injury and cancer. The method was challenged by measuring the NADH profile by interfering with anaerobic glycolysis and oxidative phosphorylation in the mitochondrial respiratory chain. Therefore, intravital NADH FLIM represents a tool for investigating cell metabolic adaptation occurring in wounds, as well as the relationship between cell metabolism and cancer.</p>
Dataset of Paper: "Evaluation of membranes performance for microplastic removal in a simple and low-cost filtration system"
<p>Dataset of Paper: "Evaluation of membranes performance for microplastic removal in a simple and low-cost filtration system":</p> <ul> <li>Zeta potential curves as a function of pH</li> <li>Results of transmembrane pressure (TMP) and flow rate of the filtration experiments. Average transmembrane pressure drop (ΔTMP) and flux (J) after 10 min of operation.</li> <li>Total number of unremoved microplastic particles per litre (NTMP), average size (DMP), and mass removal efficiency (MRE%) of each membrane when filtering water with 100 mg/L of PA and PS</li> </ul>
Contextualized Adaptive Research Description INterfaces Applying LinkedData Evaluation Survey Responses
<p>Building adaptive web forms for publishing research datasets based on contextual information and established ontologies allows the acquisition of fine-grained, structured, semantic metadata which facilitation interdisciplinary findability and reuse.</p> <p>The provided dataset contains the responses of 74 participants in a Turtle (ttl) format from an online survey experiment where they had to use a prototypical web application (CARDINAL) as a proof-of-concept in practice based on a fictious scenario about a political election poll. The evaluation was conducted in July 2020.</p>
Evaluating QualiCO: An ontology to facilitate qualitative methods sharing to support open science
<p>This dataset includes the quantitative analysis we did in our paper. It includes the time on task the participants needed as well as the scores they obtained in the task</p>
Data_Evaluation of Deep Learning Based Segmentation Methods for Industrial Burner Flames
<p>The publicated files contain data related to the research article with the title Evaluation of Deep Learning Based Segmentation Methods for Industrial Burner Flames. It contains raw image data, segmentation results and a matlab script for visualization and its derived figures.</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.