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47,155 results for “evaluations”
Data from: Evaluating the use of lidar to discern snag characteristics important for wildlife
<p>Standing dead trees (known as snags) are historically difficult to map and model using airborne laser scanning (ALS), or lidar. Specific snag characteristics are important for wildlife; for instance, a larger snag with a broken top can serve as a nesting platform for raptors. The objective of this study was to evaluate whether characteristics such as top intactness could be inferred from discrete-return ALS data. We collected structural information for 198 snags in closed-canopy conifer forest plots in Idaho. We selected 13 lidar metrics within 5 m diameter point clouds to serve as predictor variables in random forest (RF) models to classify snags into four groups by size (small [<40 cm diameter] or large [≥40 cm diameter]) and intactness (intact or broken top) across multiple iterations. We conducted these models first with all snags combined, and then ran the same models with only small or large snags. Overall accuracies were highest in RF models with large snags only (77%), but kappa statistics for all models were low (0.29–0.49). ALS data alone were not sufficient to identify top intactness for large snags; future studies combining ALS data with other remotely sensed data to improve classification of snag characteristics important for wildlife is encouraged.</p>
Dataset from paper "Dynamic Concentration Factor: A Novel Parameter For The Rigorous Evaluation Of Solar Compound Parabolic Collectors"
<ul> <li>Dynamic Concentration Factor as a function of the beam angle (α) for CPC with different acceptance angles (θ), reflectivity (r) of the collector surface ranging from 0.0 to 1.0, and optical density ranging from 0.0 to 10.0</li> <li>Diffuse Dynamic Concentration factor for CPC with different acceptance angles (θ), reflectivity (r) of the collector surface ranging from 0.0 to 1.0, and optical density ranging from 0.0 to 10.0</li> <li>Experimental validation of the Dynamic Concentration Factor using actinometry results</li> <li>Independence test of Dynamic Concentration results of the number of rays cast in the software tool.</li> <li>Full-precision data of the Dynamic Concentration Factor as a function of the beam angle (α) for CPC with different acceptance angles (θ), reflectivity (r) of the collector surface ranging from 0.0 to 1.0, and optical density ranging from 0.0 to 10.0</li> </ul>
Evaluating refugia in recent human evolution in Africa: Supplementary information
<p><em>Homo sapiens</em> have adapted to an incredible diversity of habitats around the globe. This capacity to adapt to different landscapes is clearly expressed within Africa, with Late Pleistocene <em>Homo sapiens</em> populations occupying savannahs, woodlands, coastlines and mountainous terrain. As the only area of the world where <em>Homo sapiens</em> have clearly persisted through multiple glacial-interglacial cycles, Africa is the only continent where classic refugia models can be formulated and tested to examine and describe changing patterns of past distributions and human phylogeographies. The potential role of refugia has frequently been acknowledged in the Late Pleistocene palaeoanthropological literature, yet explicit identification of potential refugia has been limited by the patchy nature of palaeoenvironmental and archaeological records, and the low temporal resolution of climate or ecological models. Here, we apply potential climatic thresholds on human habitation, rooted in ethnographic studies, in combination with high resolution model datasets for precipitation and biome distributions to identify persistent refugia spanning the Late Pleistocene (130-10 thousand years ago). We present two alternate models suggesting that between 27-66% of Africa may have provided refugia to Late Pleistocene human populations, and examine variability in precipitation, biome, and ecotone distributions within these refugial zones.</p>
Evaluation data for "Global, high-resolution, reduced-complexity air quality modeling for PM2.5 using InMAP (Intervention Model for Air Pollution)"
