Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
80
datasets available to search
ShareScore release 0.9.0
Dataset results
80 results for “evaluation framework”
Models, scripts, simulated data, and results from the article "Evaluation and comparison of methods for neuronal parameter optimization using the Neuroptimus software framework."
Open the record for dataset details and reuse information.
Open Research Data for "A New Framework for Evaluating Model Simulated Inland Tropical Cyclone Wind Fields"
<p>The (1) NOAA GFDL T-SHiELD outputs, (2) processed ASOS data, and (3) observation-based, theory-driven wind profiles data used in the manuscript "A New Framework for Evaluating Model Simulated Inland Tropical Cyclone Wind Fields". </p>
Dataset for "Adding the SMT solver OpenSMT2 to the JavaSMT Framework and Evaluation using CPAchecker"
<p>Benchmark data and code examples for the thesis. See README.md files for the exact locations.</p>
Analytical Modeling Framework to Assess the Economic and Environmental Impacts of Residential Deliveries, and Evaluate Sustainable City Logistics Strategies
Open the record for dataset details and reuse information.
Data from: Limits and relationships of Paracanthopterygii: a molecular framework for evaluating past morphological hypotheses
Open the record for dataset details and reuse information.
A quantitative framework to evaluate modeling of cortical development by neural stem cells
GEO Series GSE57595. Homo sapiens. 105 samples. Type: Expression profiling by array.
Evaluating Frameworks Assemblies In Microservices-based Systems Using Imperfect Information (Video)
<p>This video presents a novel technique, called µAzimut, whose purpose is to generate, evaluate, and compare frameworks assemblies using potentially incomplete, imprecise, and changing descriptions of nonfunctional requirements and frameworks. The frameworks assemblies evaluation is based on a support score which allows modeling imperfect architectural knowledge.</p>
Data from: Demographic inferences using short-read genomic data in an Approximate Bayesian Computation framework: in silico evaluation of power, biases, and proof of concept in Atlantic walrus
Approximate Bayesian Computation (ABC) is a powerful tool for model-based inference of demographic population histories from large genetic data sets. For most organisms its implementation has been hampered by the lack of sufficient genetic data. Genotyping-by-sequencing (GBS) provides cheap genome-scale data to fill this gap, but its potential has not fully been exploited. Here, we explored power, precision and biases of a coalescent-based ABC approach where GBS data were modeled with either a population mutation parameter (θ) or with a fixed sites (FS) approach, allowing single or several segregating sites per locus. With simulated data ranging from 500 to 50,000 loci a variety of demographic models could be reliably inferred across a range of timescales and migration scenarios. Posterior estimates were informative with 1,000 loci for migration and split time in simple population divergence models. In more complex models posterior distributions were wide and almost reverted to the uninformative prior even with 50,000 loci. ABC parameter estimates, however, were generally more accurate than an alternative composite-likelihood method. Bottleneck scenarios proved particularly difficult and only recent bottlenecks without recovery could be reliably detected and dated. Notably, minor allele frequency filters – usual practice for GBS data – negatively affected nearly all estimates. With this in mind, we used a combination of FS and θ approaches on empirical GBS data generated from the Atlantic walrus (Odobenus rosmarus rosmarus), collectively providing support for a population split before the last glacial maximum followed by asymmetrical migration and a range-wide bottleneck. Overall, this study evaluates the potential and limitations of GBS data in an ABC-coalescence framework and proposes a best-practice approach.
Molecular diffusion enhanced performance evaluation of metal-organic frameworks for carbon dixoide capture
<p>This data set contains the process-level performance ranking of 982 metal-organic frameworks (MOF) which were down-selected from 10,143 structures contained in the puplic CoRE MOF 2019 data set. To rankorder MOFs for application in post-combustion carbon dioxide capture, we have used a computational workflow that combines active-learning based structure selection, molecular-level modeling, and process-level optimization. A detailed description of the repository content is provided in the README file which is included in the zip archive “Diffusion-MOF-Screening.zip”.</p>
Evaluation Framework tests on RDF2Vec
<p>The ZIP file contains the results of the tests run through the evaluation framework available at https://git.rwth-aachen.de/KGEmbedding/evaluationFramework executed on vectors produce by RDF2Vec combined with 11 different weighting techniques, described in <br> <br> Cochez, M., Ristoski, P., Ponzetto, S.P., Paulheim, H.:<br> Biased graph walks for RDF graph embeddings.<br> In: Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics (2017).<br> <br> The framework tested the vectors upon Machine Learning tasks - classification, regression, and clustering - and semantic tasks - document modeling, semantic analogies, and entity relatedness. <br> <br> At the top level, there is a summary of the results, detailed in the inner folder. </p>
Smart Waste Management System Development and Evaluation Decision Support Framework
<p><strong>Smart Waste Management System Development and Evaluation Decision Support Framework</strong></p> <p><strong>Framework concept and goal</strong></p> <ul> <li> <p>For various cities with various infrastructure, financial capabilities, needs and goals of implementing a waste management (WM) system, a various set of WM services and technologies is needed</p> </li> <li> <p>There is a large number of studies describing individual aspects of WM systems in various specific context to solve various local problems</p> </li> <li> <p>We developed this improvement and evaluation decision support framework to provide recommendations about WM in the city, based on current goals, challenges, the characteristics of the city, city context and context of the city WM system</p> </li> <li> <p>This version of the framework is intended for the mayor of the city / city authorities</p> </li> </ul> <p> </p> <p><strong>Primary data for the framework operation and decision support</strong></p> <p>Primary data summarizes:</p> <ul> <li> <p>scientific research (173 primary studies, period 2014-2022) <br> <a href="https://ieeexplore.ieee.org/document/9815071">I. Sosunova