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470 results for “Evaluation dataset”
Dataset: Parallel Empirical Evaluations: Resilience Despite Concurrency
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Datasets of synthetic task flow graphs for evaluating a latency/energy optimization task allocation framework
<p>These datasets of synthetic task flow graphs were generated to evaluate the performance and scalability of an optimal task allocation approach for applications of various structures and sizes in an environment following the edge/hub/cloud paradigm. The system under study comprised an edge device (e.g., a single-board computer attached to an unmanned aerial vehicle (UAV)) interacting with a hub device (e.g., a laptop), which in turn communicated with a more computationally capable cloud server. The objective was the minimization of either overall latency or overall energy consumption, under memory, storage, energy, and task precedence constraints. We considered that a percentage of the tasks required fixed allocation on the edge or hub device.<br> <br>We generated 18 task flow graphs of parallel, serial, and mixed (a combination of parallel and serial) structure with 10, 100, and 1000 nodes, and various in/out degrees, utilizing the Task Graphs For Free (TGFF) random task graph generator [1],[2]. Additional task parameters (e.g., execution time, power consumption, memory, storage, output data size) were included post-generation, using representative random values. More details are provided in README.txt and in [3].<br> <br>Note: These datasets are released under a Creative Commons Attribution license. If you utilize these datasets in your work, please cite us using the corresponding Zenodo DOI https://doi.org/10.5281/zenodo.10654551.<br> <br>References:<br>[1] R. P. Dick, D. L. Rhodes, and W. Wolf, "TGFF: Task graphs for free," Proceedings of the Sixth International Workshop on Hardware/Software Codesign (CODES/CASHE), 1998, pp. 97-101, doi: 10.1109/HSC.1998.666245.<br>[2] R. P. Dick, D. L. Rhodes, and K. Vallerio, "TGFF," https://robertdick.org/projects/tgff/.<br>[3] A. Kouloumpris, G. L. Stavrinides, M. K. Michael, and T. Theocharides, "An optimization framework for task allocation in the edge/hub/cloud paradigm," Future Generation Computer Systems, vol. 155, pp. 354-366, Jun. 2024, doi: 10.1016/j.future.2024.02.005.</p>
Dataset of paper "Evaluation Of Microplastics Release From Solar Water Disinfection Poly(Ethylene Terephthalate) And Polypropylene Containers"
<p>Dataset of paper "Evaluation Of Microplastics Release From Solar Water Disinfection Poly(Ethylene Terephthalate) And Polypropylene Containers":</p><ul><li>Effect of the underlying glass fiber (GF) filter on the baseline of an HDPE MP microreflectance spectrum.</li><li>Comparison between the spectrum of a particle collected from the treated water and the spectrum of pristine high-density polyethylene polymer.</li><li>Number of total microplastics (high-density polyethylene and polypropylene) found in PET bottles.</li><li>Number of total microplastics (high-density polyethylene and polypropylene) found in TJC containers.</li><li>Comparison between the spectrum of PP particles recovered after 10 weeks of exposure time.</li><li>Number of microplastics identified as polypropylene and weathered polypropylene in the samples corresponding to the translucent jerrycans containers.</li><li>Number of microplastics identified as polypropylene and weathered polypropylene in the samples corresponding to the transparent jerrycans containers.</li><li>Number of polypropylene microplastics found in the translucent and transparent jerrycans containers.</li><li>Number of microplastics found in PET bottles and TJC containers.</li><li>Size of the microplastics found in the study. </li><li>Abundance of each target polymer found in the study based on the measured minor particle dimension.</li></ul>
Satellite Precipitation Products data performance evaluation with observed datasets
<p>Datasets used for evaluation of Satellite Precipitation Products (SPPs). We used Three open-source datasets from different sources for our evaluation of the SPPs applicability in real-time flood forecasting.</p>
Dataset for the research article titled "Evaluation of Reanalysis and Satellite Products against Ground-based Observations in a Desert Environment "
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Observational dataset for "Using Satellite and ARM Observations to Evaluate Cold Air Outbreak Cloud Transitions in E3SM Global Storm-Resolving Simulations"
