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1,855 results for “Framework”
Dataset - FetMRQC: an open-source machine learning framework for multi-centric fetal brain MRI quality control
<p>This dataset contains the data and model used in the paper</p> <blockquote> <p>Thomas Sanchez, Oscar Esteban, Yvan Gomez, Alexandre Pron, Mériam Koob, Vincent Dunet, Nadine Girard, Andras Jakab, Elisenda Eixarch, Guillaume Auzias, and Meritxell Bach Cuadra. "FetMRQC: an open-source machine learning framework for multi-centric fetal brain MRI quality control." <a href="https://arxiv.org/abs/2311.04780"><em>arXiv preprint arXiv:2311.04780</em></a> (2023).</p> </blockquote> <p>If you found this dataset useful or used it in your research, please cite this reference.</p> <p>This dataset contains manual quality annotations and image quality metrics (IQMs) obtained from 1647 stacks of T2-weighted (T2w) slices of fetal brain magnetic resonance (MR) images collected from 233 subjects at four different institutions Lausanne University Hospital (CHUV) in Switzerland, BCNatal at Hospital Sant Joan de Déu in Barcelona (Spain), University Children's Hospital Zürich (KISPI) in Switzerland and La Timone University Hospital in Marseille, France. The data were acquired on scanners from different vendors (Siemens at CHUV, BCNatal and Marseille, General Electrics at KISPI), MR sequences (Half Fourier Single-shot Turbo spin-Echo –HASTE– for Siemens scanners and Single-Short Fast Spin Echo –SS-FSE– for GE scanners), magnetic field strengths (1.5 T and 3 T), image resolutions, fields of view, repetition times and echo times, with both neurotypical and pathological cases.</p> <p>These data and the derived IQMs were used to train and evaluate models for quality assessment and quality control of fetal brain MR images. The code to reproduce the experiments is available on <a href="https://github.com/Medical-Image-Analysis-Laboratory/fetal_brain_qc">GitHub.</a></p> <p>Each entry describe the information for a single stack of T2w slices. It contains information regarding which subject it belongs to, its manual quality rating, scanner-related information and 332 IQMs, starting at the `centroid` column in the file. Further description of the data is available in the materials and methods section of the <a href="https://arxiv.org/abs/2311.04780">paper</a>.</p> <p>The model is a 2D nnUNet [1] segmentation network trained on the super-resolution reconstructed data and manual segmentations available as part of the<a href="https://www.synapse.org/#!Synapse:syn25649159/wiki/610007"> Fetal Tissue Annotation Challenge</a> (FeTA).</p> <p>Copyright (c) - All rights reserved. Medical Image Analysis Laboratory - Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland & CIBM Center for Biomedical Imaging. 2023.</p> <p>[1] Isensee, Fabian, et al. "nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation." <em>Nature methods</em> 18.2 (2021): 203-211.</p>
Datasets for "Machine-Learning-Enhanced Symbolic Regression for Methane Storage Prediction in Covalent Organic Frameworks"
<p>This collection contains the datasets and associated files used in the research presented in the manuscript titled "Machine Learning-Enhanced Symbolic Regression for Methane Storage Prediction in Covalent Organic Frameworks". The datasets are critical for the development and validation of machine learning and symbolic regression models aiming to predict methane storage capacities in covalent organic frameworks (COFs).</p> <p><strong>Included Datasets:</strong></p> <ol> <li><code>COF_Data_for_ML.csv</code>: This dataset was utilized for the development of machine learning models.</li> <li><code>COF_Data_for_SISSO.csv</code>: This dataset was employed for the development of SISSO-based symbolic regression models.</li> <li><code>ML_vs_GCMC.xlsx</code>: This comparative dataset features GCMC-calculated results alongside machine learning predictions.</li> <li><code>Feature_Combination.xlsx</code>: This file contains data detailing all the feature combinations explored in the study.</li> <li><code>ML_SISSO_GCMC.xlsx</code>: This comparative dataset includes GCMC calculations, SISSO-based symbolic regression model predictions, and ML predictions.</li> <li><code>Crystallographic_Properties_of_535k_COFs.xlsx</code>: This consolidated dataset presents the crystallographic properties of 535,293 COFs.</li> </ol> <p><strong>Software Used:</strong></p> <ul> <li>Machine Learning Computations: Scikit-Learn (<a href="https://scikit-learn.org/stable/" target="_new">https://scikit-learn.org/stable/</a>)</li> <li>GCMC Simulations: RASPA2 (<a href="https://github.com/iRASPA/RASPA2" target="_new">https://github.com/iRASPA/RASPA2</a>)</li> <li>SISSO Calculations: SISSO toolkit (<a href="https://github.com/rouyang2017/SISSO" target="_new">https://github.com/rouyang2017/SISSO</a>)</li> <li>Crystallographic property calculations: Zeo++ (<a href="https://www.zeoplusplus.org/" target="_new">https://www.zeoplusplus.org/</a>)</li> </ul> <p>The datasets are provided to enable replication of the study's findings, encourage further research in the field, and facilitate the development of advanced predictive models by the scientific community. Researchers who use these datasets are requested to cite this Zenodo entry as well as the associated paper upon its publication.</p>
