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47,155 results for “evaluations”
Dataset for an article "Numerical and Experimental Evaluation of Structured Material for Use in Multi-scale Topology Optimization"
<p><span>The dataset contains data from compression mechanical testing of 6 basic truss-based lattice cells with relative density between 0.3 and 0.7 in two directions (parallel and perpendicular to build direction). Additionally, the dataset contains simulation of the experiments by finite element method. Due to a big difference between results from experiment and simulation with nominal material model, parametric material model with Young's modulus set as parameter was used. Resulting Young's moduli that correspond with the experiments are also included. Details can be found in the article: “Numerical and Experimental Evaluation of Structured Material for Use in Multi-scale Topology Optimization”.</span></p>
Fig. 1 in Conservation status of Amphibians of Argentina: An update and evaluation of national assessments
Fig. 1. Map of the ecoregions of Argentina from Burkart et al. 1999. Numbers indicate ecoregions divisions as follows: (1) Puna, (2) High Andean, (3) Yungas Forest, (4) Dry Chaco, (5) Humid Chaco, (6) Delta and Islands of the Parana River, (7) Esteros of Ibera, (8) Paranaen Forest, (9) Campos and Malezales, (10) Espinal, (11) Pampas, (12) Monte of Sierras and Bolsones, (13) Monte de Llanuras and Mesetas, (14) Patagonian Steppe, (15) Patagonian Woodlands. Arrows indicate the four overthreatened ecoregions.
Multinational evaluation of genetic diversity indicators for the Kunming-Montreal Global Biodiversity Framework
<p>Under the recently adopted Kunming-Montreal Global Biodiversity Framework, 196 Parties committed to report the status of genetic diversity for all species. To facilitate reporting, three genetic diversity indicators were developed, two of which focus on processes contributing to genetic diversity conservation: maintaining genetically distinct populations and ensuring populations are large enough to maintain genetic diversity. The major advantage of these indicators is that they can be estimated with or without DNA-based data. However, demonstrating their feasibility requires addressing the methodological challenges of using data gathered from diverse sources, across diverse taxonomic groups, and for countries of varying socioeconomic status and biodiversity levels. Here, we assess the genetic indicators for 919 taxa, representing 5,271 populations across nine countries, including megadiverse countries and developing economies. Eighty-three percent of taxa assessed had data available to calculate at least one indicator. Our results show that although the majority of species maintain most populations, 58% of species have populations too small to maintain genetic diversity. Moreover, genetic indicator values suggest that IUCN Red List status and other initiatives fail to assess genetic status, highlighting the critical importance of genetic indicators.</p>
Data from: Evaluation of a low-cost staining method for improved visualization of sweet potato whitefly (Bemisia tabaci) eggs on multiple crop plant species
<p>The sweet potato whitefly (Bemisia tabaci) is a damaging insect pest that feeds on hundreds of crop plants. Oviposition rate is a useful metric to screen plants for whitefly resistance. Whitefly eggs are small and translucent, and can therefore be hard to count on the leaves of some crops. In this research, we tested a selective egg staining process on five crop species to determine if egg staining can improve the visualization and quantification of whitefly eggs. By comparing the egg counts before and after staining using two-sample Wilcoxon signed-rank tests (a non-parametric test for paired analyses). Two individuals counted the eggs, and for both these counters we found a significant increase in the number of visible eggs after staining on melon, tomato, and cowpea. This method could be applied to improve phenotyping for whitefly resistance in plant breeding applications.</p>
Ir and NMR for article Synthesis and Biological Activity Evaluation of New Isatin-Gallate Hybrids as Antioxidant and Anticancer Agents (in vitro) and In-silico Study as Anticancer Agents and Coronavirus Inhibitors
<p>this is IR and NMR data for article titled <strong>Synthesis and Biological Activity Evaluation of New Isatin-Gallate Hybrids as Antioxidant and Anticancer Agents (<em>in vitro</em>) and In-silico Study as Anticancer Agents and Coronavirus Inhibitors </strong></p> <p> </p>
Alloy Sustainability Database - Evaluating the Economic, Environmental, and Societal Impacts of High-Entropy Alloys