<p>This observational dataset include the DOE ARM ground based observations and satellite for the paper titled “Using Satellite and ARM Observations to Evaluate Cold Air Outbreak Cloud Transitions in E3SM Global Storm-Resolving Simulations” on GRL.</p> <p>For the original data source, all ARM observational data sets used in this study are publicly available from the ARM data archive site (https://adc.arm.gov/discovery/#/results/iopShortName::amf2019comble/datastream::anxarmbeatmM1.c1/datastream::anxarmbecldradM1.c1/datastream::anxarsclkazr1kolliasM1.c0). MODIS MOD06 L2 cloud product are publicly available from (https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MOD06 L2, DOI:10.5067/MODIS/MOD06 L2.061).</p> <p>CloudSat products can be ordered from the CloudSat Data Processing center (https://www.cloudsat.cira.colostate.edu/order/). To download CloudSat data, a new user must first create an account by filling out the signup form (https://www.cloudsat.cira.colostate.edu/accounts/signup/).</p>
Quality and Utility of European Cardiovascular and Orthopaedic Registries for the Regulatory Evaluation of Medical Device Safety and Performance Across the Implant Lifecycle: A Systematic Review - Dataset
<p><strong>Background: </strong>The European Union Medical Device Regulation (MDR) requires manufacturers to undertake post-market clinical follow-up (PMCF) to assess the safety and performance of their devices following approval and Conformité Européenne (CE) marking. The quality and reliability of device registries for this Regulation have not been reported. As part of the Coordinating Research and Evidence for Medical Devices (CORE-MD) project, we identified and reviewed European cardiovascular and orthopaedic registries to assess their structures, methods, and suitability as data sources for regulatory purposes.</p> <p><strong>Methods: </strong>Regional, national and multi-country European cardiovascular (coronary stents and valve repair/replacement) and orthopaedic (hip/knee prostheses) registries were identified using a systematic literature search. Annual reports, peer-reviewed publications, and websites were reviewed to extract publicly available information for 33 items related to structure and methodology in six domains and also for reported outcomes.</p> <p><strong>Results: </strong>Of the 20 cardiovascular and 26 orthopaedic registries fulfilling eligibility criteria, a median of 33% (IQR: 14%-71%) items for cardiovascular and 60% (IQR: 28%-100%) items for orthopaedic registries were reported, with large variation across domains. For instance, no cardiovascular and 16 (62%) orthopaedic registries reported patient/ procedure-level completeness. No cardiovascular and 5 (19%) orthopaedic registries reported outlier performances of devices, but each with a different outlier definition. There was large heterogeneity in reporting on items, outcomes, definitions of outcomes, and follow-up durations.</p> <p><strong>Conclusion: </strong>European cardiovascular and orthopaedic device registries could improve their potential as data sources for regulatory purposes by reaching consensus on standardised reporting of structural and methodological characteristics to judge the quality of the evidence as well as outcomes.</p>
Dataset: Testing for effects of growth rate on isotope trophic discrimination factors and evaluating the performance of Bayesian stable isotope mixing models experimentally: a moment of truth?
<p><span>Discerning assimilated diets of wild animals using stable isotopes is well established where potential dietary items in food webs are isotopically distinct. With the advent of mixing models, and Bayesian extensions of such models (Bayesian Stable Isotope Mixing Models, BSIMMs), statistical techniques available for these efforts have been rapidly increasing. The accuracy with which BSIMMs quantify diet, however, depends on several factors including uncertainty in tissue discrimination factors (TDFs; <em>Δ</em>) and identification of appropriate error structures. Whereas performance of BSIMMs has mostly been evaluated with simulations, here we test the efficacy of BSIMMs by raising domestic broiler chicks (<em>Gallus gallus domesticus</em>) on four isotopically distinct diets under controlled environmental conditions, ideal for evaluating factors that affect TDFs and testing how BSIMMs allocate individual birds to diets that vary in isotopic similarity. For both liver and feather tissues,<em> δ</em><sup>13</sup>C and <em>δ </em><sup>15</sup>N values differed among dietary groups. <em>Δ</em><sup>13</sup>C of liver, but not feather, was