F I G U R E 3 in The future of fish-based ecological assessment of European rivers: from traditional EU Water Framework Directive compliant methods to eDNA metabarcoding-based approaches
F I G U R E 3 Comparison of trait-based metrics expressed in relative number of individuals computed from eDNA () and traditional electro-fishing (TEF;) samples in the five river stretches (RS), A, B, C, D and E. Trait categories: BEN, benthic; EUR, eurytopic; INS, insectivorous; OMN, omnivorous; PHY, phytophilic; POT, potamodromous; RHE, rheophilic; TOL, tolerant; PEL pelagic. Significance of the differences between eDNA and TEF metrics are shown: ns, not significant (P> 0.05)
F I G U R E 4 in The future of fish-based ecological assessment of European rivers: from traditional EU Water Framework Directive compliant methods to eDNA metabarcoding-based approaches
F I G U R E 4 Boxplots (, median value;, interquartile range;, full range;, outliers) showing the variability in the eDNAadapted fish index (six metrics) computed at three sites (Brangues, Rhins and Usses) where 10 eDNA water samples were collected once. At each site, the eDNA-based six-metric fish index was computed for each of the 45 possible pairs of samples
F I G U R E 2 in The future of fish-based ecological assessment of European rivers: from traditional EU Water Framework Directive compliant methods to eDNA metabarcoding-based approaches
F I G U R E 2 Comparison of trait-based metrics expressed in number of species computed from eDNA () and traditional electrofishing (TEF;) samples in the five river stretches (RS), A, B, C, D and E. Trait categories: BEN, benthic; EUR, eurytopic; INS, insectivorous; OMN, omnivorous; PHY, phytophilic; POT, potamodromous; RHE, rheophilic; TOL, tolerant; PEL pelagic. Significance of the differences between eDNA and TEF metrics are shown: P <0.05; P <0.01; ns, not significant (P> 0.05)
F I G U R E 1 in The future of fish-based ecological assessment of European rivers: from traditional EU Water Framework Directive compliant methods to eDNA metabarcoding-based approaches
F I G U R E 1 Sampling locations along river stretches (RS) A to E () of the main channel of the Rhône River, France, using both traditional electro-fishing (TEF) and eDNA., Sites sampled every 2 months (September 2015– August 2016);, sites where ten eDNA water samples (filtration capsules) were collected once;, sites located on the tributaries or the Rhône River itself sampled once for eDNA. The 10 metric fish index and the adapted six-metric fish index were computed at all sites with a black filled symbol within natural water bodies
Data for Analysis for "A Framework for Adapting Conversational Intelligent Tutoring Systems to enable Collaborative Learning"
<p>This dataset includes, the data files for validating the statistical analysis from "A Framework for Adapting Conversational Intelligent Tutoring Systems to enable Collaborative Learning"</p> <p> </p> <p>The dataset is composed of 1500 files named following the pattern `Test-R-N-User-C-P.csv` where</p> <ul> <li>R is the n-th repetition. From 0 to 50</li> <li>N is the number of concurrent users. From 100 to 1000</li> <li>C is the treatment. chat for the framework version. chat-session for the legacy version.</li> <li>P is the problem number. 16 or 352.</li> </ul> <p>The data files corresponding to chat and problem 16 are those that in the paper are identified as Framework. The files por problem 352 are the collaborative version with students grouped.</p> <p>Each csv, is composed following the standard formate by Apache JMeter, and contains XX columns:</p> <ul> <li>timeStamp - UNIX timestamp of the request</li> <li>elapsed - Time taken to finish the request</li> <li>label - which step</li> <li>responseCode - HTTP response code</li> <li>responseMessage</li> <li>threadName</li> <li>dataType</li> <li>success - true|false</li> <li>failureMessage</li> <li>sentBytes</li> <li>grpThreads</li> <li>allThreads - Threads running</li> <li>URL - Endpoint URL</li> <li>Latency</li> <li>SampleCount</li> <li>ErrorCount - Cumulative amount of errors</li> <li>IdleTime </li> <li>Connect - Connection time</li> </ul> <p> </p>
Research Data Management Framework - POC-Study: Guideline and protocol