<p>This zip file contains data for performing Global InMAP model runs and evaluations. To the extent that any of the data is covered by third party licenses, it is the responsibility of the user to follow the terms of those licenses. A description of the contents of this directory is below:</p> <p>measurements.csv<br> Vetted global dataset of ground-level annual-average measurements of total PM2.5 and species (pNO3, pSO4, pNH4) compiled from monitoring networks, used for model performance evaluation. Data sources are: World Health Organization (Global), European Environment Agency (Europe), National Air Pollution Surveillance Program (Canada), Environmental Protection Agency (United States of America), Central Pollution Control Board (India), Australian Government State of the Environment (Australia), and Acid Deposition Monitoring Network In East Asia (EANET) (East Asia).</p> <p>population directory<br> Population count data is from the Gridded Population of The World (v4.10) projected to year 2020. The data is in 15x15 arcminute grids, except for in grid cells where the population is above 80,000, where the population data is 30x30 arcseconds.</p> <p>GlobalInMAPData_v1.ncf<br> Regular-grid Global InMAP input data for the year 2005 for use as the "InMAPData" variable in the InMAP configuration file. It was created from GEOS-Chem v.11-01 simulation outputs with the 'inmap preproc' command.</p> <p>global_inmap_004x003_v1.1.0.gob<br> Global InMAP variable grid resolution input data for coords for year 2016 for use as the "VariableGridData" variable in the InMAP configuration file. It was created with the 'inmap grid' command using GlobalInMAPData_v1.ncf and population.shp.</p> <p>2016_emissions directory<br> Total PM2.5 and precursor emissions to arrive at total PM2.5 concentrations from Global InMAP. Units for polygonized emissions inputs (shapefiles) are short (US) tons/yr, and units for gridded emissions inputs (NetCDF files) are kg/yr.</p> <p>global_emission_changes directory<br> nh3.nc, nox.nc, and sox.nc are gridded emissions for changes in inorganic precursors for comparing Global InMAP and GEOS-Chem. Units are kg/yr. NH4-gc.nc, NIT-gc.nc, and SO4-gc.nc are results for changes in concentrations arising from these changes in emissions for 3 months, 1 month, and 2 months.</p> <p>usa_emission_changes directory<br> Emissions for comparing Global InMAP and US InMAP (described in Tessum et al., 2017).<br> Emissions are derived using the United States National Emissions Inventory (NEI) 2014v.1, processed exactly as in Thakrar et al., 2020.<br> Emissions are coal-powered electricity generation (NEI Source Classification Code: 10100212) and gasoline passenger vehicles (NEI Source Classification Code: 2201210080).<br> Units are ug/s.</p> <p>Tessum, C.W.; Hill, J.D.; Marshall, J.D. InMAP: A model for air pollution interventions. PloS One 2017, 12 (4) e0176131.<br> Thakrar, S.K.; Balasubramanian, S.; Adams, P.J.; Azevedo, I.M.; Muller, N.Z.; Pandis, S.N.; Polasky, S.; Pope III, C.A.; Robinson, A.L.; Apte, J.S.; Tessum, C.W.; Marshall, J.D.; Hill; J.D. Reducing mortality from air pollution in the United States by targeting specific emission sources. Environmental Science & Technology Letters 2020, 7(9), pp.639-645.<br> Gridded Population of the World, Version 4 (GPWv4): National Identifier Grid. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://dx.doi.org/10.7927/H41V5BX1.</p>
HPCC FPGA Evaluation Data
<p>This archive contains configuration files, scripts, and full FPGA bitstreams for the execution of the HPCC FPGA benchmarks over multiple FPGAs. Moreover, the raw output files of the measurements are also provided. </p> <p>Update 1.1: Add build configuration, scripts, and output files of additional experiments.</p> <p>Update 1.2: Add artifacts for Xilinx Alveo U280</p>
Supplementary Material for "Evaluating Classifiers in SE Research: The ECSER Pipeline and Two Replication Studies"
<p>This supplementary material for the article"Evaluating Classifiers in SE Research: The ECSER Pipeline and Two Replication Studies. Dell’Anna, D.; Aydemir, F. B.; and Dalpiaz, F. Empirical Software Engineering. 2022" includes</p> <ul> <li>ECSER-ExploratoryStudy.csv: The annotated meta-data of the papers that have been published in ICSE between 2019 and 2021.</li> <li>ECSER_ROCplots+StatTest.ipynb: A python notebook that compares classifiers adn checks the statistical significance of the comparison results.</li> <li>ECSER_SummaryOfReplicationSteps.pdf: This table presents a summary of ECSER steps for the two original studies and our applications on ECSER.</li> <li>ECSER_RE: The directory that holds the datasets and code for the replication of Hay et al. [1] and additional runs on the new data sets.</li> <li>ECSER_FF: The directory that holds the code and data for the replication of Alshammari et al. [2]</li> <li>README.md presents the structure of the supplementary materials.