<p>This database encompasses 18 elements and details over 400 high entropy alloys (HEAs), Ni-based superalloys, and steels, providing an extensive review of their properties and capabilities. To assess the broader implications of these materials, we have developed nine indicators that evaluate their economic, environmental, and human health impacts. Each indicator is meticulously described, including the methods used for their calculation. This data has been compiled with the goal of integrating considerations of societal impact into the alloy design process, thereby promoting the development of materials that are not only innovative but also socially responsible.</p>
Train and Evaluation Code, Road Classification Models and Test set of the paper "Insights into the Effects of Image Overlap and Image Size on Semantic Segmentation Models Trained for Road Surface Area Extraction from Aerial Orthophotography"
<p>This repository contains the Python scripts built for training and evaluation of the implementation, together with the test data and the resulting road segmentation models corresponding to the paper "Insights into the Effects of Image Overlap and Image Size on Semantic Segmentation Models Trained for Road Surface Area Extraction from Aerial Orthophotography". The scripts make use of the Tensorflow with Keras framework and their additional required dependencies.</p> <p>The training and validation set is based on the binary SROADEX dataset (<a href="../records/6482346">https://zenodo.org/records/6482346</a>) that was re-split into tiles that feature the image resolutions (256 x 256, 512 x 512, and 1024 x 1024 pixels) and image overlaps (0% and 12.5%) considered in this study. The data have been generated using scripts developed in Python using Open Source libraries (GDAL/OGR and MapScript) for rasterization of vector cartography that represents the axes of the different types of roads (urban, interurban and rural). This binary road data contains information from 16 full orthoimages (28.5 km * 18.5 km) with spatial resolution of 0.5 m/pixel from the insular and peninsular Spanish territory. Due to the size on disk of approximately 492 gigabytes, this training and validation data is only available upon request from the corresponding author. The test set has been generated from a novel area from Palencia (Spain) and features 18 million pixels labelled with the positive "Road" class. The test sets are provided in the repository for each resolution (with no overlap), so that additional DL models can be evaluated on the same data and compared with the results achieved in this study.</p> <p>The structure of the information shared in this repository is as follows:<br>The scripts have been grouped by tile resolution (256, 512 and 1024). First, the test set and the evaluation script can be found. For each tile resolution, there are two subfolders (corresponding to the "no overlap" and "12.5% overlap"). In each case, the Python scripts for training the models in the three repetitions are shared, and the trained models (H5 format) are shared in compressed form. Finally, for each resolution we also share the testing dataset which consists of two folders.</p> <p>The material is distributed under a CC-BY 4.0 license.</p>
Technical Debt Tools: a Survey and an Empirical Evaluation
<p>Background: The life cycle of a technical debt from its identification to its payment is long and may include several<br> activities, such as identification and management. There is a lot of research in the literature to address different sets of<br> these activities by different means. Specifically, several tools have already tackled such technical debt identification<br> problem. However, only a few studies empirically assessed those tools. Method: In this article, we carried a multi-<br> method research. We first surveyed the literature for the technical debt tools available an then we evaluated two of<br> them, which aim at identification of self-admitted technical debt. Thy are named eXcomment e DebtHunter. Results:<br> We found 92 tools employing different approaches to support technical debt life cycle management. Most of them<br> (59%) address only the high level task of management, instead of actually identify and pay the debt. Additionally,<br> as for our empirical evaluation of tools, our results show that DebtHunter found only 7% of debt identified by<br> eXcomment. In the other way around, eXcomment found 19.9% the debt find by DebtHunter. Besides, both tools<br> have low levels of precision and recall. Conclusion: It is hard to find technical debt through comments. Both tools<br> can find indicators of debt items, however they struggle on the precision and recall. In fact, although eXcomment<br> and DebtHunter diverge on the amount of debt identified, they seem to converge with regard to the type o debt<br> present in the system under evaluation.</p>
Figure 3 in Evaluation of potassium borate as a volatility-reducing agent for dicamba
Figure 3. Temperature and relative humidity following herbicide application in 2020 at the locations in (A) Fayetteville and (B) Newport, AR, from July 7, 2020, through July 8, 2020 (30 h after application).