negatively related to the rate at which individuals gained body mass. For <em>Δ</em><sup>15</sup>N, we identified effects of dietary group, sex, and tissue type, as well as an interaction between sex and tissue type</span><span><span>, </span></span><span><span>with f</span></span><span>emales having higher liver <em>Δ</em><sup>15</sup>N relative to males. For both tissues, BSIMMs allocated most chicks to correct dietary groups, especially for models using combined TDFs rather than diet specific TDFs, and those applying a multiplicative error structure. These findings provide new information on how biological processes affect TDFs and confirm that adequately accounting for variability in consumer isotopes is necessary to optimize performance of BSIMMs. Moreover, they demonstrate experimentally that these types of models reliably characterize consumed diets when appropriately parameterized.<span> </span></span></p>
The track 1 evaluation dataset of ADD 2022
<p>Audio deepfake detection is an emerging topic, which was included in the ASVspoof 2021. However, the recent shared tasks have not covered many real-life and challenging scenarios. The first Audio Deep synthesis Detection challenge (ADD) was motivated to fill in the gap. The ADD 2022 (<a href="http://addchallenge.cn/add2022">http://addchallenge.cn/add2022</a>) includes three tracks: low-quality fake audio detection (LF), partially fake audio detection (PF) and audio fake game (FG). The LF track focuses on dealing with bona fide and fully fake utterances with various real-world noises etc. The PF track aims to distinguish the partially fake audio from the real. The FG track is a rivalry game, which includes two tasks: an audio generation task and an audio fake detection task. In this paper, we describe the datasets, evaluation metrics, and protocols. We also report major findings that reflect the recent advances in audio deepfake detection tasks.</p> <p>The ADD 2022 dataset is publicly available.</p> <p>This data set is licensed with a CC BY-NC-ND 4.0 license.</p> <p>If you use this dataset, please cite the following paper:</p> <blockquote> <p>Jiangyan Yi, Ruibo Fu, Jianhua Tao, Shuai Nie, Haoxin Ma, Chenglong Wang, Tao Wang, Zhengkun Tian, Ye Bai, Cunhang Fan, Shan Liang, Shiming Wang, Shuai Zhang, Xinrui Yan, Le Xu, Zhengqi Wen, Haizhou Li:<br>ADD 2022: the first Audio Deep Synthesis Detection Challenge. ICASSP 2022: 9216-9220</p> </blockquote>
Dataset for evaluation of AROME and SURFEX-SA forecasts
<p>SQL Dataset for the verification of AROME (first reference) and SURFEX-SA (produced for the paper) predictions of Austrian cities (Vienna, Linz, Klagenfurt and Innsbruck) with TAWES weather station data. In addition, data from the ensemble prediction system C-LAEF are also included. AROME and C-LAEF are originally available on 2.5 km grid, but they were bilinear interpolated to the respective TAWES coodinates. Datasets can be easily read by the verification tool "harp" in R.</p> <p>TAWES observations are free available from the GeoSphere Austria Datahub (second reference).</p>
" Description and evaluation of a new contrail cirrus 2 parameterization in the ARPEGE-Climat atmospheric 3 model " datasets
<p>This repository contains the data files used for the analyses presented in the paper. The dataset includes variables of interest for the two main simulations (CONTFREE and CONTNUDGED) for the year 2019. </p> <ul> <li>"totcon" variable represents the integrated contrail cirrus coverage.</li> <li>"rst", respectively "rstcotra", represent the net downward shortwave radiation at the top of the atmosphere for the radiative call with contrails perturbation, respectively without perturbation. The difference between these two variables provides the contrail cirrus net downward shortwave radiation contribution. </li> <li>"rlut", respectively "rlutcotra", represent the net upward longwave radiation at the top of the atmosphere for the radiative call with contrails perturbation, respectively without perturbation. The difference between these two variables provides the contrail cirrus net upward longwave radiation contribution. </li> <li>"pissr" represents the probability of the gridbox being ice supersaturated. This variable is provided for pressure levels 200,225, and 250hPa.</li> <li>"clhcalipso" represents the integrated coverage of "high clouds" (>400hPa). </li> </ul>
Dataset for "Diurnal, seasonal, and interannual variations in δ(18O) of atmospheric O2 and its application to evaluate natural/anthropogenic changes in oxygen, carbon, and water cycles" by Ishidoya et al.