</li> <li>requirements.txt lists the dependencies needed to run the code</li> </ul> <p>In the ECSER_RE directory, the code for multiple classifiers that are compared are kept in the "Classifiers" directory. The public data sets are shared in the Datasets directory. "ECSER_RE_Compare_Classifiers.ipynb" python notebook includes the code that runs each classifier. The results are presented in "ECSER_RE_results-Promise-vs-all.csv".</p> <p>In the ECSER_FF directory, the data sets are presented directly under the main directory. The notebook "ECSER-FF-Compare_Classifiers.ipynb" compares the classifiers of the original study and the results are kept under the "ECSER_FF_results" directory.</p> <p><strong>How to cite this repository </strong><br>If you use this repository, please cite the reference paper, and the repository, as below:</p> <p>Dell’Anna, Davide, Fatma Başak Aydemir, and Fabiano Dalpiaz. "Evaluating classifiers in SE research: the ECSER pipeline and two replication studies." Empirical Software Engineering 28.1 (2023): 3.</p> <p>Davide Dell'Anna, Fatma Başak Aydemir, & Fabiano Dalpiaz. (2021). Supplementary Material for "Evaluating Classifiers in SE Research: The ECSER Pipeline and Two Replication Studies" [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6266675</p> <p> </p> <p> </p> <p> </p> <ol> <li>Tobias Hey, Jan Keim, Anne Koziolek, and Walter F. Tichy. 2020. SupplementaryMaterial of "NoRBERT: Transfer Learning for Requirements Classification". https://doi.org/10.5281/zenodo.3874137</li> <li>Abdulrahman Alshammari, Christopher Morris, Michael Hilton, and JonathanBell. 2021. FlakeFlagger: Predicting Flakiness Without Rerunning Tests. In43rdIEEE/ACM International Conference on Software Engineering, ICSE 2021, Madrid,Spain, 22-30 May 2021. IEEE, 1572–1584. https://doi.org/10.1109/ICSE43902.2021.00140</li> </ol>
Song recordings and annotation files of 3 canaries used to evaluate training of TweetyNet models for birdsong segmentation and annotation
<p>Many analyses of birdsong require time-consuming manual annotation of the individual elements of song, known as syllables or notes. We developed the first automated algorithm for birdsong annotation, "TweetyNet", that is applicable to complex song such as canary song. TweetyNet is trained with a small amount of hand-labeled data using supervised learning methods. We evaluate the amount of data required for training TweetyNet models using vocalizations of two songbird species - Bengalese finches and Canaries. This dataset contains song audio files and their accompanying annotation files for the three canaries used in this analysis.</p>
Raw Data for the article: Clinical and Molecular-Based Approach in the Evaluation of Hepatocellular Carcinoma Recurrence after Radical Liver Resection
<p><strong>Background: </strong>Hepatic resection remains the treatment of choice for patients with early-stage HCC with preserved liver function. Unfortunately, however, the majority of patients develop tumor recurrence. While several clinical factors were found to be associated with tumor recurrence, HCC pathogenesis is a complex process of accumulation of somatic genomic alterations, which leads to a huge molecular heterogeneity that has not been completely understood. The aim of this study is to complement potentially predictive clinical and pathological factors with next-generation sequencing genomic profiling and loss of heterozygosity analysis.</p> <p><strong>Methods: </strong>124 HCC patients, who underwent a primary hepatic resection from January 2016 to December 2019, were recruited for this study. Next-generation sequencing (NGS) analysis and allelic imbalance assessment in a case-control subgroup analysis were performed. A time-to-recurrence analysis was performed as well by means of Kaplan-Meier estimators.</p> <p><strong>Results: </strong>Cumulative number of HCC recurrences were 26 (21%) and 32 (26%), respectively, one and two years after surgery. Kaplan-Meier estimates for the probability of recurrence amounted to 37% (95% C.I.: 24-47) and to 51% (95% C.I.: 35-62), after one and two years, respectively. Multivariable analysis identified as independent predictors of HCC recurrence: hepatitis C virus (HCV) infection (HR: 1.96, 95%C.I.: 0.91-4.24, <em>p</em> = 0.085), serum bilirubin levels (HR: 5.32, 95%C.I.: 2.07-13.69, <em>p</em> = 0.001), number of nodules (HR: 1.63, 95%C.I.: 1.12-2.38, <em>p</em> = 0.011) and size of the larger nodule (HR: 1.11, 95%C.I.: 1.03-1.18, <em>p</em> = 0.004). Time-to-recurrence analysis showed that loss of heterozygosity in the <em>PTEN</em> loci (involved in the PI3K/AKT/mTOR signaling pathway) was significantly associated with a lower risk of HCC recurrence (HR: 0.35, 95%C.I.: 0.13-0.93, <em>p</em> = 0.036).</p> <p><strong>Conclusions: </strong>multiple alterations of cancer genes are associated with HCC progression. In particular, the evidence of a specific AI mutation presented in 20 patients seemed to have a protective effect on the risk of HCC recurrence.</p>