<p>Dataset for the following paper:<br><br>Proof-Of-Concept-Studie für das FDM in den Ingenieur:innenwissenschaften</p> <p><strong>Ein Forschungsdatenmanagement-Rahmenwerk</strong><br>T. Hamann, C. Florides, A. Abdelrazeq, R. H. Schmitt</p> <p>Forschungsdatenmanagement (FDM) gewinnt seit Jahren an Bedeutung. Das Ziel, Daten wiederverwendbar aufzubereiten und nachzunutzen anstatt sie aufwändig neu zu erheben, wird von Forschenden der deutschen Ingenieur:innenwissenschaften jedoch nur selten verfolgt. Um dem entgegenzuwirken, wurde ein Rahmenwerk für das FDM in den Ingenieur:innenwissenschaften entwickelt. In einer Proof-Of-Concept-Studie soll dieses nun erstmals anhand des Forschungsprojekts KIOptiPack validiert werden.</p> <p><strong>A Research Data Management Framework</strong></p> <p>Research data management (RDM) has been gaining in importance for years. However, the goal of preparing data sustainably and reusing existing data instead of laboriously collecting it from scratch is rarely pursued by researchers in the German engineering sciences. To counteract this, a framework for RDM in the engineering sciences was developed. In a proof-of-concept study, this framework will be validated for the first time using the research project KIOptiPack.<br>Stichwörter: Forschung, Informationsmanagement, Digitalisierung<br><br><br>The authors would like to thank the Federal Government and the Heads of Government of the Länder, as well as the Joint Science Conference (GWK), for their funding and support within the framework of the NFDI4Ing consortium. Funded by the German Research Foundation (DFG) - project number 442146713.</p>
Preliminary land use scenarios for Nature Future Framework
<p>This deliverable is the product of tasks 5.2, we run high-resolution (1km) spatial land-use models that quantify potential land-use change and land management changes consistent with developed NFF storylines (T5.1). The European spatial land-use model (CLUMondo) was parametrized using economic modelling results and European SSP scenarios as input.</p>
Dataset of the publication: Strain Switching in van der Waals Heterostructures Triggered by a Spin-Crossover Metal–Organic Framework
<p>Dataset of the publication: Strain Switching in van der Waals Heterostructures Triggered by a Spin-Crossover Metal–Organic Framework</p> <p>DOI: 10.1002/adma.202110027</p> <p>Boix-Constant, Carla; Garcia-Lopez, Victor; Navarro-Moratalla, Efren; Clemente-Leon, Miguel; Zafra, Jose Luis; Casado, Juan; Guinea, Francisco; Manas-Valero, Samuel; Coronado, Eugenio</p> <p> Adv. Mater. 34, 2110027 (2022)</p>
ADAPTING SYSTEMS ENGINEERING TO EVALUATE TECH STARTUPS: AN INNOVATIVE FRAMEWORK BASED ON OMG ESSENCE
<p>The adaptation of system engineering to evaluate technical startups through an innovative framework based on the OMG Essence standard is a modern approach to simplify the process of analyzing and managing startups at various stages of their development. In this study, a universal framework was proposed that allows evaluating technical startups from the point of view of system engineering. This approach takes into account key aspects of startup development, such as requirements, stakeholders, technology, and team, which makes it an important tool for evaluating innovative projects.</p> <p>The main purpose of the proposed framework is to apply the principles of systems engineering to the evaluation of startups, focusing on technical aspects such as system architecture, integration capabilities, and the ability of the team to solve complex tasks. Traditional methods of evaluating startups, often focusing on business aspects, may not always take into account all the technical difficulties and risks that arise when developing software products. The OMG Essence-based framework fills this gap by providing a more comprehensive tool for analyzing and managing startup development.</p> <p>OMG Essence, as a standardized method, acts as a basis for formalizing practices and facilitating interaction between various project participants. The framework based on it offers the possibility of modular adaptation, which allows you to evaluate startups regardless of their complexity and current stage of development. An important feature of OMG Essence is the ability to integrate with other methodologies, which makes it universal for various fields. The application of this standard in the evaluation of startups allows you to obtain more objective results and improve the decision-making process.</p>
Dataset about An Exploratory Framework of Land-Sea Movement Model for Early Austronesians Migration
<p>Dataset about An Exploratory Framework of Land-Sea Movement Model for Early Austronesians Migration https://zenodo.org/records/14997527</p>
Resource Description Framework (RDF) Modeling of Named Entity Co-occurrences in Biomedical Literature and Its Integration with PubChemRDF