Figure 2. Exponential 2p in Evaluation of potassium borate as a volatility-reducing agent for dicamba
Figure 2. Exponential 2p curve ((O*Exp(*rate), O = scale, b = growth rate) fit to potassium tetraborate tetrahydrate (KBo) concentration and total dicamba recovered from polyurethane foam and filter paper from the two KBo rate titration experiments conducted in 2020; R2 value displays the percentage of variability explained by the fit of the line. Black dots in the middle represent mean recovered dicamba of the respective KBo concentration, and gray dots above and below the mean represent the SE.
Figure 1 in Evaluation of potassium borate as a volatility-reducing agent for dicamba
Figure 1. (A) Images of low tunnels and implementation of trial in the field, and (B) placement of air samplers and treated soil flats between two rows of bioindicator soybean located underneath a 1.5 m by 6 m by 1.2 m plastic-covered tunnel in Fayetteville, AR, in 2020.
Figure 1 in Weed Seedbank Management: Revisiting How Herbicides Are Evaluated
Figure 1. Scatter plot of principal component analysis indicating the (dis) associations between visual rating, weed seed production, weed biomass and density as a result of meta-analysis of selected research articles in Weed Science and Weed Technology.
A Benchmark Suite for Systematically Evaluating Reasoning Shortcuts
<p><strong>Codebase</strong> [<a href="https://unitn-sml.github.io/rsbench/">Github</a>] | <strong>Dataset</strong> [<a href="doi.org/10.5281/zenodo.11612556">Zenodo</a>]</p> <p> </p> <p><strong>Abstract</strong></p> <p>The advent of powerful neural classifiers has increased interest in problems that require both learning and reasoning. These problems are critical for understanding important properties of models, such as trustworthiness, generalization, interpretability, and compliance to safety and structural constraints. However, recent research observed that tasks requiring both learning and reasoning on background knowledge often suffer from <em>reasoning shortcuts</em> (RSs): predictors can solve the downstream reasoning task without associating the correct concepts to the high-dimensional data. To address this issue, we introduce <strong>rsbench</strong>, a comprehensive benchmark suite designed to systematically evaluate the impact of RSs on models by providing easy access to highly customizable tasks affected by RSs. Furthermore, rsbench implements common metrics for evaluating concept quality and introduces novel formal verification procedures for assessing the presence of RSs in learning tasks. Using rsbench, we highlight that obtaining high quality concepts in both purely neural and neuro-symbolic models is a far-from-solved problem. rsbench is available on <a href="https://unitn-sml.github.io/rsbench">Github</a>.</p> <p> </p> <p><strong>Usage</strong></p> <p>We recommend visiting the official code <a href="https://unitn-sml.github.io/rsbench/">website</a> for instructions on how to use the dataset and accompaying software code.</p> <p> </p> <p><strong>License</strong></p> <p>All ready-made data sets and generated datasets are distributed under the <a href="https://creativecommons.org/licenses/by-sa/4.0/">CC-BY-SA 4.0</a> license, with the exception of <code>Kand-Logic</code>, which is derived from <code>Kandinsky-patterns</code> and as such is distributed under the <a href="https://www.gnu.org/licenses/gpl-3.0.en.html">GPL-3.0</a> license.</p> <p> </p> <p><strong>Datasets Overview</strong></p> <ul> <li><strong>CLIP-embeddings. </strong>This folder contains the saved activations from a pretrained CLIP model applied to the tested dataset. It includes embeddings that represent the dataset in a format suitable for further analysis and experimentation.</li> <li><strong>BDD_OIA-original-dataset</strong>. This directory holds the original files from the X-OIA project by Xu et al. [1]. These datasets have been made publicly available for ease of access and further research. If you are going to use it, please consider citing the original authors.