Fairlex: A multilingual benchmark for evaluating fairness in legal text processing
<p>We present a benchmark suite of four datasets for evaluating the fairness of pre-trained legal language models and the techniques used to fine-tune them for downstream tasks. Our benchmarks cover four jurisdictions (European Council, USA, Swiss, and Chinese), five languages (English, German, French, Italian, and Chinese), and fairness across five attributes (gender, age, nationality/region, language, and legal area). In our experiments, we evaluate pre-trained language models using several group-robust fine-tuning techniques and show that performance group disparities are vibrant in many cases, while none of these techniques guarantee fairness, nor consistently mitigate group disparities. Furthermore, we provide a quantitative and qualitative analysis of our results, highlighting open challenges in the development of robustness methods in legal NLP.</p>
Identification of Performance Changes at Code Level (Jetty Evaluation Dataset)
<p><em>This is the anonymous reviewing version; the source code repository will be added after the review.</em></p> <p>This dataset provides the results of measuring jetty with the 1,000 artificial regressions with Peass and with JMH. The creation of the artificial regressions and the measurement is defined here: https://anonymous.4open.science/r/jetty-evaluation-6F58/ (Repository named jetty-evaluation, GitHub link will be provided after review) An example regression is contained in <a href="https://anonymous.4open.science/r/jetty-experiments-202D">https://anonymous.4open.science/r/jetty-experiments-202D</a> We obtained these data from measurement on Intel Xeon CPU E5-2620 v3 @ 2.40GHz.</p> <p>The dataset contains the following data:</p> <ul> <li>regression-results-peass-0.tar.xz (Results of the measurement with Peass, part 0)</li> <li>regression-results-peass-1.tar.xz (Results of the measurement with Peass, part 1)</li> <li>regression-results-peass-2.tar.xz (Results of the measurement with Peass, part 2)</li> <li>regression-results-peass-3.tar.xz (Results of the measurement with Peass, part 3)</li> <li>regression-results-jmh.tar.xz (Results of the measurement with JMH)</li> <li>tree-results.tar.xz (Metadata of the trees)</li> </ul> <p>To get the data in a usable format, extract the peass data to one folder (the folder will be named <strong>$PEASS_RESULT_FOLDER</strong>):</p> <pre><code class="language-bash">mkdir peass for file in *; do echo $file; tar -xf $file; done for i in {0..3}; do mv $i/* .; done</code></pre> <p>This will yield to a folder containing 1000 folders named regression-$i, where each consists of</p> <ul> <li>deps.tar.xz: The regression test selection results</li> <li>logs.tar.xz: The logs of the test executions</li> <li>results: The traces of the regression test selection and a file named changes_*testcase.json, which contains statistical details of the measured performance change (if present)</li> <li>jetty.project_peass: Detailed measurement data and logs of individual JVM starts</li> </ul> <p>To analyse the Peass results, run</p> <pre><code class="language-bash">cd scripts/peass ./analyzeChangeIdentification.sh $PEASS_RESULTS_FOLDER ./analyzeFrequency.sh $PEASS_RESULTS_FOLDER</code></pre> <p>This will take some time, since partial results need to be unpacked for analysis. The first script will create the following results:</p> <p>and the second will yield the following results:</p> <pre><code>Correct Measurement: 587 Not selected changes: 146 Wrong measurement result: 267 Wrong analysis (should be 0): 0 Overall: 1000 Share of changed method on correct measurements: 0.109571 0.11238 32 Method call count on correct measurement: 15638.4 32853.7 32 Average tree depth on correct measurements: 1.1022 2.6875 32 Share of changed method on wrong measurements: 0.17692 0.180365 968 Method call count on wrong measurement: 711415 180438 968 Average tree depth on wrong measurements: 1.23239 2.42252 968</code></pre> <p>To analyze the JMH data, first extract the metadata (the folder will be named <strong>$TREEFOLDER</strong>):</p> <pre><code class="language-bash">tar -xf tree-results.tar.xz</code></pre> <p>Afterwards extract the JMH results (the folder will be named $<strong>JMH_RESULTS_FOLDER</strong>):</p> <pre><code