<p>The dataset used in the paper "Diurnal, seasonal, and interannual variations in δ(18O) of atmospheric O2 and its application to evaluate natural/anthropogenic changes in oxygen, carbon, and water cycles" by Ishidoya et al. The doi of the preprint of the paper is https://doi.org/10.5194/egusphere-2024-654. The dataset contains the diurnal cycles of delta_atm(18O), delta(O2/N2), y(CO2), and delta(Ar/N2) at Tsukuba, Japan averaged for the period 2013-2022 ("data_for_Fig4.csv"), the rolling average values of delta_atm(18O) and delta(O2/N2) anomalies for 300 data at TKB during August and February, 2017 ("data_for_Fig6.csv"), and the monthly mean values of delta_atm(18O) and the delta(O2/N2) at TKB for the period 2013-2022("data_for_Fig7.csv"). The raw data before averaging are also presented ("raw_data_2013.csv" to "raw_data_2022.csv"). See text of Ishidoya et al. in more details. </p> <p> </p>
LODVec Evaluation Datasets and Experiments
<p>This dataset contains the input datasets that were used for evaluating the system LODVec. Moreover, it contains the input for the machine learning tasks, i.e., prediction classes for classification, ratings for regression and the top related entities for a set of movies and basketball players.</p> <p>The evaluation datasets for movies and music albums were derived from <a href="https://www.uni-mannheim.de/dws/research/resources/sw4ml-benchmark/" target="_blank" rel="nofollow noopener">https://www.uni-mannheim.de/dws/research/resources/sw4ml-benchmark/</a>, and belong to these researchers (University of Mannheim).</p> <p> </p> <p><a href="https://demos.isl.ics.forth.gr/lodvec/">https://demos.isl.ics.forth.gr/lodvec/</a></p>
Dataset for 'Smart Health Evaluation for Lithium-ion Battery with Super-short-segment Charging'
<p>This is the dataset used in our paper '<em>Smart Health Evaluation for Lithium-ion Battery with Super-short-segment Charging</em>', which mainly includes data from <strong>40Ah batteries</strong> at <strong>0.3C, 1C, 2C</strong>, and two <strong>280Ah batteries</strong> at <strong>20%, 60%, and 100% DOD</strong>, respectively. For specific details, please refer to Readme.text</p> <p>The data in<strong> LISHEN</strong> is the cycle test data of 40Ah battery at different discharge depths under 100%,60%,20%DOD working condition. The data in <strong>CATL</strong> and<strong> EVE </strong>is the cyclic test data of 280Ah battery at different discharge depths under 100%,60%,20%DOD conditions. <br>The CSV file has 21 or 17 columns, (17 columns for, data sequence number, good cycling, step number, step type, time (s) , total time (s) , current (a) , voltage (V) , capacity (AH) , charging capacity (AH) , discharge capacity (AH) , energy (Wh) , charging energy (Wh) , discharge energy (Wh) , absolute time (s) , power (W) , temperature (° C-RRB-) , the 21-column CSV adds four metrics (DQ/DV (AH/V) in column 17, dQm/DV (mAh/V) in column 18, contact resistance (m ω) in column 19, and module start-stop status in column 20) , More detailed files can be found in the zip file.</p>
Dataset supporting the manuscript "Comprehensive evaluation of phosphoproteomic-based kinase activity inference"
<p>Datasets involved in the benchmarking of kinase activity inference as presented in the manuscript "Comprehensive evaluation of phosphoproteomic-based kinase activity inference".</p>
An Evaluation of Self-reported Level of Entrustment across the First Year of Clinical Practice [dataset]
<p><strong>This file contains the data files from each of the four surveys (Times 1 to 4), and the questionnaires used to collect the data at Time 1, and Times 2-3.</strong></p> <p><strong>Background.</strong> Newly graduated medical students often report that they lack the skills required to care for patients, and feel unprepared for clinical practice. However, little is known about when, and if, they acquire these skills in practice.</p> <p><strong>Objective.</strong> To assess self-reported level of entrustment in, and frequency of performance of, the seven Entrustable Professional Activities (EPAs) comprising the EPA framework for interns in Ireland. EPAs describe discrete activities that are essential to a particular profession.</p> <p><strong>Methods</strong>. Self-report survey data were collected from doctors in the Republic of Ireland during their first year of clinical practice at four different time points during 2020/21.