</li> <li><strong>kand-logic-3k</strong>. This folder contains all images generated for the Kand-Logic project. Each image is accompanied by annotations for both concepts and labels.</li> <li><strong>bbox-kand-logic-3k</strong>. In this directory, you will find images from the Kand-Logic project that have undergone a preprocessing step. These images are extracted based on bounding boxes, rescaled, and include annotations for concepts and labels.</li> <li><strong>sdd-oia</strong>. This folder includes all images and labels generated using rsbench.</li> <li><strong>sdd-oia-embeddings</strong>. This directory contains 512-dimensional embeddings extracted from a pretrained ResNet18 model on ImageNet. The embeddings are derived from the sdd-oia`dataset.</li> <li><strong>BDD-OIA-preprocessed</strong>. Here you will find preprocessed data that follow the methodology outlined by Sawada and Nakamura [2]. The folder contains 2048-dimensional embeddings extracted from a pretrained Faster-RCNN model on the BDD-100k dataset.</li> </ul> <p>The original BDD datasets can be downloaded from the following Google Drive link: [<a href="https://drive.google.com/file/d/1WFiwRi_sMA_McZnkbEjh8Rnl-Im7_9Mk/view">Download BDD Dataset</a>].</p> <p> </p> <p><strong>References</strong></p> <p>[1] Xu et al., *Explainable Object-Induced Action Decision for Autonomous Vehicles*, CVPR 2020.</p> <p>[2] Sawada and Nakamura, *Concept Bottleneck Model With Additional Unsupervised Concepts*, IEEE 2022.</p> <p> </p>
CausalBench A Comprehensive Benchmark for Evaluating Causal Reasoning Capabilities of Large Language Models
<p>CausalBench is a comprehensive benchmark dataset designed to evaluate the causal reasoning capabilities of large language models. The primary uses of this dataset include, but are not limited to:</p> <p>- Testing the performance of large language models on causal reasoning tasks</p> <p>- Serving as a benchmark dataset for causal reasoning research</p> <p>- Improving and developing new causal reasoning algorithms and models</p>
Figure 6 in Evaluation of Meloidogyne incognita and Rotylenchulus reniformis nematode-resistant cotton cultivars with supplemental Corteva Agriscience nematicides
Figure 6: Field trial R. reniformis eggs per gram of root collected from four root systems. LS means for PHY 332 W3FE (R) and PHY 340 W3FE (S) cotton and nematicide combination at 40 DAP in 2021. P-value for Type III fixed effects for the Variety x Nematicide interaction was 0.0441. Nematicide treatments included no-nematicide control, Reklemel (0.28 L/ha) + Vydate C-LV (1.24 L/ha), C-LV (0.28 +1.24 L/ha), Reklemel (0.56 L/ha) + Vydate C-LV (2.5 L/ha), Reklemel (1.13 L/ha) + Vydate C-LV (5.0 L/ha), BIOST Nematicide 100 (0.026 mg ai/seed), BIOST Nematicide 100, Reklemel (0.28 L/ha) + Vydate C-LV (1.24 L/ha), BIOST Nematicide 100 (0.026 mg ai/seed), Reklemel (0.56 L/ha) + Vydate C-LV (2.5 L/ha), BIOST Nematicide 100 (0.026 mg ai/seed), Reklemel (1.13 L/ha) + Vydate C-LV (5.0 L/ha).
Figure 1 in Evaluation of Meloidogyne incognita and Rotylenchulus reniformis nematode-resistant cotton cultivars with supplemental Corteva Agriscience nematicides
Figure 1: M. incognita PHY 340 W3FE susceptible variety on the left and the resistant PHY 360 W3FE on the right at 106 DAP.
Figure 1 in Evaluation of perennial Glycine species for response to Meloidogyne incognita, Rotylenchulus reniformis, and Pratylenchus penetrans
Figure 1: Representative images of soybean and perennial Glycine species roots at 8 weeks post-inoculation of Meloidogyne incognita. Gall index rating is given in parentheses A, susceptible check G. max cv. Pickett 71. B, resistant check G. max cv. Forrest. C, G. tomentella PI 339655. D, G. latifolia PI 559298. E, G. latifolia PI 559300. F, G. tabacina PI 373990.