<p>This Zenodo record contains the co-occurrence RDF data generated in the work described in the paper “<strong>A resource description framework (RDF) model of named entity co-occurrences in biomedical literature and its integration with PubChemRDF</strong>” by Li et al., published in the Journal of Cheminformatics (<a href="https://doi.org/10.1186/s13321-025-01017-0" target="_blank" rel="noopener">https://doi.org/10.1186/s13321-025-01017-0</a>). It also contains the SPARQL query examples, the RDF schema in SHACL and ShEx, and the validation scripts.</p> <p>All content in this Zenodo record is for archival purposes. The latest version of the co-occurrence RDF data and other PubChemRDF data can be accessed via the PubChem FTP site (<a href="https://ftp.ncbi.nlm.nih.gov/pubchem/RDF/" target="_blank" rel="noopener">https://ftp.ncbi.nlm.nih.gov/pubchem/RDF/</a>). The up-to-date RDF schema in various formats is available on the PubChemRDF Schema page (<a href="https://pubchem.ncbi.nlm.nih.gov/docs/rdf-schema" target="_blank" rel="noopener">https://pubchem.ncbi.nlm.nih.gov/docs/rdf-schema</a>). A set of SPARQL query examples can be found on the PubChemRDF use case pages (<a href="https://pubchem.ncbi.nlm.nih.gov/docs/rdf-use-cases" target="_blank" rel="noopener">https://pubchem.ncbi.nlm.nih.gov/docs/rdf-use-cases</a>).</p>
Data and codes related to the article: Renard et al. A Hidden Climate Indices Modeling Framework for Multi-Variable Space-Time Data. Water Resources Research.
<p>This package contains data and codes related to the article:</p> <p>B. Renard, M. Thyer, D. McInerney, D. Kavetski, M. Leonard and S. Westra. A Hidden Climate Indices Modeling Framework for Multi-Variable Space-Time Data. <em>Water Resources Research</em>.</p> <p><strong>R scripts</strong></p> <p>The main computations of the paper have been performed using a computing code named <a href="https://github.com/STooDs-tools">STooDs</a>, which is called using the bash script launchpad.sh.</p> <p>The R scripts in this package only perform pre-processing (create configuration files) and post-processing (analyze results) steps.</p> <ul> <li>Funk.R: a set of functions called by other scripts.</li> <li>1_defineModel.R: define the model to be inferred and create STooDs configuration files in <em>dataset_XXX/runs.</em></li> <li>2_analyzeResults.R: analyze the outputs of STooDs runs.</li> <li>3_crossValidation.R: analyze the outputs of cross-validation experiments in <em>dataset_XV</em> and <em>dataset_XV_1971-1990</em>.</li> </ul> <p><strong>Data</strong></p> <p>Data for the 3 cases (full dataset and 2 cross-validation experiments) are located in folders <em>dataset_XXX/data</em>.</p> <ul> <li>dat.txt: raw dataset in text format.</li> <li>dataset.RData: dataset in RData format.</li> <li>DMI.txt, NINO.txt, SAM.txt: 3 standard climate indices.</li> <li>spaceP.txt, spaceQ.txt, spaceT.txt: properties of Precipitation (P), Streamflow (Q) and Temperature (T) stations.</li> <li>[only for cross-validation experiments] validation.RData: left-out data used for validation.</li> </ul> <p> </p> <p> </p>
Preventing technical debt with the TAP framework for Technical Debt Aware Management
<p><br> This zip file contains twelve files and one folder to replicate and verify the study evaluating the TAP Framework (for Technical debt Aware Project management).</p> <p> * TDframework_ticket_rawdata_revised_anonymised.xlsx: <br> revised and anonymized raw data for tickets statistic containing two sheets for TD ticket and maintenance ticket</p> <p> * TDframework_survey_questions_and_SPSSvariable_definition.xlsx: <br> excel sheet with all survey questions in German and English language, SPSS variable names, and short names<br> <br> <br> * TDframework_survey_results.sav:<br> survey results already in SPSS format, variables with suffix "D" are dichotomized versions of the variables as explained in the paper.</p> <p> * TDframework_survey_results.xslx:<br> Excel-Export of TDframework_survey_results.sav<br> <br> <br> * MannWhitneyUTest.spv:<br> results of the Mann-Whitney U-Test, which show the significances of the hypothesis mentioned in the paper (Section 5.2.2)<br> <br> * MannWhitneyUTest.xslx:<br> Excel-Export of MannWhitneyUTest.sav<br> <br> * Correlations.spv<br> results of the Chi-square test for correlations and the significances mentioned in the paper (Section 5.2.3)<br> <br> * Correlations.xslx:<br> Excel-Export of Correlations.sav<br> <br> <br> * Management-Survey_2021_en.pdf:<br> - original survey with englisch translation of the questions<br> <br> * Management-Survey_2021_XXX.pdf:<br> results of the management survey from <br> - team manager of the observed unit (TMO), participant A<br> - team manager of the comparison unit (TMC), participant B<br> - unit manager of the observed unit (UMO), participant C<br> - unit manager of the comparison unit (UMC), participant C<br> UMC => no survey, but email conversation between author and UMC to get the answers for the comparison unit<br> <br> * Fig6_Cause-Effect-Diagram_arrow_details.xslx<br> details about sources for the arrows in this figure<br> <br> * folder "Case Study Protocol"<br> contains the case study protocol according to Runeson's guidelines.<br> </p>