class="language-bash">tar -xvf regression-results-jmh.tar.xz</code></pre> <p>This will yield a folder containing a measurement for each regression with two files:</p> <ul> <li>basic.json: The performance measurement result of the basic version</li> <li>regression-$i.json: The performance measurement result of the version containing the regression</li> </ul> <p>Afterwards, run the analysis in the jetty-evaluation repository:</p> <pre><code class="language-bash">cd scripts/jmh ./analyzeFrequency.sh $JMH_RESULTS_FOLDER $TREEFOLDER</code></pre> <p>Since the regression are injected in the call tree of the benchmark, there are now unselected changes. The analysis will yield the following results:</p> <pre><code>Share of changed method on correct measurements: 0.184631 0.271968 587 Method call count on correct measurement: 14628.2 4979.6 587 Share of changed method on wrong measurements: 0.180981 0.235614 267 Method call count on wrong measurement: 14902.2 4333.58 267</code></pre> <p> </p>
Data from 90-day quantitative inhalation toxicology study evaluating the dose-response and fate in the lung and pleura of chrysotile-containing brake dust
<p>The final results from this multi-dose, 90-day inhalation toxicology study in the rat with life-time post-exposure observation have shown a significant fundamental difference in pathological response and tumorgenicity between brake dust generated from brake pads manufactured with chrysotile or from chrysotile alone in comparison to the amphiboles, crocidolite and amosite asbestos.</p> <p>The groups exposed to brake dust showed no significant pathological or tumorigenic response in the respiratory track compared to the air control group at exposure concentrations and deposited doses well above those at which humans have been exposed. Slight alveolar/interstitial macrophage accumulation of particles was noted. Wagner grades were 1–2 (1 = control group), similar to the TiO2 particle control group. Chrysotile was not biopersistent, exhibiting in the lung a deterioration of its matrix which results in breakage into particles and short fibers which can be cleared by alveolar macrophages and which can continue to dissolve. Particle-laden macrophage accumulation was observed, leading to a very-slight interstitial inflammatory response (Wagner grade 1–3). There was no peribronchiolar inflammation, occasional very-slight interstitial fibrosis (Wagner grade 4), and no exposure-related tumorigenic response.</p> <p>The pathological response of crocidolite and amosite compared to the brake dust and chrysotile was clearly differentiated by the histopathology and the confocal analysis. Crocidolite and amosite induced persistent inflammation, microgranulomas, persistent fibrosis (Wagner grades 4), and a dose-related lung tumor response. Confocal microscopy quantified extensive inflammatory response and collagen development in the lung, visceral and parietal pleura as well as pleural adhesions.</p> <p>These results provide a clear foundation for differentiating the innocuous effects of brake dust exposure from the adverse effects following amphibole asbestos exposure.</p>
Relationship-Based Threat Modeling: Evaluation Data
<p>The full data sets used for the evaluation of our Relationship-Based Threat Modeling approach, as described in our recent submission to EnCyCriS 2022.<br> Stef Verreydt, Laurens Sion, Koen Yskout, and Wouter Joosen. 2022. Relationship-Based Threat Modeling. In The 3rd International Workshop on Engineering and Cybersecurity of Critical Systems (EnCyCriS’22 ), May 16, 2022, Pittsburgh, PA, USA. ACM, New York, NY, USA, 8 pages. https://doi.org/10.1145/ 3524489.3527303</p>
MorphinPlan Experimental Evaluation results
<p>Results of the empirical evaluation of the MorphinPlan framework. </p>
Datasets for Yue et al. (2022), Atmospheric Measurement Techniques: "Evaluating the Consistency and Continuity of Pixel-Scale Cloud Property Data Records From Aqua and SNPP"
<p>Pixel scaled collocated AIRS, Aqua MODIS, SNPP CrIS, and SNPP VIIRS cloud retrieval products. These datasets are used to generate analyses and results presented in Yue et al. (2022, AMT): "Evaluating the Consistency and Continuity of Pixel-Scale Cloud Property Data Records From Aqua and SNPP"</p>
Metabolomics-based phenotypic screens for evaluation of drug synergy via DIMS