and J. Porras, “IoT-Enabled Smart Waste Management Systems for Smart Cities: A Systematic Review,” IEEE Access, vol. 10, no. July, pp. 73326–73363, 2022, doi: 10.1109/ACCESS.2022.3188308</a></p> </li> <li> <p>Surveys for residents, authorities and companies (Lappeenranta, Helsinki, Saint-Petersburg)</p> </li> <li> <p>practical research (2 international projects, hackathons, experience of leading Finnish companies)</p> </li> </ul> <p> </p> <p>Based on this data we build a Smart Waste Management (SWM) Model, that includes:</p> <ul> <li> <p>physical infrastructure</p> </li> <li> <p>services</p> </li> <li> <p>stakeholders</p> </li> <li> <p>data, shared between stakeholders </p> </li> </ul> <p> </p> <p><strong>Basic steps for using the framework</strong></p> <p>To get <strong>recommendations (5) </strong>about Improvement and Evaluation of the Waste Management System in your city, you will need read these <strong>Guide (1</strong>, we don't provide it in the figure<strong>)</strong> to define:</p> <ul> <li> <p><strong>Context (2)</strong> of your city and your city waste management system</p> </li> <li> <p><strong>Goals (3) </strong>of WM system Improvement and Evaluation and KPIs to measure the achievement of each goal</p> </li> <li> <p><strong>Challenges (4) </strong>for each goal and KPIs to some of the challenges (this will allow to measure the achievement of this challenges more accurately)</p> </li> </ul>
Evaluating a Telemedicine Neurological Consult Program for Drug-Induced Movement Disorders Using the RE-AIM Framework
ClinicalTrials.gov study NCT06060444. IPD Sharing: NO. Countries: 1. Publications: 0.
Evaluation of the Mechanisms of Weight-bearing of the Hemiplegic Limb During Table Tennis Sessions in the Framework of Post-stroke Rehabilitation : Pilot Study
ClinicalTrials.gov study NCT05857072. IPD Sharing: Not stated. Countries: 1. Publications: 0.
The Opinions of Multiple Stakeholders Towards Gerontechnology Evaluation Framework: Four Studies Using Delphi Techniques
ClinicalTrials.gov study NCT05595018. IPD Sharing: NO. Countries: 1. Publications: 0.
Developing an Experimental Framework to Evaluate Oncologist Emotion Regulation
ClinicalTrials.gov study NCT05365763. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Evaluating the Foot Touch Framework Model
ClinicalTrials.gov study NCT04699019. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: Demographic inferences using short-read genomic data in an Approximate Bayesian Computation framework: in silico evaluation of power, biases, and proof of concept in Atlantic walrus
Open the record for dataset details and reuse information.
Dimensions of Quality framework for evaluating management system: a case study in the construction industry
<p><span>Dimensions of Quality potential applications for improving management systems and products in building development have not been fully explored. The objective of this study is to use the service quality dimensions as a framework for quality assessment that can serve as a continuous improvement tool to evaluate whether the management system supports construction building projects and whether these projects are in line with the system. Thirteen dimensions were adapted from studies focused on improved management systems, quality product and customer services, and Brazilian requirements standards for management in civil construction: Performance, Ease of Use, Availability, Reliability, Maintainability, Durability, Conformity, Installation and Direction of Use, Technical Support, User Interface, Environmental Interface, Appearance, and Perceived Quality and Brand Image. A case study was conducted to validate the framework as a continuous improvement tool applied to the management system of a construction company. As a result, the construction company's management system was evaluated due to the excellent scores received by the ABC Building Project on all available dimensions of quality, which shows that the company's management system is suitable for this product portfolio.</span></p>
Empirical Evaluation of Diagnostic Algorithm Performance Using a Generic Framework
A variety of rule-based, model-based and datadriven techniques have been proposed for detection and isolation of faults in physical systems. However, there have been few efforts to comparatively analyze the performance of these approaches on the same system under identical conditions. One reason for this was the lack of a standard framework to perform this comparison. In this paper we introduce a framework, called DXF, that provides a common language to represent the system description, sensor data and the fault diagnosis results; a run-time architecture to execute the diagnosis algorithms under identical conditions and collect the diagnosis results; and an evaluation component that can compute performance metrics from the diagnosis results to compare the algorithms. We have used DXF to perform an empirical evaluation of 13 diagnostic algorithms on a hardware testbed (ADAPT) at NASA Ames Research Center and on a set of synthetic circuits typically used as benchmarks in the model-based diagnosis community. Based on these empirical data we analyze the performance of each algorithm and suggest directions for future development.
Towards a Framework for Evaluating and Comparing Diagnosis Algorithms
Diagnostic inference involves the detection of anomalous system behavior and the identification of its cause, possibly down to a failed unit or to a parameter of a failed unit. Traditional approaches to solving this problem include expert/rule-based, model-based, and data-driven methods. Each approach (and various techniques within each approach) use different representations of the knowledge required to perform the diagnosis. The sensor data is expected to be combined with these internal representations to produce the diagnosis result. In spite of the availability of various diagnosis technologies, there have been only minimal efforts to develop a standardized software framework to run, evaluate, and compare different diagnosis technologies on the same system. This paper presents a framework that defines a standardized representation of the system knowledge, the sensor data, and the form of the diagnosis results – and provides a run-time architecture that can execute diagnosis algorithms, send sensor data to the algorithms at appropriate time steps from a variety of sources (including the actual physical system), and collect resulting diagnoses. We also define a set of metrics that can be used to evaluate and compare the performance of the algorithms, and provide software to calculate the metrics.
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.