</p> <p><strong>Results</strong>. Response rates to the survey varied from 73.3% (126/172) at Time 1 to 25.6% (44/172) at Time 4. After 3 months, the respondents reported that they could execute all of the EPAs, inclusive of 12 essential procedural skills, with indirect supervision. As the year progressed there was an increase in the proportion of respondents reporting that they performed the EPAs at least once a week. However, the proportion of respondents performing five of the essential procedural skills (e.g. nasogastric tube insertion) remained low across all time points.</p> <p><strong>Conclusion</strong>. Consideration should be given as to how to better prepare medical students to execute these EPAs, how the interns can be better supported during the first quarter of internship. The findings from this research are positive. However, there is an urgent need to carry out formal assessments of entrustability, rather than relying on self-report.</p>
Evaluation Dataset: SemOI2 – Building Adaptive And Cost-Effective Recognition Applications With Semantic Augmentation
<p>Evaluation dataset for the paper "SemOI2 – Building Adaptive And Cost-Effective Recognition Applications With Semantic Augmentation", presented at the AAAI-Make conference 2022, to be published in: <em>A. Martin, K. Hinkelmann, H.-G. Fill, A. Gerber, D. Lenat, R. Stolle, F. van Harmelen (Eds.), Proceedings of the AAAI 2022 Spring Symposium on Machine Learning and Knowledge Engineering for Hybrid Intelligence (AAAI-MAKE 2022), Stanford University, Palo Alto, California, USA, March 21–23, 2022.</em></p>
Dataset used in the article: Evaluation of goal recognition systems on unreliable data and uninspectable agents
<p>Goal or intent recognition, where one agent recognizes the goals or intentions of another, can be a powerful tool for effective teamwork and improving interaction between agents. Such reasoning can be challenging to perform, however, because observations of an agent can be unreliable and, often, an agent does not have access to the reasoning processes and mental models of the other agent. Despite this difficulty, recent work has made great strides in addressing these challenges.</p> <p>In particular, two Artificial Intelligence (AI)-based approaches to goal recognition have recently been shown to perform well: goal recognition as planning, which reduces a goal recognition problem to the problem of plan generation; and Combinatory Categorical Grammars (CCGs), which treat goal recognition as a parsing problem. Additionally, new advances in cognitive science with respect to Theory of Mind reasoning have yielded an approach to goal recognition that leverages analogy in its decision making.</p> <p>However, there is still much unknown about the potential and limitations of these approaches, especially with respect to one another. Here, we explore this space and compare three state-of-the-art approaches to goal recognition along two different axes: reliability of observations and inspectability of the other agent's mental model. Overall, we show that no approach dominates across all cases and discuss the relative strengths and weaknesses of these approaches. Scientists interested in goal recognition problems can use this knowledge as a guide to select the correct starting point for their specific domains and tasks.</p> <p> Research Topic: Advances in Goal, Plan and Activity Recognition</p>
Recommender Evaluation Results and Dataset
<p><strong>Datasets:</strong></p> <ol> <li>MongoDB_Dataset: an archived MongoDB data used in this research. To reproduce/operate the proposed framework, this dataset must be restored in your local machine.</li> <li>Survey_Form: an example survey we used to evaluate our framework.</li> <li>Survey_Responses: an Excel file that contains summarized survey responses that we collected from the survey with participants. This file includes 30 responses in a raw-answer format.</li> </ol>
NDN Content Store and Caching Policies: Performance Evaluation - Dataset
<p>This is a dataset results from the evaluation of the following caching replacement policies: Random, Least Recently Used (LRU), Least Frequently Used (LFU), and First In First Out (FIFO) applied to NDN. It was used for the paper "NDN Content Store and Caching Policies: Performance Evaluation", DOI: https://doi.org/10.3390/computers11030037 .</p> <p>The simulation tool used is the ndnSIM-2.7.</p> <p>Includes:</p> <p>1. The actual dataset from simulations (for both NDN Testbed and Abilene).</p> <p>2. The C++ ndnSIM scenarios (for both NDN Testbed and Abilene).</p> <p>3. Matlab scripts for data extraction.</p> <p>4. Simple Bash scripts for automating the execution</p> <p> </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.