<p>Drugs used in combination can synergize to increase efficacy, decrease toxicity, and prevent drug resistance. While conventional high-throughput screens relied on univariate data are incredibly valuable to identify promising drug candidates, phenotypic screening methodologies could be beneficial to provide deep insight into the molecular response of drug combination with a likelihood of improved clinical outcomes. We developed a high-content metabolomics drug screening platform using stable isotope tracer direct infusion mass spectrometry that informs a novel algorithm to determine synergy from multivariate phenomics data. Using a cancer drug library, we validated the drug screening integrating isotope enriched metabolomics data and computational data mining on a panel of prostate cell lines and verified the synergy between CB-839 and docetaxel both in vitro (three-dimensional model) and in vivo. The proposed unbiased metabolomics screening platform can be used to rapidly generate phenotype-informed datasets and quantify synergy for combinatorial drug discovery. Drugs used in combination can synergize to increase efficacy, decrease toxicity, and prevent drug resistance. While conventional high-throughput screens relied on univariate data are incredibly valuable to identify promising drug candidates, phenotypic screening methodologies could be beneficial to provide deep insight into the molecular response of drug combination with a likelihood of improved clinical outcomes. We developed a high-content metabolomics drug screening platform using stable isotope tracer direct infusion mass spectrometry that informs a novel algorithm to determine synergy from multivariate phenomics data. Using a cancer drug library, we validated the drug screening integrating isotope enriched metabolomics data and computational data mining on a panel of prostate cell lines and verified the synergy between CB-839 and docetaxel both in vitro (three-dimensional model) and in vivo. The proposed unbiased metabolomics screening platform can be used to rapidly generate phenotype-informed datasets and quantify synergy for combinatorial drug discovery.</p>
HEAR NeurIPS 2021 Datasets (Holistic Evaluation of Audio Representations)
<p><strong>NOTES:</strong></p> <p><strong>* On Zenodo, please make sure you download datasets version 2021.3, not earlier versions. (2021.3 added the Vocal Imitations dataset at 48KHz. 2021.2 updated the tfds datasets.)</strong></p> <p><strong>* The datasets have different open licenses. Please see LICENSE.txt for each individual dataset's license.</strong></p> <p>These are the evaluation tasks for the HEAR (Holistic Evaluation of Audio Representations) 2021 NeurIPS challenge.</p> <p>The aim of this challenge is to develop a general-purpose audio representation that provides a strong basis for learning in a wide variety of tasks and scenarios. The HEAR 2021 challenge invites you to create an audio embedding that is as holistic as the human ear, i.e., one that performs well across a variety of everyday domains: What approach best generalizes to a wide range of downstream audio tasks without fine-tuning?<br> <br> HEAR 2021 evaluates audio representations using a benchmark suite across a variety of domains, including speech, environmental sound, and music.</p> <p>For more information, see the <a href="https://neuralaudio.ai/hear2021-holistic-evaluation-of-audio-representations.html">HEAR 2021 website</a> and upcoming PMLR journal article.</p> <p>Datasets were all normalized to a common human-readable format using <a href="https://github.com/neuralaudio/hear-preprocess">hearpreprocess</a>. Until 2022-04-01, datasets will be mirrored at <a href="https://data.neuralaudio.ai/">data.neuralaudio.ai</a>. This Zenodo mirror has all audio task but only at 48000Hz sampling rate. For other sampling rates (16000, 22050, 32000, 44100), please download files (requester pays) from Google Storage gs://hear2021-archive/tasks/ or AWS s3://hear2021-archive/tasks/</p>
Bioacoustic data for evaluating wind denoising and detection methods
<p>Acoustic survey data accompanying publication "Wind-robust sound event detection and denoising for bioacoustics", by Julius Juodakis and Stephen Marsland.</p> <p>Contents (see the publication for details):</p> <ul> <li>pilotdata/: short audio files used for selecting the best-fit wind model</li> <li>surveys/LSK_additional_wavs/: remaining recordings from the little spotted kiwi survey that have not been deposited previously</li> <li>surveys/rawannots/: annotations produced by the unadjusted, OLS-adjusted and QR-adjusted detectors, for two bird surveys</li> <li>surveys/reviewed/: annotations passing human review for the unadjusted and OLS-adjusted detectors, for two bird surveys</li> <li>denoising/: examples of noise and signal which were mixed to evaluate the denoising.</li> </ul> <p>Audio files are provided in WAV PCM format. Annotations and filters are intended for use with AviaNZ software (https://www.avianz.net/), and provided in AviaNZ-compatible JSON format.</p>