Figure 5 in Evaluation of Meloidogyne incognita and Rotylenchulus reniformis nematode-resistant cotton cultivars with supplemental Corteva Agriscience nematicides
Figure 5: Field trial R. reniformis eggs per gram of root collected from four root systems LS means for PHY 332 W3FE (R) and PHY 340 W3FE (S) cotton and nematicide combination at 40 DAP in 2020. P-value for Type III fixed effects for the Variety x Nematicide interaction was 0.0178. Nematicide treatments included no-nematicide control, Reklemel (0.28 L/ha) + Vydate C-LV (1.24 L/ha), C-LV (0.28 +1.24 L/ha), Reklemel (0.56 L/ha) + Vydate C-LV (2.5 L/ha), Reklemel (1.13 L/ha) + Vydate C-LV (5.0 L/ha), BIOST Nematicide 100 (0.026 mg ai/seed), BIOST Nematicide 100, Reklemel (0.28 L/ha) + Vydate C-LV (1.24 L/ha), BIOST Nematicide 100 (0.026 mg ai/seed), Reklemel (0.56 L/ha) + Vydate C-LV (2.5 L/ha), BIOST Nematicide 100 (0.026 mg ai/seed), Reklemel (1.13 L/ha) + Vydate C-LV (5.0 L/ha).
Figure 2 in Evaluation of Meloidogyne incognita and Rotylenchulus reniformis nematode-resistant cotton cultivars with supplemental Corteva Agriscience nematicides
Figure 2: Field trial M. incognita eggs per gram of root collected from four root systems LS means for PHY 360 W3FE (R) and PHY 340 W3FE (S) cotton and nematicide combination at 40 DAP in 2020. P-value for Type III fixed effects for the Variety x Nematicide interaction was 0.0368. Nematicide treatments included no-nematicide control, Reklemel (0.28 L/ha) + Vydate C-LV (1.24 L/ha), C-LV (0.28 +1.24 L/ha), Reklemel (0.56 L/ha) + Vydate C-LV (2.5 L/ha), Reklemel (1.13 L/ha) + Vydate C-LV (5.0 L/ha), BIOST Nematicide 100 (0.026 mg ai/seed), BIOST Nematicide 100, Reklemel (0.28 L/ha) + Vydate C-LV (1.24 L/ha), BIOST Nematicide 100 (0.026 mg ai/seed), Reklemel (0.56 L/ha) + Vydate C-LV (2.5 L/ha), BIOST Nematicide 100 (0.026 mg ai/seed), Reklemel (1.13 L/ha) + Vydate C-LV (5.0 L/ha).
Figure 3 in Evaluation of Meloidogyne incognita and Rotylenchulus reniformis nematode-resistant cotton cultivars with supplemental Corteva Agriscience nematicides
Figure 3: Field trial M. incognita eggs per gram of root collected from four root systems LS means for PHY 360 W3FE (R) and PHY 340 W3FE (S) cotton and nematicide combination at 40 DAP in 2021. P-value for Type III fixed effects for the Variety x Nematicide interaction was 0.0272. Nematicide treatments included no-nematicide control, Reklemel (0.21 L/ha) + Vydate C-LV (0.88 L/ha) Reklemel (0.28 L/ha) + Vydate C-LV (1.24 L/ha, Reklemel (0.56 L/ha) + Vydate C-LV (2.5 L/ha), BIOST Nematicide 100 (0.026 mg ai/seed), BIOST Nematicide 100, Reklemel (0.21 L/ ha) + Vydate C-LV (0.88 L/ha), BIOST Nematicide 100 (0.026 mg ai/seed), Reklemel (0.28 L/ha) + Vydate C-LV (1.24 L/ha), BIOST Nematicide 100 (0.026 mg ai/seed), Reklemel (0.56 L/ha) + Vydate C-LV (2.50 L/ha).
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.