Computational results for the publication "Evolution of water structures in metal-organic frameworks for improved atmospheric water harvesting"
<p>This upload contains the computationally obtained atomic coordinates for MOF-303 and MOF-333 at different water loadings.</p>
Performance of GFN1-xTB for periodic optimization of Metal-Organic Frameworks
<p>GFN-xTB optimised structures of CoRE 2014 and CoRE 2019 structures, with and without lattice optimisation.</p>
Global indicators framework for socially responsible research and innovation (RRI): How to monitor public and researcher perspectives (Supplemental material)
<p>This data upload provides a detailed account of indicators that can be used to measure RRI progress around the world against the UNESCO Recommendation for Science and Scientific Researchers at the level of individual researchers and public opinion. This data upload provides supplemental material for an article in Open Research Europe entitled, 'Global indicators framework for socially responsible research and innovation (RRI): How to monitor public and researcher perspectives'.</p> <p>Abstract for main article:</p> <p>As calls for more socially responsible research and innovation (RRI) policies and practices grow more insistent, the need for high quality indicators that can be used to evaluate progress is becoming increasingly important. Given the global nature of science, such indicators need to be relevant to countries across all world regions. Moreover, the methodological quality of indicators is critical to provide a strong foundation for long-term comparative measurement of the impacts of different kinds of policy intervention. There is a practical challenge in this effort relating to the uneven mechanisms for data collection and analysis available in different countries. There is also a geopolitical challenge in gaining buy-in from countries with very different, and sometimes competing, agendas. Here, the 2017 UNESCO-led Recommendation on Science and Scientific Researchers is highlighted as an existing vehicle that can enable cooperation on globally comparative measurement of socially responsible research and innovation. In particular, the quadrennial monitoring of the implementation of this wide-ranging global policy instrument that has been ratified by 195 countries affords a unique opportunity to add value for these countries by linking RRI to the 2017 Recommendation while establishing benchmark indicators for RRI more generally. As a practical and methodological contribution to the global community of science and innovation policymakers, researchers and research and innovation stakeholders committed to socially responsible research, this report contains specific, detailed survey questions and response options focusing on the public opinion and individual researchers’ level of measurement. It provides details of sources of benchmark survey data that have readily available open data that can be used to benchmark the development of socially responsible research and innovation over time from the vantage points of the public and researchers around the world. The aim of this kind of indicators framework is to enable evidence-based practice in socially responsible research and innovation. Robust and practical RRI measurement on a global scale can reduce the risk that well-intentioned but ill-conceived RRI policy and practice interventions create undetected, unchecked, and unreformed negative impacts in practice.</p>
Original datasets for : A computational homogenization framework with enhanced localization criterion for macroscopic cohesive failure in heterogeneous materials
<p>The original datasets from tests in the article: <strong> A computational homogenization framework with enhanced localization criterion for macroscopic cohesive failure in heterogeneous materials</strong>. The results are produced by the in-house fem codes of the Computational Mechanics group, CiTG, TU delft.</p>
Evaluating the Usability of Open Source Frameworks in Energy System Modelling (Supplementary Material)
<p>Dataset and source code for analysis of the Energy System Modelling Usability Testing (ESMUT) procedure applied in the open_MODEX project.</p> <p>This is supplementary material for the publication:</p> <pre>Berendes et al. (2022). Evaluating the Usability of Open Source Frameworks in Energy System Modelling. <em>Renewable and Sustainable Energy Reviews. DOI: </em><a href="https://doi.org/10.1016/j.rser.2022.112174">https://doi.org/10.1016/j.rser.2022.112174</a></pre> <p> </p> <p> </p>
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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.