Evaluation MyPack PEF packaging technology
<p>This data was used for the redaction of the project deliverable "D5.2-Report on FINAL evaluation packaging technologies", which is a report on the final evaluation of selected packaging technologies.</p> <p>Moreover, this data is from the scientific publication "<a href="https://popups.uliege.be/esaform21/index.php?id=1589"><strong>Relationship Between Crystallization, Mechanical and Gas Barrier Properties of Poly(ethylene furanoate) (PEF) in Multinanolayered PLA-PEF and PET-PEF Films</strong></a>"</p>
A scheduler log replayer and logs for BFTrainer evaluation
<p>This is a temporary repository to host source for paper: <code>BFTrainer: Low-Cost Training of Neural Networks on Unfillable Supercomputer Nodes.</code></p> <p>Files</p> <ul> <li><code>BFTrainer-replay.py</code> the main program to replay real scheduler to evaluate our resource allocation algorithm</li> <li><code>jobs.py</code> implements functions to manage jobs.</li> <li><code>progCBC.py</code> or <code>progGRB.py</code> the implementation of mixed Integer linear programming using Gurobi optimizer (progGRB.py). We also open source our implementation (progGRB.py) using free optimizer (e.g., CBC, Pulp and JuMP). You can get an Trial Licenses or Free Academic Licenses from Gurobi if you want to run the current version. You need to adjust the import source in the <code>BFTrainer-replay.py</code> to use the CBC based solver.</li> <li><code>trace.py</code> has functions to manage scheduler logs for the replay evaluation.</li> </ul>
Evaluation of Alternative Sources of Supplementary Cementitious Materials (SCMs) for Concrete Materials in Transportation Infrastructure
<p>Fly ash is the most utilized supplementary cementitious material (SCM) in the US. Nonetheless, rapid decline in coal-fired power generation threatens its supply. The objective of this study was to evaluate alternative SCMs for concrete transportation infrastructure in Region 6. SCMs investigated included reclaimed fly ash (RFA), reclaimed ground bottom ash (GBA), metakaolin (MK), and conventional Class F fly ash (FA) as a reference. SCMs were characterized and the fresh and hardened properties of concrete incorporating different dosages (i.e., 10, 20, and 30% cement replacement by mass) of the individual SCMs (i.e., binary systems) and blended SCM systems of RFA-MK and GBA-MK (i.e., ternary systems) were assessed. All the coal ashes met the requirements for pozzolanic component, CaO, SO3, moisture content, LOI, SAI, and water requirement to be classified as Class F pozzolan according to ASTM C618. MK met all the but the water requirement. Concrete using FA generally exhibited better workability than the control mixture (i.e., without SCMs), whereas concrete incorporating RFA, GBA, and MK presented decrements in workability. Mixtures implementing ternary systems also displayed decrements in workability. Air content of fresh concrete mixtures incorporating binary and ternary systems generally decreased. Relative to the control mixture, decrements in 28-day compressive strength (f’c) were reported when incorporating FA and RFA, yet this was generally not the case for the 90-day f’c. In the case of GBA mixtures, significant differences in f’c were not observed after 28 days nor 90 days. MK mixtures as well as RFA-MK and GBA-MK mixtures generally presented increments in 28-day and 90-day f’c. Concrete mixtures implementing coal ashes did not produce significant differences in 28-day surface resistivity (SR) at any cement replacement levels; yet after 90 days of curing, significant improvements in SR were reported. MK, RFA-MK, and GBA-MK mixtures exhibited significant increments in SR at all dosages after 28 and 90 days of curing. Notably, while the control mixture and mixtures incorporating coal ashes did not meet the 28-day SR requirement for class A1 concrete according to LaDOTD, mixtures implementing MK and ternary systems did in almost all cases. All SCMs were effective at reducing drying shrinkage. Binary systems reduced drying shrinkage by 24.2-69.1%, whereas ternary systems reduced drying shrinkage by 55.2-75.3%. With regards ASR, mixtures implementing SCMs presented significantly lower expansion and the increment in SCMs content further reduced the expansion; thus, signaling a positive effect in suppressing ASR related expansion, specially at high dosages.</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.